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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Zayene, Mohamed Amine Basly, Hend Sayadi, Fatma Ezahra National School of Engineering School of Sousse Lab. Networked Objects Control and Communication Systems Sousse Tunisia

ISBN: (纸本)9798350325539

Neurodegenerative disease especially dementia are reported as disease that leads to death, Alzheimer's Disease (AD) is kind dementia that cause progressive and irreversible brain disorder loss which leads to death. AD shows no symptoms in its early stages which makes diagnosing it its beginning a challenge and helpful for doctors as they can slow down its progress in its early stages. Computer-aided approaches such as machine learning which come up with several techniques to detect AD by extracting features from the given image data and use them to build a classifier. Recently, a subcategory of machine learning called deep learning has widely been employed to enhance the medical diagnosis by attempting notable performance. In fact, these approaches avoid the tricky manual feature extraction using Convolutional Neural Network (CNN) considered as a reference in the field of computer vision. This paper proposes a combination of machine-deep learning technics for early diagnosis of AD from positron emission tomography (PET). We first train our CNN on PET images to extract the most relevant features, then we select the most appropriate CNN's level from where the features will be extracted, which will be feed in a second step as input to a Support Vector Machine based classifier (SVM). The proposed approach achieves notable results that exceeded the performance obtained by various existing approaches. © 2023 IEEE.

关键词： Positron emission tomography

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Semantic recognition: Unified framework for joint object detection and semantic segmentation 17

Semantic recognition: Unified framework for joint object det...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Abdi, Lotfi Meddeb, Aref National Engineering School of Tunis University of Tunis El Manar Networked Objects Control and Communication Systems Laboratory Tunisia National Engineering School of Sousse University of Sousse Networked Objects Control and Communication Systems Laboratory Tunisia

ISBN: (纸本)9781450344869

Understanding contents of an image, or scene labeling, is an important yet very challenging problem in artificial intelligence and computer vision to improve road safety. Semantic labeling and object detection in road scenes are strongly correlated tasks. Motivated by the complementary effect of the two tasks, we presented a novel framework to address the scene understanding problem. In this paper we propose a new framework for semantic labeling and object detection problem which is able to combine ideas from deep Convolutional Neural Network (CNN) for object detection and fully-connected Conditional Random Field (CRF) for segmenting and labeling. Specifically, we develop a new framework uses global image features to predict detection which drastically reduces its errors from background detections and a pairwise CRF is used as a post-processing step to enforce spatial consistency in the structured prediction. By combining the consistency between final detection results of CNN and CRF based graphical models, our unified framework can effectively leverage the advantages of leading techniques i.e., CRF and CNN for these two tasks. Exten- sive experiments on the PASCAL VOC 2007/2012 data sets demonstrate the effectiveness of our framework for Scene Understanding tasks. Copyright 2017 ACM.

关键词： Semantic Segmentation

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Deep learning traffic sign detection, recognition and augmentation 17

Deep learning traffic sign detection, recognition and augmen...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Abdi, Lotfi Meddeb, Aref National Engineering School of Tunis University of Tunis Networked Objects Control and Communication Systems Laboratory El Manar Tunisia National Engineering School of Sousse University of Sousse Networked Objects Control and Communication Systems Laboratory Tunisia

ISBN: (纸本)9781450344869

Driving is a complex, continuous, and multitask process that involves driver's cognition, perception, and motor movements. The way road traffic signs and vehicle information is displayed impacts strongly driver's attention with increased mental workload leading to safety concerns. Drivers must keep their eyes on the road, but can always use some assistance in maintaining their awareness and directing their attention to potential emerging hazards. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. In-vehicle contextual Augmented Reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we present a new real-time approach for fast and accurate framework for traffic sign recognition, based on Cascade Deep learning and AR, which superimposes augmented virtual objects onto a real scene under all types of driving situations, including unfavorable weather conditions. Experiments results show that, by combining the Haar Cascade and deep convolutional neural networks show that the joint learning greatly enhances the capability of detection and still retains its realtime performance.

