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检索条件"机构=Image and Signal Processing Laboratory"

共 941 条记录，以下是21-30 订阅

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Robust Adaptive Sliding Mode Control of Upper-Limb Rehabilitation Exoskeleton System with Uncertainties

Robust Adaptive Sliding Mode Control of Upper-Limb Rehabilit...

引用

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Mouna Dali Hassen Raghda Jouirou Nasreddine Bouguila Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Uncertain robotic manipulator systems, known for their high nonlinearities, unmodeled dynamics, and uncertainties, are the subject of this paper. Based on the terminal sliding mode control algorithm and its nonsingular design method, a new robust control scheme is introduced to ensure fast and finite time convergence to the desired state. The upper bounds of unknown system uncertainties are estimated using adaptive control. The stability of the proposed control strategy is confirmed by the Lyapunov theory, and its effectiveness is tested on a two-degrees-of-freedom robotic manipulator. Robustness and rapid convergence, as well as reducing the controller's high-frequency commutation effect, are proved.

关键词： Adaptation models Uncertainty Upper bound Tracking Exoskeletons Stability analysis Robustness

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Development of an Intelligent System for Detection of Chronic Stress from Biological signal processing 9th

Development of an Intelligent System for Detection of Chroni...

引用

9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022

作者： Junqueira, Luis Pina, Marta Biomedical Engineering Laboratory of Signal Processing and Medical Image SP São Paulo Brazil Federal Institute of Education Science and Technology IFSP SP São Paulo Brazil

ISBN: (纸本)9783031494031

Our objective in this work was to develop an intelligent system able to perform an automated detection of chronic stress, based on biological signals processing and features extraction, confronted with Hans Selye clinical model of stress phase’s assessment. We recorded biological signals of blood pressure, skin surface temperature, galvanic skin resistance and heart rate of 120 health adult volunteers. Also the heart rate variability parameters were extracted for the time-domain and frequency-domain analysis. A Multi-Layer Perceptron Artificial Neural Network with a supervised learning approach was applied to generate the mathematical model. The system classification produced a precision index of 89% for identifying the stressed class, when applying the skin temperature and the heart rate variability parameters as input of the neural network, providing a satisfactory initial performance in the discrimination of stressed individuals. A worth training set with more examples mighty potentially increases the precision for identifying the stressed individuals. Despite study limitations, we consider that the use of intelligent systems to classify biological signals and identify the long term stress presence in human organism could contribute to a more objective analysis of such physiologic characteristics associated to the chronic stress and its implications in human health and performance. Future works should include different settle of physiological parameters and different machine learning techniques for analysis and classification of signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent systems

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Efficient RNN Models for IOT Intrusion Detection System

Efficient RNN Models for IOT Intrusion Detection System

引用

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Rahma Jablaoui Noureddine Liouane Laboratory of Automatic Signal and Image Processing University of Monastir Monastir Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Due to the growing number of network devices, traffics and services, designing robust Intrusion Detection System (IDS) become a crucial need in the face of complex and various network attacks as a protective measure from hackers and cybercriminals. However, the traditional Machine Learning (ML) approach shows success in many research topics but with the increase in the amount of data and the diversification of network threats methods, it seems to lack reliability and accuracy. Therefore, considering a large amount of real-world cyber traffic, Deep Learning (DL) may be able to extract big data features more effectively. In this paper, we suggest an intrusion detection system for the Internet of Things (IoT) network based on Deep Learning to recognize various assault types for both binary and multiclass classification using two variants of Recurrent Neural Network (RNN) models long short-term memory (LSTM) and Bidirectional LSTM (BiLSTM). We have experimented the models with CSE-CIC-IDS2018, which is the newest comprehensive network traffic dataset. Accuracy, precision, recall, and F1 score are a few performance criteria where the suggested approach clearly excels. After comparison, we can infer that Bi-directional LSTM outperforms LSTM and other existing efforts in the literature. The accuracy of the experimental results was high, coming in at 98.62%.

