Special Issue on Multidisciplinary Innovation in Engineering Science & Technology 2020

Dental plaque is considered a possible causative agent of major dental diseases. People must develop oral care skills at an early age with family support. This study aims to assist parents and to reduce children’s fear while cleaning their teeth. An interactive game challenges children’s brushing ability. Arduino modules and software platform was utilized to build a functional prototype. The status of the palm movements identified using a sensing module within brusher. When the cumulative number of swipes exceeds a specified target, the LED moves to the next position. Children became more attentive and adjusted to their corresponding tooth positions according to changes in the hippo’s dental light. The average brushing time increased to 226 seconds, with the assistance of the partner. An interactive novelty toothbrush with Bluetooth module was built and evaluated. The contribution of the work is observing children’s needs and guide them to brush their teeth from an intimate perspective.

Jeju Island is the largest island of South Korea and has an independent electric supply system. The island has a special energy plan that realizes itself a carbon-free region by 2030. Wind and solar energy resources are considered as an important means. The introduction of weather-variable renewable energy generation sources into the electric power system makes it unstable. The main content of this study was to investigate the capacity factor of large scale renewable energy (solar, wind) power plants operated in Jeju Island during the electric power peak time. We can see their utilization rate or contribution rate during the power peak time by the value of their capacity factors. The largest 18 solar power plants (SPP) and 8 wind power plants (WPP) on the island were chosen and the 4, 12, 25, 50 and 100% electric power peak times were set for this study. There are two electric power peak seasons, summer and winter, in Jeju Island when the electric power demand arrives at peak. Generation of renewable energy plants depends on weather and the climates of the 5 regions of Jeju Island are a little different from one another. So, we investigated the difference between the average capacity factors of the 5 region’s renewable plants. WPPs showed a high contribution to the power grid only during winter peak time and SPPs did high contribution to the power grid only during summer peak time, while WPPs showed low contribution to the power grid during summer peak time and SPPs did low contribution to power grid during winter peak time. WPPs and SPPs have inter-compensation relation in the aspect to contribute to the power grid.

In this paper, a novel quantum transmission protocol which are based on the quantum no-key protocol is proposed. First, the quantum no-key protocol is discussed to show its important and its non-efficient on data transmission over quantum channel. Then, we improve it to carry many data bits via transmission. In addition, the error correction code is discussed and emerged into the proposed protocol to secure the transmission over quantum channel noise. Finally, the evaluation of transfer cost is discussed to show the effective of our proposed system.

The development of electronic healthcare systems requires to adopt modern technologies and architectures. The use of Electronic Personal Health Record (E-PHR) should be supported by efficient storage such as cloud storage which enables more security, availability and accessibility of patients’ records. Actually, the increase of availability of E-PHR enhances parallel access, which improves the performance and the throughput of the system. Using distributed systems, users are able to communicate and to share resources to achieve specific goals. Such kind of access needs to have more coordination to maintain parallelism, which can be provided through leader election algorithms. In leader election algorithms, users elect one of them as a leader to coordinate the work and to prevent conflicts. This paper introduces an adoptive leader election algorithm (ALEA) that considers medical and healthcare specifications, since it uses leader election algorithm for E-PHR in the cloud environment. The use of ALEA improves performance by allowing more parallelism and reducing the number of coordinating messages within the system, as well as facilitating the medical specifications such as having a primary doctor or handling emergency situations. Moreover, the paper highlights the strengths and weaknesses of using Blockchain technology in the field of healthcare. In fact, the paper investigates the implementation challenges of ALEA concepts using Blockchain technology.

Auditors now can no longer rely on the old-fashioned way of auditing manually. More and more jobs, increasingly complex work environment, the demands of the times, accuracy and speed of work require auditors inevitably must adopt technology. This research began with our success as academics in the audit family. Related to Indonesia, a large country and has several hundred public accounting firms and thousands of auditors, but computerized use of audits using software is still very little. The public accounting firm still uses a manual system, using normally typed paperwork. We want to find out what can boost the use of software among auditors. Our results are useful for auditors in Indonesia. From the results of statistical tests we found that auditors use compilation software by individual auditors themselves rather than organizations and individuals

Human behavior quantification is an essential part of psychological science. One of the cases is measuring human personality. Social media provide rich text, which can be beneficial as a data source to get valuable insight. Previous researches show that social media offered favorable circumstances for psychological researchers by tracking, analyzing, and predicting human character. In this research, we propose a personality measurement design to help to assess human character through linguistic usage from human digital traces. We construct our model by classifying social media text to the pre-determined personality facet from Big Five personality traits, mapping the knowledge to the ontology model, and implementing the model as a platform dictionary. Our model is based on the Indonesian language, which to the best of our knowledge is the first in the subject area. The platform is running effectively by using a well-established sorting algorithm, called the radix tree. Our objective is to support psychological science in adapting to a new technological era.

The experimental findings of the main units of readout electronics of silicon photomultipliers (SiPMs) based on array chip (AC) ??2??030: a charge-sensitive amplifier (CSA) with an adjustable conversion factor and a base line restorer (BLR) circuit and two types of voltage comparators are considered. The electrical circuits of the units, the measurement results of static and dynamic parameters are given.

Analytical researches on a potential performance of multipath multiple-input multiple-output (MIMO) systems inspire the development of new technologies that decompose a MIMO channel into independent sub-channels on the condition of constrained transmit power. Moreover, in current studies of inter-symbol interference (ISI) MIMO systems, there is an assumption that channel state information (CSI) at receivers and/or transmitters is perfect. In this paper, we propose a hybrid design of precoding and equalization schemes based on the unweighted minimum mean square error criterion that not only eliminates the ISI but also improves the system performance. Additionally, the impact of imperfect channel knowledge at receivers on the system performance of MIMO ISI system is investigated. The simulation result shown that the proposed hybrid design of precoding and equalization with shared redundancy outperforms the conventional method in all considered scenarios. Furthermore, the proposed and the conventional schemes are extremely sensitive to the CSI factor, the performance of these systems is quickly deteriorated when the accuracy of channel estimation decreases.

Among the most popular tools in security field is the anomaly based Intrusion Detection System (IDS), it detects intrusions by learning to classify the normal activities of the network. Thus if any abnormal activity or behaviour is recognized it raises an alarm to inform the users of a given network. Nevertheless, IDS is generally susceptible to high false positive rate and low detection rate as a result of the huge useless information contained in the network traffic employed to build the IDS. To deal with this issue, many researchers tried to use a feature extraction methods as a pre-processing phase. Principal Component Analysis (PCA) is the excessively popular method used in detection intrusions area. Nonetheless, classical PCA is prone to outliers, very sensitive to noise and also restricted to linear principal components. In the current paper, to overcome that we propose a new variants of the Nonlinear Fuzzy Robust PCA (NFRPCA) utilizing the popular data sets KDDcup99 and NSL-KDD. The results of the conducted experiments demonstrated that the proposed approaches is more effective and gives a promising efficiency in comparison to NFRPCA and PCA.

Visible light communication (VLC) that simultaneously gives illumination and information transmission abilities, is recognized as a hopeful competitor for prospective wireless net- works. Furthermore, a channel adaptive collaborative constellation (CASCC) with the capacity of adapting according to the channel condition to enhance the bit error rate (BER) while concurrently improving the versatility of the receiver mobility was regarded to be further effective than the contemporary CC. Nonetheless, the early CASCC barely presents modest performance enhancement in a strong correlation channel. Hence, by this study, we provide a design to form a channel-adaptation CC, namely the enhanced CC (ECC) for VLC systems. More specifically, from the basic constellation points, we form the optimization problem of efficient size that can be solved by any convex optimization solving technique. Also, the computational simulation outcomes confirm that the ECC is more beneficial than preceding constellations in term of BER for different channels. Moreover, we also provide the result comparison of the proposed scheme with other schemes in the imperfect channel condition. Overall, by effectively reducing the distance among the constellation points, a significant signal-to-noise ratio gain can be achieved.

This paper investigates the trajectory tracking control (TTC) problem of a networked control system (NCS) against a replay-attack. The impact of data packet dropout and com- munication delay on the wireless network are taken into account. A new mathematical representation of the NCS under network issues (packet dropout, delay, and replay-attack) is proposed, the resulting closed-loop system is written in the form of an asynchronous dy- namical system. Linear matrix inequalities (LMIs) formulation and a cone complementary linearization (CCL) approach are used to calculate the controller gain F1 and the trajec- tory tracking gain F2. Finally, a DC motor simulation with MATLAB is carried out to demonstrate the effectiveness of our approach.

This paper is an extension of work originally presented in 2019 Third World Conference on Smart Trends in Systems Security and Sustainability (WorldS4) [1]. Author describes two new methods: reactive protection method (without delay after detecting an attack), which consists in virtualizing the execution environment of supercomputers processes if the calculated state descriptor falls into the “risk” zone and based on monitoring requests for allocation of resources in accordance with the rules of the security policy in the form of temporal modal structures CTL logic and method for reconfiguring the runtime environment of the supercomputers taking into account the mobility requirements (built-in computations) based on the application of the trajectories of computing state security descriptors on Kripke structures. The methods develop a number of provisions of the theory of information security, based on the new concept of Information Security of stationary and onboard supercomputer computing systems as a calculated convolution of the states of the execution environment (hardware or virtual) and system software.

Many advances in computer systems and IT infrastructures increases the risks associated with the use of these technologies. Specifically, intrusion into computer systems by unauthorized users is a growing problem and it is very challenging to detect. Intrusion detection technologies are therefore becoming extremely important to improve the overall security of computer systems. In the past decades, most of the intrusion detection systems designed suffer from the problem of high false negative and low efficiency rate. A powerful intrusion detection system (IDS) should be implemented to solve these issues and it is necessary to collect, reduce and analysis the data automatically. The integration of machine learning and artificial intelligence techniques serves this purpose in this paper. A use of particle swarm optimization (PSO) selects the optimal number of clusters and the integration of k-means based artificial neural network (ANN) achieves maximum efficiency when the number of clusters selected optimally. The proposed IDS are t bested with NSL-KD dataset and the experiment result shows the significance of the proposed IDS.

The field of humanitarian logistics has in recent times gained an increasing attention from both academics and practitioners communities alike. Although various research groups have addressed theoretical and technical developments in humanitarian logistics using conventional research tools, applied research appears to be often dependent on practitioners’ inputs. This paper is an attempt to fill the existing gaps between academic research and practitioners’ needs and proposes an integrated framework that consists of serious games and computer modelling. The serious games component aims to raise awareness on humanitarian logistics issues as well as provide a platform to facilitate the acquisition of inputs from humanitarian practitioners. Based on these inputs, a computer model will be developed. To test the framework, a real-life case study about the prepositioning of strategic stockpiles in Indonesia, one of the countries with the highest disaster risk exposure on a global scale, was used. Findings of this work highlight the role of serious games as risk-free environments for players to design strategies enhancing disaster preparedness in conjunction with broadly used research methodologies such as computer modelling.

In today’s Moroccan business environment, companies need to implement organization agility by developing an agile workforce that is able to deal with the environment volatility. Thus, the agile workforce concept has been appeared as a necessary and sufficient condition to achieve agility. Focusing on agile enablers influencing workforce agility is an important area but currently there is limited literature available. Acknowledging its importance, we continued our literature exploration in order to identify the enablers of workforce agility. Then, we describe a list of four enablers with different criteria and attributes. This paper further proposed fuzzy logic approach to evaluate different measures of the workforce agility. The results suggest that engagement, knowledge sharing, acceptance of changes and self-motivation are the most important attributes of agile workforce. Apart from that, different agile workforce attributes need to be improved in order to achieve the extremely agile level of the workforce.

With a model predictive control approach and to set the motion trajectory for an autonomous vehicle in situations where emergency braking cannot be performed, in this paper, we propose a solution to apply the law on road traffic into a set of constraints and thereby build an objective function to create motion trajectory for autonomous vehicle. The newly created trajectory is created to improve performance and enhance the ability to avoid obstacle but ensure an optimal global trajectory. The performance of this solution is assessed through simulation with different scenarios, from which there are applied research orientations on the problem of autonomous vehicle in practice.

This project aims to demonstrate the importance that digital media can have to the development of loyalty programs, namely in creating empathy and proximity relationships between brands and their target audience: young people. This study consisted of the creation of a digital platform for Toyota Portugal, named Spot Toyota, to communicate actions promoted by the car brand, especially during music festivals. With the support of advertising agency Caetsu, this mobile application was developed to bring the brand closer to a younger audience – festival fans – with potential interest in two Toyota fleet car models: Aygo and C-HR. Through strategies typical of loyalty programs, such as the awarding of vouchers or coupons, the accumulation of points or winning prizes, a system was produced with the main focus on attracting users to the platform in a continuity perspective. The working process of this investigation resulted in the design of a smartphone application, based not only on the analysis of other examples present in the market but also on the understanding of crucial subjects such as loyalty programs, UX and UI design, application of personas models, creation of wireframes and workflows, and development of usability tests.

The human gut environment can contain hundreds to thousands bacterial species which are proven that they are associated with various diseases. Although Machine learning has been supporting and developing metagenomic researches to obtain great achievements in personalized medicine approaches to improve human health, we still face overfitting issues in Bioinformatics tasks related to metagenomic data classification where the performance in the training phase is rather high while we get low performance in testing. In this study, we present discretization methods on metagenomic data which include Microbial Compositions to obtain better results in disease prediction tasks. Data types used in the experiments consist of species abundance and read counts on various taxonomic ranks such as Genus, Family, Order, etc. The proposed data discretization approaches for metagenomic data in this work are unsupervised binning approaches including binning with equal width bins, considering the frequency of values and data distribution. The prediction results with the proposed methods on eight datasets with more than 2000 samples related to different diseases such as liver cirrhosis, colorectal cancer, Inflammatory bowel disease, obesity, type 2 diabetes and HIV reveal potential improvements on classification performances of classic machine learning as well as deep learning algorithms. These binning approaches are expected to be promising pre-processing techniques on various data domains to improve the performance of prediction tasks in metagenomics.

The paper presents a novel control strategy applying the particle swarm optimization (PSO) algorithm to optimize the scaling weights coefficients of the fuzzy-PID controller for the resistance furnace temperature control system, called PSO-based fuzzy-PID controller/ algorithm. The proposed PSO-based fuzzy-PID controller in this paper consist of the fuzzy-PID controller and the PSO algorithm. The proposed fuzzy-PID controller is combination of the advantage of PID control and fuzzy logic control. Firstly, the paper presents the mathematical model of the resistance furnace by identification method, based on the experimental data. Then, the design of the fuzzy-PID controller is given in this study. And then, the paper presents the design of the temperature control board using PIC16f with the installed PSO-based fuzzy-PID algorithm. Finally, the simulation and experimental results proved the stability of the proposed PSO-based fuzzy-PID controller with the disturbance, improved the furnace temperature control quality, through obtained major control criteria, such as overshoot, steady-state error, settling time, rising time.

To have enough information on time can be helpful when companies try to reduce costs and operate more efficiently. An international company that supplies parts for the automotive industry is currently testing its new facilities in Mexico. The relocation of the raw materials and finished goods warehouses were tested using a P-Median model. The operating costs and risk factors were included in the model to provide a better solution and improve the operation of the warehouses and production lines. The research results compared different scenarios and indicated that the proposed better location isolates the forklift routes, mainly for finished products, and minimizes the cost of moving both raw materials and finished products to and from warehouses.

With a model predictive control approach including boundary analysis and uncertain prediction of activities of different road participants, this paper proposes solutions that support motion control by steering control and appropriate acceleration to create safe motion trajectories for an autonomous vehicle. The motion control support element is determined by the principle of minimal intervention and can handle complex situations, while building control model to predict real-time operation with speed factors, ability to control driving and limit the long period. The performance of this solution is assessed through simulation, then there are applied research orientations on practical autonomous vehicle accounting.

An extensive work has been carried out in the field of human transcribe-verification and transcribe-recognition by extensive scholars across the globe from past decades. In order to demeanour immense experiments for considering the performance of the newly intended models and to substantiate the efficacy of the proposed model which is moderately required. This paper monologue the problem of signature-verification and recognition using diverse ORB key points and Convolution Neural Network. This method reckons on descriptors for detection and matching. The systematic approach is tested more on an few real and few fake signatures. Many features and combination of features were proposed for signature substantiation and acknowledgement. Numerous experiments are conducted to determine the capability of the proposed models in selective genuine and forgery signature. In this context, a large signature corpus comprising of 29950 offline signatures from 605 persons is created during the course of the research work. Finally the achievement was achieved about 90% of correct accuracy of the given original signature.

Visualization plays a crucial role in the exploratory analysis of Big Data. The direct visualization of Big Data is a challenging task and difficult to analyze. Dimensionality Reduction techniques extract the features in the context of visualization. Due to the unsupervised and non-parametric nature, most of the dimensionality reduction techniques are not evaluated quantitatively and not allowed to extend for dynamic data. The proposed representative k-NN sampling-based clustering, determines the underlying structure of the data by using well-known clustering techniques. The external cluster validation index determines the order sequence of clustering techniques from which the appropriate cluster techniques are recommended for the given datasets. From the recommended set, the samples of the best clustering technique are considered as representative samples which can be used for gener- ating the visual representation. The t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is applied to generate a low-dimensional embedding model of representative samples, which is more suitable for visualization. The new data samples are added to the generated model by using the interpolation technique. The low-dimensional embedding results are quantitatively evaluated by k-NN accuracy and trustworthiness. The performance analysis of representative k-NN sampling-based clustering results and embedding results accomplished by seven di datasets.

This research aims to enhance the generated power of the small-scaled wind turbine using the eddy current brake system and Maximum Power Point Tracking (MPPT) control method. We analyzed the behavior of the generated power and power factor, with and without the MPPT control which implemented by eddy current brake system. Also, the feasibility of the system investigated using different wind conditions such as strong and calm wind conditions. The load data has different voltage respond to the system since its conditions depend on the day/night loads pattern, weather conditions, soil moisture. Moreover, the analogical experiment for small-scaled wind turbine blade destruction is analyzed to determine the maximum penetration value of mechanical power in order to retrieve an optimal angular velocity which resulting in provides a possible maximum power to loads. At the same time, emergency break is operated when angular velocity reaches to critical speed to avoid destruction. In the simulation, we collected the real load data from a mango farm in Okinawa prefecture in Japan. The results were analyzed through simulations for the different wind conditions. In the end of simulation, we could verify that either Maximum Power Point and emergency control are activated correspondingly.

A novel design of a dual motion mechanism for a high-voltage gas circuit breaker is presented. The contact motion of the circuit breaker due to the operating mechanism increases capacitive current switching (CCS) performance. CCS is one of the interrupting duties of the circuit breaker, where high voltage is applied during the half cycle from contact separation. The dual motion mechanism drives two contacts in opposite directions from each other. Operating energy is reduced because the maximum displacement of the moving parts linked to the operating mechanism is shortened. To increase CCS performance at lower operating energies, the contact on the opposite side of the contact linked to the operating mechanism requires quick motion in the CCS period with a short displacement. The dual motion mechanism reported here is composed of two grooved cams that cross each other (double grooved cams). A pin positioned at the intersection point of the grooved cams rotates a lever linked to both contacts while changing the lever ratio that shortens the path length of the pin movement. To implement an optimized displacement curve with low operating energy and low mechanical stress while keeping the CCS performance high, a shape optimization method was developed that uses a direct search to minimize the local contact forces acting on the contact positions between the grooved cams and the pin. In order to maintain the stability of the pin in motion, a position holding part was designed by considering size of the gaps between the grooved cams and the pin. The measured displacement curve was in good agreement with the ideal one. In addition, a full-scale prototype was fabricated that successfully passed a 10,000-motion test.

The human brain is one of the most complex machines on the planet. Being the only method to get real-time data with high temporal resolution from the brain makes EEG a highly sought upon signal in the neurological and psychiatric domain. However, recent developments in this field have made EEG more than just a tool for medical professionals. The decreasing size and increasing complexity of EEG acquisition systems have brought it out of the lab and into the field where it is used for varied applications like neurofeedback, person recognition and other recreational activities. Amalgamation of the EEG signal with new developing standards of Industry 4.0 to control basic IOT devices using edge computing techniques marks the next step in the design and development our low-cost yet robust Brain Computer Interface (BCI); which is just one of the many applications that a versatile and well-built EEG acquisition system can be used for.

In a biometric system technology, a person is authenticated based on processing the unique features of the human biometric signs. One of the well known biometric systems is iris recognition, this technique being considered as one of the most secure authentication solutions in the biometric field. However, several attacks do exist that are able to spoof iris. In this paper, we propose a novel approach for securing the iris recognition system by eye liveness detection technique. The proposed system detects the eye liveness, and recognises the iris. This process includes multiple steps. As per the first step, the person opens his eye and the system reads remotely the changes in pupil size as a result of the response to the ambient illumination. Then, the system starts matching the iris with a database. The second step: the person closes his eye and the system remotely detects the heartbeats signals under the skin of the eyelid. As per the third step, the person opens his eyes again and the system reads the pupil size again and compares the results of the pupil size, and then the system matches again the iris with the above database. For the iris recognition to be validated, all the above checks have to be passed. We have conducted several experiments with our proposed system, based on a brand new dataset comprised of 40 subjects. In addition, we also used public terval, CASIA-Twin and Ubiris.V1. The achieved results show the quality and viability of our proposal.

In the last few years of research and innovations, lots of spatial data in the form of points, lines, polygons and circles have been made available. Traditional indexing methods are not perfect to store spatial data. To search for nearest neighbour is one of the challenges in different fields like spatiotemporal data mining, computer vision, traffic management and machine learning. Many novel data structures are proposed in the past, which use spatial partitioning and recursive breakdown of hyperplane to find the nearest neighbour efficiently. In this paper, we have adopted the same strategy and proposed a nearest neighbour search algorithm for k-dSLst tree. k-dSLst tree is based on k-d tree and sorted linked list to handle spatial data with duplicate keys, which is ignored by most of the spatial indexing structures based on k-d tree. The research work in this paper shows experimentally that where the time taken by brute force nearest neighbour search increases exponentially with increase in number of records with duplicate keys and size of dataset, the proposed algorithm k-dSLstNearestNeighbourSearch based on k-dSLst tree performs far better with approximately linear increase in search time.

The dynamic modelling of an airship is the primary requirement in designing and developing a control system for a particular application. Extracting/predicting/modelling the aerody- namic coefficients is a crucial step towards the modelling of an airship. There is a huge amount of literature on the aerodynamic modelling of airships which presents experimental as well as analytical methods. All these techniques require some experimental data such as the geometrical data, control derivatives, etc. In this work, we are investigating an analyti- cal technique which can calculate the aerodynamic parameters for a high altitude airship. The complete airship model is implemented using the derived aerodynamic coefficients. A MATLABOR Simulink based simulation is used for the investigation.

One of the most important technological changes due to the arrival of Industry 4.0, an initial, gradual, and complex process of technology transfer is taking place, which strongly relies on the integration of universities, industries, and governments. In this context, to make the Industry 4.0 approach a reality, several requirements need to be met. One of them is the need to qualify people to work in industries. This research paper aims to clarify the required knowledge and learning for a person to operate the manufacturing processes associated with some of the capabilities of Industry 4.0. Interviews were conducted with individuals who are Industry 4.0 employees, including experts of technology, education vendors, and employers who are eager to develop and improve their projects. This study provides several results, the most important of which is the focus on the rehabilitation of operators using modern technologies in alignment with the Fourth Industrial Revolution.

Augmented Reality applications are gaining popularity while maintaining novelty. Many industries are utilizing the user interface type, and use cases are becoming repeat patterns of problem solutions. Despite this rising popularity, quality has not matured nor has the technology become mainstream. Novelty must be approached as risk, and risk must be evaluated for and tested to assure adequate levels of quality. Quality itself can also be vague and have contextual definition. For these reasons, a quality model for augmented reality was created. This work analyzes over two hundred thousand tweets, collected during 2019 and 2020, relating to augmented reality technology, and contextualizes various data points to the established AR Quality Model. The education industry had the highest mentions among the tweets within the scope of this research while the tweets labeled to the transportation industry had the highest sentiment. Furthermore, the tweets were shown to illustrate the needs of testing against the characteristics within the quality model; presence, perspective, interaction, portability and persistence.

