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IEEE International Conference on Communications Workshops, ICC

作者： Yao Ge Lei Liu Shunqi Huang David González G. Yong Liang Guan Zhi Ding Continental-NTU Corporate Lab Nanyang Technological University Singapore School of Information Science Japan Institute of Science and Technology (JAIST) Nomi Japan Wireless Communications Technologies Group Continental AG Germany Department of Electrical and Computer Engineering University of California at Davis Davis CA USA

Efficient signal detectors are rather important yet challenging to achieve satisfactory performance for large-scale communication systems. This paper considers a non-orthogonal sparse code multiple access (SCMA) configuration for multiple-input multiple-output (MIMO) systems with recently proposed orthogonal time frequency space (OTFS) modulation. We develop a novel low-complexity yet effective customized Memory approx-imate message passing (AMP) algorithm for channel equalization and multi-user detection. Specifically, the proposed Memory AMP detector enjoys the sparsity of the channel matrix and only applies matrix-vector multiplications in each iteration for low-complexity. To alleviate the performance degradation caused by positive reinforcement problem in the iterative process, all the preceding messages are utilized to guarantee the orthogonality principle in Memory AMP detector. Simulation results are finally provided to illustrate the superiority of our Memory AMP detector over the existing solutions.

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Unsupervised Infrared-Visible Person Re-Identification by Multi-Level Dual-Stream Contrastive Learning

SSRN

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SSRN 2024年

作者： Zhang, Yifeng Zhang, Canlong Ma, Haifei Li, Zhixin Wang, Zhiwen Wei, Chunrong Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China School of Electronic Engineering Guangxi University of Science and Technology Liuzhou545006 China Teachers College for Vocational and Technical Education Guangxi Normal University Guilin541004 China

Unsupervised Visible-Infrared Person Re-identification (USL-VI-ReID) aims to learn a label-free cross-modal retrieval model to reduce reliance on expensive manual annotations for cross-modal data. Previous works on USL-VI-ReID tend to generate sample labels from the perspective of inter-modality contrastive learning, while neglecting the information differences between cameras, resulting in low credibility of the generated sample labels. Additionally, traditional inter-modality contrastive learning methods use shared feature encoders, which cannot learn personalized modality features. To address these issues, this paper proposes a Dual-Stream Contrastive Learning Method (DCM) that jointly learns personalized feature encoders from both inter-modality and inter-camera perspectives, generating more reliable sample labels, which we refer to as a Multi-level Dual-Stream Contrastive Learning Method. We first establish a Dual-Stream Contrastive Learning (DCL) pre-training framework, which includes three different feature encoders for learning pedestrian representations within specific modalities, and optimize these encoders through contrastive learning using all training data. Then, based on these three feature encoders, we construct a Multi-level Dual-Stream Contrastive Learning framework (MLA) for cross-camera to cross-modal feature clustering and pseudo-label generation. This enables MLA to focus more on learning hierarchical domain information during subsequent model training, combining multi-level feature information aggregation to obtain more accurate cross-modal pseudo-labels. Finally, to further ensure the semantic consistency of the two modal labels, we insert a Cross-Modality Alignment Module (CMAM) between modalities to further improve the recognition accuracy of USL-VI-ReID. Numerous experiments demonstrate that the proposed method stands out among various USL-VI-ReID approaches, exhibiting promising performance. © 2024, The Authors. All rights reserved.

关键词： Cameras

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DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

arXiv

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arXiv 2023年

作者： Li, Zongrui Zheng, Qian Shi, Boxin Pan, Gang Jiang, Xudong School of Electrical and Electronic Engineering Nanyang Technological University Singapore The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Reflection

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Hybrid malware detection approach with feedback-directed machine learning

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science China(information sciences) 2020年第3期63卷 240-242页

作者： Zhetao LI Wenlin LI Fuyuan LIN Yi SUN Min YANG Yuan ZHANG Zhibo WANG Hunan Provincial Key Lab of Internet of Things and Information Security Xiangtan University The College of Information Engineering Xiangtan University Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science Fudan University Shanghai Institute of Intelligent Electronics & Systems Shanghai Institute for Advanced Communication and Data Science School of Cyber Science and Engineering Wuhan University

Dear editor,In recent years, the number of mobile malware has increased at an alarming rate. According to a report from G DATA [1], there are approximately 9000 new Android malware instances each *** malicious applica... 详细信息

