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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Li, Yifan Liu, Pengyu Kong, Lili Deng, Yiyang Liu, Weilong Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Hebei Hongyi Environmental Protection Technology Co. Ltd Dingzhou Hebei China Beijing-Dublin International College at BJUT Beijing University of Technology Beijing China Information Technology Co. Ltd Dingzhou Hebei China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

Aiming at the existing deep learning-based fall detection model in multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, there are problems such as leakage detection and false detection. In this paper, we take the falling person as the research object, construct a dataset containing multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, and propose a fall detection model based on improved YOLOv5. By improving the backbone network and neck network structure of the detection model, the problem of low accuracy of fall detection due to incomplete target deformation features and target detail features in complex scenes is solved. The proposed algorithm in this paper achieves an average accuracy of 92.78% for fall detection in complex scenes, and the detection speed reaches 36 frames/second. © 2024 Copyright held by the owner/author(s).

关键词： Fall detection

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RIS-Aided Cell-Free Massive MIMO systems With Low-Resolution ADCs: Uplink Performance Analysis and Optimization

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IEEE Internet of Things Journal 2025年第11期12卷 18249-18265页

作者： Xiong, Youzhi Tang, Liu Sun, Sanshan Yang, Songjie Liu, Li Mao, Sun Zhang, Zhongpei Wei, Ning Sichuan Normal University Laboratory of Information Intelligent Processing and Communications College of Physics and Electronic Engineering Chengdu610101 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Sichuan Normal University College of Computer Science Chengdu610101 China

This article investigates the uplink performance of reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems over spatially correlated Rayleigh fading channels. We consider multiple RISs and low-resolution analog-to-digital converters (ADCs) to improve the system energy efficiency (EE). We first provide an aggregated channel estimation technique with less pilot overhead. By exploiting the statistical channel state information (CSI), we further optimize the RISs’ phase shifts with the goal of minimizing the total normalized mean square error (NMSE) of the estimated aggregated channels. Subsequently, we derive the closed-form expression of the uplink spectral efficiency (SE) for quantization-aware minimum mean-square error (MMSE) combining. Third, based on the closed-form SE expression and power consumption model, we formulate and solve an optimization problem that maximizes the uplink EE under the constraints of transmit power and total ADC quantization bits. Specifically, by leveraging the Dinkelbach transform, Lagrangian dual transform, and fractional programming (FP) techniques, an alternating optimization (AO)-based algorithm is proposed to jointly obtain the bit allocation (BA) scheme among all access points (APs) and the uplink power control (PC) strategy for all users. Finally, numerical results validate the correctness of the closed-form SE expression and show the effectiveness of the proposed optimization methods for phase shift design and EE maximization. © 2014 IEEE.

关键词： Mean square error

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Enhancing WRSN Sustainability through On-demand Directional Wireless Charging with Multiple Green-powered Mobile Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 18019-18030页

作者： Ma, Xiongbo Liu, Xilong Ansari, Nirwan Ding, Jun The School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China The Advanced Networking Laboratory Helen & John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime. © 2014 IEEE.

关键词： Wireless charging

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MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

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Computers, Materials & Continua 2025年第2期82卷 2969-2984页

作者： Ruoxin Li Shaoxiong Wu Fengping Deng Zhongli Tian Hua Cai Xiang Li Xu Xu Qi Liu NARI-TECH Nanjing Control System Co. Ltd.Nanjing211106China School of Software Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044China

Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.

关键词： Electrical load forecasting cloud computing smart grid weather attributes energy consumption time-series analysis

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Efficient Design Optimization for Diffractive Deep Neural Networks

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 1199-1203页

作者： Wu, Kun Liu, Yuncheng Gao, Hui Tao, Jun Xiong, Wei Li, Xin Wuhan University School of Electronic Information Wuhan430072 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics The School of Optical and Electronic Information Wuhan430074 China Optics Valley Laboratory of Hubei Wuhan430074 China Fudan University State Key Laboratory of Integrated Chips and System School of Microelectronics Shanghai200433 China Duke Kunshan University Data Science Research Center Jiangsu Kunshan215316 China

