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Intelligent sociotechnical systems are gaining momentum in today’s information-rich society, where different technologies are used to collect data from such systems and mine this data to make useful insights about our daily activities
Kyandoghere Kyamakya; Fadi Al-Machot; Ahmad Haj Mosa; Hamid Bouchachia; Jean Chamberlain Chedjou; Antoine Bagula. Emotion and Stress Recognition Related Sensors and Machine Learning Technologies. Sensors 2021, 21, 2273 .
AMA StyleKyandoghere Kyamakya, Fadi Al-Machot, Ahmad Haj Mosa, Hamid Bouchachia, Jean Chamberlain Chedjou, Antoine Bagula. Emotion and Stress Recognition Related Sensors and Machine Learning Technologies. Sensors. 2021; 21 (7):2273.
Chicago/Turabian StyleKyandoghere Kyamakya; Fadi Al-Machot; Ahmad Haj Mosa; Hamid Bouchachia; Jean Chamberlain Chedjou; Antoine Bagula. 2021. "Emotion and Stress Recognition Related Sensors and Machine Learning Technologies." Sensors 21, no. 7: 2273.
Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITSs) are being widely adopted worldwide to improve the efficiency and safety of the transportation system
Kyandoghere Kyamakya; Jean Chedjou; Fadi Al-Machot; Ahmad Haj Mosa; Antoine Bagula. Intelligent Transportation Related Complex Systems and Sensors. Sensors 2021, 21, 2235 .
AMA StyleKyandoghere Kyamakya, Jean Chedjou, Fadi Al-Machot, Ahmad Haj Mosa, Antoine Bagula. Intelligent Transportation Related Complex Systems and Sensors. Sensors. 2021; 21 (6):2235.
Chicago/Turabian StyleKyandoghere Kyamakya; Jean Chedjou; Fadi Al-Machot; Ahmad Haj Mosa; Antoine Bagula. 2021. "Intelligent Transportation Related Complex Systems and Sensors." Sensors 21, no. 6: 2235.
In this paper, the dependence of the capacitance of lateral drain–substrate and source–substrate junctions on the linear size of the oxide trapped charge in MOSFET is simulated. It is shown that, at some range of linear sizes of the trapped charge, the capacitance of lateral junctions linearly depends on the linear size of the trapped charge. The dependence of the difference between drain–substrate and source–substrate capacitances on the linear size of trapped charges is also simulated. The revealed dependence can be used in measurements to estimate the linear size of oxide trapped charges induced by hot carrier injection, which can occur during MOSFET operation at defined conditions.
Atabek E. Atamuratov; Ahmed Yusupov; Zukhra A. Atamuratova; Jean Chamberlain Chedjou; Kyandoghere Kyamakya. Lateral Capacitance–Voltage Method of NanoMOSFET for Detecting the Hot Carrier Injection. Applied Sciences 2020, 10, 7935 .
AMA StyleAtabek E. Atamuratov, Ahmed Yusupov, Zukhra A. Atamuratova, Jean Chamberlain Chedjou, Kyandoghere Kyamakya. Lateral Capacitance–Voltage Method of NanoMOSFET for Detecting the Hot Carrier Injection. Applied Sciences. 2020; 10 (21):7935.
Chicago/Turabian StyleAtabek E. Atamuratov; Ahmed Yusupov; Zukhra A. Atamuratova; Jean Chamberlain Chedjou; Kyandoghere Kyamakya. 2020. "Lateral Capacitance–Voltage Method of NanoMOSFET for Detecting the Hot Carrier Injection." Applied Sciences 10, no. 21: 7935.
