检索结果-内蒙古大学图书馆

8th Optoelectronics Global Conference, OGC 2023

作者： Liu, Bowen Dai, Maolin Shirahata, Takuma Yamashita, Shinji Set, Sze Yun The University of Tokyo Research Center for Advanced Science and Technology Department of Electrical Engineering and Information Systems Tokyo Japan The University of Tokyo Department of Electrical Engineering and Information Systems Tokyo Japan The University of Tokyo Research Center for Advanced Science and Technology Tokyo Japan

ISBN: (纸本)9798350325324

Ultrafast laser is an ideal playground for the study of precise spectroscopy and microscopy that heavily rely on the high-performance optical frequency combs, which is the unique nature of mode-locked fiber lasers. In particular, dual-comb fiber lasers can greatly improve system performance while reducing complexity by developing self-referencing combs and coupling pulse trains with different repetition rates. Whereas the repetition rate is generally determined for a certain lasing resonator, which creates additional constraints in dual-comb applications. Here, we developed a bidirectional polarization-maintaining fiber laser, where the repetition rate of mode-locked pulse trains can be tuned continuously inside the cavity. The proposed laser outputs mutually orthogonal polarization states in opposite directions, and the maximum tuning range of the repetition rate reaches 681.6 kHz. Compared with referencing CW loop, the RF difference reaches 978.2 kHz, almost equal to 1 MHz. Meanwhile, the two directions generate pulses with nearly the same central wavelength, enabling excellent capacity in self-referencing dual-comb generation. This tunable-repetition-rate dual-comb source is promising for two-photon fluorescence microscopy, precise metrology of wavelength and synchronization of other combs based on fiber lasers with different repetition rate, etc. © 2023 IEEE.

关键词： Fiber lasers

来源：评论

学校读者我要写书评

暂无评论

Fractal dimension analysis for improved decision-making in non-stationary multi-arm bandit problems: a machine learning approach

引用

The European Physical Journal Special Topics 2025年 1-16页

作者： Nandhini, S. Kala Pandian, J. Arun Department of Computer Applications School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

In this research, we propose a novel approach to addressing the exploration–exploitation dilemma in multi-armed bandit (MAB) algorithms using fractal dimensions. The fractal dimension is used in the algorithms to represent the reward distributions of arms which represents the uncertainty of the arm in receiving the reward. The fractal dimension of the reward distribution is implemented in the most popular MAB optimization algorithms, such as Epsilon-Greedy, Upper Confidence Bound (UCB), Exponential-weight algorithm for Exploration and Exploitation (EXP3), and Thompson Sampling in this study. The algorithm prefers to choose arm with the least fractal dimension, as a lower fractal dimension represents less uncertainty of the arm. The performance of the fractal-enhanced MAB optimization algorithms is compared with traditional algorithms in non-stationary environments with various numbers of arms. The proposed approach provides a novel way to quantify and utilize the uncertainty of each arm in MAB problems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deepfake Detection using XAI based Deep Fusion Models 2

Deepfake Detection using XAI based Deep Fusion Models

引用

2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Anitha, A. Saju, Lis Maria Kamaraj, Balakrishnan School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Madurai Medical College Tamilnadu Madurai India

ISBN: (纸本)9798331530389

Deepfake technology has rapidly evolved, posing a serious threat to the authenticity of digital media and contributing to the spread of misinformation. The manipulation of media content raises significant concerns across sectors like politics, entertainment, and social media, undermining public trust in digital information. In response, this research proposes an advanced deepfake detection model that integrates Explainable Artificial Intelligence (XAI) techniques within a hybrid deep learning architecture. The proposed model combines ResNet50 and Vision Transformer (ViT) to capture spatial and contextual features effectively. While exploring additional methods such as facial landmark detection and frequency domain analysis, the final model focuses on the ResNet50-ViT combination for its superior performance to fusion model. To ensure transparency, the model employs XAI techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive explanations), offering interpretability by revealing the key factors that influence its predictions. This dual focus on high detection accuracy and interpretability ensures that the model not only detects deepfakes effectively but also builds trust in automated decision-making processes. By leveraging cutting-edge deep learning and XAI methods, this framework offers a more reliable, transparent, and adaptable solution for preserving digital media authenticity across various applications, ultimately contributing to combating misinformation and fostering trust in digital content. © 2025 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Utilizing Automatic Number Plate Recognition for an Intelligent Campus Gate Security System 6

Utilizing Automatic Number Plate Recognition for an Intellig...