We investigate in this paper many problems related to the decision-making process in the Cognitive Radio (CR), where a Secondary User (SU) tries to maximize its opportunities by finding the most vacant channel. Recently, Multi-Armed Bandit (MAB) problems attracted the attention to help a single SU, in the context of CR, makes an optimal decision using the well-known MAB algorithms, such as: Thompson Sampling, Upper Confidence Bound, e-greedy, etc. However, the big challenge for multiple SUs remains to learn collectively or separately the vacancy of channels and decrease the number of collisions among users. To solve the latter issue for multiple users, the All-Powerful Learning (APL) policy is proposed; this new policy considers the priority access and the dynamic multi-user access, where the number of SUs may change over time. Based on our APL policy, we consider as well as the Quality of Service (QoS), where SUs should estimate and then access best channels in terms of both quality and availability. The experimental results show the superiority of APL compared to existing algorithms, and it has also been shown that the SUs are able to learn channels qualities and availabilities and further enhance the QoS.

Wireless sensor network is highly resource constrained, where energy efficiency and network lifetime plays a major role for its sustenance. As the sensor nodes are battery operated and deployed in hostile environments, either recharging or replacement of batteries in sensor nodes is not possible after its deployment in inaccessible areas. In such condition, energy is the vital factor for the survival of sensor node in the sensing field. In order to increase the network lifetime and balance the energy consumption, robust routing protocols are required. The proposed network routing has three phases: 1. Network initiation phase to create a zone which enables the communication among local nodes 2. Zone co-ordinator selection phase algorithm to form zone cluster and its re-election procedure and 3. Zone head selection with its replacement phase based on energy centroid positional information and distance to the basestation to distribute load equally among zone co-ordinators, local sensor nodes. The data path between zone heads and basestation is distance centric and is optimized at one hop and dual hop levels to avoid data packet loss at zoneheads. Each zone is designed to own atmost ¼ rth of deployed sensor node count through uniform random deployment. Simulations results when basestation is placed inside sensing field indicates that the proposed network algorithm outperforms when benchmarked against similar protocols like conventional LEACH, Traditional PEGASIS, existing PRRP, ES3 protocols in terms of performance metrics like Network energy consumption, Average energy consumed by sensor node, Packet delivery ratio, Packet loss percentage and Network throughput.

For many years, a lot of researches have been made to develop Advanced Driver Assistance Systems (ADAS) that are based on integrated systems. The main objective is to help drivers. Hence, keeping them safe under different driving conditions. Visibility for drivers remains the biggest problem faced on the road in an atmosphere of fog. In this paper, we examine a system that can be employed to substantially enhance visibility through using deep neural networks. Researches done recently- which are based on deep learning for eliminating image fog- have made clear that an end-to-end proposed system is such an effective model. However, it becomes a must to extend the idea to end-to-end real-time video deshazing. In this paper, we introduce a model of image dehazing. It is based on Convolutional Neural Networks (CNN) as a basis for developing the video dehazing model. As in addition, we concatenate our model with the faster RCNN for detecting objects on the road in real time.
The experimental results on our image datasets shows the performance of our model with regard to Peak Signal to Noise Ratio (PSNR=19.823) and Structural Similarity (SSIM =0.8501). On the dataset of the synthesized videos, our model achieved a performance of PSNR = 21.4032 and SSIM = 0.9354. Moreover, with the concatenation of our dehazing model with Faster R-CNN (regions with convolutional neural networks), our proposed system displays desirable visual quality and a remarkable progress of the object detection achievement on blurred images with mean Average Precision (mAP) equal to 0.933 during the day and 0.804 during the night.

Natural disasters have caused not only economic but also human losses. These events bring with them, among other things, deficiencies in the supply of food, clothing, health and cleaning products, to name a few. This situation makes it imperative to locate facilities that can supply the needs, as mentioned earlier, to the victims in the shortest possible time. This document presents the evaluation of a logistic model of the literature whose foundations are: a) the classic p-median problem for the location of a pre-positioning warehouse; b) an extension of the (q-R) model for calculating inventories of different products, according to different types of demand, and c) the problem of multiple vehicles routing with the capacity to establish delivery routes from warehouses to the affected municipalities. This model is evaluated with 90 municipalities belonging to Veracruz, Mex. The results show that the location of the warehouse falls in the municipality of Fortín de las Flores, the inventory levels for five demands and four product kits, and product delivery routes, which are a total of 12, with which a favorable cost minimization is obtained.

Given ubiquitous digital devices with recording capability, distributed microphone arrays are emerging recording tools for hands-free communications and spontaneous tele-conferencings. However, the analysis of signals recorded with diverse sampling rates, time delays, and qualities by distributed microphone arrays is not straightforward and entails important considerations. The crucial challenges include the unknown/changeable geometry of distributed arrays, asynchronous recording, sampling rate mismatch, and gain inconsistency. Researchers have recently proposed solutions to these problems for applications such as source localization and dereverberation, though there is less literature on real-time practical issues. This article reviews recent research on distributed signal processing techniques and applications. New applications benefitting from the wide coverage of distributed microphones are reviewed and their limitations are discussed. This survey does not cover partially or fully connected wireless acoustic sensor networks.

Dengue is a viral infection disease transmitted to people through the bite of specific mosquito species living in a tropical zone. According to the World Health Organization, dengue has been listed among the top-ten diseases for 2019 as it makes 3.9 billion people in 128 countries be at risk of infection. One major cause of substantial dengue widespread is the globally warm climate that accelerates rapid growth of mosquito vectors. In this research, we aim to build data-driven models to predict dengue cases using satellite index data to represent temperature, humidity, and greenness over the surface area of Bangkok, which is our target area of dengue prediction because of its high infection cases. Oceanic Niño Index is also used as a predictor variable to represent climate variability. The modeling methods employ seven algorithms from two broad schemes of the machine learning field. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are algorithms from the subfield of computational intelligence, whereas multiple linear regression (MLR), generalized linear model (GLM), support vector regression (SVR), classification and regression tree (CART), and chi-squared automatic interaction detection (CHAID) are from the statistical learning subfield. Performances of these algorithms are evaluated on the same set of out-of-sample data. The results are that ANFIS is the best model for predicting dengue outbreak in the capital city of Thailand.

The advent of electric vehicles has changed the face of the automobile industry. The drive system properties of vehicles such as eBikes or electric cars differ fundamentally from those of a diesel engine. The lack of a conventional internal combustion engine has made the vehicles considerably silent. Nevertheless, previously hidden sources of vibration and noise have become more dominant. In addition to these emissions, other structural properties such as compliance and deformation also appear as relevant factors for the original equipment manufacturer. Usually, deterioration of these variables affects the efficiency of the power unit. In this paper, a simulation template is created to understand and analyze these properties of the drive unit. Furthermore, new enhancements to improve the key indicators, such as strain energy, natural frequencies, etc., are shown, thereby creating a potential method flow to develop better performing drive units. Numerical optimization tools are used to simulate structures with complex shapes that exactly meet the mechanical constraints and use as little material as possible. In this work, two optimized variants of electromechanical drives are presented. The first scenario illustrates the optimized model with an objective of minimizing the strain energy of the structures, whereas the second task aids in the development of a variant with superior dynamic properties than the current drive units. Ultimately, several numerical calculations are validated using experiments.

Communication is a bilateral process and being understood by the person you are talking to is a must. Without the ability to talk nor hear, a person would endure such handicap. Given that hearing and speech are missing, many have ventured to open new communication methods for them through sign language. This bilateral communication device can be utilized by both non-sign language users and Deaf-mute together in a single system. Shaped as a box (8in x 8in) with two multi-touch capable displays on both ends, the contraption has several microcontrollers and touch boards within. The latter has the technology of twelve interactive capacity touch and proximity electrode pads that react when tapped, producing quick response phrases audible via speaker or earphone. These touch boards are equipped with an MP3 decoder, MIDI synthesizer, 3.5mm audio jack and a 128MB microSD card. The touch screen modules mounted on top of the microcontrollers transfer data to and from each other in real-time via receiver-transmitter (RX-TX) full duplex UART serial communication protocol. The device is lightweight weighing at about 3 lbs. The prototype device was piloted in an academic institution of special education for deaf-mute students. Participants were 75 normal and 75 Deaf-mute people aged between 18 and 30 years. The experimental results show the overall rating of the device is 90.6%. The device is designed to promote the face-to-face socialization aspect of the Deaf-mute users to the normal users and vice versa. Several third-party applications were utilized to validate the accuracy and reliability of the device thru metrics of consistency, timing and delay, data transmission, touch response, and screen refresh rates.

The new model of the short-term exchange rate dynamics was constructed. First of all, the most interesting were the reasons of the deviation from the medium-term equilibrium. The author’s IFEER-concept (International Flows Equilibrium Exchange Rate) was used as a base and it was developed. In this study, due to the short-term modeling period the differential approach was applied. The result was an integrated version of the exchange rate dynamics model. The main result of mathematical modeling is a nonlinear multi-factor functional dependence of the exchange rate. The result dynamic functional dependence differs from the previous medium-term dependencies by the type of the internal dynamic function. Economically, this function in the short-term period is responsible for explosive changes in the exchange rate dynamics. The basis for mathematical modeling was the system of fundamental economic factors that affect the dynamics of the exchange rate. The influence of crisis events on the Russian financial market in the short term was studied. The conducted research allowed us to analyze and evaluate the quantitative impact of the short-term effects of the dynamics of the exchange rate of the Russian ruble to the US dollar.

Trust is essential in many interdependent human relationships. Trustworthiness is measured via the effectiveness of the relationships involving human perception. The decision to trust others is often made quickly (even at zero acquaintance). Previous research has shown the significance of voice in perceived trustworthiness. However, the listeners’ characteristics were not considered. A system has yet to be produced that can quantitatively predict the degree of trustworthiness in a voice. This research aims to investigate the relationship between trustworthiness and different vocal features while considering the listener’s physical characteristics, towards modelling a computational trust model. This study attempts to predict the degree of trustworthiness in voice by using an Artificial Neural Network (ANN) model. A set of 30 audio clips of white males were obtained, acoustically analyzed and then distributed to a large group of untrained Malaysian respondents who rated their degree of trust in the speakers of each audio clip on a scale of 0 to 10. The ANOVA test showed a statistically significant difference of trust ratings across different types and intensities of emotion, duration of audio clip, average fundamental frequencies, speech rates, articulation rates, average loudness, ethnicity of listener and ages of listener (p <.01). The findings conclude that Malaysians tend to trust white males who talk faster and longer, speak louder, have an f0 between 132.03Hz & 149.52Hz, and show a neutral emotion or rather stoic (arousal<.325). Results suggest that Indians are the most trusting Malaysian ethnic group, followed by Bumiputera from East Malaysia and then followed by Malays. Chinese are the least trusting Malaysian ethnic group. The data was fed into an ANN model to be evaluated, which yielded a perfect percentage accuracy (100%) in degree of trustworthiness 39.70% of the time. Given a threshold of two-point deviation, the ANN had a prediction accuracy of 76.86%.

In this paper a classification system based on a complex-valued neural network is used to evaluate the health state of joints in high voltage overhead transmission lines. The aim of this method is to prevent breakages on the joints through the frequency response measurements obtained at the initial point of the network. The specific advantage of this kind of measure is to be non-intrusive and therefore safer than other approaches, also considering the high voltage nature of the lines. A feedforward multi-layer neural network with multi-valued neurons is used to achieve the goal. The results obtained for power lines characterized by three and four junction regions show that the system is able to identify the health state of each joint, with an accuracy level greater than 90%.

A remotely mobile phone controlled electronic system for supplying water to garden plantations in a greenhouse or similar environment was designed and experimentally implemented. The system monitors the garden environmental conditions such as soil moisture, temperature, humidity and sunlight and a remote user can send commands from his mobile phone such as to switch ON/OFF water pumps, supply water for the plants for certain duration and acquire the environmental status information of the plantation. The water pumps are controlled by the Arduino microcontroller that is the core part of the electronic system. The user has the ability to monitor the environmental conditions on his mobile phone. Such systems can facilitate monitoring, give flexibility of controlling, save time and human labour and increase productivity as a result of automation and remote controlling.

Firewalls are controlled by rules which often incur anomalies. The anomalies are considered serious problems that administrators do not desire to happen over their firewalls because they cause more vulnerabilities and decrease the overall performance of the firewall. Resolving anomaly rules that have already occurred on the firewall is difficult and mainly depends on the firewall administrator’s discretion. In this paper, a model is designed and developed to assist administrators to make effective decisions for optimizing anomaly rules using the probability approach (Bayesian). In this model, the firewall needs to add four property fields (Extra fields) to the firewall rules: frequency of packets matching against rules, evidence of creating rules, the expertise of rules creator and protocol priority. These fields are used to calculate the probability of each firewall rule. The probability for each rule is used while the rules conflict and administrators need to resolve them. The rule having the highest probability value indicates that it has the highest priority in consideration. Experimental results show that the proposed model allows firewall administrators to make significant decisions about solving anomaly rules. The data structure of this model is based on k-ary tree, therefore the speed of building tree, time complexity and space complexity: O(n), O(logmn) and O(m*n) respectively. Besides, the confidence of the proposed firewall for resolving firewall rule anomalies of the administrator increase by 29.6% against the traditional firewall, and the reliability value between the inter-raters also increase by 13.1%.

Internet of Things provides a virtual view of real-life things by guiding challenges faced by persons in daily life. It is reforming our world with trillions of sensors and other IoT enabled devices by creating a smart environment. Effective use of IoT sensors in various smart IoT applications is very important. After analyzing different sensor applications, this paper presents various types of wearable sensors used for monitoring of human activities along with different locations optimal for their placement. This paper enlightens sensors suitable for any particular application. IoT has opened up a new avenue of research in the field of human activity recognition using wearable sensors. It provides an individual’s valuable information regarding functional ability and lifestyle. Human activities are monitored automatically to provide personalized support to different individuals. Recently, various researchers presented different human activity recognition systems, a few are cumulated in this paper. Furthermore, a Human Activity Recognition (HAR) system is also proposed in this paper for a smart IoT environment that would be secure enough to use.

The Hausdorff distance (HD) is defined as MAX-MIN distance between two geometric objects for measuring the dissimilarity between two objects. Because MAX-MIN distance is sensitive with the outliers, in face recognition field, average Hausdorff distance is used for measuring the dissimilarity between two sets of features. The computational complexity of HD, and also average HD, is high. Various methods have been proposed in recent decades for reducing the computational complexity of HD computing. However, these methods could not be used for reducing the computational complexity of average HD. Line Hausdorff distance (LHD) is a face recognition method, which uses weighted average HD for measuring the distance between two line edge maps of face images. In this paper, the Least Trimmed Square Line Hausdorff Distance method, LTS-LHD, is proposed for face recognition. The LTS-LHD, which is a modification of the weighted average HD, is used for measuring the distance between two line edge maps. The state – of – art algorithm, the EARLYBREAK method, is used for reducing the computational complexity of the LTS-LHD. The experimental results show that the accuracy of proposed method and LHD method are equivalent while the runtime of proposed method is 68% lower than LHD method.

Nowadays, Customer satisfaction is important for businesses and organizations. Manual methods exist, namely surveys and distributing questionnaires to customers. However, marketers and businesses are looking for quick ways to get effective and efficient feedback results for their potential customers. In this paper, we propose a new method for facial emotion detection to recognize customer’s satisfaction using machine-learning techniques. We used a facial landmark point; we extract geometric features from customer’s emotional faces using distances from landmarks points. Indeed, we used distances between the neutral side and the negative or positive feedback. After that, we classified these distances by using different classifier, namely Support Vector Machine (SVM), KNN, Random Forest, Adaboost, and Decision Tree. To assess our method, we verified our algorithm by using JAFFE datasets. The proposed method reveals 98,66% as accuracy for the most performance SVM classifier.

Road traffic accidents are primarily caused by drivers error. Safer roads infrastructure and facilities like traffic signs and signals are built to aid drivers on the road. But several factors affect the awareness of drivers to traffic signs including visual complexity, environmental condition, and poor drivers education. This led to the development of different ADAs like TSDR that enhances vehicle system. More complex algorithms are implemented for improvement but this affects the performance of a real-time system. This study implements a real-time traffic sign detection and recognition system with voice alert using Python. It aims to establish the proper trade-off between accuracy and speed in the design of the system. Four pre-processing and object detection methods in different color spaces are evaluated for efficient, accurate, and fast segmentation of the region of interest. In the recognition phase, ten classification algorithms are implemented and evaluated to determine which will provide the best performance in both accuracy and processing speed for traffic sign recognition. This study has determined that Shadow and Highlight Invariant Method for the pre-processing and color segmentation stage provided the best trade-off between detection success rate (77.05%) and processing speed (31.2ms). Convolutional Neural Network for the recognition stage not only provided the best trade-off between classification accuracy (92.97%) and processing speed (7.81ms) but also has the best performance even with lesser number of training data. Embedded system implementation utilized Nvidia Jetson Nano with interface Waveshare IMX219-77 camera, Nvidia 7” LCD and generic speaker and programmed in Python with OpenCV, sci-kit learn and Pytorch libraries. It is capable of running at an adaptive frame rate from 8-12 frames per second with no detection and down to approximately 1 frame per second when there is a traffic sign detected.

The article touches upon an urgent question of social-interactive learning. The resource under study deals with one of the possible ways to solve this problem, in the context of global socio-cultural transformations and a new paradigm for the development of society. It is the formation of a subject-subject dialogue between a teacher and a student. The second language research study showed how pre-and intermediate speakers’ performance breaks down in the face of a difficult narrative task and self-regulation and control over the mediational means are lost. More advanced speakers are able to guide themselves through the task. The main idea of such education is that good learning leads to development. It seems to us important to refer the concept of the zone of proximal development to the development of the individual, which affects the boundaries of the zone of proximal development. Electronic educational resource with the help of mediating means or sign operations makes external social interactions become “internalized”, namely, internally reconstructed psychological processes – ways of thinking and learning A student identifies active personality development prospect through the actual experience. The students’ learning curve let us prove the idea that IT technologies intensify the process of studying, but should provide not only language accomplishments but active communication with a teacher as well.

A gas sensor array is used for gas analysis to aid in an inspection. The signals from the sensor array are fed into machine learning models for learning and classification. These signals are characterized by time series fluctuating according to the environment or drift. When an unseen pattern is entered, the classification may be incorrect, resulting in decreased model performance. Creating a new model results in the problem of forgetting the old knowledge called Catastrophic Forgetting. Accordingly, this research proposes Correlation-Based Incremental Learning Network (CILN) using the Correlation Distance method to measure similarity and the Gaussian membership function to determine membership of each node. The gas sensor array data is used to verify the proposed algorithm by choosing 16 steady-state features (DR) from 13,910 records which are divided into 6 classes: 1) Ethanol, 2) Ethylene, 3) Ammonia, 4) Acetaldehyde, 5) Acetone, and 6) Toluene. The data are normalized and divided as the training sets into 10%, 20%, 30%, 40%, and 50%, respectively. The proposed algorithm was compared with well-known classifiers. CILN experiment results yield the highest accuracy of 98.96% using 50% of the training data set. It shows that CILN has the incremental learning ability and can be used with data that fluctuate according to the situation.

The paper presents a description and justification of the correctness of fuzzy recognition by a logic-predicate network. Such a network is designed to recognize complex structured objects that can be described by predicate formulas. The NP-hardness of such an object recognition requires to separate the learning process, leaving it exponentially hard, and the recognition process itself. The learning process consists in extraction of groups of features (properties of elements of an object and the relations between these elements) that are common for objects of the same class. The main result of a paper is a reconstruction of a logic-predicate recognition cell. Such a reconstruction allows to recognize objects with descriptions not isomorphic to that from a training set and to calculate a degree of coincidence between the recognized object features and the features inherent to objects from the extracted group.

Using non-invasive and non-contact sensors to measure a person’s presence or movement helps improve the quality of life for both healthy people and patients. In this paper, a method of measuring the presence and motion of a person is proposed by utilizing UWB Impulse Radar, which is low power consumption and safe to radiate to the human body. The experimental stage of this study is divided into the stage of extracting features by signal processing from radar signals, the stage of generating datasets with 3~6 kinds of labels, and the stage of performing and verifying machine learning by imaging. In this experiment, a small number of images were used because only good quality signals were selected and used by radiating radar signals to the human body. The experiment result show high accuracy when using neural networks such as GoogLeNet and SqueezeNet. Experiments in this study confirmed that radar signals could be used to detect human presence and motion as a result of studies using the proposed method.

The non-selective second language course at vocational colleges and universities makes teachers strive at fostering students’ motivation to learn by choosing from a variety of enhancing factors. Teacher’s personality and skills if they comply with pedagogical ethics are considered to be inspiring for students to learn. The aim of this piloting study was to collect empirical data and ascertain the actual motivational power of ethical teaching skills when organizing and conducting autonomous learning activities for college students on the example of educational computer games.
The ethical teaching skills applied when creating and using computer games as out-of-classroom voluntary activities were conceptualized. Their possible influence on learning motivation of college students was analysed with the use of oral and written feedback procedures, and statistical data retrieved from Metrika.Yandex and students’ certificates submitted.
This allowed to determine five specific groups of students valuing the activity, which in their turn were united into two larger formations: students with distinct intrinsic motivation in the activities-related areas (60%) and socially and psychologically responsive students (40%). A possible method to evaluate statistically the efficiency of some ethical skills specified is suggested, whereas the other skills need further research under modified parameters.
Thus, ethical teaching skills are effective in the case the college students possess an intrinsic motivation in an area actualized by an activity suggested. Therefore, they are unlikely to be regarded as an independent motivator, but the indicator of a particular intrinsic motivation characteristic to a student.

Bus Rapid Transit in Purwokerto-Purbalingga is a new mass transportation mode. Recently, the execution of the BRT has been going on for three years. In terms of service standards to ridership, the BRT has been fulfilled the requirement. However, during the execution, it shall be supported by the non-ridership (local communities) who get the impact as public engagement. The non-ridership impact is captured by observing their presumptions. This study uses quantitative method and survey technique to collect the data by spreading questionnaires to the non-ridership in Purwokerto and Purbalingga. The collected data is analyzed by Analysis of Variance (ANOVA) and Structural Equation Modelling Partial Least Square (SEM-PLS). The ANOVA results show that gender, age ratio, and residence (living area) are significant presumption factors. According to SEM-PLS model, the R-squared of non-ridership presumption variables toward the BRT execution as excellent public transportation is at 51.8% (moderate level). It is found that the economic variable affects the excellent public transportation variable is at 41.4%, and the social variable have a correlation with the excellent public transportation variable is at 36.2%, but not with the environment variable (5.6% only). Following up these findings, it is recommended that public engagement through the non-ridership presumption will lead the BRT provider to purpose some programs to improve the service and increase the occupancy. So that, the proposed program will attract the sense of awareness and public engagement of the non-ridership toward the BRT execution.

Senior projects allow students to move the learning process from basic knowledge to an interdisciplinary approach. The purpose of this research is (1) to analysis attitude and perception, which is a collaboration between teachers and students to develop a model for clustering of appropriate advisors and advisee who cooperate in senior project, and (2) to develop factors that are significant to predict the right match in senior projects course. Data collection was a questionnaire consisting of 463 samples from 7 administrators, 68 lecturers, 26 staff and 362 students from two institutions: The Rajabhat Mahasarakham University, and the University of Phayao. The research methodology was designed and divided into three sections: preparation, implementation, and conclusion. The result shows that the satisfaction and the overall acceptance level were at a high level (mean = 4.04, S.D. = 0.88). Moreover, the developed model has the highest level of efficiency (accuracy = 98.06%). While the factors that are important for matching recommendations consists of 9 factors: policies of the organization, vision of the organization, mission of the organization, experience and achievements of researchers, qualifications of research team, interest in the research topics, impressions and examples in the past, technology and laboratory support, and budget support. For the future, the researchers are aimed to research on the development of students’ academic achievement and aims to promote a learning culture based on the results of this research.

The performance ratio (PR) based on IEC 61724 standard is calculated under the influence of seasonal variations and the capability of the system. On the other hand, The National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy proposed the weather-corrected performance ratio (PRcorr). This PRcorr index calculates the performance of the PV system which has taken the weather variations that influence the cell temperature into account. Both techniques were compared with the simulation program to analyze the PR index according to system conditions. The study site is the on-grid PV rooftop at Kasetsart University Chalermphakiat Sakon Nakhon (CSC). The PV panel is oriented to the southwest, 215 degrees azimuth. The angle of the inclination of the panel is 17 degrees. The result shows the trend of monthly PRcorr with little variations. At the same time, the PR value over the year has a lot of variations. The variation of PRcorr and PR is 2.39 and 5.07, respectively. PRcorr has low variability due to the correction of weather factors. The average cell temperature is an important variable. To calculate the average temperature of the panels, one year of data is needed to filter out distorted information. The system available condition is an important factor for the on-grid PV system at low voltage.

