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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Shibo Guo Kang Zhang Zhiyan Zhou Xinyu Zhou Weiyu Gong Zhenghao Wang Kaiyue Zhu Haibin Wu Aili Wang School of Measurement-Control Technology and Communications Engineering Harbin University of Science and Technology China

ISBN: (纸本)9781450397810

In recent years, elevator safety accidents occur frequently, and relevant news has aroused people's strong attention to elevator safety. Among them, electric vehicle explosion accident hot spot is the highest. In this paper, according to the elevator safety hazard source (including illegal vehicle such as storage battery into, passengers trapped, robbed or distress due to illness, the elevator is high concentration flammable gases or burning smoke) design storage battery detection module, voice call module, combustible gas monitoring module for joint operations, monitoring, alarm, the outside world communication as one of the complete system. The system uses Arduino Mega 2560 as the main control chip, ESP32-CAM wireless camera module uses the improved YOLOv4 model for real-time analysis and processing of illegal vehicles, LD3320 speech recognition module and SYN6288 Chinese speech synthesis chip to achieve the voice call function. MQ-2 gas sensitive smoke sensor and LCD1602 LIQUID crystal display module to achieve gas monitoring function, HC-05 Bluetooth module to achieve communication function feedback alarm information to the elevator monitoring and management center, if necessary, can automatically call the alarm phone. The experiment shows that the detection confidence of battery car in elevator environment can reach 0.75, the recognition accuracy of voice call in quiet environment is 96%, and the recognition accuracy of voice call in noise environment is 90%. The alarm can be realized by combustible gas monitoring. The system can monitor the hidden danger in the elevator in real time, solve the problems such as the elevator supervision is weak, the emergency rescue is not timely, improve the efficiency of solving the elevator safety problems.

关键词： voice call combustible gas monitoring battery car identification ASPP-YOLOv4

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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 5

Combined Classification of Hyperspectral and LiDAR Data base...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Zhou, Binbin Wang, Qingyan Zhang, Junping Wang, Yujing School Of: Measurement-Control And Communication Engineering Harbin University Of Science And Technology Harbin150080 China School Of: Electronics And Information Engineering Harbin Institute Of Technology Harbin150001 China

ISBN: (纸本)9798400709272

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample differences and lack of correlation in physical features between modalities, which can impact classification performance. To fully integrate the heterogeneous information of multimodal data and improve classification performance, we propose a dual-channel cross-transformer feature fusion extraction network. The framework leverages self-attention mechanisms to aggregate features within each modality, and the feature fusion module based on cross-modal attention fully considers the complementary information between modalities. Classification tasks are performed using the fused spatial-spectral features obtained from the joint representation of modalities. Extensive experiments conducted on the Houston and MUUFL datasets demonstrate the effectiveness of the proposed model compared to existing methods. © 2023 ACM.

关键词： Classification (of information)

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Channel pruning method driven by similarity of feature extraction capability

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Soft Computing 2025年第2期29卷 1207-1226页

作者： Sun, Chuanmeng Chen, Jiaxin Li, Yong Wang, Yu Ma, Tiehua State Key Laboratory of Dynamic Measurement Technology North University of China Shanxi Taiyuan030051 China School of Electrical and Control Engineering North University of China Shanxi Taiyuan030051 China State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing400044 China

Channel pruning is a method to compress convolutional neural networks, which can significantly reduce the number of model parameters and the computational amount. Current methods that focus on the internal parameters of a model and feature mapping information rely on artificially set a priori criteria or reflect filter attributes by partial feature mapping, which lack the ability to analyze and discriminate the channel feature extraction and ignore the basic reasons for the similarity of the channels. This study developed a pruning method based on similar structural features of channels, called SSF. This method focuses on analysing the ability to extract similar features between channels and exploring the characteristics of channels producing similar feature mapping. First, adaptive threshold coding was introduced to numerically transform the channel characteristics into structural features, and channels with similar coding results could generate highly similar feature mapping. Secondly, the spatial distance was calculated for the structural features matrix to obtain the similarity between channels. Moreover, in order to keep rich channel classes in the pruned network, different class cuts were made on the basis of similarity to randomly remove some of the channels. Thirdly, considering the differences in the overall similarity of different layers, this study determined the appropriate pruning ratio for different layers on the basis of the channel dispersion degree reflected by the similarity. Finally, extensive experiments were conducted on image classification tasks, and the experimental results demonstrated the superiority of the SSF method over many existing techniques. On ILSVRC-2012, the SSF method reduced the floating-point operations (FLOPs) of the ResNet-50 model by 57.70% while reducing the Top-1 accuracy only by 1.01%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Coexisting fast–slow dendritic traveling waves in a 3D-array electric field coupled neuronal network

