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Hyperspectral Target Detection Using Diffusion Model and Convolutional Gated Linear Unit

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Li, Zhaokui Mo, Fan Zhao, Xiaobin Liu, Cuiwei Gong, Xuewei Li, Wei Du, Qian Yuan, Bo Shenyang Aerospace University School of Computer Science Shenyang110136 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Institute of Applied Ecology Chinese Academy of Sciences CAS Key Laboratory of Forest Ecology and Silviculture Shenyang110016 China Beijing Institute of Technology School of Information and Electronics Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

Deep learning can effectively extract latent information from data to enhance target-background separation in hyperspectral target detection (HTD). However, these models typically require extensive labeled samples, while available target spectra in hyperspectral images (HSIs) are scarce. In addition, existing deep models struggle with target detection in complex backgrounds due to subtle spectral differences. To address these issues, we propose a novel HTD method based on diffusion model and convolutional gated linear unit (HTD-DMCG). First, the diffusion model is integrated with MixUp for data augmentation to generate a diverse and sufficiently large sample set. Next, a transformer architecture using a convolutional gated linear unit (GLU) is designed to effectively capture global dependencies and local feature correlations, leading to more discriminative feature representations. In addition, a new target aggregation and background separation loss is introduced, which emphasizes target sample aggregation while increasing the distance between targets and background samples to enhance separability. The HTD-DMCG method is compared against classical and state-of-the-art HTD methods on four real HSI datasets. Extensive experiments show that it can effectively outperform existing methods in target detection performance. © 1980-2012 IEEE.

关键词： Image coding

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Feature-Based Aggregation and Deep Reinforcement Learning:A Survey and Some New Implementations

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 1-31页

作者： Dimitri P.Bertsekas the Department of Electrical Engineering and Computer Science and the Laboratory for Information and Decision SystemsMassachusetts Institute of Technology

In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.

关键词： Reinforcement learning dynamic programming Markovian decision problems aggregation feature-based architectures policy iteration deep neural networks rollout algorithms

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TRANSFORMATION AND VERIFICATION OF OFFICE PROCEDURES

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IEEE TRANSACTIONS ON SOFTWARE engineering 1985年第8期11卷 724-734页

作者： CHANG, SK CHAN, WL Information Systems Laboratory Department of Electrical and Computer Engineering Illinois Institute of Technology

An office procedure constitutes a structured set of office activities that are geared to accomplishing a specific office task. Office activities include editing text, generating form, filing, communicating, and decision making. A unified model, the office procedure model (OPM), is proposed to model office procedures. The OPM describes the relationships among messages, databases, alerters, and activities, and it can also be used to integrate and coordinate the activities of an office procedure. The OPM permits the specification of office protocols in an office information system, which is an office environment in which computer systems are employed to aid in the performance of office activities. A methodology is also proposed for the verification of office procedures. Using this methodology, potential problems in office procedure specification, such as deadlock and unspecified message reception, can be analyzed effectively.

关键词： Protocol Analysis Message Exchange Theory Office Automation Office Information System Office Procedure Model Petri Net

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The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review

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PeerJ computer Science 2025年 11卷 e2400-e2400页

作者： Gadekallu, Thippa Reddy Yenduri, Gokul Kaluri, Rajesh Rajput, Dharmendra Singh Lakshmanna, Kuruva Fang, Kai Chen, Junxin Wang, Wei Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Computer Science Engineering and Information Systems Vellore Institute of Technology University Tamil Nadu India Zhongda Group Jiaxing China College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India School of Mathematics and Computer Science Zhejiang Agricultural and Forestry University Hangzhou China School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China School of Medical Technology Beijing Institute of Technology Beijing China

The generative pre-trained transformer (GPT) is a notable breakthrough in the field of artificial intelligence, as it empowers machines to effectively comprehend and engage in interactions with humans. The GPT exhibits the capacity to enhance inclusivity and accessibility for students with learning disabilities in the context of higher education, hence potentially facilitating substantial advancements in the field. GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities. This motivated us to conduct an extensive review to assess the effectiveness of GPT in enhancing accessibility and inclusivity in higher education for students with learning disabilities. This review offers a comprehensive analysis of the GPT and its significance for enhancing inclusivity in the field of higher education. In this research, we also examined the possible challenges and constraints associated with the integration of GPT into inclusive higher education, along with potential solutions. Overall, this review is intended for educators, students with and without learning disabilities, policymakers, higher education institutes, researchers, and educational technology developers. This review aims to provide a comprehensive understanding of GPT in promoting inclusive higher education for people with various learning disabilities, its impacts on inclusive higher education, emerging challenges, and potential solutions. Copyright 2025 Gadekallu et al.

