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Your search keyword '"Ihsen Alouani"' showing total 21 results
Start Over Author "Ihsen Alouani" Topic [spi.tron]engineering sciences [physics]/electronics
21 results on '"Ihsen Alouani"'

1. Enabling User Relaying in MCM-NOMA Under Doubly Selective Channels Using Iterative Interference Cancellation Schemes for Wireless IoT Networks

Author

Anis Amazigh Hamza, Iyad Dayoub, Ihsen Alouani, Abderrahmane Amrouche, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université des Sciences et de la Technologie Houari Boumediene = University of Sciences and Technology Houari Boumediene [Alger] (USTHB), Institut National des Sciences Appliquées (INSA), and no funding information

Subjects
IoT, Data_CODINGANDINFORMATIONTHEORY, multi-carrier modulation, non-orthogonal multiple access, [SPI.TRON]Engineering Sciences [physics]/Electronics, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Computer Science::Networking and Internet Architecture, Electrical and Electronic Engineering, Interference, cooperative relaying, doubly selective channels, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Instrumentation, Computer Science::Information Theory
Abstract

Cell-edge users of the future cellular internet of things (IoT) with massive IoT sensors can suffer from extremely severe channel conditions, especially under very high-speed scenarios. In this paper, we present a performance improvement method for cell-edge users of multi-carrier modulation (MCM)-based non-orthogonal multiple access (NOMA) downlink systems. To this end, we consider the implementation of cooperative user relaying NOMA (CUR-NOMA) and derive its lower bound end-to-end bit error rate (E2E-BER) under doubly selective channels. In addition, the imperfect successive interference cancellation (SIC) process is analyzed, wherein two interference cancellation schemes are combined to remove the NOMA induced inter-user interference (IUI) and the doubly selective channel induced inter-carrier interference (ICI). Furthermore, numerical simulations are performed to prove the efficiency of the introduced schemes with imperfect channel state information (CSI) when compared to the theoretical perfect SIC with a perfect CSI case.

Published
2022

2. SIT: Stochastic Input Transformation to Defend Against Adversarial Attacks on Deep Neural Networks

Author

Amira Guesmi, Ihsen Alouani, Mouna Baklouti, Tarek Frikha, Mohamed Abid, Université de Sfax - University of Sfax, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), and no information

Subjects
Convolutional Neural Networks, Convolution, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI.TRON]Engineering Sciences [physics]/Electronics, Machine Learning, Kernel, [SPI]Engineering Sciences [physics], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Stochastic processes, Hardware and Architecture, Image reconstruction, Security, Feature extraction, Training, [INFO]Computer Science [cs], Electrical and Electronic Engineering, Robustness, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software, Adversarial Attacks
Abstract

International audience; Deep Neural Networks (DNNs) have been deployed in a wide range of applications, including safety-critical domains, owing to their proven efficiency in solving complex problems. However, these systems have been shown vulnerable to adversarial attacks: carefully crafted perturbations that threaten their integrity and trustworthiness. Several defenses have been recently proposed. However, most of these techniques are costly to deploy since they require retraining and specific fine-tuning procedures. While there are pre-processing defenses that do not require retraining, these were shown to be ineffective against adaptive white-box attacks. In this paper, we propose a model-agnostic defense against adversarial attacks using stochastic pre-processing. Based on a process of down-sampling/up-sampling, we transform the input to a new sample that is: (i) close enough to the initial input to be classified correctly, and (ii) different enough to ignore any potential adversarial noise within it. The proposed defense is generic, easy to deploy and does not require any specific training or fine tuning. We tested our technique comparatively to state-of-the-art defenses under grey-box and strong white-box scenarios. Experimental results show that our defense achieves robustness of up to 94% and 93% against PGD and Cand#x0026;W attacks, respectively, under strong white-box scenario. IEEE

Published
2022

3. Entropy-Based Ultra-Wide Band Radar Signals Segmentation for Multi Obstacle Detection

Author

Atika Rivenq, Amira Mimouna, Abdelmalik Taleb-Ahmed, Anouar Ben Khalifa, Ihsen Alouani, Najoua Essoukri Ben Amara, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Laboratory of Advanced Technology and Intelligent Systems (LATIS), and Ecole Nationale d'Ingénieurs de Sousse (ENISo)