Solving ordinary differential equations (ODE) on heterogenous or multi-core/parallel embedded systems does significantly increase the operational capacity of many sensing systems in view of processing tasks such as self-calibration, model-based measurement and self-diagnostics. The main challenge is usually related to the complexity of the processing task at hand which costs/requires too much processing power, which may not be available, to ensure a real-time processing. Therefore, a distributed solving involving multiple cores or nodes is a good/precious option. Also, speeding-up the processing does also result in significant energy consumption or sensor nodes involved. There exist several methods for solving differential equations on single processors. But most of them are not suitable for an implementation on parallel (i.e., multi-core) systems due to the increasing communication related network delays between computing nodes, which become a main and serious bottleneck to solve such problems in a parallel computing context. Most of the problems faced relate to the very nature of differential equations. Normally, one should first complete calculations of a previous step in order to use it in the next/following step. Hereby, it appears also that increasing performance (e.g., through increasing step sizes) may possibly result in decreasing the accuracy of calculations on parallel/multi-core systems like GPUs. In this paper, we do create a new adaptive algorithm based on the Adams–Moulton and Parareal method (we call it PAMCL) and we do compare this novel method with other most relevant implementations/schemes such as the so-called DOPRI5, PAM, etc. Our algorithm (PAMCL) is showing very good performance (i.e., speed-up) while compared to related competing algorithms, while thereby ensuring a reasonable accuracy. For a better usage of computing units/resources, the OpenCL platform is selected and ODE solver algorithms are optimized to work on both GPUs and CPUs. This platform does ensure/enable a high flexibility in the use of heterogeneous computing resources and does result in a very efficient utilization of available resources when compared to other comparable/competing algorithm/schemes implementations.
Vahid Tavakkoli; Kabeh Mohsenzadegan; Jean Chedjou; Kyandoghere Kyamakya. Contribution to Speeding-Up the Solving of Nonlinear Ordinary Differential Equations on Parallel/Multi-Core Platforms for Sensing Systems. Sensors 2020, 20, 6130 .
AMA StyleVahid Tavakkoli, Kabeh Mohsenzadegan, Jean Chedjou, Kyandoghere Kyamakya. Contribution to Speeding-Up the Solving of Nonlinear Ordinary Differential Equations on Parallel/Multi-Core Platforms for Sensing Systems. Sensors. 2020; 20 (21):6130.
Chicago/Turabian StyleVahid Tavakkoli; Kabeh Mohsenzadegan; Jean Chedjou; Kyandoghere Kyamakya. 2020. "Contribution to Speeding-Up the Solving of Nonlinear Ordinary Differential Equations on Parallel/Multi-Core Platforms for Sensing Systems." Sensors 20, no. 21: 6130.
The core objective of this paper is to develop and validate a comprehensive visual sensing concept for robustly classifying house types. Previous studies regarding this type of classification show that this type of classification is not simple (i.e., tough) and most classifier models from the related literature have shown a relatively low performance. For finding a suitable model, several similar classification models based on convolutional neural network have been explored. We have found out that adding/involving/extracting better and more complex features result in a significant accuracy related performance improvement. Therefore, a new model taking this finding into consideration has been developed, tested and validated. The model developed is benchmarked with selected state-of-art classification models of relevance for the “house classification” endeavor. The test results obtained in this comprehensive benchmarking clearly demonstrate and validate the effectiveness and the superiority of our here developed deep-learning model. Overall, one notices that our model reaches classification performance figures (accuracy, precision, etc.) which are at least 8% higher (which is extremely significant in the ranges above 90%) than those reached by the previous state-of-the-art methods involved in the conducted comprehensive benchmarking.
Vahid Tavakkoli; Kabeh Mohsenzadegan; Kyandoghere Kyamakya. A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction. Sensors 2020, 20, 5672 .
AMA StyleVahid Tavakkoli, Kabeh Mohsenzadegan, Kyandoghere Kyamakya. A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction. Sensors. 2020; 20 (19):5672.
Chicago/Turabian StyleVahid Tavakkoli; Kabeh Mohsenzadegan; Kyandoghere Kyamakya. 2020. "A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction." Sensors 20, no. 19: 5672.
