Journal of Circuits Systems and Computers最新文献_第3页

Saliency-Guided Sparse Low-Rank Tensor Approximation for Unsupervised Anomaly Detection of Hyperspectral Remote Sensing Images 高光谱遥感图像无监督异常检测的显著性引导稀疏低秩张量近似

4区工程技术

Journal of Circuits Systems and Computers Pub Date : 2023-11-10 DOI: 10.1142/s0218126624501457

Zhiguo Du, Lian Yang, Mingxuan Tang

引用次数: 0

A Pre-Silicon Detection Based on Deep Learning Model for Hardware Trojans 基于深度学习模型的硬件木马预硅检测

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Journal of Circuits Systems and Computers Pub Date : 2023-11-10 DOI: 10.1142/s0218126624501445

Pengcheng Ma, Zhen Wang, Yong Wang

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Voltage Controlled Ring Oscillator with Phase Compensation Technique for Jitter Reduction in 180 nm CMOS Technology 基于相位补偿技术的180nm CMOS减抖压控环振荡器

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Journal of Circuits Systems and Computers Pub Date : 2023-11-07 DOI: 10.1142/s0218126624501433

Abhishek Mishra, Anil Singh, Alpana Agarwal

引用次数: 0

CCCBTA-based Floating Inductance Simulator with CM/VM PID Controllers 基于cccbta的浮动电感模拟器及CM/VM PID控制器

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Journal of Circuits Systems and Computers Pub Date : 2023-11-03 DOI: 10.1142/s021812662450141x

M. Sagbas, Z.G.C. Taskiran, U.E. Ayten

引用次数: 0

Low-Leakage Double-Body MOSFET: A Promising Circuit-Level Technique for Deep-Submicron Analog Integrated Circuit Design 低漏双体MOSFET:深亚微米模拟集成电路设计的一种有前途的电路级技术

4区工程技术

Journal of Circuits Systems and Computers Pub Date : 2023-11-03 DOI: 10.1142/s0218126624501421

Mohammad Moradinezhad Maryan, Seyed Javad Azhari

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Dual cross-attention multi-stage embedding network for low-light image enhancement 弱光图像增强的双交叉注意多级嵌入网络

4区工程技术

Journal of Circuits Systems and Computers Pub Date : 2023-11-03 DOI: 10.1142/s0218126624501172

Junyu Fan, Jinjiang Li, Zhen Hua

{"title":"Dual cross-attention multi-stage embedding network for low-light image enhancement","authors":"Junyu Fan, Jinjiang Li, Zhen Hua","doi":"10.1142/s0218126624501172","DOIUrl":"https://doi.org/10.1142/s0218126624501172","url":null,"abstract":"The low-light image enhancement task aims to improve the visibility of information in the dark to obtain more data and utilize it, while also improving the visual quality of the image. In this paper, we propose a dual cross-attention multi-stage embedding network (DCMENet) for fast and accurate enhancement of low-light images into high-quality images with high visibility. The problem that enhanced images tend to have more noise in them, which affects the image quality, is improved by introducing an attention mechanism in the encoder–decoder structure. In addition, the encoder–decoder can focus most of its attention on the dark areas of the image and better attend to the detailed features in the image that are obscured by the dark areas. In particular, the poor performance of the Transformer when the dataset size is small is solved by fusing the CNN-Attention and Transformer in the encoder. Considering the purpose of the low-light image enhancement task, we raise the importance of recovering image detail information to the same level as reconstructing the lighting. For features such as texture details in images, cascade extraction using spatial attention and pixel attention can reduce the model complexity while the performance is also improved. Finally, the global features obtained by the encoder–decoder are fused into the shallow feature extraction structure to reconstruct the illumination while guiding the network for the focused extraction of information in the dark. The proposed DCMENet achieves the best results in both objective quality assessment and subjective evaluation, while for the computer vision tasks working in low-light environments as well, the enhanced images using the DCMENet proposed in this paper show the best performance.","PeriodicalId":54866,"journal":{"name":"Journal of Circuits Systems and Computers","volume":"180 S454","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffusion Convolution Neural Network-based Multiview Gesture Recognition for Athletes in Dynamic Scenes 基于扩散卷积神经网络的动态场景运动员多视角手势识别

4区工程技术

Journal of Circuits Systems and Computers Pub Date : 2023-11-01 DOI: 10.1142/s0218126624501147

