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36. Ternary SRAM circuit designs with CNTFETs

Author

Doaa K. Abdelrahman, 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), Queen's University [Belfast] (QUB), and no information found

Subjects
[PHYS]Physics [physics], Applied Mathematics, ternary memory, CNTFET, soft errors, SRAM, multiple-valued logic (MVL), SNM, Computer Science Applications, Electronic, Optical and Magnetic Materials, [SPI]Engineering Sciences [physics], amplitude–duration criterion, Electrical and Electronic Engineering, SEU
Abstract

International audience; SummaryStatic random-access memory (SRAM) is a cornerstone in modern microprocessors architecture, as it has high power consumption, large area, and high complexity. Also, the stability of the data in the SRAM against the noise and the performance under the radian exposure are main concern issues. To overcome these limitations in the quest for higher information density by memory element, the ternary logic system has been investigated, showing promising potential compared with the conventional binary base. Moreover, carbon nanotube field effect transistor (CNTFET) is a new alternative device with proper features like low power consumption and threshold voltage dependency on CNTAQAUTHOR: Please provide the expanded form of CNT at first mention in the abstract and in the body if it is an acronym. diameter. This paper proposes a new design for ternary SRAM using CNTFET and its evaluation by comparing it against two other designs in many aspects. Moreover, we investigated the static noise margin for the three designs to discuss their stability. Furthermore, we studied the reliability of the designs by evaluating the soft errors effect.

Published
2023

38. Toward a Reliable Estimation of Fluid Concentration Using High Frequency Acoustic Waves: A Machine Learning Approach

Author

Venu Babu Thati, Ibrahim Zaaroura, Malika Toubal, Nikolay Smagin, Souad Harmand, Julien Carlier, Ihsen Alouani, 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), 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), Transduction, Propagation et Imagerie Acoustique - IEMN (TPIA - 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), Matériaux et Acoustiques pour MIcro et NAno systèmes intégrés - IEMN (MAMINA - IEMN), COMmunications NUMériques - IEMN (COMNUM - IEMN), This work was supported by the CNRS. The authors wish to thank all the members of the CMNF platform at IEMN - UMRCNRS 8520, LAMIH - UMR CNRS 8201, and UPHF. This work was also supported by the French RENATECH network., Renatech Network, and CMNF

Subjects
[SPI]Engineering Sciences [physics], Acoustic waves, Machine learning, Fluid concentration estimation, Data processing & augmentation, Deep neural network, Electrical and Electronic Engineering, Instrumentation
Abstract

dataset associé: This article includes datasets hosted on IEEE DataPort(TM) DOI: 10.21227/8nnk-kd35dataset name: FLUID CONCENTRATION ESTIMATION; International audience; Using acoustic waves to estimate fluid concentration is a promising technology due to its practicality and non-intrusive aspect, especially for medical applications. The existing approaches are exclusively based on the correlation between the reflection coefficient and the concentration. However, these techniques are limited by the high sensitivity of the reflection coefficient to environment conditions changes, even slight ones. This introduces inaccuracies that cannot be tolerated in medical applications. This paper proposed a deep learning model, Fluid Concentration Estimation Convolutional Neural Network (FCE-CNN), to estimate fluid concentration. Instead of using only the reflection coefficient, we train our model to detect concentration-related patterns based on the whole received acoustic signal. FCE-CNN shows promising results that overcome the state-of-the-art limitations. Specifically, our model that is able to estimate fluid concentration with 98.5% accuracy using ultra high frequency acoustic waves.

Published
2022

39. 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

40. BM-Seg: A new bone metastases segmentation dataset and ensemble of CNN-based segmentation approach

Author

Marwa Afnouch, Olfa Gaddour, Yosr Hentati, Fares Bougourzi, Mohamed Abid, Ihsen Alouani, Abdelmalik Taleb Ahmed, 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), Computer & Embedded Systems Laboratory (CES-ENIS), École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax (ENIS), Hedi Chaker Hospital [Sfax], Istituto di Scienze Applicate e Sistemi Intelligenti 'Eduardo Caianiello' (ISASI), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Queen's University [Belfast] (QUB), and AcknowledgmentsThe authors would like to express their heartfelt gratitude to Drs. Zaineb Mnif and Hela Fendri for their helpful advice on various technical issues examined in this work, and to the Tunisian Ministry of Health for permission to conduct this study. This work was supported by the PhD School ’Sciences & Technologies’ of the National Engineering School of Sfax (ENIS) and the Computer and Embedded Systems (CES) Laboratory.

