2023 6th World Conference on Computing and Communication Technologies (WCCCT)最新文献_第3页

Reconfigurable Intelligent Surface Aided DFRC Vehicular Networks 可重构智能地面辅助DFRC车辆网络

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052208

Junming Feng, Peichang Zhang, Lei Huang, Gong-bin Qian

{"title":"Reconfigurable Intelligent Surface Aided DFRC Vehicular Networks","authors":"Junming Feng, Peichang Zhang, Lei Huang, Gong-bin Qian","doi":"10.1109/WCCCT56755.2023.10052208","DOIUrl":"https://doi.org/10.1109/WCCCT56755.2023.10052208","url":null,"abstract":"Reconfigurable Intelligent Surface (RIS) has been considered as a promising technology for 6G networks, which is capable of directionally reflecting the incident signal by adjusting phase shift of its reflecting elements, and thus to achieve beamforming. In this paper, we investigate the potential of employing RIS in dual-functional radar-communication (DFRC) vehicular networks. Considering a vehicle-to-infrastructure (V2I) communication in non-line-of-sight(N-LoS) scenario, where RIS is deployed to establish sensing and communication links, we propose a scheme to optimize the beamforming based on the detected vehicle position for the sake of avoiding the relatively large channel estimation overhead. More specifically, we build a codebook to maintain the optimal phase shift of RIS corresponding to each candidate angle offline, which can be used for detecting the direction of vehicle in N-LoS scenario. In addition, the position-based channel state information (CSI) is introduced and used for maximize the downlink rate. Simulation results show that by deploying RIS in DFRC vehicular networks, the base station can detect the vehicle position in N-LoS scenario and provide it with reliable communication rates without channel estimation.","PeriodicalId":112978,"journal":{"name":"2023 6th World Conference on Computing and Communication Technologies (WCCCT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132313731","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

The Analysis of Mobility Patterns during the COVID-19 Pandemic in Thailand Using Time Series Clustering 使用时间序列聚类分析泰国COVID-19大流行期间的流动模式

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052489

Weeriya Supanich, Suwanee Kulkarineetham, B. Vanishkorn

引用次数: 0

A 2.4 GHz SiGe Envelope Tracking Power Amplifier for LTE Applications 用于LTE应用的2.4 GHz SiGe包络跟踪功率放大器

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052136

YongKang Jing, Bihui Zhu

引用次数: 0

RIS Relaying UAV-Aided WPCN for Throughput Maximization RIS中继无人机辅助WPCN的吞吐量最大化

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052414

Xinhong Pan, Zhihai Zhang, Heyun Lin, Jiaying Zhang

引用次数: 2

WCCCT 2023 Cover Page wcccc2023封面

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/wccct56755.2023.10052164

引用次数: 0

A Chinese Speech Recognition System Based on Binary Neural Network and Pre-processing 基于二值神经网络和预处理的汉语语音识别系统

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052123

Lunyi Guo, Yiming Deng, Liang Tang, Ronggeng Fan, Bo Yan, Zhuoling Xiao

{"title":"A Chinese Speech Recognition System Based on Binary Neural Network and Pre-processing","authors":"Lunyi Guo, Yiming Deng, Liang Tang, Ronggeng Fan, Bo Yan, Zhuoling Xiao","doi":"10.1109/WCCCT56755.2023.10052123","DOIUrl":"https://doi.org/10.1109/WCCCT56755.2023.10052123","url":null,"abstract":"Neural networks have made excellent progress in the field of speech recognition. However, more research needs to be done in some scenarios where computational resources are limited or real-time, and low power consumption is required. In this paper, we propose a lightweight speech recognition model based on pre-processing + binary neural network, which can significantly reduce the number of weight parameters while ensuring an acceptable error rate. The speech pre-processing part converts the 1D speech signal to the 2D Mel spectrum and uses Voice Activate Detection (VAD) to make the speech Mel spectrum input variable. The speech data set is also expanded using data augmentation methods. For convolutional layers, the weights are binarized to reduce the number of model parameters and improve computational and storage efficiency. The number of model parameters after quantization is 6.94% of the number of full precision model parameters, and the error rate on the ST CMD speech dataset increases by only 2.07%. Finally, a circuit structure based on binary weights for convolutional computation is designed, and a single multiplication can be implemented using only the hardware resources of the 7 Look Up Table (LUT).","PeriodicalId":112978,"journal":{"name":"2023 6th World Conference on Computing and Communication Technologies (WCCCT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126021091","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

