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A mechanical arm based on image recognition and remote control

A mechanical arm based on image recognition and remote contr...

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2023 international conference on Mechatronics and Smart systems, CONF-MSS 2023

作者： Li, Kangmin Zhongfang Yachen Planning and Architectural Design Co. LTD Beijing China

Mechanical arm, image recognition and wireless transmission have been a hot topic and technology in recent years. This paper mainly introduces the object recognition mechanical arm based on the mechanical arm and wireless transmission technology. The system is modular, and the whole equipment system is divided into remote control system and mechanical arm system according to function. The remote control system is an upper computer software which integrates image recognition, related data processing module and wireless transmission module. The remote end includes wireless transmission module, steering gear control module and image acquisition module. The image is collected and sent to the remote control system, and the motion trajectory of the mechanical arm is obtained after calculation, and the control signal of the stepping motor on the mechanical arm is further calculated, and finally sent to the mechanical arm system to achieve the purpose of remote control. At the same time, during the movement of the mechanical arm, the relevant data is collected by the sensor on the mechanical arm and sent back to the remote control terminal for display and judgment. In this project, the mechanical arm system can automatically recognize the target on a two-dimensional plane and move the mechanical arm according to the object position. Meanwhile, the upper computer can display relevant data information. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： image recognition

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UAVs-Borne MIMO Radars Layout Optimization based on Clustered signal Integration 8

UAVs-Borne MIMO Radars Layout Optimization Based on Clustere...

引用

8th international conference on signal and image Processing, ICSIP 2023

作者： Zhang, Zhao Li, Xiaolong Chen, Zhe Wang, Mingxing Gao, Longji Sun, Zhi University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350397932

This paper considers the layout problem for the unmanned aerial vehicles (UAVs)-borne multi-input multi-output (MIMO) radar system in the situation of signal integration to improve the detection probability and radar effective coverage area (ECA). Because of the coherent constraints between UAV node echo signals, the signals can only be integrated coherently when the distances between nodes are relatively short;otherwise, the signals must be integrated incoherently. Thus, we propose a system model of the clustered UAV MIMO radar based on hybrid integration for the varying distances between the nodes. The model groups the system's nodes into clusters, where the distance between nodes within the same cluster is small and the distance between nodes from different clusters is larger. A clustering criterion and an approximation of the ECA solution calculation are also included in the model. Then, we propose an optimization solution for radar node placement based on Cuckoo search-particle swarm optimization (CS-PSO). Finally, compared to CS-based, PSO-based, and random deployment methods, the simulation results examine the feasibility and effectiveness of the proposed method. © 2023 IEEE.

关键词： MIMO systems

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Multispectral Pedestrian Detection Via Two-Stream YOLO With Complementarity Fusion For Autonomous Driving 3

Multispectral Pedestrian Detection Via Two-Stream YOLO With ...

引用

3rd IEEE international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2023

作者： Chan, Hung-Tse Tsai, Po-Ting Hsia, Chih-Hsien Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Kang Chiao International School Xiugang Campus New Taipei Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan

ISBN: (纸本)9798350333862

With the booming development of autonomous cars, the core technologies of vehicle-to-everything (V2X), automated driving systems (ADS), shared mobilities, and electrified vehicles (EV) have gained much attention for research. These technologies use sensors to obtain information about the vehicle's surroundings and algorithms and analyze or make decisions. For example, pedestrian detection tasks use images taken by visible cameras. However, visible image information does not perform well in harsh environments (e.g., light confusion, insufficient illumination value), and thermal cameras rely on temperature for imaging, so they can be used to compensate for visible information and complementarity between visible and thermal image information. Due to the diversity of scenes and unstable illumination, the task of pedestrian detection using multispectral information is challenging. To solve the problem, we propose a multispectral two-stream model based on one-stage architecture to capture multispectral information simultaneously and then enhance the multispectral complementarity by an adaptive multispectral weight adjustment method. This model has better accuracy than the recent ones and effectively uses the information between different modalities. © 2023 IEEE.

关键词： Autonomous vehicles

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Security Improvement for Deep Learning-based Semantic Communication systems

Security Improvement for Deep Learning-Based Semantic Commun...

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2024 international conference on Advanced Technologies for Communications, ATC 2024

作者： Tan, Hung Ngo Luu Nguyen, Van-Dinh Huynh-The, Thien Nguyen, Toan-Van Vo, Phuong Luu School of Computer Science and Engineering International University-VNU HCMC Viet Nam Vietnam National University Ho Chi Minh City Viet Nam College of Engineering and Computer Science Center for Environmental Intelligence VinUniversity Hanoi Viet Nam Hcmc University of Technology and Education Department of Computer and Communications Engineering Viet Nam

ISBN: (纸本)9798350353983

Semantic communication is becoming increasingly popular for wireless image transmission due to its superior communication efficiency. However, current deep learning-based semantic systems designed for semantic communication, though efficient, remain vulnerable to eavesdropping and often overlook security measures at the physical layer. To address this issue, this paper presents a deep learning-based system with joint source-channel coding (JSCC) and cyclical consistent generative adversarial network that enhances the security of semantic communication systems. We also design a convolutional neural network for the encoder and decoder which is trained to extract and transmit semantic features while minimizing the risk of privacy leakage. The artificial noise at the physical layer is employed at the source to degrade the eavesdropping ability of the eavesdropper. We show through experiments that under the artificial noise strategy, the legitimate user achieves a higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) than the eavesdropper. Moreover, the semantic system with JSCC offers better SSIM and PSNR than the separated source and channel coding models while preserving the confidentiality of semantic information during wireless transmission. This enhanced security framework opens new opportunities for secure and reliable communication of semantic information in diverse applications. © 2024 IEEE.

