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Contrastive MLP Network Based on Adjacent Coordinates for Cross-Domain Zero-Shot Hyperspectral Image Classification

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Jiaojiao Zhang, Zhiyuan Song, Rui Xu, Haitao Li, Yunsong Du, Qian Xidian University State Key Laboratory of Integrated Service Networks Xi’an710071 China CAS Key Laboratory of Spectral Imaging Technology Xi’an710119 China Chinese Academy of Sciences National Space Science Center China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

With the breakthrough of transfer learning and meta-learning, cross-domain few-shot hyperspectral image classification (CDFSL HSIC) technology has recently achieved satisfactory performance under limited annotations. Nevertheless, the most practical applications are zero-shot scenarios, which are intractable for CDFSL technology, such as the extraterrestrial detection scene, where unexplored objects are recognized by scientists to be more valuable for research. To conquer the zero-shot problem under domain shift, a two-stage contrastive MLP network (MAC-CDZS) is proposed, which constitutes a pioneering effort in the cross-domain zero-shot (CDZS) HSIC task. Firstly, given the remarkable performance of MLPs within a diminutive model size and their enhanced capacity for extracting spatial-spectral features of HSIs, the MLP framework has been strategically chosen as the foundational backbone of the first stage in the MAC-CDZS for facilitating efficient feature extraction. Secondly, to alleviate the potential category collapse, the second-stage fine-tuning framework is introduced, which extends the first-stage backbone by incorporating the elaborate adjacent coordinate module and contrastive learning paradigm for more harmonious classification performance. Specifically, the adjacent coordinate module is creatively designed to adequately mine the adjacent coordinates among samples for ameliorating category collapse from the perspective of grasping more reliable priors. Furthermore, a contrastive learning paradigm is innovatively constructed, comprising a Spatial Augmentation (SA) module tailored for hyperspectral patches and a construction strategy of sample pair under zero-shot conditions, which aims to boost the representation capability and alleviate the class collapse. The superior performance of the MAC-CDZS is demonstrated by experimental results on four benchmark datasets. © 2025 IEEE. All rights reserved.

关键词： Zero-shot learning

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NFT-Based Digital Identity Authentication Framework for the Metaverse Environments

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IEEE Transactions on Consumer Electronics 2025年

作者： Khan, Wazir Zada Siddiqa, Ayesha Alanazi, Faisal Khan, Muhammad Khurram University of Wah Department of Computer Science Wah Cantt Pakistan Prince Sattam bin Abdulaziz University College of Engineering Department of Electrical Engineering Al-Kharj 11942 Saudi Arabia King Saud University Center of Excellence in Information Assurance Riyadh11451 Saudi Arabia

The Metaverse creates a 3D virtual environment similar to the real world, enabling immersive interactions across diverse fields such as education, healthcare, and gaming. A critical aspect of these interactions is digital identity authentication, which ensures secure and trustworthy user experiences. This paper proposes a novel Non-Fungible Token (NFT) based digital identity authentication framework for the Metaverse, leveraging blockchain technology and Elliptic Curve Cryptography (ECC) to enhance security and user trust. The framework is tested using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, demonstrating its resilience against replay and man-in-the-middle (MITM) attacks. Our contributions include: (1) a secure NFT-based authentication mechanism, (2) a formal security analysis validating the framework’s robustness, and (3) a comprehensive discussion of practical implications. The proposed framework addresses key gaps in existing methods, offering a scalable and user-friendly solution for digital identity authentication in the Metaverse. © 1975-2011 IEEE.

关键词： Authentication

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A SURVEY OF SOFTWARE-DESIGN TECHNIQUES

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IEEE TRANSACTIONS ON SOFTWARE engineering 1986年第6期12卷 713-721页

作者： YAU, SS TSAI, JJP Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60201 Department of Electrical Engineering and Computer Science University of Illinois at Chicago Chicago IL 60680

Software design is the process that translates the requirements into a detailed design representation of a software system. Good software design is a key to producing dependable and understandable software. To support software design, many methods and tools have been developed. Important techniques for software design are surveyed, including architectural and detailed design stages. A review is also given of recent advances in distributed software system design methodologies. Various design verification and validation techniques that ensure software quality are discussed. In addition, consideration is given to current software metrics and error-resistant software design methodologies.

