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arXiv 2025年

作者： Yang, Zheng Wang, Ning Sun, Yanshi Ding, Zhiguo Schober, Robert Karagiannidis, George K. Wong, Vincent W.S. Dobre, Octavia A. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou350117 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Khalifa University Abu Dhabi United Arab Emirates University of Manchester ManchesterM1 9BB United Kingdom Germany Group Electrical & Computer Engineering Dept. Aristotle University of Thessaloniki Thessaloniki54 124 Greece Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering Memorial University St. John’sNL Canada

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models

arXiv

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arXiv 2025年

作者： Ma, Zicheng Fan, Chuanliu Wang, Zhicong Chen, Zhenyu Lin, Xiaohan Li, Yanheng Feng, Shihao Zhang, Jun Cao, Ziqiang Gao, Yi Qin Changping Laboratory Beijing102200 China School of Computer Science and Technology Soochow University Suzhou China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing100871 China

Large language models have made remarkable progress in the field of molecular science, particularly in understanding and generating functional small molecules. This success is largely attributed to the effectiveness of molecular tokenization strategies. In protein science, the amino acid sequence serves as the sole tokenizer for LLMs. However, many fundamental challenges in protein science are inherently structure-dependent. The absence of structure-aware tokens significantly limits the capabilities of LLMs for comprehensive biomolecular comprehension and multimodal generation. To address these challenges, we introduce a novel framework, ProtTeX, which tokenizes the protein sequences, structures, and textual information into a unified discrete space. This innovative approach enables joint training of the LLM exclusively through the Next-Token Prediction paradigm, facilitating multimodal protein reasoning and generation. ProtTeX enables general LLMs to perceive and process protein structures through sequential text input, leverage structural information as intermediate reasoning components, and generate or manipulate structures via sequential text output. Experiments demonstrate that our model achieves significant improvements in protein function prediction, outperforming the state-of-the-art domain expert model with a twofold increase in accuracy. Our framework enables high-quality conformational generation and customizable protein design. For the first time, we demonstrate that by adopting the standard training and inference pipelines from the LLM domain, ProtTeX empowers decoder-only LLMs to effectively address diverse spectrum of protein-related tasks. Copyright © 2025, The Authors. All rights reserved.

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Stress Prediction of Working Employees in Different Environments Using Machine Learning Techniques

Stress Prediction of Working Employees in Different Environm...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Shanmugapriya P P. Balasubramanie C.R. Dhivyaa D. Senthil Raia P. Dhivya Department of CSE Kongu Engineering College Erode Tamilnadu India School of Computer Science & Engineering VIT Vellore Tamilnadu India Dept of Maths K S Rangasamy College of Technology Tiruchengode Tamilnadu India Department of CT-PG Kongu Engineering College Erode Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Mental stress is a growing concern, especially among employees across sectors, affecting both well-being and job performance. Stressors like excessive workload, tight deadlines, and high employer expectations often lead to health issues such as anxiety, depression, cognitive decline, cardiovascular diseases, and suicidal thoughts. This study aims to predict employee stress using physiological and behavioral indicators. Real-time data was collected from employees in medical, teaching, and IT sectors through an online stress survey. Three machine learning models, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were utilized for prediction. Among the models, the Random Forest (RF) algorithm showed the best performance with a 92% accuracy rate. The study's findings highlight the effectiveness of real-time data and machine learning in stress prediction. A web-based application was also developed to help organizations monitor and manage employee well-being. This research demonstrates the potential of predictive analytics and machine learning to support stress management, encouraging proactive measures for a healthier work environment.

关键词： Support vector machines Radio frequency Surveys Human factors Nearest neighbor methods Predictive models Real-time systems Biomedical monitoring Random forests Stress measurement

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Adaptive Perception for Unified Visual Multi-modal Object Tracking

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hu, Xiantao Zhong, Bineng Liang, Qihua Shi, Liangtao Mo, Zhiyi Tai, Ying Yang, Jian Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining Guilin541004 China Guangxi Normal University Security Guilin541004 China Guangxi Normal University Intelligent Technology Guilin541004 China Guangxi Normal University Ministry of Education Guilin541004 China Wuzhou University School of Data Science Wuzhou543002 China Wuzhou University Software Engineering Wuzhou543002 China Nanjing University State Key Laboratory for Novel Software Technology Suzhou215163 China Nanjing University of Science and Technology School of Computer Science Nanjing210094 China Nanjing University of Science and Technology Engineering Nanjing210094 China

Recently, many multi-modal trackers prioritize RGB as the dominant modality, treating other modalities as auxiliary, and fine-tuning separately various multi-modal tasks. This imbalance in modality dependence limits the ability of methods to dynamically utilize complementary information from each modality in complex scenarios, making it challenging to fully perceive the advantages of multi-modal. As a result, a unified parameter model often underperforms in various multi-modal tracking tasks. To address this issue, we propose APTrack, a novel unified tracker designed for multi-modal adaptive perception. Unlike previous methods, APTrack explores a unified representation through an equal modeling strategy. This strategy allows the model to dynamically adapt to various modalities and tasks without requiring additional fine-tuning between different tasks. Moreover, our tracker integrates an adaptive modality interaction (AMI) module that efficiently bridges cross-modality interactions by generating learnable tokens. Experiments conducted on five diverse multi-modal datasets (RGBT234, LasHeR, VisEvent, DepthTrack, and VOT-RGBD2022) demonstrate that APTrack not only surpasses existing state-of-the-art unified multi-modal trackers but also outperforms trackers designed for specific multi-modal tasks. © 2020 IEEE.