关键词： Deep learning Accuracy Recurrent neural networks Intrusion detection Bidirectional control Telecommunication traffic Feature extraction

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An effective deep CNN-LSTM based intrusion detection system for network security

An effective deep CNN-LSTM based intrusion detection system ...

引用

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Rahma Jablaoui Noureddine Liouane Laboratory of Automatic Signal and Image Processing University of Monastir Monastir Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Deep learning (DL) models have become prevalent and consistently exhibit outstanding performance across diverse domains, particularly in information security. As a subset of machine learning (ML), DL has proven adept at handling complex, unstructured, and large-scale data, making it well-suited to tackle the evolving challenges of cybersecurity. By leveraging DL techniques, we can develop robust intrusion detection systems (IDS) to counter a wide range of network attacks, serving as a crucial defense against hackers and cybercriminals. The integration of DL into IDS holds immense potential for significantly enhancing network security and mitigating cyber risks in various real-world scenarios. This work proposes an IDS architecture that integrates Convolutional Neural Network (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal feature extraction and sequential analysis to predict and classify malicious cyberattacks in IoT network traffic. Our experiments with the CSE-CIC-IDS2018 dataset, the latest comprehensive network traffic dataset, demonstrate the superiority of our approach in terms of accuracy, precision, recall, and F1 score. Our model achieves an unprecedented accuracy of over 99% in both binary and multiclass classification, surpassing existing efforts in the literature.

关键词： Deep learning Sequential analysis Accuracy Computer hacking Intrusion detection Telecommunication traffic Network security

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Optimal sliding mode control for a pendulum system

Optimal sliding mode control for a pendulum system

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2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022

作者： Raghda, Jouirou Anouar, Benamor Wafa, Boukadida Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia

ISBN: (数字)9781665497947

ISBN: (纸本)9781665497947

This paper is dedicated to study the performance of the optimal control for linear systems by sliding mode of the first order with the integration of a Linear Quadratic Regulator LQR, applied to the pendulum. We are interested in the design of an optimal and robust control law which is, derived, to satisfy the precision, the convergence, the speed of and the asymptotic stabilization of the system despite the perturbations and the uncertainties born and a multi-optimization. The goal is to minimize the phenomenon of chattering. Simulation, results have been presented to confirm the usefulness and the effectiveness of the proposed control strategy. © 2022 IEEE.

关键词： Pendulums

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A SCIL-based Transformer for PolSAR image Classification

A SCIL-based Transformer for PolSAR Image Classification

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IET International Radar Conference 2023, IRC 2023

作者： Xin, Xinyue Li, Ming Wu, Yan Zhang, Peng Xu, Dazhi Zheng, Jia The National Laboratory of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an China The Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9781839539954

Transformer has been applied for polarimetric synthetic aperture radar (PolSAR) image processing due to its ability to construct long-range dependency. However, Transformer lacks the learning of local spatial information within samples and correlation information among samples, which limits its performance in practical tasks. To address these issues, this paper proposes a novel Transformer structure based on spatial information learning and correlation information learning (SCIL-based Transformer) for PolSAR image classification. Firstly, a global-local spatial information learning (GL-SIL) module is constructed to fully extract the global-local spatial information within samples. Secondly, a correlation information learning (CIL) module is designed to achieve the learning of correlation information among samples by modeling the dependency across samples. Compared to the self-attention network in Transformer, the above two modules not only add the learning of local spatial information within samples and the correlation information among samples, but also greatly reduce the network complexity. Experiments on two real PolSAR datasets have shown that the proposed SCIL-based Transformer has better performance than other state-of-the-art algorithms. © The Institution of Engineering & Technology 2023.

关键词： Contrastive Learning

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Performance Analysis of Improved Sub-band Sparse Adaptive Algorithm for Acoustic Channel Identification

Performance Analysis of Improved Sub-band Sparse Adaptive Al...

