1. Introduction

Intensive research interests have been in adaptive transmission techniques over wireless channels to efficiently support multimedia services, Internet access, 4G and future 5G mobile communications. One important issue is the ability to combat inter-symbol interference (ISI) in the wideband transmission over multipath fading channels.

Multicarrier modulation technique, such as orthogonal frequency division multiplex (OFDM), appears to be a promising solution to this problem. It is expended in various broadcast technologies like wireless local area network (WLAN), worldwide interoperability for microwave access (WiMAX), long term evolution (LTE), digital video broadcasting (DVB) and digital audio broadcasting (DAB) [1]. OFDM is a multicarrier system which employs a prominent number of close spaced carriers that are regulated with low data rate. In order to avoid mutual interference, the signals are made as orthogonal to each other. Coded Orthogonal Frequency Division Modulation (COFDM) is a kind of OFDM where error correction coding is comprised into the signal [2]. This system is able to attain excellent performance on frequency selective channels because the merged benefits of multicarrier modulation and coding. The multicarrier transmission reaches better results if incorporated with additive techniques in order to achieve higher efficiency in terms of throughput and ASE. The AMC-COFDM systems allows to choose the most appropriate Modulation and Coding Scheme (MCS) depending on the quality of the received signal and the channel conditions.

The aim of this paper is to develop an adaptive technique that improve the COFDM system performances: keep the error probability below a specified threshold or maximize the ASE. The MCS selection is based on the amount of received packets with errors, having as target that of minimizing that number and reducing the implementation complexity by using NLMS algorithm.

The rest of this paper is organized as follows. Section 2 gives the COFDM description where we show how the COFDM is better than OFDM.  In Section 3, we present the details of AMC system and explain NLMS algorithm for channel estimation. Simulation results have been discussed in Section 4 and finally, conclusions are provided in the last section.

2. COFDM System Description

The Coded Orthogonal Frequency Division Multiplexing (COFDM) system comprises of three main elements. These are Guard interval/cyclic prefix (CP), Channel coding/interleaving and IFFT. These technical aspects make the system tolerant to multipath fading and ISI [3]. Description of this transceiver system is listed:

The digital bits are randomly generated, convolutional coding is applied with different coding rate schemes (R=1/2, 2/3 or 3/4). The coded data is then interleaved to eliminate the effect of selective fading. The data is thereafter modulated using different proposed schemes (M-PSK, M-QAM). A serial-to-parallel conversion is performed. Thereafter, an IFFT is executed and guard time is added prior to parallel-to-serial conversion for making OFDM symbols tolerant to ISI. The data is thereafter digital-to-analog converted (DAC) and transmitted over the Rayleigh fading channel. At the receiver level, time and frequency synchronization is performed. The GI is removed and serial-to-parallel conversion is performed, and then an FFT is executed. The channel is estimated for each sub-carrier and the channel frequency selectivity is compensated for (equalized). After a final parallel-to-serial conversion, the data can then be demodulated and decoded. In order to achieve better performance, the receiver has to know the channel effect.

3.  AMC-COFDM Proposed System

Figure 1 illustrates the block scheme of structure of the proposed AMC-COFDM system, exploiting her potentialities taking that the change of modulation level and coding rate according to the CSI, for each symbol. The block diagram represents the whole system model. Basically, the proposed system is divided in two main sections namely transmitter and receiver:

Figure 1. The proposed AMC-COFDM transceiver block diagram.

At the transmitter level, the digital bits are randomly generated and encoded using an adaptive conventional coding technique, where three coding rates are proposed (1/2, 3/4, 2/3) with a constraint length of 3 and a polynomial generator [7,5]8. The sequence at the encoder output is permuted. The employed interleaver should have the capability of breaking up low-weight input sequences so that the permuted sequences have large distances between the 1’s.  Thus the incoming serial coded sequence is divided into low-rate data streams that are simultaneously transmitted, where each of the low-rate data streams is mapped using an adaptive modulation technique. There are four available modulation types for modeling the data onto sub-carriers: BPSK, QPSK, 16-QAM and 64-QAM used with gray coding in the constellation map. Depending on the feedback information, from the receiver, the mapping scheme and coding rate are adjusted.

The next stage consists to perform invers fast Fourier transform (IFFT), insert a guard area (GI) by the addition of cyclic prefix (CP). thus combating the inter-symbol interference (ISI) and inter-carrier interference (ICI) introduced by the multipath fading channel. As a last step Before the transmission through channel, a parallel to serial conversion is made.