Subjects
UWB radar, Computer science, Entropy, Ultra-wideband, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], law.invention, Differential entropy, [SPI]Engineering Sciences [physics], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], law, Robustness (computer science), Radar imaging, [INFO]Computer Science [cs], Computer vision, Segmentation, Electrical and Electronic Engineering, Radar, Entropy (energy dispersal), Instrumentation, business.industry, segmentation, 010401 analytical chemistry, object detection, [SPI.TRON]Engineering Sciences [physics]/Electronics, 0104 chemical sciences, Obstacle, Artificial intelligence, ITS, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The development of safe intelligent transportation systems (ITS) has driven extensive research to come up with efficient environment perception techniques with a variety of sensors. In short range settings, Ultra Wide-Band (UWB) radars represent a promising technology for building reliable obstacle detection systems as they are robust to environmental conditions. However, UWB radars suffer from a segmentation challenge: localizing relevant regions of interest (ROIs) within its signals. This article proposes a segmentation approach to detect ROIs in an environment perception-dedicated UWB radar. Specifically, we implement a differential entropy analysis to detect ROIs. We evaluate our technique on a benchmark of more than 47 thousands samples. The obtained results show higher performance in terms of obstacle detection compared to state-of-the-art techniques, and a stable robustness even with low amplitude signals.

Published
2021

4. Soft Spatial Attention-Based Multimodal Driver Action Recognition Using Deep Learning

Author

Mohamed Ali Mahjoub, Ihsen Alouani, Imen Jegham, Anouar Ben Khalifa, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), and Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)

Subjects
Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Driver action recognition, 01 natural sciences, Motion (physics), kinect sensor, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Silhouette, [SPI]Engineering Sciences [physics], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO]Computer Science [cs], Computer vision, Electrical and Electronic Engineering, Instrumentation, [PHYS]Physics [physics], business.industry, Deep learning, 010401 analytical chemistry, Frame (networking), multimodal, deep learning, [SPI.TRON]Engineering Sciences [physics]/Electronics, 0104 chemical sciences, Visualization, Task analysis, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, spatial soft attention
Abstract

International audience; Driver behaviors and decisions are crucial factors for on-road driving safety. With a precise driver behavior monitoring system, traffic accidents and injuries can be significantly reduced. However, understanding human behaviors in real-world driving settings is a challenging task because of the uncontrolled conditions including illumination variation, occlusion, and dynamic and cluttered background. In this paper, a Kinect sensor, which provides multimodal signals, is adopted as a driver monitoring sensor to recognize safe driving and common secondary most distracting in-vehicle actions. We propose a novel soft spatial attention-based network named the Depth-based Spatial Attention network (DSA), which adds a cognitive process to deep network by selectively focusing on the driver's silhouette and motion in the cluttered driving scene. In fact, at each time t, we introduce a new weighted RGB frame based on an attention model designed using a depth frame. The final classification accuracy is substantially enhanced compared to the state-of-the-art results with an achieved improvement of up to 27%. © 2001-2012 IEEE.

Published
2021

5. Defensive approximation: securing CNNs using approximate computing

Author

Mouna Baklouti, Mohamed Abid, Nael Abu-Ghazaleh, Khaled N. Khasawneh, Amira Guesmi, Tarek Frikha, Ihsen Alouani, Université de Sfax - University of Sfax, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), George Mason University [Fairfax], University of California [Riverside] (UCR), University of California, Partially supported by NSF grants CNS-1646641, CNS-1619322 and CNS-1955650, Université catholique de Lille (UCL)-Université catholique de Lille (UCL), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), University of California [Riverside] (UC Riverside), and University of California (UC)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer systems organization, Network reliability, Computer science, Embedded systems, 0211 other engineering and technologies, 02 engineering and technology, Convolutional neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), Image (mathematics), [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], Redundancy, Robustness (computer science), Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 021110 strategic, defence & security studies, business.industry, Embedded and cyber-physical systems, Deep learning, Robotics, Energy consumption, [SPI.TRON]Engineering Sciences [physics]/Electronics, 020202 computer hardware & architecture, Dependable and fault-tolerant systems and networks, Range (mathematics), Noise (video), Artificial intelligence, Networks, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Cryptography and Security (cs.CR), Algorithm, Network properties
Abstract