We propose a multilevel method to speed highly optimized parallel codes whose runtime increases faster than their workload. This method requires the ability to solve large in- stances by decomposing them into smaller instances. Using a simple parallel computing model, we derive a mathematical model that predicts whether or not our method can im- prove performance and also predicts the amount of improvement attainable. Our method is tested and shown to be effective on three highly optimized BLAS (Basic Linear Alge- bra Subprograms) routines from Intel’s Math Kernel Library (MKL). Those routines are cblas dgemm, cblas dtrmm and cblas dsymm. On the Intel Knights Landing (KNL) platform our method speeds cblas dgemm by 33%, cblas dtrmm by 50% and cblas dsymm by 49% on double-precision matrices of size 16K x 16K, when the KNL’s default memory-clustering configuration (cache-quadrant) is used.

In modern deregulated power industry, private sector has invested a lot to supply for extended power demand using the preexisting power system framework. This resulted into increased loading of transmission lines which has to work now to hit their thermal limits. The overloading of transmission line resulted in congestion and hence increase in loss of power in the system. One of the efficient ways to reduce congestion is by enhancing the available transfer capacity (ATC) of the power system. ATC enhancement can be achieved by application of FACTS devices. This paper presents an innovative method to mitigate congestion by locating TCSC in the IEEE 30 bus system. The allocation of TCSC is done by using ACPTDF sensitivity factors while the parameter setting is done by applying Grey Wolf Optimization (GWO) method. The effective application of GWO is demonstrated in this paper to reduce active power loss, enhancement of ATC value with reduction of reactive power loss and to optimize TCSC size through a multi objective function. The suitability of algorithm is established through concerned figures and tables.

The aim of this paper is to identify a critical link in the man – machine – environment system in the case of an adverse event, such as a hay baling fire, based on a comprehensive risk assessment method. The rate of spread of fires in agriculture depends on meteorological conditions, with large areas affected and potentially endangering the surrounding buildings, facilities. Access to fires is difficult and can extend to forests. Water is often lacking at the scene of a fire, which should be extinguished, and water sources are usually located over long distances. The paper addressed a specific example using a comprehensive method. The process of risk assessment in the work process was determined by the following steps: assessment of the overall risk of the work equipment, assessment of environmental impact, assessment of the person’s ability to manage risk, calculation of the resulting risk value, comparison of calculated risk value and acceptability of risk value, proposal of measures. The result of the analysis was the finding that the primary cause of the fire is the environment, i.e. high ambient temperature. The critical element in hay baling work system is the work environment. The risk ratio was estimated at 5.78. The level of risk was low due to the rapid intervention of the human factor. Based on the results, the technical measures mentioned in the paper were proposed to the operator. The paper found that maintenance of the machine is important for protection against agricultural fires, where the human factor plays an important role in the man – machine – environment system.

Vehicle systems are controlled by embedded electronic devices called electronic control units (ECUs). These ECUs are connected together with network protocols. The Controller Area Network (CAN) protocol is widely implemented due to its high fault tolerance. However, the CAN is a serial broadcast bus, and it has no protection against security threats. In this paper, we propose a fast stream cipher based on a chaos neural network (CNN) that is able to generate pseudo-random numbers at a high speed, faster than that of the Advanced Encryption Standard, to protect ECUs on the CAN bus by encrypting CAN messages. We discuss the chaotic orbit of the CNN and statistical properties of pseudo-random numbers from the CNN. For a stream cipher, it is very important to share the symmetric key. We designed a symmetric key that is shared with ID-based encryption without the need to use digital certificates. We evaluated our method’s performance with embedded boards and showed that the stream cipher is efficient for the embedded software of the ECU. Further, it does not need a hardware security module to accelerate the encryption.

Vehicle systems are controlled by embedded electronic devices called electronic control units (ECUs). These ECUs are connected together with network protocols. The Controller Area Network (CAN) protocol is widely implemented due to its high fault tolerance. However, the CAN is a serial broadcast bus, and it has no protection against security threats. In this paper, we propose a fast stream cipher based on a chaos neural network (CNN) that is able to generate pseudo-random numbers at a high speed, faster than that of the Advanced Encryption Standard, to protect ECUs on the CAN bus by encrypting CAN messages. We discuss the chaotic orbit of the CNN and statistical properties of pseudo-random numbers from the CNN. For a stream cipher, it is very important to share the symmetric key. We designed a symmetric key that is shared with ID-based encryption without the need to use digital certificates. We evaluated our method’s performance with embedded boards and showed that the stream cipher is efficient for the embedded software of the ECU. Further, it does not need a hardware security module to accelerate the encryption.

Low power OTAs are the most preferred circuits in the realization of continuous time filters of analog front end of wearable healthcare devices. A low transconductance OTA with series parallel current mirror to realize large time constant of the filter is designed. The differential pair of the OTA uses bulk driven PMOSFETs and the subthreshold operation of the circuit achieves 44 nW power with supply voltage of ±0.4 V. The designed OTA has DC gain of 29.59 dB and UGBW of 34.28 KHz. Using the proposed OTA, a multifunction filter which can operate as low pass and high pass filter, having cut-off frequency in the range 25 Hz – 225 Hz is designed in gpdk 180 nm CMOS technology. The simulation is performed using Cadence virtuoso design environment.

Optimal integration of distributed generation (DG) into the distribution system results in reduced power losses and improved bus voltages. In this article, a combination of two techniques has been analyzed:
The integration of DG and reconfiguration of the distribution system by removing the Normally Open Point NOP in different places of the system.
These two techniques are applied to a real distribution network ” distribution network of Kenitra city in Morroco”, considering as key objectives the reduction of power loss and improvement of voltage profile.
To investigate the effectiveness and robustness of our system a model was performed using ETAP. The simulation results improve that we can minimize greatly the power losses in the distribution network by the implementation of DGs and reconfiguring our distribution network.

Global changes caused by the IV Industrial revolution and globalization processes resulted in a redistribution of roles of participants in innovative infrastructures of countries. Universities are leading both in terms of generating R&D products and in terms of developing business activities. Now there is a problem of insufficient methodological support of technological universities for pricing R&D products developed and prepared for transfer to the business environment. Existing methods and models do not meet the needs of the market, which is growing rapidly. At the same time, the market is characterized by a high degree of volatility. The purpose of the article is to develop a method for modelling prices for R&D products from universities to the business environment, which takes into account: the specifics of the R&D product, modern market features for this R&D product; the nature of the transfer and commercialization of this R&D product. The article identifies the factors that determine the processes of transfer, commercialization and market launch of R&D products, which affect the pricing of R&D products. Groups of characteristics that characterize systematize these factors: 1) consumer value of R&D product; 2) market susceptibility of R&D product; 3) transfer and commercialization processes of R&D product. Justified a number of factor attributes within the formed groups and assigned them the values of linguistic terms for adjusting the price of R&D product using fuzzy set theory algorithms. The method takes into account elements of cost, revenue and comparative estimation approaches. The method makes it possible to adjust prices for R&D products, taking into account heterogeneous features in the composition of R&D products and compare them with market analogues of R&D products. This contributes to achieving a higher level of pricing accuracy for R&D products when they are transferred from the university to the business environment. The resulting prices are compared with market prices for competitive analogues, which makes it possible to determine the scenario of transfer and commercialization of R&D product; justify the strategy of market development of R&D product; increase the level of manoeuvrability of pricing management for R&D product. The model was tested on a number of R&D products developed at Lviv Polytechnic National University (Ukraine). Application of the proposed method is advisable in the short-and medium-term forecasting period.

In this paper, the performance evaluation of a line-start three-phase Synchronous Reluctance Motor (SynRM) with symmetrical distributed brass rotor bars is presented. The machine, which has been designed from a conventional three-phase induction motor (IM) NEMA frame stator is proposed as an alternative to a squirrel cage induction motor (SCIM). The 2D Finite Element Analysis (FEA) under ac magnetic transient solution was used to study some performance parameters of interest during starting transients. The experimental measurements were carried out in order to validate the numerical computation, to analyze the starting transients, and to explore the dynamic responses due to load variations. The FEA and experimental results of the synchronous reluctance motor with brass rotor bars (SynRM-BRBs) are compared to the results of a conventional three-phase SCIM of the same NEMA frame stator. The results evidenced that the reluctance torque developed by the SynRM-BRBs has a compounding effect on the accelerating torque, reaching its steady-state operational condition faster than the SCIM. The dynamic response of the SynRM-BRBs is faster in contrast to the SCIM during load variations. Furthermore, it was noted through measured results that the proposed line-start three-phase SynRM had a reduced dynamic no-load, and load current as opposed to the SCIM, thus positioning itself as a good candidate to replace the SCIM in applications that require a line-start ac motor with good starting transients and fast dynamic responses.

he dog is one of the first animals domesticated by human, and for thousands of years, it has been artificially bred into hundreds of types in order to provide certain traits that humans want. Nowadays, the selection of dogs by potential adopters has become a problem due to the availability of different type of breeds with their physical and mental characteristics. This study aims to design a decision support system through an analytical model that uses variety of data on the characteristics of purebred dogs obtained from different sources. Data from official sources is obtained from international purebred dog organizations that set the standards for each breed type, while data from unofficial sources is obtained from the dog lovers community, experts, and kennel owners. The result of the study provides appropriate recommendations to potential adopters in selecting a breed that suits their preferences and needs.

This paper focuses on a string encoding algorithm, which produces sparse distributed representations of text data. A characteristic feature of the algorithm described here, is that it works without tokenizing the text and can avoid other data preparation steps, such as stemming and lemmatization. The text can be of arbitrary size, whether it is a single word or an entire book, it can be processed in the same way. Such approaches to text vectorization are not common in the machine learning literature. This sets the presented encoder apart from conventional text vectorizers. Two versions of the encoding algorithm are described and compared – the initial one and an improved version. The goal is to produce a robust data preparation procedure, capable of handling highly corrupted texts.

Over the last decades, numerous distributed consensus-based algorithms have found a wide application as a complementary mechanism for data aggregation in wireless sensor networks. In this paper, we provide an analysis of the generalized Metropolis-Hastings algorithm for data aggregation with a fully-distributed stopping criterion. The goal of the implemented stopping criterion is to effectively bound the algorithm execution over wireless sensor networks. In this paper, we analyze and compare the performance of the mentioned algorithm with various mixing parameters for distributed averaging, for distributed summing, and for distributed graph order estimation. The algorithm is examined under different configurations of the implemented stopping criterion over random geometric graphs by applying two metrics, namely the mean square error and the number of the iterations for the consensus. The goal of this paper is to examine the applicability of the analyzed algorithm with the stopping criterion to estimating the investigated aggregate functions in wireless sensor networks. In addition, the performance of the algorithm is compared to the average consensus algorithm bounded by the same stopping criterion.

Practically level of design element (i.e., explore, sharing, connect) is an essential of heutagogical approach. The self-determined learning process can be at ease with the implementation of these elements, and the critical step is reliability to measure teaching and learning feedback. Although various instruments were proposed in the literature to assess heutagogy elements, the specific potential Rasch Measurement Model to determine the practicality levels of heutagogy element is less emphasized. This paper aimed to validate the research instrument (six constructs with 65 items). The instrument was administered to N=175 students for a pilot study. The Rasch model was conducted to examine reliability (0.93, 0.94) with ? = 0.97, separation index (3.75, 4.01) for item and person, respectively. Besides, item fit (three-item dropped), polarity and standardized correlation residual (no overlapping items). The findings have shown that the instrument has high validity and reliability for use in measuring the practical level of heutagogy elements.

With recent advancements in medical filed, the quantity of healthcare care data is increasing at a faster rate. Medical data classification is considered as a major research topic and numerous research works have been already existed in the literature. Presently, deep learning (DL) models offers an efficient method for developing a dedicated model to determine the class labels of the respective medical data. But the performance of the DL is mainly based on the hyperparameters such as, learning rate, batch size, momentum, and weight decay, which need expertise and wide-ranging trial and error. Therefore, the process of identifying the optimal configuration of the hyper parameters of a DL is still remains a major issue. To resolve this issue, this paper presents a new hyperparameters tuned DL models for intelligent medical diagnosis and classification. The proposed model is mainly based on four major processes namely pre-processing, feature extraction, classification and parameter tuning. The proposed method makes use of simulated annealing (SA) based feature selection. Then, a set of DL models namely recurrent neural network (RNN), gated recurrent units (GRU) and long short term memory (LSTM) are used for classification. To further increase the classification performance, differential evolution (DE) algorithm is applied to tune the hyperparameters of the DL models. A detailed simulation analysis takes place using three benchmark medical dataset namely Diabetes, EEG Eye State and Sleep stage dataset. The simulation outcome indicated that the DE-LSTM model have shown better performance with the maximum accuracy of 97.59%, 88.52% and 93.18% on the applied diabetes, EEG Eye State and Sleep Stage dataset.

In the recent times power shortage has been a major setback to deal for the effective operation of power systems. Bridging the gap between generation and demand is known as Demand Side Management (DSM). For an effective DSM strategy to be implemented, it is crucial that both utility and customers be involved. By DSM, the energy generated is used more effectively. This reduces the burden of the utility to invest on additional generation. In this work, a DSM strategy has been performed on two systems: (i) on RTS 24 bus system with wind energy sources distributed at some nodes of the system (ii) on an institutional load with installed solar power plant. A generic DSM strategy to effectively utilize the generated energy and to minimize the utility bills for the customer has been proposed. An instantaneous billing scheme has been proposed. By implementing the instantaneous billing scheme, customers can be persuaded to change their consumption behavior, matching the demand with available generation. The results obtained are promising, with a resulting flat load profile and reduced utility bills for the customer.

A data analytic approach presented in this work covers both data descriptive and predictive modeling with two main objectives: (1) discovering factors related to longevity of populations in the two most populated nations, China and India, and (2) generating life expectancy predictive models for both countries. Descriptive modeling methods to explore major environmental and economic factors anticipating to affect longevity patterns of people are web graph analysis and chi-squared automatic interaction detection (CHAID) techniques. Web graph analysis has been applied for the ease of visualization and CHAID is for discovering factors leading to longevity. From the analysis results, particulate emission including ozone pollution and PM2.5 concentrations is the most important factor threatening life of populations in both China and India. To predict number of years an individual is expected to live based on the available environmental and economic factors, several statistical and machine learning techniques are applied and it turns out that a linear regression model yields the most accurate prediction result.

This paper provides an overview of Intelligence, Surveillance, and Reconnaissance (ISR) data with respect on NATO standards and recommendations; further presents methods, tools, and experiences in ISR data processing in military operations. The steps of the Intelligence cycle and disciplines Business Intelligence (BI), Data Warehousing, Data Mining, and Big Data are presented in the introduction. The literature review is oriented to the analysis of intersections between ISR and BI methods. The next chapter describes the ISR data processing in detail; there are listed structures, formats, standards, and data from the operational point of view. The ISR operational picture is explained, and steps of the ISR data mart is completed. The last part is oriented to Big Data processing; NoSQL, in-memory and streaming databases. The last two chapters are focused on the description of research results in the processing of ISR data. The ISR data mart experiment processes the radio transmission data that consists of detected radio signals. Results are visualized in RapidMiner Studio. The Big Data experiment is realized in Apache Hadoop.

The accurate identification and characterization of microbiological species is a must that allows us to design engineered pharmacology to tackle down the diversity of diseases derived of them. This paper presents a study about the usage of both classical dynamical and electrodynamics in conjunction to the Feynman’s path integral to describe as well as identify the displacement of bacteria in closed spaces. Our methodology consists in the in the usage of the usage of probability amplitude to investigate the theoretical motility of bacteria can be disturbed through electrical interactions from the fact that them contain ions in their biochemical composition.Our study yields that bacteria might exhibit a pattern of probabilities as function of space and time. It would be advantageous for an engineered nanodevice that would sense them uniquely through electrical interactions.

The ability to verify the critical risk factors related to an effective diagnosis is very crucial for improving accuracy on coronary heart disease prediction. The objective of this research is to find the best predictive model for coronary heart disease diagnosis. Three approaches are set up to achieve the goals (1) investigating the classifier algorithms that are most suitable for the Thai heart disease dataset in this study (2) exploring features analyzed to be the significant risk factors in the predictive model, both major risk factors, and socioeconomic status and (3) rediscretizing the predefined clinical values on certain major risk factors. In order to achieving the optimal model before incorporating with feature selection process, several classifier approaches are conducted in this experiment. The study shows that the most effective classifiers ranked from the highest accuracy are Support Vector Machine, Naïve Bayes, Decision Tree, and Multi Layer Perceptron. Support Vector Machine produces the highest accuracy of 88.18%, with respect to both major risk factors and socioeconomic factors. Moreover, when adjusted thirteen major risk factors and five socioeconomic factors altogether, the accuracy is proved to be better than conducting each one alone. To investigate the better predictive performance of our study, feature selection methods of both filter and wrapper groups are employed with exploring the hybrid models to identify the most relevant features for Thai coronary heart disease. Relief Attribute Evaluation with Bayes Theorem is proved to be the best one with the accuracy of 92.59%, classified by SVM. To prove the accuracy enhancement, we perform rediscretization model on predefined medical values to examine different physical and personalized information of each person which can be incurred the coronary heart disease in different situation. The findings found that equal-depth rediscretization values on 7 major risk factors as Obesity, Hypertension, age, LDL, HDL, Fasting Blood Sugar, and Triglyceride, influences and improves with the better accuracy than predefined values of 95.50% classified by SVM. Thus, this finding shows that the proposed technique definitely outperforms predefined values from medical field.

Nigerian power utility companies particularly the distribution and generation aspects were recently in the process of national power reform converted from public to private service by privatization. Prior to these development, power utility companies’ performance is low due to poor operational style that leads to inadequate revenue generation. Thus, the task before the privatized companies includes autonomy, high reliability operation and brake-even management. To achieve these goal, frequent outages and system collapses must be minimized. One of the methods of achieving this is using power flow to improve the reliability of power system which will subsequently improve other lacking factors. A developed software for Newton-Raphson power flow was tested with a known solution network and the results obtained are accurate and reliable. Therefore, this paper presents an application of this software on real-time transmission network. Nigerian 330kV transmission grid is considered as case study. The power flow analysis of this grid was carried out and the network operational parameters were obtained. These results are stated and carefully analyzed. In practice, power utility distribution network of medium voltage of 11kV feeder was also tested with this NR-Software in ascertaining network reliability and in the course of adding public transformers to utility feeder network.

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide su the optimal split value.

Age and growth are two essential rice biophysics parameters used to determine the health parameters and production rate. The spatial data of both parameters can utilize remote sensing technology, which in turn makes use of several vegetation indices to achieve accurate estimation. However, due to the rapid changes in rice plants’ characteristics, it is essential to study vegetation index utilization using a multitemporal method to improve its accuracy. Therefore, this research uses a multitemporal Enhanced Vegetation Index (EVI) to estimate rice’s age and growth model. The multitemporal EVI patterns were observed to estimate the Time Early Planting (TEP) and the maximum EVI value of rice in an area. The results showed that the maximum EVI value in the rice fields of Demak Regency has a class range of 0.4 to more than 0.9. The highest value is in the class of 0.80 – 0.85 covering 12023.28 ha, followed by 0.75 – 0.80 at 11834.19 ha. Furthermore, the multitemporal EVI method on Landsat 8 images was used to estimate the rice age with accuracy or RMSE of 7.7 days. The result also showed that this value is good enough because the RMSE is still in the same range of paddy growth phases.

The paper is devoted to the presentation of a new methodological approach (new philosophy) to the formulation and solution of directed search and synthesis of electromechanical objects for a given function problems. The object under the study is a class of electrical machines and electromechanical devices which operation is carried out with a variable structure or geometry of the active zone. The research is relevant due to the characteristic trend in the evolution of modern technology which is associated with the creation of complex technical systems with the ability to change the structure and spatial geometry of the executive body in accordance with changing of external factors. The novelty of the synthesis methodology is determined by genetic nature of the technical evolution of electromechanical objects and by genetic programs of structure formation using. According to the results of research the genetic principles and macrogenetic programs of electromechanical objects with variable spatial geometry of active parts have been determined for the very first time. The area of existence and results of genetic synthesis of “elastic” electromechanics objects are presented. The reliability of genetic models, genetic programs and the results of the electromechanical objects with adaptive spatial geometry of the active zone synthesis is confirmed by the results of evolutionary experiments.

The pre-reason of the Smolensk catastrophe was a dense fog. The pilots took three trials to find the proper way to airfield. Each case the tower communicated: you are on the curse and path. Pilots, however, resigned. In third critical trial the co-pilot prolonged the response second ring to 8 seconds and the engines could not already take the plane up. It collided with a thick tree and made an upside down. Next it crashed on the ground and left a bloody trace of merely ~100 m long. This means that a mean acceleration at initial speed of 100 m/s and linear braking reached -50 m/s2. The real values could be even higher as the peak slowdown is always higher the mean. The clue of the Smolensk crash was then a fog and high azimuth error of radar. If it was correct but the ceiling too low, the plane could lose the under-carriage but avoided the upside down and the bloody crash.

Modern digitalized cyber domains are extremely complex ensemble. Cyber attacks or incidents against system may affect capricious effects for another system or even for physical devices. For understanding and training to encounter those effects requires an effective and complex simulation capability. Cyber Security Exercises are an effective expedient for training and learning measures and operations with their outcomes in that complex cyber domain. Learning in cyber security exercises is relevant for different level actors in organisation hierarchy. Technical experts are able to train the technical capabilities whereas decision makers are able to train the decision-making capabilities under hectic cyber incident. In this paper, the pedagogical aspects of cyber security exercises are discussed in accordance with the law of the lifecycle of the cyber security exercise: planning phase, implementation phase, and feedback phase.

The most common non-communicable diseases, such as cardiovascular diseases and cancer, are a problem in global and national growth. The World Health Organization considers it a priority to study the specific causes of these diseases for trend monitoring. The aim of this paper is to identify a hierarchy of clusters of Portuguese mortality by non-communicable diseases using the agglomerative hierarchical cluster analysis. The Euclidean distance with complete linkage and average linkage criteria are used. These methods identify six clusters with both criteria, indicating some order of disease severity in the way clusters joint together. Special attention should be given to diseases in the last two clusters, where the last one is formed by ischemic heart disease, cerebrovascular diseases and larynx / trachea / bronchi and lung malignant tumor, all for males. In fact, these clustering results show that male gender seems to be a risk factor for at least two groups of the non-communicable diseases. Other suggested risk factors and / or pathophysiological mechanisms that in a direct or indirect way may enhance the common development of the pathologies found in the clusters arising from this study should also be an object of priority study.

The study aims to demonstrate how the use of the design process can align a brand’s strategy with the interests of its target audience through a digital product based on a case study. Currently, Lexus internal studies show that there is a possibility to meet the needs of new audiences, beyond the traditional ones (men, 50 In order to achieve this goal, the following objectives have been defined: communicate the brand and its cars’ main values and philosophy, namely ecology, economy, safety and comfort; promote its services and products; and design an interface that guarantees users easy, pleasant and attractive navigation. The work process consisted of identifying the brand’s strategy and values, identifying users, analyzing the main competing brands in the market, and designing a prototype within the framework of User-Centered Design, for which it had to address crucial issues such as the application of character models, UX and UI design, the creation of wireframes and user flows, interface design and the development of usability tests. The results demonstrate that it is possible to align the strategic interests of brands with the needs, objectives and expectations of users in a context of increasing global concerns of citizens related to reuse and sustainability. In this sense, it is vitally important that brands adopt Design processes in order to converge their own brand interests with people’s demands.

This paper proposes a closed association rule generation technique to investigate the association patterns of diseases that are frequent co-occurrence. Diseases records of 5,000 patients are studied to find the association patterns of disease co-occurrence. The CHARM algorithm is adapted to find frequent diseases that can cover all-important patterns with a small number. Then the association patterns of disease co-occurrence are created in a form of association rules from the frequent diseases. The rules represent diseases associated with other diseases. Accuracy and prediction ratio are defined to evaluate the generated association patterns. From the experimental results, the generated association patterns give 79.76% of accuracy and 84.03% of prediction ratio although the number of generated association patterns is small. Moreover, the top-10 association patterns of disease co-occurrence are investigated. Besides, the 5 most frequent diseases are found to deeply study the other related diseases of them. From the investigation, we found that diabetes mellitus, metabolic disorders, and renal failure are highly related to hypertensive diseases with 88.81% of confidence. In addition, we found that influenza and pneumonia, plastic and other anemias are highly related to metabolic disorders.