As shown in the diagram block, at the reception level the inverse procedure is performed, where the serial to parallel conversion, the CP remove, and FFT are executed. To obtain the original data bits, the reverse process (demapping, de-interleaving, Viterbi decoding) is executed.

Besides, the NLMS channel estimator method is used. The knowledge on the Channel Impulse Response (CIR) provided by this method to the detector, is transmitted also through feedback channel. The basic concept of this channel estimation algorithm is to know sequence of bits, which is reoccurred in every transmission burst. The transferred symbols going through the channel transmission, can be regarded as a circular convolution between the CIR and the transmitted data block. In the receiver side, the channel coefficients are normally unknown. It demands to be efficiently estimated to maintain a low computational complexity.

3.1.  Least Mean Square (LMS) Algorithm

The Least Mean Square (LMS) algorithm was first developed in 1959 by Widrow and Hoff through their studies of pattern recognition [4]. From then it became one of the most widely used algorithms in adaptive filtering. The LMS is an adaptive filter algorithm known as stochastic gradient-based algorithm since it utilizes the gradient vector of the filter tap weights to converge on the optimal wiener solution [5,6]. It is well known by his computational simplicity and implementation facility. This simplicity that made it the benchmark against which all other adaptive filtering algorithms are judged. With each iteration of our algorithm, the tap weights of the adaptive filter are updated according to the following relation:

Where  represents the input vector of time delayed input values:

The vector is the coefficients of adaptive FIR filter tap weight vector at time  . The parameter µ is step size (a small positive constant) which controls the influence of the updating factor. The choice of a suitable value for μ is imperative to the performance of the LMS algorithm, if it is too small the time the adaptive filter takes to converge on the optimal solution will be too long; if μ is too large the adaptive filter becomes unstable and its output diverges [7,8].

The three distinct steps required for each LMS algorithm iteration are given as follows:

• First, the output of the FIR filter is calculated by following equation:

• Then, we calculate the error estimation value using the following relation:

• Thereafter, the tap weights of the FIR vector are updated using the relation (1) in preparation for the next iteration.

The primary trouble of the LMS algorithm is its sensitivity to the scaling of its input signals. This causes it very hard to select h that guarantees stability of the algorithm. Also, among its disadvantages is having a fixed step size parameter for every iteration [9]. This requires an understanding of the statistics of the input signal prior to commencing the adaptive filtering operation. In practice this is rarely achievable.

3.2.  Normalized Least Mean Square (NLMS)Algorithm

The normalized least mean square algorithm (NLMS) is a variant of the LMS algorithm that resolves these problems by annealing with the power of the input signal and calculating maximum step size value, it is proportional to the inverse of the total expected energy of the instantaneous values of the coefficients of the input vector . [i.e. Step size=1/dot product (input vector, input vector)].

The recursion formula for the NLMS algorithm is given by the following equation:

• Implementation of the NLMS algorithm:

We implement the NLMS algorithm in MatLab. It gives a great stability with unknown signals since the step size parameter is selected based on the current input values. The NLMS algorithm practical implementation is similar to that of the LMS algorithm. Each iteration of this algorithm requires these following steps:

first, we calculate the output of the adaptive filter using the following relation:

Then, we calculate the error estimation value using the relation (4).

Thereafter, the step size value for the input vector is determined by:

And then, the filter tap weights are updated using the relation (5) [i.e.

] in preparation for the next iteration.

3.3.  Analysis of Simulated Algorithms

In this section, we will examine the performance of LMS algorithm in comparison with the NLMS algorithm. The adaptive filter is a 1025th order FIR filter. The step size was set to 0.0005.

Figure 2. Channel estimation using the LMS algorithm.

Figure 3. Channel estimation using the NLMS algorithm.

Figure 2 shows the original and predicted values of channel coefficients with LMS algorithm, but the tracking is not efficient. It does not predict the coefficients exactly. So, NLMS algorithm is adopted to have better tracking compared to LMS. It is shown in figure 3.

The LMS algorithm is the most popular adaptive algorithm because of its simplicity. However, the LMS algorithm suffers from slow and data dependent convergence behavior. The NLMS algorithm, an equally simple, but more robust variant of the LMS algorithm, exhibits a better balance between simplicity and performance than the LMS algorithm. Due to its good properties the NLMS has been largely used in real-time applications.

4. Simulation Results

This paper considers a NLMS-AMC-COFDM binary transmission system. The Simulation model was implemented in MatLab. The system parameters used in simulations are given in Table 1.

Table 1. Parameters definition.

