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Ihsen Alouani
IEMN-DOAE, INSA Hauts-De-France, Université Polytechnique Hauts-De-France, Valenciennes, France

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Journal article
Published: 04 May 2021 in IEEE Design & Test
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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 C&W attacks, respectively, under strong white-box scenario.

ACS Style

Amira Guesmi; Ihsen Alouani; Mouna Baklouti; Tarek Frikha; Mohamed Abid. SIT: Stochastic Input Transformation to Defend Against Adversarial Attacks on Deep Neural Networks. IEEE Design & Test 2021, PP, 1 -1.

AMA Style

Amira Guesmi, Ihsen Alouani, Mouna Baklouti, Tarek Frikha, Mohamed Abid. SIT: Stochastic Input Transformation to Defend Against Adversarial Attacks on Deep Neural Networks. IEEE Design & Test. 2021; PP (99):1-1.

Chicago/Turabian Style

Amira Guesmi; Ihsen Alouani; Mouna Baklouti; Tarek Frikha; Mohamed Abid. 2021. "SIT: Stochastic Input Transformation to Defend Against Adversarial Attacks on Deep Neural Networks." IEEE Design & Test PP, no. 99: 1-1.

Journal article
Published: 20 April 2021 in Microelectronics Journal
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Ternary number system offers higher information processing within the same number of digits when compared to binary systems. Such advantage motivated the development of ternary processing units especially with CNTFET which offers better power and delay results compared to CMOS-based realization. In this paper, we propose a variety of circuit realizations for the ternary memory elements that are needed in any processor including ternary D-latch, and ternary D-flip-flop. These basic building blocks are then used to design a ternary register file with multiple read and write ports. This paper is an attempt to investigate the performance aspects of using ternary RF to open the gate of more contributions and research in the direction of full ternary computer architecture. The proposed designs have been compared in terms of power, area, and latency at different supply voltages and operating temperatures.

ACS Style

Amr Mohammaden; Mohammed E. Fouda; Ihsen Alouani; Lobna A. Said; Ahmed G. Radwan. CNTFET design of a multiple-port ternary register file. Microelectronics Journal 2021, 113, 105076 .

AMA Style

Amr Mohammaden, Mohammed E. Fouda, Ihsen Alouani, Lobna A. Said, Ahmed G. Radwan. CNTFET design of a multiple-port ternary register file. Microelectronics Journal. 2021; 113 ():105076.

Chicago/Turabian Style

Amr Mohammaden; Mohammed E. Fouda; Ihsen Alouani; Lobna A. Said; Ahmed G. Radwan. 2021. "CNTFET design of a multiple-port ternary register file." Microelectronics Journal 113, no. : 105076.

Journal article
Published: 08 January 2021 in IEEE Sensors Journal
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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 paper 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.

ACS Style

Amira Mimouna; Anouar Ben Khalifa; Ihsen Alouani; Najoua Essoukri Ben Amara; Atika Rivenq; Abdelmalik Taleb-Ahmed. Entropy-Based Ultra-Wide Band Radar Signals Segmentation for Multi Obstacle Detection. IEEE Sensors Journal 2021, 21, 8142 -8149.

AMA Style

Amira Mimouna, Anouar Ben Khalifa, Ihsen Alouani, Najoua Essoukri Ben Amara, Atika Rivenq, Abdelmalik Taleb-Ahmed. Entropy-Based Ultra-Wide Band Radar Signals Segmentation for Multi Obstacle Detection. IEEE Sensors Journal. 2021; 21 (6):8142-8149.

Chicago/Turabian Style

Amira Mimouna; Anouar Ben Khalifa; Ihsen Alouani; Najoua Essoukri Ben Amara; Atika Rivenq; Abdelmalik Taleb-Ahmed. 2021. "Entropy-Based Ultra-Wide Band Radar Signals Segmentation for Multi Obstacle Detection." IEEE Sensors Journal 21, no. 6: 8142-8149.

Article
Published: 30 October 2020 in Journal of Signal Processing Systems
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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 %.

ACS Style

Alexandre Chabot; Ihsen Alouani; Réda Nouacer; Smail Niar. A Memory Reliability Enhancement Technique for Multi Bit Upsets. Journal of Signal Processing Systems 2020, 93, 439 -459.

