检索结果-内蒙古大学图书馆

Light Shift Detection Using a Multiple Photodetection Method Suitable for a Chip-Scale Atomic Clock

Light Shift Detection Using a Multiple Photodetection Method...

2023 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFCS 2023

作者： Fukuoka, Masahiro Goka, Shigeyoshi Tokyo Metropolitan University Graduate School of System Design Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350311426

In this study, we demonstrated light shift detection in a running atomic clock using the multi-photodetection method. This method enables the observation of atomic resonances with different intensities using the intensity distribution of the laser beam. As it requires only two photodiodes to detect light shifts, it is easily applicable to miniature vapor-cell-based atomic clocks such as chip-scale atomic clocks and miniature atomic clocks. © 2023 IEEE.

关键词： chip-scale atomic clock coherent population trapping intensity distribution laser light shift VCSEL

来源：评论

学校读者我要写书评

暂无评论

Enhance Intrusion Detection by Analyzing the Behavior of Labeled and Unlabeled Classification to Obtain Better Accuracy 2

Enhance Intrusion Detection by Analyzing the Behavior of Lab...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Suriya Prakash, J. Srinidhi, N. Latha, A. Chaithra Kiran, S. Department of Computer Science and Engineering Karnataka Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Sri Krishna Institute Of Technology Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka Bengaluru India KSIT Department of Computer Science and Design Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519056

Intrusion Detection System has accelerated globally as a result of the need to identify intrusions that occur in network data flow. The two IDS methods that are primarily used by machine learning and network pattern detection are anomaly-based and signature-based methods. A network can be made foolproof by keeping it safe by keeping an eye out for any hazardous activity. In network dataflow systems, network traffic can be produced via simulators. Simulators are a useful tool for detecting harmful behavior. Intrusions that are host-based, protocol-based, application protocol-based, network-based, and hybrid-based are some of the most prevalent kinds that have been discovered. In this paper,open source datasets ISP datasets and WIDE datasets are employed for experimentation. The crux of this paper is to carry out network classification by using minimal training sets and classifying maximal test sets for procuring accurate results. Lastly, the Labelled and Unlabelled technique is used to compare various accuracy outcomes. The suggested scheme's effectiveness is confirmed by the empirical investigation conducted on ISP and WIDE data. In this paper, the labeled and unlabeled classification proposed here identifies the intruder application with minimal labeled dataset. Most importantly automatic labeling of large test sets with small training set is accomplished. The analysis process for classification methods involving ISP and WIDE datasets by an experimental way has produced efficient classification results. The Labeled classification algorithm produces 93% accuracy and the unlabeled classification algorithm produces 84% accuracy. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

An Explainable Transfer Learning Based Approach for Detecting Face Mask 1

引用

7th International Conference on computer Vision and Image Processing, CVIP 2022

作者： Anjali, T. Masilamani, V. Department of Computer Science and Engineering Indian Institute of Information and Technology Design and Manufacturing Kancheepuram Chennai600127 India

ISBN: (数字)9783031314070

ISBN: (纸本)9783031314063

COVID-19 has made a serious impact throughout the world. Wearing a face mask properly in public is considered an effective strat¬egy to prevent infection. To contribute to community health, this work intends to develop an accurate real-time system for detecting non-mask faces in public. The proposed system can be used by law enforcement authorities to monitor and enforce the proper use of masks. A novel trans¬fer learning-based method to detect face masks efficiently is proposed in this paper. Inspired by EfficientNetV2, a transfer learning model has been proposed to use for detecting whether a person is wearing a mask or not. To our knowledge, the EfficientNetV2 has not been used for detect¬ing face masks. The model building is done based on two standard face mask datasets. The images in the first dataset consist of multiple peo¬ple wearing masks, not wearing masks, and wearing the mask incorrectly. The second dataset consists of masked faces and faces without masks. To validate the generalization capability of the proposed model, the trained model is tested on two new standard datasets. In addition to that, the testing is done on a dataset created by ourselves. The proposed model performs well even on images with distortions such as blurred and noisy images. The model predicts whether the person in the input image is wearing a mask or not and also the correctness of mask-wearing with significantly better accuracy than the existing methods. The explain-ability of the proposed model is explained using a class activation map. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing EEG Analysis for Rapid Brain Activities Detection in Patients 1