Since diffractive deep neural network (D2NN) provides a full optical solution to implement deep neural networks (DNNs), it offers ultrafast operation speed and virtually unlimited bandwidth, yielding an alternative-yet-competitive approach for computer-based neural networks. A D2NN is composed of several 3D-printed phase masks as hidden layers and a number of optical detectors at the output. To enable automatic and efficient design of D2NNs, we propose an iterative optimization method to determine the optimal design parameters of D2NNs. During each iteration step, we first optimize the physical parameters for masks (e.g., thicknesses) while fixing the detector parameters (e.g., locations). Next, we exhaustively search the detector parameters with fixed masks. These two steps are repeated until convergence is reached. Our numerical experiments demonstrate that the proposed optimization algorithm can produce a high-performance D2NN achieving 97% accuracy for recognizing handwritten digits. © 2024 IEEE.

关键词： Deep neural networks

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Poly(lactic acid)/Poly(butylene adipate-co-terephthalate)films with simultaneous high oxygen barrier and fast degradation properties

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Green Energy & Environment 2025年第1期10卷 1-10页

作者： Mengjing Yang Yuxi Mao Penghui Zhang Jie Li Zeming Tong Zhenguo Liu Yanhui Chen Institute of Flexible Electronics Northwestern Polytechnical UniversityXi'an710072China Key Laboratory of Flexible Electronics of Zhejiang Province Ningbo Institute of Northwestern Polytechnical University218 Qingyi RoadNingbo315103China School of Chemistry and Chemical Engineering Shaanxi Key Laboratory of Macromolecular Science and TechnologyKey Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi'an 710072China

Although poly(lactic acid)(PLA)is a good environmentally-friendly bio-degradable polymer which is used to substitute traditional petrochemical-based polymer packaging films,the barrier properties of PLA films are still insufficient for high-barrier packaging *** this study,oxygen scavenger hydroxyl-terminated polybutadiene(HTPB)and cobalt salt catalyst were incorporated into the PLA/poly(butylene adipate-co-terephthalate)(PLA/PBAT),followed by melting extrusion and three-layer co-extrusion blown film process to prepare the composite *** oxygen permeability coefficient of the composite film combined with 6 wt%oxygen scavenger and 0.4 wt%catalyst was decreased significantly from 377.00 cc·mil·m^(-2)·day^(-1)·0.1 MPa^(-1) to 0.98 cc·mil·m^(-2)·day^(-1)·0.1 MPa^(-1),showing a remarkable enhancement of 384.69 times compared with the PLA/PBAT composite ***,the degradation behavior of the composite film was also accelerated,exhibiting a mass loss of nearly 60%of the original mass after seven days of degradation in an alkaline environment,whereas PLA/PBAT composite film only showed a mass loss of 32%.This work has successfully prepared PLA/PBAT composite films with simultaneously improved oxygen barrier property and degradation behavior,which has great potential for high-demanding green chemistry packaging industries,including food,agricultural,and military packaging.

关键词： Barrier property Oxygen scavenging Blow molding Eco-packaging Degradable composite film

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Universal Fan-In/Fan-Out Solution for Few-Mode Multi-Core Fibers Using Multi-Mode Fibers

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Journal of Lightwave technology 2025年第11期43卷 5498-5505页

作者： Hua, Xuchen Du, Haoze Liu, Zhuyixiao Zhang, Fengming Yang, Chen Zhao, Zhiyong Tang, Ming Yangtze Optical Fibre and Cable Joint Stock Limited Company State Key Laboratory of Optical Fiber and Cable Manufacture Technology Hubei Wuhan China National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Hubei Wuhan430074 China Optics Valley Laboratory Hubei Wuhan430074 China

With the development of AI, the capacity demand for interconnection between computing component is increasing. As a solution has been vigorously developed in the past half of century, optical interconnection has been put forward with higher demands. Few-mode multi-core fiber (FM-MCF), a key technology within spatial division multiplexing (SDM), has consistently pushed the boundaries of single optical fiber communication capacity records. However, the non-standardized fiber design of FM-MCF poses a significant challenge in terms of multiplexing and demultiplexing. The difficulty lies in the preservation of high-order modes and mode field adaptation. To address these challenges, we propose a universal fiber-core multiplexing/demultiplexing solution for FM-MCF, leveraging multi-mode optical fiber (MMF) as a versatile bridging medium. The simulation and experimental results demonstrate excellent insertion loss and high-order mode retention capabilities. This solution offers compatibility with a wide range of FM-MCF designs, making it highly adaptable for various research applications. Furthermore, by using commercially available MMF, the solution reduces both cost and complexity, presenting a practical and scalable approach to advancing FM-MCF technology as a next-generation transmission medium for AI-driven computing systems. © 1983-2012 IEEE.