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2024 International Conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Alilouche, Ali Bendoumia, Redha Guessoum, Abderrezak University of Blida 1 Signal Processing and Image Laboratory BP N° 270 road of Soumaa Blida Algeria University of Blida 1 Detection Information and Communications Laboratory BP N° 270 road of Soumaa Blida Algeria

ISBN: (纸本)9798350353754

In this paper, we present the performance analysis of new Sub-band Improved Proportionate NLMS algorithm to improve the convergence rate of dispersive acoustical channel identification. Although the existing proportionate algorithms lately implemented had a good convergence speed in sparse systems, however they suffer from slow convergence speed in case of dispersive channels. This proposed algorithm based on Improved Proportionate NLMS incorporated into sub-band adaptive filtering structure denoted by S-IPNLMS shows good results in the latter case. In this approach, sub-band adaptive filtering is utilized to address challenges encountered in long adaptive filters, such as their slow convergence rate. Simulation results show that the sub-band IPNLMS performs better than the sub-band NLMS, PNLMS when the acoustical impulse response is dispersive. © 2024 IEEE.

关键词： Adaptive filtering

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Fast Convergence and Low Complexity Algorithm for Multichannel Adaptive Filtering

Fast Convergence and Low Complexity Algorithm for Multichann...

引用

Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed Zerouali Mohamed Djendi Signal Processing and Image Laboratory (LATSI) University of BLIDA 1 Blida Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

In this research, we propose a new multi-channel adaptive filtering method with low complexity and optimized convergence rate. The basic strategy behind the proposed algorithm is to generalize the decorrelation technique used in FNLMS algorithm to the multi-channel case by estimating the prediction parameters for each channel to decorrelate the multi-channel input signals. In simulation we confirm the high performances of the proposed algorithm with comparison of the two dimensions multi-channel Affine Projection (multi-channel APA) and the multi-channel Normalized Least Mean Square (multi-channel NLMS) algorithms.

关键词： Adaptive systems Echo cancellers Filtering Filtering algorithms Prediction algorithms Decorrelation Complexity theory Telecommunications Intelligent systems Convergence

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Spoken Digit Recognition Using Convolutional Neural Network

Spoken Digit Recognition Using Convolutional Neural Network

引用

2022 Applied Informatics International Conference, AiIC 2022

作者： Safie, S.I. Universiti Kuala Lumpur Malaysian Institute of Industrial Technology Signal and Image Processing Laboratory Johor Bahru Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

This paper presents a spoken digit recognition study based on features extracted from Bark spectrogram and classifies it using convolutional neural network (CNN). In this work, training and test databases of the spoken digits were developed from TI20 corpus. 6,506 spoken digits from 16 individuals have been used in the study. A 198 x 50 feature map has been extracted from each spoken digit to be fed as input to the CNN. A 5-layer convolutional filters have been added as layers in the CNN architecture. Adam optimization algorithm is then used to train the CNN. A confusion matrix resulting from classification on the test database has been presented in this paper. It is shown in this paper that the proposed CNN architecture performing on the test database achieved an average accuracy of over 99% for the known spoken digits and wrongly classify as digit up to 3.4% for the unknown spoken words. © 2022 IEEE.

关键词： Speech recognition

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Automatic Pavement Crack Detection Based on YOLOv5-AH 12

Automatic Pavement Crack Detection Based on YOLOv5-AH

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Dong, Zhaohui Zhu, Guijie Fan, Zhun Liu, Jiacheng Li, Huanlin Cai, Yuwei Huang, Huaxing Shi, Ze Ning, Weibo Wang, Liu Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou515063 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Object detection on pavement cracks plays a significant role in the overall condition assessment of the pavement. However, the automatic crack detection remains a tough work due to the complex background and the scale change. In order to solve the related issues, a deep learning model named YOLOv5-AH is proposed on the basis of the YOLOv5. We integrate convolutional block attention module (CBAM) to extract attention area, which can emphasize the crack features and suppress the background information. Moreover, we optimize the network architecture by adding one more prediction head to detect different-scale cracks. To further improved our proposed YOLOv5-AH, a series of advanced strategies are integrated into the model training including data augmentation. A pavement crack dataset in different enviroments is built by a mobile robot. The precision and recall of our crack detection method reach 0.924 and 0.988 respectively. The prediction speed of the model can achieve real-time effect (72FPS). © 2022 IEEE.

关键词： Crack detection

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