检索结果-内蒙古大学图书馆

IEEE Journal on Flexible Electronics 2025年第12期3卷 526-532页

作者： Zhang, Hanbo Lou, Yuqing Zhang, Zhihang Li, Yongfu Rokhan, Fakhrul Wang, Guoxing Zhao, Jian Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication System Engineering Selangor Serdang43400 Malaysia

This paper proposed a fully LTPS-TFT-based bi-directional biomedical pixel interface with integrated a high linearity stimulator and a high-performance bio-potential sensing front-end circuit to amplify and digitize biological signals for high robustness data transmission. All circuits are designed and manufactured under a 3-μm LTPS process. We demonstrated the experiment in saline using the stimulator circuit to simulate stimulation inside the organism, achieving stimulation with different average currents under different duty cycles. The proposed front-end circuit has a gain of 40 dB. The referred input voltage noise of the entire signal chain is 86.2 μV/√Hz, and the ENOB is 9.13 bits at an input of 0.5 mVPP. Simultaneously conducting statistical testing on 20 chips has achieved a 1.05 dB SNDR standard deviation. Finally, we use a function signal generator to simulate the input signals of ECoG, ECG, and EOG, then use front-end circuits to read and reconstruct them to demonstrate that the proposed pixel interface can correctly read common biomedical signals for healthcare applications. © 2022 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

MV-DUO: multi-variate discrete unified optimization for psychological vital assessments

引用

Neural Computing and Applications 2024年第31期36卷 19777-19793页

作者： Pethuraj, Mohamed Shakeel Burhanuddin, M.A. Dzakiyullah, Nur Rachman Department of Information System Faculty of Computer and Engineering Universitas Alma Ata Yogyakarta Indonesia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka Malaysia

Psychological vital assessments are required for monitoring health conditions and observing body reactions toward diseases and medications. Wearable sensors play a vital role in sensing body vitals and presenting them as signals for computer-based analysis. The problem relies on the signal decoding due to its input stream that turns out to be discrete/ continuous. Therefore for addressing the above specific issue, this article introduces MV-DUO (Multi-Variate Discrete Unified Optimization) method. This method addresses the above problem from a multi-variate perspective by sensing differential signals across healthy and unhealthy conditions. The healthy and unhealthy conditions are trained using neural learning by augmenting/ ceasing external vital data. The unification is performed using single-point artificial ecosystem-based optimization for identifying discrete sequences collaborated with continuous signals. The single-point reference is grouped based on the maximum continuity fitness observed under various sensing intervals. In this process, the non-grouped sequences are identified as unhealthy or discrete for which additional detection training and classification are required. Considerably the changes between successive sensing intervals are used for variations detection from unified high-fitness groups. Those grouped instances are used for training new vital changes observed at distinct intervals. This improves detection accuracy under controlled errors. For the varying sensing intervals, the proposed method achieves 14.13% high accuracy, 8.29% high grouping rate, 10.77% less error, and 10.07% less detection time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： computer aided analysis

来源：评论

学校读者我要写书评

暂无评论

Use of Euclidean Distance to Improve Fingerprint Matching Accuracy 4th

Use of Euclidean Distance to Improve Fingerprint Matching Ac...

引用

4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Bhargava, Neeraj Yadav, Rajendra Choudhary, Usha Department of Computer Science School of Engineering System Sciences MDS University Rajasthan Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819769919

Fingerprint authentication is the most sophisticated method of all biometric techniques and has been thoroughly verified through various applications. Fingerprint matching has been done using several fingerprint recognition techniques. The methods for matching fingerprints have changed significantly over time, but Euclidean distance is still widely used since it is efficient and straightforward. Euclidean distance is still used in minutiae-based fingerprint matching to produce accurate results. The computational geometry, image processing, and patter recognition fields all make substantial use of Euclidean distance, a fundamental distance matching approach. The purpose of this work is to highlight the importance of normalization in improving contrast, particularly in noisy fingerprint photographs where accuracy is critical. We analyzed how to make fingerprint matching more robots against a reference template by utilizing Euclidean distance between small spots. We also analyzed algorithm’s effectiveness in producing dependable and accurate fingerprint matching results using MATLAB. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computational geometry

来源：评论

学校读者我要写书评

暂无评论

Transformer Internal and Inrush Current Fault Detection Using Machine Learning

引用

Intelligent Automation & Soft Computing 2023年第4期36卷 153-168页

作者： R.Vidhya P.Vanaja Ranjan N.R.Shanker Department of Electronics and Communication Engineering Loyola-ICAM College of Engineering and TechnologyChennai600034India Embedded System Technologies Department of Electrical and Electronics EngineeringCollege of EngineeringGuindyChennai 600025India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai 600055India

Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external ***,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush *** saturates due to magnetizing inrush currents and causes false tripping of the differential ***,iden-tiﬁcation of tripping in protection relay either due to inrush current or internal faults needs to be *** the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal *** transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush *** inrush currents,CT temperature is *** monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates *** wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identiﬁcation of the false tripping of differential protection system of transformer.

