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An Interference Method based on Periodic Sawtooth Wave Phase Modulation

An Interference Method Based on Periodic Sawtooth Wave Phase...

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Artificial Intelligence, internet of Things and Cloud Computing technology (AIoTC), international conference on

作者： Yuan Li College of Information and Electronic Engineering Shandong Technology and Business University Yantai China

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

This paper proposes a new jamming method for linear frequency modulation radar: sawtooth wave phase modulation repeater jamming. The LFM signal is phase modulated by the periodic sawtooth waves and then forwarded out. It causes the LFM echo to spread in pairs in the range frequency domain, and the interference spectrum covers the frequency domain range of the distance image, confusing the LFM distance image. The advantage of this jamming method is that the jamming signal and the target echo have the same kernel function in the frequency domain. Because it is coherent jamming, it can obtain a certain pulse compression gain. The article provides a detailed signal design model of sawtooth wave phase modulation repeater jamming, and conducts a detailed analysis of the signal. Simulation experiments verify the effectiveness of this method.

关键词： Chirp modulation Phase modulation Frequency-domain analysis Noise Radar Interference Radar imaging Repeaters Jamming Kernel

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Pedestrian recognition method based on Jetson nano 23

Pedestrian recognition method based on Jetson nano

引用

7th international conference on Big Data and internet of Things, BDIOT 2023

作者： Zhang, Lemei Xin, Yu Zhang, Letao North China University of Technology China Minzu University of China China

ISBN: (纸本)9798400708015

This paper presents a novel pedestrian recognition method based on Jetson Nano, leveraging its advantages as an edge computing device with low latency and high privacy. Traditional methods for pedestrian recognition often require substantial computing resources and a strong network connection, making them unsuitable for edge devices. Thus, this paper proposes an edge computing method specifically designed for Jetson Nano, utilizing CUDA and TensorRT to achieve higher performance. This method utilizes a convolutional neural network to extract image features, which are then activated through Reshape, Transpose, and Sigmoid functions. The Slice operation is employed to extract objects within areas with relatively high probabilities. The position of the object is then adjusted using mathematical operations such as Mul, Sub, Add, Pow, etc., resulting in more accurate detection results. This paper demonstrates the effectiveness of using Jetson Nano as an edge computing device for pedestrian recognition and the potential of CUDA and TensorRT for optimizing performance on edge devices. The proposed method has broad applications in various fields, such as surveillance systems, autonomous vehicles, and robotics, where real-Time pedestrian recognition tasks are essential. © 2023 ACM.

关键词： Edge computing

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IoT based Wearable Glasses for Visually Impaired Users 17

IoT based Wearable Glasses for Visually Impaired Users

引用

17th IEEE international conference on Advanced Networks and Telecommunications systems, ANTS 2023

作者： Boppana, Lakshmi Keerthi Kiran, A. Pranay Teja, B. Laxmi Priya, P. Nit Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350307672

The human eye is an organ that gives us the sense of sight. Visual impairment is one of the important issues to consider in the current environmental situation, as it has a major effect on human health. There are currently 39 million visually impaired people in the world. internet of Things (IoT) has transformed devices into remote monitoring in the healthcare sector around the clock. An assistive technology involves the development of devices to meet the needs of people suffering with different disabilities. The prototype presented in this paper is an IoT-based wearable glasses for visually impaired users, which is developed to assist them to read the text from the picture available in front of them at a certain distance and converts it into audio, enabling the users to hear through earphones connected to the glasses. This prototype can also translate the English text captured from the image into another language of the user's choice. The proposed wearable glasses are equipped with a panic button to allow the visually impaired person to inform care providers about the location of the person in an emergency. © 2023 IEEE.

关键词： Glass

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An Intrusion Detection System for Securing IoT based Sensor Networks from Routing Attacks 7th

An Intrusion Detection System for Securing IoT Based Sensor ...

引用

7th international conference on Computer, Communication and signal Processing, ICCCSP 2023

作者： Subramani, Shalini Selvi, M. Kumar, S. V. N. Santhosh Thangaramya, K. Anand, M. Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India Computer Science and Engineering College of Engineering Guindy Anna University Chennai600025 India Department of Electronics and Communication Saveetha Engineering College Chennai602105 India

ISBN: (纸本)9783031398100

internet of Things is made up of sensor nodes that can sense the given environment, gather the data from environment and then communicates the data to the sink node and the internet through wireless communication. As it follows the routing protocols used in Wireless Sensor Networks, the Denial of Service (DoS) attacks including selective forwarding and flooding are launched by the attackers through Routing Protocol for Low-Power and Lossy Network (RPL) attacks. Intrusion detection systems are able to handle the RPL based DoS attacks and can prevent the attackers from participation in the routing process. Therefore, we propose a new fuzzy temporal random forest algorithm for detecting the attacks more efficiently. In addition, a new intelligent agent based intrusion prevention system which uses fuzzy temporal inference rules for intrusion prevention is also proposed in this work. The proposed model has been compared with other machine learning algorithms such as decision trees, logistic regression, random forest, K-Nearest Neighbor and also the deep learning algorithm namely the Convolution neural networks. based on the experimentations carried out, we could prove that the proposed algorithm provides higher intrusion detection accuracy, reduces the false positive rate and detection time when compared with the related work. © IFIP international Federation for Information Processing 2023.