The insulation condition of a transformer decides the longevity of the equipment. The unpredicted failure of power transformer will lead to major disaster in the distribution network and it affects both environment and public safety. Nowadays synthetic oil and natural esters are alternatives to transformer oil because of the biodegradable nature. In this paper, investigations were carried out to study the performance of the blended ester. The different properties investigated were viscosity, breakdown voltage, flash point, dielectric dissipation factor and moisture content. Comparisons of the properties were made between mineral oil, vegetable oil without additives and with additives. Further Investigation was carried out to study the impact of antioxidants and degasification. The results indicated that the addition of antioxidants and degasification of the vegetable oil improve significantly its voltage withstanding capacity. The Indigenous oil is code named as DM; Indigenous oil with DBPC is codenamed as DM1, Indigenous oil with BHA is codenamed as DM2. The results have been tabulated and found to be satisfactory.

Dynamic modeling has been a crucial study in many areas of the engineering field. In this paper, we apply the Newton-Euler equation of motion to a two-DOF parallel mechanism solar tracker which is a close loop mechanism. The aim of this study is to show a simulation of the dynamical model with feedback control using a PD controller to orientate the solar panel perpendicular to the sun rays. The mechanism is modeled in the form of a system of algebraic differential equations. First, kinematic constraint equations were constructed in the form of algebraic equations to specify the dynamic interactions at joints. We use the Baumgarte stabilization method, a constraint violation method to eliminate computational error incurred by numerical approximation. Then, the dynamic equations of the system were formulated using the Newton- Euler equation of motion. To describe the translation and rotation motions, we apply Cartesian coordinates and Euler parameters. Simulation of driving the solar panel to reach the desired configuration is made, and the result shows that the PD controller provides good performance of the mechanism regardless of the complexity of the dynamic behavior of the mechanism.

Electrical and optical properties of polymer nanocomposite thin films have been analyzed to study their reliability and competency as a component for optoelectronic devices such as LED and solar cells. Polymer nanocomposite encounters various challenges, such as the dispersion of nanoparticles in the matrix that hinders their efficiency for potential devices. In this paper, two types of polymer nanocomposites have been fabricated, and their Johnson noise, current density-voltage, and optical have been measured. The first type of nanocomposite produced through an in-situ method, that is by impregnating CdS or CdSe nanoparticles in conjugated polymer, P3HT (NP-CdX:P3HT). The nucleation of the nanoparticles was done using gas exposure. The second type is by directly adding CdS or CdSe quantum dots into P3HT (QD-CdX:P3HT). Both kinds of polymer nanocomposite thin films were fabricated using modified Langmuir-Blodgett technique. Results showed that for frequency above 10 Hz, the Johnson noise was less than 1× 10-27 A2/Hz, regardless of the quantity of quantum dots or nanoparticles. The J-V results show (NP-CdX:P3HT) electrical performance compared with QD-CdX:P3HT. High polymer crystallization of NP-CdX:P3HT thin films is revealed by UV-Vis absorbance spectra. The quantum confinement effect is evidence through peak shifting and depreciation of absorption. The photoluminescence intensity of thin films decreased when they were exposed to the gas. It can be concluded that the NP-CdX:P3HT nanocomposites can be further studied as they have greater potential to be exploited in optoelectronic devices.

This paper is an extension of Novel Flexible Chip-less Bow-Tie RFID tag in which we presented the design, testing and fabrication of the tag and compared the results with Octagonal Chipless RFID tag. The chipless RFID tag was designed by using simulation software CST microwave studio and fabricated by using laser etching technique on a flexible polymer substrate Polyethylene Terephthalate (PET). The tag operates at frequency ranging from 8 to 18 GHz uses the Frequency Selective Surface (FSS) approach. A series of experiments are performed to measure the Radar Cross Section (RCS) in an anechoic chamber. The tag design is composed of six concentric Bow-Tie shaped loop resonators with one unitary element. In this paper, we demonstrated the Singularity Expansion Method (SEM) based circuit modelling and the transient behaviour of the RFID tag is performed. The coupling coefficients and the induced currents over the surface of Bow tie shaped rings are evaluated. The maximum read range is evaluated and the Bow-Tie RFID tag is proved to be more accurate and efficient with the variation of distance up to 1.8m at 0dBm which is extendable to 2.14m for higher input power. This range is maximum to our knowledge for such a high-frequency range of 8-18GHz. The 4-bits Bow-Tie Chipless RFID tag design is compact and can be deployed commercially for general IoT applications.

This paper presents a comparative study for using the classic networks in remote sensing images classification. There are four deep convolution models that used in this comparative study; the DenseNet 196, the NASNet Mobile, the VGG 16, and the ResNet 50 models. These learning convolution models are based on the use of the ImageNet pre-trained weights, transfer learning, and then adding a full connected layer that compatible with the used dataset classes. There are two datasets are used in this comparison; the UC Merced land use dataset and the SIRI-WHU dataset. This comparison is based on the inspection of the learning curves to determine how well the training model is and calculating the overall accuracy that determines the model performance. This comparison illustrates that the use of the ResNet 50 model has the highest overall accuracy and the use of the NASNet Mobile model has the lowest overall accuracy in this study. The DenseNet 169 model has little higher overall accuracy than the VGG 16 model.

Estimation of displacement is an information required for daily operation monitoring systems to monitor human health or to locate users in buildings, basements, tunnels and similar places which under the same conditions that the global positioning signal (GPS) level is from very weak to completely absent; and is the measurement technique by using multimetric data fusion. Most current displacement estimation methods require a lot of infrastructures and devices such as UWB, wifi access points, cameras. Hence, estimation methods that ultilize inertial measurement unit (IMU) and integrate acceleration to get diplacement are effective alternatives since the three-axis accelerometer embedded in IMU usually low cost, easy to adjust and low noise. The advantage of this approach is that the IMU-based device is compact, easy to install and put on user’s body. However, these methods expose some weaknesses when used in large-scale indoor structures such as multi-storey buildings due to the need to compensate azimuth estimation which is drifted overtime and is employed for calculating displacement with refer to earth frame as a base station. This article proposes a low-cost wireless displacement estimation system developed with IMU. The system employs a Kalman-filter type in indirect form for orientation estimates and Median-filter algorithm for classification of motion modes. In order to verify the proposed system in terms of accuracy and feasibility, a device was designed in a wearable form and tetsted on a multi-storey building in university. The wearable device ultilized IMU model MPU9250 and results recored wirelessly via Xbee devices in order to test the system performance in such senarios as climbing/descending staircases only, climbing/descending staircases through one floor combined with walking. Experiments are repeated for root mean square error (RMSE) computation based on the ground-truth. The proposed system performance is evaluated accordingly to RMSE. The experimental results demonstrate RMSE of 3.56%, 1.43%, for climbing/descending staircases only, climbing/descending staircases one floor combined with walking, respectively.

As manufacturing efficiency has become a main focus of today’s business, it is very critical to surge the throughput by developing different test strategies. With throughput, testing cost also has been recognized as the major challenge in the future of leading semiconductors. Reducing test time is a significant effort to maximize throughput as the complexity increases in future generation outcomes and devices. So, low-cost Automatic Test Equipment (ATE) with parallel test can be promoted as the obvious solution for challenges said above. In parallel testing, multiple devices-under-test (DUT) can be tested at a time that enhances way of testing by increasing product flow, limiting gross test times, and efficient usage of tester. The proposed Integrated Circuit (IC) tester is used to implement multi-site testing (Quad-Site testing) and concurrent testing. It exhibits multi-site efficiency which substantially enhances the throughput by reducing test time. Modular, re-configurable test system provides cost-saving solution. To confirm these effects, authors have presented experimental results for Quad site testing of different ICs namely Decoder, Buffer, Multiplexer and Logic gates. This portable IC Tester handles variety of IC packages like Dual Inline Package (DIP), Small Outline Integrated Circuit (SOIC), Thin Shrink Small Outline Package (TSSOP). With functional test, the proposed tester also verified the AC Parametric tests (i) Propagation Delay is 20ns (ii) Operating frequency with 50MHz for Decoder IC (74HC138). The proposed IC tester consumes 70% less power and throughput enhanced by 11% compared to existing IC testers.

This work presents an innovative solution for parking-sharing of private institutions based on daily occupancy patterns and using different real time tracking methods of vacant parking slots. The research objective consists in finding the most accurate cars detection method, for determining of vacant parking slots and updating them on application web page. Beside the technical innovation represented by image processing algorithms used, this paper promotes the concept of sharing economy with many social benefits like car flow optimization, reducing fuel, pollution, loss of time and creating financial advantages for parking owners. The main software component is a web application which is connected with Raspberry Pi microcontroller, 2 Pi cameras and one fix camera for parking management. It facilitates reserving a place, opening the barrier and allows entering, exiting and revising the number of vacant slots and synchronization with the web application and the supporting database. The web application provides the following facilities: real time parking status view, reservation on a specific time by license plate number, administration module that includes payment system and updates about users and prices, implementation of the gamification concept in the management of parking spaces. The solution was piloted at Lucian Blaga University of Sibiu (LBUS) Romania. The developed solution is flexible, extensible and applicable to crowded university cities, but also to other private organizations that have inefficiently operated parking slots.

Visualization plays an important role in teaching and learning. The ability of the learner to grasp the visual contents are better than that of textual contents. Traditional teaching methods often revolved around instructions and recitation techniques. However, most of these approaches were dormant and did not call for active learning. The proposed AGDO- Analyze, Group, Design & Optimize methodology was inspired by these obstacles and creates an engaging and wholesome experience for the students. A sample of 47 undergraduate students was used to analyses the feasibility and effectiveness of this teaching mechanism. Students first tackled and analyzed the problem statement with their preconceived knowledge. They were then segregated into groups wherein they designed solutions collectively. Finally, they tried to find an optimized solution considering all the suggested designs. Observations were validated based on feedback and how well the students were able to perform. The results revealed that approaches such as AGDO facilitate an immersive learning environment and ensure the quality of teaching. The implications and methodology were further discussed.

Hearing impairments have a negative impact in the lives of individuals living with them and those around such individuals. Different applications and technological tools have been developed to help reduce this negative impact. Most mobile applications that have been developed that use Speech-to-Text technology have been inconsistent such that they are not inclusive of all types of hearing impaired individuals, only work under specifically predefined environments and do not support conversations with multiple participants. This makes the present tools less effective and makes hearing impaired participants feel like they are not completely part of the conversation. This paper presents a model that aims to address this by introducing the use of Multiple-Speaker Classification technology in the design of mobile applications for hearing impaired people. Furthermore we present a prototype of a mobile application called Deaf Chat that uses the newly designed model. A survey was conducted in order to evaluate the potential that this application has to address the needs of hearing-impaired people. The results of the evaluation presented a good user acceptance and proved that a platform like Deaf Chat could be useful for the greater good of those who have hearing impairment.

This paper highlights the importance of on-campus online job recruitment system and its role in helping students grab the available job opportunities. It highlights the problems associated with the traditional way of hiring, especially for college students. It also presents some findings and results obtained through various surveys conducted within the campus before and after the deployment of this system. The work presented in this paper is based on an e-recruitment system built for one of the leading engineering institute in Nepal, Pulchowk campus. With some features like job recommendation based on various levels of skill, smart multi-criteria search, graduate tracking, this system proves to be useful for all i.e. companies, students and the campus as well.

An experimental study on the effect of a cover number of solar distillers with a continuous seawater feeding system was conducted. The seawater feeding in this study was a continuous feeding, which was not utilized yet in the previous studies. Three identical distillers i.e. single caver distiller, double cover distiller, and triple cover distiller were designed and examined. The material tested in this study was seawater taken from Tanjungkarang beach, Mataram, NTB, Indonesia, and converted into distilled water through distillation processes. The overall size of the distillers used was 1136 mm x 936 mm x 574 mm (outer dimension), while the absorber plat size was 0.8 m x 1 m. The experiment was performed in July 2019 from 09.00 to 16.00 local time. The results showed that increasing the number of glass cover decreased the amount of distilled water. The single cover distiller resulted in 949 ml a day, while the double and triple cover distillers resulted in 260 ml and 88 ml a day. The distiller with 3 glass covers was hotter than others so that the seawater vapour could not condense on the glass cover. Therefore, the distiller with a single glass cover was recommended.

Several challenges like changes in brightness, dynamic background, occlusion and inconsistency of camera position make the recognition of hand gestures dif?cult in any vision-based method. Diversity in finger shape, size, distribution and motion dynamics is also a big constraint. This leads to the motivation in developing a dense Scale Invariant Feature Transform (SIFT) flow based architecture for recognizing dynamic hand gestures. Initially, a combination of three frames differencing and skin filtering technique is used for hand detection to reduce the computational complexity followed by a SIFT flow technique to extract the features from the detected hand region. SIFT flow vectors obtained from every pixel can lead to over?lling, data redundancy and dimension disaster. A dual layer belief propagation algorithm is utilized to optimize the feature vectors to resolve the dimensionality problem. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers are used to evaluate the performance of the developed framework. Experiments were conducted on hand gesture database for HCI, Sebastien Marcel Dynamic Hand Posture Database and RWTH German finger spelling database. The simulation results demonstrate that the developed architecture has excellent performance on the uneven background and varying camera position and it is robust against image noise. A comparative analysis with the state of the art methods illustrates the effectiveness of the architecture.

The effect of the moving dispersive conducting complex scatterer on the scattering of incident H-wave is investigated herein. In this research, a simulation shows how the scattered phase and magnitude of a moving (rotating and translating) circular cylinder with higher conductivity, made of dispersive material, is affected in the case of incident H-wave (TE-mode) polarization. The Franklin transformation is applied to study the scattering of incident wave by a rotating dispersive higher conductivity cylinder, and then the effect of translating dispersive higher conductivity cylinder is investigated by applying the Lorentz transformation. This effect is studied at different speed of rotation. Also, this work shows that the pattern of scattered phase and magnitude are changed in terms of incident frequency. Moreover, a created model is used to demonstrate the impact of moving dispersive higher conductivity cylinder using backscattered static data which is generated using a comprehensive computational electromagnetics software(FEKO). The comparison between patterns of scattering of incident H-wave by a moving dispersive and non-dispersive higher conductivity cylinder is considered to show clear behavior of scattering patterns, in terms of the material of scatterer.

This paper examines the impact of innovation on the performance of manufacturing enterprises in Vietnam. Innovation is measured by product innovation (3 observed variables), technology innovation (8 observed variables), and organization innovation (6 observed variables) while firm performance is measured by revenue and profit. The OLS regression model was used with data collected from 806 enterprises in four industrial sectors. The results show that innovation has a positive effect on firm performance. From the results, some implications are proposed to improve the performance of manufacturing enterprises in Vietnam.

In this paper, we present a new method to deduce minimal cut sets depending on the minimal path sets of the complex systems (networks) to generate the Incidence Matrix, and then compared it with the truth table of the system. This comparison, based on some algebraic properties, gives minimal-cut sets of the complex network with an algorithm in Mathematica software. In addition, the minimal cut sets completely characterize the operating state of the system and equal to the complex system structural function information. So, the distinguish of the operational states of the system give us information about the binary operational states for some components. The system failure time is also given immediately if the failure times of the component parts are known.

Horse power – the prime mover that has been there with humans for ages; chiefly used for transportation in the early days, and later, also used as an energy source leading to the conception of the term – horse power (hp). The current paper presents an interdisciplinary effort that brings forward an approach for evaluation of the potential of renewable power extractable from this prime mover once again. Specifically, the focus is towards extracting power from the equine treadmills (dry-type) whereby the incline of the machine is replaced by an equivalent (or pseudo-equivalent) energy generation mechanism leading to the coupling of renewable power with an equine workout. This approach comprises aerobic power (evaluated from oxygen uptake data) as well as anaerobic power (evaluated from plasma lactate data) – each of which is estimated from the difference values between equine running on a flat treadmill and when the mill is inclined. Furthermore, a literature review mentioning different inventions as well as some research efforts directed towards tapping somatic energy of animals is included in the manuscript. Also, a section dedicated towards assumptions/weak points helps judge the applicability of the presented work.

We describe the control design strategy used to control a soft robotic artificial muscle composed of silicone rubber using hand gesture signals. This artificial muscle is actuated with pneumatics, and therefore, the control strategy employed is through the regulation of air pressure within the inner chambers. Using the hand gestures of bringing the hands apart and together, the artificial muscle can be made to expand and contract with the gesture interface from the leap motion sensor. The advantage of the employed hand gesture control compared to switch control is that it provides a more natural interface for the regulation of air pressure within the artificial muscle through the use of electronic and automatic control. Possible areas of application include the use of the soft muscles for rehabilitation purposes and the combined system for developing a physiotherapy gaming device to exercise the hands and fingers of individuals that need to strengthen the muscles of the hands and fingers.

Durum wheat (Triticum turgidum L. var durum) is among the most important crops in the world. High and stable grain yield in diverse environments is the major objective in durum breeding programs. This trait is linked to the quantitative trait loci (QTL). For the detection of QTL linked to the grain yield, it is necessary to construct a high-density genetic linkage map. The aims of this study were to detect the candidate genes comprised in the QTLs on 2A chromosome linked to grain yield and annotate the single nucleotide polymorphisms (SNPs). The linkage map of Lahn/Cham1 population was used to identify QTLs. In multi-environmental analysis and employing bioinformatic approaches, 583 sequences corresponding to the SNPs markers selected from the detected QTL regions were analyzed, 122 SNP sequences were annotated of which 53% of the candidate genes were involved in stresses tolerance, 29.5% in plant development and growth, and 3.3% in cell transport. Moreover, 1.6% of candidate genes were retrotransposon and transposon 2.4% with unknown function. Further 9.8% were related in other cellular processes. The results also showed that 66.7% of the candidate genes harbored on 4B chromosome, were involved in stresses tolerance and 33.3% in plant development and growth. Additionally, in the specific and stressed environments analysis, the DNA sequences of the four QTL detected on 2A chromosome were used for homology search, 546 candidate genes were identified of which some were present in several QTL (F-box gene family, hydroxyproline-rich glycoprotein-like), retrotransposons and transposons and others. This study provided information on employing SNPs markers to detect candidate genes linked with grain yield trait in durum wheat in contrasting environments (dry, cold, hot).

A real-time embedded system requires plenty of measurements to fallow the Nyquist criteria. The hardware built for such a large number of measurements, is facing the challenges like storage and transmission rate. Practically it is very much complex to build such costly hardware. Compressive Sensing (CS) will be a future alternate technique for the Nyquist rate, specific to some applications where sparsity property plays a major role. Software implementation of Compressive Sensing takes more time to reconstruct a signal from CS measurements using the Matching Pursuit (MP) algorithm because of fetching, decoding, and execution policy. It is necessary to build hardware in CS. The author proposes one such VLSI Architecture (Hardware) for 256 X 256 and 512 X 512image. Various random matrices like Bernoulli, Partial Hadmard, Uniform Spherical, and Random Matrix are used to build hardware. FHT (Foreward Transform) with ±2 to 6 threshold is applied to get CS measurements. The reconstruction time, Signal to Noise ratio (SNR), and Mean Square Error (MSE) are measured. Multiple time experiments are carried out and results show that for an image of size 256 x 256, SNR is 25 dB and MSE is 166. For the image of size 512 x 512, the values are 27dB and 182. However, both the input images are resized to 256 X 256 so the reconstruction time is 2.62µsec? which is less is compared to software implementation.

This paper addresses essential features/issues and proposes solutions that would improve the overall performance of a multi-phase buck converter. Low efficiency at light load is addressed with phase shedding, load fast transient and regulated output voltage spiking/dipping is addressed with novel helper technique at the point of load (output node). An Integrated current sensing is utilized to implement over-current-protection (OCP), in addition to its inherent function in current mode control. The phase shedding developed technique was used to enhance the efficiency of the converter. The number of phases rather increases or decreases, depending on the desired load. The proposed fast transient helper circuit is tested by inserting a 500mA transient current step in 100µs. Worst case spike of a 79.1mV was achieved at the output node; which is a reduction of 49.4% of the original response (160mV without the helper circuit). Worst case of a 35mV of output voltage dip was achieved; which is a reduced by 45.4% compared to the original response (77.1mV without the helper circuit). An integrated current sensing technique using current mirroring to equalize the drain voltages of main and replica (sense) PMOS devices was utilized. With this technique, the current in the replica (sense) PMOS device is a scaled down version of the current in the main PMOS device. The sensed currents from all three phases are added up and converted to a voltage. This voltage is compared to a reference voltage that represents the limit for over-current. This reference voltage is set to be 20% higher than the average total currents in the three phases combined.

In this paper, performances of two variations of chaos based algorithms are compared. First algorithm is a self-adaptive color image encryption algorithm is proposed based on Radial Hilbert Transform and chaos. This technique uses chaotic random phase masks operated on the transformed pixels to increase the randomness in confusion and diffusion operations. Also, a random jumbling process is used at the final stage to increase the randomness in the cipher image. Part of the plain image is used to generate the keys for encrypting another part. Second algorithm is inspired by the bio operations resembling confusion and diffusion. Use of a scrambler improves the performance of this algorithm. Proposed work elaborates the results of the suitability analysis conducted on various kinds of input images, namely, satellite images, face images and handwritten signature images. Performance parameters considered for the analysis include horizontal correlation, vertical correlation, diagonal correlation, and net changing pixel rate, unified average change in intensity, entropy and encryption time taken by the encryption techniques.

Caregivers experience lower back pain due to their awkward postures while handling patients. Therefore, a monitoring system to supervise caregivers’ postures using wearable sensors is being developed. This study proposed a postural recognition method for caregivers during postural change while handling a patient on a bed. The proposed method recognizes foot positions and arm movements by a machine learning algorithm using inertial data on the trunk and foot pressure data obtained from wearable sensors. An experiment was conducted to evaluate whether the proposed method could recognize three foot positions and three arm movements. Participants provided postural change for a simulated patient on a bed (patient: supine to lateral recumbent) under nine conditions, including different combinations of the three foot positions and three arm movements; the experiment was repeated ten times for each condition. Experimental results showed that the proposed method using an artificial neural network with all features obtained from an inertial measurement unit and insole pressure sensors could recognize arm movements and foot positions with an accuracy of approximately 0.75 and 0.97, respectively. These results suggest that the proposed method can be used in a monitoring system tracking a caregiver’s posture.

The purpose of this research is to study the relevance of factors for the analysis of the effectiveness of suitable educational institutions that illustrate the significance of the characteristics and attributes of the student’s academic achievements and to identify the acceptance and tolerance of each attribute, which supports lifelong learning. The data used in this research is 1109 students who used and tested the institution recommender system based on student context and educational institution application. The research methodology focuses on the study of user involvement and application analysis. There are six significant phases of the research: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The machine learning tools and data mining techniques are k-means, k-medoids, decision trees, cross-validation methods, and confusion matrix. From the data analysis, it can be concluded that the overall level of satisfaction with the application is accepted (average = 3.70, S.D. = 0.84). In addition, the prototype model has been developed for predicting and recommending appropriate institutions for the learner has moderate accuracy levels (92.25%), and the results of the self-test data model are very accurate at the highest level, which is equal to 93.78%. Finally, this research demonstrates the relevance and success of education engineering projects. It demonstrates a worthy accomplishment. For future research, the researchers aim to construct and develop applications that promote and support the findings of this research.

Visual Simultaneous Localization and Mapping known as V-SLAM, is an essential task for autonomous vehicles. It can be carried out using several sensors, in particular with on board cameras. To locate a vehicle, SLAM algorithms are based on two main tasks. The first task (front-end kernel) is intended to process images in order to provide features (called also landmarks or primitives) of the perceived environment. The second task (back-end kernel) is intended for localization and environment reconstruction.
This work focuses on the front-end task which uses extractors (detectors and descriptors) in a stereo-vision system. Several feature detectors and descriptors exist in the state of the art. The aim of this paper is to evaluate the possible combinations of detectors and descriptors to achieve a precise localization while considering the processing times. The study is extended to bio-inspired extractors. The evaluation is achieved with SLAM algorithms over well-known indoor and outdoor datasets. Experimental results highlight the performance of bio-inspired extractors and their potential integration in designing vision systems for real-time SLAM applications.

