2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)最新文献_第4页

Large-Scale Distance Matrix Calculation Method Based on Contraction Hierarchies 基于收缩层次的大规模距离矩阵计算方法

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906326

Weijun Wang, Haixia Pan, Yefan Cao

引用次数: 0

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906374

Kun Yu

引用次数: 0

A Small Target Detection Method Based on Feature Enhancement and Positioning Optimization 基于特征增强和定位优化的小目标检测方法

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906335

Qingshu Li, Xiǎohóng Shí, Qi Xu, Wei Huang, Peng Yang

{"title":"A Small Target Detection Method Based on Feature Enhancement and Positioning Optimization","authors":"Qingshu Li, Xiǎohóng Shí, Qi Xu, Wei Huang, Peng Yang","doi":"10.1109/CCET55412.2022.9906335","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906335","url":null,"abstract":"Feature Pyramid Network (FPN) is a basic but important component in target detection system. Together with target detection algorithms, such as SSD, Faster R-CNN and YOLO series, they have achieved good detection results for large targets with high resolutions, but the performance is less effective when it comes to detect small targets that contain relatively little semantic information. And small target detection is quite common in daily life, such as face recognition at long distances, traffic sign detection in automatic driving, etc. That means it is significant to break the bottleneck of target detection and get better accuracy performance. In this article, based on the FPN, we propose an improved network structure (IMFPN) from two aspects to get a better accuracy result in small target detection task. In the first aspect, we improve the feature map pyramid structure for feature enhancement, reduce the problem of information loss during feature map fusion and get the semantic information of multiscale feature maps. In the second aspect, we concentrate on the problem of information loss in the pooling process of feature maps, we propose an improved version of the PRRoI pooling method that combines RoI Pooling and RoI Align Pooling. And we also optimize the positioning of the frame through a new IoU calculation standard. Based on these above ideas and methods, we propose a small target detection method based on feature enhancement and positioning optimization.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"974 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116203658","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

Vision Transformer Based on Knowledge Distillation in TCM Image Classification 基于知识升华的中医图像分类视觉变换

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906332

Ge Yuyao, Cheng Yiting, Wang Jia, zhou Hanlin, Chen Lizhe

引用次数: 2

An Improved Clock Cycle Measurement Method for High-Speed Serial Signal with Duty-Cycle-Distortion Jitter 含占空比失真抖动的高速串行信号时钟周期测量方法的改进

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906325

Tong Wu, K. Song, Hongwei Zhao

{"title":"An Improved Clock Cycle Measurement Method for High-Speed Serial Signal with Duty-Cycle-Distortion Jitter","authors":"Tong Wu, K. Song, Hongwei Zhao","doi":"10.1109/CCET55412.2022.9906325","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906325","url":null,"abstract":"Software clock data recovery (CDR) is a critical component of a high-speed serial link that recovers the reference clock from a serial signal, which are generally used in real-time sampling oscilloscopes and electronic design automation software. The common method for calculating the period of the reference clock is to extract the rising and falling edges of the signal, and then calculate the difference between adjacent transition edges, so that a pulse width sequence is obtained. By performing statistics on this sequence, it can be found that the value of the position of the first peak of the statistical histogram is the period of the reference clock, the position of the second peak is twice the period of the clock, etc. The period of the clock can be calculated by weighted average. However, high-speed serial signals inevitably have duty-cycle-distortion (DCD) jitter, and DCD seriously affect the determination of the position of the peak of the above statistical histogram, there are many spurious peaks near the actual peak. This article proposes an improved method of calculating the clock period, which can eliminate the influence of DCD and to obtain an accurate clock period.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114166681","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

Trusted Management Infrastructure with Blockchain for Edge Device in Smart City 基于区块链的智慧城市边缘设备可信管理基础设施