关键词： Augmented reality

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Driver information system: A combination of augmented reality and deep learning 17

Driver information system: A combination of augmented realit...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

ISBN: (纸本)9781450344869

Improving traffic safety is one of the important goals of Intelligent Transportation systems (ITS). In vehicle-based safety systems, it is more desirable to prevent an accident than to reduce severity of injuries. One of the solutions to prevent accidents is to provide information on the surrounding environment of the driver. Augmented Reality Head-Up Display (AR-HUD) can facilitate a new form of dialogue between the vehicle and the driver;and enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we propose a fast deeplearning-based object detection approaches for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. A single Convolutional Neural Network (CNN) predicts region of interest and class probabilities directly from full images in one evaluation. We also investigated potential costs and benefits of using dynamic conformal AR cues in improving driving safety. Copyright 2017 ACM.

关键词： Augmented reality

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Synchronous system design for a high speed treatment and optimized dynamic power

Synchronous system design for a high speed treatment and opt...

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2017 International Conference on Engineering and Technology, ICET 2017

作者： Ben Haj Hassine, Siwar Ouni, Bouraoui Networked Objects Control and Communication Systems Lab National Engineering School of Sousse Sousse University BP 264 Sousse Sousse Tunisia

ISBN: (纸本)9781538619490

In the design of current microelectronic systems, low power is extremely needed since it is the key concern for embedded applications. Besides, a faster treatment is definitely required in a word overshadowed by digital technologies. For that, we propose a new design of synchronous circuit based on a detector avoider block that is intended to optimize two important parameters: power and response time, simultaneously. Our proposed design has been implemented and compared to existing ones. The design results have shown a significant gain in terms of dynamic power as well as in terms of response time compared to existing designs, without negatively influencing other constraints. To further improve the efficiency of our method, the gain has been proved by the physical level. © 2017 IEEE.

关键词： Embedded systems

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Experimental validation of flatness control for path planning and tracking for a quadrotor

Experimental validation of flatness control for path plannin...

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2017 International Conference on Engineering and MIS, ICEMIS 2017

作者： Chelly, Nizar Mekki, Hassen Ibrahim, Anis Belhadj Networked Objects Control Communication Systems Lab National School of Engineering Sousse Sousse University Tunisia National School of Engineering Sousse Sousse University Tunisia

ISBN: (纸本)9781509067787

In this paper, we investigates the problem of planning and tracking trajectory for a non linear system which is the quadrotor using the flatness theory. The proposed flatness algorithm is real time implemented. The Experimental results using this solution validate the design control along a pre defined trajectory as well as the response of the AR Drone under disturbances is evaluated. © 2017 IEEE.

关键词： Nonlinear systems

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An FPGA-SoC based Hardware Acceleration of Convolutional Neural Networks 9

An FPGA-SoC based Hardware Acceleration of Convolutional Neu...

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9th IEEE International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Bouaafia, Soulef Messaoud, Seifeddine Khemiri, Randa Sayadi, Fatma Ezahra Monastir University Lab. Electronics and Microelectronics Faculty of Sciences of Monastir Monastir Tunisia Sousse University Lab. Networked Objects Control and Communication Systems National Engineering School of Sousse Sousse Tunisia

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Deep learning has evolved as a discipline that has demonstrated its capacity and usefulness in tackling complicated learning issues as a result of recent improvements in digital technology and the availability of authentic data. Convolutional neural networks (CNNs) in particular have demonstrated their usefulness in image processing and computer vision applications. They do, however, need heavy CPU operations and memory bandwidth, which prevents general-purpose CPUs from attaining desirable performance levels. To boost CNN throughput, hardware accelerators such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been deployed. FPGAs, in particular, have lately been used to accelerate the development of deep learning networks due to their ability to optimize parallelism and power efficiency. Based on hardware-software architecture, this research provides a CNN acceleration model for video compression applications. Vivado High Level Synthesis is used to accelerate the CNN model in order to develop Intellectual Property (IP) cores. © 2022 IEEE.