In the past few years, an increasing number of machine-learning and deep learning structures, such as Convolutional Neural Networks (CNNs), have been applied to solving a wide range of real-life problems. However, these architectures are vulnerable to adversarial attacks. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine learning classifiers. We show that our approximate computing implementation achieves robustness across a wide range of attack scenarios. Specifically, for black-box and grey-box attack scenarios, we show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has access to the internal implementation of the approximate classifier. We explain some of the possible reasons for this robustness through analysis of the internal operation of the approximate implementation. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5 and an Alexnet CNNs by up to 99% and 87%, respectively for strong grey-box adversarial attacks along with up to 67% saving in energy consumption due to the simpler nature of the approximate logic. We also show that a white-box attack requires a remarkably higher noise budget to fool the approximate classifier, causing an average of 4db degradation of the PSNR of the input image relative to the images that succeed in fooling the exact classifier, Comment: ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2021)

Published
2021

6. A memory reliability enhancement technique for multi bit upsets

Author

Alexandre Chabot, Smail Niar, Ihsen Alouani, Reda Nouacer, Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), and no information

Subjects
Computer science, 02 engineering and technology, Theoretical Computer Science, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], MBU, Memory, Fault injection, 0202 electrical engineering, electronic engineering, information engineering, Redundancy (engineering), Time overhead, [INFO]Computer Science [cs], Electronic systems, 020206 networking & telecommunications, Reliability, Computing systems, Reliability engineering, [SPI.TRON]Engineering Sciences [physics]/Electronics, Hardware and Architecture, Control and Systems Engineering, Modeling and Simulation, Signal Processing, 020201 artificial intelligence & image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Information Systems, Voltage
Abstract

International audience; Technological advances allow the production of increasingly complex electronic systems. Nevertheless, technology and voltage scaling increased dramatically the susceptibility of new devices not only to Single Bit Upsets (SBU), but also to Multiple Bit Upsets (MBU). In safety critical applications, it is mandatory to provide fault-tolerant systems, providing high reliability while meeting applications requirements. The problem of reliability is particularly expressed within the memory which represents more than 80 % of systems on chips. To tackle this problem we propose a new memory reliability techniques referred to as DPSR: Double Parity Single Redundancy. DPSR is designed to enhance computing systems resilience to SBU and MBU. Based on a thorough fault injection experiments, DPSR shows promising results; It detects and corrects more than 99.6 % of encountered MBU and has an average time overhead of less than 3 %.

Published
2021

7. LSTM-based system for multiple obstacle detection using ultra-wide band radar

Author

Ihsen Alouani, Anouar Ben Khalifa, Najoua Essoukri Ben Amara, Amira Mimouna, Atika Rivenq, Abdelmalik Taleb-Ahmed, Laboratory of Advanced Technology and Intelligent Systems (LATIS), Ecole Nationale d'Ingénieurs de Sousse (ENISo), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), and Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)

Subjects
Computer science, UWB Radar, Ultra-wideband, law.invention, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI.TRON]Engineering Sciences [physics]/Electronics, Intelligent Transportation Systems, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], Deep Learning, law, Obstacle, 11. Sustainability, Electronic engineering, [INFO]Computer Science [cs], Radar, LSTM, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Obstacle Detection
Abstract

International audience; Autonomous vehicles present a promising opportunity in the future of transportation systems by providing road safety. As significant progress has been made in the automatic environment perception, the detection of road obstacles remains a major challenge. Thus, to achieve reliable obstacle detection, several sensors have been employed. For short ranges, the Ultra-Wide Band (UWB) radar is utilized in order to detect objects in the near field. However, the main challenge appears in distinguishing the real target’s signature from noise in the received UWB signals. In this paper, we propose a novel framework that exploits Recurrent Neural Networks (RNNs) with UWB signals for multiple road obstacle detection. Features are extracted from the time-frequency domain using the discrete wavelet transform and are forwarded to the Long short-term memory (LSTM) network. We evaluate our approach on the OLIMP dataset which includes various driving situations with complex environment and targets from several classes. The obtained results show that the LSTM-based system outperforms the other implemented related techniques in terms of obstacle detection.