关键词： Denial-of-service attack

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Exploration on Simulation and Optimization of Intelligent Transportation System based on Artificial Intelligence and Virtual Simulation

Exploration on Simulation and Optimization of Intelligent Tr...

引用

2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Wang, Lanfeng Xiao, Xiaobing Wei, Yuhua Vocation College Guangzhou Huali Science and Technology Guangzhou China Guangdong Province Huali Technician College Guangzhou China

ISBN: (纸本)9798350313413

In the context of urbanization, with the innovation of computer information technology, the target demand of intelligent transportation is also constantly improving, and the design and research of intelligent transportation system (ITS) based on artificial intelligence and virtual simulation is becoming more and more important. In the construction of the entire intelligent transportation optimization system, how to improve the accuracy of traffic flow prediction and reduce the incidence of safety accidents is currently a key issue that needs to be urgently solved. This article conducted research on the optimization methods of intelligent transportation on the internet of Things, analyzed the application and process steps of artificial intelligence and virtual simulation in intelligent transportation, and combined intelligent transportation video and image storage calculation formulas. based on the data results, the following conclusions were drawn through discussion: six urban intersection samples were selected through simulation experiments. The application of ITS based on artificial intelligence and virtual simulation improved the flow prediction compared with the traditional scheme, and the overall average increase was 10.35%. At the same time, the safety accident rate also improved, and the overall average decrease was 7.85%. This indicated that the intelligent transportation optimization system based on artificial intelligence and virtual simulation had good results in practical applications. This study can also optimize traffic signal control and path planning, improve traffic efficiency, save energy consumption, reduce carbon emissions, and make positive contributions to sustainable urban development. © 2023 IEEE.

关键词： artificial Intelligence intelligent transportation system design virtual simulation

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A Federated Learning Approach for Enhancing Autonomous Vehicles image Recognition 4

A Federated Learning Approach for Enhancing Autonomous Vehic...

引用

4th international conference on Computer Communication and the internet, ICCCI 2022

作者： Stergiou, Konstantinos D. Psannis, Konstantinos E. Vitsas, Vasileios Ishibashi, Yutaka University of Macedonia Department of Applied Informatics Thessaloniki Greece International Hellenic University Department of Information and Electronic Engineering Thessaloniki Greece Nagoya Institute of Technology Graduate School of Engineering Nagoya Japan

ISBN: (数字)9781665469920

ISBN: (纸本)9781665469920

Traffic sign recognition is vital for the success of the autonomous vehicles paradigm in mobile edge computing. We present a Federated Learning implementation based on a Lenet-5 CNN architecture which can enhance edge computing systems on unsupervised feature classification problems. Experiments demonstrated that our model can deliver satisfactory performance on traffic sign image recognition. Also our research has shown that less resources can be utilized on client devices to run the local training models. However, heavy data processing must be shifted to intermediate edge computing layers requiring more computing resources and the optimization of parameters such as latency, bandwidth, and privacy. © 2022 IEEE.

关键词： image recognition

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A Defect Identification Method for Synchronous Condenser based on Gramian Angular Fields Enhancement and Optimization Graph Convolutional Neural Network 6

A Defect Identification Method for Synchronous Condenser Bas...

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6th IEEE international conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Li, Wenbo Zhang, Shuai Yang, Yong Jin, Yongtao Zhao, Lin Lu, Hongkun Wei, Bo E. Energy Technology Co. Ltd. Electric Power Research Institute State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China E. Energy Technology Co. Ltd. Hangzhou China Electric Power Research Institute State Grid Zhejiang Electric Power Co. Ltd. Zhejiang Hangzhou China

ISBN: (纸本)9798350374315

To achieve intelligent and reliable recognition of the operating conditions of synchronous condenser equipment, a typical defect recognition method of synchronous condenser based on gramian angular fields enhancement and optimization graph convolutional neural network is proposed. Firstly, the one-dimensional voiceprint signal of the camera is mapped into a two-dimensional image by using the K-field method, and then the original data sets under different working conditions are constructed. Then, the Gram angular field map under different working conditions is divided into several superpixel blocks by using super pixel segmentation method, which are used as nodes and graph structure data are constructed according to color and texture characteristics. Finally, the constructed graph structure is input into the graph convolution neural network model with attention mechanism for training and learning, so as to realize accurate recognition of the state of synchronous condenser equipment under multi-typical working conditions. A simulation test system including typical defects of the synchronous condenser equipment under normal, unbalanced and low oil pressure conditions is established. The test results show the proposed method can effectively characterize the three typical working states of the synchronous condenser, and the defect recognition accuracy is significantly improved compared with the conventional method, and it has good stability. The recognition rate can still be more than 93 % in a noise environment of 5 dB, which is about 5.3% higher than that of the conventional method. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on interference avoidance technology for low Earth orbit satellite based on spatial processing 2

Research on interference avoidance technology for low Earth ...