In this section throughput vs. SNR performance is compared for various combination “modulation – code rate” schemes (BPSK1/2, QPSK 3/4, 16QAM 3/4 and 64QAM 2/3) with NLMS-Coded OFDM. In adaptive modulation and coding (AMC) technique modulation scheme and convolution code rate are altered according to the variation in the communication channel. The transmitter will choose the appropriate combination scheme is depend upon the SNR threshold.

Figure 4. Throughput of AMC schemes for NLMS-COFDM system.

Figure 4 presents the throughput versus SNR graphs for different proposed combination schemes selected by channel estimator using the COFDM scenario. We observe that 64QAM with code rate 2/3 offers a significant performance gain of 3dB to 4dB. As we see here, at a given SNR NLMS-COFDM can provide a significant increase in throughput when combined with suitable link adaptation, since higher throughput modes can be used at much lower values of SNR.

AMC increases the robustness of the system. In this way, the spectral efficiency of NLMS-COFDM and NLMS-AMC -COFDM are exploited in figure 5 to improve the performance. At lower SNR values BPSK is preferred because it gives lower BER values compared to remaining techniques. As modulation order increases, SE also increases for a given SNR value.

Figure 5. Spectral Efficiency of AMC-NLMS-COFDM.

5. Conclusion

This paper exploits the Coded OFDM performance in terms of SE and throughput for different modulation and code rate schemes over a fading channel. COFDM is powerful modulation technique to achieve higher bit rate and to eliminate ISI. So, the COFDM has chosen for high speed digital communications of the next generation. This paper discusses the importance of Adaptive Modulation Coding technique. NLMS channel estimator is considered because of its low complexity. The most suitable MCS is chosen at the transmitter on the basis of channel estimator in terms of BER.

