检索结果-内蒙古大学图书馆

63rd IEEE Conference on Decision and Control, CDC 2024

作者： He, Jiabao Ziemann, Ingvar Rojas, Cristian R. Hjalmarsson, Hakan Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Pennsylvania PhiladelphiaPA19104 United States

ISBN: (纸本)9798350316339

While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical analysis of SIMs remains an open problem, especially in the non-asymptotic regime. In this work, we provide a finite sample analysis for a class of SIMs, which reveals that the convergence rates for estimating Markov parameters and system matrices are O(1 / SN), in line with classical asymptotic results. Based on the observation that the model format in classical SIMs is non-causal because of a projection step, we choose a parsimonious SIM that bypasses the projection step and strictly enforces a causal model to facilitate the analysis, where a bank of ARX models are estimated in parallel. Leveraging recent results from a finite sample analysis of an individual ARX model, we obtain a union error bound for an array of ARX models and proceed to derive error bounds for system matrices using robustness results for the singular value decomposition. © 2024 IEEE.

关键词： Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

Enhanced Feature-based Image Stitching for Endoscopic Videos in Pediatric Eosinophilic Esophagitis

Enhanced Feature-based Image Stitching for Endoscopic Videos...

引用

Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Xiong, Juming Li, Muyang Deng, Ruining Yao, Tianyuan Bao, Shunxing Tyree, Regina N. Hiremath, Girish Huo, Yuankai Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Division of Pediatric Gastroenterology Hepatology and Nutrition Vanderbilt University Medical Center NashvilleTN United States

ISBN: (纸本)9781510685949

Video endoscopy represents a major advance in the investigation of gastrointestinal diseases. Reviewing endoscopy videos often involves frequent adjustments and reorientations to reconstruct a comprehensive view of the examined area, a process that is not only time-consuming but also prone to errors. Image stitching techniques offer a solution by enabling a continuous and complete visualization of the examined area. However, endoscopic images, particularly those of the esophagus, present unique challenges. The smooth surface, lack of distinct feature points, and non-horizontal orientation complicate the stitching process, rendering traditional feature-based methods often ineffective for these types of images. To solve this problem, we propose a novel preprocessing pipeline designed to enhance endoscopic image stitching through advanced computational techniques. Our approach converts endoscopic video data into continuous 2D images by following four key steps: (1) keyframe selection, (2) image rotation adjustment to correct distortions, (3) surface unwrapping using polar coordinate transformation to generate a flat image, and (4) feature point matching enhanced by Adaptive Histogram Equalization for improved feature detection. We evaluate stitching quality through the assessment of valid feature point match pairs. Experiments conducted on 20 pediatric endoscopy videos demonstrate that our method significantly improves image alignment and stitching quality compared to traditional techniques, laying a robust foundation for more effective panoramic image creation. © 2025 SPIE

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Wireless modulation classification based on Radon transform and convolutional neural networks

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6263-6272页

作者： Ghanem, Hanan S. Al-Makhlasawy, Rasha M. El-Shafai, Walid Elsabrouty, Maha Hamed, Hesham F. A. Salama, Gerges M. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia61111 Egypt Department of Computers and Systems Electronics Research Institute Cairo Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electronics and Communications Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions. © 2022, The Author(s).

关键词： Fading channels

来源：评论

学校读者我要写书评

暂无评论

Dementia Insights: A Context-Based MultiModal Approach

arXiv

引用

arXiv 2025年

作者： Mehdoui, Sahar Sinene Bouzid, Abdelhamid Sierra-Sosa, Daniel Elmaghraby, Adel Department of Computer Science and Engineering University of Louisville United States Department of Electrical Engineering and Computer Science The Catholic University of America United States

Dementia, a progressive neurodegenerative disorder, affects memory, reasoning, and daily functioning, creating challenges for individuals and healthcare systems. Early detection is crucial for timely interventions that may slow disease progression. Large pre-trained models (LPMs) for text and audio, such as Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), and Contrastive Language-Audio Pretraining (CLAP), have shown promise in identifying cognitive impairments. However, existing studies generally rely heavily on expert-annotated datasets and unimodal approaches, limiting robustness and scalability. This study proposes a context-based multimodal method, integrating both text and audio data using the best-performing LPMs in each modality. By incorporating contextual embeddings, our method improves dementia detection performance. Additionally, motivated by the effectiveness of contextual embeddings, we further experimented with a context-based In-Context Learning (ICL) as a complementary technique. Results show that GPT-based embeddings, particularly when fused with CLAP audio features, achieve an F1-score of 83.33%, surpassing state-of-the-art dementia detection models. Furthermore, raw text data outperforms expert-annotated datasets, demonstrating that LPMs can extract meaningful linguistic and acoustic patterns without extensive manual labeling. These findings highlight the potential for scalable, non-invasive diagnostic tools that reduce reliance on costly annotations while maintaining high accuracy. By integrating multimodal learning with contextual embeddings, this work lays the foundation for future advancements in personalized dementia detection and cognitive health research. © 2025, CC BY.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

引用

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Epileptic Seizure Detection using Denoising Autoencoder 4

Epileptic Seizure Detection using Denoising Autoencoder

引用

4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Joshi, Sukumar Rout, Pravat Kumar Samanta, Indu Sekhar Cherukuri, Murthy Swain, Kunjabihari Siksha 'o' Anusandhan University Dept. of Electrical Engineering Bhubaneswar India Siksha 'o' Anusandhan University Department of Electrical and Electronics Engineering India Siksha 'o' Anusandhan University Dept. of Computer Science and Engineering Bhubaneswar India Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India

