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Djaffar Ould Abdeslam received the M.Sc. degree in electrical engineering from the University of Franche-Comté, Besançon, France, in 2002. He received a Ph.D. degree in 2005 and the Habilitation degree in electrical engineering in 2014 from the University of Haute-Alsace, Mulhouse, France. Since 2007, he has been an associate professor at the University of Haute-Alsace. His research interest includes artificial neural networks applied to power systems, signal processing for power quality improvement, smart metering, smart building, and smart grids. He holds the PES, Allowance for Scientific Excellence, given by the French Ministry of Education in 2012 and in 2018.
Although atrial fibrillation (AF) Arrhythmia is highly prevalent within a wide range of populations with major associated risks and due to its episodic occurrence, its recognition remains a challenge for doctors. This paper aims to present and experimentally validate a new efficient approach for the detection and classification of this cardiac anomaly using multiple Electrocardiogram (ECG) signals. This work consists of applying Stockwell transform (ST) with compact support kernel (ST-CSK) for ECG time-frequency analysis. The estimation of the atrial activity (AA) is then achieved after analyzing P-waves of the ECG signals for each heartbeat. ECG signals segmentation allows characterizing the AA by making use of its (t, f) flatness, (t, f) flux, energy concentration and heart rate variability. The features matrix is employed as an input of the support vector machines (SVM) working in binary and asymmetrical mode with an embedded reject option. The proposed algorithm is trained and then tested using different ECG sources namely two databases provided by PhysionNet (MIT-BIH Arrhythmia, MIT-BIH Atrial Fibrillation) and recorded ECG signals using MySignals HW development platform with raspberry Pi 3 model B\(+\). The used method has achieved \(98.46\%\) and \(97.81\%\) as sensitivity and specificity, respectively. The obtained results confirm that the proposed approach represents a promising tool for Atrial Fibrillation Episodes (AFE) recognition with significant separability between Normal atrial activity and atrial activity with AF even under real and clinical conditions.
Hocine Hamil; Zahia Zidelmal; Mohamed Salah Azzaz; Samir Sakhi; Redouane Kaibou; Djaffar Ould Abdeslam. AF episodes recognition using optimized time-frequency features and cost-sensitive SVM. Physical and Engineering Sciences in Medicine 2021, 1 -12.
AMA StyleHocine Hamil, Zahia Zidelmal, Mohamed Salah Azzaz, Samir Sakhi, Redouane Kaibou, Djaffar Ould Abdeslam. AF episodes recognition using optimized time-frequency features and cost-sensitive SVM. Physical and Engineering Sciences in Medicine. 2021; ():1-12.
Chicago/Turabian StyleHocine Hamil; Zahia Zidelmal; Mohamed Salah Azzaz; Samir Sakhi; Redouane Kaibou; Djaffar Ould Abdeslam. 2021. "AF episodes recognition using optimized time-frequency features and cost-sensitive SVM." Physical and Engineering Sciences in Medicine , no. : 1-12.
Yacine Triki; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. An Accurate Orthogonal Signal Generator for Voltage Control in Synchronous Reference Frame of Stand-Alone Single-Phase Voltage Source Inverters. European Journal of Electrical Engineering 2021, 23, 113 -122.
AMA StyleYacine Triki, Ali Bechouche, Hamid Seddiki, Djaffar Ould Abdeslam. An Accurate Orthogonal Signal Generator for Voltage Control in Synchronous Reference Frame of Stand-Alone Single-Phase Voltage Source Inverters. European Journal of Electrical Engineering. 2021; 23 (2):113-122.
Chicago/Turabian StyleYacine Triki; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. 2021. "An Accurate Orthogonal Signal Generator for Voltage Control in Synchronous Reference Frame of Stand-Alone Single-Phase Voltage Source Inverters." European Journal of Electrical Engineering 23, no. 2: 113-122.
A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.
Mahfoud Drouaz; Bruno Colicchio; Ali Moukadem; Alain Dieterlen; Djafar Ould-Abdeslam. New Time-Frequency Transient Features for Nonintrusive Load Monitoring. Energies 2021, 14, 1437 .