Methods used to evaluate the impact of Intelligent Transport System (ITS) services on road safety are usually based on expert assessments or statistical studies. However, commonly used methods are challenging to apply in the planning process of ITS services. This paper presents the methodology of research using surrogate safety measures calculated and calibrated with the use of simulation techniques and a driving simulator. This approach supports the choice of the type of ITS services that are beneficial for traffic efficiency and road safety. This paper presents results of research on the influence of selected scenarios of variable speed limits on the efficiency and safety of traffic on the sections of motorways and expressways in various traffic conditions. The driving simulator was used to estimate the efficiency of lane-keeping by the driver. The simulation traffic models were calibrated using driving simulator data and roadside sensor data. The traffic models made it possible to determine surrogate safety measures (number of conflicts and their severity) in selected scenarios of using ITS services. The presented studies confirmed the positive impact of Variable Speed Limits (VSLs) on the level of road safety and traffic efficiency. This paper also presents recommendations and plans for further research in this area.
Jacek Oskarbski; Tomasz Kamiński; Kyandoghere Kyamakya; Jean Chamberlain Chedjou; Karol Żarski; Małgorzata Pędzierska. Assessment of the Speed Management Impact on Road Traffic Safety on the Sections of Motorways and Expressways Using Simulation Methods. Sensors 2020, 20, 5057 .
AMA StyleJacek Oskarbski, Tomasz Kamiński, Kyandoghere Kyamakya, Jean Chamberlain Chedjou, Karol Żarski, Małgorzata Pędzierska. Assessment of the Speed Management Impact on Road Traffic Safety on the Sections of Motorways and Expressways Using Simulation Methods. Sensors. 2020; 20 (18):5057.
Chicago/Turabian StyleJacek Oskarbski; Tomasz Kamiński; Kyandoghere Kyamakya; Jean Chamberlain Chedjou; Karol Żarski; Małgorzata Pędzierska. 2020. "Assessment of the Speed Management Impact on Road Traffic Safety on the Sections of Motorways and Expressways Using Simulation Methods." Sensors 20, no. 18: 5057.
In this paper, different physical models of single trap defects are considered, which are localized in the oxide layer or at the oxide–semiconductor interface of field effect transistors. The influence of these defects with different sizes and shapes on the amplitude of the random telegraph noise (RTN) in Junctionless Fin Field Effect Transistor (FinFET) is modelled and simulated. The RTN amplitude dependence on the number of single charges trapped in a single defect is modelled and simulated too. It is found out that the RTN amplitude in the Junctionless FinFET does not depend on the shape, nor on the size of the single defect area. However, the RTN amplitude in the subthreshold region does considerably depend on the number of single charges trapped in the defect.
Atabek E. Atamuratov; Mahkam M. Khalilloev; Ahmed Yusupov; A. J. García-Loureiro; Jean Chamberlain Chedjou; Kyamakya Kyandoghere. Contribution to the Physical Modelling of Single Charged Defects Causing the Random Telegraph Noise in Junctionless FinFET. Applied Sciences 2020, 10, 5327 .
AMA StyleAtabek E. Atamuratov, Mahkam M. Khalilloev, Ahmed Yusupov, A. J. García-Loureiro, Jean Chamberlain Chedjou, Kyamakya Kyandoghere. Contribution to the Physical Modelling of Single Charged Defects Causing the Random Telegraph Noise in Junctionless FinFET. Applied Sciences. 2020; 10 (15):5327.
Chicago/Turabian StyleAtabek E. Atamuratov; Mahkam M. Khalilloev; Ahmed Yusupov; A. J. García-Loureiro; Jean Chamberlain Chedjou; Kyamakya Kyandoghere. 2020. "Contribution to the Physical Modelling of Single Charged Defects Causing the Random Telegraph Noise in Junctionless FinFET." Applied Sciences 10, no. 15: 5327.