关键词： Optical interconnects

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Uniformly dispersed zinc-tin alloy as high-performance anode for aqueous zinc ion batteries

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Journal of Materials science and technology 2025年 219卷 10-18页

作者： Ge, Xumeng Peng, Zhi Zhang, Qingqing Zhu, Jing Zhao, Ningning Zhang, Zekun Meng, Wei Li, Bin Wang, Ling Tian, Huajun Dai, Lei He, Zhangxing School of Chemical Engineering North China University of Science and Technology Tangshan063009 China Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education and School of Energy Power and Mechanical Engineering North China Electric Power University Beijing102206 China

Uncontrolled dendrite and side reactions of aqueous zinc ion batteries (AZIBs) hinder their commercial application. To overcome these obstacles, a novel zinc alloy anode for multifunctional AZIBs was designed by incorporating metal elements into the zinc anode. The metal elements are intended to improve the overall electrochemical performance of the battery by solving the zinc anode problem in an "incorporation" manner. In this study, the effect of Sn-induced surface structure reconstruction on the diffusion and deposition behavior of Zn2+ was investigated using binary zinc alloy (Zn@Sn) as a zinc anode. The zinc anode with Zn (002) crystal plane as the preferred crystal plane was able to inhibit the disordered growth of zinc dendrites, and the introduction of Sn elements enhanced the anti-hydrogen evolution reaction ability of the zinc anode. At a current density of 1.2 mA cm–2, the Zn@Sn symmetric cell was able to maintain stable operation for 1000 h, demonstrating a more prominent deposition/stripping stability. This work provides a promising strategy and new insights into the design of electrolyte-anode interfacial protection. © 2024

关键词： Tin alloys

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RSAPower: Random Style Augmentation Driven Structure Perception Network for Generalized Retinal OCT Fluid Segmentation

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2353-2367页

作者： Lu, Chenggang Guo, Zhitao Zhang, Dan Mou, Lei Yuan, Jinli Ma, Shaodong Chen, Da Zhao, Yitian Xia, Kewen Zhang, Jiong Hebei University of Technology School of Artificial Intelligence School of Electronic and Information Engineering Tianjin300401 China Chinese Academy of Sciences Laboratory of Advanced Theranostic Materials and Technology Ningbo Institute of Materials Technology and Engineering Ningbo China Innovation and Research Institute of Hebei University of Technology in Shijiazhuang Shijiazhuang China Ningbo University of Technology School of Cyber Science and Engineering Ningbo China Ningbo Cixi Institute of Biomedical Engineering China Qilu University of Technology Jinan China

Optical Coherence Tomography (OCT) imaging is extensively utilized for non-invasive observation of pathological conditions, such as retinal fluid-associated diseases. Accurate fluid segmentation in OCT images is therefore critical for quantifying disease severity and aiding clinical decision-making. However, achieving precise segmentation remains challenging due to pathological variations in shape and size, uncertain boundaries, and low contrast of fluid. Most importantly, variability in OCT image styles across different vendors and centers significantly affects fluid segmentation, leading to poor generalization to unseen domains. To address this, we propose a novel method, RSAPower, to enhance the generalization ability of fluid perception networks via style augmentation for retinal fluid segmentation. Specifically, RSAPower comprises a plug-and-play random style transform augmentation (RSTAug) module and a novel fluid perception network (FLPNet) for end-to-end training. The RSTAug module generates new random-style data from the source domain, preserving realistic pathological and structural features. The FLPNet benefits from a novel hybrid structure attention (HSA) module to perceive fluid's spatial features and long-range dependence. Furthermore, FLPNet adapts to the diverse augmented data through a saliency-guided multi-scale attention (SGMA) block, boosting its segmentation performance. We validate RSAPower against various state-of-the-art methods using two publicly available datasets, Retouch and Kermany. Experimental results demonstrate the proposed method's superior generalization ability and effectiveness in fluid segmentation. © 1982-2012 IEEE.

关键词： Optical coherence tomography

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