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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Zhang, Jian Bai, Shichao Xu, Lei Zhang, Hongwei Fang, Hongyu Li, Xiaohui Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei China

ISBN: (纸本)9781728105352

In the uplink grant-free non-orthogonal multiple access (NOMA) scenario, since the active user at the sender has a structured sparsity transmission characteristic, the compressive sensing recovery algorithm is initially applied to the joint detection of the active user and the transmitted data. However, the existing compressive sensing recovery algorithms with unknown sparsity often require noise power or signal-to-noise ratio (SNR) as the priori conditions, which greatly reduces the algorithm adaptability in multi-user detection. Therefore, an algorithm based on cross validation aided structured sparsity adaptive orthogonal matching pursuit (CVA-SSAOMP) is proposed to realize multi-user detection in dynamic change communication scenario of channel state information (CSI). The proposed algorithm transforms the structured sparsity model into a block sparse model, and without the priori conditions above, the cross validation method in the field of statistics and machine learning is used to adaptively estimate the sparsity of active user through the residual update of cross validation. The simulation results show that, compared with the traditional orthogonal matching pursuit (OMP) algorithm, subspace pursuit (SP) algorithm and cross validation aided block sparsity adaptive subspace pursuit (CVA-BSASP) algorithm, the proposed algorithm can effectively improve the accurate estimation of the sparsity of active user and the performance of system bit error ratio (BER), and has the advantage of low-complexity. © 2019 IEEE.

关键词： Multiuser detection

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Continuous Distributed processing of Software Defined Radar

Continuous Distributed Processing of Software Defined Radar

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Bing Qiu, Qiang Gong, Shiqi Liu, Yongjun Lei, Yu Institute of Computing Technology CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Xidian University National Laboratory of Radar Signal Processing Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665498142

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing. © 2021 IEEE.

关键词： distributed processing Software-defined radar stream processing engine

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Study of Resistive Properties and Neural Response of Zro2/Tio2 Heterojunction Nanowire Array (Nwa) Rram

SSRN

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SSRN 2024年

作者： Sun, Tangyou Qin, Zubin Yu, Fantao Gao, Shuai Wangyang, Peihua Tang, Xiaosheng Li, Haiou Zhang, Fabi Xu, Zhimou Cai, Ping Jiang, Chunsheng Xue, Xiaogang Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China College of Optoelectronic Engineering Chongqing University Chongqing400044 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Guangxi Key Laboratory of Information Materials Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guilin541004 China School of Optoelectronic Engineering Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin541004 China

Nowadays, researcher has an eye on enhancing the performance of resistive random-access memory (RRAM) and exploring new demands and applications, particularly in the field of artificial intelligence and neural computing. It is crucially important for accurately replicating the observed plasticity within synapses to achieve superior RRAM performance in low-dimensional nanomaterials. In this work, we focus on addressing certainly unsatisfactory electrical property of titanium oxide nanowire array (NWA)-based RRAM, for example short retention time (~ 103 s). Theoretically and experimentally, a novel and solution-based RRAM structure, namely ZrO2/TiO2(NWA), has been conducted. With the help of ZrO2 insertion layer, the RRAM devices shows impressive resistive switching characteristics, including endurance cycling (> 105 cycles), and a long retention time (~ 9×104 s). These findings hold significant implications for digital computing systems. Moreover, successful implementation of the proposed device enables the emulation of fundamental synaptic biological features such as long-term potentiation (LTP), long-term depression (LTD), non-linear transmission properties, and learning experiences. This research provides evidence of the immense potential of the ZrO2/TiO2(NWA) RRAM device in non-volatile memory (NVM) and biomimetic neuromorphic systems. © 2024, The Authors. All rights reserved.

关键词： Heterojunctions

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Fil-Cad: A Fault Detection Method for Iiot Based on Federated Incremental Learning with Class Accuracy Distillation

SSRN

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SSRN 2024年

作者： Liu, Yanhua Fang, Wenyu Huang, Wei Wang, Xiaofeng Zhao, Baokang Liu, Ximeng College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer National University of Defense Technology Changsha410073 China

Fault detection is a research topic attracting much attention in IIoT. However, the continuous accumulation and growth of IIoT data leads to the catastrophic forgetting problem in the fault detection model and data heterogeneity, which creates a bottleneck for the performance and generalization of federated learning in fault detection. To improve the fault detection performance of federated learning models in data incremental scenarios, we propose a federatedincremental-learning-based fault detection method for IIoT, namely FIL-CAD. Firstly, comparing the knowledge distillation, the accuracies of different data classes in the global and local models are chosen as the model lightweight learning features, and the loss function is weighted. Secondly, we introduce the concept of data learning depth. It computes different model weights in the aggregation based on their degrees of data sample learning and model training participation and obtains the global model through weighted aggregation. Additionally, FIL-CAD supports the combination of differential privacy to protect data privacy. The proposed method is compared with four benchmark methods on four datasets, which include CWRU and MFPT bearing datasets. Then, the experimental results show that FIL-CAD performs optimally in accuracy and F1-Score, and the average accuracy is improved by 1.32%-18.52%, demonstrating the superiority of FIL-CAD. © 2024, The Authors. All rights reserved.