Qingyun Wang, Hua Li

{"title":"Diffusion Convolution Neural Network-based Multiview Gesture Recognition for Athletes in Dynamic Scenes","authors":"Qingyun Wang, Hua Li","doi":"10.1142/s0218126624501147","DOIUrl":"https://doi.org/10.1142/s0218126624501147","url":null,"abstract":"This paper focuses on deep vision sensing-assisted gesture recognition for athletes in dynamic scenes. Although many research attention had been devoted to this field in recent years, most of existing works failed to fully take characteristics of dynamic scenes into consideration. To deal with this challenge, this paper proposes a diffusion convolution neural network-based multiview gesture recognition approach in dynamic scenes. For one thing, the dynamic spatiotemporal slice position selection based on the body mask heatmap is adopted to calculate positions of horizontal and vertical slices. Thus, the dynamic selection of slice positions in two directions can be realized, and then the extraction of bi-directional spatiotemporal slice images can be completed. For another, action sequences through the 3D residual neural network are learned, and the spatiotemporal information among frames are mined through recurrent networks. Through their combination, a multi-view gesture recognition approach for athletes is constructed. In the experiments, two standard datasets UCF101 and HMDB51 are utilized to establish simulation environment. The proposed method can reach the accuracy beyond 95% on the two datasets. Compared with several typical recognition methods, the proposed method shows higher accuracy.","PeriodicalId":54866,"journal":{"name":"Journal of Circuits Systems and Computers","volume":"408 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135011757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Adversarial Machine Learning-based Fast Detection Method for Denial of Service-Oriented Cyber Attacks in Internet of Vehicles 基于对抗性机器学习的车联网服务网络攻击拒绝快速检测方法

4区工程技术

Journal of Circuits Systems and Computers Pub Date : 2023-11-01 DOI: 10.1142/s0218126624501226

Mingxu Wang, Mingchen Xu

{"title":"An Adversarial Machine Learning-based Fast Detection Method for Denial of Service-Oriented Cyber Attacks in Internet of Vehicles","authors":"Mingxu Wang, Mingchen Xu","doi":"10.1142/s0218126624501226","DOIUrl":"https://doi.org/10.1142/s0218126624501226","url":null,"abstract":"Denial of Service (DoS)-Oriented cyber attack has been a major threat for physical security in many kinds of network media, including the Internet of Vehicles (IoV). This paper focuses on the scenario of IoV, and proposes a machine learning-based fast detection method for adversarial neural network-based fast detection method for DoS-oriented cyber attacks. First, by analyzing the implementation principles and attack characteristics of three attack types, three aspects of statistical features are extracted: maximum matching packet growth rate, source address entropy value, and flow table similarity. Then, they are used as the input features to establish an adversarial machine learning-based DoS cyber attack detection method. On this basis, the field features of six stream rules are extracted, and two DoS cyber attack detection methods via machine learning are formulated. The proposals are able to detect the low-rate DoS-based cyber attacks against the data layer. The experimental results show that the proposed DoS attack detection method based on machine learning can effectively detect three DoS attacks under IoV, and these two algorithms have higher detection rates when compared with other algorithms.","PeriodicalId":54866,"journal":{"name":"Journal of Circuits Systems and Computers","volume":"47 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135012046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Access Control for Secure Authentication using Dual VC Scheme based on CP-ABE in DID DID中基于CP-ABE的双VC安全认证数据访问控制

4区工程技术

Journal of Circuits Systems and Computers Pub Date : 2023-10-31 DOI: 10.1142/s0218126624500944

Taehoon Kim, Yong-Woon Hwang, Daehee Seo, Im-Yeong Lee

{"title":"Data Access Control for Secure Authentication using Dual VC Scheme based on CP-ABE in DID","authors":"Taehoon Kim, Yong-Woon Hwang, Daehee Seo, Im-Yeong Lee","doi":"10.1142/s0218126624500944","DOIUrl":"https://doi.org/10.1142/s0218126624500944","url":null,"abstract":"In a Decentralized Identifier (DID), the Holder does not depend on a third party but controls self-identity information and is authenticated by the Verifier. Therefore, the Verifier can request verification data for access control from the Verifiable Data Registry (VDR) and credentials to authenticate the Holder. Data access control should be used when requesting data access such that only authorized Verifiers can access it. Consequently, studies on secure and efficient data access control have been conducted, and among them a scheme using Ciphertext Policy Attribute-based Encryption (CP-ABE) is underway. However, when the CP-ABE scheme is applied to the DID, the Holder’s extended Self-Sovereign Identity (SSI), which proves that the Holder has approved access to the Holder’s data stored in the VDR, is not ensured. Furthermore, the VDR does not verify the Verifier’s data access rights, resulting in unauthorized verification and illegal access to data by the user. And issue infringement of the Holder’s privacy, where Verifiers can infer the Holder by sharing and connecting the same DID-based Verifiable Presentations (VPs) of the Holder. Also, it leads to overheads in the amount of computation and search time for encryption/decryption. Therefore, in this paper, we propose a data access control for secure authentication by solving the security vulnerabilities of CP-ABE and using a CP-ABE-based dual Verifiable Credential (VC) scheme in DID.","PeriodicalId":54866,"journal":{"name":"Journal of Circuits Systems and Computers","volume":"34 32","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135765495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kidney Stone Detection from CT Images using ALEXNET and Hybrid ALEXNET-RF Models 基于ALEXNET和混合ALEXNET- rf模型的CT图像肾结石检测

4区工程技术

Journal of Circuits Systems and Computers Pub Date : 2023-10-28 DOI: 10.1142/s021812662450107x

M. Revathi, G. Raghuraman

引用次数: 0