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Robust Deep Learning-Based Physical Layer Communications: Strategies and Approaches

IEEE Network 2025年

作者： Zhu, Fenghao Wang, Xinquan Zhu, Chen Gong, Tierui Yang, Zhaohui Huang, Chongwen Chen, Xiaoming Zhang, Zhaoyang Debbah, Merouane Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Zhejiang University Polytechnic Institute Hangzhou310015 China Nanyang Technological University 639798 Singapore Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates

Deep learning (DL) has emerged as a transformative technology with immense potential to reshape the sixth-generation (6G) wireless communication network. By utilizing advanced algorithms for feature extraction and pattern recognition, DL provides unprecedented capabilities in optimizing the network efficiency and performance, particularly in physical layer communications. Although DL technologies present the great potential, they also face significant challenges related to the robustness, which are expected to intensify in the complex and demanding 6G environment. Specifically, current DL models typically exhibit substantial performance degradation in dynamic environments with time-varying channels, interference of noise and different scenarios, which affect their effectiveness in diverse real-world applications. This paper provides a comprehensive overview of strategies and approaches for robust DL-based methods in physical layer communications. First we introduce the key challenges that current DL models face. Then we delve into a detailed examination of DL approaches specifically tailored to enhance robustness in 6G, which are classified into data-driven and model-driven strategies. Finally, we verify the effectiveness of these methods by case studies and outline future research directions. © 1986-2012 IEEE.

关键词： Feature Selection

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China SYSTEMA Research Center European University Cyprus Nicosia Cyprus

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multistage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

arXiv

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arXiv 2025年

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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A BiOI/TiO_(2)heterogeneous interface-based fiber memristor for intelligent textile system and high-precision hand gestures recognition

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Nano research 2025年第5期18卷 637-648页

作者： Chao Jiang Shenglong Huang Yantao Yu Zixiang Zhao YiLun Cheng Yusuf Abdullahi Hassan Chuanghong Zhao Dou Zhang Zizhao Ding Powder Metallurgy Research Institute State Key Laboratory of Powder MetallurgyCentral South UniversityChangsha 410083China College of Physics and Electronic Information&Research Center for Novel Solar-Blind Ultraviolet and Infrared Photoelectronic Detectors of Henan province Luoyang Normal UniversityLuoyang 471934China EPS Department of Mill Maintenace Zijin AGM INCDurban BacklandsGeorgetownGuyana College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518060China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen 518060China

Textile memristor is the decision-making center of a bionic neuromorphic intelligent textile system and presents a promising opportunity for the incorporation of intricate information processing components within the architecture of flexible electronic *** architecture,characterized by its cross-bar configuration and adaptability,positions textile memristors as a highly promising avenue for the future advancement of wearable electronic ***,carbon fiber is chosen as electrode,and the memristor functional layer is made up of a high-performance and reliable BiOI/TiO_(2)heterogeneous interface synthesized *** textile memristor exhibits an average set voltage of≈0.78 V,set voltage deviation of 12.3%,long retention time(>10^(4)s),and high on/off ratio(>10^(5)).Simultaneously,the observed self-rectification,room-temperature negative differential resistance(NDR)effect,and biological synaptic phenomenon offer the possibility of further neuromorphic ***,the pressure sensor was integrated onto the textile memristor to construct an intelligent textile *** through self built datasets and neural network calculations,the gesture recognition rate of the system reached 98.5%,maintaining a high level under the influence of 20%Gaussian noise(64%).This textile memristor arrays supply great memristive performance,steady device distribution,and good mechanical strength,indicating a reliable direction for the next generation of integrated textile electrical systems.

关键词： neuromorphic computing intelligent textile system heterogeneous interface textile memristor

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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Implications of neuroimaging findings in addiction

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Psychoradiology 2023年第1期3卷 18-23页

作者： Xinwen Wen Lirong Yue Zhe Du Linling Li Yuanqiang Zhu Dahua Yu Kai Yuan School of Life Science and Technology Xidian UniversityXi’an 710126China School of Biomedical Engineering Medical SchoolShenzhen UniversityShenzhen 518060China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen UniversityShenzhen 518060China Department of Radiology Xijing HospitalFourth Military Medical UniversityXi’an 710032China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information EngineeringInner Mongolia University of Science and TechnologyBaotou 014010China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xidian UniversityXi’an 710126China Xi’an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information School of Life Science and TechnologyXidian UniversityXi’an 710126China

Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is *** leads to structural and fun... 详细信息

关键词： motivation finding seeking

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Development and Preliminary Application of Pedestrian Anger Scale: A Pliot study in Central China

SSRN

引用

SSRN 2024年

作者： Wan, Ping Shao, Haoying Li, Jinggang Ma, Xiaofeng Jiangxi Provincial Key Laboratory of Comprehensive Stereoscopic Traffic Information Perception and Fusion East China Jiaotong University Nanchang330013 China School of Transportation Engineering East China Jiaotong University Jiangxi Nanchang330013 China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan430063 China