引用

6th IEEE Symposium on Computers and Informatics, ISCI 2024

作者： Limon, John Christopher Botangen, Khavee Agustus Malaca, Marc Joel Bermoza, Ryan Vidania, Noli Central Luzon State University Department of Information Technology Science City of Muñoz Philippines Central Luzon State University Cyber-Physical Systems and IoT Lab Department of Information Technology Science City of Muñoz Philippines

ISBN: (纸本)9798350353853

The Central Luzon State University (CLSU) campus, covering 658 hectares, experiences thousands of vehicle entries and exits daily. Ensuring campus safety and security is of utmost importance, and efficient monitoring of vehicle entry and exit at campus gates is vital. Traditional manual methods of vehicle identification and recording at the campus gates are often time-consuming, error-prone, and inefficient. This paper presents the design and development of an Automatic Number Plate Recognition (ANPR) system for vehicle monitoring at CLSU. The system includes two main components: an ANPR module that captures real-time number plate images using optical character recognition (OCR) and advanced image processing, and a web-based application for camera management and data handling. The application supports data management, alerts for unrecognized vehicles, analytics, and report generation. Experimental results showed high accuracy, with a success rate of approximately 92 percent for both 4-wheeled and 2-wheeled vehicles, when excluding vehicles outside the camera's range. This system enhances campus gate security by providing real-time and accurate vehicle identification, reducing manual errors, and enabling efficient monitoring of vehicle movements. It facilitates comprehensive data collection which can be analyzed to identify traffic patterns, optimize parking occupancy, and inform overall campus vehicle management and security strategies. Furthermore, the system potentially allows for seamless integration with existing security systems such as CCTV cameras and alarm systems, enhancing the overall security infrastructure of the university community. © 2024 IEEE.

关键词： information management

来源：评论

学校读者我要写书评

暂无评论

Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection 38

Suitable is the Best: Task-Oriented Knowledge Fusion in Vuln...

引用

38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Wang, Jingjing Huang, Minhuan Nie, Yuanpin Li, Xiang Du, Qianjin Kong, Wei Deng, Huan Kuang, Xiaohui Institute of Systems Engineering Academy of Military Sciences PLA China Department of Computer Science and Technology Tsinghua University China School of Information Science and Engineering Zhejiang Sci-Tech University China

Deep learning technologies have demonstrated remarkable performance in vulnerability detection. Existing works primarily adopt a uniform and consistent feature learning pattern across the entire target set. While designed for general-purpose detection tasks, they lack sensitivity towards target code comprising multiple functional modules or diverse vulnerability subtypes. In this paper, we present a knowledge fusion-based vulnerability detection method (KF-GVD) that integrates specific vulnerability knowledge into the Graph Neural Network feature learning process. KF-GVD achieves accurate vulnerability detection across different functional modules of the Linux kernel and vulnerability subtypes without compromising general task performance. Extensive experiments demonstrate that KF-GVD outperforms SOTAs on function-level and statement-level vulnerability detection across various target tasks, with an average increase of 40.9% in precision and 26.1% in recall. Notably, KF-GVD discovered 9 undisclosed vulnerabilities when employing on C/C++ open-source projects without ground truth. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fusion of speech and handwritten signatures biometrics for person identification

引用

International Journal of Speech technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Overview and Analytical Study on Automatic Bird Repellent Laser System for Crop Protection

引用

Journal of Harbin Institute of technology(New Series) 2024年第1期31卷 38-53页

作者： Sireesha Abotula Srinivas Gorla Prasad Reddy PVGD Mohankrishna S Department of Computer Science and Engineering GITAM Deemed to be UniversityVisakhapatnam 530045India Department of Information Technology Andhra UniversityVisakhapatnam 530003India Department of Computer Science and Systems Engineering Andhra UniversityVisakhapatnam 530003India

Birds are a huge hazard to agriculture all around the world,causing harm to profitable field *** use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This study presents a comprehensive overview of current bird repellant approaches used in agricultural contexts,as well as potential new ways. The bird repellent techniques include Internet of Things technology,Deep Learning,Convolutional Neural Network,Unmanned Aerial Vehicles,Wireless Sensor Networks and Laser biotechnology. This study’s goal is to find and review about previous approach towards repellent of birds in the crop fields using various technologies.

