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Anti-Collision Phase Extraction in RFID systems 12

Anti-Collision Phase Extraction in RFID Systems

引用

IEEE 12th international conference on RFID technology and Applications (RFID-TA)

作者： Tang, Hao Zhou, Yan Song, Wenfei Chang, Lili Chen, Rui Xidian Univ State Key Lab Integrated Serv Networks ISN Xian Shaanxi Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China

ISBN: (数字)9781665465946

ISBN: (纸本)9781665465946

With the widespread use of radio frequency identification (RFID) in the internet of things (IoTs), RFID is becoming increasingly important. As RFID tag phase-based positioning is more accurate than signal strength-based positioning, phase extraction is indispensable for RFID-based high-precision positioning systems. In the situation of tag collision, we propose a phase extraction approach based on the electronic product code (EPC) signal segment rather than 16-bit random or pseudo-random number (RN16) signal segment by evaluating the conflict handling mechanism of the ISO/IEC 18000-6c communication protocol. Experiments in both indoor and outdoor situations have demonstrated the feasibility of the proposed phase extraction method using EPC signal segments.

关键词： RFID Phase extraction EPC signal segment ISO/IEC 18000-6c Tag collision

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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

引用

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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Vehicle Accident Detection and Prevention using IoT and Deep Learning

Vehicle Accident Detection and Prevention using IoT and Deep...

引用

2022 IEEE international conference on signal Processing, Informatics, Communication and Energy systems, SPICES 2022

作者： Lakshmy, S. Gopan, Renjith Meenakshi, M.L. Adithya, V. Elizabeth, Mariya R Mar Baselios College of Engineering and Technology Department of Electronics and Communication Trivandrum India

ISBN: (纸本)9781665449403

Road accidents have become an issue of major concern to the people. This paper presents an accident prevention mechanism developed through alcohol detection using an MQ3 alcohol sensor followed by automatic engine locking. The detection part uses an SW-420 vibration sensor to detect any sort of abnormal vibrations that may occur from a collision. This is accompanied by supervised deep learning CNN algorithms. The accident scene image is captured using a front camera built in the vehicle to be used by the deep learning model for accident prediction. Accident detection is followed by communication to the nearest emergency center using GPS and GSM modules. © 2022 IEEE.

关键词： internet of things

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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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Research on Communication Delay Mechanism of Intelligent Network Connected Vehicle Data Transmission System

Research on Communication Delay Mechanism of Intelligent Net...

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image Processing, Computer Vision and Machine Learning (ICICML), international conference on

作者： Di Qi College of Automotive and Marine Engineering Dalian Vocational and Technical College Dalian China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

With the rapid development of information technology and the increasing maturity of intelligent vehicle network technology, the internet of Vehicles system is an important part of intelligent transportation, and the real-time and accuracy of its data transmission is very important to improve road traffic efficiency and safety. This paper discusses the mechanism of communication delay in automobile intelligent network data transmission system in detail, analyzes the key factors affecting the transmission distance, transmission speed and network congestion of communication delay signal, and proposes an optimization method of cache strategy based on popularity and location prediction. The effectiveness of the strategy is verified by simulation experiments and practical tests, which provides strong support for the further development of automotive network systems.

关键词： Intelligent networks Connected vehicles Transportation Real-time systems Stability analysis Road traffic Delays Safety Data communication internet of Vehicles

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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...

引用

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

引用

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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Role of Big Data, AI and Deep Learning in Medical image Training Models and Decision Support System Using I4.0 Technologies 10th

Role of Big Data, AI and Deep Learning in Medical Image Trai...

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10th EAI international conference on IoT Technologies for Health-Care, HealthyIoT 2023, and 4th EAI international conference on Wearables in Healthcare, HealthWear 2023

作者： Bhatt, Kavita Kumar, S. Mohan School of Engineering and Technology CMR University Bangalore560037 India

ISBN: (纸本)9783031719103

The area of diagnostic decision assistance in radiology is going through a rapid shift with the availability of a lot of patient information and the advancement of new AI (Artificial Intelligence) techniques of ML (Machine Learning), like DL (Deep Learning). They have the potential to offer imaging professionals tools that will increase the precision and effectiveness of diagnosis and therapy. This paper will discuss the development of the area of radiology and general trends emphasizing advancements in diagnostic decision assistance from the earliest rule-based expert systems to contemporary cognitive assistants utilizing I4.0 technology. The accuracy, dependability, and productivity of electronic equipment in the healthcare industry must be improved with the use of the IoMT (internet of Medical Things). Researchers are building a digital healthcare system by connecting the already available medical resources and healthcare services. Although IoT is converging across many disciplines, our attention is on the scientific contributions of IoT in the healthcare domain. In terms of medical services in healthcare, this article discusses IoT applications, user contributions, and upcoming problems specifically in medical imaging hence the name internet of Medical Imaging Things. It also gives a complete overview of the latest developments of ML approaches broadly utilized for Medical Imaging Diagnostic using I4.0 by classifying the study as per the ML methods, equipment, and machinery used and a foundation for future research. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Radiology

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Research on Measuring Method of Ball Indentation based on MATLAB image Edge Detection 4th

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4th international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3D IT-MSP and DL 2022

作者： Zhou, Yuan Wang, Xin Liu, Ya Wang, Xiaoyuan Li, Zheng Wang, Yanying Shandong Institute of Inspection On Product Quality Jinan250102 China

ISBN: (纸本)9789819912292

Ball pressure test is an important test to evaluate the heat resistance of non-metallic materials. At present, most of the tests are judged by manual measurement, with low accuracy. In this paper, the digital image measurement technology of MATLAB is used to transform and enhance the collected indentation image. The Gaussian Laplace operator is used to detect the edge of the image, find the maximum value of the coordinate difference between each element point on the image edge and other element points, traverse all the element points on the image edge, and put the obtained maximum value into the maximum value array;continue to find the maximum value in the maximum value array to obtain the diameter of the ball pressure indentation image, thus improving the measurement accuracy and realizing the intelligent measurement of the diameter of the ball pressure detection image. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heat resistance

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