To investigate the causes of pedestrian frustration in urban transportation, and to ensure the safety of pedestrian lives as well as maintain public transportation order this study developed and validated a psychological measurement tool—the Pedestrian Anger Scale (PAS) in central China. This scale aims to quantify the pedestrians’ various anger experiences triggered by diverse stimulating factors while walking Through exploratory factor analysis (EFA) and subsequent confirmatory factor analysis (CFA), a 19-item Pedestrian Anger Scale (PAS) was compilated finally. The scale is divided into four subscales, focusing on (1) Hostility from other drivers (α = 0.74), (2) obstacles and inconveniences in traffic (α = 0.85), (3) public disorder (α = 0.65), and (4) violations of motorized and non-motorized vehicles. (α = 0.89). The overall Cronbach’s alpha coefficient for the scale is 0.92, demonstrating a high level of internal consistency and ensuring the reliability of the measurement. Furthermore, this study investigated the relationships between pedestrian anger and demographic characteristics, as well as self-reported traffic violations and accidents. The results indicate significant correlations between the PAS scores and factors such as gender, age, sleep duration, having children or not, and experiencing a traffic accident or not during walking within the past three years. These findings offer valuable insights into the optimal design of urban slow-moving traffic system for creating more harmonious travelling environment. © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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A new algorithm for removing random motion noise of human body based on total variation

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Biomedical Signal processing and Control 2025年 110卷

作者： Yunqing Liu Jiaqi Shang Wei Chu Fei Yan Dongpo Xu Siyuan Wu He Huang Xin Chen School of Electronics Information Engineering Changchun University of Science and Technology China Jilin Provincial Science and Technology Innovation Center of Intelligent Perception and Information Processing China

Millimeter-wave radar sensors have garnered significant attention in non-contact monitoring for detecting human vital signs, particularly respiration and heart rate. These sensors offer advantages such as compact size, lightweight design, and versatility in sensing across diverse scenarios. Among them, frequency-modulated continuous wave (FMCW) radar demonstrates considerable potential for vital sign monitoring. However, a critical challenge persists, human random motion noise, which spans the entire frequency domain, significantly interferes with accurate heartbeat signal detection. To address this issue, this paper proposes a total variation model-based approach. This method involves reconstructing the thoracic signal matrix, converting it into a grayscale image, and obtaining the sparse characteristics of noise and the underlying image structure based on the second-order gradient information in different directions of the image. By introducing the Lp pseudo-norm, the second-order regularization constraint terms in the horizontal and vertical directions are designed respectively. Meanwhile, a global sparse constraint term was designed based on the prior characteristics of the noise. Then, the noise and the sparse characteristics of the underlying image structure are utilized for denoising, and finally, the thoracic cavity signal is reverse-reconstructed. This framework effectively suppresses random body motion noise while preserving vital sign information. Experimental results demonstrate a notable improvement in signal-to-noise ratio (SNR), along with enhanced measurement accuracy for both respiratory rate and heart rate. The findings of this study not only advance the theoretical framework for non-contact vital sign monitoring but also underscore the practical utility of FMCW radar in real-world applications.

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A Multi-attribute Decision-making based Importance Assessment Method of Sensor Nodes

A Multi-attribute Decision-making based Importance Assessmen...

引用

Computer Technology and information Science (ISCTIS), International Symposium on

作者： Xiusheng Duan Jingchao Yang Dehan Kong Weilong Zhang Department of Artificial Intelligence and Big Data Hebei Polytechnic Institute Hebei Jiaotong Vocational and Technical College Shijiazhuang China Hebei Provincial University Road Traffic Perception and Intelligent Application Technology Research and Development Center Department of Information Engineering Hebei University of Environmental Engineering Qinhuangdao China

In this paper, an air defense early warning sensor network node importance assessment method based on multi-attribute decision was proposed. Firstly, according to the characteristics of the air defense early warning sensor network, the factors that affect the node importance degree were analyzed, and the node importance degree was divided into three aspects, which were performance importance degree, function importance degree and structure importance degree. Secondly, three calculation methods of importance degree were introduced. Then the synthesis method of comprehensive importance degree based on TOPSIS method was proposed. Finally, the validity of the method and model proposed in this paper was verified.

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Contrastive Learning based Collaborative Modelling of Heterogeneous Data for Few-shot Fault Diagnosis

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Zhong, Kai Zhu, Hengchang Zhang, Xiaoming Huang, Darong Han, Min Anhui University Institutes of Physical Science and Information Technology Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Hefei 230601 China Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology School of Artificial Intelligence Anhui Hefei 230601 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian 116024 China

Few-shot diagnosis has received extensive attention recently. Existing methods rarely consider the consistency within and between heterogeneous data, leading to suboptimal diagnosis performance. To address this issue, a contrastive learning based collaborative modelling for few-shot diagnosis is proposed. First of all, a heterogeneous data enhancement workflows with distribution consistency assessment is designed to acquire sufficient industrial process information, which can also mitigate the inconsistency between enhanced data and original data. Following this, convolutional networks with customized structures are used to extract the multimodal features from heterogeneous signals. After that, the collaborative modelling and diagnosis module is devised through the joint optimization of contrastive loss and cross entropy loss, which can shorten the distance of similar samples in feature space and retain cross structure consistency. Finally, the effectiveness and superiority of the proposed method are substantiated through simulated and the real world cases. © 2020 IEEE.

关键词： collaborative modelling contrastive learning Few-shot diagnosis heterogeneous data

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