关键词： Software Systems Software Design Design Methodology Programming Data Structures Formal Specifications Software Metrics Design Methodologies Design Representation Design Verification And Validation Distributed Software System Design Error Resistant Software Design Software Design Technique

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DESIGN CONSIDERATIONS FOR SINGLE-CHIP computerS OF THE FUTURE

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IEEE TRANSACTIONS ON computerS 1980年第2期29卷 108-116页

作者： PATTERSON, DA SEQUIN, CH Computer Science Division Department of Electrical Engineering and Computer Sciences University of California

In the mid 1980"s it will be possible to put a million devices (transistors or active MOS gate electrodes) onto a single silicon chip. General trends in the evolution of silicon integrated circuits are reviewed and design constraints for emerging VLSI circuits are analyzed. Desirable architectural features in modern computers are then discussed and consequences for an implementation with large-scale integrated circuits are investigated. The resulting recommended processor design includes features such as an on-chip memory hierarchy, multiple homogeneous caches for enhanced execution parallelism, support for complex data structures and high-level languages, a flexible instruction set, and communication hardware. It is concluded that a viable modular building block for the next generation of computing systems will be a self-contained computer on a single chip. A tentative allocation of the one milion transistors to the various functional blocks is given, and the result is a memory intensive design.

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Predicting Response to Neoadjuvant Chemotherapy using Multi-Modal MRI Radiomics and Machine Learning Approaches

Predicting Response to Neoadjuvant Chemotherapy using Multi-...

引用

IEEE International Symposium on Biomedical Imaging

作者： Abdelrahman Gamal Ahmed Sharafeldeen Eman Alnaghy Reham Alghandour Khadiga M. Ali Sameh Shamaa Amal Aboueleneen Ahmed Elsaid Tolba Samir Elmougy Mohammed Ghazal Sohail Contractor Ayman El-Baz Department of Computer Science Faculty of Computers and Information Mansoura University Egypt Department of Bioengineering University of Louisville USA Department of Radiology Faculty of Medicine Mansoura University Mansoura Egypt Department of oncology Faculty of Medicine Mansoura University Mansoura Egypt Pathology Department Faculty of Medicine Mansoura University Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE Department of Radiology University of Louisville USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

A precise computer-aided diagnosis (CAD) system is introduced to predict two different tumor responses to neoadjuvant chemotherapy (NAC) by studying the correlation between radiological and clinical markers, and treatment response: complete response and stable disease. Predicting the NAC response assists physicians in managing the treatment plan effectively and reduces unnecessary use of NAC. First, the breast tumor is delineated from MRI modalities. Then, the proposed system extracts functional features, including the apparent diffusion coefficient (ADC), from diffusion-weighted imaging (DWI), and 2 nd -order texture features, such as gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM), from various MRI modalities. Afterward, statistical features are extracted from these matrices. Moreover, ADC features are represented statistically using percentiles with 2% intervals. Subsequently, a feature selection approach, namely the genetic algorithm (GA), is employed for each fused features individually (i.e., ADC, GLCM, and GLRLM) to extract the most relevant features. The resulting feature set undergoes a multi-stage feature generation system, introducing new features (i.e., probabilities), which are fused with its input and clinical markers and are then fed into a lightGBM classifier for final prediction. Our system was evaluated on 54 patients using leave-one-subject-out (LOSO) cross-validation, achieving a sensitivity of 89.89%, a specificity of 89.29%, and an accuracy of 89.09%, emphasizing the promise of our system in predicting breast tumor response to NAC.