关键词： Channel coding

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MultiRAT DYMO: Enhanced Routing Protocols for VANETs 9

MultiRAT DYMO: Enhanced Routing Protocols for VANETs

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9th international conference on Communication, image and signal Processing, CCISP 2024

作者： Suthaputchakun, Chakkaphong Li, Tianru Foh, Chuan Sun, Zhili Chatzimisios, Periklis Bangkok University Electrical and Computer Engineering Phathumthani Thailand Institute for Communication Systems University of Surrey Surrey United Kingdom School of Science & Technology International Hellenic University Thessaloniki Greece

ISBN: (纸本)9798350356656

This paper describes our design of multihop multiRAT routing protocol based on the traditional DYMO (Dynamic MANET On Demand) routing protocol. Since the tradition routing protocols including DYMO only support singleRAT communications. With the increasing demand of multiRAT communications, there is a need for a multihop multiRAT routing protocol. Among the well-accepted routing protocols, DYMO is one of the popular routing protocols, which is also a successor of the widely-used AODV (Ad-Hoc on Demand Distance Vector). To support multiRAT, a route discovery process in DYMO is enhanced. A route request message RREQ will be broadcasted from the source to its neighbors through multiRAT instead of singleRAT as done traditionally. Then, the intermediate hosts also perform the same procedure by broadcasting the received RREQ to the next hop via all RATs they have in hands. The process continues until reaching the destination. The destination then sends a route reply message RREP back to the source through the reverse order of the intermediate hosts and the specified RATs. Therefore, a routing table stored on each host has be enhanced to include the information regarding the RAT used for each route entry, so that the RREP can be successfully sent through the right RATs along the discovered route back to the source. With the performance evaluation based on the simulation in OMNET++, the result shows that the proposed enhanced multiRAT DYMO achieves high QoS in terms of reception rate and end-to-end delay, and successfully create routes in multiRAT environment with the mix use of different RATs in each data route. © 2024 IEEE.

关键词： Routing protocols

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international Workshop on New Approaches for Multidimensional signal Processing, NAMSP 2023

International Workshop on New Approaches for Multidimensiona...

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international Workshop on New Approaches for Multidimensional signal Processing, NAMSP 2023

ISBN: (纸本)9789819727438

The proceedings contain 25 papers. The special focus in this conference is on New Approaches for Multidimensional signal Processing. The topics include: Weighted Tensor Least Angle Regression for Solving Sparse Weighted Multilinear Least Squares Problems;research on Behavior Control Method in 3D Virtual Animation Design based on the Purpose of Improving the Effect of Overseas Dissemination;the Positive Exertion of "Fuzzy Control" in Art Appreciation Class;discussion on the Establishment and Application of Intelligent Design Platform for Concrete Proportioning;locally Adaptive Processing of Color Tensor images Represented as Vector Fields;Energy Efficient VgSOT-MTJ based 1 Bit Subtractor;Hybrid Prediction Model for Mechanical Properties of Low Alloy Steel based on SVR-MLP;A Human-Inspired Semantic SLAM based on Parking-Slot Number for Autonomous Valet Parking;review of the Security Risks and Practical Concerns with Current and Future (6G) Communications technology;effect of Rehabilitation Robot Training on Cognitive Function in Stroke Patients: A Systematic Review and Meta-analysis;The Application Value of Virtual Reality Navigation Combined with Rapid On-Site Evaluation in CT-Guided Lung Biopsy;Gray and White Matters Segmentation in Brain CT images Using Multi-task Learning from Paired CT and MR images;wearable Long-Term Graph Learning for Non-invasive Mental Health Evaluation;music Personalized Recommendation System based on Deep Learning;handwritten Mathematic Expression Conversion to Docx;application of image Processing in Air-Ground Combined Fire Fighting System;Design and Realization of Mobile Terminal Side Time Synchronization based on FPGA;exploration of Drone Trajectory Planning in Unknown Environments Using Reinforcement Learning;an image-Content-based Adaptive Tile Partitioning Algorithm.

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Automatic Modulation Classification based on Complex-Valued Convolutional Neural Network and Semi-Supervised Learning 23

Automatic Modulation Classification Based on Complex-Valued ...