AMA Style

Alexandre Chabot, Ihsen Alouani, Réda Nouacer, Smail Niar. A Memory Reliability Enhancement Technique for Multi Bit Upsets. Journal of Signal Processing Systems. 2020; 93 (4):439-459.

Chicago/Turabian Style

Alexandre Chabot; Ihsen Alouani; Réda Nouacer; Smail Niar. 2020. "A Memory Reliability Enhancement Technique for Multi Bit Upsets." Journal of Signal Processing Systems 93, no. 4: 439-459.

Journal article
Published: 25 August 2020 in IEEE Sensors Journal
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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%.

ACS Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. Soft Spatial Attention-Based Multimodal Driver Action Recognition Using Deep Learning. IEEE Sensors Journal 2020, 21, 1918 -1925.

AMA Style

Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub. Soft Spatial Attention-Based Multimodal Driver Action Recognition Using Deep Learning. IEEE Sensors Journal. 2020; 21 (2):1918-1925.

Chicago/Turabian Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. 2020. "Soft Spatial Attention-Based Multimodal Driver Action Recognition Using Deep Learning." IEEE Sensors Journal 21, no. 2: 1918-1925.

Journal article
Published: 08 August 2020 in Signal Processing: Image Communication
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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.

ACS Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. A novel public dataset for multimodal multiview and multispectral driver distraction analysis: 3MDAD. Signal Processing: Image Communication 2020, 88, 115960 .

AMA Style

Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub. A novel public dataset for multimodal multiview and multispectral driver distraction analysis: 3MDAD. Signal Processing: Image Communication. 2020; 88 ():115960.

Chicago/Turabian Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. 2020. "A novel public dataset for multimodal multiview and multispectral driver distraction analysis: 3MDAD." Signal Processing: Image Communication 88, no. : 115960.

Journal article
Published: 16 July 2020 in Future Generation Computer Systems
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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 towards 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.

ACS Style

Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub; Atika Rivenq. A novel multi-view pedestrian detection database for collaborative Intelligent Transportation Systems. Future Generation Computer Systems 2020, 113, 506 -527.

AMA Style

Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub, Atika Rivenq. A novel multi-view pedestrian detection database for collaborative Intelligent Transportation Systems. Future Generation Computer Systems. 2020; 113 ():506-527.

Chicago/Turabian Style

Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub; Atika Rivenq. 2020. "A novel multi-view pedestrian detection database for collaborative Intelligent Transportation Systems." Future Generation Computer Systems 113, no. : 506-527.

Conference paper
Published: 01 July 2020 in 2020 International Joint Conference on Neural Networks (IJCNN)
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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.

ACS Style

Valerio Venceslai; Alberto Marchisio; Ihsen Alouani; Maurizio Martina; Muhammad Shafique. NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips. 2020 International Joint Conference on Neural Networks (IJCNN) 2020, 1 -8.

AMA Style

Valerio Venceslai, Alberto Marchisio, Ihsen Alouani, Maurizio Martina, Muhammad Shafique. NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips. 2020 International Joint Conference on Neural Networks (IJCNN). 2020; ():1-8.

Chicago/Turabian Style

Valerio Venceslai; Alberto Marchisio; Ihsen Alouani; Maurizio Martina; Muhammad Shafique. 2020. "NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips." 2020 International Joint Conference on Neural Networks (IJCNN) , no. : 1-8.

Journal article
Published: 27 March 2020 in Electronics
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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.

ACS Style

Amira Mimouna; Ihsen Alouani; Anouar Ben Khalifa; Yassin El Hillali; Abdelmalik Taleb-Ahmed; Atika Menhaj; Abdeldjalil Ouahabi; Najoua Essoukri Ben Amara. OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception. Electronics 2020, 9, 560 .

AMA Style

Amira Mimouna, Ihsen Alouani, Anouar Ben Khalifa, Yassin El Hillali, Abdelmalik Taleb-Ahmed, Atika Menhaj, Abdeldjalil Ouahabi, Najoua Essoukri Ben Amara. OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception. Electronics. 2020; 9 (4):560.