关键词： Solid modeling Chemotherapy Breast tumors Magnetic resonance imaging Machine learning Feature extraction Breast cancer Genetic algorithms Radiomics Diseases

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Near-Field Motion Parameter Estimation: A Variational Bayesian Approach

arXiv

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arXiv 2025年

作者： Meng, Chunwei Wang, Zhaolin Wei, Zhiqing Liu, Yuanwei Feng, Zhiyong Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong

A near-field motion parameter estimation method is proposed. In contract to far-field sensing systems, the near-field sensing system leverages spherical-wave characteristics to enable full-vector location and velocity estimation. Despite promising advantages, the near-field sensing system faces a significant challenge, where location and velocity parameters are intricately coupled within the signal. To address this challenge, a novel subarray-based variational message passing (VMP) method is proposed for near-field joint location and velocity estimation. First, a factor graph representation is introduced, employing subarray-level directional and Doppler parameters as intermediate variables to decouple the complex location-velocity dependencies. Based on this, the variational Bayesian inference is employed to obtain closed-form posterior distributions of subarray-level parameters. Subsequently, the message passing technique is employed, enabling tractable computation of location and velocity marginal distributions. Two implementation strategies are proposed: 1) System-level fusion that aggregates all subarray posteriors for centralized estimation, or 2) Subarray-level fusion where locally processed estimates from subarrays are fused through Guassian product rule. Cramér-Rao bounds for location and velocity estimation are derived, providing theoretical performance limits. Numerical results demonstrate that the proposed VMP method outperforms existing approaches while achieving a magnitude lower complexity. Specifically, the proposed VMP method achieves centimeter-level location accuracy and sub-m/s velocity accuracy. It also demonstrates robust performance for high-mobility targets, making the proposed VMP method suitable for real-time near-field sensing and communication applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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Beyond the Lungs: Extending the Field of View in Chest CT with Latent Diffusion Models

arXiv

引用

arXiv 2025年

作者： Zuo, Lianrui Xu, Kaiwen Su, Dingjie Yu, Xin Krishnan, Aravind R. Liu, Yihao Bao, Shunxing Li, Thomas Sandler, Kim L. Maldonado, Fabien Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University Nashville United States Insitro South San Francisco United States Department of Computer Science Vanderbilt University Nashville United States Department of Biomedical Engineering Vanderbilt University Nashville United States Department of Radiology Vanderbilt University Medical Center Nashville United States Department of Medicine Vanderbilt University Medical Center Nashville United States Department of Thoracic Surgery Vanderbilt University Medical Center Nashville United States

The interconnection between the human lungs and other organs, such as the liver and kidneys, is crucial for understanding the underlying risks and effects of lung diseases and improving patient care. However, most research chest CT imaging is focused solely on the lungs due to considerations of cost and radiation dose. This restricted field of view (FOV) in the acquired images poses challenges to comprehensive analysis and hinders the ability to gain insights into the impact of lung diseases on other organs. To address this, we propose SCOPE (Spatial Coverage Optimization with Prior Encoding), a novel approach to capture the inter-organ relationships from CT images and extend the FOV of chest CT images. Our approach first trains a variational autoencoder (VAE) to encode 2D axial CT slices individually, then stacks the latent representations of the VAE to form a 3D context for training a latent diffusion model. Once trained, our approach extends the FOV of CT images in the z-direction by generating new axial slices in a zero-shot manner. We evaluated our approach on the National Lung Screening Trial (NLST) dataset, and results suggest that it effectively extends the FOV to include the liver and kidneys, which are not completely covered in the original NLST data acquisition. Quantitative results on a held-out whole-body dataset demonstrate that the generated slices exhibit high fidelity with acquired data, achieving an SSIM of 0.81. Copyright © 2025, The Authors. All rights reserved.

关键词： Lung cancer

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Improved Parallel Cross-Convolutional Neural Network Based Network Traffic Classification and Anomaly Detection for Real-Time Analysis

Improved Parallel Cross-Convolutional Neural Network Based N...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rana Veer Samara Sihman Bharattej R Vasanthakumar G U Saef Obidhusin S. Senthil Kumar Govardhan S D Doctorate of Business Administration National Louis University FL USA Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College Of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Nowadays, the rapid growth of network traffic and increasing sophistication of cyber threats have made it essential to develop efficient and effective network traffic analysis techniques. The existing Convolutional Neural Networks (CNN) have learnt patterns and anomalies in network traffic for real-time classification. However, it has led to challenges including complexity of network traffic patterns and low false positives. Hence, this research proposes an Improved Parallel Cross-CNN (IPC-CNN) to provide better results by learning diverse feature representations with multiple convolutional branches simultaneously. Initially, the input data is acquired from USTC-TFC2016 dataset which contains real- world network traffic payloads including time series characteristics and then fed into preprocessing with normalization as well as one-hot encoding. Here, the normalization helps to standardize the traffic data and one-hot- encoding is used to transform the categorical data into numerical format. Finally, IPC-CNN is employed to detect and classify the network traffic payloads by extracting spatial features through parallel convolutional layers. From the results, the proposed IPC-CNN achieved better results in terms of accuracy (99.21%) when compared with existing Genetic Algorithm-Support Vector Machines (GA-SVM).

