检索结果-内蒙古大学图书馆

Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems

引用

PeerJ Computer science 2025年 11卷 e2582-e2582页

作者： Ali, Shoukath K. Philip, Sajan P. Khan, Arfat Ahmad Moses, Leeban Cengiz, Korhan Akleylek, Sedat Ivković, Nikola Department of Electronics and Communication Engineering Presidency University Karnataka Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Electrical-Electronics Engineering Istinye University Istanbul Turkey Institute of Computer Science University of Tartu Tartu Estonia Faculty of Organization and Informatics University of Zagreb Pavlinska Varaždin Croatia

Channel estimation poses a main challenge in intelligent reflecting surface (IRS)assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. Copyright 2025 K et al. Distributed under Creative Commons CC-BY 4.0

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

Loss Functions in Deep Learning: A Comprehensive Review

arXiv

引用

arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Integer Ambiguity Baseline Rectifying for GNSS Augmentation Services by Linear Programming Method

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Chen, Jinpei Zhi, Nan Hu, Jinlin Xia, Yaowei Lu, Mingquan Feng, Shaojun Tsinghua University Department of Electronic Engineering Beijing100084 China Fudan University School of Information Science and Technology Shanghai200433 China Qianxun Spatial Intelligence Inc. Shanghai200438 China College of Surveying and Geo-Informatics Tongji University Shanghai200092 China Southeast University School of Instrument Science and Engineering China Imperial College London Department of Civil and Environmental Engineering London United Kingdom

The advent of the Global Navigation Satellite System (GNSS) has greatly enhanced satellite positioning technology, with Precise Point Positioning (PPP) emerging as a prominent technique. Despite the advantages of PPP, such as flexibility and reduced operational costs, it faces challenges from phase noise and atmospheric delays, which necessitate reliable ambiguity resolution. Our study introduces a novel non-statistical estimation method—baseline rectification for integer ambiguities. This approach employs integer linear programming, integrated with sparse statistics, to formulate the problem. It establishes a theoretical foundation for the method's effectiveness, facilitating the verification and, crucially, the rectification of integer ambiguities to their true values. Real-world data testing has empirically proven that during times of moderate ionospheric activity (as indicated by Rate of Total Electron Content Index (ROTI)), our method can effectively restore baseline ambiguities, thereby increasing the number of usable satellites in correction service and ultimately enhancing positioning accuracy (Root Mean Squared Error (RMSE) decreases 87.57%). This paper explores the foundational theoretical concepts and mathematical modeling involved in our study. The efficacy of our proposed method has been substantiated through simulations, demonstrating its effectiveness. Furthermore, the application of our method to real-world data has shown significant improvements in positioning accuracy. © 1965-2011 IEEE.

关键词： Integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Redistribution of humanitarian items in disaster management multi-period location–allocation problem under type-2 neutrosophic environment

引用

Applied Soft Computing 2025年 177卷

作者： Shaw, Lipika Das, Soumen Kumar Roy, Sankar Kumar Sakalauskas, Leonidas Weber, Gerhard-Wilhelm Dan, Hiroshige Department of Applied Mathematics Vidyasagar University West Bengal Midnapore721102 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Pure and Applied Science Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Applied Informatics Vytautas Magnus University Vileikos st 8-510 LT-44404 Kaunas Lithuania Faculty of Engineering and Management Poznan University of Technology Poznan Poland METU Ankara Turkey School of Creative Science and Engineering Waseda University Waseda Japan

Disasters cause significant damage to both human life and property, as well as the necessary resources required for survival, within a short period of time. The sudden disturbance to society caused by a disaster often leaves many people homeless. With the increased frequency of disasters, researchers have become increasingly concerned with proposing suitable models to handle these situations. Only a suitable humanitarian logistic operation can rescue those affected by disasters by providing life-saving items. To this end, this assay introduces a multi-period, multi-objective facility location redistribution model that is considered under some uncertain environmental conditions. The primary objectives of this study are to minimize total transportation costs and required time while maximizing the satisfaction of demand for new relief camps. The concept of redistribution is employed to facilitate rapid responses in emergency situations and reduce logistics waste. Some parameters in the model are handled using type-2 neutrosophic numbers, which are subsequently converted into crisp values through a ranking function. Subsequently, an improved neutrosophic TOPSIS approach is developed based on neutrosophic programming and fuzzy TOPSIS to obtain non-dominated solution. Three numerical examples are provided to illustrate the problem briefly. Following this, a comparative study is conducted between the proposed approach and two existing techniques: neutrosophic programming and fuzzy TOPSIS. Additionally, a stability analysis is incorporated to assess the resilience of the designed model. In conclusion, the paper offers significant insights and highlights areas for future research in this field. © 2025 Elsevier B.V.

