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Joint Resources Optimization for Soft Video Transmission over IRS-assisted SR Network

IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 6087-6100页

作者： Luo, Lei Wu, Junjie Jin, Zhi Guo, Hongwei Zhu, Ce Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China University of Electronic Science and Technology School of Information and Communication Engineering Chengdu610051 China Sun Yat-sen University School of Intelligent Systems Engineering Guangzhou510275 China

Intelligent reflective surface (IRS) assisted symbiotic radio (SR) network has been proposed as a promising solution for the sixth generation (6G) mobile wireless system, which achieves mutualistic spectrum sharing and highly reliable backscattering communication with extremely low energy cost. On the other hand, exponential growth in video traffic makes wireless video transmission more challenging in the 6G era. With the assistance of IRS based secondary link in SR network, an efficient soft video transmission scheme (IRSCast) is proposed to achieve linear quality transition under the drastically varying wireless channel. To minimize the transmission distortion of the video signal, a multivariable optimization problem is formulated to jointly optimize the wireless resources, including transmission power, active beamforming of the primary transmitter (PTx), and passive beamforming of the secondary transmitter (STx). Then, an alternating optimization method is utilized to decouple the multivariate optimization problem into multiple univariate sub-problems that are finally solved by semi-positive definite relaxation and Lagrange multiplier methods. The simulation results demonstrated that the proposed IRSCast method significantly improves the objective and subjective quality of the received video. © 1991-2012 IEEE.

关键词： Television transmission

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MLP-Mixer based Masked Auto encoders Are Effective, Explainable and Robust for Time Series Anomaly Detection 51st

MLP-Mixer based Masked Auto encoders Are Effective, Explaina...

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51st International Conference on Very Large Data Bases, VLDB 2025

作者： Tang, Qideng Dai, Chaofan Wu, Yahui Zhou, Haohao National Key Laboratory of Information Systems Engineering National University of Defense Technology China National University of Defense Technology China

Time series anomaly detection remains one of the most active research areas in data mining due to its wide range of real-world applications. In recent years, numerous deep learning-based methods have been proposed for this task. However, deep learning-based methods fail to detect subsequence anomalies with long durations, lack explain ability, and are vulnerable to training set contamination. This paper addresses these issues by proposing a novel deep learning framework for effective, explainable, and robust time series anomaly detection. Our framework, MMA, incorporates the MLP-Mixer backbone with a Masked Auto encoder-based anomaly detection approach to allow for a significantly larger input window size (10 to 20 times larger than the input window sizes of current models). This larger input window enables our model to detect challenging subsequence anomalies. Meanwhile, a contrast learning module is proposed to aid in detecting subtle anomalies that fail to be identified by residual errors. Furthermore, a dynamic anomaly filtering method is introduced to mitigate the impact of subsequence anomalies on the reconstruction of surrounding normal regions to reduce false alarms. Extensive experiments on univariate and multivariate time series datasets demonstrate that our proposed framework significantly outperforms state-of-the-art methods across rigorous evaluation metrics. Additionally, MMA has a strong ability to reconstruct potential normal patterns in anomalous regions, providing high levels of explain ability. Moreover, MMA demonstrates high robustness to various types of training set pollution. © 2025, VLDB Endowment, All rights reserved.

关键词： Mixers (machinery)

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A novel active-passive protection strategy endows carbonyl iron with excellent intelligent anti-corrosion and microwave absorption dual functional characteristics

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Nano Research 2025年第1期18卷 487-500页

作者： Wei Tian Quanyuan Feng Linbo Zhang Yinfan Liu Xian Jian Longjiang Deng Institute of Microelectronics School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 611756China National Engineering Researching Centre of Electromagnetic Radiation Control Materials Key Laboratory of Multi-Spectral Absorbing Materials and Structures of Ministry of EducationState Key Laboratory of Electronic Thin Films and Integrated DevicesSchool of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 611731China The Yangtze Delta Region Institute(Huzhou)&School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaHuzhou 313001China

