2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)最新文献_第2页

Non-Quantization Keys-Based Physical Layer Secure Transmission Scheme Using One-Time Pad 基于非量化密钥的一次性键盘物理层安全传输方案

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9996019

Meng Wang, Kaizhi Huang, Zheng Wan, Xiaoli Sun, Zengchao Geng, Kai Zhao

引用次数: 0

A QSPI Interface Supporting ‘Bits-Decoding’ for High-Speed Access to Flash 支持“位解码”的高速访问Flash的QSPI接口

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995778

Yu Zhang, Chongfei Shen, Lang Tan, Zhiyuan Gao, Zhijie Chen, Xu Liu, Peiyuan Wan

引用次数: 0

Hybrid Attention Cascade Multi-View Stereo Network 混合注意级联多视点立体网络

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995975

Weiqiang Liu, Rongshan Chen, Huarong Xu, Lifen Weng

引用次数: 0

Intelligent Root Cause Detection for LTE Network Fault Based on Machine Learning 基于机器学习的LTE网络故障智能根本原因检测

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995909

Junyi Tang, Shouliang Li

{"title":"Intelligent Root Cause Detection for LTE Network Fault Based on Machine Learning","authors":"Junyi Tang, Shouliang Li","doi":"10.1109/ASID56930.2022.9995909","DOIUrl":"https://doi.org/10.1109/ASID56930.2022.9995909","url":null,"abstract":"With the Long-Term Evolution (LTE) of mobile networks, the types of network fault have become more complex and diverse. In order to ensure reliable and safe run of the network, it is increasingly difficult for traditional manual administration and maintenance to cope with the complicated and heavy network faults. The super machine learning ability of artificial intelligence can well realize intelligent prediction and processing of network faults by sorting and analyzing a large amount of Key Performance Indicator (KPI) data on network operation and maintenance. In the face of complex network fault types, the number of labeled KPI samples used for training the model is very limited and it is difficult to obtain the samples. This paper proposes a multiclass classification algorithm based on semi-supervised transductive support vector machine for fault root cause detection in LTE networks, and compares its performance with supervised learning and unsupervised learning algorithms. Theoretical analysis and achieved results show that the algorithm proposed in this paper achieves a good learning effect on the mixed sample training set composed of a small number of labeled samples and a large number of unlabeled samples, and has a high classification accuracy for unlabeled samples.","PeriodicalId":183908,"journal":{"name":"2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"7 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":"122619986","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

A Higher Performance Accelerator for Resource-Limited FPGA to Deploy Deeper Object Detection Networks 资源有限的FPGA部署更深层次目标检测网络的高性能加速器

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995953

Hao Yu, Sizhao Li

{"title":"A Higher Performance Accelerator for Resource-Limited FPGA to Deploy Deeper Object Detection Networks","authors":"Hao Yu, Sizhao Li","doi":"10.1109/ASID56930.2022.9995953","DOIUrl":"https://doi.org/10.1109/ASID56930.2022.9995953","url":null,"abstract":"Nowdays, CNN models become more and more popular in lots of fields due to its high performance. Unfortuna-tely, the complexity of the model increases along with accuracy, which limited its applications in some fields. Until now, a lot of researches are focused on some shallow networks such as Alexnet, VGG16. The state of art models in computer vision has over one hundred layers using ResNet structure. Besides, the power consumption of the model and latency of inference also leads to the difficulties to use AI models in reality. To solve the problem, we proposed an accelerator structure to apply yolov5 model to FPGA boards. Two types of parallelisms and pipeline structure are applied. Besides, to eliminate the time of loading and saving to off-chip buffer, ping-pong buffer are used. We improve the pipeline performance by rescheduling the mac operation. Eventually, we test the performance of accelerator on ZC702. So it can be easily implemented on some resource-limited boards. The acc-elerator can speed up the inference 6 times than CPU, 17.4 times than ARM CPU on ZC702. And the throughput of single DSP outperforms the previous works.","PeriodicalId":183908,"journal":{"name":"2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"16 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":"117072638","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