Enhancing EEG Analysis for Rapid Brain Activities Detection ...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Yadav, Abhivyakti Ahmed, R Kaif Annamalai, R. Mercy, S.Sudha CSE Amrita School of Computing Chennai India R.M.K. Engineering College Department of Computer Science and Design Chennai India

ISBN: (纸本)9798331518943

Electroencephalogram (EEG) analysis plays a critical role in diagnosing various neurological conditions by detecting abnormal brain activities. However, the complex nature of EEG signals poses challenges for traditional analysis methods, which often struggle to handle diverse and high-dimensional data effectively. This paper develops a new technique of EEG signal classification using machine learning and deep learning models. Specifically, attention has been given to the CatBoost, ResNet34D, EfficientNetB0, and EfficientNetB2 models. The proposed approach utilized a vast EEG dataset with 11,000 samples of spectrograms, and it conducted an in-depth comparison of these models. Data preprocessing included features developed with statistical measures and the formation of uniform EEG spectrograms. All these models were evaluated in terms of their KL divergence score, defined as a measure of the divergence between the predicted and target distributions. From all the models considered, CatBoost proved to be one with an excellent performance, having had a KL score value at 0.78 compared to that of deep learning models. This work demonstrates the potential of gradient boosting for EEG-activity classification tasks and can be of benefit as a source of insight for interested applications in real-time brain activity monitoring and diagnostics of neurological issues. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

The Jade Gateway to Exergaming: How Socio-Cultural Factors Shape Exergaming Among East Asian Older Adults

引用

Proceedings of the ACM on Human-computer Interaction 2024年第CHI PLAY期8卷 1-34页

作者： Hadi Mogavi, Reza Son, Juhyung Yang, Simin Wang, Derrick M. Choong, Lydia Alhilal, Ahmad Zhou, Peng Yuan Hui, Pan Nacke, Lennart E. HCI Games Group Stratford School of Interaction Design and Business University of Waterloo WaterlooON Canada Guangzhou China Hong Kong University of Science and Technology Hong Kong Aarhus University Aarhus Denmark The Hong Kong University of Science and Technology University of Helsinki Hong Kong The Department of Systems Design Engineering University of Waterloo Canada The Cheriton School of Computer Science University of Waterloo Canada

Exergaming, blending exercise and gaming, improves the physical and mental health of older adults. We currently do not fully know the factors that drive older adults to either engage in or abstain from exergaming. Large-scale studies investigating this are still scarce, particularly those studying East Asian older adults. To address this, we interviewed 64 older adults from China, Japan, and South Korea about their attitudes toward exergames. Most participants viewed exergames with a positive inquisitiveness. However, socio-cultural factors can obstruct this curiosity. Our study shows that perceptions of aging, lifestyle, the presence of support networks, and the cultural relevance of game mechanics are the crucial factors influencing their exergame engagement. Thus, we stress the value of socio-cultural sensitivity in game design and urge the HCI community to adopt more diverse design practices. We provide several design suggestions for creating more culturally approachable exergames. © 2024 Owner/Author.