关键词： Fault detection

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

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Frontiers of Engineering Management 2022年第4期9卷 550-562页

作者： Haochen SUN Xiaofan LIU Zhanwei DU Ye WU Haifeng ZHANG Xiaoke XU College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China Web Mining Laboratory Department of Media and CommunicationCity University of Hong KongHong KongChina WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkHong KongChina Computational Communication Research Center Beijing Normal University(Zhuhai)Zhuhai 519087China School of Journalism and Communication Beijing Normal UniversityBeijing 100875China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical ScienceAnhui UniversityHefei 230601China

Wearing masks is an easy way to operate and popular measure for preventing *** masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains ***,we aim to investigate the epidemic prevention effect of masks in different real-life gathering *** study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering *** transmission of COVID-19 is simulated using the Monte Carlo simulation *** heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general ***,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to *** masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and ***,the use of masks alone in primary schools and hospitals cannot control *** high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for *** the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be ***,asymptomatic infection has varying effects on the prevention effect of masks in different *** effect can be weakened or eliminated by increasing the usage rate of masks in high schools and ***,the effect on primary schools and hospitals cannot be *** study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

关键词： COVID-19 masks behavioral heterogeneity asymptomatic infection

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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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TransMixer: A Hybrid Transformer and CNN Architecture for Polyp Segmentation

TransMixer: A Hybrid Transformer and CNN Architecture for Po...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanglin Huang Donghui Tan Yuan Zhang Xuanya Li Kai Hu Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Affiliated Hospital of Xiangnan University Chengzhou China Baidu Inc Beijing China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Learning how to fully extract global representations and local features is a key factor in improving the performance of polyp segmentation. In this paper, we explore the potential of combined techniques of Transformers and convolutional neural networks (CNNs) to address the challenges of polyp segmentation. Specifically, we present TransMixer, a hybrid interaction fusion architecture of the Transformer branch and the CNN branch, which is able to enhance the local details of global representations and the global context awareness of local features. To achieve this, we first bridge the semantic gap between the Transformer branch and the CNN branch through the Interaction Fusion Module (IFM), and then make full use of both respective properties to enhance polyp feature representations. After that, we further propose the Hierarchical Attention Module (HAM) to collect polyp semantic information from high-level features to gradually guide the recovery of polyp spatial information in low-level features. Quantitative and qualitative results show that the proposed model is more robust to various complex situations compared to existing methods, and achieves state-of-the-art performance in polyp segmentation.

关键词： Deep learning Bridges Semantics Context awareness Benchmark testing Transformers Feature extraction

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Adjusting Dynamics of Hopfield Neural Network via Time-variant Stimulus

arXiv

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arXiv 2024年

作者： Peng, Xuenan Li, Chengqing Zeng, Yicheng Li, Chun-Lai School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing & Information Processing the Ministry of Education Xiangtan University Hunan Xiangtan411105 China School of Physics and Optoelectronic Engineering Xiangtan University Hunan Xiangtan411105 China School of Computer Science Xiangtan University Hunan Xiangtan411105 China

As a paradigmatic model for nonlinear dynamics studies, the Hopfield Neural Network (HNN) demonstrates a high susceptibility to external disturbances owing to its intricate structure. This paper delves into the challenge of modulating HNN dynamics through time-variant stimuli. The effects of adjustments using two distinct types of time-variant stimuli, namely the Weight Matrix Stimulus (WMS) and the State Variable Stimulus (SVS), along with a Constant Stimulus (CS) are reported. The findings reveal that deploying four WMSs enables the HNN to generate either a four-scroll or a coexisting two-scroll attractor. When combined with one SVS, four WMSs can lead to the formation of an eight-scroll or four-scroll attractor, while the integration of four WMSs and multiple SVSs can induce grid-multi-scroll attractors. Moreover, the introduction of a CS and an SVS can significantly disrupt the dynamic behavior of the HNN. Consequently, suitable adjustment methods are crucial for enhancing the network’s dynamics, whereas inappropriate applications can lead to the loss of its chaotic characteristics. To empirically validate these enhancement effects, the study employs an FPGA hardware platform. Subsequently, an image encryption scheme is designed to demonstrate the practical application benefits of the dynamically adjusted HNN in secure multimedia communication. This exploration into the dynamic modulation of HNN via time-variant stimuli offers insightful contributions to the advancement of secure communication *** Codes 68T07 Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamics

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Bi-Contrastive Alignment for Incomplete Multi-view Clustering

Bi-Contrastive Alignment for Incomplete Multi-view Clusterin...

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Chinese Control and Decision Conference, CCDC

作者： Shengxia Gao Yaoying Wang Yuqing Shi Kaiwu Zhang Shiqiang Du Chinese National Information Technology Research Institute Northwest Minzu University Lanzhou China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Province Northwest Minzu University Lanzhou China College of Electrical Engineering Northwest Minzu University Lanzhou China Key Laboratory of Linguistic and Cultural Computing of Ministry of Education Northwest Minzu University Lanzhou China College of Mathematics and Computer Science Northwest Minzu University Lanzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Multi-view clustering (MVC) represents a significant challenge in the realms of machine learning and data mining. Recently, the advent of contrastive learning (CL) has catalyzed the development of numerous innovative methods within this field. The efficacy of these methods typically hinges on the availability of consistent and comprehensive data. Nevertheless, real-world scenarios of data collection and transmission frequently encounter issues like partial unaligned-views (PU), which result in incomplete datasets and pose formidable challenges for effective MVC. To address this prevalent issue, our paper introduces an innovative framework, termed bi-contrastive alignment for incomplete multi-view clustering (BCA). BCA specifically targets the challenge of incomplete data stemming from PU. It encompasses a unique module, semantic guided bi-contrastive alignment (SGBA), designed to counteract the PU by harnessing semantic similarities. Moreover, the framework augments data alignment by integrating a contrastive learning-based corrective component into the loss function. The comparative experiments demonstrate that the ARI metric of our method surpasses the second-best contrasting method on all four datasets.

关键词： Measurement Semantics Contrastive learning Machine learning Fasteners Data collection Benchmark testing

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