This study conducted experiments to investigate the effects of resting actions of e-learners during break times on keeping and improving the concentrations of learners in e-learning. Two smart toys (a dog-type robot “aibo” and a toy drone) were used for the resting actions. Two types of experiments were conducted to examine the effects: one is a work type experiment (simple mathematical calculation) and the other is a memorizing type experiment (learning of English words). In those experiments, the learners played with one of the smart toys during the resting times between the learning sessions and performed certain actions accordingly. The experimental results were combined with the previous study using a humanoid robot “RoBoHoN.” A questionnaire was also employed to investigate the feeling of the learners. The experiments showed that the “playing with aibo” action refreshed the learners effectively in the mathematical experiment, while the “playing with RoBoHoN” action was effective in the learning experiment.

The proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering the prediction of the content popularity and user location to minimise the latency and maximise the cache hit rate. Here, a backpropagation neural network is applied to predict the content popularity, and prediction by partial matching is chosen to predict the user location. The simulation results reveal our proposed cache policy is around 27%-60% improved in the cache hit ratio and 14%-60% reduced in the average latency, compared with the two conventional reactive policies, i.e., LFU and LRU policies.

The revolution in the electrification of conventional vehicles due to increased petroleum prices and environmental concerns has had its impact on three-wheeler vehicles as well. Motorized three-wheeled vehicles, known as Auto-rickshaws, are a standard mode of trans- portation in India. Existing battery operated electric rickshaws (known as E-rickshaws) available in major cities of India faces challenges like insufficient charging facility, low driving range, high battery cost, battery replacement/disposal, etc. A battery-ultracapacitor (UC) hybrid energy source is proposed in this paper to overcome these issues. Two energy sources of complementary characteristics, when operated in tandem, can enhance the over- all performance of a vehicle in terms of weight, volume, and efficiency. An Erickshaw with battery-UC hybrid energy sources is modeled in Matlab-Simulink in this paper. The vehicle dynamics are calculated real-time based using a GPS based PerformanceBox tool of VBOX motorsport for Surat city. A fuzzy logic-based approach was employed for efficient energy management between these two sources with efficient utilization of regenerative power generated while braking. The system was later tested on Real-Time Hardware-in-Loop (HIL) environment to validate the simulation results. It was observed that the addition of an additional source with complementary characteristics had enhanced the performance of vehicle operation.

The adoption of Engine-in-the-loop technology shows real behaviour. This study presents a test runs simulation platform with real engine data. In addition, a test bench model is a demand approach that offers a significant potential to provide an excellent reproducibility of test runs. The platform includes the data integration to upgrade tests run and a comparison with previous results using the advancing control techniques designed. The dynamometer system presents significantly non-linearity. The adaptive control approach, integrated into the Model Predictive Control on the vehicle, allows increasing the tests run performance. The results show how the real data can improve performance and the validation of the system integrating the updated driving cycle and maintaining EiL approach. The conclusion showed the significant benefits regarding the control methods used.

English remains the language of choice for database courses and widely used for instruc- tion in nearly all South African universities, and also in many other countries. Novice programmers of native origins are mostly taught Structured Query Language (SQL) through English as the medium of instruction. Consequently, this creates a myriad of problems in understanding the syntax of SQL as most native learners are not too proficient in English. This could affect a learner’s ability in comprehending SQL syntaxes. To resolve this problem, this work proposes a tool called local language narrations to SQL (Local-Nar-SQL) that uses a type of Finite Machine, such as a Jumping Finite Automaton to translate local lan- guage narratives into SQL queries. Further, the generated query extracts information from a sample database and presents an output to the learner. This paper is an extension of work originally presented in a previous study in this field. A survey involving 145 participants concluded that the majority found Local-Nar-SQL to be helpful in understanding SQL queries from local languages. If the proposed tool is used as a learning aid, native learners will find it easier to work with SQL, which will eliminate many of the barriers faced with English proficiencies in programming pedagogies.

From amateur players who enjoy sports throughout their lives to top athletes who participate in international competitions, interest in improving sports skills is growing. Coaches and their coaching are indispensable for improving sports skills, but it is difficult for many athletes, especially amateur athletes, to secure coaching. However, we thought that anyone could easily receive coaching through the use of an artificial intelligence (AI) coach. In order to bring about AI coaching, learning is important. The set of learning data must include data such as players’ skills as they correspond to their gaze and performance.
In particular, it is thought that analyzing gaze movement during sports may provide insight into exceptional athletic skills. In this study, we propose a skill sampling method of collecting learning data for the specific purpose of creating an AI coach, using a wireless eye movement measurement device and 4K imaging.

This paper showcases our previous and continuously improving development at Ateneo Innovation Center (AIC) and partners in designing and further enhancing the existing Low-cost Phototherapy Light System (LPLS) and Improved Low-cost Phototherapy Light System (ILPLS) to the new Smart Low-cost Phototherapy Light System (Smart LPLS) with non-invasive jaundice monitoring for newborns with Neonatal Jaundice (NNJ). Developing this tool will help determine the intensity of yellowish color in infants and can monitor NNJ in a non-invasive way. The system is envisioned to be integrated with Mobile or Near Cloud as part of Smart Nursing Station together with other hospital equipment for monitoring, collection, and management of medical records and services. Its solar-power features for off-grid and remote deployments were also explored. This contribution is an extension of the Intelligent Sensors and Monitoring System for Low-cost Phototherapy Light for Jaundice Treatment that was presented in the International Symposium on Multimedia and Communication Technology (ISMAC) in 2019.

Smart agriculture concept uses innovative solutions including IoT and Cloud storage features, dedicated sensors for monitoring basic agricultural parameters, new communications protocols, etc. SmartAgro architecture comprises a telemetry system for Key Performance Indicators (KPIs) such as air & soil temperature, air & soil relative humidity, leaf wetness, etc. The current paper outlines the reliability of the implemented system by comparing and analyzing data collected in spring 2019 and spring 2020. The relevance of this season consists in great air variations due to the transition from winter to summer. Being monitored in a vine area near Bucharest, these data may be useful for different statistics related to grapes culture in this season and can be used by interested parties for future predictions related to vine crops. Moreover, in this paper, data fusion will allow advanced data management and coherence achievement among collected data.

This paper is an extension of work originally presented in IPFA 2019. In the original work, a new memory bit-counting method in physical failure analysis (PFA) using laser deprocessing technique (LDT) is introduced. In the present paper, LDT will be further exploited and the methodology applied to PFA will be fully discussed. Compared to the conventional methods that involve high-cost equipment such as focused ion beam (FIB) and reactive ion etcher (RIE), the novel LDT method using a laser system instead lowers the cost by more than 5 times and shortens the failure analysis (FA) cycle time by up to 45%. The new improved methodology can significantly increase PFA throughput especially in semiconductor foundries, and facilitate more applications in other types of FA labs.

Big data is a vast data set that was used in many areas. Online applications are subject to theft of confidential information because opponents can exploit software errors to access private data, and because curious or malicious officials can capture and lose data. CryptDB is a functional system that provides security and confidentiality through a set of operations. The obvious confidentiality of these attacks is for applications supported by SQL databases. It works by executing SQL queries on encrypted data using robust coding systems that support SQL. Word2Vec outputs word vectors that can be displayed as a large piece of text, or even we first train data. Word2Vec forms and word similarity assessment. Without a doubt, this article calls for proper research that sheds light on the security features using CryptDB to prevent data theft and privacy breaches in the server. The motivation of this research is to have an overview of CryptDB and Word2Vec implementation on the existing research approaches.

The large and ever-increasing variety of remote sensing sensors used in satellite imagery today explains why detecting changes between identical locations in images that are captured, at two separate times, from heterogeneous capturing systems is a major and challenging recent research problem in the field of satellite imaging for fast and accurate determination of temporal changes. This work presents an original concentric circular invariant convolution model that aims at projecting the first satellite image into the imaging modality of the second image. This allows the two images to have identical statistics so that one can then effectively use a classical monomodal change detection method. The invariant circular convolution kernel parameters are estimated in the least squares sense using a conjugate gradient routine whose optimal direction is determined by a quadratic line search algorithm. After the projection of the before image into the imaging modality domain associated with the after image is achieved, a basic pixel- by-pixel difference permits the estimation of a relevant soft difference map which is segmented into two classes by a multilevel Markovian technique. A series of experiments conducted on several pair of satellite images acquired under different imaging modalities, resolution scales, noise characteristics, change types and events, validates the effectiveness of this strategy. The experimentation shows that the proposed model can process different image pairs with less re-striction about the source images and natural event, coming from different sensors or from the same sensor, for detecting natural changes.

In this paper, a portable Frequency Modulated Continuous Wave (FMCW) radar system was designed and implemented for human movements and breathing detection. The radar operates with a frequency band ranges from 4.7 to 4.9GHz. The radar sub-systems were designed and simulated using up to date computer-aided-design tools before implementation. The Voltage Controlled Oscillators (VCO), high gain antenna, low loss power divider/combiner, and a high selectivity bandpass filter were implemented, and their parameters were measured using a microwave analyzer. The simulated results and the measured results show a significant correlation. The new RF front end module components enhanced the radar signal-noise-ratio (SNR), and the breathing rate detection. The Digital Signal Processing (DSP) is implemented on the Field Programmable Gate Array (FPGA) board for movement and breathing signals detection processing in real-time. Highly sensitive detection, configurability, low cost, low power consumption, and portability were considered in the designed system. We believe that the enhanced reconfigurable radar system will be helpful in several biomedical monitoring applications.

Knowledge-based approach is wield used in various NLP applications. For example, to evaluate the semantic similarity between words, the semantic evidence in lexical ontologies (wordnets) is commonly used. The success of the English WordNet (EnWN) in this domain has inspired the creation of several wordnets in different languages, including the Arabic WordNet (ArWN). The English synsets have been extended to Arabic synsets through translation, which have introduced semantic gaps in ArWN structure. Therefore, compared to EnWN, ArWN has limited coverage in terms of lexical and semantic knowledge. This paper explores to what degree the richness of the wordnets’ semantic structure influences the semantic evidence that can be used in wordnet-based applications, in particular the effect of filling the semantic gaps in ArWN. The paper studies the performance of applying English-based and Arabic-based similarity measures over ArWN. A set of experiments was performed by applying six path-based semantic similarity measures over Arabic benchmark dataset to investigate the usability and efficacy of the enriched structure of ArWN. The Performance measures, Person Correlation and Mean Square Error, are computed against and compared to human judgment benchmark. The obtained results demonstrate that the semantic similarity between words can be significantly improved when filling the semantic gaps. In addition, the experiment findings show that Arabic-based measures competitively perform well compared to the English-based measures. Further, ArWN enhanced structure is also available for public.

In this paper we study a cell division cycle modeled by a system of partial differential equations with an age structure. This model translates the many regulatory mechanisms within the cell cycle where it introduces the notion of phases. The individual cell can be either in I phases where the transition between theses phases are ordered and unidirectional. The model is related to the suns and stars caluculus via the dual semigroups of operators that are considered as solution of an abstract integral equation equivalent to a Volterra type equation of the form w(t) = ?(wt). We will determine the core operator ? and prove that the semigroup solution of the model possesses the asynchronous exponential property. The model permits different types of controls where the provided framework allows better control on the model parameters and yields the characterization of the intrinsic rate of natural increase through properties of the core operator ?. Finally, we demonstrate that the asymptotic behavior of the model is governed by the simple dominant eigenvalue and its associated eigenvector, that leads to the dispersion of the cell structure through the future generations.

Preventive care approaches have attracted much attention in Japan, which is one of the world’s most super-aged societies. These approaches aim to decrease the number of people who require nursing care or other human support. Our research group has developed several kinds of preventive care systems, including a fall prevention system, a cognitive training system, and an Intelligent Dialogue Agent (IDA). In this paper, we focus on familiarizing older adults with the IDA – a conversational agent that encourages older adults to engage it in natural conversations, while monitoring their health in the process – and we introduce a Speech Content Coordinating Function (SCCF) to the IDA to further improve older adult’s familiarity with and interest in the agent. We also have a plan to develop a Comprehensive Preventive Care System (CPCS) which can encourage proactive conversation and provide effective monitoring of older adults as well as habitual cognitive training. To evaluate the basic characteristics and impressions of the CPCS, a prototype version that consists of the IDA and the Mechanism for Cognitive Training (MCT) is developed. The results of the experiments indicated that the SCCF can adequately adjust speech content depending on the user’s circumstances. We also confirmed that the prototype CPCS achieves synergistic effects (e.g., more detailed monitoring of older adult’s health condition, increased frequency of cognitive training) when its components are used together.

In this paper we propose the application of supervised learning techniques to recognize stress situations in drivers by analyzing their Skin Potential Response (SPR) and the Electrocardiogram (ECG). A sensing device is used to acquire the SPR from both hands of the drivers, and the ECG from their chest. We also consider a motion artifact removal algorithm that allows the generation of a single cleaned SPR signal, starting from the two SPR signals, which could be characterized by artifacts due to vibrations or movements of the hands on the wheel. From both the cleaned SPR and the ECG signals we compute some statistical features that are used as input to six Machine Learning Algorithms for stress or non-stress episodes classification. The SPR and ECG signals are also used as input to Deep Learning Algorithms, thus allowing us to compare the performance of the different classifiers. The experiments have been carried out in a firm specialized in developing professional car driving simulators. In particular, a dynamic driving simulator has been used, with subjects driving along a straight road affected by some unanticipated stress-evoking events, located at different positions. We obtain an accuracy of 88.13% in stress recognition using a Long Short-Term Memory (LSTM) network.

In this paper we are demonstrating the importance of building and achieving a renewable electrical power system that works in synchronization with the grid. The average of consumption, generation and the negative environmental effect of generating power were analyzed. This paper is divided into three parts, one was analyzed with Green Building Studio software and the other with Building Information software. Using BIM software, the levels of energy consumption were analyzed for a building in Baghdad – Iraq and in Craiova _ Romania, alongside pollution levels produced, and the possibility of energy generation utilizing PV cells and wind, the output power that can be achieved in respect to temperature and irradiance levels. Using Green Building software, energy consumption system was analyzed in the level of green building.

Face recognition technology becoming pervasive in the fields of computer vision, image processing, and pattern recognition. However, face recognition accuracy rates will decrease if training is done on disguised images with covered objects on a face area. This paper aims to propose a state-of-the-art face recognition methodology which could be applied in Internet of Things (IoT) devices as an input source; then face segmentation and training process will be executed in the cloud via internet; the recognition result will be sent to the connected applications which determines a safety check for personal or public security. This paper focuses on implementation of face segmentation and training process for IoT. Face extraction from the background and disguised part is applied by Fully Convolutional Networks (FCN), and then deep convolution neural network is employed for face training and testing process. This algorithm has been experimented on a challengeable face dataset. The proposed face recognition system is applied to IoT services which have multiple applications, such as, personal home security and public library space management. Compared to recognition without face segmentation, the results of proposed methodology indicate a better accuracy regarding recognition rate.

3D city modeling may be considered as one of the key applications, that are provided by the Automated Feature Extraction (AFE) techniques from LiDAR data. The authors attempt to prove that with growing availability of LiDAR surveying methods the resulted 3D city models become the most significant modeled features for any urban environment. Our paper represents the conceptual multifunctional approach within the AFE frameworks, that has been introduced through consequent steps of the phased methodological flowchart with its two branches: High Polyhedral Modeling (HPM) and the Low Polyhedral Modeling (LPM) of buildings. Both branches result in the heavyweight models, and in the lightweight ones, correspondingly. The research purpose of this paper is to outline our multifunctional approach (functionalities of Building Extraction, Building Extraction in Rural Areas, Change Detection, and Digital Elevation Model Generation) to the fully automated extraction of urban features, and present our original contributions to the relevant algorithmic solutions within both HPM, and LPM pipelines, as well as represent desktop, web-, and cloud-based software elaborated for these intentions. Original enhancements and optimizations of the adopted AFE-techniques have been bounded to the phases of the methodological flowchart, while some derivative results have been presented not only as the software description, but also in the discussion chapter. Joint implementation of various functionalities in a web-based application (the Server) is presented with several interface samples of research in urban block and district scopes, while a cloud-based application (the Geoportal) is an Internet-toolbox for solutions in the scope of a whole city.

The paper is aimed as a concise and relatively self-contained description of the educational environment TOST, used in teaching and learning Operating Systems basics such as Processes, Multiprogramming, Timesharing, Scheduling strategies, and Memory management. TOST also aids education in some important IT concepts such as Deadlock, Mutual exclusion, and Concurrent processes synchronization. The presented integrated environment allows the students to develop and run programs in two simple programming languages, and at the same time, the data in the main system tables can be monitored. The paper consists of a description of TOST system, the features of the built-in programming languages, and demonstrations of teaching the basic Operating Systems principles. In addition, some of the well-known concurrent processing problems are solved to illustrate TOST usage in parallel programming teaching.

Technology Acceptance Model (TAM) is broadly accepted and has proved applicable in identifying consumers’ willingness to utilize information and communication technology (ICT). The theory proposes that Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) are actually determining factors of individual attitudes, while attitude is a determining factor of Behavioural Intention (BI) and Behavioural Intention (BI) influences usage. It is necessary to understand the usage and modifications that have been made to Technology Acceptance Model (TAM) since user acceptance and confidence are of great importance for advance improvement and successful implementation of any new technology. Numerous frameworks and models have been designed and created to describe user acceptance of modern innovations. These models introduced factors that contribute towards user acceptance. This is a review paper on understanding the usage, modifications, limitations, and criticisms of Technology Acceptance Model (TAM). The model has been utilized to measure new different technologies for usage as well as adoption. The literature indicates that the modification of the model was mostly the addition or removal of variables and in some cases the addition of moderators or mediators. The model has limitations identified in literature such as the problem of reliably quantifying behaviour in an observed investigation. Moreover, there are notable criticisms identified in literature such as TAM’s incapability of noticing other issues, for example, cost and structural imperatives that pushes users to adopt an innovation. TAM will continue to be accepted and modified according to the successful application of any new technology. This study can be used to enhance users’ knowledge of usage, modifications, limitations, and criticisms of Technology Acceptance Model (TAM).

The main advantage of software-defined visible light communication (VLC) systems is that they allow us to reuse modulation, coding, synchronization and medium access techniques used in conventional radio systems. This article focuses on the implementation of an uplink alternative using an infrared (IR) light communication system. For this goal, analog front-ends are designed and implemented. Therefore, they convert a radio frequency signal into an optical signal. In addition, a Universal Software Radio Peripheral (USRP) is used as a software-defined radio platform. The obtained bit error rate is 5.33×?10?^(-5) using QPSK modulation with a bit rate of 4 Mbps at a distance of 160 cm between transmitter and receiver.

Finger skills are achieved through the training process. This training process will be more straightforward if a training system is used. A novel finger motoric training system is introduced in this paper. This system is to facilitate a beginner piano player to learn the basic piano skill. The system is comprised of a tracking finger sensor, a personal computer, and multimedia feedback. Subjects train with passive and active mode. The passive mode, the subject’s fingers are doing nothing when the information is given, whereas the subjects are playing a virtual piano under the sensor. While the information which fingers have to tap are provided by visual, audio, and enhanced with electrical haptic sensation. The experimental results demonstrate that the system equipped with electro-tactile feedback makes the subjects more responsive to both modes. Moreover, this system enabled the user to accelerate learning a new skill.

A happy employee with high morale is the best asset of an organization. The morale of an individual working in an organization can be impacted by both positive and negative ways by physical environment factors of the workplace. The physical environment of an organization impacts a lot on the work performance of an individual. It becomes more significant in case of aviation maintenance organization formally called Maintenance Repair and Overhaul (MRO) organization as the work performed by an individual requires full accuracy and directly linked with the safety of the aircraft and its occupants. This can only be achieved by providing a safe and positive working atmosphere to individuals working in MROs. Considering the same, this study targets to deliver a brief status report of physical environment factors disturbing work performance in Maintenance Repair Overhaul (MRO) agencies. This study initially explains the impact of different physical environment factors. The study also discusses the impact of unsafe work due to improper working environment in aircraft maintenance organizations. The individual’s working in MROs located in the United Arab Emirates were considered for testing the physical environment factors for this case study. This case study aims to provide the status of different physical environment factor of organization causing problems at work for individuals working in aircraft maintenance organizations. The result of the study indicated that MROs in UAE are providing a conducive working atmosphere to employees, however, the lack of workplace is still an area for improvement.

The prospects for the introduction of assembly automation in the industry are largely determined by the reliability of the assembly process. One of the reasons for reducing its reliability is parts jamming. In order to increase the technological reliability of the assembly process, various physical and technical effects are applied. The article presents the results of an experimental study using the rotational movement effect and low-frequency vibrations of the base part in order to reduce assembly efforts and the probability of part jamming.

Nowadays, smartphones and laptops equipped with cameras have become an integral part of our daily lives. The pervasive use of cameras enables the collection of an enormous amount of data, which can be easily extracted through video images processing. This opens up the possibility of using technologies that until now had been restricted to laboratories, such as eye-tracking and emotion analysis systems, to analyze users’ behavior in the wild, during the interaction with websites. In this context, this paper introduces a toolkit that takes advantage of deep learning algorithms to monitor user’s behavior and emotions, through the acquisition of facial expression and eye gaze from the video captured by the webcam of the device used to navigate the web, in compliance with the EU General data protection regulation (GDPR). Collected data are potentially useful to support user experience assessment of web-based applications in the wild and to improve the effectiveness of e-commerce recommendation systems.

The fast-paced development of smart technologies and the prevalence of vehicles, created an urgent demands to study and improve safety issues related to driving. In order to reduce traffic accidents, driving behavior was found to be very important issues to study and investigate. Recently, the advent and widespread of smartphone platforms with advanced computing competence and embedment of a variety sensing elements have greatly contributed to the development of solutions that can detect, and evaluate driving behavior and skills. In this study the development of a real-time smartphone-based identification and classification system for highway driving maneuvers is presented. The proposed system has been designed to detect ten different maneuvers usually performed by drivers when driving on highways. The methodology is based on separating the identification and the classification processes. The identification process is performed by a hybrid pattern matching scheme that combines Dynamic Time Warping (DTW) and neural networks. While a second neural network has been used to classify maneuvers, severity based statistical and time features. The separation of the identification and classification processes simplifies and accelerates the learning processes of the neural networks and greatly improves both system’s reliability and accuracy

Long-range, Low-Power Wide Area Network (LoRaWAN) is a very scalable solution for the Internet of Things (IoT). Due to the air channel environment’s complexity, connectivity is a crucial parameter for successfully planning and deploying the IoT networks. Measurements and simulations have been used to evaluate LoRaWAN propagation models in the Urban environment, but it is a challenging task. While practical propagation evaluation has been prohibitively expensive, the theoretical modeling results have been less accurate. This paper uses real-world measurements and a trace-driven simulation technique to evaluate the RF propagation models’ prediction performance for LoRaWAN 868 MHz propagation. First, a novel LoRaWAN trace- driven simulation of Glasgow city centre has been performed. Second, LoRaWAN 868 MHz measurements have been used to perform a critical analysis of LoRaWAN trace-driven Radio Frequency (RF) propagation models and validation. The processed trace dataset is composed of GPS coordinates, and the corresponding LoRaWAN received signal strength. The dataset has been extracted from 5017 datasets of LoRaWAN measurements taken from Glasgow city centre. A trace simulation program built-in ICS-Telecom was used to simulate LoRaWAN propagation in the real-world urban environment. Comparison of LoRaWAN simulation traces and the real-world data was performed to evaluate the prediction performance accuracy of Deygout 94, ITU-R 525/526, and COST-Walfish Ikegami (COST-WI) propagation models. All models over-estimated LoRaWAN trace-simulated RSS levels in comparison to collected measurement samples. While Deygout 94 prediction accuracy was higher with mean absolute error (MAE) at 0.83 dBm and standard deviation (SD) at 4.17 dBm, COST-WI performed poorly with MAE and SD at 2.87 dBm and 10.96 dBm respectively.