Carbonyl iron absorbers(CI)face significant challenges in practical applications,such as corrosion,interface bonding failure,detachment,and high maintenance ***,we have developed intelligent self-healing technology based on proactive/passive mechanisms via in situ synthesis of self-healing factors(polydopamine/benzotriazole(PDA/BTA))and physical barrier layers(SiO_(2)/1,1,1,3,3,3-hexamethyl disilazane,SiO_(2)/HMDS)to enhance corrosion resistance,while also being compatible with efficient microwave absorption *** unique multiscale structure gives full play to the utilization of the roles of each functional layer,including the intelligent self-healing features of PDA/BTA,physical shielding and spatial confinement characteristics of SiO_(2)/HMDS,and magnetic-dielectric synergistic mechanism resulted from the good impedance matching characteristics,the conduction loss,the interfacial polarization loss and natural *** asfabricated composites achieved an exceptional minimum reflection loss value of-55.4 dB at 10.7 GHz and the effective absorption band of 7.6 ***,it still exhibits obvious self-healing and corrosion resistance characteristics after 360 h corrosion treatment,ascribed to the self-healing mechanism of PDA/BTA and the blocking intervention effect of SiO_(2)/*** work is considered to pave the way for the synthesis of high-performance magnetic absorbers,especially in enhancing their intelligent self-healing ability in corrosive environments.

关键词： microwave absorption intelligent self-healing technology carbonyl iron multiscale structure magneticdielectric synergistic mechanism

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A Novel Approach for Image Encryption with Chaos-RNA

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Computers, Materials & Continua 2023年第10期77卷 139-160页

作者： Yan Hong Shihui Fang Jingming Su Wanqiu Xu Yuhao Wei Juan Wu Zhen Yang State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine Anhui University of Science and TechnologyHuainan232001China School of Electrical and Information Engineering Anhui University of Science and TechnologyHuainan232001China School of Information Science and Engineering Henan University of TechnologyZhengzhou450001China

In today’s information society,image encryption technology is crucial to protecting Internet ***,traditional image encryption algorithms have problems such as insufficient chaotic characteristics,insufficient randomness of keys,and insecure Ribonucleic Acid(RNA)*** address these issues,a chaos-RNA encryption scheme that combines chaotic maps and RNA encoding was proposed in this *** initial values and parameters of the chaotic system are first generated using the Secure Hash Algorithm 384(SHA-384)function and the plaintext ***,the Lühyperchaotic system sequence was introduced to change the image’s pixel values to realize block scrambling,and further disturbance is achieved through spiral index sequence to enhance encryption ***,to obtain the final encrypted image,the diffusion is accomplished through different RNA encoding rules and operation rules corresponding to the chaotic sequence generated by an improved one-dimensional chaotic map(1DCM).Here innovatively propose four new RNA operation rules,increasing the difficulty of *** results demonstrate that the normalized pixel change rate(NPCR)and the unified average changed intensity(UACI)values of the tested encrypted images were 99.61%and 33.46%,*** average ciphertext entropy value in the Red Green Blue(RGB)channels were 7.9986,7.991,and ***,this algorithm exhibits a low correlation coefficient and enhanced *** encryption method effectively improves the security and reliability of image encryption compared to other similar techniques.

关键词： Chaos-RNA SHA-384 Lühyperchaotic system block scrambling RNA diffusion

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Extending Token Computation for LLM Reasoning 24

Extending Token Computation for LLM Reasoning

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8th International Conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Li, Bingli Vargas, Danilo Vasconcellos Laboratory of Intelligent Systems The Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Fukuoka Japan