Chicago/Turabian Style

Amira Mimouna; Ihsen Alouani; Anouar Ben Khalifa; Yassin El Hillali; Abdelmalik Taleb-Ahmed; Atika Menhaj; Abdeldjalil Ouahabi; Najoua Essoukri Ben Amara. 2020. "OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception." Electronics 9, no. 4: 560.

Conference paper
Published: 25 March 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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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.

ACS Style

Karim M. A. Ali; Ihsen Alouani; Abdessamad Ait El Cadi; Hamza Ouarnoughi; Smail Niar. Cross-layer CNN Approximations for Hardware Implementation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 151 -165.

AMA Style

Karim M. A. Ali, Ihsen Alouani, Abdessamad Ait El Cadi, Hamza Ouarnoughi, Smail Niar. Cross-layer CNN Approximations for Hardware Implementation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():151-165.

Chicago/Turabian Style

Karim M. A. Ali; Ihsen Alouani; Abdessamad Ait El Cadi; Hamza Ouarnoughi; Smail Niar. 2020. "Cross-layer CNN Approximations for Hardware Implementation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 151-165.

Review article
Published: 27 January 2020 in Forensic Science International: Digital Investigation
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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.

ACS Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. Vision-based human action recognition: An overview and real world challenges. Forensic Science International: Digital Investigation 2020, 32, 200901 .

AMA Style

Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub. Vision-based human action recognition: An overview and real world challenges. Forensic Science International: Digital Investigation. 2020; 32 ():200901.

Chicago/Turabian Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. 2020. "Vision-based human action recognition: An overview and real world challenges." Forensic Science International: Digital Investigation 32, no. : 200901.

Journal article
Published: 16 December 2019 in Cognitive Systems Research
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While background subtraction techniques have been widely applied to detect moving objects in a video stream captured by a static camera, detecting moving objects using a moving camera still represents a challenging task. In this context, pedestrian detection using a camera placed on the top of a vehicle’s windshield has been rarely investigated. This is mainly due to the background ego-motion. Since the scene captured by the camera seems in motion, it is very difficult to distinguish the moving pedestrians from the others that belong to the static part of the scene. For this reason, a compensation step is needed to suppress the ego-motion. This paper presents a study on the main challenges facing pedestrian detection systems as well as methods proposed to handle these challenges. A novel trajectory classification framework for detecting pedestrians even in challenging real-world environments is proposed. The proposed method models the background motion between two consecutive frames in order to compensate the camera motion. Then, it defines a classification process that differentiates between the background and the foreground in the frame. Using the defined foreground, we consequently identify the presence of pedestrians in the scene. The proposed method was validated on a public benchmark dataset: CVC-14 containing both visible and far infrared video sequences in day and night times. Experimental results confirm the effectiveness of the proposed approach in capturing the dynamic aspect between frames and therefore detecting the presence of pedestrians in the scene.

ACS Style

Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub; Najoua Essoukri Ben Amara. Pedestrian detection using a moving camera: A novel framework for foreground detection. Cognitive Systems Research 2019, 60, 77 -96.

AMA Style

Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara. Pedestrian detection using a moving camera: A novel framework for foreground detection. Cognitive Systems Research. 2019; 60 ():77-96.

Chicago/Turabian Style

Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub; Najoua Essoukri Ben Amara. 2019. "Pedestrian detection using a moving camera: A novel framework for foreground detection." Cognitive Systems Research 60, no. : 77-96.

Journal article
Published: 08 November 2019 in IEEE Design & Test
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While Convolutional Neural Networks (CNNs) are going mainstream and deployed in widespread domains including safety-critical systems, the impact of reliability issues in CNN-hosting systems are still under-explored. In this paper, we experimentally evaluate the inherent fault tolerance of CNNs through fault injection experiments. Our experiments demonstrate that quantization increases reliability. We also show the most vulnerable bits in the IEEE 754 format representation. At the layer-level, a study on the network architecture was performed to identify the most vulnerable layers. This exploratory study is useful for comprehensive critical system reliability enhancement since only the vulnerable parts will be protected.

ACS Style

Mohamed A. Neggaz; Ihsen Alouani; Smail Niar; Fadi Kurdahi. Are CNNs Reliable Enough for Critical Applications? An Exploratory Study. IEEE Design & Test 2019, 37, 76 -83.

