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Automatic Face Expression Neutralisation and Posture Correction via 3D Contrastive Learning

Procedia Computer Science 2023年 225卷 269-278页

作者： Grina Wiem Douik Ali University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 University of Monastir National Engineering School of Monastir Rue Ibn Jazzar Monastir 5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 Tunisia

The performance of recognition systems can be significantly affected by various factors, with facial expression poses and lighting changes being the main confounding factors. In order to minimize their impact, we propose an intriguing method in this study that enables the generation of high-quality images specifically tailored to the target domain. Our objective is to utilize disentangled representation to effectively model the decomposition of data variations and generate neutral facial expression images with frontal posture and adaptive illumination. To achieve this, we incorporate 3D priors in the adversarial learning during the training process, simulating the generation of an analytical 3D face deformation as well as rendering operations. Additionally, we employ contrastive learning to control the disentanglement of the generated faces while preserving the essential properties of facial features. This technique enables us to learn an embedding space in which similar data samples are represented closely, while distinct samples are kept far apart from each other. Furthermore, we conduct an analysis of the learned latent space and introduce several other significant properties that enhance the reinforcement of factor disentanglement. These properties include an imitation learning algorithm, which facilitates the acquisition of meaningful patterns and characteristics.

关键词： generative adversarial network expression neutralization disentanglement 3D contrastive learning

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Sliding mode control of quadrotor based on differential flatness

Sliding mode control of quadrotor based on differential flat...

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2018 International Conference on control, Automation and Diagnosis, ICCAD 2018

作者： Abadi, Amine Hadj Brahim, Anis Ben Mekki, Hassen Amraoui, Adnen El Ramdani, Nacim Networked Objects Control and Communication Systems Laboratory National Engineering School of Sousse University of Sousse Tunisia University of Sousse National Engineering School of Sousse Tunisia Univ. Orléans INSA CVL PRISME EA 4229 OrléansF45072 France

ISBN: (纸本)9781538654071

In this work, the differential flatness property and the sliding mode controller design are proposed for a quadrotor in order to track a predefined reference trajectory despite the presence of uncertain parameters. Firstly, differential flatness is utilized to resolve the problem for trajectory generation and tracking for the quadrotor. Next, a sliding controller is combined with a flatness design to guarantee the robustness of the tracking strategy. The numerical simulations of quadrotor system are done in order to evaluate the performance of the suggested control scheme. © 2018 IEEE.

关键词： Sliding mode control

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Alzheimer's Disease Detection Based on Features Level Selection for CNN-SVM Combination

Alzheimer's Disease Detection Based on Features Level Select...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

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

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.

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Parallel video processing on FPGA architecture

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International Journal of High Performance systems Architecture 2018年第3期8卷 169-178页

作者： Touil, Lamjed Abdelali, Abdessalem Ben Kechiche, Lilia Chaieb, Chiheb Ouni, Bouraoui Mtibaa, Abdellatif Networked Objects Control and Communications Systems Laboratory ENISo Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Tunisia Laboratory of Electronics and Microelectronics University of Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Tunisia Networked Objects Control and Communications Systems Laboratory ENISo University of Sousse Tunisia

Real-time video applications are becoming widely used in many domains with more demand for high performance. Video processing is intensive and habitually has accompanying real-time or super-real-time requirements. Such us, multiple cameras are used in monitoring and surveillance systems in automatically real-time analyse video to detect unusual events. Due to the strong computational imposed by video algorithms, real-time video treatment is notably amenable to concurrent processing. Classical implementation solutions whether based on general purpose processors or dedicated ones like DSP cannot fulfil wanted performance. In this article, we focus on the applicability of computing reconfigurable architectures to parallel video processing applications. The experiment results show that the proposed hardware-oriented multi-treatment architecture can provide an average frame rate of 45 frames/s at high definition resolution. Statistics show a consumption about 18% of logic resources and 27% of on chip memory which gives the possibility to integrate additional treatments. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Reconfigurable architectures

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Research on Multi-objective Active Power Optimization Simulation of Novel Improved Water Cycle Algorithm

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IAENG International Journal of Applied Mathematics 2021年第1期51卷 1页