Published
2021

8. CNTFET-Based Ternary Multiply-and-Accumulate Unit

Author

Amr Mohammaden, Mohammed E. Fouda, Ihsen Alouani, Lobna A. Said, Ahmed G. Radwan, Nile University (NU), University of California [Irvine] (UC Irvine), University of California (UC), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Cairo University, and This research received no external funding.

Subjects
ternary multiplier, Computer Networks and Communications, ternary logic gates, CNTFET, ternary full adder, multiply-and-accumulate, [SPI.TRON]Engineering Sciences [physics]/Electronics, Hardware and Architecture, Control and Systems Engineering, Signal Processing, [INFO]Computer Science [cs], Hardware_ARITHMETICANDLOGICSTRUCTURES, Electrical and Electronic Engineering
Abstract

International audience; Multiply-Accumulate (MAC) is one of the most commonly used operations in modern computing systems due to its use in matrix multiplication, signal processing, and in new applications such as machine learning and deep neural networks. Ternary number system offers higher information processing within the same number of digits when compared to binary systems. In this paper, a MAC is proposed using a CNTFET-based ternary logic number. Specifically, we build a 5-trit multiplier and 10-trit adder as building blocks of two ternary MAC unit designs. The first is a basic MAC which has two methods to implement, serial and pipeline. The second is an improved MAC design that optimizes the number of transistors, offers higher performance and lower power consumption. The designed MAC unit can operate up to 300 MHz. Finally, a comparative study in terms of power, delay, and area variations is conducted under different supply voltages and temperature levels.

Published
2022

9. A novel public dataset for multimodal multiview and multispectral driver distraction analysis: 3MDAD

Author

Mohamed Ali Mahjoub, Anouar Ben Khalifa, Ihsen Alouani, Imen Jegham, Laboratory of Advanced Technology and Intelligent Systems (LATIS), Ecole Nationale d'Ingénieurs de Sousse (ENISo), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), and Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)

Subjects
Computer science, Multispectral, Multispectral image, Advanced driver assistance systems, 02 engineering and technology, Machine learning, computer.software_genre, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Multiview, [SPI]Engineering Sciences [physics], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Distraction, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Electrical and Electronic Engineering, Intelligent transportation system, Set (psychology), Modalities, Modality (human–computer interaction), business.industry, Driver distraction, 020206 networking & telecommunications, Public dataset, Safe driving, [SPI.TRON]Engineering Sciences [physics]/Electronics, Signal Processing, Multimodal, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software
Abstract

International audience; Driver distraction and fatigue have become one of the leading causes of severe traffic accidents. Hence, driver inattention monitoring systems are crucial. Even with the growing development of advanced driver assistance systems and the introduction of third-level autonomous vehicles, this task is still trending and complex due to challenges such as the illumination change and the dynamic background. To reliably compare and validate driver inattention monitoring methods, a limited number of public datasets are available. In this paper, we put forward a public, well-structured and complete dataset, named Multiview, Multimodal and Multispectral Driver Action Dataset (3MDAD). The dataset is mainly composed of two sets: the first one recorded in daytime and the second one at nighttime. Each set consists of two synchronized data modalities, both from frontal and side views. More than 60 drivers are asked to execute 16 in-vehicle actions under a wide range of naturalistic driving settings. In contrast to other public datasets, 3MDAD presents multiple modalities, spectrums and views under different time and weather conditions. To highlight the utility of our dataset, we independently analyze the driver action recognition results adapted to each modality and those obtained of several combinations of modalities.

Published
2020

10. Deep CNN-based Pedestrian Detection for Intelligent Infrastructure

Author

Bilel Tarchoun, Anouar Ben Khalifa, Mohamed Ali Mahjoub, Imen Jegham, Ihsen Alouani, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), and Institut National des Sciences Appliquées (INSA)

Subjects
Computer science, Pedestrian detection, Real-time computing, Advanced driver assistance systems, 02 engineering and technology, Pedestrian, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Task (project management), Databases, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], 0202 electrical engineering, electronic engineering, information engineering, Training, [INFO]Computer Science [cs], Intelligent transportation system, Lighting, Detector, Intelligent decision support system, Detectors, 020206 networking & telecommunications, Cameras, [SPI.TRON]Engineering Sciences [physics]/Electronics, Task analysis, Feature extraction, 020201 artificial intelligence & image processing, Transfer of learning, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