关键词： Bird repellent crop protection IoT UAV Deep Learning

来源：评论

学校读者我要写书评

暂无评论

CSST Dense Star Field Preparation:A Framework for Astrometry and Photometry for Dense Star Field Images Obtained by the China Space Station Telescope(CSST)

引用

Research in Astronomy and Astrophysics 2024年第7期24卷 158-169页

作者： Yining Wang Rui Sun Tianyuan Deng Chenghui Zhao Peixuan Zhao Jiayi Yang Peng Jia Huigen Liu Jilin Zhou College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan 030024China School of Astronomy&Space Science Nanjing UniversityNanjing 210093China Peng Cheng Lab Shenzhen 518066China

The China Space Station Telescope(CSST)is a telescope with 2 m diameter,obtaining images with high quality through wide-field *** its first observation cycle,to capture time-domain observation data,the CSST is proposed to observe the Galactic halo across different *** data have significant potential for the study of properties of stars and ***,the density of stars in the Galactic center is high,and it is a well-known challenge to perform astrometry and photometry in such a dense star *** paper presents a deep learning-based framework designed to process dense star field images obtained by the CSST,which includes photometry,astrometry,and classifications of targets according to their light curve *** simulated CSST observation data,we demonstrate that this deep learning framework achieves photometry accuracy of 2%and astrometry accuracy of 0.03 pixel for stars with moderate brightness mag=24(i band),surpassing results obtained by traditional ***,the deep learning based light curve classification algorithm could pick up celestial targets whose magnitude variations are 1.7 times larger than magnitude variations brought by Poisson photon *** anticipate that our framework could be effectively used to process dense star field images obtained by the CSST.

关键词： techniques photometric-methods data analysis-astrometry

来源：评论

学校读者我要写书评

暂无评论

Autism Detection using InceptionV3 and Security using Blockchain 8

Autism Detection using InceptionV3 and Security using Blockc...

引用

8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Annie Silviya, S.H. Seetha, R. Raja, S. Sriman, B. Rupesh, S. Gopala Krishnan, J. Rajalakshmi Institute of Technology Computer Science Engineering Chennai India Vellore Institute of Technology Computer Science Engineering and Information Systems Vellore India Panimalar Engineering College Computer Science and Engineering Chennai India Institute of Science and Technology Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&d Chennai India Rajalakshmi Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

The mental health and well-being of children are critical components of their overall development and future success. In India, only 1 in 6 8 children are diagnosed with autism, since monitoring and addressing the mental health issues among children is a challenge because of various factors. Studies have proven that autism detection can be made possible with the assessment of specific facial features that are used as primary markers for the proposed model. The proposed solution aims to leverage technological advancements, such as deep learning, for detecting autism in children using the aforementioned markers. The model gathers data on the children's facial features and predicts any underlying mental disorder. Since the application collects and analyzes sensitive information from the users, an additional security feature is implemented in the proposed solution using blockchain. Key features of the solution include standardized assessment tools, real-time tracking capabilities, personalized interventions, and seamless communication channels between parents, caregivers, and mental health professionals. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

The impact of information dissemination on vaccination in multiplex networks

引用

science China(information sciences) 2022年第7期65卷 185-196页

作者： Xiao-Jie LI Cong LI Xiang LI Adaptive Networks and Control Lab Department of Electronic EngineeringFudan University Research Center of Smart Networks and Systems School of Information Science and EngineeringFudan University MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University

The impact of information dissemination on epidemic control is essentially subject to individual behaviors. Vaccination is one of the most effective strategies against the epidemic spread, whose correlation with the information dissemination should be better understood. To this end, we propose an evolutionary vaccination game model in multiplex networks by integrating an information-epidemic spreading process into the vaccination dynamics, and explore how information dissemination influences vaccination. The spreading process is described by a two-layer coupled susceptible-alert-infected-susceptible(SAIS) model, where the strength coefficient between two layers characterizes the tendency and intensity of information *** find that the impact of information dissemination on vaccination decision-making depends on not only the vaccination cost and network topology, but also the stage of the system evolution. For instance, in a two-layer BA scale-free network, information dissemination helps to improve vaccination density only at the early stage of the system evolution, as well as when the vaccination cost is smaller. A counter-intuitive conclusion that more information transmission cannot promote vaccination is obtained when the vaccination cost is larger. Moreover, we study the impact of the strength coefficient and individual sensitivity on the fraction of infected individuals and social cost, and unveil the role of information dissemination in controlling the epidemic.

关键词： evolutionary game theory epidemic modeling network dynamics

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：