引用

23rd IEEE international conference on Communication technology, ICCT 2023

作者： Liu, Congguang Cai, Zhuoran Zhang, Baojie School of Physics and Electronic Information YanTai University YanTai China

ISBN: (纸本)9798350325959

In recent years, with the rapid development of 5G and intelligent communication, the proliferation of wireless terminal devices has led to the scarcity of spectrum resources. Cognitive radio, as a key technology for spectrum regulation and management, plays a crucial role in automatic modulation classification, which is an essential part of cognitive radio. Therefore, automatic modulation classification techniques have become increasingly important. This paper proposes an automatic modulation classification method based on Complex-Valued Convolutional Neural Network and Semi-Supervised Learning (CCNN-SSL). Deep learning has been a hot research topic in recent years, and its remarkable performance in image recognition has been widely recognized. It has also been found that applying deep learning to signal modulation classification achieves promising results. However, existing deep learning methods for modulation classification are primarily based on fully supervised learning, relying on a large amount of annotated data for neural network training. In practical signal recognition tasks, annotating a significant amount of data is costly. The proposed method effectively addresses this issue, and the CCNN model is capable of extracting features from the real and imaginary parts of complex-valued signals, thereby improving the classification accuracy. The performance of CCNN-SSL is validated on the RML2016.10A and RML2016.10B datasets, demonstrating the superiority of the proposed approach. © 2023 IEEE.

关键词： automatic modulation classification cognitive radio complex convolutional neural network semi-supervised learning

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A lossless information embedding algorithm based on data reconstruction

A lossless information embedding algorithm based on data rec...

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2022 international conference on signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Zhang, Yi Zhou, Quan Sun, Xuemin Huyan, Lang Liu, Juanni Liu, Mengyao Key Laboratory of Science and Technology on Space Microwave China Academy of Space Technology Xi’an China China Academy of Space Technology Xi’an China

ISBN: (纸本)9781510664616

At present, the network connection scale in the communication and transmission system is expanding with the increasing traffic, it is not easy to process the data, which is inefficient and difficult to ensure the quality of service. In order to increase the system transmission capacity and make the processing more efficient, a lossless information embedding algorithm based on data reconstruction is proposed, according to the characteristics of large variance and discrete distribution of different image, text, and voice data in the communication transmission system. Combined with the data characteristics of each group, interval mapping is carried out for segmented data, which is reorganized by certain rules to increase the redundancy of this segment and expand the data embedding range. The proposed algorithm realizes the lossless embedding of information and the original carrier data and embedded information are recovered losslessly. Simulation results show that the algorithm expands the redundancy range of segmented data and increases the capacity of information embedding, which is suitable for different data transmission systems. © 2023 SPIE.

关键词： Redundancy

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Tower-Type Detection of UAV Aerial image based on YOLOV5 Network Model 5th

Tower-Type Detection of UAV Aerial Image Based on YOLOV5 Net...

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5th international conference on 3D Imaging Technologies—Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Kou, Xiaoshi Li, Zhe Tian, Yangyang Yan, Lei Liu, Ling Wang, Bowen State Grid Henan Electric Power Company Research Institute Zhengzhou450052 China Beijing Qianfang Innovation Technology Co. Ltd. Beijng102299 China

ISBN: (纸本)9789819751808

With the application and promotion of UAVs in power system, the needs of information mining or target recognition of UAV aerial image is also increasingly stronger. UAV aerial image has advantages of high inspection efficiency and low strength;it can facilitate the establishment and processing of various databases. Due to the different tower types having different detection requirements and image collection processes, it needs to identify and classify the type. The tower-type recognition network method is established in this paper, firstly, the role of YOLOV5 model Input, Backbone, Neck, and Prediction in the role of UAV aerial image tower type recognition is analyzed;secondly, the UAV aerial image model recognition test environment is built, then the training effect can finally classify the data collected by the UAV aerial image by real-time identification tower type verification method and can establish a standardized detection database. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aerial photography

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Fault Detection Method of Insulator image based on Gabor Texture and Convolutional Neural Network 1

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18th international conference on Intelligent Information Hiding and Multimedia signal Processing, IIH-MSP 2022

作者： Chu, Xiaoquan Gao, Yunpeng Gong, Wenwen Han, Fujia Department of Computer Science and Technology Tsinghua University Beijing100084 China China Electric Power Research Institute Beijing100192 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Insulator is the main work of daily inspection of circuit inspectors, and the quality of insulator is related to line safety. To enhance the efficiency of insulator fault recognition, an insulator image fault recognition method based on Gabor Texture and convolutional neural network (CNN) is proposed. Firstly, the insulator image is collected, and the appropriate area is selected for regional segmentation to separate the insulator area. Secondly, the fault area of the insulator image is selected as the area of interest to mark, and the corresponding normal area is combined to construct the insulator image data set. Then, the selected regions are filtered using Gabor wavelet to obtain feature images at different scales and orientations. Finally, combined with the original image, a classification model of the CNN is established. Through training and testing, the parameters of the convolutional network are continuously adjusted to build the optimal classification model. Establish Gabor analysis and CNN prediction models for classification and recognition. Experiments show that the method could quickly identify insulator faults, assist circuit inspection, and have strong practicability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fault detection

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