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54. Saidi Mohamed, Habib Cherif, Othman Hasnaoui, Jamel Belhadj, "Design and Comparative Analysis of Hybrid Energy Systems for Grid-Connected and Standalone Applications in Tunisia: Case Study of Audiovisual Chain", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 144–153, 2023. doi: 10.25046/aj080316
55. Orapin Channumsin, Jirapun Pimpol, Tattaya Pukkalanun, Worapong Tangsrirat, "Tunable Resistorless Phase Shifter Realization with a Single VDGA", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 136–143, 2023. doi: 10.25046/aj080315
56. Alvin Sahroni, Isnatin Miladiyah, Nur Widiasmara, Hendra Setiawan, "Analysis of Linear and Non-Linear Short-Term Pulse Rate Variability to Evaluate Emotional Changes during the Trier Social Stress Test", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 69–79, 2023. doi: 10.25046/aj080309
57. Kangrong Tan, Shozo Tokinaga, "Markov Regime Switching Analysis for COVID-19 Outbreak Situations and their Dynamic Linkages of German Market", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 11–18, 2023. doi: 10.25046/aj080302
58. Mario Cuomo, Federica Massimi, Francesco Benedetto, "Detecting CTC Attack in IoMT Communications using Deep Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 130–138, 2023. doi: 10.25046/aj080215
59. Latifah Sarah Supian, Danial Haikal Mohd Razali, Chew Sue Ping, Nurul Sheeda Suhaimi, Sharifah Aishah Syed Ali, Nani Fadzlina Naim, Harry Ramza, "Assessment of Scattered-Bend Loss in Polymer Optical Fiber (POF) Displacement Sensor", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 123–129, 2023. doi: 10.25046/aj080214
60. Temsamani Khallouk Yassine, Achchab Said, Laouami Lamia, Faridi Mohammed, "Hybrid Discriminant Neural Networks for Performance Job Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 116–122, 2023. doi: 10.25046/aj080213
61. Mohammed Al-Shanfari, "The First Application of the Multistage One-Shot Decision-Making Approach to Reevaluate a Technology Project Decision Problem", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 97–107, 2023. doi: 10.25046/aj080211
62. Ivana Marin, Sven Gotovac, Vladan Papić, "Development and Analysis of Models for Detection of Olive Trees", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 87–96, 2023. doi: 10.25046/aj080210
63. Paulo Gustavo Quinan, Issa Traoré, Isaac Woungang, Ujwal Reddy Gondhi, Chenyang Nie, "Hybrid Intrusion Detection Using the AEN Graph Model", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 44–63, 2023. doi: 10.25046/aj080206
64. Zhanna Dedovets, Mikhail Rodionov, Anna Novichkova, "A Model for Teaching Mathematics to Gifted Students Based on an Effective Combination of Various Approaches for their Preparation", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 138–148, 2023. doi: 10.25046/aj080117
65. Brian Song, Jeongkyu Lee, "Northern Leaf Blight and Gray Leaf Spot Detection using Optimized YOLOv3", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 125–130, 2023. doi: 10.25046/aj080115
66. Ossama Embarak, "Dismantle Shilling Attacks in Recommendations Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 684–691, 2021. doi: 10.25046/aj060174
67. Richard Romero Izurieta, Segundo Moisés Toapanta Toapanta, Luis Jhony Caucha Morales, María Mercedes Baño Hifóng, Eriannys Zharayth Gómez Díaz, Oscar Marcelo Zambrano Vizuete, Luis Enrique Mafla Gallegos, José Antonio Orizaga Trejo, "Prototype to Identify the Capacity in Cybersecurity Management for a Public Organization", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 108–115, 2023. doi: 10.25046/aj080113
68. Mostapha Harmouzi, Aziz Amari, Lhoussaine Masmoudi, "Conception and Simulation of an Electronic Nose Prototype for Olfactory Acquisition", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 101–107, 2023. doi: 10.25046/aj080112
69. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, Thiagarajan Murugasamy, "An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 84–94, 2023. doi: 10.25046/aj080110
70. Bezzaoucha-RebaÏ Souad, "On the Polytopic Modelling & Robust H∞ Control of Nonlinear Systems Subject to Cyber-attack: Application to Attitude Stabilization of Quadrotor", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 78–83, 2023. doi: 10.25046/aj080109
71. Himanshu Bana, Rahul Dev Garg, "Analysis and Trend Estimation of Rainfall and Seasonality Index for Marathwada Region", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 30–37, 2023. doi: 10.25046/aj080104
72. Anh-Thu Mai, Duc-Huy Nguyen, Thanh-Tin Dang, "Transfer and Ensemble Learning in Real-time Accurate Age and Age-group Estimation", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 262–268, 2022. doi: 10.25046/aj070630
73. Mikhail Lavrentiev, Dmitry Kuzakov, Andrey Marchuk, "Optimizing Sensors Locations for Tsunami Warning System", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 256–261, 2022. doi: 10.25046/aj070629
74. Djamila Hammouche, Karim Atif, "Consideration of Ambiguity in the Analysis Phase of Data Warehouses", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 244–247, 2022. doi: 10.25046/aj070627
75. Jasmin Softić, Zanin Vejzović, "Operating Systems Vulnerability – An Examination of Windows 10, macOS, and Ubuntu from 2015 to 2021", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 230–235, 2022. doi: 10.25046/aj070625
76. Vanamala H R, Samriddha Shukla, Vijaya krishna A, "Ensemble Extreme Learning Algorithms for Alzheimer’s Disease Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 204–211, 2022. doi: 10.25046/aj070622
77. Lai Kecheng, He Qikun, Hu Ning, Fujinami Tsutomu, "Estimating Subjective Appetite based on Cerebral Blood Flow", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 195–203, 2022. doi: 10.25046/aj070621