关键词： Convolutional neural networks

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Analysis and optimisation of 8 × 1 double 'U' slotted patch array for future 5G communications

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International Journal of systems, control and communications 2020年第3期11卷 305-319页

作者： Zaidi, Abir Baghdad, Abdennaceur Awan, Wahaj Abbas Faleh, Souhir Ballouk, Abdelhakim Badri, Abdelmajid Mohammedia Laboratory EEA and TI Hassan II University Casablanca Morocco Department of Electrical and Computer Engineering COMSTAS University Islamabad Sahiwal Pakistan Networked Objects Control and Communication Systems Laboratory National Engineers School of Sousse University of Sousse Sousse Tunisia

In this manuscript, an 8 × 1 rectangular 'U' slotted patch antenna array designed for future 5G communications is presented. The proposed antenna is designed using the 3D EM CSTv18 microwave studio based on the finite difference time domain (FDTD) method, and compared using HFSSv19 EM (high frequency structure simulator) based on the finite element (FEM) method. Three scenarios are considered in this work, the first one is the analysis of antenna performance of single element, regular antenna array and slotted antenna array in order to evince the performance of the proposed antenna for future 5G communications. The designed array antenna presents good results in term of return loss, efficiency, VSWR, input impedance and gain. Moreover, a comparison between the proposed work and other related works is presented. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Millimeter waves

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Face Expression Neutralisation Using Improved Variational Autoencoder 6

Face Expression Neutralisation Using Improved Variational Au...

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6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Wiem, Grina Ali, Douik University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse National Engineering School of Monastir Rue Ibn Jazzar Monastir5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Pôle Technologique de Sousse Route de Ceinture Sahloul Sousse4054 Tunisia

ISBN: (纸本)9781665451161

There is a high demand for realistic facial expressions in modern computer graphics and multimedia research. Unfortunately, synthesizing face expressions takes time, effort, and hard labour since high naturalism of face expressions is required. Despite recent advances in synthesizing realistic facial images, current generative models conflict to catch more involved image types, potentially due to their latent space simplified architecture. Furthermore, the union of a Generative Adversarial Network (GAN) with a Variational Autoencoder (VAE) by using usually the Adam optimizer has recently been studied and investigated in cutting-edge research. Hence, at tiem of learning the generator takes a long time and then overfit for a particular time instance. So, in this work, we will suggest integrating the VAE to GAN's architecture by using the Ranger optimizer into an unsupervised generative model that at once learns to encode, generate and compare dataset samples to bring out various real neutral faces in a reduced and optimal time. Next, we proposed the probability distributions called Kullback-Leibler Divergence (KLD) as an objective weighting scheme that helps us to measure just how much information we lose when we choose an approximation during training. Also, the Mean Square Error (MSE), the most commonly used loss function for regression is used as a metric of evaluation for the experiment tests. © 2022 IEEE.

关键词： Mean square error

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Video saliency using supervoxels 10th

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10th KES International Conference on Intelligent Interactive Multimedia systems and Services, IIMSS 2017

作者： Kalboussi, Rahma Abdellaoui, Mehrez Douik, Ali Networked Objects Control and Communication Systems Laboratory National Engineering School of Sousse Pôle technologique de Sousse Route de Ceinture Sahloul Sousse4054 Tunisia

ISBN: (纸本)9783319594798

Physiology and neural systems researchers revealed that the visual system is attracted by some parts of an image more than others. Different computational models were developed to simulate the visual system. In this paper we propose a video saliency model that helps to predict and detect the regions of interest in each video frame. We use a supervoxel segmentation as an indicator of dynamic objects. Based on the observation that dynamic objects attract attention when an observer watches a video sequence, supervoxel segmentation provides a first estimation for what belongs to foreground and background. Then, a saliency score is attributed to each supervoxel according to its motion distinctiveness. Experiments over two benchmark datasets, using several evaluation metrics have shown that our proposed method outperforms five saliency detection methods. © Springer International Publishing AG 2018.

关键词： Computers

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