关键词： Contrastive Learning

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A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks

arXiv

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arXiv 2025年

作者： Li, Dong Wan, Guihong Wu, Xintao Wu, Xinyu Nirmal, Ajit J. Lian, Christine G. Sorger, Peter K. Semenov, Yevgeniy R. Zhao, Chen Department of Computer Science Baylor University TX United States Department of Dermatology Massachusetts General Hospital Harvard Medical School MA United States Laboratory of Systems Pharmacology Harvard Medical School MA United States Department of Electrical Engineering and Computer Science University of Arkansas AR United States Department of Dermatology Brigham and Women’s Hospital Harvard Medical School MA United States Department of Pathology Brigham and Women’s Hospital Harvard Medical School MA United States

Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain-specific adaptation, and the lack of standardized evaluation benchmarks. This survey provides a comprehensive review of CPathFMs in computational pathology, focusing on datasets, adaptation strategies, and evaluation tasks. We analyze key techniques, such as contrastive learning and multi-modal integration, and highlight existing gaps in current research. Finally, we explore future directions from four perspectives for advancing CPathFMs. This survey serves as a valuable resource for researchers, clinicians, and AI practitioners, guiding the advancement of CPathFMs toward robust and clinically applicable AI-driven pathology solutions. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Formulations and Approximations of Branch Flow Model for General Power Networks

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Journal of Modern Power systems and Clean Energy 2022年第5期10卷 1110-1126页

作者： Zhao Yuan Electrical Power Systems Laboratory(EPS-Lab) Department of Electrical and Computer EngineeringUniversity of IcelandReykjavikIceland IEEE

The formulations and approximations of the branch flow model for general(radial and mesh) power networks(General-BranchFlow) are given in this paper. Using different sets of the power flow equations, six formats of the exact General-BranchFlow model are listed. The six formats are mathematically equivalent with each other. Linear approximation and second-order cone programming(SOCP) are then used to derive the six formats of the convex General-BranchFlow model. The branch ampacity constraints considering the shunt conductance and capacitance of the transmission line Π-model are derived. The key foundation of deriving the ampacity constraints is the correct interpretation of the physical meaning of the transmission line Π-model. An exact linear expression of the ampacity constraints of the power loss variable is derived. The applications of the General-BranchFlow model in deriving twelve formats of the exact optimal power flow(OPF) model and twelve formats of the approximate OPF model are formulated and analyzed. Using the Julia programming language, the extensive numerical investigations of all formats of the OPF models show the accuracy and computational efficiency of the General-BranchFlow model. A penalty function based approximation gap reduction method is finally proposed and numerically validated to improve the AC-feasibility of the approximate General-BranchFlow model.

关键词： Branch flow linear approximation second-order cone programming ampacity constraint optimal power flow radial network mesh network

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MODELING AND PERFORMANCE ANALYSIS OF MULTIHOP PACKET RADIO NETWORKS

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PROCEEDINGS OF THE IEEE 1987年第1期75卷 135-155页

作者： TOBAGI, FA Department of Electrical Engineering Computer Systems Laboratory University of Stanford Stanford CA USA

The design of packet radio networks involves a large number of issues which interact in a very complex fashion. Many of these pertain to the RF channel and its use, others pertain to the operational protocols. Clearly, no single model can be formulated which incorporates all the necessary parameters and leads to the optimum solution. The one essential element which complicates matters is that, contrary to point-to-point networks in which each channel is utilized by a single pair of nodes, the radio channel in packet radio networks is a multiaccess broadcast resource: i) in a given locality determined by radio connectivity, the channel is shared by many contending users, hence the need for channel access protocols; ii) radio is a broadcast medium and thus the action taken by a node has an effect on the actions taken by neighboring nodes and their outcome. Despite the complexity of the problem, there has been significant progress worth reporting on. The work accomplished so far has been either the analysis of specific examples of networks or an attempt to create models that would be useful in the design of general networks. The purpose of this paper is to survey the various modeling techniques that have been used for the performance analysis of packet radio networks, and to discuss the assumptions underlying these models, their scope of applicability, and some of the results obtained.

关键词： Performance analysis Spread spectrum communication Packet radio networks Radio broadcasting Packet switching Mobile communication Protocols Radio frequency Digital control Radio control

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A frequency response, harmonic distortion, and intermodulation distortion test for BIST of a sigma-delta ADC

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-ANALOG AND DIGITAL SIGNAL PROCESSING 1996年第8期43卷 608-613页

作者： Toner, MF Roberts, GW Microelectronics and Computer Systems Laboratory Department of Electrical Engineering McGill University Montreal Canada

Built-In-Self-Test (BIST) for VLSI systems is desirable for production-time testing and in-the-field diagnostics, This brief discusses a Mixed Analog-Digital BIST (MADBIST) for a frequency response test, a harmonic distortion test and an intermodulation distortion test of an analog-to-digital converter, The MADBIST strategy for the frequency response, harmonic distortion, and intermodulation distortion tests of the ADC is introduced, accuracy issues are discussed, and preliminary experimental results are presented.

关键词： Frequency response Harmonic distortion Intermodulation distortion Built-in self-test Cellular neural networks Neural networks Circuit testing Cellular networks Optical interconnections Optical receivers

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