We develop, for the first time, and validate through some illustrative examples a new neuro-processor based concept for solving (single-vehicle) traveling salesman problems (TSP) in complex and dynamically reconfigurable graph networks. Compared to existing/competing methods for solving TSP, the new concept is accurate, robust, and scalable. Also, the new concept guarantees the optimality of the TSP solution and ensures subtours avoidance and thus an always-convergence to a single-cycle TSP solution. These key characteristics of the new concept are not always satisfactorily addressed by the existing methods for solving TSP. Therefore, the main contribution of this paper is to develop a systematic analytical framework to model (from a nonlinear dynamical perspective) the TSP, avoid/eliminate subtours, and guarantee/ensure convergence to the true/exact TSP solution. Using the stability analysis (nonlinear dynamics), analytical conditions are obtained to guarantee both robustness and convergence of the neuro-processor. Besides, a bifurcation analysis is carried out to obtain ranges (or windows) of parameters under which the neuro-processor guarantees both TSP solution’s optimality and convergence to a single-cycle TSP solution. In order to validate the new neuro-processor based concept developed, two recently published application examples are considered for both benchmarking and validation as they are solved by using the developed neuro-processor.
Jean Chamberlain Chedjou; Kyandoghere Kyamakya; Nkiediel Alain Akwir. An Efficient, Scalable, and Robust Neuro-Processor-Based Concept for Solving Single-Cycle Traveling Salesman Problems in Complex and Dynamically Reconfigurable Graph Networks. IEEE Access 2020, 8, 42297 -42324.
AMA StyleJean Chamberlain Chedjou, Kyandoghere Kyamakya, Nkiediel Alain Akwir. An Efficient, Scalable, and Robust Neuro-Processor-Based Concept for Solving Single-Cycle Traveling Salesman Problems in Complex and Dynamically Reconfigurable Graph Networks. IEEE Access. 2020; 8 (99):42297-42324.
Chicago/Turabian StyleJean Chamberlain Chedjou; Kyandoghere Kyamakya; Nkiediel Alain Akwir. 2020. "An Efficient, Scalable, and Robust Neuro-Processor-Based Concept for Solving Single-Cycle Traveling Salesman Problems in Complex and Dynamically Reconfigurable Graph Networks." IEEE Access 8, no. 99: 42297-42324.
Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose instances have been already seen during training. Activity recognition methods based on real-life settings should cover a growing number of activities in various domains, whereby a significant part of instances will not be present in the training data set. However, to cover all possible activities in advance is a complex and expensive task. Concretely, we need a method that can extend the learning model to detect unseen activities without prior knowledge regarding sensor readings about those previously unseen activities. In this paper, we introduce an approach to leverage sensor data in discovering new unseen activities which were not present in the training set. We show that sensor readings can lead to promising results for zero-shot learning, whereby the necessary knowledge can be transferred from seen to unseen activities by using semantic similarity. The evaluation conducted on two data sets extracted from the well-known CASAS datasets show that the proposed zero-shot learning approach achieves a high performance in recognizing unseen (i.e., not present in the training dataset) new activities.
Fadi Al Machot; Mohammed R. Elkobaisi; Kyandoghere Kyamakya. Zero-Shot Human Activity Recognition Using Non-Visual Sensors. Sensors 2020, 20, 825 .
AMA StyleFadi Al Machot, Mohammed R. Elkobaisi, Kyandoghere Kyamakya. Zero-Shot Human Activity Recognition Using Non-Visual Sensors. Sensors. 2020; 20 (3):825.
Chicago/Turabian StyleFadi Al Machot; Mohammed R. Elkobaisi; Kyandoghere Kyamakya. 2020. "Zero-Shot Human Activity Recognition Using Non-Visual Sensors." Sensors 20, no. 3: 825.
The concept presented in this paper is based on previous dynamical methods to realize a time-varying matrix inversion. It is essentially a set of coupled ordinary differential equations (ODEs) which does indeed constitute a recurrent neural network (RNN) model. The coupled ODEs constitute a universal modeling framework for realizing a matrix inversion provided the matrix is invertible. The proposed model does converge to the inverted matrix if the matrix is invertible, otherwise it converges to an approximated inverse. Although various methods exist to solve a matrix inversion in various areas of science and engineering, most of them do assume that either the time-varying matrix inversion is free of noise or they involve a denoising module before starting the matrix inversion computation. However, in the practice, the noise presence issue is a very serious problem. Also, the denoising process is computationally expensive and can lead to a violation of the real-time property of the system. Hence, the search for a new ‘matrix inversion’ solving method inherently integrating noise-cancelling is highly demanded. In this paper, a new combined/extended method for time-varying matrix inversion is proposed and investigated. The proposed method is extending both the gradient neural network (GNN) and the Zhang neural network (ZNN) concepts. Our new model has proven that it has exponential stability according to Lyapunov theory. Furthermore, when compared to the other previous related methods (namely GNN, ZNN, Chen neural network, and integration-enhanced Zhang neural network or IEZNN) it has a much better theoretical convergence speed. To finish, all named models (the new one versus the old ones) are compared through practical examples and both their respective convergence and error rates are measured. It is shown/observed that the novel/proposed method has a better practical convergence rate when compared to the other models. Regarding the amount of noise, it is proven that there is a very good approximation of the matrix inverse even in the presence of noise.