关键词： Game design

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review: Development of AI Based computer Vision Scrap Sorting System for Metal Scrap

A Systematic Review: Development of AI Based Computer Vision...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Gedam, Pragati B. Khan, Atiya Purohit, Neha Jha, V.K. G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India Jawaharlal Nehru Aluminium Research Development and Design Centre Department of Aluminium Electrolysum Nagpur India

ISBN: (纸本)9798331509828

The metals recycling industry has undergone a revolution thanks to the quick development of computer vision and artificial intelligence (AI), which have made it possible to sort metal trash using efficient and automated techniques. The existence of pollutants, the requirement for high-speed processing without sacrificing precision, and variations in scrap form, size, and texture are some of the ongoing difficulties. Though they provide strong feature extraction capabilities, current methods like Convolutional Neural Networks (CNNs), Scale-Invariant Feature Transform (SIFT), and Histogram of Oriented Gradients (HOG) frequently necessitate substantial preprocessing and sizable, precisely labeled datasets in order to integrate them with classification models like Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Furthermore, scalability and resilience in real-world scenarios continue to be major challenges. An AI-driven computer vision system is proposed in this work for identifying and sorting different kinds of metal trash, such as alloys made of copper, iron, and aluminium. Utilizing sophisticated feature extraction techniques and combining SVM and ANN models, the system seeks to maximize classification performance while tackling issues with operational effectiveness and dataset variability. YOLO LabelImg makes annotations easier by producing accurate bounding boxes, which are essential for tasks involving object identification and categorization. The study emphasizes the value of integrating models to increase accuracy and investigates how AI-powered solutions might raise the efficiency, dependability, and affordability of applications for sorting metal waste. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Protecting SDIIOT Network from Key Breach Attack by using Secret Hash Chain Techniques 2

Protecting SDIIOT Network from Key Breach Attack by using Se...

引用

2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Arora, Jatin Saluja, Kamal Ghantasala, G S Pradeep Rajesh Sharma, R. Sungheetha, Akey Singh, Susheel Kumar Vidyullatha, Pellakuri Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Heera Lal Yadav Balika Degree College UP Lucknow India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350364699

Attacks in a software-defined network (SDN) are hard to find. In light of this, this article offers a versatile modular architecture that enables DDoS attack prevention and mitigation in SDN environments. The interlinked computational devices present in the small items over the communication channel, which allows those devices to transmit industrial data, is known as the Industrial Internet of Things (IIoT). Since the conversation is conducted online, security and privacy breaches are possible. As a result, the authenticated key agreement (AKA) of communicating devices is crucial. However, every IOT device is limited in computational resources, so it might be difficult to add robust privacy and security features. AKA research is now focused on forward secrecy (FS) feasibility that provides control over future keys when a secret key is compromised. IoT devices with limited resources cannot process public key cryptosystems because of intensive calculations. The primary goal of AKA with FS for IIoT is to provide a lightweight security framework. Instead of using a public key security framework, it is using multiple hash values. The security strength of the suggested framework is built on challenge-response mechanisms, one-way hash functions, and symmetric key encryption. The inclusion of this suggested architecture demonstrates that it provides a protection against a variety of attacks, secret key negotiation, anonymity, and unlinkability. The proposed approach is analyzed to assess the performance of the security framework and demonstrates that it is a lightweight mechanism for IIoT. © 2024 IEEE.

关键词： Public key cryptography

来源：评论

学校读者我要写书评

暂无评论

Research on Virtual Reality Application System Using Internet of Things and Image Recognition Technology

Research on Virtual Reality Application System Using Interne...

引用

2023 International Conference on Applied Physics and Computing, ICAPC 2023

作者： Chen, Yi Wu, Qiongfei Xie, Qubo Wuhan Institute of Design and Science Department of Information Engineering Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350359244

This paper is based on the Internet of Things technology, combined with image recognition technology, the construction of intelligent three-dimensional virtual information management system. The system is visually controlled by the method of 3D visual display, which makes the operation of the system more convenient and active. By 3D modeling, mapping and modeling of buildings, roads, trees, computer rooms and other scenes and equipment with 3DMAX software, the 3D model built by 3DMAX is converted to fbx format. We need to edit and develop 3D scenes. The MS SQL Server database is invoked to intelligently navigate each cabinet, server, and network device in the equipment room, and display the property and state information to achieve effective control of the system information. Color image modeling, boundary recognition and feature extraction, color image segmentation based on distributed multi-attribute information fusion are analyzed. The distributed 3D model based on this algorithm has better visualization performance and better representation characteristics. © 2023 IEEE.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Ensemble Classifier using Wisconsin Dataset For Breast Cancer Prediction

Machine Learning Based Ensemble Classifier using Wisconsin D...