引用

2nd international conference on signal image Processing and Communication, ICSIPC 2022

作者： Zhao, Changlin Liu, Ziwei Zhang, Gengxin College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China

ISBN: (纸本)9781510657694

With the rapid development of low earth orbit (LEO) satellite systems, the interference between LEO satellite constellations using the same frequency has become increasingly prominent. Because of the large number and high dynamic characteristic of LEO systems, the traditional interference avoidance methods have great limitations. based on existing interference avoidance methods, this paper establishes a mathematical model of the interference avoidance for LEO satellite systems, and proposes an interference avoidance method using spatial processing. Aiming at the fatal main lobe interference avoidance problem, the method turns main lobe interference into side lobe interference by switching the beam of earth station to other satellites within the visual range. Then it uses adaptive beamforming method of side lobe null broadening to suppress side lobe interference in high dynamic environment. The interference scenario between OneWeb and Starlink constellations is used for simulation. The results turn out that the proposed method can effectively reduce the interference between LEO constellations and improve the link quality. © 2022 SPIE.

关键词： Orbits

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A Rolling Bearing Fault Diagnosis Method based on Synchrosqueezed Wavelet Transform and SE-ResNet 24

A Rolling Bearing Fault Diagnosis Method based on Synchrosqu...

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9th international conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Liu, Ye Xu, Yanhe Qin, Hui Niu, Xinqiang School of Civil and Hydraulic Engineering Huazhong University of Science and Technology China State Key Laboratory of Hydroscience and Engineering Tsinghua University China

ISBN: (纸本)9798400718137

Rolling bearings are vital components in rotary machines where even minor faults can lead to the collapse of the entire mechanical system, resulting in significant economic losses. The condition monitoring and fault diagnosis of rolling bearings are crucial for safe operation of complex industrial systems. Traditional fault diagnosis methods mainly depend on expert experience, which leads to obvious deficiencies in diagnostic efficiency, especially in terms of accuracy. To address the issues of traditional bearing fault diagnosis relying on expert experience and low efficiency and accuracy of fault diagnosis, a bearing fault diagnosis model combining synchrosqueezed wavelet transform (SWT) and SE-ResNet is proposed. Firstly, the one-dimensional non-stationary bearing vibration signal is converted into a high-frequency two-dimensional time-frequency image through SWT, which serves as the input for the convolutional neural network;Secondly, an attention mechanism is introduced to construct an SE-ResNet model for extracting two-dimensional image features, which enhances the attention of important features and thus enhances the network’s representation ability;Finally, the results of fault diagnosis are output through Softmax layer. To validate the capacity of the proposed model, experiments with the bearing dataset collected from Case Western Reserve University (CWRU) were conducted. The results show that the transfer learning accuracy of the CWRU dataset is 86.25% to 99%. This method has good feasibility, good convergence performance, and has high practical application value. © 2024 Copyright held by the owner/author(s).

关键词： Synchros

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Multi-Objective Trapping of UAVs Swarm based on Gene Regulatory Networks 13

Multi-Objective Trapping of UAVs Swarm Based on Gene Regulat...

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13th IEEE international conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Xu, Peng Shi, Ze Fan, Zhun College of Engineering International Cooperation Base of Evolution Intelligence and Robotics of Guangdong Province Shantou University Shantou515063 China

ISBN: (纸本)9798350315196

Encirclement mode formation in swarm robotic systems is a long-term hot research topic. Most of the encirclement control models designed by traditional methods are based on global information. This paper proposes a distributed unmanned swarm control method. The approach is based on gene regulatory network models and does not rely on any direct communication. The gene regulatory network model will generate a concentration field based on the input target and obstacle information, which will be used by the unmanned swarm to autonomously assign targets and guide the unmanned swarm to move towards the target. In this method, the UAV only needs to obtain the local information within the sensing range through the sensors carried by itself, and can complete the assignment and encirclement tasks of multiple targets by itself, getting rid of the unmanned swarm's dependence on global information. Finally, the validity and reliability of the proposed model are verified by simulating in an obstacle environment. © 2023 IEEE.

关键词： Genes

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