2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)最新文献_第3页

Location Based Channel Resource Allocation for V2V Communications 基于位置的V2V通信信道资源分配

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995793

Xinyu Xie, Jianghong Shi, Qi Yang

引用次数: 0

A Cityscape Image Detail Extraction Enhancement Method for Lightweight Semantic Segmentation 基于轻量级语义分割的城市景观图像细节提取增强方法

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995858

Xinhe Yu, Huarong Xu, Lifen Weng

引用次数: 1

A Fast Current Sensing Front-End IC Design for Nanopore-Based DNA Sequencing 基于纳米孔DNA测序的快速电流传感前端集成电路设计

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995851

Xu Liu, Qiumeng Fan, Xin Hu, Peiyuan Wan, Zhijie Chen

引用次数: 0

Joint Spectrum Resource Allocation and Power Control for LTE- V2V Communication LTE- V2V通信联合频谱资源分配与功率控制

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995754

Shujie Wu, Qi Yang, Xuemin Hong

引用次数: 0

Analysis and Optimization of the Branch Prediction Unit of SweRV EH1 SweRV EH1支路预测单元分析与优化

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9996038

Changbiao Yao, Ziqin Meng, Wen Guo, Jianyang Zhou, Zichao Guo

{"title":"Analysis and Optimization of the Branch Prediction Unit of SweRV EH1","authors":"Changbiao Yao, Ziqin Meng, Wen Guo, Jianyang Zhou, Zichao Guo","doi":"10.1109/ASID56930.2022.9996038","DOIUrl":"https://doi.org/10.1109/ASID56930.2022.9996038","url":null,"abstract":"With the continuous improvement of processor performance requirements, technologies such as superscalar, deep pipeline, and multi-core which can improve instruction parallelism are frequently used. Under this technical background, branch prediction errors will increase the delay used to flush the pipeline and greatly reduce the performance of the processor. Therefore, for high-performance processors, branch predictors with high prediction accuracy are particularly important. Based on the open source RISC-V processor core SweRV EH1, this paper adopts two prediction predictors, the hybrid predictor, and the TAGE predictor to improve the prediction performance of the original processor. This paper uses the riscv-tests self-checking test scheme to verify the instruction set of the optimized processor and completes the prototype verification on the Kintex-7 KC705 FPGA. Based on PowerStone and CoreMark test programs, this paper separately evaluates the branch prediction performance and processor performance of the processor core with two kinds of branch predictors. Experiments show that the implementation of the hybrid predictor and the TAGE predictor respectively improves the branch prediction accuracy of PowerStone programs by 3.65% and 3.39%; the average branch prediction rate respectively reaches 85.98% and 90.06%. The performance of SweRV EH1 is respectively improved by 2.56% and 5.43%.","PeriodicalId":183908,"journal":{"name":"2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126617768","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

Efficient Automatic Detection of Uterine Fibroids Based on the Scalable EfficientDet 基于可扩展的高效检测系统的子宫肌瘤自动检测

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9996062

Tiantian Yang, P. Li, Peizhong Liu

{"title":"Efficient Automatic Detection of Uterine Fibroids Based on the Scalable EfficientDet","authors":"Tiantian Yang, P. Li, Peizhong Liu","doi":"10.1109/ASID56930.2022.9996062","DOIUrl":"https://doi.org/10.1109/ASID56930.2022.9996062","url":null,"abstract":"Uterine fibroids refer to benign tumors formed by uterine smooth muscle tissue hyperplasia, high frequency in women between 30 and 50 years old. By the age of 50 years, 80% of women have one or more uterine fibroids, and about half of these patients are symptomatic and in need of treatment. It's ranking the third highest incidence of all gynecological diseases. Generally, it is a benign tumor, but it can also have certain effects on women's bodies, such as causing infertility. Early detection and treatment are essential measures to reduce morbidity. Ultrasound is the preferred imaging method, and with the continuous development of deep learning in the field of medical image analysis, many applications related to object detection have good performance. Computer-assisted diagnosis can further solve the subjective uncontrollability problem caused by different doctors' reading films. Because doctors' inexperience and fatigue can reduce the diagnostic accuracy of uterine fibroids, this paper proposes a scalable EfficientDet to detect the ultrasound images of uterine fibroids and uses the Convolutional Neural Network (CNN) to extract their features. The backbone network uses EfficientNet, and then it is used together with BiFPN to improve the accuracy of the model. This method can not only benefit non-professional ultrasonologists but also provide sufficient auxiliary diagnostic effects for high-quality ultrasonologists to provide a reliable basis for future treatment and surgical resection. Finally, the effectiveness of this method is experimentally compared with other existing methods. Our method has an average accuracy of 98.88% and an f1-score of 98%. We demonstrate that the methods of this study are superior to other neural networks. And it can bring sufficient benefits to ultrasonologists. We summarize and analyze various detection algorithms, and discuss their possible future research hotspots.","PeriodicalId":183908,"journal":{"name":"2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123513757","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