78. Luís Cláudio Dallier Saldanha, "A Review of the Role of Information Technology in Brazilian Higher Educational Institutions during Covid-19 Pandemic", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 189–194, 2022. doi: 10.25046/aj070620
79. Nomawethu Tungela, Tiko Iyamu, "A Structuration View of the South African National Health Insurance Readiness", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 174–180, 2022. doi: 10.25046/aj070618
80. Segundo Moisés Toapanta Toapanta, Rodrigo Humberto Del Pozo Durango, Luis Enrique Mafla Gallegos, Eriannys Zharayth Gómez Díaz, Yngrid Josefina Melo Quintana, Joan Noheli Miranda Jimenez, Ma. Roció Maciel Arellano, José Antonio Orizaga Trejo, "Prototype to Mitigate the Risks, Vulnerabilities and Threats of Information to Ensure Data Integrity", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 139–150, 2022. doi: 10.25046/aj070614
81. Tarek Nouioua, Ahmed Hafid Belbachir, "The Security of Information Systems and Image Processing Supported by the Quantum Computer: A review", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 77–86, 2022. doi: 10.25046/aj070609
82. Chen Zhao, Weisheng Kong, Federico Percacci, Patrik Gnos, "Field Oriented Control and Commutation Based on Sensorless Methods for High-Speed Electrical Motors of Unmanned Multicopters", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 58–69, 2022. doi: 10.25046/aj070607
83. Kohei Yamagishi, Tsuyoshi Suzuki, "Regular Tessellation-Based Collective Movement for a Robot Swarm with Varying Densities, Scales, and Shapes", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 31–38, 2022. doi: 10.25046/aj070604
84. Lixin Wang, Jianhua Yang, Austin Lee, Peng-Jun Wan, "Matching TCP Packets to Detect Stepping-Stone Intrusion using Packet Crossover", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 13–19, 2022. doi: 10.25046/aj070602
85. Brahim Zraibi, Mohamed Mansouri, Salah Eddine Loukili, Said Ben Alla, "Hybrid Neural Network Method for Predicting the SOH and RUL of Lithium-Ion Batteries", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 193–198, 2022. doi: 10.25046/aj070520
86. Ferdinand Friedrich, Christoph Ament, "Model Order Reduction and Distribution for Efficient State Estimation in Sensor and Actuator Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 146–156, 2022. doi: 10.25046/aj070516
87. Jabrane Slimani, Abdeslam Kadrani, Imad EL Harraki, El hadj Ezzahid, "Long-term Bottom-up Modeling of Renewable Energy Development in Morocco", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 129–145, 2022. doi: 10.25046/aj070515
88. Marzouk Hassan, Abdelmajid Badri, Aicha Sahel, "A Self-Adaptive Routing Algorithm for Real-Time Video Transmission in VANETs", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 91–101, 2022. doi: 10.25046/aj070512
89. Bouassale Nasr-Eddine, Sallaou Mohamed, Aittaleb Abdelmajid, Benaissa Elfahim, "DEM models Calibration and Application to Simulate the Phosphate Ore Clogging", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 79–90, 2022. doi: 10.25046/aj070511
90. Gulshan Sharma, "Frequency Oscillation Suppression of Interlinked Solar PV-Thermal Power System using HVDC Link", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 73–78, 2022. doi: 10.25046/aj070510
91. Erick Esparza Tapia, Eva Selene Hernández Gress, Martin Flégl, "Sensitive Analysis in Holding and Penalty Costs for the Stochastic Sequencing Problem in Agile Manufacturing", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 62–72, 2022. doi: 10.25046/aj070509
92. Khaing Hsu Wai, Nobuo Funabiki, Khin Thet Mon, May Zin Htun, San Hay Mar Shwe, Htoo Htoo Sandi Kyaw, Wen-Chung Kao, "A Proposal of Code Modification Problem for Self-study of Web Client Programming Using JavaScript", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 53–61, 2022. doi: 10.25046/aj070508
93. Pang-Yen Lou, Yung-Yuan Ho, Chua-Chin Wang, Wei-Chih Chang, "Analysis of Layout Arrangement for CMOS Oscillators to Reduce Overall Variation on Silicon", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 46–52, 2022. doi: 10.25046/aj070507
94. Abdul Ahad Jhumka, Robert Tat Fung Ah King, Chandana Ramasawmy, "Assessing the Impact of Integrating Solar PV System using the Equal Area Criterion Method", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 35–40, 2022. doi: 10.25046/aj070505
95. Muhammad Musleh Uddin, Kazi Rafiqul Islam, Md. Monirul Kabir, "An Improved Model to Analyze the Impact of Cyber-Attacks on Power Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 27–34, 2022. doi: 10.25046/aj070504
96. Lukanyo Mbali, Oliver Dzobo, "Design of an Off-Grid Hybrid Energy System for Electrification of a Remote Region: a Case Study of Upper Blink Water Community, South Africa", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 17–26, 2022. doi: 10.25046/aj070503
97. Bahram Ismailov Israfil, "Deep Learning in Monitoring the Behavior of Complex Technical Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 10–16, 2022. doi: 10.25046/aj070502
98. Hiroaki Hanai, Akira Mishima, Atsuyuki Miura, Toshiki Hirogaki, Eiichi Aoyama, "Realization of Skillful Musical Saw Bowing by Industrial Collaborative Humanoid Robot", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 1–9, 2022. doi: 10.25046/aj070501
99. Kohki Nakane, Akihiro Sugiura, Hiroki Takada, "Estimating a Minimum Embedding Dimension by False Nearest Neighbors Method without an Arbitrary Threshold", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 114–120, 2022. doi: 10.25046/aj070415
100. Kamil Halouzka, Ladislav Burita, Aneta Coufalikova, Pavel Kozak, Petr Františ, "A Comparison of Cyber Security Reports for 2020 of Central European Countries", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 105–113, 2022. doi: 10.25046/aj070414