ISBN 978-1-7281-7514-0 ; e-ISBN 978-1-7281-7513-3; International audience; Autonomous driving systems and driver assistance systems are becoming the center of attention in transport technology. Given its safety criticality, pedestrian detection is a highly important task. Transport oriented intelligent systems use embedded sensors for the detection task. However, vehicle side detection is starting to show its limitations especially when dealing with certain challenges such as occlusions. In this paper, we propose an infrastructure side perception system that has a bird's eye view. We introduce a new deep pedestrian detector that can use the detection results to warn nearby vehicles of the presence of pedestrians on the road. The results show that our proposed system is able to detect pedestrians in most conditions with 70.41% precision and 69.17% recall.

Published
2020

11. Neuroattack: undermining spiking neural networks security through externally triggered bit-flips

Author

Alberto Marchisio, Valerio Venceslai, Maurizio Martina, Muhammad Shafique, Ihsen Alouani, Vienna University of Technology (TU Wien), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Politecnico di Torino = Polytechnic of Turin (Polito), This work has been partially supported by the Doctoral CollegeResilient Embedded Systems which is run jointly by TU Wien’sFaculty of Informatics and FH-Technikum Wien., Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), and Institut National des Sciences Appliquées (INSA)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer science, Distributed computing, Reliability (computer networking), Machine Learning (stat.ML), 02 engineering and technology, 01 natural sciences, SNN, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, Spiking Neural Networks, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], Hardware, Statistics - Machine Learning, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Fault-Injection Attacks, Resilience (network), Adversarial Attacks, 010302 applied physics, Spiking neural network, Deep Neural Networks, Resilience, Machine Learning, Spiking Neural Networks, Reliability, Adversarial Attacks, Fault-Injection Attacks, Deep Neural Networks, DNN, SNN, Security, Resilience, Cross-Layer, Biological system modeling, Reliability, 020202 computer hardware & architecture, [SPI.TRON]Engineering Sciences [physics]/Electronics, Cross layer, Cross-Layer, Security, Noise (video), Biological neural networks, Cryptography and Security (cs.CR), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, DNN
Abstract

Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploiting low-level reliability issues through a high-level attack. Particularly, we trigger a fault-injection based sneaky hardware backdoor through a carefully crafted adversarial input noise. Our results on Deep Neural Networks (DNNs) and SNNs show a serious integrity threat to state-of-the art machine-learning techniques., Accepted for publication at the 2020 International Joint Conference on Neural Networks (IJCNN)

Published
2020

12. Are CNNs reliable enough for critical applications? An exploratory study

Author

Mohamed Ayoub Neggaz, Fadi J. Kurdahi, Ihsen Alouani, Smail Niar, Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA), Centro de Estudios Científicos (CECs), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), and Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)

Subjects
Computer science, Reliability (computer networking), Feature extraction, Exploratory research, 02 engineering and technology, Multiple Event Upsets, Machine learning, computer.software_genre, Convolutional neural network, Reliability engineering, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, [SPI]Engineering Sciences [physics], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Engines, Electrical and Electronic Engineering, Implementation, Quantization (signal), Fault tolerant systems, business.industry, Convolutional Neural Networks, Reliability, 020202 computer hardware & architecture, [SPI.TRON]Engineering Sciences [physics]/Electronics, Hardware and Architecture, Artificial intelligence, business, Embedded Systems, computer, Single Event Upsets, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software
Abstract

International audience; Resource-constrained CNN implementations are subject to various reliability threats. This article provides an exploratory study investigating the impact of faults (soft errors modeled as bit flips) across the parameters of CNNs and the impact on the CNN weights, which can be used to create reliability-aware design guidelines. -Theocharis Theocharides, University of Cyprus -Muhammad Shafique, Technische Universitat Wien

Published
2020

13. OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Author

Atika Menhaj, Yassin El Hillali, Ihsen Alouani, Amira Mimouna, Anouar Ben Khalifa, Abdeldjalil Ouahabi, Abdelmalik Taleb-Ahmed, Najoua Essoukri Ben Amara, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), and Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)