AMA StyleMahfoud Drouaz, Bruno Colicchio, Ali Moukadem, Alain Dieterlen, Djafar Ould-Abdeslam. New Time-Frequency Transient Features for Nonintrusive Load Monitoring. Energies. 2021; 14 (5):1437.
Chicago/Turabian StyleMahfoud Drouaz; Bruno Colicchio; Ali Moukadem; Alain Dieterlen; Djafar Ould-Abdeslam. 2021. "New Time-Frequency Transient Features for Nonintrusive Load Monitoring." Energies 14, no. 5: 1437.
The State of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries. In this paper, a Robust Adaptive Online Long Short-Term Memory (RoLSTM) method is proposed to extract SOC estimation for Li-ion Batteries in Electric Vehicles (EVs). This real-time, as its name suggests, method is based on a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) units and using the Robust and Adaptive online gradient learning method (RoAdam) for optimization. In the proposed architecture, one sequential model is defined for each of the three inputs: voltage, current, and temperature of the battery. Therefore, the three networks work in parallel. With this approach, the number of LSTM units are reduced. Using this suggested method, one is not dependent on precise battery models and can avoid complicated mathematical methods. In addition, unlike the traditional recursive neural network where content is re-written at any time, the LSTM network can decide on preserving the current memory through the proposed gateways. In that case, it can easily transfer this information over long paths to receive and maintain long-term dependencies. Using real databases, the experiment results illustrate the better performance of RoLSTM applied to SOC estimation of Li-Ion batteries in comparison with a neural network modeling and unscented Kalman filter method that have been used thus far.
Gelareh Javid; Djaffar Ould Abdeslam; Michel Basset. Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks. Energies 2021, 14, 758 .
AMA StyleGelareh Javid, Djaffar Ould Abdeslam, Michel Basset. Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks. Energies. 2021; 14 (3):758.
Chicago/Turabian StyleGelareh Javid; Djaffar Ould Abdeslam; Michel Basset. 2021. "Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks." Energies 14, no. 3: 758.
The importance of looking into microgrid security is getting more crucial due to the cyber vulnerabilities introduced by digitalization and the increasing dependency on information and communication technology (ICT) systems. Especially with a current academic unanimity on the incremental significance of the microgrid’s role in building the future smart grid, this article addresses the existing approaches attending to cyber-physical security in power systems from a microgrid-oriented perspective. First, we start with a brief descriptive review of the most commonly used terms in the latest relevant literature, followed by a comprehensive presentation of the recent efforts explored in a manner that helps the reader to choose the appropriate future research direction among several fields.
Bushra Canaan; Bruno Colicchio; Djaffar Ould Abdeslam. Microgrid Cyber-Security: Review and Challenges toward Resilience. Applied Sciences 2020, 10, 5649 .
AMA StyleBushra Canaan, Bruno Colicchio, Djaffar Ould Abdeslam. Microgrid Cyber-Security: Review and Challenges toward Resilience. Applied Sciences. 2020; 10 (16):5649.
Chicago/Turabian StyleBushra Canaan; Bruno Colicchio; Djaffar Ould Abdeslam. 2020. "Microgrid Cyber-Security: Review and Challenges toward Resilience." Applied Sciences 10, no. 16: 5649.
One of the most significant indicator of heart disease is arrhythmia showing heartbeat patterns. Thus, early and accurate detection of arrythmia types by categorization of heartbeats is important. In this paper, we introduce an ECG beat classifier system integrating two main parts: feature extraction and classification. For the first part, we consider the features observed in the time–frequency (t, f) plane where the ECG is projected using a variant of Stockwell transform. For the second part, the framework of semi-supervised SVM with asymmetric costs (AS3VM) has been applied for assessment of the obtained feature sets performance. Notice that four heartbeat types have been considered: normal beats (N), left and right bundle branch blocks (L and R) and premature ventricular contractions (V). The proposed method has been evaluated on PhysionNet’s MIT-BIT arrythmia database. The obtained results show that the suggested approach achieves significant separability of the classes and thus, able to make prediction accuracies of \(99.35\%\), \(98.73\%\), \(98.57\%\) and \(99.44\%\) for, respectively, N, L, R and V beats.