ISBN: (纸本)9798400718182

Large Language Models (LLMs) are pivotal in advancing natural language processing but often struggle with complex reasoning tasks due to inefficient attention distributions. In this paper, we explore the effect of increased computed tokens on LLM performance and introduce a novel method for extending computed tokens in the Chain-of-Thought (CoT) process, utilizing attention mechanism optimization. By fine-tuning an LLM on a domain-specific, highly structured dataset, we analyze attention patterns across layers, identifying inefficiencies caused by non-semantic tokens with outlier high attention scores. To address this, we propose an algorithm that emulates early layer attention patterns across downstream layers to re-balance skewed attention distributions and enhance knowledge abstraction. Our findings demonstrate that our approach not only facilitates a deeper understanding of the internal dynamics of LLMs but also significantly improves their reasoning capabilities, particularly in non-STEM domains. Our study lays the groundwork for further innovations in LLM design, aiming to create more powerful, versatile, and responsible models capable of tackling a broad range of real-world applications. © © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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DIProT:A deep learning based interactive toolkit for efficient and effective Protein design

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Synthetic and systems Biotechnology 2024年第2期9卷 217-222页

作者： Jieling He Wenxu Wu Xiaowo Wang Ministry of Education Key Laboratory of Bioinformatics Center for Synthetic and Systems BiologyBioinformatics DivisionBeijing National Research Center for Information Science and TechnologyDepartment of AutomationTsinghua UniversityBeijingChina

The protein inverse folding problem,designing amino acid sequences that fold into desired protein structures,is a critical challenge in biological *** numerous data-driven and knowledge-driven methods,there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein *** this paper,we present DIProT,an interactive protein design *** leverages a non-autoregressive deep generative model to solve the inverse folding problem,combined with a protein structure prediction *** integration allows users to incorporate prior knowledge into the design process,evaluate designs in silico,and form a virtual design loop with human *** inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets,with promising sequence recovery and inference *** studies further illustrate how DIProT can facilitate user-guided protein design.

关键词： Computational Protein design Interactive design toolkit Protein inverse folding Non-autoregressive decoding

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A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1681-1694页

作者： Qiuyu Zhang Lipeng Wang Hao Meng Wen Zhang Genghua Huang the College of Intelligent Systems Science and Engineering Harbin Engineering UniversityHarbin 150001China Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://***/zqy411470859/ship_dataset.

关键词： 3D point clouds dataset dynamic tail wave fog simulation rainy simulation simulated data

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Effects of various information scenarios on layer-wise relevance propagation-based interpretable convolutional neural networks for air handling unit fault diagnosis

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Building Simulation 2024年第10期17卷 1709-1730页

作者： Chenglong Xiong Guannan Li Ying Yan Hanyuan Zhang Chengliang Xu Liang Chen School of Urban Construction Wuhan University of Science and TechnologyWuhan 430065China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Key Laboratory of Intelligent Buildings Technology School of Information and Electrical EngineeringShandong Jianzhu UniversityJinan 250101China Key Laboratory of Low-grade Energy Utilization Technologies and Systems(Chongqing University) Ministry of Education of ChinaChongqing UniversityChongqing 400044 China State Key Laboratory of Green Building in Western China Xi’an University of Architecture&TechnologyXi’an 710055China

Deep learning(DL),especially convolutional neural networks(CNNs),has been widely applied in air handling unit(AHU)fault diagnosis(FD).However,its application faces two major ***,the accessibility of operational state variables for AHU systems is limited in practical,and the effectiveness and applicability of existing DL methods for diagnosis require further ***,the interpretability performance of DL models under various information scenarios needs further *** address these challenges,this study utilized publicly available ASHRAE RP-1312 AHU fault data and employed CNNs to construct three FD models under three various information ***,the layer-wise relevance propagation(LRP)method was used to interpret and explain the effects of these three various information scenarios on the CNN *** R-threshold was proposed to systematically differentiate diagnostic criteria,which further elucidates the intrinsic reasons behind correct and incorrect decisions made by the *** results showed that the CNN-based diagnostic models demonstrated good applicability under the three various information scenarios,with an average diagnostic accuracy of 98.55%.The LRP method provided good interpretation and explanation for understanding the decision mechanism of CNN models for the unlimited information *** the very limited information scenario,since the variables are restricted,although LRP can reveal key variables in the model’s decision-making process,these key variables have certain limitations in terms of data and physical explanations for further improving the model’s ***,an in-depth analysis of model parameters—such as the number of convolutional layers,learning rate,βparameters,and training set size—was conducted to examine their impact on the interpretative *** study contributes to clarifying the effects of various information scenarios on the diagnostic performance and interpr