关键词： Multicarrier modulation

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. Control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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Hyperspectral Target Detection Using Diffusion Model and Convolutional Gated Linear Unit

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Li, Zhaokui Mo, Fan Zhao, Xiaobin Liu, Cuiwei Gong, Xuewei Li, Wei Du, Qian Yuan, Bo Shenyang Aerospace University School of Computer Science Shenyang110136 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Chinese Academy of Sciences CAS Key Laboratory of Forest Ecology and Silviculture Institute of Applied Ecology Shenyang110016 China Beijing Institute of Technology School of Information and Electronics Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

Deep learning can effectively extract latent information from data to enhance target-background separation in hyperspectral target detection (HTD). However, these models typically require extensive labeled samples, while available target spectra in hyperspectral images (HSI) are scarce. Additionally, existing deep models struggle with target detection in complex backgrounds due to subtle spectral differences. To address these issues, we propose a novel HTD method based on diffusion model and convolutional gated linear unit (HTD-DMCG). First, the diffusion model is integrated with MixUp for data augmentation to generate a diverse and sufficiently large sample set. Next, a Transformer architecture utilizing a convolutional gated linear unit is designed to effectively capture global dependencies and local feature correlations, leading to more discriminative feature representations. Additionally, a new target aggregation and background separation loss is introduced, which emphasizes target sample aggregation while increasing the distance between targets and background samples to enhance separability. The HTD-DMCG method is compared against classical and state-of-the-art HTD methods on four real HSI datasets. Extensive experiments show that it can effectively outperform existing methods in target detection performance. © 1980-2012 IEEE.

关键词： Image coding

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An Image Semantic Communication System Based on GAN and Relative Position-Encoding

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IEEE Communications Letters 2025年

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptography Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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From Partial Calibration to Full Potential: A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Gain-Phase Uncertainty

arXiv

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arXiv 2025年

作者： Xu, He Wu, Tuo Liu, Wei Elkashlan, Maged Al-Dhahir, Naofal Debbah, Mérouane Yuen, Chau So, Hing Cheung School of Cyber Science and Engineering Ningbo University of Technology Ningbo315211 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Kowloon Hong Kong School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Department of Electrical Engineering City University of Hong Kong China

Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is essential in multipath wireless communication scenarios, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method attained improved estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical multipath environments. © 2025, CC BY.

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Design of NB-IoT based Portable pH Detector

Design of NB-IoT based Portable pH Detector

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2024 International Conference on Green Energy and Mechatronics engineering, ICGEME 2024

作者： Yan, Qiuzhen Guo, Dong Chen, Nan Lv, Xinyu Li, Siyuan School of Computer Science and Technology Zhejiang University of Water Resources and Electric Power Zhejiang Hangzhou310018 China College of Electrical Engineering Zhejiang University of Water Resources and Electric Power Zhejiang Hangzhou310018 China

pH is a key factor affecting water quality. Traditional pH detection methods are mostly manually operated, with a large workload, low efficiency, and low detection accuracy. In order to improve the efficiency and convenience of water quality detection, we have designed a portable pH value detector based on NB-IoT(Narrow Band Internet of Things) by combining single chip microcomputer and wireless transmission technology. It uses STM8L152 microcontroller as the core and adopts pH composite electrode to measure the pH value of water samples, which simultaneously achieve online detection and numerical display of pH value. The monitoring values can be transmitted to the main control system through the NB-IoT network, enabling wireless collection and monitoring of environmental water quality pH values at multiple points. The use of this portable pH detector is beneficial for conducting water quality testing and preventing water pollution. © Published under licence by IOP Publishing Ltd.

关键词： Water hardness

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Age of Information Analysis of Ber/Geo/1/1 Queue With On-Off Service

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IEEE Internet of Things Journal 2025年

作者： Zeng, Hao Qu, Yi Chen, Zhengchuan Akar, Nail Wang, Min Wu, Dapeng O. Quek, Tony Q. S. Chongqing University School of Microelectronics and Communication Engineering Chongqing China Bilkent University Electrical and Electronics Engineering Department Bilkent Ankara06800 Turkey Chongqing University of Posts and Telecommunications School of Optoelectronics Engineering Chongqing China City University of Hong Kong Department of Computer Science Hong Kong Singapore University of Technology and Design 487372 Singapore Yonsei University Yonsei Frontier Lab Korea Republic of

The Age of Information (AoI), which measures the time since the generation of the latest update, quantifies information freshness in timeliness-critical systems. Minimizing AoI and characterizing it precisely are crucial for system efficiency and decision-making. This work investigates AoI under external interference modeled as an On-Off process, providing a foundation for future research in more complex scenarios. We consider a discrete-time remote status-updating system with a monitor and a sensor, where the sensor observes a physical process, generates timestamped updates, and sends them to the monitor. Both inter-arrival and service times follow geometric distributions, with service interrupted according to a two-state On-Off process. We analyze AoI under two queuing disciplines: 1) non-preemptive, where arriving updates are discarded if the server is occupied, and 2) preemptive, where in-service updates are replaced with new ones during the Off state. For both, we derive closed-form expressions for average AoI and peak AoI (PAoI). We also explore the relationship between discrete-time and continuous-time systems, showing that the latter is the limiting case of the former. Numerical results validate the theoretical analysis, revealing a linear relationship between the relative normalized increase in average PAoI and AoI and the proportion of Off state time. Frequent On-Off switching and higher service rates under the same system load help mitigate freshness deterioration caused by interruptions. The On-Off process is shown to have a large impact on the average AoI (resp. PAoI) of systems with relatively high (resp. low) arrival and service rates. © 2014 IEEE.

关键词： Continuous time systems

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