Subjects
multi-modality, fusion, Computer Networks and Communications, Computer science, media_common.quotation_subject, lcsh:TK7800-8360, Context (language use), 02 engineering and technology, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], law.invention, Task (project management), [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], Human–computer interaction, law, Perception, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Electrical and Electronic Engineering, Radar, Intelligent transportation system, media_common, business.industry, Deep learning, 010401 analytical chemistry, lcsh:Electronics, intelligent transportation systems, object detection, Object detection, [SPI.TRON]Engineering Sciences [physics]/Electronics, 0104 chemical sciences, public dataset, Hardware and Architecture, Control and Systems Engineering, Signal Processing, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

Published
2020
Full Text
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14. Cross-layer CNN approximations for hardware implementation

Author

Hamza Ouarnoughi, Karim M. A. Ali, Ihsen Alouani, Smail Niar, Abdessamad Ait El Cadi, Rincón, Fernando, Barba, Jesús, So, Hayden K. H., Diniz, Pedro, Caba, Julián, Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), and Institut National des Sciences Appliquées (INSA)

Subjects
Computer science, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Convolution, Reduction (complexity), [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Pruning (decision trees), Field-programmable gate array, Quantization (image processing), ComputingMilieux_MISCELLANEOUS, FPGA, 0105 earth and related environmental sciences, Artificial neural network, business.industry, 020208 electrical & electronic engineering, CNNs, Approximate computing, Object detection, [SPI.TRON]Engineering Sciences [physics]/Electronics, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Computer hardware, MNIST database
Abstract

Oral Session 7 - Design Space Exploration & Estimation TechniquesISBN 978-3-030-44533-1 ; e-ISBN 978-3-030-44534-8; International audience; Convolution Neural Networks (CNNs) are widely used for image classification and object detection applications. The deployment of these architectures in embedded applications is a great challenge. This challenge arises from CNNs’ high computation complexity that is required to be implemented on platforms with limited hardware resources like FPGA. Since these applications are inherently error-resilient, approximate computing (AC) offers an interesting trade-off between resource utilization and accuracy. In this paper, we study the impact on CNN performances when several approximation techniques are applied simultaneously. We focus on two of the widely used approximation techniques, namely quantization and pruning. Our experimental results showed that for CNN networks of different parameter sizes and 3% loss in accuracy, we can obtain up to 27.9%–47.2% reduction in computation complexity in terms of FLOPs for CIFAR-10 and MNIST datasets.

Published
2020

15. Power-efficient reliable register file for aggressive-environment applications

Author

Ozcan Ozturk, Ihsen Alouani, Smail Niar, Hamzeh Ahangari, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA), Bilkent University [Ankara], Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Ahangari, Hamzeh, and Öztürk, Özcan

Subjects
normal operating conditions, Computer science, reduced power budget, power aware computing, microprocessor chips, floating point critical bits, Register file, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI]Engineering Sciences [physics], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], serious design challenge, embedded system manufacturers, 0202 electrical engineering, electronic engineering, information engineering, flip-flops, RF architecture, Data processing, adjacent byte-level narrow-width values, highly aggressive operating conditions, counterpart register, integrated circuit reliability, on-board data processing, fault immune computing systems, 020202 computer hardware & architecture, Significand, Hardware and Architecture, aggressive environments, Instruction pipeline, embedded systems, low power budget, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Computer hardware, Floating point, Exploit, floating point RF reliability enhancement techniques, Power budget, processor pipeline, aggressive-environment applications, critical element, adjacent register hardened RF, integer registers, nonutilised bits, [INFO]Computer Science [cs], Electrical and Electronic Engineering, reliability, business.industry, Power efficient, [SPI.TRON]Engineering Sciences [physics]/Electronics, floating point arithmetic, authors propose, embedded processors, power-efficient reliable register file, error hardening, business, Software
Abstract

International audience; In a context of increasing demands for on-board data processing, insuring reliability under reduced power budget is a serious design challenge for embedded system manufacturers. Particularly, embedded processors in aggressive environments need to be designed with error hardening as a primary goal, not an afterthought. As Register File (RF) is a critical element within the processor pipeline, enhancing RF reliability is mandatory to design fault immune computing systems. This study proposes integer and floating point RF reliability enhancement techniques. Specifically, the authors propose Adjacent Register Hardened RF, a new RF architecture that exploits the adjacent byte-level narrow-width values for hardening integer registers at runtime. Registers are paired together by special switches referred to as joiners and non-utilised bits of each register are exploited to enhance the reliability of its counterpart register. Moreover, they suggest sacrificing the least significant bits of the Mantissa to enhance the reliability of the floating point critical bits, namely, Exponent and Sign bits. The authors' results show that with a low power budget compared to state of the art techniques, they achieve better results under both normal and highly aggressive operating conditions.