Redouane Lekhal; Zahia Zidelmal; Djaffar Ould-Abdesslam. Optimized time–frequency features and semi-supervised SVM to heartbeat classification. Signal, Image and Video Processing 2020, 14, 1471 -1478.
AMA StyleRedouane Lekhal, Zahia Zidelmal, Djaffar Ould-Abdesslam. Optimized time–frequency features and semi-supervised SVM to heartbeat classification. Signal, Image and Video Processing. 2020; 14 (7):1471-1478.
Chicago/Turabian StyleRedouane Lekhal; Zahia Zidelmal; Djaffar Ould-Abdesslam. 2020. "Optimized time–frequency features and semi-supervised SVM to heartbeat classification." Signal, Image and Video Processing 14, no. 7: 1471-1478.
This paper presents a new maximum power point tracking (MPPT) algorithm based on the adaptive linear neuron concept. This algorithm is designed to extract the maximum power in single-stage grid-connected photovoltaic (PV) system configuration. In the considered system, a PV panel is directly connected to a grid through a three-phase pulse-width modulation inverter. The control is achieved in the synchronous dq frame, and the proposed MPPT estimates directly the optimal d-axis duty cycle component. Furthermore, in order to achieve a unity power factor operation, the q-axis reference current is set to zero. In this work, only one proportional-integral controller is used to maintain the reactive power to zero value. To verify the effectiveness of the proposed algorithm, the grid-connected system is implemented and simulated under MATLAB-Simulink software. The obtained results are compared to those achieved by the conventional perturb and observe based MPPT technique under fast and slow irradiance changes. The simulation results show that the proposed method leads to achieve incomparable performances such as unity efficiency and zero oscillations in the PV panel in both transient and steady-state operations.
Yacine Triki; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. Unity Efficiency and Low-Cost MPPT Method for Single-Stage Grid-Connected PV System. Lecture Notes in Electrical Engineering 2020, 539 -552.
AMA StyleYacine Triki, Ali Bechouche, Hamid Seddiki, Djaffar Ould Abdeslam. Unity Efficiency and Low-Cost MPPT Method for Single-Stage Grid-Connected PV System. Lecture Notes in Electrical Engineering. 2020; ():539-552.
Chicago/Turabian StyleYacine Triki; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. 2020. "Unity Efficiency and Low-Cost MPPT Method for Single-Stage Grid-Connected PV System." Lecture Notes in Electrical Engineering , no. : 539-552.
Fatima Zohra Boudjella; Fouad Boukli Hacène; Abdelhak Bouchakour; Mostéfa Brahami; Djaffar Ould-Abdeslam. Simulation and realisation of a three-phase inverter controlled through sinus triangle and space vector pulse width modulation for photovoltaic systems. International Journal of Ambient Energy 2020, 1 -9.
AMA StyleFatima Zohra Boudjella, Fouad Boukli Hacène, Abdelhak Bouchakour, Mostéfa Brahami, Djaffar Ould-Abdeslam. Simulation and realisation of a three-phase inverter controlled through sinus triangle and space vector pulse width modulation for photovoltaic systems. International Journal of Ambient Energy. 2020; ():1-9.
Chicago/Turabian StyleFatima Zohra Boudjella; Fouad Boukli Hacène; Abdelhak Bouchakour; Mostéfa Brahami; Djaffar Ould-Abdeslam. 2020. "Simulation and realisation of a three-phase inverter controlled through sinus triangle and space vector pulse width modulation for photovoltaic systems." International Journal of Ambient Energy , no. : 1-9.
Yacine Triki; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. Unity Efficiency and Zero-Oscillations Based MPPT for Photovoltaic Systems. Applied Solar Energy 2020, 56, 75 -84.