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Chinese Physics B 2024年第6期33卷 614-626页

作者：魏熙乐任泽宇卢梅丽樊亚琴常思远 The Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China School of Information Technology Engineering Tianjin University of Technology and EducationTianjin 300074China

Coexistence of fast and slow traveling waves without synaptic transmission has been found in hhhippocampal tissues,which is closely related to both normal brain activity and abnormal neural activity such as epileptic discharge. However, the propagation mechanism behind this coexistence phenomenon remains unclear. In this paper, a three-dimensional electric field coupled hippocampal neural network is established to investigate generation of coexisting spontaneous fast and slow traveling waves. This model captures two types of dendritic traveling waves propagating in both transverse and longitude directions: the N-methyl-D-aspartate(NMDA)-dependent wave with a speed of about 0.1 m/s and the Ca-dependent wave with a speed of about 0.009 m/s. These traveling waves are synaptic-independent and could be conducted only by the electric fields generated by neighboring neurons, which are basically consistent with the in vitro data measured experiments. It is also found that the slow Ca wave could trigger generation of fast NMDA waves in the propagation path of slow waves whereas fast NMDA waves cannot affect the propagation of slow Ca waves. These results suggest that dendritic Ca waves could acted as the source of the coexistence fast and slow waves. Furthermore, we also confirm the impact of cellular spacing heterogeneity on the onset of coexisting fast and slow waves. The local region with decreasing distances among neighbor neurons is more liable to promote the onset of spontaneous slow waves which, as sources, excite propagation of fast waves. These modeling studies provide possible biophysical mechanisms underlying the neural dynamics of spontaneous traveling waves in brain tissues.

关键词： hippocampal network epileptiform dendritic oscillation traveling wave electric field coupling

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Research on a transmission line fault detection method based on improved YOLOv7-Tiny 5

Research on a transmission line fault detection method based...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Zhang, Zhen Wang, Qingyan Chen, Qingguo Liang, Xintao School Of Measurement And Control Technology And Telecommunications Engineering Harbin University Of Science And Technology Harbin150080 China School Of Electrical And Electronic Engineering Harbin University Of Science And Technology Harbin150080 China

ISBN: (纸本)9798400709272

In response to the challenges faced by most fault detection algorithms for transmission power lines, such as detecting small targets, issues of missed detections and false negatives, a large number of model parameters, and high computational requirements that hinder deployment on unmanned aerial vehicles, this study proposes a novel model called YOLOvS. YOLOvS addresses these challenges by incorporating lightweight models, introducing improved attention mechanisms, and utilizing an enhanced CIoU to achieve fast and accurate detection of typical faults in transmission power lines captured by aerial imagery. Experimental validation confirmed that the YOLOvS algorithm improves the detection accuracy of small targets while maintaining overall detection performance. Furthermore, it achieved a reduction of 21% in model size compared to YOLOv7-Tiny and a 20% decrease in computational requirements. These advancements significantly contribute to lowering the hardware costs associated with deploying the algorithm on unmanned aerial vehicles. © 2023 ACM.

关键词： Fault detection

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The control System of Intelligent Drug Delivery Car Based on Vision Guidance 7

The Control System of Intelligent Drug Delivery Car Based on...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Wang, Gang Ding, Simin Jing, Shufan Jilin Communications Polytechnic School of Mechanical Engineering Changchun China Jilin Institute of Chemical Technology School of Information And Control Engineering Changchun China

ISBN: (纸本)9798350359145

In response to the "contactless delivery"problem in hospitals, the control system of intelligent drug delivery car was designed based on vision guidance. The system takes raspberry pie as the control core and completes font recognition through high-definition camera;It determines the position and direction of the car through the OpenMV scanning marker line to complete the tracking and obstacle avoidance;It detects whether the drug is put down through the HX711 pressure sensor;It completes human-computer interaction through Bluetooth communication;It combines PID algorithm to realize closed loop control of speed, and controls the moving and changing direction of the car through DC motor module. The test results show that the system has high stability and good robustness, which has certain reference value for the intelligence of public medical service industry. © 2023 IEEE.