AMA Style

Mohamed A. Neggaz, Ihsen Alouani, Smail Niar, Fadi Kurdahi. Are CNNs Reliable Enough for Critical Applications? An Exploratory Study. IEEE Design & Test. 2019; 37 (2):76-83.

Chicago/Turabian Style

Mohamed A. Neggaz; Ihsen Alouani; Smail Niar; Fadi Kurdahi. 2019. "Are CNNs Reliable Enough for Critical Applications? An Exploratory Study." IEEE Design & Test 37, no. 2: 76-83.

Conference paper
Published: 22 August 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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“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.

ACS Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. MDAD: A Multimodal and Multiview in-Vehicle Driver Action Dataset. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 518 -529.

AMA Style

Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub. MDAD: A Multimodal and Multiview in-Vehicle Driver Action Dataset. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():518-529.

Chicago/Turabian Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. 2019. "MDAD: A Multimodal and Multiview in-Vehicle Driver Action Dataset." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 518-529.

Conference paper
Published: 30 April 2019 in Proceedings of the 16th ACM International Conference on Computing Frontiers
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ACS Style

Alexandre Chabot; Ihsen Alouani; Smail Niar; Reda Nouacer. A new memory reliability technique for multiple bit upsets mitigation. Proceedings of the 16th ACM International Conference on Computing Frontiers 2019, 145 -152.

AMA Style

Alexandre Chabot, Ihsen Alouani, Smail Niar, Reda Nouacer. A new memory reliability technique for multiple bit upsets mitigation. Proceedings of the 16th ACM International Conference on Computing Frontiers. 2019; ():145-152.

Chicago/Turabian Style

Alexandre Chabot; Ihsen Alouani; Smail Niar; Reda Nouacer. 2019. "A new memory reliability technique for multiple bit upsets mitigation." Proceedings of the 16th ACM International Conference on Computing Frontiers , no. : 145-152.

Research article
Published: 15 April 2019 in IET Computers & Digital Techniques
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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.

ACS Style

Ihsen Alouani; Hamzeh Ahangari; Ozcan Ozturk; Smail Niar. Power‐efficient reliable register file for aggressive‐environment applications. IET Computers & Digital Techniques 2019, 14, 1 -8.

AMA Style

Ihsen Alouani, Hamzeh Ahangari, Ozcan Ozturk, Smail Niar. Power‐efficient reliable register file for aggressive‐environment applications. IET Computers & Digital Techniques. 2019; 14 (1):1-8.

Chicago/Turabian Style

Ihsen Alouani; Hamzeh Ahangari; Ozcan Ozturk; Smail Niar. 2019. "Power‐efficient reliable register file for aggressive‐environment applications." IET Computers & Digital Techniques 14, no. 1: 1-8.

Conference paper
Published: 01 December 2018 in 2018 30th International Conference on Microelectronics (ICM)
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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.

ACS Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. Safe Driving : Driver Action Recognition using SURF Keypoints. 2018 30th International Conference on Microelectronics (ICM) 2018, 60 -63.

AMA Style

Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub. Safe Driving : Driver Action Recognition using SURF Keypoints. 2018 30th International Conference on Microelectronics (ICM). 2018; ():60-63.

Chicago/Turabian Style

Imen Jegham; Anouar Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub. 2018. "Safe Driving : Driver Action Recognition using SURF Keypoints." 2018 30th International Conference on Microelectronics (ICM) , no. : 60-63.

Conference paper
Published: 01 October 2018 in 2018 IEEE 36th International Conference on Computer Design (ICCD)
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Deep learning systems such as Convolutional Neural Networks (CNNs) have shown remarkable efficiency in dealing with a variety of complex real life problems. To accelerate the execution of these heavy algorithms, a plethora of software implementations and hardware accelerators have been proposed. In a context of shrinking devices dimensions, reliability issues of CNN-hosting systems are under-explored. In this paper, we experimentally evaluate the inherent fault tolerance of CNNs by injecting errors within network modules, namely processing elements and memories. Our experiments demonstrate a non uniform sensitivity between different parts of the system. While CNNs are relatively resilient to errors occurring in processing elements, transient faults hitting memories lead to catastrophic degradation of accuracy.