A PCA Based SVM Hardware Trojan Detection Approach 基于PCA的SVM硬件木马检测方法

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995991

Peng Liu, Liji Wu, Zhenhui Zhang, Dehang Xiao, Xiangmin Zhang, Lili Wang

{"title":"A PCA Based SVM Hardware Trojan Detection Approach","authors":"Peng Liu, Liji Wu, Zhenhui Zhang, Dehang Xiao, Xiangmin Zhang, Lili Wang","doi":"10.1109/ASID56930.2022.9995991","DOIUrl":"https://doi.org/10.1109/ASID56930.2022.9995991","url":null,"abstract":"In recent years, with the globalization of semiconductor processing and manufacturing, integrated circuits have gradually become vulnerable to malicious attackers. In order to detect Hardware Trojans (HTs) hidden in integrated circuits, it has become one of the hottest issues in the field of hardware security. In this paper, we propose to apply Principal Component Analysis (PCA) and Support Vector Machine (SVM) to hardware Trojan detection, using PCA algorithm to extract features from small differences in side channel information, and then obtain the principal components. The SVM detection model is optimized by means of cross-validation and logarithmic interval. Finally, it is determined whether the original circuit contains a hardware Trojan. In the experiment, we use the SAKURA-G FPGA board, Agilent oscilloscope, and ISE simulation software to complete the experimental work. The test results of five different HTs show that the average True Positive Rate (TPR) of the proposed method for HTs can reach 99.48%, along with an average True Negative Rate (TNR) of 99.2%, and an average detection time of 9.66s.","PeriodicalId":183908,"journal":{"name":"2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"57 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":"121883716","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

Automatic Recognition of Standard Liver Sections Based on Vision-Transformer 基于视觉变换的标准肝脏切片自动识别

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995936

Jiansong Zhang, Yongjian Chen, Peizhong Liu

{"title":"Automatic Recognition of Standard Liver Sections Based on Vision-Transformer","authors":"Jiansong Zhang, Yongjian Chen, Peizhong Liu","doi":"10.1109/ASID56930.2022.9995936","DOIUrl":"https://doi.org/10.1109/ASID56930.2022.9995936","url":null,"abstract":"Acquisition of ultrasound standard views is a prerequisite for performing ultrasound diagnosis. With the aim of solving the clinical imaging challenge that adult liver standard sections have long been constrained by physicians' subjective experience, this paper collects 12 common liver ultrasound standard sections from the Second Hospital of Fujian Medical University and investigates and discusses the adaptability of the Vision-Transformer(ViT)-based deep learning automatic recognition method in liver ultrasound standard sections. Using a regional pixel set segmentation operation on liver ultrasound images, we found that the ViT model achieved recognition accuracy of 92.9% in the available ultrasound dataset when the basic segmentation module was 16*16 and the depth was 12. We also compared other mainstream deep learning frameworks based on convolutional neural networks, and the ViT model outperformed all other methods, guided by the features of the visual attention mechanism. The work in this paper provides a rich research base for deep learning of liver ultrasound based on the visual attention mechanism, and to a certain extent standardises the medical examination of the liver in adults by ultrasound-based means.","PeriodicalId":183908,"journal":{"name":"2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"39 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":"116709640","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

GIDL Analysis of 1T1C Structure for Sub-20nm DRAM Cell 亚20nm DRAM电池1T1C结构的GIDL分析

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995836

Yalin Zheng, Danfeng Chen, Shan He, Donghui Guo

引用次数: 0

Mixed Wave-Front Signal Model for 5G Indoor Passive Sounding and Channel Parameter Estimation 5G室内无源探测混合波前信号模型及信道参数估计

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995791

Jialin Shi, Xuemin Hong, Ao Peng

引用次数: 1

An Novel Anomaly Detection Method for Tiny Defects on Translucent Glass 一种新的半透明玻璃微小缺陷异常检测方法

2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2022-12-02 DOI: 10.1109/ASID56930.2022.9995999

Die Hu, X. Liu, Lei Wang

引用次数: 0