The performance of indoor illumination control in many applications, such as in an intelligent building, relies on the quality of the light sensors. In many cases, the light level is not uniform and depends on the direction of the illumination source. It usually requires multiple sensors set up in different directions to gather the overall light level. We propose a system that can provide multi-directional light sensing data by rotating a single sensor. This approach overcomes the problem of static sensors network by dynamically changing the measuring angular of the light sensor. We present a sensor system prototype using the ESP8266 controller board, BH1750 light sensor, stepper motor, and 3D printed rotation base mechanism. The system can calculate the sensing angle and transmit sensing data to the monitoring unit or Internet of Things platforms for visualization and analysis. The testing results in normal workrooms show that the rotating sensor can measure the light level in different directions and detect the direction of the main illumination source. Even blocking some directions, the sensor still is able to accurately measure and provide sensing information on the remaining directions. Our sensor system is useful in both whole lighting and local lighting control applications.

This article is a continuation of the work presented in the Third World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). In this version we focus on matching IT and business processes from different management levels by using IT-Governance (ITG) Risk and Compliance frameworks in a smart way. In fact, every information system (IS) has two main interfaces with two key systems: Operating System (OS) and Decision System (DS). Every system has his own frameworks and best practices for efficient management. IT Governance is the ability to control IT strategy implementation by ensuring business and IT alignment for profitable digital services. Also, the variety of IT Governance frameworks for each hierarchical level in the enterprise, should be efficiently deployed together in order to have coherent recommendations for IS, OS and DS users. It is why we use COBIT 5 as a strategic IT Governance framework able to match and integrate other frameworks and best practices dealing with IT services, IT risks and compliance as well. We based the proposed IT GRC smart adviser on artificial intelligence and knowledge management as technical axes. This article presents, IT Governance frameworks, their matching problems and related works. Then it proposes a smart architecture with new functionalities to match COBIT processes with other frameworks in order to cover different management levels. The technical contribution of this article is to propose and implement an IT GRC smart adviser based on many frameworks and best practices to optimize and improve IT Strategies. The simulation was presented, the obtained results were compared to human experts’ decisions and big similarities were observed. The aim of this article is to integrate knowledge from many IT GRC frameworks for better IT governance in a smart and easy way.

In order to balance the power in intelligent distribution networks (Smart Grid or Microgrid), it is proposed to organize a «multi operated virtual power plant». The resources of active and reactive power for which can be obtained using an AC voltage controller with a phase-angle control for regulation of operating modes of the ohmic load of consumers, for example, distributed systems of electric space heating or electric water heating, etc. A method is proposed and the results of the analysis of the phase-angle control modes by gate turn off (GTO) thyristors of the AC voltage regulator, which provides the generation of virtual reactive power by consumers of active power, are presented. The process equipment of such a virtual power plant is fully suitable both for the dynamic production of a virtual resource of active power to balance, for example, the power of dynamic distributed renewable energy sources (virtual power plant (VPP) mode), and to regulate reactive power to ensure adequate voltage levels and increase stock stability of operation of electric load units (virtual reactive power plant (VRPP) mode). References 24, figures 8.

Video and its processing are an interesting area as the increase in usage of internet videos, online streaming, CCTV, impact of internet on normal crowd increased. The need to know about video and its processing become an eminent area in research in current era. The paper tries to cover the traditional video processing, the advancement in video codec from the initial year, its origin, features, drawbacks and advancement lead to next stage. It provides an insight to need of video compression, steps involved in it, followed by overall review about video compression in various areas. The detailed explanation with reason of emergence, origin, characteristics are pointed. This information helps to add knowledge about the past and that helps to focus on the advancement and transitions that can be done to the video codecs. It summarizes the advancement in recent video processing using CNN, NN, deep learning too.

This paper is an extension of work originally presented in PM2.5 Prediction-based Weather Forecast Information and Missingness Challenges: A Case Study Industrial and Metropolis Areas, which focused on imputation algorithm to solve missingness challenge and demonstrated a basic prediction system to prove the proposed algorithm, II-MPA. Distributed and decentralized systems, recently, have been proven for their effectiveness in multiple perspectives. This paper introduces “Overmind”, the solution that governs and builds the network of decentralized machine learning as a prediction framework named after its functionality: it aims to discover a set of data and associated attributes for assigning machine learnings in the collaborative decentralized manner. Overmind also empowers feature transfer learning with data preservation. It demonstrates how discovered features are transferred and shared among synergic agents in the network. This model is tested and evaluated the accuracy against the traditional single machine learning prediction model in the original work. The results are satisfactory in both prediction performance and transfer learning.

Accurate machining control is indispensable for the smart factories of tomorrow. Variations in controller responses may cause unacceptable process deviations during machining leading to productivity losses and possible damage. In the present work, a complex order PI?+ j?D?+ j? (COPID) controller was designed to effectively control surface roughness generation while machining CNT Al-Mg hybrid composites. Performance of the designed complex order controller was compared against the conventional PID and fractional order PID (FOPID) controllers for the machined surface roughness system. Output signal responses indicate that the complex order controller attains the desired surface roughness set point with zero percent overshoot in almost same settling time (46 sec) as PID (41 sec) and FOPID (46 sec) controllers. The PID and FOPID output signals registered overshoots of 96.8 % and 36.7 % respectively. Similarly, in case of control signals (feed rate) the COPID controller successfully minimised peak overshoot to 4.6 %; as compared to 64.2 % and 96.5 % in case of the PID and FOPID controllers respectively. The COPID controller was also effective in reducing its peak time response metric (2.001 % for control signal and no peak time for output signal due to zero overshoot). In comparison, the PID and FOPID controllers recorded higher response peak times (7 sec / 26 sec for the PID ouput/control and 5 sec / 7.84 sec for FOPID output/control signal responses). Overshoot elimination in output signal (surface roughness) is crucial for consistency of the machined surface quality. Similarly, overshoot minimisation in control signal (feed rate) is critical because excessive feed rate can damage the cutting tool, work piece, machinery and is a potential safety hazard for the machine operator as well. Hence, the COPID controller can be safely and extensively applied in smart industrial control systems of the future.

Digital twins act through application programming interfaces to their physical counterparts to monitor, model, and control them. Beyond these traditional functions of digital twins, they must also act to secure their physical counterparts. A multi-model scheme is presented to help digital twins towards the task of securing the physical system. Additionally, this work includes an analysis of more than four hundred thousand tweets each relating to digital twin technology and cybersecurity which were collected during June and July in 2020. Of the first corpus of tweets collected by searching for #digitaltwin during the research period, only a small population of 10% reference security concepts. In the second and larger corpus of collected tweets, the top mentioned industries were health, education, and public. A naïve Bayes model reached a 70.3% accuracy at differentiating tweets that were either related to cybersecurity or the internet of things. The study also indicates that cybersecurity tweets are consistently more negative in many areas of sentiment when compared to tweets about the internet of things. The sentiment findings of cybersecurity tweets will reinforce the need to address culture in cybersecurity posture while the security multi-model schema contributes to the state of the art.

Currently, increasing penetration of the Wind Energy Conversion System (WECs) in Power generation systems has influenced the supply of electrical power reliability for the consumer in comparison with other traditional sources. In this paper, the performance and efficiency of a new optimization approach referred to as the “Differential Evolution Optimization Algorithm” (DEOA) to measure the reliability of power adequacy systems (RPAs). The proposed intelligent algorithm which relies on the Population-based Intelligent Search (PBIs) technique is viewed as a feasible alternative for the Monte Carlo simulation (MCS) method in the assessment of the non-chronological system. The benefit of utilizing this algorithm is apparent in the manner it expedites the calculation and achieves greater precision with less calculation effort. Additionally, there is a deeper understanding of the effect of the increasing levels of wind energy on generation adequacy from the WECs sources to satisfy future power electricity demands. In addition, the effectiveness of the suggested algorithm in assessing the RPAs was compared to the analytical and MCS method.

 Power converters in automotive industries are needed to source electronic devices. DC-DC Buck converter allows to drive loads ensuring their safety operation taking the car battery voltage as an input to generate a sub-voltage or current. By means of a control loop, the system makes sure that the output variable has the desired value in each operation condition. The Peak Current Mode Control (PCMC) is used to control the output current. Whenever the current reaches the reference maximum value (by load specs), the system acts to lower it. Intrinsically over-current protection is guaranteed. The paper presents an improvement of the basic PCMC, where, instead of just controlling the peak value, the loop controls also the average, more delicate feature, resulting in a more reliable driver system. The circuit is able to source 3A average-4.5V from a 13.5V nominal battery voltage, with a peak current of 3.3A and a 0.6A maximum ripple (3 10%A). The concept has been designed and simulated on Simulink and then tested on hardware using dSpace, a Rapid Control Prototyping set-up.

In recent decades, the challenge of managing the development teams spread across different time zones has become increasingly common, raising the importance of the development of Global Software Development (GSD) techniques, in order to tackle its particular problems. This work discuss these issues in the context of Sidia, an R&D institute which implements technological solutions for global companies. The main partner of Sidia is a mobile multinational company located in overseas. The development team must cooperate with the overseas team, even though there are no overlapping working hours between both teams. Besides, the teams have a different set of skills regarding design, quality assurance, and software engineering. In order to address theses problems, we propose the Blueprint model, a Kanban and Scrum based model that supports the development of GSD systems, the allocation of tasks and teams, and the efficient communication. Finally, we discuss the aspects and lessons learned of development of project and deploy of a new model for systems development on a real-word project.

Ultra-high resolution broadcasting such as 4K and 8K are becoming more popular for at-home use. The position of the viewer’s mean gaze point when they are looking at a larger display depends on the video clip, viewing position, and viewing distance. The International Telecommunication Union provided the document BT-2022; as the standard viewing conditions for the subjective evaluations of flat-panel TV displays. However, it is rare that a television is viewed at home under standard viewing conditions. In most homes, a TV of any size is watched from the viewer’s desired position according to the layout size of the room hosting the TV. No study has been conducted to determine how the movement of a viewer’s line of sight changes when a display with different resolution is viewed from a position different from the standard viewing condition. If the gaze position when viewing TV differs due to the difference in resolution, the size of the display, and the difference in observation position, it affects the method of TV production. This would also affect consumers considering the resolution, installation location, and viewing location of a TV. Here, we clarified that the mean gaze position and its standard deviation are almost the same viewing conditions (i.e., resolution, display size, and viewing position) are changed, even when the same contents are viewed. Our findings demonstrate that consumers could buy a display of their preferred resolution and size according to their room layout.

The paper examines the impact on the product life cycle of reducing the duration of the development stages of aviation technology through the use of multi-criteria optimization. The subject of the study is the nose of a supersonic aircraft and the process of linking it. This example describes in detail the application of a comprehensive design methodology for aircraft components using top-level criteria as a way to significantly reduce the development time and the risks of obtaining negative test results when entering a full-scale sample.

Technologies are growing with the passage of time and providing solutions for the existing problems. One of such problems is to manage the crowd according to available capacity. Especially when the crowd density is dynamic because of dynamic position of the persons (Pilgrims). Not only dynamic position of the pilgrims makes crowd capacity dynamic but also special events increase and decrease in number of pilgrims that affect the capacity in a specific place. Therefore, it is not an easy job to estimate the available capacity according to the dynamic position and event. To overcome such problems of dynamic position and event-based crowd management different techniques and approaches are adopted. To solve the above-mentioned problem, this paper proposes a proactive approach to estimate the space occupy by the pilgrims in different positions in a zone or level. Maximum capacity in each zone and level is define on the basis of each position. On the basis of maximum and occupy capacities, available (remaining) capacity has been estimated in a zone and level according to the events such as Pilgrimage, Ramadan, Jummah etc. The occupied and available capacities in a zone, level or in the whole building can be estimated with the help of technologies such as Wireless Sensor Network (WSN), cloud computing, Internet of Things (IoT) and sensor topologies according to position and event. Simulation shows different scenarios according to the zones and different levels of building to prove the proactive approach. According to the results, it is concluded that zones, levels and point of entrances should be allocated to the pilgrims to avoid the congestion problems. Further for massive crowd and large area multi sink solution is better than single sink solution to estimate available capacity.

Teaching tax-related regulations have always been a challenge due to the inclusion of external variables that hinder the learning process, such as the high complexity of tax systems and legislation variability. Universities have sought different alternatives to support the teaching process both outside and inside the classroom, ranging from recreational activities to active learning. This article will show the experience resulting from using a chatbot to support learning in accounting students for the teaching of tax regulations related to the Chilean tax system, comparing two types of tools, on the one hand, a free conversation chatbot using natural language processing versus a rule-based chatbot driven by a decision tree. The experimentation process was carried out with 50 higher education students, divided into an experimental group and a control group, in two different courses. The results obtained demonstrated in both cases greater effectiveness of the use of the chatbot in learning the tax matter, both in the free conversation chatbot where the experimental group obtained a 15.7% improvement versus the control group that obtained a 1.05% improvement, as in the chatbot that applied decision tree where the experimental group obtained a 32% improvement versus the control group with 5.2%. Considering the complexity of the content in tax matters and the little innovation in the existing teaching subjects in the area and that the students improve learning using both chatbot tools compared to other learning techniques, is considered a relevant contribution to teaching innovation.

This paper describes methods of eliminating Unmanned Aerial Vehicles (UAV) non-destructively, using Electronic Warfare Methods. The aim is to introduce certain methods of UAV detection and elimination in a complex environment and terrain, e.g., in an urban and battlefield environment, that will result in finding the control device position and the UAV itself. Neural networks, cyber penetration elements, and wireless network scanning programs are all used to address this issue. The output of this article is a new concept of a comprehensive solution, which can be implemented into the existing complex system of electronic defence against UAVs, e.g., within the allied base. Conclusions will be also used to further improve the above-mentioned topics at the authors’ workplace, within the frame of long-term projects and specifically as a part of solutions applicable to the force protection of combat support units, namely field artillery, which is described here in detail.

One of the open domain challenges for Spoken Dialogue System (SDS) is to maintain a natural conversation for rarely visited domain i.e. domain with fewer data. Spoken Language Understanding (SLU) is a component of SDS that converts user utterance into a semantic form that a computer can understand. If we scale SDS open domain challenge to SLU then it should be able to convert user utterance to a semantic form even if less data is available for the rarest visited domain. The SLU reported in literature incorporate classifiers for the task of identifying the domain of user utterance, understanding the intent of the user, and filling slots-value pair. Thus, to address open domain challenges, classifiers in SLU must be robust to scarce training data. This paper presents investigations to improve the performance of SLU to convert user utterance into semantic form even if less training data is available. Eleven classification algorithms from machine learning have experimented under deficient data. The evaluation matrices used are accuracy, f-score, and inter cross-entropy. Comprehensive experimentation is carried out on the two publicly available datasets DSTC2 and DSTC3 were carried out.The accuracy for Support Vector Machine (SVM) , Stochastic Gradient Descent (SGD) and Decision tree are 0.940, 0.960 , 0.955 for DSTC2 and 0.916, 0.900, 0.901 for DSTC3 database respectively. The F-score for SVM, SGD and Decision tree are 0.855, 0.868, 0.849 for DSTC2 dataset and 0.725, 0.715, 0.700 for DSTC3 database, respectively. The ICE for SVM and SGD are 1.191,1.100 for DSTC2 dataset and 3.180,2.999 for DSTC3 database, respectively. The performance of SLU based on SVM and SGD was found to be the best among all. The worst performance in terms of all three evaluation metrics was displayed by SLU incorporating Automatic Relevance Determination (ARD) and Relevance Vector Machine (RVC) classifier.

In a radial distribution system, component failure leads to service interruption and subsequent disconnection of a few load points. An abnormal variation in the voltage levels and high power loss gives rise to reliability issues. In this work, feeder reconfiguration is performed to improve reliability, minimize voltage deviation and power loss. A Binary Particle Swarm Optimization based search function handles the reconfiguration. The goal of this search function is to identify the optimal tie switches which minimize the multi-objective function. The search algorithm based on Binary Particle Swarm Optimization is utilized to handle the reconfiguration which identifies the optimal tie switches for minimizing the multi-objective function. The effect of the various algorith parameters on the convergence rate is studied. The proposed algorithm has been tested on Standard IEEE 33 and 69 test systems and the obtained results show a boost in the system reliability.

Sign language is a form of communication language designed to link a deaf-mute person to the world. To express an idea it requires the use of hand gestures and body movement. However, the bulk of the general population remain uneducated to understand the sign language. Therefore, a translator is required to facilitate the communication. This paper wishes to extend the previously proposed Convolutional Neural Network (CNN) model for predicting American Sign Language with a MobileNetV2-based transfer learning model. The latter model effectively generalized on a dataset which is around 18 times larger with 5 additional groups of hand signs. Over 98% of the recognition accuracy had been reported. Because of its relatively fewer parameters and less intensive computational operations compared to other deep learning architectures, the model was also ideal to be implemented on mobile devices. The model will serve as the key to deploying a sign language translator software on smartphone to enhance communication e the general public.

The fight against poverty in the Peruvian Andes is a complex task in which various professionals, such as engineers, economists, anthropologists, among others, participate. The uncertainty of the decisions taken today, no matter how appropriate they may seem, such as million-dollar investments in irrigation infrastructure, can result in over-production and, therefore, in economic recessions. For this reason, a new mathematical simulation model is proposed using system dynamics to predict recession phenomena that can occur in months or after a few years of auspicious economic growth, and that can cause sales prices to be below production costs. The author has developed the conceptualization of the production system of irrigation improvement projects in several years of multidisciplinary work in the Cusco region of Peru. The primary objective of irrigation projects is to improve the socio-economic conditions of the farmer. Techniques as the fulfillment of goals have been used to quantify qualitative dimensions such as strengthening organizations and trainings that are key to guaranteeing irrigation improvement projects’ sustainability in the long term. Therefore, it has been possible to identify the variables and relationships of this type of socio-economic system. To validate the model, we verified that the simulated data are consistent with the historical data collected. Likewise, if the values of the various proposed models’ variables are adequately modified, these can be applied to other types of production systems under different market conditions. The dimensions addressed, such as supply, demand, sale price, land, production volume, public budget, etc., enhance the research’s importance, making the simulation model formally expressed also acquire nuances from economic theory for the fight against poverty-based on water. One of the study’s conclusions is to understand the production systems, it is necessary to see them in the context of their regional economy’s behavior.

Dithering or error diffusion is a technique used to obtain a binary image, suitable for printing, from a grayscale one. At each step, the algorithm computes an allowed value of a pixel from a grayscale one, applying a threshold and, therefore, causing a conversion error. To obtain the optical illusion of a continuous tone, the obtained error is distributed to adjacent pixels. In literature there are many algorithms of this type, to cite some Jarvis, Judice and Ninke (JJN), Stucki, Atkinson, Burkes, Sierra but the most known and used is the Floyd-Steinberg. We compared various types of dithering, which differ from each other for the weights and number of pixels involved in the error diffusion scheme. All these algorithms suffer from two problems: artifacts and slowness. First, we address the artifacts, which are undesired texture patterns generated by the dithering algorithm, leading to a less appealing visual results. To address this problem, we developed a stochastic version of Floyd-Steinberg’s algorithm. The Weighted Signal to Noise Ratio (WSNR) is adopted to evaluate the outcome of the procedure, an error measure based on human visual perception that also takes into account artifacts. This measure behaves similarly to a low-pass filter and, in particular, exploits a contrast sensitivity function to compare the algorithm’s result and the original image in terms of similarity. We will show that the new stochastic algorithm is better in terms of both WSNR measurement and visual analysis. Secondly, we address the method’s inherent computational slowness: we implemented a parallel version of the Floyd-Steinberg algorithm that takes advantage of GPGPU (General Purtose Graphics Processing Unit) computing, drastically reducing the execution time. Specifically, we observed a quadratic time complexity with respect to the input size for the serial case, whereas the computational time required for our parallel implementation increased linearly. We then evaluated both image quality and the performance of the parallel algorithm on a exhaustive image database. Finally, to make the method fully automatic, an empirical technique is presented to choose the best degree of stochasticity.

Transformer health assessment techniques, based on applicable standards, such as the dissolved gas analysis (DGA), through laboratory testing or online monitoring are used to analyze the symptoms of a failure which develops in transformers from an early stage. The DGA from a sample of dielectric liquid taken from the main tank generates information on the state of degradation of the active part. It was found that of all the furan derivatives which result from the degradation of insulation, 2-furfuraldehyde (2-FAL) is the only derivative which dissipates in large quantities in oil. Because of this, and due to its thermal stability as compared to other derivatives, 2-FAL is the best unit of measurement to determine and monitor the degree of polymerization (DP) of insulation. The poor accessibility of paper samples has led to difficulties in testing the ageing state of paper directly by measuring the tensile strength and the DP. It was developed methods to indirectly assess the ageing state of paper, by means of the chemical markers in oil which are associated with paper ageing. This article presents a method to determine the DP of solid insulation in transformers, which provides a faster and more accurate interpretation, as compared to the classical ones. The 2-FAL data resulting from the lab are recorded in a MySQL database, which is embedded in an intelligent system for diagnosis of DP (ISDDP) based on Adaptive Neuro-Fuzzy Inference System (ANFIS), generating at the output the report with the interpretation of the faults as word files. The automation of the DP diagnostic process will be achieved, allowing the operator to make timely decisions, to avoid any possible damages.

In today’s modern world, reliable automatic personal recognition is a crucial area of discussion, primarily because of the increased security risks. A large number of systems first require the recognition of a person before they can access their services. Biometric recognition can be used, which can be understood as automatic identification or automatic verification of persons based on physiological or behavioural characteristics. Examples of biometric characteristics may be the fingerprint, face, signature, hand geometry. There are many approaches, and each has its pros and cons. However, there is currently no current extensive research and evaluation of hand-based biometric systems. Given the user-friendliness of these systems and the needs of society, this article aims to compare different methods of biometric recognition based on hand, so that the article reduces the entry barrier into this area of research. Furthermore, the article aims to determine the research gap and suggest possible directions for research in the future.

Many recent studies on autonomous driving have focused on model-based control. A number of studies has addressed that simple models such as the Kinematic Bicycle Model are easier to design controls for autonomous driving systems. However, such a simple vehicle model has a weakness in that it is subject to modeling errors. This is because it does not take into account the nonlinear characteristics due to road conditions and driving conditions (environmental disturbances: road friction coefficient, large steering, acceleration, sideslip, etc.) Therefore, the purpose of this study is to identify vehicles with high accuracy and in real time, adapting to environmental disturbances.
This study propose a vehicle model based on the Kinematic Bicycle Model. The nonlinear characteristics of the vehicle are represented by the deviation of the front wheel steering angle of the Kinematic Bicycle Model. This deviation is trained and estimated online using a three-layer Neural Network. In other words, the AI is adaptive learning of modeling errors caused by nonlinear characteristics of the vehicle.
This paper presents an example of model-based control using model predictive control.

Computer vision offers several strategies that permit computers to comprehend the substance of inputted data to extract the relevant highlights features. That gives the possibility to develop several successful recognition systems like face identification. One of the enormous difficulties these days is the way to have a prompt identification face in a multi-client identification system. We propose in this paper, an optimization of some face identification systems in big data environments that manages the cost of an appropriate identification time while holding a good performance of the system. The used systems are based on: CNN with Inception V3, PCA, LDA and LBPH. Our experimental results indicate that the performance can be preserved while reducing considerably the running time. LBPH with a Radius equal to 2 and 8 neighbors gives the best accuracy of that is equal to 95,71% with an identification time of 4.44 seconds.

A modern process plant is controlled, operated and safeguarded by the sophisticated distributed control system or a programmable logic controller and an emergency shutdown system. This process plant’s operation could be seriously affected by lightning, electrical switching, and power outrage. Lightning propagation can travel kilometers away, which has the risk of disturbing the control system’s operation. The process plant’s control system could damage when it is subjected to surges induced by lightning, power outrage, or heavy load switching. To protect equipment damage from these surges surge protection device is required. A number of the process plant uses flammable material for its production or during the manufacturing processes, explosive gas or vapor is released created an explosive atmosphere known as hazardous (Ex) area. Surge protection device mounted in the Ex area or used in the Ex control loop needs to be carefully selected to protect the equipment and at the same time, it does not become a source of ignition. This paper provides practical consideration and comprehensive detail in revealing the science and engineering behind Ex application on surge protection devices. The technical presentation is limited to equipment protection; structural protection against lightning will not be discussed here.

In many countries, irrigation water is one of the major contributors to water scarcity. In the present study, a novel optimized irrigation system which minimizes water consumption in irrigation is presented. The system is based on a predictive control algorithm, which foresees the water need of the crop, and regulates the time and amount of irrigation to maintain the soil moisture around an optimal level, while taking into account system constraints. The predictive feature of the algorithm requires a model of the soil moisture, which is obtained from the actual meteorological data of the Kingdom of Bahrain. The optimization problem is formulated as an Economic Model Predictive Control (EMPC) problem and implemented using MATLAB. The simulation experiments show that the novel system yields a reduction of water consumption around 8% and 16% compared with the PID and On-off controllers, respectively, while maintaining an optimal soil moisture level.