Vahid Tavakkoli; Jean Chamberlain Chedjou; Kyandoghere Kyamakya. A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a “Time-Varying Matrix”. Sensors 2019, 19, 4002 .
AMA StyleVahid Tavakkoli, Jean Chamberlain Chedjou, Kyandoghere Kyamakya. A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a “Time-Varying Matrix”. Sensors. 2019; 19 (18):4002.
Chicago/Turabian StyleVahid Tavakkoli; Jean Chamberlain Chedjou; Kyandoghere Kyamakya. 2019. "A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a “Time-Varying Matrix”." Sensors 19, no. 18: 4002.
One of the main objectives of Active and Assisted Living (AAL) environments is to ensure that elderly and/or disabled people perform/live well in their immediate environments; this can be monitored by among others the recognition of emotions based on non-highly intrusive sensors such as Electrodermal Activity (EDA) sensors. However, designing a learning system or building a machine-learning model to recognize human emotions while training the system on a specific group of persons and testing the system on a totally a new group of persons is still a serious challenge in the field, as it is possible that the second testing group of persons may have different emotion patterns. Accordingly, the purpose of this paper is to contribute to the field of human emotion recognition by proposing a Convolutional Neural Network (CNN) architecture which ensures promising robustness-related results for both subject-dependent and subject-independent human emotion recognition. The CNN model has been trained using a grid search technique which is a model hyperparameter optimization technique to fine-tune the parameters of the proposed CNN architecture. The overall concept’s performance is validated and stress-tested by using MAHNOB and DEAP datasets. The results demonstrate a promising robustness improvement regarding various evaluation metrics. We could increase the accuracy for subject-independent classification to 78% and 82% for MAHNOB and DEAP respectively and to 81% and 85% subject-dependent classification for MAHNOB and DEAP respectively (4 classes/labels). The work shows clearly that while using solely the non-intrusive EDA sensors a robust classification of human emotion is possible even without involving additional/other physiological signals.
Fadi Al Machot; Ali Elmachot; Mouhannad Ali; Elyan Al Machot; Kyandoghere Kyamakya. A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors. Sensors 2019, 19, 1659 .
AMA StyleFadi Al Machot, Ali Elmachot, Mouhannad Ali, Elyan Al Machot, Kyandoghere Kyamakya. A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors. Sensors. 2019; 19 (7):1659.
Chicago/Turabian StyleFadi Al Machot; Ali Elmachot; Mouhannad Ali; Elyan Al Machot; Kyandoghere Kyamakya. 2019. "A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors." Sensors 19, no. 7: 1659.
Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which are more universal and where the emotion recognition system is trained using a specific group of subjects and then tested on totally new persons and thereby possibly while using other sensors of same physiological signals in order to recognize their emotions. In this paper, we explore a novel robust subject-independent human emotion recognition system, which consists of two major models. The first one is an automatic feature calibration model and the second one is a classification model based on Cellular Neural Networks (CNN). The proposed system produces state-of-the-art results with an accuracy rate between 80% and 89% when using the same elicitation materials and physiological sensors brands for both training and testing and an accuracy rate of 71.05% when the elicitation materials and physiological sensors brands used in training are different from those used in training. Here, the following physiological signals are involved: ECG (Electrocardiogram), EDA (Electrodermal activity) and ST (Skin-Temperature).
