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Residual multimodal Transformer for expression-EEG fusion continuous emotion recognition

CAAI Transactions on Intelligence Technology 2024年第5期9卷 1290-1304页

作者： Xiaofang Jin Jieyu Xiao Libiao Jin Xinruo Zhang College of Information and Communication Engineering Communication University of ChinaBeijingChina School of Computer Science and Electronic Engineering University of EssexColchesterUK

Continuous emotion recognition is to predict emotion states through affective information and more focus on the continuous variation of emotion. Fusion of electroencephalography (EEG) and facial expressions videos has been used in this field, while there are with some limitations in current researches, such as hand-engineered features, simple approaches to integration. Hence, a new continuous emotion recognition model is proposed based on the fusion of EEG and facial expressions videos named residual multimodal Transformer (RMMT). Firstly, the Resnet50 and temporal convolutional network (TCN) are utilised to extract spatiotemporal features from videos, and the TCN is also applied to process the computed EEG frequency power to acquire spatiotemporal features of EEG. Then, a multimodal Transformer is used to fuse the spatiotemporal features from the two modalities. Furthermore, a residual connection is introduced to fuse shallow features with deep features which is verified to be effective for continuous emotion recognition through experiments. Inspired by knowledge distillation, the authors incorporate feature-level loss into the loss function to further enhance the network performance. Experimental results show that the RMMT reaches a superior performance over other methods for the MAHNOB-HCI dataset. Ablation studies on the residual connection and loss function in the RMMT demonstrate that both of them is functional.

关键词： facial expression recognition human-machine interaction information fusion physiology regression analysis

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Adversarial attacks and defenses for digital communication signals identification

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Digital communications and Networks 2024年第3期10卷 756-764页

作者： Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Information and Communication Engineering Harbin Engineering UniversityHarbin150000China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849USA

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关键词： Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators

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Reflections on strategies for psychological health education for college students based on data mining

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International Journal of Business Intelligence and Data Mining 2024年第3-4期25卷 394-408页

作者： Meng, Die Ma, Beibei Li, Shiying College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450001 China

In order to improve the mental health level of college students, a data mining based mental health education strategy for college students is proposed. Firstly, analyse the characteristics of data mining and its potential value in mental health education. Secondly, after denoising the mental health data of college students using wavelet transform, data mining methods are used to identify the psychological crisis status of college students. Finally, based on the psychological crisis status of college students, measures for mental health education are proposed from the following aspects: building a psychological counselling platform, launching psychological health promotion activities, establishing a psychological support network, strengthening academic guidance and stress management. The example analysis results show that after the application of the strategy in this article, the psychological health scores of college students have been effectively improved, with an average score of 93.5 points. © 2024 Inderscience Enterprises Ltd.

关键词： Electronic health record

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An Energy Distribution Correlation Judgment Method for Interrupted Sampling Repeater Jamming Suppression

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Progress In Electromagnetics Research B 2024年 105卷 59-78页

作者： Li, Ji Su, Fan Wang, Wei Yan, Rui Li, Jialiang College of Computer and Communication Engineering Changsha University of Science & Technology Changsha410000 China

Interrupted Sampling Repeater Jamming (ISRJ) can produce several false targets through intermittent sampling and forwarding of the intercepted signals. The paper proposes an interference identification and suppression method based on Short-Time Fourier Transform-Energy Distribution Correlation Judgment (STFT-EDCJ) to lessen the impact of the false targets mixed in echo pulses. Firstly, the method obtains the energy distribution of echoes in the time-frequency domain employing the short-time Fourier transform, extracts the time slice of higher energy targets through energy peak detection, and then calculates the Pearson correlation coefficient (PCC) of the energy distribution in the frequency domain of each target time slice to construct the Target PCC Datasets (TPCCD). Secondly, it distinguishes between the real target and false targets after echo pulse pressure by the range and specificity of TPCCD. Finally, it uses mapping the time domain position of the false targets to suppress interference. The abundant simulation results verify the proposed method’s effectiveness, and the Monte Carlo simulation demonstrates the method’s effectiveness under ISRJ models. © (2023), (Electromagnetics Academy). All Rights Reserved.

关键词： Monte Carlo methods

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A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images

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International Journal of Intelligent Networks 2024年第1期5卷 10-18页

作者： George, Neetha Shine, Linu N, Ambily Abraham, Bejoy Ramachandran, Sivakumar Department of Electronics and Communication Engineering Rajiv Gandhi Institute of Technology Kottayam Kerala India Department of Electronics and Communication Engineering College of Engineering Trivandrum Kerala India Department of Electronics and Communication Engineering Government Engineering College Idukki Kerala India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Thiruvananthapuram India Department of Electronics and Communication Engineering Government Engineering College Wayanad Kerala India

The advancements in medical imaging techniques have brought exponential increase in the quantity and complexity of data which often require human expertise for interpretation and decision making. However, in real-world clinical settings, there is often a shortage of experts available for timely diagnosis and triage. An automated diagnostic technique aids clinicians in the precise diagnosis and effective management of diseases. In this article, a two-stage classification model using convolutional neural network (CNN) is proposed for the classification and severity analysis of retinal and choroidal diseases in optical coherence tomography (OCT) images. The proposed model can identify abnormal conditions such as Pachychoroid disorders, macular edema, and Drusen. The images are initially classified into four categories-healthy, Drusen, Pachychoroid and macular edema classes. The severity of Pachychoroid conditions, including central serous chorioretinopathy, polypoid choroidal vasculopathy, and choroidal neovascularization, is subsequently determined through a second-level classification. A modified version of the VGG16 architecture is used for the initial classification. A fine-tuned CNN with the same architecture is then employed to determine the severity of Pachychoroid diseases. Further, we make use of the UNet architecture for assessing the severity of macular edema. The proposed hybrid approach for classification and analysis achieved promising results, demonstrating consistency on par with human experts in diagnosing and grading ocular diseases. © 2024