关键词： Hybrid malware detection approach with feedback-directed machine learning

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Blockchain for Energy Market: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Jiang, Tianqi Luo, Haoxiang Yang, Kun Sun, Gang Yu, Hongfang Huang, Qi Vasilakos, Athanasios V. Glasgow College University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China Guangyuan Power Supply Company State Grid Sichuan Electric Power Company Guangyuan628033 China Peng Cheng Laboratory Shenzhen518066 China Sichuan Provincial Key Laboratory of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu611731 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China Center for AI Research University of Agder Grimstad4879 Norway

The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. Additionally, the energy market can address future production control and environmental concerns such as energy shortages and environmental deterioration. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. These factors have hindered the smooth operation of the energy market. Researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the

关键词： Deterioration

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Deep learning for the design of non-Hermitian topolectrical circuits

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Physical Review B 2024年第9期109卷 094103-094103页

作者： Xi Chen Jinyang Sun Xiumei Wang Hengxuan Jiang Dandan Zhu Xingping Zhou College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Portland Institute Nanjing University of Posts and Telecommunications Nanjing 210003 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Institute of AI Education Shanghai East China Normal University Shanghai 200333 China Institute of Quantum Information and Technology and Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing 210003 China

Non-Hermitian topological phases can produce some remarkable properties, compared with their Hermitian counterpart, such as the breakdown of conventional bulk-boundary correspondence and the non-Hermitian topological edge mode. Here, we introduce several algorithms with multilayer perceptron (MLP), and convolutional neural network (CNN) in the field of deep learning, to predict the winding of eigenvalue non-Hermitian Hamiltonians. Subsequently, we use the smallest module of the periodic circuit as one unit to construct high-dimensional circuit data features. Further, we use the Dense Convolutional Network (DenseNet), a type of convolutional neural network that utilizes dense connections between layers to design a non-Hermitian topolectrical Chern circuit, as the DenseNet algorithm is more suitable for processing high-dimensional data. Our results demonstrate the effectiveness of the deep learning network in capturing the global topological characteristics of a non-Hermitian system based on training data.

关键词： Topological phases of matter Non-Hermitian systems Su-Schrieffer-Heeger model

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A Wideband Linear-to-Circular Polarizer based on Hexagonal FSS on PDMS Substrate for CubeSats 4

A Wideband Linear-to-Circular Polarizer based on Hexagonal F...

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4th IEEE International Conference on Electronic information and Communication technology, ICEICT 2021

作者： Mirza, Hidayath Soh, Ping Jack Sheikh, Rais Ahmad Hossain, Toufiq M. Yan, Sen Zhang, Jiahao Luo, Fengyi Dept. of Electrical Engineering College of Engineering Jazan University Jazan Saudi Arabia Centre for Wireless Communications University of Oulu P.O. Box 4500 OuluFI-90014 Finland Dept. of Electrical Engineering College of Engineering Jazan University Jazan Saudi Arabia School of Engineering and Information Technology University of New South Wales Canberra CampbellACT2600 Australia School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan China

ISBN: (纸本)9781665432030

A novel flexible single-layered and single-sided linear to-circular polarizer is proposed in this work. The proposed polarizer is designed for a deployable format for application in a 3U Cube Satellites. It features a near-zero 3 dB AR, with a size of 0. 22 λ0 × 0. 27λ0 × 0. 03λ0 and a 3dB AR fractional bandwidth of 34.39 %, starting from 1.95 GHz to 2.76 GHz. It performs with a conversion efficiency bandwidth covering 90% of 43.47 %, starting from 1.8 GHz to 2.8 GHz. This indicates the polarizer’s wideband CP performance, with a pure CP centered at 2.41 GHz. ©2021 IEEE

关键词： Frequency selective surfaces

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DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing

arXiv

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arXiv 2024年

作者： Gu, Xueying Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. School of Internet of Things Engineering Jiangnan University Wuxi214122 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China State Key Lab. of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China 999077 Hong Kong

Intelligent Transportation systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, risking real-time system stability. Vehicle Edge Computing (VEC) addresses this by offloading tasks to Road Side Unit (RSU), ensuring timely services. Our previous work FLSimCo algorithm, which uses local resources for Federated Self-Supervised Learning (SSL), though vehicles often can't complete all iterations task. Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training. Meanwhile, setting an offloading threshold further prevents inefficiencies. Simulation results show that the enhanced algorithm reduces energy consumption, improves offloading efficiency and the accuracy of Federated SSL. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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