关键词： Adaptation models Telecommunication traffic Feature extraction Encoding Real-time systems Convolutional neural networks Integrated circuit modeling Anomaly detection Payloads Genetic algorithms

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Machine learning assisted identification of homobilayer sliding ferroelectrics with large out-of-plane polarization and low sliding energy barriers

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Physical Review B 2025年第9期111卷 094106-094106页

作者： Xian Wang Yifan Li Ying Zhang Peng Wang Yi-Ming Zhao Jun Zhou Jie Yang Xuesen Wang Lei Shen Department of Physics National University of Singapore Singapore 117551 Singapore Department of Mechanical Engineering National University of Singapore Singapore 117575 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore National University of Singapore (Chongqing) Research Institute Chongqing 401123 China College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao 266590 China Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (ASTAR6) Singapore 138634 Singapore Key Laboratory of Material Physics Ministry of Education School of Physics Zhengzhou University Zhengzhou 450001 China

Two-dimensional (2D) ferroelectric materials hold great promise for ultrahigh-density data storage and ultrafast memory applications in flexible or wearable electronic devices. Because of depolarizing field effects and high-energy barriers for switching, only a handful of 2D ferroelectrics have been reported experimentally, such as In2Se3 and CuInP2S6. Through high-throughput calculations we identified a catalog of 25 synthesizable sliding ferroelectrics with large out-of-plane polarization and low sliding energy barriers. The high-throughput process started with 6351 monolayer materials in 2dmatpedia, narrowed down to 79 monolayer semiconductors with honeycomb structures, leading to the construction of 474 homobilayers sliding ferroelectrics, and ultimately identifying 25 high-performance 2D sliding ferroelectrics. Using big-data analysis and machine learning, we also revealed strong correlations between polarization and key physical properties, especially a hidden factor of effective van der Waals radius, which has been overlooked in previous fewer-sample studies. Based on these mechanistic insights, we proposed machine learning descriptors for predicting sliding ferroelectric properties.

关键词： Van der Waals forces

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Time-varying linear control for tiltrotor aircraft

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Chinese Journal of Aeronautics 2018年第4期31卷 632-642页

作者： Jing ZHANG Liguo SUN Xiangju QU Liuping WANG School of Aeronautic Science and Engineering Beihang University School of Electrical and Computer Engineering RMIT University

Tiltrotor aircraft have three flight modes： helicopter mode, airplane mode, and transition mode. A tiltrotor has characteristics of highly nonlinear, time-varying flight dynamics and inertial/-control couplings in its transition mode. It can transit from the helicopter mode to the airplane mode by tilting its nacelles, and an effective controller is crucial to accomplish tilting transition missions. Longitudinal dynamic characteristics of the tiltrotor are described by a nonlinear Lagrangeform model, which takes into account inertial/control couplings and aerodynamic *** commands for airspeed velocity and attitude in the transition mode are calculated dynamically by visiting a command library which is founded in advance by analyzing the flight envelope of the tiltrotor. A Time-Varying Linear（TVL） model is obtained using a Taylorexpansion based online linearization technique from the nonlinear model. Subsequently, based on an optimal control concept, an online optimization based control method with input constraints considered is proposed. To validate the proposed control method, three typical tilting transition missions are simulated using the nonlinear model of XV-15 tiltrotor aircraft. Simulation results show that the controller can be used to control the tiltrotor throughout its operating envelop which includes a transition flight, and can also deal with vertical gust disturbances.

关键词： Constrained optimal control Inertia/control couplings Tiltrotor aircraft Time-varying control Transition mode

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