Mouhannad Ali; Fadi Al Machot; Ahmad Haj Mosa; Midhat Jdeed; Elyan Al Machot; Kyandoghere Kyamakya. A Globally Generalized Emotion Recognition System Involving Different Physiological Signals. Sensors 2018, 18, 1905 .
AMA StyleMouhannad Ali, Fadi Al Machot, Ahmad Haj Mosa, Midhat Jdeed, Elyan Al Machot, Kyandoghere Kyamakya. A Globally Generalized Emotion Recognition System Involving Different Physiological Signals. Sensors. 2018; 18 (6):1905.
Chicago/Turabian StyleMouhannad Ali; Fadi Al Machot; Ahmad Haj Mosa; Midhat Jdeed; Elyan Al Machot; Kyandoghere Kyamakya. 2018. "A Globally Generalized Emotion Recognition System Involving Different Physiological Signals." Sensors 18, no. 6: 1905.
The evacuation of people from a building on fire is a task which can prove to be very difficult, in particular because of the influence of human behavior, but also of the type of people or the evacuation place configuration. Thus, it is crucial to think on how to organize the evacuation upstream for a situation of emergency can give rise disorganization, on one hand because of panic which grips evacuees, and on the other end because of the large quantity of evacuees in dangerous conditions. These recent years, several fire evacuation models have been proposed. Unfortunately, most of these models do not clearly define the parameters to be considered for their effective evaluations. These models consider, more generally, the number of survivors as a key parameter of evaluation. The purpose of this paper is to propose an intelligent Agent-Based Model enabling the modelling and simulation of evacuation of people from a building on fire. Our proposed model is based on four parameters that allow her practical evaluation. A case study of simulation is carried out in a building having the general configuration of Kinshasa supermarkets. This model is general enough for it to be implemented in several types of commercial buildings without major changes.
Selain Kasereka; Nathanaël Kasoro; Kyandoghere Kyamakya; Emile-Franc Doungmo Goufo; Abiola P. Chokki; Maurice V. Yengo. Agent-Based Modelling and Simulation for evacuation of people from a building in case of fire. Procedia Computer Science 2018, 130, 10 -17.
AMA StyleSelain Kasereka, Nathanaël Kasoro, Kyandoghere Kyamakya, Emile-Franc Doungmo Goufo, Abiola P. Chokki, Maurice V. Yengo. Agent-Based Modelling and Simulation for evacuation of people from a building in case of fire. Procedia Computer Science. 2018; 130 ():10-17.
Chicago/Turabian StyleSelain Kasereka; Nathanaël Kasoro; Kyandoghere Kyamakya; Emile-Franc Doungmo Goufo; Abiola P. Chokki; Maurice V. Yengo. 2018. "Agent-Based Modelling and Simulation for evacuation of people from a building in case of fire." Procedia Computer Science 130, no. : 10-17.
In Active and Assisted Living environments (AAL), a major service that can be provided is the automated assessment of old people’s well-being. Therefore, activity recognition is required to detect what types of help disabled persons need to support them in their daily life activities. Unfortunately, it is still a difficult task to estimate the size of the required window for online sensor data streams to recognize a specific activity, especially when new sensor events are recorded. This paper proposes a windowing algorithm which presents promising results to recognize complex human activities for multi-resident homes. The approach is based on the analysis of the sensor data to identify the best fitting sensors that should be considered in a specified window. Moreover, the second part of the paper proposes a set of different statistical spatio-temporal features to recognize human activities. In order to check the overall performance, this approach is tested using the CASAS dataset and artificially generated laboratory data using our HBMS simulator. The results show a high performance based on different evaluation metrics compared to other approaches. We believe that the proposed windowing approach provides a good approximation of the required window size in order to robustly detect human activities in comparison to other windowing approaches.
Fadi Al Machot; Ahmad Haj Mosa; Mouhannad Ali; Kyandoghere Kyamakya. Activity Recognition in Sensor Data Streams for Active and Assisted Living Environments. IEEE Transactions on Circuits and Systems for Video Technology 2017, 28, 2933 -2945.