In this study, the ASTM A48 Class 30 gray cast iron was cast in a medium frequency induction furnace. The ultimate tensile strength (UTS), ultimate flexural strength (UFS) and microstructure were determined in accordance with ASTM E8/E 8M-80 and ASTM A438-80 (1997). From the results of this study, the graphite size at overheating temperatures 1350oC was smaller than that at 1300oC. Moreover, if the holding time was sufficient, the nested sheet graphite would be completely destroyed. Particularly, in the case of holding time from 5 to 20 minutes, the UTS, UFS are increased and the graphite shape got fine. The smallest graphite size and the highest UTS, UFS values were achieved if 20 minutes holding time is carried out. However, the holding time increased to 30 minutes, the graphite size tended to grow larger, and the UTS, UFS values suffered a decline. In addition, decreasing the average pouring temperature from 1350oC, 1300oC to 1260oC caused the upward trend of the graphite average sizes which were fit with the downward trend in the UTS and UFS values. From the above results, it is possible to determine the casting conditions that can improve the quality of cast iron products in induction furnace are: the optimal average pouring temperature is 1300-1350oC, while the optimal holding time is 10-30 minutes.

Connected and self-driving cars have emerged over the last decade as a leading example of cyber-physical systems, which seek to considerably enhance traffic safety, reduce emissions, decrease costs, and improve efficiency. Google, TESLA, Uber ATG are becoming pioneers in the autonomous vehicles industry. Autonomous vehicles can have a large codebase and with a large volume of messages exchanged. The concepts of connected driving, cooperative driving, and intelligent transportation systems increase the connectivity of vehicles to the internet or other cloud services. High connectivity, misconfiguration, and insecure coding widen the attack surface of autonomous vehicles. A conceptual model of autonomous vehicles is presented in this paper to better understand autonomous vehicles’ attack surface. In addition, specific threat modeling techniques are discussed. Experiments were carried out to demonstrate the security risks to autonomous vehicles due to third party Electronic Control Units.

This paper presents a low voltage, low power, fast settling switched capacitor based Proportional to Absolute Temperature (PTAT) current reference circuit. Unlike in a conventional resistor based PTAT current source, the proposed circuit saves a significant amount of silicon area on chip and hence the circuit becomes less susceptible to process variations. It creates a reference current of 1 nA from a 0.5 V power supply at room temperature (27?C). It has PTAT characteristics in the temperatures from 10?C to 80?C. The circuit draws a very low power of 1.5 nW and exhibits a good supply voltage sensitivity of 3.2 %/V. A startup circuit connected to the PTAT source improves the transient response by reducing the settling time. To test the PTAT current reference circuit, a low power log-domain filter which can be used for biomedical applications is realised and biased with the proposed PTAT current source. Results show that the filter cutoff frequency is constant over temperature variations. The CMOS technology used for designing the circuits is UMC 65 nm and tool used for simulations is Cadence Virtuoso.

Academic certifications are achievements desired by people, because they have a direct impact, positively, on their social lives. Such an important document, still widely issued in paper format, may be subject to forgery or impossibility of verification due to the unavailability of the issuing entity. This work consists of identifying, analyzing and testing some of the blockchain-based tools that are emerging, to offer more efficiency, reliability and independent degrees. A concept proof is presented, through the implementation of a prototype capable of issuing, verifying and sharing certificates. The results of this experiment are presented, analyzing the use of blockchain technology for this purpose. Finally, the work presents an overview of the current state of development and maturity in which these tools are found, reporting the advances and limitations, and exposing issues that still need to be resolved.

It is well documented that the average length of the queries submitted to Web search engines is rather short, which negatively impacts the engines’ performance, as measured by the precision metric. It is also well known that ambiguous keywords in a query make it hard to identify what exactly search engine users are looking for. One way to tackle this challenge is to consider the context in which the query is submitted, making use of query-sensitive similarity measures (QSSM). In this paper, a particular QSSM known as the query-document similarity measure (QDSM) is evaluated, QDSM is designed to determine the similarity between two queries based on their terms and their ranked lists of relevant documents. To this extent, F-measure and the nearest neighbor (NN) have been employed to assess this approach over a collection of AOL query logs. Final results reveal that both the Average Link Algorithm and Ward’s method present better results using QDSM than cosine similarity.

A new method of analysis and classification of rock acoustic emission signals is proposed. It is based on symbol description of signals and involves the following processing. First, signal segments containing pulses are detected. Second, noise of the detected pulses is reduced by the wavelet filtration method. Fourth-order symlets and adaptive threshold scheme based on empirical Bayes method are used. Application of wavelet filtration allows us to increase the signal-to-noise ratio by 9 dB, on the condition that initial SNR is 8.9 dB. Each pulse is described by a descriptive matrix which represents a square binary matrix reflecting pulse amplitude-phase structure and characterizing the positions of pulse extrema relatively each other. Based on the similarity degree of descriptive matrices, classes are formed. The proposed algorithm classifies correctly 78% of pulses. Each class is considered as a symbol and a set of obtained symbols forms an alphabet. Then, changes of alphabet parameters on subsequent fragments of the signal are studied. The developed method was realized in the form of an application program with the help of which acoustic emission signals were analyzed. The signals were recorded at “Karymshina” site (IKIR FEB RAS), located in a seismically active region, Kamchatka peninsula, in 2016–2019. It was discovered during the analysis that in case of an acoustic emission anomaly, alphabet content redistributed in favor of the symbols described by larger-size descriptive matrices (up to 350 extrema). At the same time, the number of such symbols increased. Analysis of alphabet averaged dynamics discovered a well exhibiting tendency of alphabet cardinality decrease relatively an average 10–18 days before earthquakes and recovery of the value over the period from 2 to 8 days after them. The obtained estimates of the pre-seismic anomaly occurrence periods are consistent with the results of early studies. The proposed method updates the existing methodology of short-term earthquake precursor formation. Thus, applying the developed method for analysis, it is possible to identify pre-seismic anomalies of acoustic emission that is topical in the creation of strong earthquake preparation warning system operating in automatic mode.

A nonlinear adaptive robust controller is proposed in this paper for trajectory-tracking control problems of robot manipulators. On one hand, to effectively approximate the systematic dynamics, a simple time-delay estimator is first adopted. On the other hand, to minimize the control error, the controller is designed based on a sliding mode structure using the obtained estimation results. A fast learning mechanism is then proposed for automatically tuning control gains. Another proper adaptation law is furthermore developed to support the nominal inertia-matrix selection of the time-delay estimation. Effectiveness of the closed-loop system is intensively discussed using Lyapunov-based constraints and extended simulation results.

Optical sensors exist in various forms and one of them is fiber bragg gratings (FBG). Performance evaluation was simulated for FBG sensing. The simulation involves different grating profiles tested with complete optical sensing system. The system involves a broadband light source, FBG of various profiles and optical spectrum analyser for data interpretation. Different profile of gratings imprinted on an FBG outputs wavelength spectra and simulated results satisfy this theory obtained from literatures. Temperature sensing was also simulated for the FBG at various temperature ranges that suit different industrial demands. At low temperatures FBG does give fairly noticeable sensing capabilities. When reaching 400C and above, the FBG was still capable of providing response, however when in real environments the fiber may suffer from thermal damage. FBG performance when integrated within a system with long distances of fibers were also simulated. Power attenuation was noticed at the reflectivity spectra.

Previous research has found that first-years in college is challenging due to changes in academic demands and the adaptation process. In addition to this, research on career development has found that barriers and supports may influence career interest, motivation to continue and student retention. This study explores the influence of specific supports and barriers among first-year engineering college students. To reach this goal, a case study was conducted with 425 engineering students at three universities in Lima, Peru. Based on Social Cognitive Career Theory (SCCT), we performed a Structural Equation Model (SEM) to assess effects of contextual factors such as barriers and supports on self-efficacy, coping self-efficacy, and goals. Results showed that certain supports and barriers influence self-efficacy, cope self-efficacy and goals. Effects of these factors in first-year students and possible strategies for retention in STEM careers are discussed.

Dynamic positioning, a system to maintain vessel position and heading, is a technology that is used in many vessels and being intensively used as research topic in marine engineering. In order to make this system work properly, an accurate parameters value is needed. This research focuses on finding several parameters needed in this control system, which are resistance and added mass. These parameters are identified using CFD simulation. The method has an advantage of being fairly high accuracy and has a lower cost than the experimental method. The results released from this simulation verified by several empirical methods, namely Holtrop and Mennen for resistance and Ellipsoid for added mass. Baruna Jaya III is used as an implementation object for the simulation. The simulation resulted in a small error compared to the verification method. So, this computational fluid dynamics simulation can be an alternative method for obtaining resistance and added mass values on ships.

According to IEC 61805 and ISO 26262 standards requirement inclusion of LBIST (Logic Built in Self-Test) became mandatory to achieve safety critical application such as automotive field. In such systems, once device is switched ON LBIST (Logic Built in Self-Test) is activated and testing of digital logic is performed. After safety subsystem says that the LBIST passed, the SoC (System on Chip) moves into the functional mode otherwise, the SoC moves into a safe state. In this entire start-up sequence the LBIST interacts extensively with the safety sub-system of the SoC. Startup sequence remains un-verified at RTL (Register-Transfer Level) leading to painful ECOs (Engineering Change Orders) and post Silicon issues in some cases. LBIST verification can only run if scan chains are present in design which is not the case at RTL. The paper describes design of a Design-for-Testability (DFT) technique to enable LBIST based system verification with different test approaches at RTL which eliminates the possibility of ECOs by catching most of the issues at RTL level. Simulation results are demonstrating the feasibility of the approach with emphasizing the benefits obtained on significant computational modules.

Advances in channel modeling allow wireless communication designers to accurately model and understand the channel’s phenomena within different propagation scenarios. A precise channel model results in the wireless system’s optimized performance while considering trade-offs due to the effects of the channel. The geometric-based stochastic channel model considers different interacting objects affecting the parameters using the indicated parameters measured, such as the delay, power profile, and angles of departure and arrival in MIMO wireless systems to multiple paths taken by the propagating signal. These different paths are known as multipath components (MPCs). Studies and measurements suggest that MPC appears in groups based on similarity or dissimilarity measures of the MPCs known as multipath clusters. The clustering of these MPCs can be done automatically and manually. The automatic approach clusters the measured or simulated data using an algorithm; on the other hand, the manual approach uses a visual representation and an expert evaluation to cluster the data. This paper aims to implement visual mapping of the multipath cluster dataset approach into parallel coordinates plot, t-SNE, principal component analysis, heatmaps, and dendrograms to find an optimal visualization of data to enhance the validation and interpretation of MPC further. The dataset is simulated and n a MATLAB environment.

Human emotional states can transform naturally and are recognizable through facial expressions, voices, or body movements, influenced by received stimuli. However, the articulation of emotions is not practicable by every individual, even when feelings of joy, sadness, or otherwise are experienced. Biomedically, emotions affect brain wave activities, as the continuously functioning brain cells communicate through electrical pulsations. Therefore, an electroencephalogram (EEG) is used to capture input from brain signals, study impulses, and determine the human mood. The examination generally included observing a person’s frame of mind in response to a given stimulus where the immediate results were inconclusive. In this study, the associated classifications were normal, focused, sad, and shocked. The raw brainwave data from 50 subjects were recorded by employing a single-channel EEG called the Neurosky Mindwave. Meanwhile, the assessments were performed while the candidates’ minds were stimulated by listening to music, watching videos, or reading books. The Fast Fourier Transform (FFT) method was utilized for feature extractions, along with the K-nearest neighbours (K-NN) for classifying brain impulses. The parameter k had a value of 15, and the average classification accuracy was 83.33%, while the highest accuracy for the focused emotional state was 93.33%. The Neurosky Mindwave in conjunction with the FFT and KNN techniques is potential analytical solutions to facilitate the enhanced identification of human emotional conditions.

The 5G, as a new source of telecommunication infrastructure technology, has attracted many stakeholders to promote the progress of its standards and the development of its technology industry. The 5G reinforces new technologies and delivers vehicles for everything services (V2X) to drivers and passengers. It also offers several advantages. On the other hand, due to pervasive network connectivity, there are significant trust, security, and privacy issues for vehicles this affects the overall performance of 5G V2X. This paper provides a comprehensive review; and particularly; an overview of the mobile communication system and introduces the Next Generation (NG). additionally, the paper concentrates on the fifth-generation structure and its use cases. The 5G-V2X as a new field of communication requires more attention and focus, moreover, it aims to present the problems facing this new technology as well, and some future directions for research have been highlighted.

Forecasting the occurrence and development of thunderstorms near insured mobile objects, such as aircraft carriers, oil tankers, requires the usage of appropriate single-point monitoring systems (SPMS) of thunderstorm activity. These systems must be small and included in the equipment of the objects. A SPMS based on EH location finder meets the requirements of small dimensions. But the primary processing algorithms for them used to determine the location of the radiation source lead to errors in the calculation of the bearing per atmospheric discharge. This inevitably occurs due to the presence of an anomalous component of the magnetic field of an arbitrarily oriented dipole. The error depends on the orientation of the equivalent dipole of the radiation source. If the dipole moment is vertical, the error is minimal, otherwise the direction finding error is completely determined by the horizontal projection of the equivalent dipole and can be equal to ? radians. Information of a thunderstorm activity collected by SPMS can be used by secondary processing algorithms to identify a real location of the thunderstorm foci. It is especially important when processing pre-storm radiation which consist of intra- and inter-cloud discharges. Secondary processing algorithms reduce errors by analyzing the parameters of all registered lightning discharges over a certain time interval. The proposed algorithms divide the entire space into a set of three-dimensional cells and determine the their grade of membership to a thundercloud or its projection. The presented methods for analyzing the location parameters of radiation sources defined across the entire set of received signals and ways to display them reduce the uncertainty of determining the coordinates of the thunderstorm location. Processing of pre-thunderstorm radiation is essential for predicting the development of thunderstorms.

This paper proposes a photovoltaic (PV) integrated shunt active power filter (SAPF) for reactive power compensation, uneven loading and compensation of harmonic current. The control of SAPF is implemented by using adaptive based current estimation algorithm to maintain power factor near to unity and source current sinusoidal at source side. The weights are calculated online by recursive least mean square algorithm once the three-phase load current is detected. Secondly, the unit vector templates (UVT) are multiplied with these weights and thus the load current with fundamental active component is derived. PV system enhanced by Maximum Power Point Tracking (MPPT) plays the role of maintaining the dc link voltage constant. Finally, a current control scheme based on hysteresis is used for converter switching and thus the source current to follow reference current precisely. The superiority of the proposed method of compensation of harmonic current and reactive power using SAPF with Recursive Least Mean Square (RLS) algorithm is proved by the simulation results based on MATLAB/SIMULINK.

Cyberattacks such as spear phishing and malspam pretending to be companies, institutes, and government officials are increasing and evolving. Malware has a variety of purposes, such as collecting personal information and illegal access to the system. New types of malware are increasing every day, and many malware programs spread all over the Internet, causing severe problems. To analyze such malware effectively, analysts first need to understand the inner structure of the malware. They can try to analyze malware manually and automatically. However, attackers who create malware use many different kinds of techniques, such as anti-reverse engineering, to hinder and delay analysis. They also extend malware life through a combination of different techniques, such as social engineering and anti-debugging. These techniques make the malware more sophisticated; thus, it is hard for an analyst to detect the malware. Anti-debugging, one way to protect malware, is a deadly poison to malware analysts because it makes the analysis more difficult by detecting a debugger or debugging environments. Therefore, this paper describes malware’s anti-debugging techniques and how to defeat them through anti-anti-debugging mechanisms. It applies its findings to analyze a sample program, packed files, and actual malware with anti-debugging modules and performs various experiments to verify the proposed techniques. After the experiments, it confirms whether its countermeasure is useful for malware analysis.

The pressing and most urgent challenge for most of the Posts in Southern Africa is to transit from its historical reputation of being a snail-paced, inefficient, loss-making and ineffective service provider to an agile, innovative, solution-driven and highly competitive service provider that strive for excellence. In this context, adoption, and diffusion of technology and digital transformation by Posts should deliver the necessary traction to steer the digital transformation journey of the postal industry in Southern Africa with the necessary velocity while cognizant and mindful of the appropriateness of the technology in the African context in the light of a VUCA (Volatile, Uncertain, Complex and Ambiguous) world. The traditional technology adoption models are characterized by “linearity” which is the opposite of a dynamic setting characterized by “causality” which is the fundamental principle of a system thinking approach. The multi-stakeholders in the postal sector with often competing interests renders a linear approach defunct and requires a system approach to digital transformation and technology adoption. The conceptual framework developed from the critical review of literature offers considerable leverage to postal sector in developing countries and to Southern Africa in particular. The proposed conceptual framework integrates dimensions such as Postal Industry 4.0 Envisioning, Strategy, Institutional factors, Organizational factors, Individual factors, Industry 4.0 environment and outcomes.

The emergence of the COVID-19 pandemic earlier this year destabilised the operations in the education sector, particularly in institutions of higher learning. Most of these institutions are now expected to teach online, assess their students using non-venue examinations and offer remote laboratory practical and experiments. However, many of these institutions were not prepared for such dynamic change in a such short space of time. Consequently, most of the institutions moved their mid-year examinations to the October/November period. In addition, they are finding methods to conduct laboratory practical and experiments. Prominent researchers agree that institutions of higher learning are challenged by the current dispensation where academics are expected to implement new pedagogical approaches and take advantage of information and communications technology (ICT) for teaching and learning. Every module requires a specific teaching method, as a result, academics need to know about various methods available. Generally, the sciences and engineering modules are taught within the classroom environment and require experiential platforms to solidify the theoretical knowledge gained. The current study is aimed at assessing the evolution of teaching approaches and technologies in distance education regarding online delivery particularly in science, engineering and technology. Various search engines such as Google Scholar, Scopus, Sabinet, ProQuest and EBSCO were used to obtain relevant literature to depict popular teaching approaches and the relevant technologies. In order to access relevant literature, various key strings were used. The findings of the study revealed problem-based, apprenticeship and experimental, and competency-based learning as the most popular teaching approaches, particularly between 2010 and 2020. Project-based learning, case-based learning and communities of practice equally share the third position followed by integrative learning. Students and academics show that instant feedback provided by virtual labs yielded encouraging results. This shows that many students and their academics prefer the introduction of the virtual labs in their learning environment, especially from 2012 until recent. The literature further confirms the current findings that institutions of higher learning need to equip academics to be able to integrate technology within their teaching methods for students to continue to learn at ease.

The Internet of Things (IoT) application has been experiencing increasingly progressive demand, especially for embedded devices (ED). However, the ED has limited capabilities, low power consumption resources, and low bandwidth in connecting to the Internet by using Wireless Sensor Networks (WSNs). Therefore, the WSNs form creates a necessity for new technologies and protocols for the IoT implementation. Thus, IPv6 Low Power Area Network (6LoWPAN) was designed by the Internet Engineering Task Force (IETF) to overcome the Internet Protocol (IP) based communication that allows direct communication between each ED. Nevertheless, the communication between ED using 6LoWPAN becomes challenging in designing routing protocols to achieve the efficient performance Quality of Service (QoS). Among the existing protocols for the 6LoWPAN network, RPL is considered effective for the 6LoWPAN system. However, the Power Consumption (PC) and routing overhead of RPL is high when it was implemented in a real scenario. Therefore, HRPL was proposed to enhance the RPL by introducing the rebroadcast technique in order to minimize the routing overhead at the same time reduced the PC usage. Thus, this paper is an extended version, in which 6LoWPAN Smart Home Testbed (6LoSH) was developed to investigate the impact of the number of nodes on PC usage for both protocols (HRPL and RPL) in a real scenario. The result shows for this instance, HRPL has succeeded in reducing the use of PC for both experiments (simulation and 6LoSH). On the other hand, the number of nodes had given an impact on PC usage. For further work, we plan to use multiple topologies and the different number of nodes to explore the HRPL for some performance metrics such as convergent time and latency.

The fifth?generation (5G) wireless system was implemented due to the massive connectivity, especially Internet of Things (IoT) including industrial automation, Vehicle to Everything (V2X) communications, which is shelter an extensive range of applications services and use cases and Intelligent Transportation Systems (ITS). A cellular Non-Orthogonal Multiple Access (NOMA) based on radio resource allocation schemes need to be carefully designed to serve and support the diversity of applications and services, through increased safety and traffic efficiency usage of connected vehicle and ranging from infotainment services. In this paper, we develop a novel scheduling and resource allocation algorithm for vehicular communications named Priority -based Scheduling Algorithm for NOMA integrated V2X (PSA). The novel scheme could make the limited radio Resource Blocks (RB) to be assigned in a more efficiently manner to the different traffic classes (safety and non-safety traffic) imposed by V2X exigency, in order to improve the performances of the system in terms of average throughput, Quality of Service (QoS), Average Blocking Rate (ABR) and to ensure fairness criteria between Vehicular User Equipments (VUEs). The performance of PSA is shown for urban macro-cell and rural macro-cell scenarios and we compared it with the conventional OMA and some recent works for NOMA based allocation.

We consider the estimation of empirical probability distributions, both discrete and con- tinuous. We focus on deriving formulas to estimate number of categories for the discrete distribution, when the number of categories is hidden, and the means and methods to esti- mate the number of components in the Gaussian mixture model representing a probability density function given implicitly in terms of its realizations. To reach the stated goals, we solve certain combinatorial problems for discrete distribution and develop methods to compute the expected Kullback-Leibler divergence for Gaussians. The last mentioned result is needed to develop the theory of continuous distributions. Sample applications and an extensive numerical study are given.

High Intensity Focused Ultrasound (HIFU) was widely used for treating tumors non-invasively. In this treatment, ultrasound is focused on the target volume inside the human body to ablate cancerous tissues and Magnetic Resonance Imaging (MRI) is mainly used to grasp the target position and to measure the temperature distributions around the target. However, MRI is very expensive, and a large space is required.
In this paper, we presented a method for measuring the temperature distribution using an ultrasound diagnostic device, which is inexpensive and commonly used in many clinics, and actually showed the results of heating experiments on a human shaped agar phantom. The proposed method for measuring the temperature distribution around the heated target was conducted by performing image processing on two ultrasound images before and after heating. Furthermore, it was confirmed that it was possible to grasp the three-dimensional temperature distribution from the images in multiple layers. The effectiveness of the temperature distribution measurement results by the proposed method was shown by comparing the temperature measurement results with the infrared thermal camera. The error between the results was approximately 1 ?.
It was found that the non-invasive measurement method of the three-dimensional temperature distribution around the target volume using the ultrasound images was useful for effective HIFU treatments.

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Discrete component based ultra-wideband SPDT switch modelling, design and implementation challenges are reported in this article. The basic SPDT switch is planned to design using PIN diodes (bare die). Nonlinear parameters extraction steps of PIN diode from datasheets have been proposed in this article instead of using readymade nonlinear PIN diode models. Equalizers are designed as per requirement using discrete components and quarter wave transmission lines for different source and load impedances. Then all the designed components are integrated with a low noise amplifier (LNA). Extraction of PIN diodes’ nonlinear parameters are carried out using diode equations along with datasheet parameters. Layout has been implemented using microstrip and CPWG techniques. Quickly responding driver is designed using transistors and logic gates. CPWG based layout achieves better isolation compared to microstrip line-based layout at highest frequencies. Measured and simulated responses are tallied and they are matching together respectively except few exceptions. Measured responses also validates the well agreement between the proposed nonlinear diode modelling and equalizer design with different input and output impedances.

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Interpolation tools are used daily in hydrology and climatology. With the purpose to regionalize spot´s registration parameters, such as depth of precipitation, temperature, humidity, among others. The accuracy of these methods is not fully validated. This research presents a comparative study between the most used interpolation methods for the regionalization of hydrological and climatological parameters. Comparative analysis of spatial interpolation; it was carried out using the IDW, Kriging and Spline methods, for this the ARGIS software was used, because its widespread and widespread use. The depth of precipitation was considered as a parameter for the comparison. Accuracy was determined by cross validation. Also, for the coefficient of determination and the comparison for visual errors such as “bull’s eyes”. The research was spatially restricted to a politically delimited region; Boyacá, Colombia, South America. The best method for spatial interpolation in this case was shown to be Spline.