ACS Style

Mohamed A. Neggaz; Ihsen Alouani; Pablo R. Lorenzo; Smail Niar. A Reliability Study on CNNs for Critical Embedded Systems. 2018 IEEE 36th International Conference on Computer Design (ICCD) 2018, 476 -479.

AMA Style

Mohamed A. Neggaz, Ihsen Alouani, Pablo R. Lorenzo, Smail Niar. A Reliability Study on CNNs for Critical Embedded Systems. 2018 IEEE 36th International Conference on Computer Design (ICCD). 2018; ():476-479.

Chicago/Turabian Style

Mohamed A. Neggaz; Ihsen Alouani; Pablo R. Lorenzo; Smail Niar. 2018. "A Reliability Study on CNNs for Critical Embedded Systems." 2018 IEEE 36th International Conference on Computer Design (ICCD) , no. : 476-479.

Conference paper
Published: 01 October 2018 in 2018 International Symposium on Rapid System Prototyping (RSP)
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The embedded systems industry is moving towards the integration of higher performance, yet less reliable electronic components into new product generations. 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). However, the system reliability assessment at the design phase of fault-tolerant computer systems is a complex and critical task. In this context, it is mandatory to enhance reliability analysis and evaluation techniques at early-stage of the system development. In this paper, we present a technique for reliability evaluation of embedded systems at early-stage by taking into account the application behavior and SBU/MBU phenomena. Instead of using the random fault injection, our approach models the architecture behavior under real working conditions. Our results demonstrate the efficiency of the proposed fault injection simulation platform for early-stage reliability studies.

ACS Style

Alexandre Chabot; Ihsen Alouani; Smail Niar; Reda Nouacer. A Comprehensive Fault Injection Strategy for Embedded Systems Reliability Assessment. 2018 International Symposium on Rapid System Prototyping (RSP) 2018, 22 -28.

AMA Style

Alexandre Chabot, Ihsen Alouani, Smail Niar, Reda Nouacer. A Comprehensive Fault Injection Strategy for Embedded Systems Reliability Assessment. 2018 International Symposium on Rapid System Prototyping (RSP). 2018; ():22-28.

Chicago/Turabian Style

Alexandre Chabot; Ihsen Alouani; Smail Niar; Reda Nouacer. 2018. "A Comprehensive Fault Injection Strategy for Embedded Systems Reliability Assessment." 2018 International Symposium on Rapid System Prototyping (RSP) , no. : 22-28.

Journal article
Published: 29 November 2017 in IEEE Embedded Systems Letters
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Approximate computing is a design paradigm considered for a range of applications that can tolerate some loss of accuracy. In fact, the bottleneck in conventional digital design techniques can be eliminated to achieve higher performance and energy efficiency by compromising accuracy. In this paper, a new architecture that engages accuracy as a design parameter is presented where an approximate parallel multiplier using heterogeneous blocks is implemented. Based on design space exploration, we demonstrate that introducing diverse building blocks to implement the multiplier rather than cloning one building block achieves higher precision results. We show experimental results in terms of precision, delay and power dissipation as metrics and compare with 3 previous approximate designs. Our results show that the proposed heterogeneous multiplier achieves more precise outputs than the tested circuits while improving performance and power tradeoffs.

ACS Style

Ihsen Alouani; Hamzeh Ahangari; Ozcan Ozturk; Smail Niar. A Novel Heterogeneous Approximate Multiplier for Low Power and High Performance. IEEE Embedded Systems Letters 2017, 10, 45 -48.

AMA Style

Ihsen Alouani, Hamzeh Ahangari, Ozcan Ozturk, Smail Niar. A Novel Heterogeneous Approximate Multiplier for Low Power and High Performance. IEEE Embedded Systems Letters. 2017; 10 (2):45-48.

Chicago/Turabian Style

Ihsen Alouani; Hamzeh Ahangari; Ozcan Ozturk; Smail Niar. 2017. "A Novel Heterogeneous Approximate Multiplier for Low Power and High Performance." IEEE Embedded Systems Letters 10, no. 2: 45-48.