关键词： air handling unit(AHU) fault diagnosis convolutional neural network(CNN) layer-wise relevance propagation(LRP) interpretation and explanation various information scenarios

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Introducing ionic mediators to construct peak-splitting manganese oxide structures for efficient oxygen reduction reactions

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Journal of Materials science & technology 2024年第34期201卷 139-148页

作者： Haoliang Ma Xiong Li Mengmeng Tian Lanlan Li Xiaofei Yu Xinghua Zhang Zunming Lu Xiaojing Yang School of Materials Science and Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control TechnologyHebei University of TechnologyTianjin 300130China School of Artificial Intelligence and Information Technology Nanjing University of Chinese MedicineNanjing 210023China

Manganese oxides have received extensive attention in Oxygen Reduction Reaction(ORR)***-ever,the poor electrical conductivity and oxygen adsorption capacity of manganese oxides limit their development,so it is necessary to prepare catalysts with highly active *** propose a method to introduce an ionically conductive medium and induce structural distortion via thermodynamics,re-sulting in novel peak-splitting highly active structures for enhanced oxygen reduction *** the aid of refined structural analysis,High-angle annular dark-field(HAADF-STEM)imaging,and theoretical calculations,it is elucidated that this peak-splitting structure results from thermodynamically induced structural distortions and atomic *** introduction of the ionically conductive medium promotes the formation of Ag-O-Mn conductive bond bridges,which regulate the energy level matching of manganese oxides and oxygen *** thermodynamically induced structural distortion ex-poses new atomic planes in the material,and the creation of this highly active surface is accompanied by a redistribution of surface charges,which modulates the adsorption of manganese active sites with oxy-gen intermediates and greatly enhances the oxygen reduction ***,this Ag-OMS-2 with a novel split structure exhibits excellent activity at 0.836 V,superior to Pt/C kinetics and good *** preparation of such highly active structures is instructive for the development of manganese oxides.

关键词： Fuel cells Electrochemistry Manganese Structural distortion Ionically conductive medium

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Recent Advances in Parity-Time Symmetry-Enabled Electromagnetic Sensors

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Progress In Electromagnetics Research B 2024年 105卷 107-121页

作者： Yang, Minye Ye, Zhilu Chen, Pai-Yen Erricolo, Danilo Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States State Key Laboratory for Manufacturing Systems Engineering Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education School of Electronic Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering The Key Laboratory of Biomedical Information Engineering of Ministry of Education Center for Mitochondrial Biology and Medicine School of Life Science and Technology International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology Xi’an Key Laboratory for Biomedical Testing and High-end Equipment Xi’an Jiaotong University Shaanxi Xi’an710049 China

Parity-time (PT) reversal symmetry, as a representative example in the field of non-Hermitian physics, has attracted widespread research interest in the past few years due to its extraordinary wave dynamics. PT symmetry enables unique spectral singularities including the exceptional point (EP) degeneracy where two or more eigenvalues and eigenvectors coalesce, as well as the coherent perfect absorber-laser (CPAL) point where laser and its time-reversal counterpart (i.e., coherent perfect absorber) can coexist at the same frequency. These singular points not only give rise to new physical phenomena but also provide new plausibility for building the next-generation sensors and detectors with unprecedented sensitivity. To date, investigations into EPs and CPAL points have unveiled their great potential in various sensing scenarios across a broad spectral range, spanning optics, photonics, electronics, and acoustics. In this review article, we will discuss ongoing developments of EP- and CPAL-based sensors composed of PT-synthetic structures and offer a glimpse into the future research directions in this emerging field. © (2024), (Electromagnetics Academy). All rights reserved.

关键词： Eigenvalues and eigenfunctions

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