作者： Chen, Gonggui Han, Ying Zhang, Zhizhong Zeng, Xianjun Li, Shuaiyong Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Communication Network and Testing Technology Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China

Water cycle algorithm (WCA) is a heuristic algorithm proposed in recent years. To overcome the insufficiency of standard WCA algorithm in solving the non-convex optimal power flow (OPF) problems, the multi-objective novel improved water cycle algorithm (MONIWCA) is proposed in this paper. The evaporation process is improved in WCA by introducing evaporation rate and the normal distribution optimization mechanism is used to mutate the individual position. The modified WCA also adopts a constraint-based strategy to ensure zero constraint violations. In order to obtain a high-quality Pareto optimal solution set (POS) and select the best compromise solution (BCs), a global ranking strategy is proposed. The global ranking strategy includes the novel constraint handling method, the rank index calculation and the BCs on fuzzy satisfaction theory to deal with the complex constraints of the optimization problem. The MONIWCA has been simulated under the constraints of zero violations on IEEE 30, IEEE 57 and IEEE 118 standard test systems, including six dual-objective cases and one tri- objective case. The simulation results are compared with the multi-objective particle swarm optimization (MOPSO). The results show that the improved method can effectively solve the MOOPF problem, not only to obtain a uniform continuous Pareto solution set but also to achieve a better compromise solution. In addition, the two performance indicators of the generational distance (GD) and the spacing (SP) also show that the MONIWCA algorithm has uniform distribution, high convergence and strong stability. © 2021, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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Deep Learning For Intra Frame Coding

Deep Learning For Intra Frame Coding

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Maraoui Amna Werda Imen Sayadi Fatma Ezahra Electronics and Microelectronics Laboratory University of Monastir Monastir Tunisia Electronic and Information Technology Laboratory University of Sfax Sfax Tunisia Networked objects control and communication systems Laboratory University of Sousse Sousse Tunisia

ISBN: (纸本)9781665427159

The evolution of the coding unit module from the High Efficiency Video Coding (HEVC) video standards to the Joint Exploration Model (JEM) extensively enhanced compression performance while severely increases coding complexity caused by the brute force search built on Rate Distortion Optimization (RDO). Effectively, compared to the predecessor HEVC standard that makes use of the quad-tree (QT) block partitioning module, the novel quad-tree binary-tree (QTBT) block partitioning process proposed within the JEM encoder uses additional block sizes and shapes which induces additional flexibility. In this paper, we suggest a deep convolutional neural network (CNN) based method to reduce the block partitioning module complexity for both HEVC and JEM encoders at all intra-configurations. The results show that the CNN approach leads to better optimization performance with the HEVC encoder reaching up to 59%. However, the CNN model is more robust with several JEM versions.

关键词： Deep learning Shape Rate-distortion Encoding Complexity theory Convolutional neural networks Optimization

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Comparison of RNN-Based Models for Sign Language Recognition and Translation

Comparison of RNN-Based Models for Sign Language Recognition...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Marah El Askri Riadh Bchir Hend Basly Fatma Ezahra Sayadi National Engineering School of Sousse (ENISO) Laboratory of Networked Objects Control and Communication Systems (NOCCS) University of Sousse Sousse Tunisia Faculty of Sciences of Monastir University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The recognition of human activities using vision based methods has become a key area of interest in video analytics research. In the past years, several sophisticated deep learning (DL) algorithms have surfaced, designed to recognize complex human actions depicted in video streams. These approaches have exhibited remarkable competence in tasks related to video analytics. This paper explores the field in greater depth by centering its focus on Sign Language (SL) as a unique and complex form of human activity. SL plays is pivotal in facilitating communication for people who are deaf or hard of hearing, enabling them to interact and engage. The main idea is to recognize Arabic Sign Language especially Tunisian accent, and subsequently translate it into spoken language utilizing artificial intelligence (AI) methods. The goal of this work is to facilitate communication between individuals who are deaf and those who are not. So, this paper presents the comparative results of four deep-learning models: Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Reccurent Unit (GRU), and Bidirectional Gated Reccurent Unit (Bi-GRU), aiming to improve the recognition accuracy of dynamic signs. The results indicate that the BiLSTM outperforms other variants, showcasing an accuracy of 98%.

