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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…

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 communication 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…

The basics of unconventional design of current circuits of two-valued and multi-valued memory elements (ME) for storing current digital signals and flip-flops of the main types on their basis are considered. A nontraditional method of ME synthesis is proposed, which is based on the mathematical tool of linear algebra. Linear equations, structural and functional schemes…

Recitation and listening of the Holy Quran with Tajweed is an essential activity as a Muslim and is a part of the faith. In this article, we use a machine learning approach for the Quran Reciter recognition. We use the database of Twelve Qari who recites the last Ten Surah of Quran. The twelve Qari…

In an enterprise Wifi network, indoor and dense, co-channel interference is a major issue. Wifi controllers help tackle this problem thanks to radio resource management (RRM). RRM is a fundamental building block of any controller functional architecture. One aim of RRM is to process the radio plan such as to maximize the overall network transmit…

Neurological signals are generally very weak in amplitude and strongly noisy. As a result, one of the major challenges in neuroscience is to be able to eliminate noise and thus exploit the maximum amount of information contained in neurological signals (EEG…). In this paper, we aimed at studying the N400 wave of the Event-Related Potentials…

Time scheduling as seen in timetabling processes with few and/or competing resources has exposed complex interoperable time scheduling. Attempts to resolving these time scheduling processes has been undertaken, using several classical methods, with difficulty due to inherent complexities, constraints and conflicting issues. The use of ontology-based approaches to resolve time complexity is recently adopted due…

In this paper, an analytical modeling is proposed to compute the static and dynamic characteristics of linear variable reluctance motors with taking account of the saturation and the non-linearity of the magnetic circuit. The proposed model is based on the Fourier series expression of the phase flux in which the variation of the linkage flux…

Music genre classification is an important multimedia research domain, including aspects of music piece representation, distances between genres, and categorization of music databases. The objective of this study was to develop a model for automatic classification of musical genres from audio data by using features from low-level time and frequency domains. These features can highlight…

Advances in technology have enabled the installation of an increasing number of sensors in various mechanical systems allowing for more detailed equipment health monitoring capabilities. It is hoped the sensor data will enable development of predictive tools to prevent system failures. This work describes continued analysis of sensor data surrounding a seizure of a turbocharger…

Automatic speech emotion recognition (SER) techniques based on acoustic analysis show high confusion between certain emotional categories. This study used an indirect approach to provide insights into the amplitude-frequency characteristics of different emotions in order to support the development of future, more efficiently differentiating SER methods. The analysis was carried out by transforming short 1-second…

Today, computing devices of various types with wireless interconnections are used for diverse tasks and increasingly in ad hoc manners. It is not always obvious which devices are present, reachable, and connected when users and their devices are mobile. In such mobile computing environment, the number of registered lines on the network via network operators…

The objective of this paper is to investigate three different approaches of modeling, design and discrete-time implementation of PI closed-loop control strategies in SIMULINK simulation environment, applied to a centrifugal chiller system. Centrifugal chillers are widely used in large building HVAC systems. The system consists of an evaporator, a condenser, a centrifugal compressor and an…

This article examines the hydrogen storage phenomenon in a spherical cavity. The hydrogen gas or liquid is subjected to high pressures, leading to significant loss of mass of hydrogen, and requires materials that can withstand these high pressures also minimize losses. For all these reasons, the problem is considered at the quantum scale. So in…

Probabilities topic models are active research area in text mining, machine learning, information retrieval, etc. Most of the current statistical topic modeling methods, such as Probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA). They are used to build models from unstructured text and produce a term-based representation to describe a topic by choosing…

Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning,…

Hereby in this paper, we are going to refer image classification. The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images. For feature extraction functions we evaluate…

In this paper, Constant False Alarm Rate (CFAR) detection of spatially distributed targets embedded in compound Gaussian clutter with Inverse Gamma texture is addressed. By taking into account the fact that clutter parameters are unknown in practical situations, we propose mean level based on Lookup Tables detectors, that operate as a two-step approach, which consists…

This paper is an extension of work originally presented at conference Applied Machine Intelligence and Informatics (SAMI), 2017 IEEE 13th International Symposium. The article performs a comparative study between the image compression technique Local Binary Compressed format (LBC) proposed by the authors and the standard image compression techniques used today (BMP, JPEG, PNG and GIF).…

Vehicle analysis is a very useful component in various real world applications. In this paper, we have developed a Vehicle Make and Model Recognition (VMMR) system using Support Vector Machine (SVM). Scale Invariant Feature Transform (SIFT) and Speed-Up Robust Transform (SURF) are used to extract local features from an image. Bag-of-Features (BoF) model is used…

Data visualization is an effective tool for communicating the results of opinion surveys, epidemiological studies, statistics on consumer habits, etc. The graphical representation of data usually assists human information processing by reducing demands on attention, working memory, and long-term memory. It allows, among other things, a faster reading of the information (by acting on the…

Feature extraction is playing a significant role in bio-signal processing. Feature identification and selection has two approaches. The standard method is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The…

In the aim to manage and retrieve the organizational knowledge, in the last years numerous proposals of models and tools for knowledge management and knowledge representation have arisen. However, most of them store knowledge in a non-structured or semi-structured way, hindering the semantic and automatic processing of this knowledge. In this paper we present a…

Big Data Analytics (BDA) is important to utilize data from hospital systems to reduce healthcare costs. BDA enable queries of large volumes of patient data in an interactively dynamic way for healthcare. The study objective was high performance establishment of interactive BDA platform of hospital system. A Hadoop/MapReduce framework was established at University of Victoria…