2023 5th International Conference on Electronic Engineering and Informatics (EEI)最新文献_第5页

Enabling Transcranial Electrical Stimulation via STI01: Experimental Simulations and Hardware Circuit Implementation 通过STI01实现经颅电刺激:实验模拟和硬件电路实现

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212634

Guanjie Xu, Gaomin Su, Hao Fang, Yue Li

引用次数: 0

Design of Communication Countermeasures Blue Team Operation Based on Case Knowledge 基于案例知识的沟通对策蓝队运作设计

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212526

Zi-xuan He, Bo Liu, Yong Wang, Ye-jun Li

引用次数: 0

Design of Mobile Phone Back Clip for Field Emergency Communication Based on Beidou Short Message 基于北斗短消息的现场应急通信手机背夹设计

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212869

Songyi Wang, Yunfei Du, Xiangzhu Hou, Xinming Huang

引用次数: 0

Research on Dual Band Covariant Correlation Fusion Radar Fuze Detection Method 双波段协变相关融合雷达引信探测方法研究

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212716

Qianyu Wang, Lingyi Wu, Xi Pan

{"title":"Research on Dual Band Covariant Correlation Fusion Radar Fuze Detection Method","authors":"Qianyu Wang, Lingyi Wu, Xi Pan","doi":"10.1109/EEI59236.2023.10212716","DOIUrl":"https://doi.org/10.1109/EEI59236.2023.10212716","url":null,"abstract":"With the increasing complexity of the electromagnetic environment and the continuous innovation of modern jammer technology, radar fuze is facing more and more complex situation in combat. Therefore, the multimode composite detection radar fuze has been gradually developed. As a typical mode of composite detection, dual-band detection improves the radar fuze performance of target detection and anti-jamming capability by fusing the information obtained from two bands. This paper proposes a dual band covariance correlation fusion radar fuze detection method to improve the anti-jamming capability of radar fuze and detection capability at low signal-to-noise ratio. The transmitted signal is designed as a FMCW waveform with dual band collaboratively and randomly hopping parameters, and the beat signal with randomly hopping parameters is adaptively fused by the dual band covariant correlation fusion algorithm to achieve accurate ranging at low signal-to-noise ratio. The simulation results demonstrate that the dual band covariant correlation fusion algorithm has satisfying detection performance, can improve the signal-to-noise ratio and has excellent anti-jamming performance.","PeriodicalId":363603,"journal":{"name":"2023 5th International Conference on Electronic Engineering and Informatics (EEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134362700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Ultra -wideband Indoor Positioning Base Station Location Based on Improved Particle Swarm Optimization Algorithm 基于改进粒子群算法的超宽带室内定位基站定位研究

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212636

Rui Xue, Zedong Liang

{"title":"Research on Ultra -wideband Indoor Positioning Base Station Location Based on Improved Particle Swarm Optimization Algorithm","authors":"Rui Xue, Zedong Liang","doi":"10.1109/EEI59236.2023.10212636","DOIUrl":"https://doi.org/10.1109/EEI59236.2023.10212636","url":null,"abstract":"In order to explore the influence of Ultra-Wide Band (UWB) base station location on indoor positioning accuracy, a UWB-based three-dimensional spatial positioning model is established, and the UWB base station location is equivalent to the intelligent optimization problem of multiple points. Particle Swarm Optimization (PSO) algorithm is a classical intelligent optimization algorithm, which can be used to solve the UWB base station location problem through function mapping. Aiming at the problem of slow convergence and easy to fall into local optimum in the location process of traditional PSO algorithm, a particle swarm update strategy is introduced into this paper. The strategy introduces an adaptive mutation mechanism in the PSO algorithm, and updates the particle state twice according to the probability principle to improve the traversal and optimization ability of the particle swarm. Theoretical analysis and experimental results show that under the same optimal fitness, the improved PSO compared with PSO algorithm not only converges faster, but also has higher location accuracy; under the same parameters and simulation conditions, the improved PSO compared with PSO algorithm has shorter execution time.","PeriodicalId":363603,"journal":{"name":"2023 5th International Conference on Electronic Engineering and Informatics (EEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124033765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Marine Radar Small Target Classification Based on Normalized Time-Frequency Image and Residual Network 基于归一化时频图像和残差网络的舰船雷达小目标分类

