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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Beam Scanning for Integrated Sensing and Communication in IRS-aided mmWave Systems 24

Beam Scanning for Integrated Sensing and Communication in IR...

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24th IEEE International Workshop on signal processing Advances in Wireless Communications, SPAWC 2023

作者： Li, Renwang Shao, Xiaodan Sun, Shu Tao, Meixia Zhang, Rui Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Shanghai Jiao Tong University Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9781665496261

This paper investigates an intelligent reflecting surface (IRS) aided millimeter-wave integrated sensing and communication (ISAC) system. Specifically, based on the passive beam scanning in the downlink, the IRS finds the optimal beam for reflecting the signals from the base station to a communication user. Meanwhile, the IRS estimates the angle of a nearby target based on its echo signal received by the sensing elements mounted on the IRS (i.e., semi-passive IRS). We propose an ISAC protocol for achieving the above objective via simultaneous (beam) training and sensing (STAS). Then, we derive the achievable rate of the communication user and the Cramer-Rao bound (CRB) of the angle estimation for the sensing target in closed-form. The achievable rate and CRB exhibit different performance against the duration of beam scanning. Specifically, the average achievable rate initially rises and subsequently declines, while the CRB monotonically decreases. Consequently, the duration of beam scanning should be carefully selected to balance communication and sensing performance. Simulation results have verified our analytical findings and shown that, thanks to the efficient use of downlink beam scanning signal for simultaneous communication and target sensing, the STAS protocol outperforms the benchmark protocol with orthogonal beam training and sensing. © 2023 IEEE.

关键词： Millimeter waves

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Image Watermarking Technique Using Convolutional Autoencoder

Image Watermarking Technique Using Convolutional Autoencoder

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Advances in Electronics, Control and Communication Systems (ICAECCS), International Conference on

作者： Elchaimae Rebahi Mustapha Hemis Bachir Boudraa Speech Communication and Signal Processing Laboratory Faculty of Electrical Engineering University of science and technology Houari BOUMEDIENE (USTHB) BP 32 El Alia Algiers Algeria

Due to the rapid growth in communication networks and data sharing, protecting copyrights has become increasingly important. digital watermarking is seen as a promising solution to resolve this issue. A new blind digital watermarking based on deep convolutional auto-encoders is presented in this paper. The system facilitates the embedding and extraction of a watermark in an image for its protection. The proposed architecture comprises four modules: an “encoder” that compresses the original image and watermark, an “embedder” that combines the two, a “decoder” that upsamples the combined result to reconstruct the watermarked image, and an “extractor” for watermark retrieval. The experimental results indicate that the watermarked images maintain their visual quality after insertion, and the extracted watermark remains robust against various various attacks such as filtering, rotation, and noise.

关键词： Visualization Image resolution Image coding Filtering Watermarking Control systems Robustness

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Multi-View Speaker Embedding Learning for Enhanced Stability and Discriminability

Multi-View Speaker Embedding Learning for Enhanced Stability...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Liang He Zhihua Fang Zuoer Chen Minqiang Xu Ying Meng Penghao Wang School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China iFly Digital Technology Hefei China

Deep neural network models based on x-vector have become the most popular framework for speaker recognition, and the quality of speaker features (embeddings) is important for open-set tasks such as speaker verification and speaker diarization. Currently, the most popular loss function is based on margin penalty, however, it only considers enlarging the inter-class distance while neglecting to reduce the intra-class feature differences. Therefore, we propose a multi-view learning approach that divides the training process into two views from the speaker embedding level. The classification view focuses on distinguishing the discriminability of different speakers, while the clustering view focuses on shrinking the feature boundaries of the same speaker, making intra-class differences smaller. The combined effect of the two perspectives achieves large inter-class distance and small intra-class distances, resulting in the extraction of more discriminative and stable speaker embeddings. We test the performance of the method on both speaker verification and speaker diarization tasks, and the results demonstrate the effectiveness of our approach.

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New Structure of CCR with an AOANN Threshold

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Journal of Optical Communications 2021年第1期42卷 103-109页

作者： Rabehi, Abdelhalim Djebbari, Ali Hafaifa, Ahmed Souahlia, Abdelkerim Taleb-Ahmed, Abdelmalik Telecommunications and Digital Signal Processing Laboratory Faculty of Electrical Engineering University of Djillali Liabes Sidi Bel Abbes22000 Algeria Applied Automation and Industrial Diagnostics Laboratory Faculty of Science and Technology University of Ziane Achour Djelfa17000 Algeria Faculty of Science and Technology University of Ziane Achour Djelfa17000 Algeria LAMIH Laboratory UMR C.N.R.S 8530 University of Valenciennes and Hainaut-Cambrésis. Mont Houy Valenciennes59313 France

In this paper, artificial neural network-based adaptive optimal threshold estimation for a two-dimensional optical code division multiple access conventional correlation receiver is proposed. A multilayer perceptron neural network with back-propagation learning algorithm is considered. This estimator uses the weight (w) and the length (F) of the code word, the number of active users (Ν) and the signal to noise ratio as inputs to estimate the required optimal threshold. We have evaluated the proposed approach on a data set of 46,200 samples. We have found that it gives accurate results: 0.029 for the root mean square error, 0.37% for the relative root mean square error and 99.984% for the correlation coefficient (R), which reflects the efficiency of the proposed optimal threshold estimator. © 2018 Walter de Gruyter GmbH, Berlin/Boston 2018.