AMA StyleFadi Al Machot, Ahmad Haj Mosa, Mouhannad Ali, Kyandoghere Kyamakya. Activity Recognition in Sensor Data Streams for Active and Assisted Living Environments. IEEE Transactions on Circuits and Systems for Video Technology. 2017; 28 (10):2933-2945.
Chicago/Turabian StyleFadi Al Machot; Ahmad Haj Mosa; Mouhannad Ali; Kyandoghere Kyamakya. 2017. "Activity Recognition in Sensor Data Streams for Active and Assisted Living Environments." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 10: 2933-2945.
This chapter presents a study on the modeling and performance evaluation of chaos-based coherent and incoherent systems, i.e., chaotic direct-sequence code-division multiple-access (CDS-CDMA) and differential chaos-shift keying (DCSK), for low-data-rate applications in wireless communications. This study is motivated by the design of a secure physical layer for wireless-based applications with low data rate and in small transmission areas. A wireless channel affected by noise, fading, multipath, and delay-spread for low-data-rate transmission of chaotically spreading signals is described and mathematically modeled. Discrete-time models for the transmitter and receiver of CDS-CDMA and DCSK systems under the impact of the wireless channel are developed. Bit error rate (BER) performance of the systems is estimated by means of both theoretical derivation and discrete integration. Simulated performances are shown and compared with the corresponding estimated ones, where the effects of the ratio \(E_b/N_0\), spreading factor, number of users, sample rate, and the number of transmission paths on the BER are fully evaluated. The obtained results showed that the low-rate chaos-based systems can exploit the multipath nature of wireless channels in order to improve their BER performances. This feature indicates that chaos-based communication systems are a promising and robust solution for enhancing physical layer security in low-rate wireless personal area networks (LR-WPANs).
Nguyen Xuan Quyen; Kyandoghere Kyamakya. Chaos-Based Digital Communication Systems with Low Data-Rate Wireless Applications. Developments in Advanced Control and Intelligent Automation for Complex Systems 2017, 239 -269.
AMA StyleNguyen Xuan Quyen, Kyandoghere Kyamakya. Chaos-Based Digital Communication Systems with Low Data-Rate Wireless Applications. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2017; ():239-269.
Chicago/Turabian StyleNguyen Xuan Quyen; Kyandoghere Kyamakya. 2017. "Chaos-Based Digital Communication Systems with Low Data-Rate Wireless Applications." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 239-269.
Antoine Kayisu; Meera K. Joseph; Kyandoghere Kyamakya. ICT and COMPRAM to assess road Traffic Congestion Management in Kinshasa. 2017 IST-Africa Week Conference (IST-Africa) 2017, 1 -10.
AMA StyleAntoine Kayisu, Meera K. Joseph, Kyandoghere Kyamakya. ICT and COMPRAM to assess road Traffic Congestion Management in Kinshasa. 2017 IST-Africa Week Conference (IST-Africa). 2017; ():1-10.
Chicago/Turabian StyleAntoine Kayisu; Meera K. Joseph; Kyandoghere Kyamakya. 2017. "ICT and COMPRAM to assess road Traffic Congestion Management in Kinshasa." 2017 IST-Africa Week Conference (IST-Africa) , no. : 1-10.
Kabuya K. Isaac; Meera K. Joseph; Kyandoghere Kyamakya. Complex societal problem related to the internet access and electricity access in DRC. 2017 IST-Africa Week Conference (IST-Africa) 2017, 1 -10.
AMA StyleKabuya K. Isaac, Meera K. Joseph, Kyandoghere Kyamakya. Complex societal problem related to the internet access and electricity access in DRC. 2017 IST-Africa Week Conference (IST-Africa). 2017; ():1-10.
Chicago/Turabian StyleKabuya K. Isaac; Meera K. Joseph; Kyandoghere Kyamakya. 2017. "Complex societal problem related to the internet access and electricity access in DRC." 2017 IST-Africa Week Conference (IST-Africa) , no. : 1-10.