AMA StyleYacine Triki, Ali Bechouche, Hamid Seddiki, Djaffar Ould Abdeslam. Unity Efficiency and Zero-Oscillations Based MPPT for Photovoltaic Systems. Applied Solar Energy. 2020; 56 (2):75-84.
Chicago/Turabian StyleYacine Triki; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. 2020. "Unity Efficiency and Zero-Oscillations Based MPPT for Photovoltaic Systems." Applied Solar Energy 56, no. 2: 75-84.
For lithium-ion batteries, active balancing can bring advantages compared to passive balancing in terms of lifetime and available capacity. Most known balancing techniques suffer from a low efficiency or high complexity and cost. This paper proposes a new technical solution based on a non-isolated DC/DC converter and a low-speed switching matrix to overcome efficiency and power limitations of present balancing methods. The proposed circuit allows balancing current paths which are not possible with previous methods and results in balancing efficiencies of over 90%. The performance comparison is based on batch numerical simulations using calculated and measured efficiency values. The simulation results are compared to the approved active balancing method cell-to-cell and to passive balancing. A study case with eight randomly distributed battery cells shows an improvement in overall balancing efficiency of up to 47.4% and a reduction in balancing time of up to 36.9%. The available capacity increases from 97.2% in a passively balanced system to 99.7% for the new method. A hardware prototype was set up to demonstrate the working principle and to verify the numerical simulations.
Manuel Raeber; Andreas Heinzelmann; Djaffar Ould Abdeslam. Analysis of an Active Charge Balancing Method Based on a Single Nonisolated DC/DC Converter. IEEE Transactions on Industrial Electronics 2020, 68, 2257 -2265.
AMA StyleManuel Raeber, Andreas Heinzelmann, Djaffar Ould Abdeslam. Analysis of an Active Charge Balancing Method Based on a Single Nonisolated DC/DC Converter. IEEE Transactions on Industrial Electronics. 2020; 68 (3):2257-2265.
Chicago/Turabian StyleManuel Raeber; Andreas Heinzelmann; Djaffar Ould Abdeslam. 2020. "Analysis of an Active Charge Balancing Method Based on a Single Nonisolated DC/DC Converter." IEEE Transactions on Industrial Electronics 68, no. 3: 2257-2265.
In this paper, a new neural network-based virtual flux (NN-VF) estimator is proposed for sensorless control of a pulse-width modulation rectifier under unbalanced and distorted grid conditions. In this estimator, the VF vector is reconstructed through an emulated ideal integrator. Thereafter, positive and negative sequence VF components are extracted using a NNbased positive and negative sequence components separator. Lyapunov’s theory-based convergence analysis is performed for optimal tuning of the NN-VF estimator. Accordingly, accurate and fast estimation is achieved. A VF-based predictive direct power control (VF-PDPC) model including the estimated VF positive sequence components is formulated. A startup procedure is considered for smooth starting under unbalanced grid conditions. Feasibility and robustness of the developed VF-PDPC are verified by experiments. Mainly, obtained results demonstrate that the startup procedure reduces settling time and avoids over currents; the VF-PDPC achieves better current waveforms than those obtained by the conventional PDPC under unbalanced grid conditions; and the NN-VF estimator presents similar dynamic performances as the second-order generalized integrator which uses grid voltages measurement.
Adel Rahoui; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. Virtual Flux Estimation for Sensorless Predictive Control of PWM Rectifiers Under Unbalanced and Distorted Grid Conditions. IEEE Journal of Emerging and Selected Topics in Power Electronics 2020, 9, 1923 -1937.
AMA StyleAdel Rahoui, Ali Bechouche, Hamid Seddiki, Djaffar Ould Abdeslam. Virtual Flux Estimation for Sensorless Predictive Control of PWM Rectifiers Under Unbalanced and Distorted Grid Conditions. IEEE Journal of Emerging and Selected Topics in Power Electronics. 2020; 9 (2):1923-1937.
Chicago/Turabian StyleAdel Rahoui; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. 2020. "Virtual Flux Estimation for Sensorless Predictive Control of PWM Rectifiers Under Unbalanced and Distorted Grid Conditions." IEEE Journal of Emerging and Selected Topics in Power Electronics 9, no. 2: 1923-1937.