关键词： Wavelet threshold inrush transformer breather current transformer thermal image

来源：评论

学校读者我要写书评

暂无评论

Real-Time 4-Mode MDM Transmission Using Commercial 400G OTN Transceivers and All-Fiber Mode Multiplexers

引用

ZTE communications 2024年第1期22卷 106-110页

作者： REN Fang LI Yidan YE Bing LIU Jianguo CHEN Weizhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of WDM System Design ZTE CorporationBeijing 100029China

Weakly-coupled mode division multiplexing(MDM)technique is considered a promising candidate to enhance the capacity of an optical transmission system,in which mode multiplexers/demultiplexers(MMUX/MDEMUX)with low insertion loss and modal crosstalk are the key *** this paper,a low-modal-crosstalk 4-mode MMUX/MDEMUX for the weakly-coupled triple-ring-core few-mode fiber(TRC-FMF)is designed and fabricated with side-polishing *** measurement results show that a pair of MMUX/MDEMUX and 25 km weakly-coupled TRC-FMF MDM link achieve low modal crosstalk of lower than−17.5 dB and insertion loss of lower than 11.56 dB for all the four *** on the TRC-FMF and all-fiber MMUX/MDEMUX,an experiment for 25 km real-time 4-mode 3-λwavelength division multiplexing(WDM)-MDM transmission is conducted using commercial 400G optical transport network(OTN)*** experimental results prove weakly-coupled MDM techniques facilitate a smooth upgrade of the optical transmission system.

关键词： optical fiber couplers mode(de)multiplexers mode division multiplexing transmission

来源：评论

学校读者我要写书评

暂无评论

Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

引用

Frontiers of computer Science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

来源：评论

学校读者我要写书评

暂无评论

Leveraging Data Analytics to Enhance Smart Home Efficiency and Security 2

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Vivekanandan, Karthi Kaleeswari, B. Natchiammai, P.L. Anthonisamy, Arun Vijayabharathi, R. Alagarsamy, Manjunathan Builders Engineering College Department of Electrical and Electronics Engineering Tirupur India Department of Electronics and Communication Engineering Chennai India Mookambigai College of Engineering Department of Electronics and Communication Engineering Pudukkottai India Panimalar Engineering College Department of Computer Science and Business System Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices' energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security. © 2024 IEEE.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays 1

引用

1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Karkera, Thejas Singh, Chandra Bekal, Anush Shetty, Shailesh Prajwal, P. Department of Electronics Communication Engineering Sahyadri College of Engineering and Management Mangalore India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

Covid-19 pandemic is spreading across the world at a breakneck pace. As of September 2022, JHU CSSE COVID-19 Data and Our World in Data have recorded up to 5.2 M deaths due to Covid-19. One of the essential steps of fighting this pandemic is to detect the disease early. Studies show some abnormalities in the chest X-rays of the Covid-19 patients, and these features help classify the covid-19 positive patients from the negative patients. In recent years in the field of medical x-rays, Deep Learning models have proven to have the ability to learn and identify features that a trained person can only identify. Due to the exponential increase in the covid-19 cases, the waiting time for covid results has increased, resulting in the late diagnosis of patients for Covid-19. Classifying a patient as Covid-19 positive for a genuinely covid positive patient is as essential as classifying a patient Covid-19 negative who is genuinely covid-negative. Here we present a detailed quantitative analysis on the performance of state-of-the-art models like ResNet-50, Dense-Net, Mobile-Net-V2, and MNAS-Net on classifying patients for Covid positive, Pneumonia positive, and Normal by evaluating Accuracy, Precision, Recall, and Jaccard Index. We propose a new deep neural network classification model for low-end devices that uses two Attention mechanisms. Figure 1. shows the attention map with confidence percentage for each classes. Our model uses fewer parameters and FLOPS than other state-of-the-art models and recorded a 2% increase in Accuracy and other evaluation parameters. The dataset used for implementation is a public dataset COVID-DATASET and NIH Chest X-ray from kaggle. It has over 1000 images of Covid Chest X-ray, Pneumothrax, Mass, Pneumonia, Cardiomegaly, Nodule, Effusion, Atelectasis and inflitration. All the model implementations are implemented on PyTorch. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

引用

Journal of Modern Power systems and Clean Energy 2023年第4期11卷 1282-1293页

作者： Fangyuan Si Yinghua Han Qinqin Xu Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern UniversityShenyangChinaand F.Si is also with the State Key Laboratory of Power System and Generation EquipmentDepartment of Electrical EngineeringTsinghua UniversityBeijingChina School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdaoChina School of Control Engineering Northeastern University at QinhuangdaoQinhuangdaoChina

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility *** the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge ***,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing *** results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running *** analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.

关键词： We-Market bidding energy sharing prosumer peer-to-peer cloud-edge

来源：评论

学校读者我要写书评

暂无评论

Adaptive Intrusion Detection in Cyber-Physical systems using Reinforcement Learning-Based Autoencoders 2

Adaptive Intrusion Detection in Cyber-Physical Systems using...

引用

2nd IEEE International Conference on Integrated Intelligence and communication systems, ICIICS 2024

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection system (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：