引用

2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Ghantasala, G. S. Pradeep Kunchala, Anjaneyulu Sathiyaraj, R. Venkateswarulu Naik, B. Raparthi, Yaswanth Vidyullatha, P. Alliance University Faculty of Science & Technology Alliance College of Engineering and Design Bengaluru India Gitam University Gitam School of Technology Department of Computer Science and Engineering Karnataka Bangalore India Computer Science Engineering Narasimha Reddy Engineering College Hyderabad India Computer Science Engineering Vellore Institute of Technology TN Vellore India Computer Science Engineering Koneru Lakshmaiah Education Foundation A.P Guntur India

ISBN: (纸本)9798350315455

Subclass of Artificial Intelligence, machine learning integrates several optimizations, probabilistic, and statistical methodologies to help computers learn from prior occurrences and find it challenging to spot patterns in intricate datasets, noised and large datasets. As a result, machine learning in cancer diagnosis and detection has increased. In women, breast cancer is the utmost common malignancy. The study aims to predict breast cancer by comparing widely used machine learning algorithms and methodologies, neural networks, Random Forest, and Boosting, upon Wisconsin Diagnosis Breast Cancer data set to inspect the performance of critical characteristics such as precision and accuracy. This study's accurate, competitive results can be used for detection and treatment. © 2023 IEEE.

关键词： Artificial intelligence Boosting Cancer prediction Neural network Random Forest

来源：评论

学校读者我要写书评

暂无评论

The design Innovation Space of Intelligent Personal Assistants (IPAs) in Healthy Buildings in the Era of Artificial Intelligence 26th

The Design Innovation Space of Intelligent Personal Assistan...

引用

26th International Conference on Human-computer Interaction, HCII 2024

作者： Liu, Yuqi Zhang, Jiawen Zeng, Yibei Liu, Yunlu Shidujamam, Mohammad Academy of Arts and Design Tsinghua University Beijing China Department of Service Design School of Design Royal College of Art London United Kingdom Fine Arts College Shanghai Normal University Shanghai China Faculty of Science Engineering and Computing Kingston University London United Kingdom Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (纸本)9783031768088

The implementation of Intelligent Personal Assistants (IPA) in healthy buildings cannot just elevate the buildings, intelligence level, but also boost their interactivity and interaction, enhance the quality of life for residents, and establish tailored health management approaches. Given China’s status as the world’s most populous country and a significant consumer market for emerging smart personal assistant products, this study focuses on the Chinese market. Utilizing a sample of 665 user data, it defines the demand, function, and design domains of IPA products in healthy buildings. Furthermore, it establishes a diversified design innovation space model for IPAs in healthy buildings amidst the artificial intelligence era. Initially, the study quantitatively assesses the impact of demographic variables on the acceptance of IPAs, offering a preliminary clarification and definition of the user profile of Chinese consumers. Additionally, by analyzing user needs and preferences, it explores the specific factors that guide users, purchasing decisions and categorizes these factors into four levels based on their significance. In terms of functional domains, this study has comprehensively gathered and categorized 30 service functions pertinent to Intelligent Personal Assistants (IPA), encompassing both current and anticipated functionalities. Utilizing questionnaire responses and statistical analysis, the study ranked the significance and precedence of these service functions, while employing principal component analysis to categorize user needs for smart speakers into seven distinct dimensions. Additionally, focusing on design domains, this research summarizes the diverse and innovative spatial models for intelligent personal assistants within architectural spaces, drawing insights from the functional domain analysis. This comprehensive study serves as a crucial foundation for the innovative design of IPA products in healthy buildings, while also facilitating the expans

关键词： Architectural design

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：