Review of 5G MIMO Enhancement Technologies 5G MIMO增强技术综述

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052403

Hua Xu, Jincan Xin, Sen Xu, Hua Zhang, S. Xiong

引用次数: 1

Hazardous Behavior Identification Based on BIM and AutoML Applied to Prefabricated Construction 基于BIM和AutoML的装配式建筑危险行为识别

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052373

Nairui Hou

{"title":"Hazardous Behavior Identification Based on BIM and AutoML Applied to Prefabricated Construction","authors":"Nairui Hou","doi":"10.1109/WCCCT56755.2023.10052373","DOIUrl":"https://doi.org/10.1109/WCCCT56755.2023.10052373","url":null,"abstract":"With the continuous development of China’s construction projects, safety hazards have emerged endlessly. Safe and standard construction practices are essential in construction projects. In recent years, construction safety accidents caused by construction workers not wearing helmets correctly and safety accidents caused by workers smoking and igniting flammable materials to start site fires have accounted for a large proportion of the safety accidents that have occurred on major construction sites. However, traditional manual supervision is time-consuming, inefficient, subjective, and sometimes challenged by conflicts of interest. Over the years, deep learning-based target object detection has achieved satisfactory results, but the complex deep learning setup is not easily mastered by construction personnel. Therefore, it is of great practical importance to prevent and reduce construction safety accidents by automatically detecting behavioral norms for personnel entering job sites. To this end, this paper presents an automated machine learning (AutoML) framework for helmet detection and smoking recognition. This paper aims to benchmark the AutoML algorithm for helmet detection and smoking. The final results of the experiments show that for helmets, AutoML could achieve 99.0% precision, 91.89% recall, and 99.24% mAP, which demonstrates the usability of AutoML in the construction datasets.","PeriodicalId":112978,"journal":{"name":"2023 6th World Conference on Computing and Communication Technologies (WCCCT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133344878","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

Snapshot Ensemble One-Dimensional Convolutional Neural Networks for Ballistic Target Recognition 用于弹道目标识别的快照集成一维卷积神经网络

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052300

Qian Xiang, Xiaodan Wang, Jie Lai, Yafei Song, Jiaxing He, Lei Lei

{"title":"Snapshot Ensemble One-Dimensional Convolutional Neural Networks for Ballistic Target Recognition","authors":"Qian Xiang, Xiaodan Wang, Jie Lai, Yafei Song, Jiaxing He, Lei Lei","doi":"10.1109/WCCCT56755.2023.10052300","DOIUrl":"https://doi.org/10.1109/WCCCT56755.2023.10052300","url":null,"abstract":"Aiming at improving the performance of ballistic target HRRP recognition, a ballistic target HRRP recognition method based on snapshot ensemble one-dimensional convolutional neural network (SSE-1DCNN) is proposed. The snapshot ensemble model is constructed by integrating a single 1DCNN estimator with a cosine annealing scheduler. The snapshot ensemble integrates 1DCNN estimators at different loss function minima in the same training process, which ensures the diversity of training estimators and avoids the increase in training cost for estimator ensemble. In addition, the AdamW algorithm is introduced to improve the convergence speed and degree of the ensemble training. The experimental results show that the use of snapshot ensemble can effectively improve the recognition of ballistic target HRRP by the 1DCNN, and the introduction of AdamW effectively improves the convergence effect. Compared with a single 1DCNN estimator, the optimal average recognition accuracy of SSE-1DCNN is improved by 0.31% withinside the hyperparameter experimental setting ranges.","PeriodicalId":112978,"journal":{"name":"2023 6th World Conference on Computing and Communication Technologies (WCCCT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125972288","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: 3

Timestamp Free Synchronization with Clock Skew Estimation in Wireless Sensor Networks 基于时钟偏差估计的无线传感器网络无时间戳同步

2023 6th World Conference on Computing and Communication Technologies (WCCCT) Pub Date : 2023-01-06 DOI: 10.1109/WCCCT56755.2023.10052472

Shaoqing Wang, Lei Sun

引用次数: 0