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212915

Jing-Yi Li, P. Shui

{"title":"Marine Radar Small Target Classification Based on Normalized Time-Frequency Image and Residual Network","authors":"Jing-Yi Li, P. Shui","doi":"10.1109/EEI59236.2023.10212915","DOIUrl":"https://doi.org/10.1109/EEI59236.2023.10212915","url":null,"abstract":"In this article, a small target classification method is proposed, which can effectively distinguish different types of sea surface targets under different sea states and different radars. The proposed classification method is mainly based on normalized time-frequency spectrograms and Residual Network (ResNet). The time-frequency images of the radar returns reflect the motion characteristics of the target, the fluctuation of the target Radar Cross Section (RCS) and the relationship between the target and the wave. The effect of different radar parameters on target classification can be eliminated by standardizing range resolution and carrier frequency of radar returns. By exploiting ResNet, the proposed method can effectively extract the deeply hidden features of normalized time-frequency images and solve the problems of gradient disappearance, gradient explosion and degradation in deep network. Datasets consisting of four types of targets, floating ball, floating boat, yacht and low altitude Unmanned Aerial Vehicle (UAV) are constructed to test the performance of the proposed method. The experiment results show that the proposed classifier combines normalized time-frequency images and ResNet attains better classification performance compared with the existing classification methods.","PeriodicalId":363603,"journal":{"name":"2023 5th International Conference on Electronic Engineering and Informatics (EEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129163430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scaled IoT Intrusion Detection Model based on Improved PSO Algorithm Optimization 基于改进粒子群算法优化的规模化物联网入侵检测模型

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212914

Yongrui Wang, Han Yang, He Liu, Haoyang Dang

{"title":"Scaled IoT Intrusion Detection Model based on Improved PSO Algorithm Optimization","authors":"Yongrui Wang, Han Yang, He Liu, Haoyang Dang","doi":"10.1109/EEI59236.2023.10212914","DOIUrl":"https://doi.org/10.1109/EEI59236.2023.10212914","url":null,"abstract":"An improved convolutional nveural network intrusion detection training model is proposed to address the issues of slow convergence and low accuracy that may arise from the large randomness of initial parameters in traditional neural network intrusion detection models. The proposed approach utilizes an enhanced particle swarm optimization (PSO) algorithm to optimize the convolutional neural network intrusion detection model, thereby improving its global search capability. The improved PSO algorithm is employed to optimize the neural network hyperparameters, with the loss function used as the population location. Once the optimal parameters are obtained, a scaled convolutional neural network is constructed, and the model is trained using backpropagation. The initial model defaults to using the Gram's corner field method to transform the data into three-dimensional format, incorporating a softpool pooling layer to enhance feature extraction. Additionally, a scaled neural network structure is employed to maximize the perceptual field, and the CUDA parallel computing method is utilized to accelerate computation speed. Experimental results demonstrate that the proposed method exhibits certain advantages over the traditional PSO algorithm in terms of optimizing the neural network intrusion detection training method, as evaluated comprehensively in terms of efficiency and accuracy. These findings highlight the potential of the proposed method for improving the efficiency and accuracy of neural network intrusion detection training when compared to the traditional PSO algorithm.","PeriodicalId":363603,"journal":{"name":"2023 5th International Conference on Electronic Engineering and Informatics (EEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128736414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on N4 Interface Decoupling Test N4接口解耦测试研究

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212765

Aihua Zhang, Peng Wang, Yuanyang Yin

引用次数: 0

Beam based RAN Slicing and Tenant Profit Optimization 基于波束的RAN切片与租户利润优化

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212695

Xiao Fu, Xinrui Gong, Xiaofeng Liu, Rui Sun, Qingguo Shen, Xiqi Gao

引用次数: 0

TCAM-Hash: A Combined Routing Lookup for Network Applications TCAM-Hash:用于网络应用的组合路由查找

2023 5th International Conference on Electronic Engineering and Informatics (EEI) Pub Date : 2023-06-30 DOI: 10.1109/EEI59236.2023.10212979

Keran Zhang, Wenjun Huang, Nailiang Kuang, Sheng Zhong, Yong Liang, Hangzai Luo

引用次数: 0