关键词： Disaster prevention

来源：评论

学校读者我要写书评

暂无评论

Uncovering Cognitive Taskonomy Through Transfer Learning in Masked Autoencoder-Based fMRI Reconstruction 4th

Uncovering Cognitive Taskonomy Through Transfer Learning in...

引用

4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Qu, Youzhi Xia, Junfeng Jian, Xinyao Li, Wendu Peng, Kaining Liang, Zhichao Wu, Haiyan Liu, Quanying Department of Biomedical Engineering Southern University of Science and Technology Shenzhen China Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine The University of Pennsylvania Philadelphia United States Centre for Cognitive and Brain Sciences and Department of Psychology University of Macau Macau China

ISBN: (纸本)9789819640003

Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks. Our experimental results demonstrate that the MAE model effectively captures the temporal dynamics patterns and interactions within the brain regions, enabling robust cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from the transfer learning framework reveals the relationships among cognitive tasks, highlighting subtask correlations within motor tasks and similarities between emotion, social, and gambling tasks. Our study suggests that the fMRI reconstruction with MAE model can uncover the latent representation and the obtained taskonomy offers guidance for selecting source tasks in neural decoding tasks for improving the decoding performance on target tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Global decomposition of networks into multiple cores formed by local hubs

引用

Physical Review E 2025年第5期111卷 054302-054302页

作者： Wonhee Jeong (정원희) Unjong Yu (유운종) Sang Hoon Lee (이상훈) The Research Institute of Natural Science Gyeongsang National University Jinju 52828 Korea Present address: Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA. Department of Physics and Photon Science & Research Center for Photon Science Technology Gwangju Institute of Science and Technology Gwangju 61005 Korea Department of Physics Gyeongsang National University Jinju 52828 Korea Future Convergence Technology Research Institute Gyeongsang National University Jinju 52849 South Korea

Networks are ubiquitous in various fields, representing systems where nodes and their interconnections constitute their intricate structures. We introduce a network decomposition scheme to reveal multiscale core-periphery structures lurking inside, using the concept of locally defined nodal hub centrality and edge-pruning techniques built upon it. We demonstrate that the hub-centrality-based edge pruning reveals a series of breaking points in network decomposition, which effectively separates a network into its backbone and shell structures. Our local-edge decomposition method iteratively identifies and removes locally least connected nodes, and uncovers an onionlike hierarchical structure as a result. Compared with the conventional k-core decomposition method, our method based on relative information residing in local structures exhibits a clear advantage in terms of discovering locally crucial substructures. As an application of the method, we present a scheme to detect multiple core-periphery structures and the decomposition of coarse-grained supernode networks, by combining the method with the network community detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis

引用

Computer Methods and Programs in Biomedicine Update 2025年 7卷

作者： Sheakh, Md. Alif Azam, Sami Tahosin, Mst. Sazia Karim, Asif Montaha, Sidratul Fahim, Kayes Uddin Shafiabady, Niusha Jonkman, Mirjam De Boer, Friso Health Informatics Research Laboratory (HIRL) Department of Computer Science and Engineering Daffodil International University Dhaka 1341 Bangladesh Faculty of Science and Technology Charles Darwin University Darwin 0909 NT Australia Department of Computer Science and Engineering University of Calgary Calgary Canada Department of Information Technology Australian Catholic University North Sydney 2060 NSW Australia

Endometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are employed to increase the quality of the images, including normalization, Non-Local Means denoising, and alpha-beta enhancement. Effective segmentation is achieved through a combination of Otsu thresholding, morphological operations, distance transformations, and the watershed approach to identify major regions of interest. Through a sequence of blocks, the ECgMLP architecture processes input images to remove unimportant patterns. Model hyperparameters are improved via ablation research. The evaluations show a maximum accuracy of 99.26 % for identifying multi-class histopathological categories of endometrial tissue, which is higher than the previous best technique. The proposed model offers an automated, correct diagnosis, enhancing clinical processes. This proposition could be added to the current tools for finding endometrial cancer early, leading to better patient outcomes. © 2025

关键词： Deep learning Endometrial cancer gMLP Histopathological Watershed

来源：评论

学校读者我要写书评

暂无评论

A Unified Multi-Domain Face Normalization Framework for Cross-domain Prototype Learning and Heterogeneous Face Recognition