Subjects
[PHYS]Physics [physics], [SPI]Engineering Sciences [physics], Artificial Intelligence, BM-Seg, Bone metastases, Generation, General Engineering, Convolutional neural network, CT-scans, Semantic segmentation, Computer Science Applications
Abstract

International audience; A B S T R A C TIn recent years, Machine Learning approaches (ML) have shown promising results in addressing many tasks inmedical image analysis. In particular, the analysis of Bone Metastases (BM) has attracted considerable interestfrom both the medical and computer vision communities due to its critical and challenging aspect. Despitethe research efforts, the detection of BM is still an open problem, mainly due to the lack of available datasets.This is due to two main obstacles: (i) the enormous time required for data collection and annotation, and(ii) privacy constraints. To overcome these challenges, we propose BM-Seg, a new dataset for segmenting BMfrom CT-scans. Our BM-Seg dataset consists of 1517 CT images from 23 patients where BM and bone regionswere labeled by three radiologists. BM-Seg is constructed to cover the diversity of bone metastases in termsof location, organ and severity.We also propose a new CNN-based approach to segmentation of BM, presenting two main contributions.First, we introduce Hybrid-AttUnet++, a new Unet++ derived architecture with dual decoders that performssegmentation of BM and bone regions simultaneously. Second, we use an ensemble of trained HybridAttUnet++ models (EH-AttUnet++) to optimize segmentation performance. Our experiments show that theEH-AttUnet++ architecture achieves better performance compared to state-of-the-art approaches for variousevaluation metrics. The purpose of this work is to provide a benchmark dataset with new state-of-the-artperformance in bone metastasis segmentation. This will facilitate further research in this area and help to putautomatic detection and segmentation of bone metastases into practice.

Published
2023

43. Identification of Ultra High Frequency Acoustic Coda Waves Using Deep Neural Networks

Author

Venu Babu Thati, Nikolay Smagin, Julien Carlier, Hatem Dahmani, Ihsen Alouani, 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), Matériaux et Acoustiques pour MIcro et NAno systèmes intégrés - IEMN (MAMINA - 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), Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), The authors would like to thank all the members of the CMNF platform at IEMN, UMR CNRS 8520., Renatech Network, 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), 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
data processing and augmentation, Computer science, Acoustics, Chaotic, 01 natural sciences, Physics::Geophysics, Coda, [SPI]Engineering Sciences [physics], Sensitivity (control systems), Electrical and Electronic Engineering, signal processing, Instrumentation, [PHYS]Physics [physics], Signal processing, Artificial neural network, Coda waves, business.industry, Deep learning, 010401 analytical chemistry, deep neural network, source identification, 0104 chemical sciences, Ultra high frequency, Ultrasonic sensor, Artificial intelligence, business
Abstract

International audience; Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in the non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution. While existing deterministic solutions reach their limit in handling complex data, emerging techniques such as deep learning-based algorithms have shown a promising potential to overcome these limitations. This paper proposes a deep neural network that paves the way to make the complex features of coda waves more handleable to reliably exploit coda waves in several applications even in a changing or unstable environment. Specifically, designed a Coda-Convolutional Neural Network that is able to identify coda waves with 95.65% precision in a silicon chaotic cavity including 5 emitters and 16 receivers using ultra high frequency ultrasonic coda waves.

Published
2021

44. CNTFET‐based ternary address decoder design

Author

Rawan Mohammed, 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), and no information

Subjects
[SPI]Engineering Sciences [physics], Applied Mathematics, ternary address decoder, multiple-valued logic, ternary, Electrical and Electronic Engineering, CNTFET, Computer Science Applications, Electronic, Optical and Magnetic Materials
Abstract

International audience; With the end of Moore's law, new paradigms are investigated for more scalable computing systems. One of the promising directions is to examine the data representation toward higher data density per hardware element. Multiple valued logic (MVL) emerged as a promising system due to its advantages over binary data representation. MVL offers higher information processing within the same number of digits when compared with binary systems. Accessing memory is considered one of the most power- and time-consuming instructions within a microprocessor. In the quest for building an entire ternary computer architecture, we propose investigating the potential opportunities of ternary address decoders. This paper presents three different designs for ternary address decoder based on CNTFET. The first design is based on a cascade of Ternary to Binary blocks (T2B) and a binary decoder. The second design is built using the hierarchical structure and enables signals. The third is designed utilising a pre-decoder and ternary logic gates. A comparison of the proposed designs and the binary address decoder in terms of power and delay under different supply voltage and temperature values is introduced. Simulation results show that the second design has the least power and delay of the proposed ternary designs.