Published
2020

16. Vision-based human action recognition: an overview and real world challenges

Author

Imen Jegham, Ihsen Alouani, Mohamed Ali Mahjoub, Anouar Ben Khalifa, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), and Institut National des Sciences Appliquées (INSA)

Subjects
Computer science, 02 engineering and technology, Large range, Action recognition, Motion (physics), Field (computer science), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Pathology and Forensic Medicine, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], Application domain, 0202 electrical engineering, electronic engineering, information engineering, Datasets, [INFO]Computer Science [cs], Vision based, 020206 networking & telecommunications, Data science, [SPI.TRON]Engineering Sciences [physics]/Electronics, Computer Science Applications, Bad weather, Medical Laboratory Technology, Activity recognition, Real world challenges, 020201 artificial intelligence & image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Law, Vision-based, Information Systems
Abstract

International audience; Within a large range of applications in computer vision, Human Action Recognition has become one of the most attractive research fields. Ambiguities in recognizing actions does not only come from the difficulty to define the motion of body parts, but also from many other challenges related to real world problems such as camera motion, dynamic background, and bad weather conditions. There has been little research work in the real world conditions of human action recognition systems, which encourages us to seriously search in this application domain. Although a plethora of robust approaches have been introduced in the literature, they are still insufficient to fully cover the challenges. To quantitatively and qualitatively compare the performance of these methods, public datasets that present various actions under several conditions and constraints are recorded. In this paper, we investigate an overview of the existing methods according to the kind of issue they address. Moreover, we present a comparison of the existing datasets introduced for the human action recognition field.

Published
2020

17. HEAP

Author

Atika Rivenq, Amira Guesmi, Mouna Baklouti, Mohamed Abid, Ihsen Alouani, Tarek Frikha, École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax (ENIS), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA), and NONE FOUND.

Subjects
Floating point, Design space exploration, Computer science, Single Precision, 02 engineering and technology, 01 natural sciences, Power budget, Single-precision floating-point format, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI]Engineering Sciences [physics], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Approximate Computing, Heap (data structure), 010302 applied physics, Genetic Algorithm, low power, Energy consumption, Design Space Exploration, [SPI.TRON]Engineering Sciences [physics]/Electronics, 020202 computer hardware & architecture, Computer engineering, Systems design, Floating Point Multiplication, Multiplier (economics), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

Session 2 - Security, verification and reliability; International audience; Floating point arithmetic is one of the most commonly used units in nowadays computing systems and is deployed for a wide range of domains and applications. While floating point operators offer high precision calculations, a plethora of applications such as multimedia processing and machine learning tolerate errors and computation imprecision. In a context of limited power budget embedded systems, saving resources and energy with an acceptable precision loss is a challenging design task. Approximate computing is an emerging systems design paradigm that offers promising balance between accuracy on the one hand and power consumption and resource utilization on the other hand. While state of the art approximate techniques offer a wide design space at the operator level, few are the works that consider exploring different techniques to build a heterogeneous comprehensive approximate design. In this paper, we propose HEAP: a heterogeneous approximate floating point multiplier. Based on a design space exploration process, we present an approximation at the transistor level that reduces energy consumption of up to 68%. Experimental study on a set of machine learning applications shows promising results with comparable accuracy to exact multiplier based systems. © 2019 Association for Computing Machinery.

Published
2019

18. MDAD: a multimodal and multiview in-vehicle driver action dataset

Author

Ihsen Alouani, Imen Jegham, Anouar Ben Khalifa, Mohamed Ali Mahjoub, Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), and Institut National des Sciences Appliquées (INSA)

Subjects
Computer science, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, Field (computer science), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Distraction, 0202 electrical engineering, electronic engineering, information engineering, Distracted driving, [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS, Modality (human–computer interaction), Modalities, business.industry, 010401 analytical chemistry, Testbed, 0104 chemical sciences, [SPI.TRON]Engineering Sciences [physics]/Electronics, Action (philosophy), RGB color model, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