关键词： Optical tomography

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A novel graph oversampling framework for node classification in class-imbalanced graphs

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Science China(Information Sciences) 2024年第6期67卷 214-229页

作者： Riting XIA Chunxu ZHANG Yan ZHANG Xueyan LIU Bo YANG Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University College of Artificial Intelligence Jilin University College of Computer Science and Technology Jilin University College of Communication Engineering Jilin University

Graph neural network(GNN) is a promising method to analyze graphs. Most existing GNNs adopt the class-balanced assumption, which cannot deal with class-imbalanced graphs well. The oversampling technique is effective in alleviating class-imbalanced problems. However, most graph oversampling methods generate synthetic minority nodes and their edges after applying GNNs. They ignore the problem that the representations of the original and synthetic minority nodes are dominated by majority nodes caused by aggregating neighbor information through GNN before oversampling. In this paper, we propose a novel graph oversampling framework, termed distribution alignment-based oversampling for node classification in classimbalanced graphs(named Graph-DAO). Our framework generates synthetic minority nodes before GNN to avoid the dominance of majority nodes caused by message passing in GNNs. Additionally, we introduce a distribution alignment method based on the sum-product network to learn more information about minority nodes. To our best knowledge, it is the first to use the sum-product network to solve the class-imbalanced problem in node classification. A large number of experiments on four real datasets show that our method achieves the optimal results on the node classification task for class-imbalanced graphs.

关键词： graph neural network class-imbalanced graphs sum-product network oversampling node classification

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Enhancing epigraphy: a deep learning approach to recognize and analyze Tamil ancient inscriptions

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Neural Computing and Applications 2024年第31期36卷 19839-19861页

作者： Bhuvaneswari, S. Kathiravan, K. Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India

This research paper presents a novel algorithmic approach for character recognition and contextual analysis of temple inscriptions, specifically focusing on Tamil ancient script. The methodology combines advanced preprocessing techniques, deep learning models, and contextual analysis to address the challenges posed by noisy images, script variations, and historical context understanding. We compiled a dataset of 100 high-resolution images of temple inscriptions from various regions and periods. The preprocessing phase involves Noise Reduction, Contrast Enhancement, Orientation Correction, and Adaptive Binarization algorithms to enhance the quality of the inscription images. The character recognition stage employs Convolutional Neural Networks with Transfer Learning, further enhanced by the Multi-head Attention mechanism in Vision Transformers (ViT). The character segmentation algorithm used was the Stroke Width Transform. Transfer Learning was incorporated to adapt the pre-trained ViT model to our specific task. This approach significantly improves the model’s ability to recognize characters from diverse scripts and languages. The results demonstrate the effectiveness of the proposed methodology. The character recognition accuracy metrics include a precision of 97.25%, a recall of 95.05%, and an F1-score of 95.17%. Additionally, the model achieved a recognition rate of 98.92% for key terms related to historical events, deities, and rulers. It also demonstrated a 94% recognition rate for context-specific phrases and a 95% recognition rate for historical dates. Contextual analysis results indicate that the model successfully identifies specific terms, phrases, and historical references, contributing to a deeper understanding of the inscriptions. The model's ability to recognize characters from multiple scripts underscores its adaptability to diverse inscriptions. In conclusion, this research provides a comprehensive and efficient solution for character recognition and

关键词： Image segmentation

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Covert communication of Active RIS-Assisted Uplink SGF-NOMA Systems

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IEEE Wireless communications Letters 2025年第6期14卷 1836-1840页

作者： Yin, Yiyang Yang, Liang Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Jishou University College of Computer Science and Electronic Engineering Hunan University School of Communication and Electronic Engineering China

This paper investigates the covert performance of an active reconfigurable intelligent surface (A-RIS)-assisted uplink non-orthogonal multiple access (NOMA) system with semi-grant-free (SGF) transmission in the presence of an illegal user. Using SGF transmission, grant-free (GF) users bypass the authorization process and transmit signals by sharing resource blocks reserved for grant-based (GB) users. Among the K GF users, the one with the best channel conditions shares the resource block with the GB user, ensuring service quality and avoiding collisions. Based on this, we derive the exact expressions for detection error probability (DEP) and covert rate (CR) to evaluate the system's covert performance. In conclusion, simulation results confirm the accuracy of our analysis and offer valuable insights. © 2012 IEEE.

关键词： Clutter (information theory)

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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EARTHEN LAMP-SHAPED DGS DUAL BAND MICROSTRIP PATCH ANTENNA FOR HIGH RETURN LOSS IN BIOMEDICAL APPLICATION

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第6期83卷 23-37页

作者： Gour, Sonam Rathi, Amit Department of Electronics & Communication Engineering Manipal University Jaipur Jaipur India Department of Computer Engineering Poornima College of Engineering Jaipur India

This study illustrates a dual-band circular and pentagon antenna with dual-band configuration for the high return loss. When an antenna has a greater return loss, less power is reflected toward the transmitter, implying that the antenna and transmission line are better suited. Due to its accessibility, the FR4 substrate is used to replicate the design. With a bandwidth of 150 MHz and 200 MHz, respectively, the antenna’s dual-band performance is between 2.32 GHz to 2.47 GHz and 2.9 GHz to 3.1 GHz. The body phantom design in CST Studio is used to test the design’s biocompatibility. It is demonstrated by the improved antenna in the industrial, scientific, and medical (ISM) band region that it can be utilized for telemetry or medical services. © 2024 Begell House Inc.. All rights reserved.

关键词： Biocompatibility

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