关键词： controlled drug delivery

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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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Roadside IRS Assisted Task Offloading in Vehicular Edge Computing Network 19th

Roadside IRS Assisted Task Offloading in Vehicular Edge Com...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xie, Yibin Shi, Lei Li, Zhehao Ding, Xu Liu, Feng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031545207

Vehicular edge computing (VEC) has been recognized as a promising technique to process delay-sensitive vehicular applications. Nevertheless, in order to accommodate the rapid growth in the number of connected vehicles, it’s inevitable that there will be an increasing deployment of conventional infrastructure with limited communication ranges. This could potentially lead to escalating costs and impede the full realization of the VEC system. In this paper, a roadside intelligent reflecting surface (IRS) assisted VEC network is introduced, where the IRS is deployed outside the coverage of roadside units (RSUs) to extend the service range. Furthermore, the maximum total number of successful offloading tasks problem within the scheduling time problem is formulated, encompassing the optimization of offloading decisions, computation resource allocation and phase shift of IRS. To tackle the formulated challenging problem, we first decouple the original problem into two subproblems. Then, a heuristic algorithm is proposed, where a many-to-one matching algorithm is proposed to joint optimize offloading decision and the computation resource, and an iterative algorithm is utilized to optimize the phase shift coefficients of IRS. The simulation results validate the effectiveness of the proposed algorithm in comparison to other schemes, and the IRS can effectively maintain network performance even when there are intervals in RSU coverage areas. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Resource allocation

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DSCCNet for high-quality 4K computer-generated holograms

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Optics Express 2025年第6期33卷 13733-13747页

作者： Xu, Zhenqi Leng, Junmin Dai, Ping Wang, Chao Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China School of Information and Communications Engineering Beijing Information Science and Technology University Beijing100101 China

With the increasing demand for high-quality 3D holographic reconstruction, visual clarity and accuracy remain significant challenges in various imaging applications. Current methods struggle for higher image resolution and to resolve such issues as detail loss and checkerboard artifacts. To address these challenges, we propose the model Depthwise Separable Complex-valued Convolutional Network (DSCCNet) for phase-only computer-generated holography (CGH). This deep learning framework integrates complex-valued convolutions with depthwise separable convolutions to enhance reconstruction precision and improve model training efficiency. Additionally, the diffuser is employed to reduce checkerboard artifacts in defocused parts of 3D CGH. Experimental results demonstrate that DSCCNet can obtain 4K images reconstructed with more intricate details. The reconstruction quality of both 2D and 3D layered objects is enhanced. Validation on 100 images from the DIV2K dataset shows an average PSNR above 37 dB and an average SSIM above 0.95. The proposed model provides an effective solution for high-quality CGH applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computer generated holography

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Joint Optimization of PAoI and Queue Backlog with Energy Constraints in LoRa Gateway Systems 19th

Joint Optimization of PAoI and Queue Backlog with Energy ...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Shi, Lei Ji, Rui Wei, Zhen Feng, Shilong Li, Zhehao School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031545306

Peak Age of Information(PAoI), as a performance indicator representing the freshness of information, has attracted the attention of researchers in recent years. The data packet transmission rate in the LoRa network determines the information freshness level for system packets. In order to study the optimal scheduling of data packets, we try to use the PAoI to describe the real-time level of the end devices(EDs) and reduce it. We use edge servers to process monitoring data packets at the edge of the network to improve the efficiency of EDs and the information freshness level of data. Since packet transmission will be constrained by EDs battery queue energy and gateway queue backlog, we propose an optimization problem that aims to minimize the long-term average PAoI of EDs while ensuring network stability. With the Lyapunov optimization framework, the long-term stochastic optimization problem is transformed into a single-slot optimization problem. Furthermore, to avoid the problem of too large search space, we propose a dynamic strategy space reduction algorithm (SSDR) to shrink the strategy space. The simulation experiments show that our SSDR algorithm can optimize the PAoI index of EDs in various situations and satisfy the constraints of long-term optimization. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Scheduling algorithms

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