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906373

Xiao-qiang Wang, Chen Zhang, Xiao Chang

{"title":"Trusted Management Infrastructure with Blockchain for Edge Device in Smart City","authors":"Xiao-qiang Wang, Chen Zhang, Xiao Chang","doi":"10.1109/CCET55412.2022.9906373","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906373","url":null,"abstract":"As smart cities continue to be developed, network traffic surges. Although edge computing improves the quality of service by shifting computing tasks from cloud computing centers to locations closer to the edge through a multi-layer distributed computing model, there are security risks in the face of central node failures and internal malicious attacks, which make it difficult to provide stable and reliable services. In this paper, we design a trusted management model of edge device in smart city with blockchain, introducing blockchain technology into the research of smart city construction. It uses the blockchain distributed architecture and decentralization idea to realize the trusted collection and storage of sensory data. And based on this architecture, a new reputation-based PoW consensus algorithm is proposed to provide a trust mechanism for IoT devices. The algorithm greatly increases the cost of malicious attacks launched by the IoT device, effectively prevents malicious attacks on devices, and achieves trusted management of edge device behavior. The smart city application based on the proposed method verifies its feasibility, effectively prevents malicious attacks from nodes, and enhances the information security of the system.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124051915","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: 1

Driver Identification with Time and Frequency Features Derived from Vehicular Acceleration Data 基于车辆加速度数据的时频特征驾驶员识别

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906391

Sheng-Kai Lin

{"title":"Driver Identification with Time and Frequency Features Derived from Vehicular Acceleration Data","authors":"Sheng-Kai Lin","doi":"10.1109/CCET55412.2022.9906391","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906391","url":null,"abstract":"With the increase of the related facilities and services around the driver, the driver’s identity becomes more and more important, so the research on the driver’s identification is also increasing gradually. Since the emergence of the online car-hailing platform represented by Uber, people’s travel has become more and more convenient, but there have also been many problems surrounding the identity of the driver. For example, the actual information of the driver does not match the information registered on the platform, which increase safety risk for passengers. In this paper, we propose a novel driver identification scheme that first converts the raw data of the x and y axes of the accelerometer into feature vectors by a novel data transformation method which adds frequency domain perspective on the basis of time domain perspective, adopts sliding window and fast Fourier transform and then uses these feature vectors as the input of neural network. Finally, we identify the driver through our designed driver identification algorithm, which accepts as input the probability distribution of the network output. In our experiments, we experiment with 10 drivers and use accuracy, precision, and recall as outcome metrics. Experimental results show that the performance based on time and frequency features is better than that of time or frequency features alone.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124132502","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

Joint optimization of Task Offloading and Computing Resource Allocation in MEC-D2D Network MEC-D2D网络中任务卸载与计算资源分配的联合优化

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906362

Zhaoyuan Liu, Jingyi Fan, S. Geng, Peng Qin, Xiongwen Zhao

引用次数: 0

Marine Predators Algorithm Based on Trigonometric Function 基于三角函数的海洋捕食者算法

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906320

Xinyu Zou, Jinhong Li

引用次数: 0

Research on Banana Leaf Disease Detection Based on the Image Processing Technology 基于图像处理技术的香蕉叶片病害检测研究

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906377

Huang Jianqing, Yuan Qi, Liu Debing, Zhang Jiarong

{"title":"Research on Banana Leaf Disease Detection Based on the Image Processing Technology","authors":"Huang Jianqing, Yuan Qi, Liu Debing, Zhang Jiarong","doi":"10.1109/CCET55412.2022.9906377","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906377","url":null,"abstract":"The banana diseases directly affected banana quality and yield, so a detection method for banana leaf diseases based on image processing is proposed. Firstly, the color segmentation method is used to remove the green background such as healthy leaves, and weed from original banana leaf disease image collected by smart phone. And the segmented image is converted to V component gray image of YUV color space and then the Ostu segmentation which uses the minium intra-cluster or the maximum inter-cluster gray variance to segment the image, and the area threshold method are used to remove the non green background such as soil, and dead grass, thus extracting a complete disease regions. Next, these color features such as the color means, color variances, and color skewnesses of R, G, B, Y, U and V component in the disease regions, are extracted and secleted to construct a set of feature vector of each banana leaf disease with standard image samples. Finally, the minimum euclidean distance classifier is used for banana leaf disease recognition. The results show that the method can effectively segment the disease regions from the original banana leaf disease image and has high recognition rate with 91.7% for gray leaf spot disease and 90% for sigatoka leaf spot disease. Therefore, it is proven that this method has high accuracy and reliability in banana leaf disease recognition, having better promotion and application value.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114448280","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