引用

IEEE Transactions on Information Forensics and Security 2025年

作者： Pang, Meng Zhang, Wenjun Lu, Yang Cheung, Yiu-Ming Zhou, Nanrun Nanchang University School of Mathematics and Computer Sciences China Xiamen University Department of Computer Science and Technology School of Informatics China Hong Kong Baptist University Department of Computer Science Hong Kong Hong Kong Shanghai University of Engineering Science School of Electronic and Electrical Engineering China

Face normalization is a critical technique for improving the robustness and generalizability of face recognition systems by reducing intra-personal variations arising from expressions, poses, occlusions, illuminations, and domain shifts. Existing normalization methods, however, often lack the flexibility to handle multi-factorial variations and exhibit limited cross-domain adaptability. To address these challenges, we propose a Unified Multi-Domain Face Normalization Network (UMFN), which is designed to process facial images with diverse variations from various domains and reconstruct frontal, neutralized facial prototypes in the target domain. As an unsupervised domain adaptation model, the UMFN facilitates concurrent training across multiple cross-domain datasets and demonstrates robust prototype reconstruction capabilities. Notably, the UMFN functions as a joint prototype and feature learning framework, extracting domain-agnostic identity features through a decoupling mapping network and adversarial training with a feature domain classifier. Furthermore, we design an efficient Heterogeneous Face Recognition (HFR) network that integrates these domain-agnostic features and the identity-discriminative features extracted from normalized prototypes, enhanced by contrastive learning to improve identity recognition accuracy. Empirical evaluation on multiple cross-domain benchmark datasets validate the effectiveness of the UMFN for face normalization and the superiority of the HFR network for heterogeneous face recognition. © 2005-2012 IEEE.

关键词： adversarial learning contrastive learning cross-domain prototype learning Face normalization heterogeneous face recognition

来源：评论

学校读者我要写书评

暂无评论

MambaHSISR: Mamba Hyperspectral Image Super-Resolution

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xu, Yinghao Wang, Hao Zhou, Fei Luo, Chunbo Sun, Xin Rahardja, Susanto Ren, Peng College of Oceanography and Space Informatics Qingdao266580 China College of Control Science and Engineering Qingdao266580 China University of Exeter Department of Computer Science ExeterEX4 4QJ United Kingdom City University of Macau Faculty of Data Science Macau China Singapore Institute of Technology 138683 Singapore

One of the main challenges facing hyperspectral image super-resolution is the complex high-dimensional data processing. Mamba leverages its ability to model long-range dependencies of linear complexity to capture the global spatial and spectral information of high-dimensional data while maintaining linear complexity. However, its visual state space equation mainly focuses on the band dimension mapping of the image, while ignoring the modeling of the spatial dimension. To overcome this limitation, we develop a Mamba hyperspectral image super-resolution framework, which comprises three essential components. The first component, i.e., the spatial Mamba subnetwork, models the spatial dimensions of hyperspectral data. It captures long-range dependencies in the pixel space, thereby integrating global spatial information into the framework. The second component, i.e., the spectral Mamba subnetwork, serves to capture long-range spectral dependencies. The third component, i.e., reconstruction, generates hyperspectral images with rich spatial and spectral details through pixel interpolation. Our Mamba framework fully develops the potential of the Mamba model in hyperspectral image super-resolution, significantly enhancing the restoration quality and accuracy of hyperspectral images. Extensive experiments on the Houston and QUST-1 datasets show that our framework outperforms state-of-the-art methods in both quantitative metrics and visual quality across diverse scenarios. © 1980-2012 IEEE.

关键词： Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models

引用

PeerJ Computer science 2025年 11卷 e2646-e2646页

作者： Zga, Adel Zitouni, Farouq Harous, Saad Sallam, Karam Almazyad, Abdulaziz S. Xiong, Guojiang Mohamed, Ali Wagdy Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang China Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt Applied Science Research Center Applied Science Private University Amman Jordan

This study conducts a comparative analysis of the performance of ten novel and wellperforming metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. These algorithms are applied to solve four well-established photovoltaic models: the singlediode model, the double-diode model, the triple-diode model, and different photovoltaic module models. The study focuses on key performance metrics such as execution time, number of function evaluations, and solution optimality. The results reveal significant differences in the efficiency and accuracy of the algorithms, with some algorithms demonstrating superior performance in specific models. The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. This optimizer achieved a root mean square error of 9.8602187789E−04 for the single-diode model, 9.8248487610E−04 for both the double-diode and triplediode models and 1.2307306856E−02 for the photovoltaic module model. This consistent success indicates that the Growth Optimizer is a strong contender for future enhancements aimed at further boosting its efficiency and effectiveness. Its

关键词： Mean square error

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：