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FFGAN: An Auto-supervised Approach for Frontal Face Generation Via Disentangled Contrastive Learning

Procedia Computer Science 2024年 246卷 3644-3653页

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

This work proposes a novel generative model, FF-GAN (Frontal Face Generative Adversarial Network), for generating high-quality and diverse frontal faces. FF-GAN utilizes contrastive learning to effectively learn the underlying representation of frontal faces from unlabeled image datasets. This approach allows the model to capture the essential characteristics of frontal faces without the need for explicit pose annotations. We evaluate the performance of FF-GAN using established metrics like FID (Fréchet Inception Distance), IS (Inception Score), and SSIM (Structural Similarity Index Measure). The results demonstrate that FF-GAN achieves superior performance compared to existing methods, generating highly realistic and visually appealing frontal faces with exceptional structural coherence. This research contributes to the field of facial image generation by introducing an effective unsupervised learning approach based on contrastive learning for generating high-quality frontal faces.

关键词： Unsupervised Facial Pose Analysis Contrastive Learning Disentanglement Pose Estimation

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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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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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Person Identification with Voice and Ear-print in Degraded Conditions for Smart Home Access 22

Person Identification with Voice and Ear-print in Degraded C...

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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 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

Since the smartphone is adapted to the ambient intelligence and the smart home systems are remotely accessed through the smartphones, there is a need for a secure authentication system based on some biometrics proprieties that can be taken from a smartphone. The identification of persons through ear and voice print is one of the basic biometric matters. The earlier research in ear recognition have shown that human ear is one of the representative human biometrics with uniqueness and stability. Indeed, the human voice is a perfect source of data for person identification in many applications. In this paper, we propose a fusion between the ear and voice biometrics in degraded conditions in a smart home context at 3 levels (feature, score, and decision). The experiments are conducted on the EVDDC database and a chimeric database (TIMIT and USTB-I). The best results are obtained with the feature level fusion (95.8%) with the KNN classifier. © 2023 IEEE.

关键词： Biometrics

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A Comparative Study of PCA and LDA for Dimensionality Reduction in a 4-Way Classification Framework

Research Square

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Research Square 2024年

作者： Mabrouk, Besma Jazzar, Nessrine Sallemi, Lamia Hamida, Ahmed Ben Advanced Technologies for Medicine and Signals ATMS Department of Electrical and Computer Engineering National Engineers School Sfax University Tunisia Networked Objects Control and Communication Systems National Engineering School Sfax Tunisia

Alzheimer’s disease (AD), recognized as the second-most impactful neurological disorder and currently incurable, stands as the leading cause of dementia. An imperative research focus is efficiently diagnosing the stages of patients, distinguishing early or late Mild Cognitive Impairment and AD from those with normal cognitive function. Advancements in anatomical and diffusion-weighted imaging, coupled with machine learning techniques, have significantly progressed in this predictive domain. However, in real-world trials, datasets often contain numerous features, and the curse of dimensionality can introduce challenges such as increased computational complexity, overfitting, and diminished model interpretability. To address these issues, the present study explores the efficacy of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) as dimensionality reduction techniques. LDA, a supervised technique emphasizing class separability, surpasses PCA, particularly in selecting features that significantly contribute to discriminating between classes. The 3D-LDA features obtained were subsequently assessed across various machine learning algorithms, leading to the establishment of a 4-way classification framework that utilized the K-Nearest Neighbors model. The outcome of this evaluation yielded an impressive accuracy rate of 87% in predicting the four different classes. © 2024, CC BY.

关键词： Nearest neighbor search

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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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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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Hybrid approach for speaker recognition based on formant and pitch extraction

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

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International Conference on Cyberworlds

作者： Sana Boujnah Radhia Ferjaoui Anouar Ben Khalifa National Engineering School of Tunis University of Tunis El Manar Tunisia NOCCS-Laboratory of Networked Objects Control and Communication Systems Eniso Tunisia Research Laboratory of biophysics and Medical technologies(LRBTM) ISTMT University of Tunis El Manar Tunis Tunisia Ecole Nationale d’Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Institut National des Technologies et des Sciences du Kef Université de Jendouba Le Kef Tunisie

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Person Identification with Voice and Ear-print in Degraded Conditions for Smart Home Access

Person Identification with Voice and Ear-print in Degraded C...

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International Conference on Cyberworlds

作者： Sana Boujnah Radhia Ferjaoui Anouar Ben Khalifa National Engineering School of Tunis University of Tunis El Manar Tunisia NOCCS-Laboratory of Networked Objects Control and Communication Systems Eniso Tunisia University of Tunis El Manar Research Laboratory of biophysics and Medical technologies(LRBTM) ISTMT Tunis Tunisia Université de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisie Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie

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