This paper presents a new signal representation called Frequency Invariant Transformation of Periodic Signals (FIT-PS) in the context of Non-Intrusive Load Monitoring (NILM). Compared to former approaches, where a conglomeration of different signal forms has been used, the presented approach is based on a single signal form containing all information. The core idea of this work is to use the original current waveform relative to the reference voltage as a signature for NILM. In general, the relation of sampling and grid frequency is subject to continuous fluctuations. Therefore, FIT-PS converts uncorrelated sample data to a fixed multiple of the grid frequency. The advantages are that the information of the current signal, as well as the phase shift between voltage and current signal, are completely contained in the FIT-PS signal representation. For classification, a neural net was applied to the HELD1 [1] dataset. Features created by FIT-PS are superior to the standard features. With 18 different devices, a detection rate of up to 90% is achieved. In particular, when several consumers are active at the same time, the new signal representation is much more robust and leads to a better detection rate. However, a Long Short-Term Memory (LSTM) net with FIT-PS signal representation provides the best results.
Pirmin Held; Steffen Mauch; Alaa Saleh; Djaffar Ould Abdeslam; Dirk Benyoucef. Frequency Invariant Transformation of Periodic Signals (FIT-PS) for Classification in NILM. IEEE Transactions on Smart Grid 2018, 10, 5556 -5563.
AMA StylePirmin Held, Steffen Mauch, Alaa Saleh, Djaffar Ould Abdeslam, Dirk Benyoucef. Frequency Invariant Transformation of Periodic Signals (FIT-PS) for Classification in NILM. IEEE Transactions on Smart Grid. 2018; 10 (5):5556-5563.
Chicago/Turabian StylePirmin Held; Steffen Mauch; Alaa Saleh; Djaffar Ould Abdeslam; Dirk Benyoucef. 2018. "Frequency Invariant Transformation of Periodic Signals (FIT-PS) for Classification in NILM." IEEE Transactions on Smart Grid 10, no. 5: 5556-5563.
This paper presents a smart battery charger based on the cascaded boost-buck converter supplied by a photovoltaic (PV) panel. This topology offers a great advantage since it operates in boost mode when the battery voltage is higher than the PV output voltage and operates in buck mode when the battery voltage is lower than the PV voltage output. Due to the presence of inductances at input and output of the cascaded boost-buck converter, the currents across the PV panel and the battery presented very low ripples. In this work, the control of the two converters is simplified since they are driven simultaneously within a same duty cycle. This reduces the implementation cost and complexity. The proposed battery charger operates intelligently; depending on the sun irradiance level and the battery state of charge, the algorithm automatically switches to maximum power point tracking mode, constant current mode, constant voltage mode, or floating charging mode. To verify the overall operation of the system, numerical simulations are performed for two battery voltage levels and including the different phases of charges. According to the obtained good results, the suggested smart charger can be inserted as battery management device in PV energy system.
Yacine Triki; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. A Smart Battery Charger Based on a Cascaded Boost-Buck Converter for Photovoltaic Applications. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society 2018, 3466 -3471.
AMA StyleYacine Triki, Ali Bechouche, Hamid Seddiki, Djaffar Ould Abdeslam. A Smart Battery Charger Based on a Cascaded Boost-Buck Converter for Photovoltaic Applications. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. 2018; ():3466-3471.
Chicago/Turabian StyleYacine Triki; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. 2018. "A Smart Battery Charger Based on a Cascaded Boost-Buck Converter for Photovoltaic Applications." IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society , no. : 3466-3471.
Ali Bechouche; Djaffar Ould Abdeslam; Hamid Seddiki; Adel Rahoui. Neural Filter Based Integrator for Virtual Flux Estimation in Direct Power Control of Three-Phase PWM Rectifiers. IFAC-PapersOnLine 2017, 50, 7013 -7018.