Congenital Heart Defect Recognition Model Based on YOLOV5 基于YOLOV5的先天性心脏缺陷识别模型

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995989

Huiling Wu, Bingzheng Wu, S. He, Peizhong Liu

{"title":"Congenital Heart Defect Recognition Model Based on YOLOV5","authors":"Huiling Wu, Bingzheng Wu, S. He, Peizhong Liu","doi":"10.1109/ASID56930.2022.9995989","DOIUrl":"https://doi.org/10.1109/ASID56930.2022.9995989","url":null,"abstract":"Congenital heart defect is an abnormality of the atrial ventricle or the large vascular structure connected to it. It is currently the most common fetal congenital defect, and the incidence accounts for about 30% of congenital defects. Fetal heart abnormalities ultrasound planes screening and the diagnosis of fetal heart defect is an important part of prenatal screening. In China, there is a large population base and obvious differences in medical resources in different regions. In this case, it is difficult for sonographers to diagnose congenital heart defect, and sonographers with rich experience and relevant qualifications are required to make the diagnosis, but the resources of sonographers are limited. This study proposes a deep learning method based on convolutional neural network (YOLOv5) to automatically identify and classify whether fetal-related cardiac ultrasound planes are abnormal. This study method can effectively identify and remind the sonographers of the possible abnormal fetal heart ultrasound section, improve the work efficiency of the sonographers, and reduce the burden of the sonographers. All the datasets used in this method are from university cooperative hospitals with a data volume of 1695, which can be divided into abnormal planes training set (595), normal planes training set (800) and anomalous planes test set (146), and normal planes test set (154). The Mean Average Precision (MAP) on the validation set reached 96.1%, the precision reached 85.2% and recall reached 96.5% in multiple repeated trials. We conduct some comparative experiments with different neural network methods and demonstrate that this method can not only improve the diagnostic efficacy of sonographers on congenital heart defect, but also hope to provide high-quality teaching tools to help low-qualified sonographers pay attention to and learn about fetal congenital heart defects.","PeriodicalId":183908,"journal":{"name":"2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124857768","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

Fine-Tuning Music Generation with Reinforcement Learning Based on Transformer 基于Transformer的强化学习微调音乐生成

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995896

Xuefei Guo, Hongguang Xu, Ke Xu

引用次数: 0

Layer Resolved Defect Detection in Transparent Plate 透明板的层分辨缺陷检测

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995826

Xing Zheng, Xianjin Lin, Yun Gao, Peng Zheng, Lei Wang

引用次数: 0

MIMO Design and Experimental Verification of Wall Perspective Imaging Radar System 墙透视成像雷达系统的MIMO设计与实验验证

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9996082

Yunhua Ding, Zhihua He, Tao Liu, Xiaoji Song, Yi Su

{"title":"MIMO Design and Experimental Verification of Wall Perspective Imaging Radar System","authors":"Yunhua Ding, Zhihua He, Tao Liu, Xiaoji Song, Yi Su","doi":"10.1109/ASID56930.2022.9996082","DOIUrl":"https://doi.org/10.1109/ASID56930.2022.9996082","url":null,"abstract":"Wall Perspective imaging radar uses the penetrating property of electromagnetic waves to image the inside of media, which can be applied to the detection of pipelines and buried objects inside walls, security and other fields, and has important application value. The existing wall Perspective imaging radar mostly adopts single transmitting and single receiving system, which cannot meet the demand of fast scanning imaging, and the penetrating imaging radar of MIMO system is less studied. To address this problem, this paper proposes a perspective imaging MIMO radar array design method based on the equivalent array. The method uses the principles of equivalent phase center and spatial convolution to transform the MIMO array design into virtual equivalent array design, firstly, the virtual equivalent array is designed according to the radar system index, and then the MIMO configuration is designed by combining the near-field array beam coverage. In addition, a wall perspective MIMO radar imaging system is built using a vector network analyzer and a 2D mobile platform to verify the feasibility of the proposed wall perspective imaging MIMO radar array design method.","PeriodicalId":183908,"journal":{"name":"2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122042226","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