ISBN: (纸本)9798350349856

Epileptic seizures are a common neurological disorder characterized by abnormal brain activity. Early and accurate detection of seizures plays a crucial role in effective treatment and improving the quality of life for individuals with epilepsy. This research paper proposes a innovative strategy for epileptic seizure detection utilizing a denoising autoencoder (DAE). The DAE is a feature extraction technique to enhance the discriminatory power of electroencephalogram (EEG) signals, commonly used for seizure detection. The suggested method's excellent sensitivity and specificity in correctly recognizing epileptic seizures is supported by the results of experiments. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Load Forecasting via Personalized Model Obfuscation

Privacy-Preserving Load Forecasting via Personalized Model O...

引用

IEEE General Meeting Power& Energy Society

作者： Shourya Bose Yu Zhang Kibaek Kim Department of Electrical & Computer Engineering University of California Santa Cruz CA Mathematics and Computer Science Division Argonne National Laboratory IL

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated learning (FL) has been proposed. This paper addresses the performance challenges of short-term load forecasting models trained with FL on heterogeneous data, emphasizing privacy preservation through model obfuscation. Our proposed algorithm, Privacy Preserving Federated Learning (PPFL), incorporates personalization layers for localized training at each smart meter. Additionally, we employ a differentially private mechanism to safeguard against data leakage from shared layers. Simulations on the NREL ComStock dataset corroborate the effectiveness of our approach.

关键词： Training Privacy Load forecasting Federated learning Simulation Predictive models Data models Smart meters Voltage control Load modeling

来源：评论

学校读者我要写书评

暂无评论

Reduced-order Gaussian Processes for partially unknown nonlinear control systems

引用

IEEE Transactions on Automatic Control 2025年

作者： Awan, Asad Ullah Zamani, Majid Technical University of Munich Department of Electrical and Computer Engineering Munich Germany Ludwig Maximilian University of Munich Department of Computer Science Munich Germany University of Colorado Boulder Department of Computer Science United States

In this work, we develop a scheme for constructing continuous approximations (referred to as abstractions) of a class of discrete-time control systems with partially unknown dynamics. The abstraction, itself a nonlinear discrete-time control system (preferably with a significantly lower dimension than the original one) can be used as a substitute in the controller design process. The technique consists of using data sampled from the concrete system to find a lower dimensional subspace of its state space (which we call the active subspace), and constructing an abstraction candidate using Gaussian Process (GP) regression. We derive sufficient conditions under which the GP candidate is shown to be the abstraction of the original system while quantifying the error bound between the output of the abstraction and that of the concrete system. A numerical example is presented to illustrate the effectiveness of this approach. © 1963-2012 IEEE.

关键词： Discrete time control systems

来源：评论

学校读者我要写书评

暂无评论

BC-SBOM: Blockchain-based SBOM Management System

BC-SBOM: Blockchain-based SBOM Management System

引用

International Conference on Advanced Communication Technology (ICACT)

作者： Ahyun Song Euiseong Seo Heeyoul Kim Department of Computer Science and Engineering Sungkyunkwan University Republic of Korea Division of Computer Science and Engineering Kyonggi University Republic of Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This paper presents BC-SBOM, a novel blockchain-based system designed to enhance the management of Software Bills of Materials (SBOMs). By leveraging blockchain technology, BC-SBOM ensures secure storage and sharing of SBOMs, while providing a comprehensive global view of dependencies among software components. The system also supports rapid propagation of alerts for newly discovered vulnerabilities, thereby increasing responsiveness to potential threats. Offering superior reliability, transparency, and availability compared to traditional SBOM tools, BC-SBOM aims to significantly improve the management of complex software systems and contribute to the advancement of software security practices.

关键词： Bills of materials Software systems Communications technology Blockchains Software reliability Security Secure storage

来源：评论

学校读者我要写书评

暂无评论

Advancing Residential Energy Management: Integrating Renewable Resources and Vehicle-to-Grid Systems for Optimized electrical Grid Performance 7

Advancing Residential Energy Management: Integrating Renewab...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Chandrashekar, Rakesh Rani, D. Shobha Nijhawan, Ginni Balaram, A. Chaturvedi, Prateek Babu, B. Sankara New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Telangana Hyderabad India Lovely Professional University Punjab Phagwara India Mlr Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Griet Department of Computer Science and Engineering Bachupally Hyderabad India

ISBN: (纸本)9798350350067

The term 'smart grid' describes the future generation of electrical power networks, which are very complicated systems. It must take into account all aspects of the electrical system, increasing its intelligence and adaptability. In response to shifts in the electrical market, this idea is put out with the intention of controlling the rising demand while guaranteeing higher service quality and enhanced safety. This research presents an urban scenario that incorporates dispersed energy sources into a smart grid. The research incorporates renewable energy sources like solar panels and tiny windmills into the system, as well as battery storage and grid-connected vehicles that can power the electrical grid by drawing electricity from the storage device. So, to maximize the energy production and absorption systems as well as the recharging and discharging time for a home user's electric car, a mixed integer linear programming is proposed. Additionally, a number of case studies are provided by changing important variables using the Taguchi technique of design of experiments in order to identify the best solution and demonstrate how each influences the system's complexity. © 2024 IEEE.

关键词： Vehicle-to-grid

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：