Ahmad Haj Mosa; Kyandoghere Kyamakya; Ralf Junghans; Mouhannad Ali; Fadi Al Machot; Markus Gutmann. Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost truck detection using a single presence detection sensor. Transportation Research Part C: Emerging Technologies 2016, 73, 105 -127.
AMA StyleAhmad Haj Mosa, Kyandoghere Kyamakya, Ralf Junghans, Mouhannad Ali, Fadi Al Machot, Markus Gutmann. Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost truck detection using a single presence detection sensor. Transportation Research Part C: Emerging Technologies. 2016; 73 ():105-127.
Chicago/Turabian StyleAhmad Haj Mosa; Kyandoghere Kyamakya; Ralf Junghans; Mouhannad Ali; Fadi Al Machot; Markus Gutmann. 2016. "Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost truck detection using a single presence detection sensor." Transportation Research Part C: Emerging Technologies 73, no. : 105-127.
This paper investigates the control of a 5-DOF upper-limb exoskeleton robot used for passive rehabilitation therapy. The robot is subject to uncertain dynamics, disturbance torques, unavailable full-state measurement, and different types of actuation faults. An adaptive nonlinear control scheme, which uses a new reaching law-based sliding mode control strategy, is proposed. This scheme incorporates a high-gain state observer with dynamic high-gain matrix and a fuzzy neural network (FNN) for state vector and nonlinear dynamics estimation, respectively. Using dynamic parameters, the scheme provides an efficient mean for simultaneously tackling the effects of FNN approximation errors, disturbance torques and actuation faults without any prior bounds knowledge and fault detection and diagnosis components. Using simulation results, it is shown that with the presented scheme, faster response, fewer oscillations during transient phase, good tracking accuracy, and chattering-free control torques with lower amplitudes are obtained.
Baraka Olivier Mushage; Jean Chamberlain Chedjou; Kyandoghere Kyamakya. Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation. Nonlinear Dynamics 2016, 87, 2021 -2037.
AMA StyleBaraka Olivier Mushage, Jean Chamberlain Chedjou, Kyandoghere Kyamakya. Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation. Nonlinear Dynamics. 2016; 87 (3):2021-2037.
Chicago/Turabian StyleBaraka Olivier Mushage; Jean Chamberlain Chedjou; Kyandoghere Kyamakya. 2016. "Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation." Nonlinear Dynamics 87, no. 3: 2021-2037.
Supporting drivers by Advanced Driver Assistance Systems (ADAS) significantly increases road safety. Driver’s emotions recognition is a building block of advanced systems for monitoring the driver’s comfort and driving ergonomics additionally to driver’s fatigue and drowsiness forecasting. This paper presents an approach for driver emotions recognition involving a set of three physiological signals (Electrodermal Activity, Skin Temperature and the Electrocardiogram). Additionally, we propose a CNN (cellular neural network) based classifier to classify each signal into four emotional states. Moreover, the subject-independent classification results of all signals are fused using Dempster-Shafer evidence theory in order to obtain a more robust detection of the true emotional state. The new system is tested using the benchmarked MAHNOB HCI dataset and the results show a relatively high performance compared to existing competing algorithms from the recent relevant literature.
Mouhannad Ali; Fadi Al Machot; Ahmad Haj Mosa; Kyandoghere Kyamakya. CNN Based Subject-Independent Driver Emotion Recognition System Involving Physiological Signals for ADAS. Advanced Microsystems for Automotive Applications 2016 2016, 125 -138.
AMA StyleMouhannad Ali, Fadi Al Machot, Ahmad Haj Mosa, Kyandoghere Kyamakya. CNN Based Subject-Independent Driver Emotion Recognition System Involving Physiological Signals for ADAS. Advanced Microsystems for Automotive Applications 2016. 2016; ():125-138.
Chicago/Turabian StyleMouhannad Ali; Fadi Al Machot; Ahmad Haj Mosa; Kyandoghere Kyamakya. 2016. "CNN Based Subject-Independent Driver Emotion Recognition System Involving Physiological Signals for ADAS." Advanced Microsystems for Automotive Applications 2016 , no. : 125-138.