Published
2022

45. Deep learning-based hard spatial attention for driver in-vehicle action monitoring

Author

Imen Jegham, Ihsen Alouani, Anouar Ben Khalifa, Mohamed Ali Mahjoub, 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), 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), Queen's University [Belfast] (QUB), and no information

Subjects
In-vehicle action monitoring, [PHYS]Physics [physics], Recognition, [SPI]Engineering Sciences [physics], Action, Artificial Intelligence, General Engineering, Hybrid network, Driver, Deep learning, Hard attention, Computer Science Applications
Abstract

International audience; Distracted driving is one of the main causes of deaths and injuries in the world. Monitoring driver behaviors through Driver Action Recognition (DAR) contributes significantly to building safer transportation systems. However, in naturalistic driving settings, this task is complex and challenging because of numerous difficulties, such as high illumination variation and cluttered and dynamic background. In this paper, we introduce a novel hard attention network that highlights the most pertinent driving-scene information while filtering out irrelevant data. Specifically, only local discriminative salient regions are exploited through a hard attention mechanism. The experimental results indicate that our approach significantly enhances DAR performance. We evaluated our network on three diverse state-of-the-art datasets recorded in real-world conditions: it achieves up to 95.83% in terms of safe driving recognition and up to 99.07% in terms of distraction detection. The proposed approach outperforms the soft attention-based DAR not only in detection and recognition performance but also in computation complexity by 38.71% less runtime. For reproducible research, the code is available at https://github.com/JEGHAMI/HSA

Published
2023

46. 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

47. 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

48. 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

49. A novel multi-view pedestrian detection database for collaborative Intelligent Transportation Systems

Author

Mohamed Ali Mahjoub, Atika Rivenq, Ihsen Alouani, 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 - 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), Université de Sousse, 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
Scheme (programming language), Collaborative intelligence, Computer Networks and Communications, Computer science, Pedestrian detection, 02 engineering and technology, computer.software_genre, Task (project management), [SPI]Engineering Sciences [physics], 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Intelligent transportation system, ComputingMilieux_MISCELLANEOUS, computer.programming_language, Environment perception, Monocular, Database, 020206 networking & telecommunications, Multi-view, Hardware and Architecture, Infrastructure to vehicle, 020201 artificial intelligence & image processing, computer, CNN, Software
Abstract

International audience; Recent advances in machine-learning, especially in deep neural networks have significantly accelerated the development and deployment of transport-oriented intelligent designs with increasingly high efficiency. While these technologies are exceptionally promising toward revolutionizing our current mobility and reducing the number of road accidents, the way to safe Intelligent Transportation Systems (ITS) remains long. Since pedestrians are the most vulnerable road users, designing accurate pedestrian detection methods is a priority task. However, traditional monocular pedestrian detection methods are limited, especially in occlusion handling. Hence, a collaborative perception scheme in which vehicles no longer restrict their input data to their immediate embedded sensors and rather exploit data from remote sensors is necessary to achieve a more comprehensive environment perception. In this work, we propose a novel public dataset: Infrastructure to Vehicle Multi-View Pedestrian Detection Database (I2V-MVPD) that combines synchronized images from both a mobile camera embedded in a car and a static camera in the road infrastructure. We also propose a new multi-view pedestrian detection framework based on collaborative intelligence between vehicles and infrastructure. Our results show a significant improvement in detection performance over monocular detection.

Published
2020

50. On the Error Rate Performance of Full-Duplex Cooperative NOMA in Wireless Networks

Author

Anis Amazigh Hamza, Iyad Dayoub, Ihsen Alouani, Abderrahmane Amrouche, 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 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), 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 catholique de Lille (UCL)-Université catholique de Lille (UCL), Université des Sciences et de la Technologie Houari Boumediene = University of Sciences and Technology Houari Boumediene [Alger] (USTHB), and no information

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 0508 media and communications, 05 social sciences, 050801 communication & media studies, Electrical and Electronic Engineering
Abstract

International audience; Error rate analyses of cooperative non-orthogonal multiple access (CNOMA) systems are of paramount importance to investigate the communication reliability for each user and facilitate the development of enhancement algorithms. Although CNOMA has recently attracted great attention, its error performance, particularly that of full-duplex cooperative NOMA (FD-CNOMA), is still underexplored in the literature. In this paper, we investigated the error performance of FD-CNOMA systems under imperfect successive interference cancellation (SIC) and residual self-interference (RSI), where new closed-form expressions of the exact bit error rates (BER) are derived for both users. Through the derived BER expressions, high-SNR analyses are conducted to show that FD-CNOMA has an error floor. Based on the derived expressions, we proposed a novel SINR-based selective FD-relaying, which minimizes the end-to-end (e2e) BER and improves the overall system performance. The analyses are extended to cover pulse-amplitude modulation (PAM) and quadrature-amplitude modulation (QAM) with arbitrary modulation orders. Monte Carlo simulations and numerical results are presented to corroborate the derived analytical expressions and give valuable insights into the error performance of FD-CNOMA systems.

Published
2022

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