关键词： Sign language Accuracy Recurrent neural networks Visual analytics Logic gates Streaming media Artificial intelligence Information technology Long short term memory Streams

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CNL-ResUnet: A novel deep learning architecture for stroke image segmentation

CNL-ResUnet: A novel deep learning architecture for stroke i...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Nesrine Jazzar Besma Mabrouk Ali Douik Networked Objects Control and Communication Systems National Engineering School of Sousse University of Sousse Tunisia National Engineering School of Sfax University of Sfax Tunisia Department of Electrical and Computer Engineering Advanced Technologies for Medicine and Signals ATMS National Engineers School Sfax University Tunisia Networked Objects Control and Communication Systems National Engineering School Sousse Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Segmentation of stroke lesions holds significant importance in enabling timely diagnosis and subsequent treatment by medical professionals. Automating this process presents technical challenges given the diverse appearance and evolving nature of lesions, discrepancies among medical experts, limited dataset availability, and the necessity for multiple MRI modalities for comprehensive imaging. This research presents an innovative method called CNL-ResUNet, specifically designed for 2D stroke image segmentation. CNL-ResUNet ingeniously incorporates residual blocks into its architecture, capitalizing on their innate capacity to facilitate seamless information flow across layers, thereby empowering the network to efficiently distill complex patterns from sparse datasets. It integrates customized skip connections to reduce semantic disparities between the encoder and decoder layers. Additionally, the architecture incorporates an innovative Classifier and Localizer (CNL) module, providing supplementary classification and localization insights with enhanced accuracy. By combining these insights with the segmentation results, CNL-ResUNet adeptly reduces the occurrence of false positives and false negatives. To evaluate the model’s performance, experiments were conducted utilizing the SISS dataset from the Ischemic Stroke Lesion Segmentation Challenge 2015. The outcomes from these experiments highlight the superiority of our approach, revealing significant qualitative and quantitative improvements across standard evaluation metrics like the Dice coefficient and Accuracy. Our proposed network demonstrates potential for advancing automated tools in stroke segmentation.

关键词： Deep learning Training Image segmentation Accuracy Semantics Stroke (medical condition) Robustness

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Sliding Mode control of Quadrotor based on Differential Flatness

Sliding Mode Control of Quadrotor based on Differential Flat...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Amine Abadi Anis Ben Hadj Brahim Hassen Mekki Adnen El Amraoui Nacim Ramdani National Engineering School Of Sousse Networked Objects Control and Communication Systems Laboratory Tunisia National Engineering School Of Sousse University of Sousse Univ. Orléans Orléans France

ISBN: (纸本)9781538654088

关键词： Trajectory Mathematical model Sliding mode control Robustness Drones Atmospheric modeling

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Hybrid approach for speaker recognition based on formant and pitch extraction 22

Hybrid approach for speaker recognition based on formant and...

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22nd International Conference on Cyberworlds, CW 2023

作者： Boujnah, Sana Ferjaoui, Radhia Khalifa, Anouar Ben University of Tunis El Manar Tunisia National Engineering School of Tunis Tunisia NOCCS-Laboratory of Networked Objects Control and Communication Systems Eniso Tunisia Istmt Tunis Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9798350315653

Human voice is an ideal data source for identifying people in many applications. Because of the increasing need for security in different public places, voice biometrics may be a good solution, as we can easily take voice records. This paper provides a brief overview of the approaches utilized in recognizing speakers, and then presents a novel approach for recognizing speakers in degraded smart-home conditions. The suggested approach includes a pre-processing phase, a feature extraction phase, and a classification phase, where the feature extraction phase consists of formant extraction to get the spectrum energy maxima of speech audio, dynamic time warping (DTW)to find an optimal alignment between two provided temporal sequences under definite restrictions, and refinement process to improve the results of the DTW system output. The experiments are carried out on a database containing 1,248 samples in order to validate the suggested approach. The latter has good results as regards the state of the art with 94.5% accuracy. © 2023 IEEE.

关键词： Feature extraction

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