关键词： signal to noise ratio

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Universal Decoding for the Typical Random Code and for the Expurgated Code

Universal Decoding for the Typical Random Code and for the E...

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IEEE International Symposium on Information Theory

作者： Ran Tamir Neri Merhav Signal and Information Processing Laboratory ETH Zurich Switzerland The Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

We provide two results concerning the optimality of the stochastic-mutual information (SMI) decoder, which chooses the estimated message according to a posterior probability mass function, which is proportional to the exponentiated empirical mutual information induced by the channel output sequence and the different codewords. First, we prove that the error exponents of the typical random codes under the optimal maximum likelihood (ML) decoder and the SMI decoder are equal. As a corollary to this result, we also show that the error exponents of the expurgated codes under the ML and the SMI decoders are equal. These results strengthen the well-known result due to Csiszár and Körner, according to which, the ML and the maximum-mutual information (MMI) decoders achieve equal random-coding error exponents, since the error exponents of the typical random code and the expurgated code are strictly higher than the random-coding error exponents, at least at low coding rates. The universal optimality of the SMI decoder, in the random-coding error exponent sense, is easily proven by commuting the expectation over the channel noise and the expectation over the ensemble. This commutation can no longer be carried out, when it comes to typical and expurgated exponents. Therefore, the proof of the universal optimality of the SMI decoder must be completely different and it turns out to be highly non-trivial.

关键词： Maximum likelihood estimation Codes Channel estimation Encoding Mutual information Maximum likelihood decoding

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Robust Optimization-based Neural Architectures Against Adversarial Attacks

Robust Optimization-based Neural Architectures Against Adver...

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IEEE SSD International Multi-Conference on Systems, signals and Devices

作者： Khaoula Ben Ali Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Noureddine Liouane Laboratory of Automatic Signal Image Processing National Engineering School Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent years have seen a rapid development in Machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are image classifications powered by CNNs. Despite the great performance of CNNs in complicated scenarios, they remain sensitive to so-called adversarial attacks, and deliberate perturbations leading them to incorrect predictions. Besides more innocuous consequences, this has serious security implications for critical applications, in-cluding medical diagnostics, where misclassifications might result in disastrous outcomes. This research work discusses adversarial attacks on CNNs and other DNNs in computer vision, studying a full range of the generation and detection methods with details while discussing intrinsic vulnerability and robustness. It also proposes a learning framework that will enhance the robustness and security of DNNs and CNNs against such adversarial perils. The ultimate goal is directed to an improvement in the reliability of such models in absolutely critical scenarios for safe deployment into applications where accuracy is crucial.

关键词： computer vision Accuracy Perturbation methods computer network reliability Machine learning Robustness Security Medical diagnosis Resilience Image classification

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Regression Neural Network for Soil Moisture Estimation

Regression Neural Network for Soil Moisture Estimation

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Blaž Pongrac Dušan Gleich Laboratory of Signal Processing and Remote Control Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

ISBN: (纸本)9781728173344

Over the past few years image categorization using deep learning became very popular, because it can handle large databases and has shown good recognition results. This paper presents the complex valued convolutional network (CV-CNN) for Synthetic Aperture Radar (SAR) soil moisture estimation. The CV-CNN consist in general of a real or complex valued input layer, output layer and one or more hidden layers. Hidden layers represent any combination of convolutional layers, pooling layers, activation functions, and are fully defined within complex valued domain. This paper proposes a deep Convolutional Neural Network (CNN) for soil moisture parameter estimation. A 9 layer convolutional neural network was used, consisting of convolutional, pooling, dropout, fully connected and regression layers. We used 1000 ground measurements for each SAR acquisition using L-band fully polarimetric SAR data. The experimental results were verified using 100 ground points.

关键词： Soil measurements Moisture measurement Soil moisture Neural networks Estimation Moisture Radar polarimetry

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Rate Maximization in Sweeping Robot-Assisted Reconfigurable Intelligent Surface Communication Systems

Rate Maximization in Sweeping Robot-Assisted Reconfigurable ...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Zhongyu Wang Gengxin Zhao Yashuai Cao Jiawei Zhang Zheng Chang Hebei Key Laboratory of Information Transmission and Signal Processing School of Information Science and Engineering Yanshan University Qinhuangdao China Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyäaskylä Jyväskylä Finland

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Reconfigurable intelligent surface (RIS) assisted communication is recognized as a promising technology in academia and industry because it offers a novel and promising solution for implementing both secure and good-quality wireless communication networks. The paper proposes installing an RIS on a sweeping robot to act as the mobile passive relay, which can flexibly deploy the location of RIS and enhance communication capacity. In this paper, the maximization problem of user throughput is formulated, and we jointly optimize transmission power at AP and reflection phase shifts at RIS. It is difficult to deal with the optimization problem. Therefore, we separate the two variables into two subproblems, i.e., the transmit power optimization problem and the reflection phase-shift optimization problem. We can solve these problems by semidefinite relaxation (SDR) and alternating optimization algorithms. The simulation results demonstrate the advantages of the proposed algorithm, which enhances the communication capacity efficiently.

关键词： Wireless communication Industries Service robots Simulation Conferences Reconfigurable intelligent surfaces Throughput Reflection Relays Optimization

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