AMA StyleAli Bechouche, Djaffar Ould Abdeslam, Hamid Seddiki, Adel Rahoui. Neural Filter Based Integrator for Virtual Flux Estimation in Direct Power Control of Three-Phase PWM Rectifiers. IFAC-PapersOnLine. 2017; 50 (1):7013-7018.
Chicago/Turabian StyleAli Bechouche; Djaffar Ould Abdeslam; Hamid Seddiki; Adel Rahoui. 2017. "Neural Filter Based Integrator for Virtual Flux Estimation in Direct Power Control of Three-Phase PWM Rectifiers." IFAC-PapersOnLine 50, no. 1: 7013-7018.
This paper proposes a new AC voltage sensorless control scheme for three-phase pulse-width modulation rectifier. A new startup process to ensure a smooth starting of the system is also proposed. The sensorless control scheme uses an adaptive neural (AN) estimator inserted in voltage-oriented control to eliminate the grid voltage sensors. The developed AN estimator combines an adaptive neural network in series with an adaptive neural filter. The AN estimator structure leads to simple, accurate and fast grid voltages estimation, and makes it ideal for low cost digital signal processor implementation. Lyapunov based stability and parameters tuning of the AN estimator are performed. Simulation and experimental tests are carried out to verify the feasibility and effectiveness of the AN estimator. Obtained results show that; the proposed AN estimator presented faster convergence and better accuracy than the second order generalized integrator based estimator; the new startup procedure avoided the over-current and reduced the settling time; the AN estimator presented high performances even under distorted and unbalanced grid voltages.
Adel Rahoui; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. Grid Voltages Estimation for Three-Phase PWM Rectifiers Control Without AC Voltage Sensors. IEEE Transactions on Power Electronics 2017, 33, 859 -875.
AMA StyleAdel Rahoui, Ali Bechouche, Hamid Seddiki, Djaffar Ould Abdeslam. Grid Voltages Estimation for Three-Phase PWM Rectifiers Control Without AC Voltage Sensors. IEEE Transactions on Power Electronics. 2017; 33 (1):859-875.
Chicago/Turabian StyleAdel Rahoui; Ali Bechouche; Hamid Seddiki; Djaffar Ould Abdeslam. 2017. "Grid Voltages Estimation for Three-Phase PWM Rectifiers Control Without AC Voltage Sensors." IEEE Transactions on Power Electronics 33, no. 1: 859-875.
In this paper, the use of a Compact Support Kernel (CSK) instead of the Gaussian window in the S-transform is proposed. The CSK is derived from the Gaussian but overcomes its practical drawbacks while preserving a large number of its useful properties. The width of the CSK is controlled by some parameters making it more flexible. These parameters are selected to optimize the energy concentration in the time–frequency domain. Compared to other versions of S-transform, other time–frequency representations and continuous wavelet transform, the achieved results obtained using synthetic and real data show a significant improvement in the time and frequency resolution, energy concentration and instantaneous frequency estimation.
Z. Zidelmal; H. Hamil; A. Moukadem; A. Amirou; D. Ould-Abdeslam. S-transform based on compact support kernel. Digital Signal Processing 2016, 62, 137 -149.
AMA StyleZ. Zidelmal, H. Hamil, A. Moukadem, A. Amirou, D. Ould-Abdeslam. S-transform based on compact support kernel. Digital Signal Processing. 2016; 62 ():137-149.
Chicago/Turabian StyleZ. Zidelmal; H. Hamil; A. Moukadem; A. Amirou; D. Ould-Abdeslam. 2016. "S-transform based on compact support kernel." Digital Signal Processing 62, no. : 137-149.
The aim of this paper is to improve the energy concentration of the Stockwell transform (S-transform) in the time-frequency domain. A modified S-transform is proposed with several parameters to control the width of a hybrid Gaussian window. A constrained optimization problem is proposed based on an energy concentration measure as objective function and inequalities constraints to define the bounds of the Gaussian window. An active-set algorithm is applied to resolve the optimization problem. The optimization of the energy concentration in the time-frequency plane can lead to more reliable applications for non-stationary signals. The simulation results show a significant improvement of the proposed methodology most notably in the presence of noise comparing with the standard S-transform and existing modified S-transform in the literature. Moreover, comparison with other known time-frequency transforms such as Short-time Fourier transform (STFT) and smoothed-pseudo Wigner-Ville distribution (SPWVD) is also performed and discussed. The proposed S-transform is tested also on real non-stationary signals through an example of split detection in heart sounds.