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Gender detection and age estimation become an active research area and a very important field today, wish has been widely used in various applications including them: biometrics, social network, Targeted advertising, access control, human-computer interaction, electronic customer, etc. The need to further improve the recognition or classification rate keeps increasing day after day. In this paper, we explore how deep learning techniques can help in the classification of gender from human face images and moreover raise the recognition rate. We propose in this contribution an approach called SH-CNN based on Discrete Shearlet Transform (DST) as a first step of feature extraction layer, and Deep Convolutional Neural Network (DCNN) as a second automatic feature extraction layer and also a classification step. The idea behind our contribution is to generate trough DST several features (in different decomposition and orientation) of an image. These features of each image will be the input of the DCNN, to enrich the training step, and so, improve the recognition rate. The obtained results have shown that the proposed approach import a significant enhancement.

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In an intelligent transportation system (ITS), time is a big challenge since processing a huge amount of data in a short time is very difficult, especially when the processed data is heterogeneous, consisting of a mixture of emergency data, normal data, and noise. In an ITS, an ambulance is one of the priority vehicles, and the data sent by the ambulance to the infrastructure and other vehicles must be treated first because if the ambulance does not receive a status of road from the infrastructure in time, it could take the wrong road or takes a road where there is a traffic jam has a high chance of arriving late, which could put the patient’s life in danger.
Prioritizing treatment of this type of data has become paramount and vital in such cases. This paper proposes modifying the big data process to include handling the intelligent transport system’s urgent data type. We will add a classification step that allows us to classify the data according to the priority degree. We use the SVM algorithm of machine learning because it has given good results concerning data classification.

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This paper covers the study of high-frequency (~ 1 GHz) ZnO piezoelectric transducer integrated on a silicon substrate able to generate both compressional and shear acoustic waves. First, to promote the longitudinal mode, an electrical matching of the transducer in this high-frequency range is effectuated. Second, to promote shear waves, new deposition conditions were applied, giving thin zinc oxide films of inclined c-axis. The RF microprobe was used to validate the transducer design and to conduct the viscosity measurements. Thus, the shear and the volume viscosity of a water droplet were measured.

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This work presents the analysis and formulation for optimizing the dynamic model and parameter estimation of all the six joints of a 6DOF industrial robot manipulator by utilizing swarm intelligence to optimize two excitation trajectories for the first three links at the arm and the last three links at the wrist of the robot manipulator. Numerical techniques were used to reduce the observation matrix to a minimum linear combination of parameters, thereby maximizing the identifiable parameters, and the Linear Least Square method was used for parameter identification. An improved particle swarm optimization algorithm with mutation and archived elite learning was proposed for solving the dynamic optimization problem of the industrial robotic manipulator. The basic parameters of the algorithm have been optimized for robotic manipulator analysis. The proposed algorithm is computationally economical while completely dominating other Evolutionary algorithms in solving robot optimization problems. The algorithm was further used to analyze 36 benchmark functions and produced competitive results.

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Traditional networks have difficulty in meeting the technological developments and the continuous increase in the size of data to be processed. Software-Defined Network (SDN) approach has emerged as an alternative to traditional networks. SDN separates the control and data planes from each other and manages the network over the control plane with flexibility and cost advantages. In networks with large data flow, SDN with multiple controllers will be useful to manage the network because a single controller may cause network interruptions and data loss. After deciding on the use of multiple controllers in the SDN approach, problems are encountered with the number of controllers that should be used and the placement of these controllers. Due to the increase in the coronavirus epidemic that has affected the whole world in recent months, the need for pandemic hospitals has increased. However, considering the establishment costs of pandemic hospitals, it will not be possible to establish these hospitals in every desired location in the impact area. For this reason, positioning pandemic hospitals at more strategic points will provide these hospitals with a wider coverage area and more functionality at lower costs. In this paper, a genetic algorithm-based SDN is presented for pandemic hospital localization. The number of coronavirus infected people of each city in Turkey is taken into consideration as well as the city distances in the ULAKNET data set within the Topology Zoo database. For the best localization of pandemic hospitals, the Dijkstra algorithm is used to best cover the cities where the coronavirus epidemic is at a minimum distance from the cities. In this paper, the number of controllers is set as 10, and the experimental results are given with maps and graphics.

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Biological networks represent biological systems, and various graph analysis algorithms have been applied to solve various real-world problems. Network motif analysis, as one of network analyses, is detecting frequently and uniquely over-occurring subgraph patterns in a network. The detection process requires high computational resources, and various tools have been developed to provide efficient solutions. However, they still lack extensible output options and easy accessibility, which restricts substantial scale of experiments for many biological applications. Therefore, we provide NemoSuite (Network Motif in a Suite) as a web-interactive tool for detection and analysis of network motifs. Including both of network-centric and motif- centric approaches, it is a greatly accessible tool emphasizing on efficiency, usability, and extensibility.

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Manufacturing industry is facing several challenges in power quality and energy consumptions that play a significant role in the cost of goods sold in addition to the operational efficiency of manufacturing plants. The current techniques are not enough to manage the manufacturing processes from power perspectives as it is focusing only on the monitoring of the power grids using smart meters connected to SCADA systems. In this paper, a novel technique is proposed that is based on energy-aware manufacturing process control model using internet of things technology. The model is applied on cabling industry use case in addition to implementation of a MATLAB model for this purpose whereas the IoT physical layer is collecting , analyzing and communicating the electric power parameters correlated with the manufacturing process parameters received from PLC IoT nodes. Accordingly, the effective power quality is enhanced using manufacturing process control rather than additional correction nodes in traditional techniques. On applying the model on cabling use case, the Total Harmonic Distortion (THD-current) is improved 10 times to be 3.1% against 31.4% and the power factor is improved by 33% from 0.7 to 0.93 without additional correction nodes.

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Automatic Dependent Surveillance-Broadcast (ADS-B) is the future of aviation. It is a vast system that provides situational awareness for the aviator and regulator at a very low cost and does so with the aid of multiple disparate systems working closely together and communicating with one another. ADS-B uses the Global Navigation Satellite System (GNSS/ GPS) to locate elements. Weather information and ground-based information is also transmitted wirelessly. The system is designed to be open, unencrypted, and accessible to actors throughout the world. However, this leaves it open to attacks. The use of GNSS and other wireless technologies also carries over their security vulnerabilities into ADS-B. Certain issues have arisen due to both component-system failures and malicious attacks. Most obvious solutions impinge on the openness and transparency of the system. Past research has indicated that security must be built into a system design itself and cannot be retrofitted. We want to showcase such a design process for ADS-B. Our pathway to do so is to first create Universal Modeling Language (UML) diagrams to showcase security and safety issues and responses. These UML diagrams will then help us to model state and sequence diagrams. These will then be used to create a TLA+ model of one selected security methodology. We then run the TLC model checking on it to find loopholes and plug gaps in our scheme. We managed to create such models and prove deadlock-free running using only software tools. Our eventual goal is to develop a comprehensive formal specification for ADS-B model-creation and checking.

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Environmental monitoring has gained significant importance in outdoor air quality measurement and assessment for fundamental survival as well as ambient assisted living. In real-time outdoor urban scale, instantaneous air quality index estimation, the electrochemical sensors warm-up time, cross-sensitivity computation-error, geo-location typography, instantaneous capacity or back up time; and energy efficiency are the six major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and pro-longed lags in air quality index mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient-aware, multi-variable air quality sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter (2.5), ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality monitoring for the chosen geo-spatial cluster (Qatar University).

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In this study, we developed and evaluated an automatic abstractive summarization algorithm in Japanese using a neural network. We used a sequence-to-sequence encoder-decoder model for practical purposes. The encoder obtained a feature-based input vector of sentences using the bidirectional encoder representations from transformers (BERT) technique. A transformer-based decoder returned the summary sentence from the output as generated by the encoder. This experiment was conducted using the Livedoor news corpus with the above model. However, two problems were revealed. One is the repetition of a specific phrase while the model is generating text. The other is that the model can not handle out-of-vocabulary words. As solutions, we use repeat block in n-gram words and WordPiece. In addition, to evaluate the performance of the model, we compared the summarization accuracy between our model and a long short term memory based pointer-generator network. As revealed by the results, our model comprehends the meanings of sentences better than a pointer-generator network but makes more word-based mistakes.

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IoT integrates and connects intelligent devices or objects with varied architectures and resources. The number of IoT devices is growing exponentially. Due to the massive wave of IoT objects, their diversity and heterogeneity among their architectures, the existing communication protocols for wireless networks become ineffective in the context of IoT. Wireless Sensor Network (WSN) has the potential to be integrated to the internet of things (IoT). The issues of the routing of WSNs impose nearly similar prerequisites for IoT routing technique. Most of the traditional routing protocols are not appropriate for WSNs and IoT because of resource constraints, computational overhead and environmental interference and do not take into account the different factors affecting energy parameter and do not accommodate node mobility. Routing algorithms must ensure the data transmission in an efficient way, having proper knowledge of the IoT system. For this reason, many intelligent systems have been utilized to design routing algorithms to handle the network’s dynamic state. In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. The proposed routing algorithm is simulated using MATLAB for performance evaluations. The evaluation results have recorded an improvement in conservation of energy, of almost 50% less consumed energy even with an increase in the number of nodes, by comparing with an existing routing technique based on ant system, a current routing protocol for IoT and the conventional ACO algorithm.

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The demand on livestock as a source of protein is increasing with the dramatic increase of world population which approaches 8 billion. As a result, the monitoring of the performance of livestock breeding is becoming essential. The technique which could increase livestock production and improve the efficiency of operation is digitalization of livestock management . This could be achieved by models’ realization that relates the rates of productivity and the parameters of operation which count on collecting big volume of data that results from digitalization process. In fact, the adoption of IoT technology in livestock environments is usually challenged by energy problems and power efficient communication technologies. In this paper a modified AODV protocol is proposed to enhance the traditional AODV protocol used in MANET networks that was mainly concerned with optimal route selection based on shortest path to send the data packets through it. The proposed technique models and simulates operational parameters of intermediate nodes as a factor to decide on the optimal path selection in IoT based MANET networks in addition to the shortest path that leads to transmission power gain of the source node. The results show a significant power performance enhancement that reaches average power saving of 33.3% compared to the traditional AODV protocol.

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Virtual reality is a technology that literally allows constructing of a new reality for its users. VR has huge potential, it is able to change social life, communications, and the academic sphere, but VR may also be applied by criminals. In the present paper properties of potentially committed crimes in VR are analyzed. It is concluded that VR provides criminals with some advantages. The first advantage is the multijurisdictional problem in prosecuting the offender that is usual for most of IT. The second advantage is that a virtual environment gives a realistic experience to the user and literally replaces the real world. The third advantage is an integration of virtual-world environments with haptic devices that may factually affect the user in the real world. All of these allow the commission of new kinds of crimes where properties of conventional and cybercrimes are combined. Among such kinds of crimes analyzed in the paper are cybercrimes that affect the body and mental state of victims. VR is researched as a first tool that in combination with haptic devices gives criminals the opportunity to commit crimes against sexual freedom and to cause physical harm. Thus, VR crimes need a special legal framework that will take into consideration this kind of crime.

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To evaluate and compare methods in computer vision, scientists must use a benchmark dataset and unified sets of measurements. The UCI-EGO dataset is a standard benchmark dataset for evaluating Hand Pose Estimation (HPE) on depth images. To build robotic arms that perform complex operations such as human hands, the poses of the human hand need to be accurately estimated and restored in 3D space. In this paper, we standardized the UCI-EGO dataset to evaluate 3D HPE from point cloud data of the complex scenes. We also propose a method for fine-tuning a set parameter to train the estimation model and evaluating 3D HPE from point cloud data based on 3D Convolutional Neural Networks (CNNs). The CNNs that we use to evaluated currently the most accurate in 3D HPE. The results of the 3D HPE from the point cloud data were evaluated in two branches: using the hand data segment and not using the hand data segment. The results show that the average of 3D joint errors of the 3D HPE is large on the UCI-EGO dataset (87.52mm) and that the error without using the hand data segment is many times higher than the estimated results when using the hand data segment (0.35ms). Besides, we also present the challenges of estimating 3D hand pose and the origin of the challenge when estimating real image dataset.

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Graphene oxide (GO) was prepared by the modified Hummer’s method, but the mass ratio of graphite to sodium nitrate (NaNO3) was varied from 2:1, 1:1, and 1:2. The primary reason for the variation was to determine the optimum conditions that would afford more oxygen functional groups to improve the material for application in adsorption. The final products, termed GO2:1, GO1:1 and GO1:2, were analyzed by several instrumental techniques. The layered structure of the GO sheet was established by transmission electron microscopy, while powder X-ray diffraction showed that the GO2:1 material was more crystalline than either GO1:1 or GO1:2. Raman spectroscopy revealed the presence of a greater defect density in GO2:1. The presence of oxygen functional groups was verified by Fourier transform infrared spectroscopy, and these were quantified by the Boehm titration method. Overall, GO2:1 had a larger oxygen content than either GO1:1 or GO1:2, and a larger specific surface area. A preliminary study on the adsorption properties of the samples revealed that GO2:1 exhibited the highest percentage removal for Cu2+ in aqueous solution. Thus, the preparation of graphene oxide with a smaller amount of NaNO3 yielded a material with a greater oxygen content, which showed suitable properties for the adsorption of contaminants in wastewaters.

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In this study, we study on the downlink indoor coverage performance of unmanned air vehicle (UAV) base stations. We consider a probabilistic expression for air-to-ground (ATG) path loss, and a deterministic one for additional indoor losses in order to provide a practical model. One of our important assumptions is that the UAV base station operates at the frequencies reserved for 4th Generation (4G) – Long Term Evaluation (LTE) based mission critical services in Turkey–around 2.6 GHz–. Therefore, we are able to neglect intercell interference issue, and we may rely on signal-to-noise ratio (SNR) for our coverage definition. We consider four different SNR requirement throughout our performance evaluation, and investigate the effect of UAV altitude and other related parameters on the radius of service area. We first observe that rural coverage performance is always better than urban conditions –this result is fully compatible with the fact that attenuation levels significantly arise in urban regions–. In addition, we show that increasing quality of service (QoS) requirement and/or using a more directive antenna unit decrease the coverage radius as they are expected. Thereupon, we conclude that it is possible to obtain the optimum altitude level –by employing the framework proposed here– in order to satisfy certain service criteria.

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The objective of the metaheuristics, together with obtaining quality results in reasonable time, is to be able to control the exploration and exploitation balance within the iterative processes of these methodologies. Large combinatorial problems present ample search space, so Metaheuristics must efficiently explore this space; and exploits looking in the vicinity of good solutions previously located. The objective of any metaheuristic process is to achieve a ”proper” balance between intensive local exploitation and global exploration. In these processes two extreme situations can occur, on the one hand an imbalance with a bias towards exploration, which produces a distributed search in the search space, but avoiding convergence, so the quality of the solutions will be low, the other case is the bias towards exploitation, which tends to converge prematurely in local optimals, impacting equally on the quality of the solutions. To make a correct balance of exploration and exploitation, it is necessary to be able to control adequately the parameters of the Metaheuristics, in order to infer in the movements taking advantage of the maximum capacity of these. Among the most widely used optimization techniques to solve large problems are metaheuristics, which allow us to obtain quality results in a short period of time. In order to facilitate the use of the tools provided by the metaheuristic optimization techniques, it is necessary to reduce the difficulties in their configuration. For this reason, the automatic control of parameters eliminates the difficult task of obtaining a correct configuration. In this work we implemented an autonomous component to the Intelligent Water Drops algorithm, which allows the control of some parameters dynamically during the execution of the algorithm, achieving a good exploration-exploitation balance of the search process. The correct functioning of the proposal is demonstrated by the Set Covering Problem, which is a classic problem present in the industry, along with this we have made an exhaustive comparison between the standard algorithm and the autonomous version that we propose, using the respective statistical tests. The proposal presents promising results, along with facilitating the implementation of these techniques to industry problems.

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In this work, a study of the process variation effects on the threshold voltage of a floating- gate device is proposed. The study demonstrates the sensitivity of the threshold voltage to five geometrical parameters including gate length, gate width, tunneling gate oxide thickness, bottom oxide-nitride-oxide oxide thickness, and nitride spacer thickness. This paper also proposed a detailed flow to fabricate the floating-gate device for CMOS 180nm process, which is used to design the floating-gate device for the study. This paper used the TCAD tools including Athena, Devedit3D, and Atlas for the simulations.

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This paper presents a simple method for correcting the error of commutation windings for sensorless control of a brushless DC motor with a small inductance. Switching error analysis is performed based on the phase currents of the switched-off and switched-on phases. To correct the commutatiuon signals for the inverter, a speed-independent function is used, calculated from the back-EMF, by selecting its coefficients. The back-EMF is calculated for the system obtained by transformed a three-phase system to a two-phase one, which reduces the system dimension. An algorithm for start-up the motor with sensorless control based on the method of align and damping the rotor vibrations in a stable equilibrium position is proposed. The back-EMF is approximated by a function based on a piecewise linear function and an inscribed ellipse, for a more accurate description of the shape of the back-EMF. This approximation is used when simulation the motor. The simulation confirms the effectiveness of the proposed method for correcting the commutation error in the case of sensorless control of a low-power BLDC motor.

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The Coriolis force in the ocean at mid to high latitudes can cause significant deviation of flow over bottom topography, including formation of Taylor columns. Structures in a tidal zone will experience zero inertial current between every tidal change. Around periods of directional change, the Coriolis force may be tapped into for energy. Factors like timescales and other environmental factors like local currents could influence the flow characteristics in an undesirable way and are outside of the scope of this study. The focus of this study is to assess how the design of a structure influences the asymmetric flow patterns produced around it by an incident quasigeostrophic flow. Analytical solutions existing for inviscid quasigeostrophic flow over isolated elongated elliptical topography are used for flows with small Rossby numbers. These solutions are used to predict and explore the characteristics of the flows expected during a change in the tidal cycle. Results show that a linear array placed perpendicular to a quasi-geostrophic flow will experience flow acceleration on the left-hand side when looking downstream. On the other hand, a linear array placed parallel to the quasi-geostrophic flow will experience a sharp velocity gradient over the array. This suggests that an array placed perpendicular to the quasi-geostrophic flow will provide for a more robust design when compared to a linear array placed parallel to the flow.

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The angle of attack is a critical flight parameter for commercial aviation aircraft, because automatic envelope protection systems rely on it to keep the aircraft within its safe flight envelope. Faulty measurements of the angle of attack could have catastrophic effects, leading to aircraft loss of control in flight and fatalities, as demonstrated by the recent accidents involving the Boeing 737-MAX. This paper presents a novel approach to the measurement of the angle of attack, which uses one virtual sensor and two physical sensors to implement a physical-analytical redundant system that is robust to a single fault of the physical sensors. The virtual sensor is based on an innovative and reliable estimator of the angle of attack. It was originally developed to provide General Aviation pilot with an accurate indication of trend toward stall, and has been suitably customized to fit its application to commercial aviation. One of the peculiarities of the redundant measurement system is that its implementation on-board several existing commercial aviation aircraft only needs the integration of a software code and does not require any installation of additional physical sensors. The proposed approach demonstrated very interesting performance, assessed in simulation through several Monte Carlo analyses. Its exploitation could contribute to reduce the angle of attack related accidents, improving the safety of the air transport system.

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Capturing similarity in gene sequences of a target organism to detect significant regions of comparison will most likely occur because genes share a related descendant. Local sequence alignment for the targeted organisms can help preserve associations among sequences of related organisms. Such homologous genes possess identical sequences with common ancestral genes. The genes may be similar to common traits, and varying purposes, but they descend from a common ancestor. Basic local alignment search tool (BLAST) from the National Center for Biotechnology Information. (NCBI) has been used by different researchers to resolve the various forms of alignment problems. However, much literature to bare the efficacy of standard protein-protein BLAST (BLASTp) on the MATLAB platform has not been seen. In this study, a position-specific iteration BLASTp of 20 anopheles insecticide target protein sequence was performed on NCBI Ensembl against genomes of Anopheles (target organism), then against humans, fruit-fly, zebrafish, and chicken genomes (non-target organisms) to eliminate the targets with homology to non-target organisms. Furthermore, the same iteration was repeated for the genomes of Anopheles and non-target organisms using a posterior probability algorithm built into MATLAB as a tool for protein to protein search BLAST. Outputs from NCBI and MATLAB were put forward to determine the optimality of an optimized search algorithm on MATLAB. The MATLAB-Blastp method based on the application of posterior probability has helped to avoid errors occurring in the early stages of alignment. Moreover, the same results were obtained for the sought features on NCBI Blastp with a refined understanding of how feature values are generated from MATLAB posterior probability built-in algorithm for position-specific BLAST.

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This article presents an analytical model of a single-phase ferromagnetic core power in- ductor (reactor) based on a magnetic equivalent circuit (MEC). The MEC model consists of magnetomotive forces (MMFs) and reluctances for all flux paths: magnetic core and leakage flux paths. The MEC elements are found established on the characteristics of the ferromagnetic material and the reactor’s dimensions. Then the inductances of the reactor are determined using the MEC for a group of Silicon Steel (Si-Fe) sheets, which are juxtaposed with the inductances obtained using the finite element analysis (FEA) method. This comparison corroborates the MEC presented here. Furthermore, for the unsaturated (linear) region of the B-H characteristics of the same sets of Si-Fe materials, the inductive reactance closed-form solution of the reactor is obtained as a function of the design parameters. As an application example of the presented analytical model, reactor design optimizations (single-objective and multi-objective) are formulated and solved based on the derived closed-form reactance expression resulting in a reactor not only optimized in reactance but also in terms of material use and size.

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Classic surgical skill evaluation is performed by an expert surgeon examining an apprentice in a hospital operating room. This method suffers from being subjective and expensive. As surgery becomes more complex and specialized, there is an increase need for an automated surgical skill evaluation system that is more objective and determines more exactly the skills (or lack thereof) the apprentice has. The main purpose of our proposed approach is to use an existing skill database with known proficiency levels to evaluate the skills of a given apprentice. The skill of the apprentice will be assessed to be similar to the closest skill example found in the database (case-based reasoning). A key element of the system is the skill distance measure employed, as each skill example is a multidimensional time series (sequence) with widely varying values. In this paper, we discuss a new surgery skill distance measure denoted as Procrustes dynamic time warping (PDTW). PDTW integrates the search for exact alignment between two skill sequences using DTW and Procrustes distance as a measure for the similarity. The Procrustes approach is a shape distance analysis that involves rotation, scaling, and translation. We evaluated our proposed distance on three surgical motion data, a widely used JIGSAWS robot surgery dataset, a wearable sensor dataset, and a Vicon motion system dataset. The results showed that the proposed framework produced a better performance for surgeon skill assessment when PDTW was used compared to other time series distances on all three datasets. Also, some experimental results for the JIGSAWS dataset outperformed existing deep learning-based methods.

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In vital energy applications especially the agricultural environments, the service of adaptive power utilization plays an essential role in facilitating the usage of Internet of Things systems. Such environments are distinguished by the large range of lands where most of the region lacks the commercial power lines. Reaching some high or deep sensing points is also difficult in such environments. The adaptive power system will pave the way to make a smart service for users to create a platform for real time interaction. It can enhance the reliability, stability and sustainability of power supply, and provide more humanized and various intelligent services for the users. The usage of adaptive power can be improved effectively using IoT technology with its strong data processing and reliable communication. In this paper, an algorithm is proposed to offer a sustainable power service for smart agriculture system to guarantee continuous system operation. It mainly rely on controlling the load demands and managing the renewable energy. A model is built on Matlab that governs the proposed algorithm and the results of the simulation are discussed.

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Contracts may range from a simple agreement between a tenant and a landlord or a gym contract, or it could be as important as an employment or marital contract. No matter the level of importance, individuals are legally obligated to obey and carry out all clauses in the contract. In this paper, we have identified that the majority of people seldom read through the entire contracts for several reasons such as the size of the contracts i.e. bulky contracts or the inability to fully comprehend a contract. As a solution to the identified problem, this paper presented a software tool that automatically comprehends and summarises legal contracts. We designed context-free grammar (CFG) rules for the recognition of critical clauses found in contracts. These CFG rules were implemented in the software tool. An evaluation of this tool showed that it was able to identify critical clauses in contracts to an accuracy of 79.2%.

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