“Driver’s distraction is deadly!”. Due to its crucial role in saving lives, driver action recognition is an important and trending topic in the field of computer vision. However, a very limited number of public datasets are available to validate proposed methods. This paper introduces a new public, well structured and extensive dataset, named Multiview and multimodal in-vehicle Driver Action Dataset (MDAD). MDAD consists of two temporally synchronised data modalities from side and frontal views. These modalities include RGB and depth data from different Kinect cameras. Many subjects with various body sizes, gender and ages are asked to perform 16 in-vehicle actions in several weather conditions. Each subject drives the vehicle on multiple trip routes in Sousse, Tunisia, at different times to describe a large range of head rotations, changes in lighting conditions and some occlusions. Our recorded dataset provides researchers with a testbed to develop new algorithms across multiple modalities and views under different illumination conditions. To demonstrate the utility of our dataset, we analyze driver action recognition results from each modality and every view independently, and then we combine modalities and views. This public dataset is of benefit to research activities for humans driver action analysis.

Published
2019

19. A new memory reliability technique for multiple bit upsets mitigation

Author

Alexandre Chabot, Reda Nouacer, Ihsen Alouani, Smail Niar, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA), Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), and Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010302 applied physics, Computer science, 02 engineering and technology, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI.TRON]Engineering Sciences [physics]/Electronics, 020202 computer hardware & architecture, Reliability engineering, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], visual_art, 0103 physical sciences, Electronic component, 0202 electrical engineering, electronic engineering, information engineering, Redundancy (engineering), visual_art.visual_art_medium, [INFO]Computer Science [cs], Static random-access memory
Abstract

International audience; Technological advances make it possible to produce increasingly complex electronic components. Nevertheless, these advances are convoyed by an increasing sensitivity to operating conditions and an accelerated aging process. In safety critical applications, it is vital to provide solutions to avoid these limitations and to guarantee a high level of reliability. In most of the existing methods in the literature only Single Event Upsets (SEU) are assumed. The next generations of embedded systems must on one side support Multiple-Bit Upsets (MBU) and avoid to induce a significant memory and processing overheads on the other side. This paper proposes a new method to increase the reliability of SRAM, without dramatically increasing costs in memory space and processing time. Our method, named DPSR for Double Parity Single Redundancy, offers a high level of reliability and takes into fault patterns occurring in real conditions

Published
2019

20. Safe Driving : Driver Action Recognition using SURF Keypoints

Author

Imen Jegham, Ihsen Alouani, Mohamed Ali Mahjoub, Anouar Ben Khalifa, Laboratory of Advanced Technology and Intelligent Systems (LATIS), Ecole Nationale d'Ingénieurs de Sousse (ENISo), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA), and NONE FOUND.

Subjects
Safe driving, Computer science, "Safety", "Face", Advanced driver assistance systems, 02 engineering and technology, "Lighting", Computer security, computer.software_genre, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], "Roads", Distraction, 0202 electrical engineering, electronic engineering, information engineering, Distracted driving, [INFO]Computer Science [cs], Road traffic, 010401 analytical chemistry, Frame (networking), "Vehicles", [SPI.TRON]Engineering Sciences [physics]/Electronics, 0104 chemical sciences, Key (cryptography), "Microelectronics", Action recognition, 020201 artificial intelligence & image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computer, "Wheels"
Abstract

International audience; Driver distraction is one of the main factors of fatal road traffic injuries. According to the national Highway Traffic Safety Administration (NHTSA), in USA, 3450 are killed by distracted driving, in 2016. In order to save lives, Advanced Driver Assistance Systems (ADAS), more specifically those systems for distracted driver action recognition are introduced. Our method aim to extract, from each frame, a region of interest (KOI) that contains body parts performing in-vehicle actions. These regions hold the most important key points after eliminating those common ones that are similar to the key points of the safe driving actions. The proposed approach was evaluated on the distracted driver detection dataset. Experimental results illustrate the performance of the proposed approach.

Published
2018

21. Auto-tuning fault tolerance technique for DSP-based circuits in transportation systems

Author

Ihsen Alouani, Niar, S., El-Hillali, Y., Rivenq, A., Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), and Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], [INFO]Computer Science [cs], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI.TRON]Engineering Sciences [physics]/Electronics
Abstract

International audience

Published
2016

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