Ali Moukadem; Zied Bouguila; Djaffar Ould Abdeslam; Alain Dieterlen. A new optimized Stockwell transform applied on synthetic and real non-stationary signals. Digital Signal Processing 2015, 46, 226 -238.
AMA StyleAli Moukadem, Zied Bouguila, Djaffar Ould Abdeslam, Alain Dieterlen. A new optimized Stockwell transform applied on synthetic and real non-stationary signals. Digital Signal Processing. 2015; 46 ():226-238.
Chicago/Turabian StyleAli Moukadem; Zied Bouguila; Djaffar Ould Abdeslam; Alain Dieterlen. 2015. "A new optimized Stockwell transform applied on synthetic and real non-stationary signals." Digital Signal Processing 46, no. : 226-238.
International audienceThis paper presents a novel method for QRS detection in electrocardiograms (ECG). It is basedon the S-Transform, a new time frequency representation (TFR). The S-Transform providesfrequency-dependent resolution while maintaining a direct relationship with the Fourierspectrum. We exploit the advantages of the S-Transform to isolate the QRS complexes in thetime–frequency domain. Shannon energy of each obtained local spectrum is then computedin order to localize the R waves in the time domain.Significant performance enhancement is confirmed when the proposed approach is testedwith the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivityof 99.84%, a positive predictivity of 99.91% and an error rate of 0.25%. Furthermore, to bemore convincing, the authors illustrated the detection parameters in the case of certainECG segments with complicated patterns
Z. Zidelmal; A. Amirou; D. Ould-Abdeslam; A. Moukadem; A. Dieterlen. QRS detection using S-Transform and Shannon energy. Computer Methods and Programs in Biomedicine 2014, 116, 1 -9.
AMA StyleZ. Zidelmal, A. Amirou, D. Ould-Abdeslam, A. Moukadem, A. Dieterlen. QRS detection using S-Transform and Shannon energy. Computer Methods and Programs in Biomedicine. 2014; 116 (1):1-9.
Chicago/Turabian StyleZ. Zidelmal; A. Amirou; D. Ould-Abdeslam; A. Moukadem; A. Dieterlen. 2014. "QRS detection using S-Transform and Shannon energy." Computer Methods and Programs in Biomedicine 116, no. 1: 1-9.
Ali Bechouche; Djaffar Ould Abdeslam; Tahar Otmane-Cherif; Hamid Seddiki. Adaptive Neural PLL for Grid-connected DFIG Synchronization. Journal of Power Electronics 2014, 14, 608 -620.
AMA StyleAli Bechouche, Djaffar Ould Abdeslam, Tahar Otmane-Cherif, Hamid Seddiki. Adaptive Neural PLL for Grid-connected DFIG Synchronization. Journal of Power Electronics. 2014; 14 (3):608-620.
Chicago/Turabian StyleAli Bechouche; Djaffar Ould Abdeslam; Tahar Otmane-Cherif; Hamid Seddiki. 2014. "Adaptive Neural PLL for Grid-connected DFIG Synchronization." Journal of Power Electronics 14, no. 3: 608-620.
Ali Moukadem; Djaffar Ould Abdeslam; Alain Dieterlen. Segmentation and Classification of Heart Sounds Based on the S-Transform. Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals 2014, 61 -86.
AMA StyleAli Moukadem, Djaffar Ould Abdeslam, Alain Dieterlen. Segmentation and Classification of Heart Sounds Based on the S-Transform. Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals. 2014; ():61-86.
Chicago/Turabian StyleAli Moukadem; Djaffar Ould Abdeslam; Alain Dieterlen. 2014. "Segmentation and Classification of Heart Sounds Based on the S-Transform." Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals , no. : 61-86.