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A Spectral-Spatial Attention Network for Hyperspectral Unmixing

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Tao, Xuanwen Koirala, Bikram Rasti, Behnood Plaza, Antonio Scheunders, Paul University of Antwerp Imec-VisionLab Department of Physics Belgium Technische Universität Berlin Faculty of Electrical Engineering and Computer Science Berlin 10587 Germany University of Extremadura Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Cáceres 10071 Spain

Hyperspectral unmixing, an essential and fundamental task in remote sensing, focuses on estimating endmembers (spectrally pure components) and their fractional abundances within each mixed pixel of a hyperspectral image. With the advent of deep learning (DL), the field of hyperspectral unmixing has made significant progress. Among DL approaches, autoencoder-based models have shown promising results. However, most unmixing methods estimate the endmembers by the weights of the linear layers in the decoder of their networks, making their performance highly dependent on weight initialization. Moreover, noise is not explicitly accounted for in most recent methods that use spectral angle distance (SAD) loss. To avoid the initialization problems, we developed an innovative inversion strategy to directly estimate the endmembers. Moreover, to optimally account for noise, an end-to-end network is proposed that integrates both denoising and unmixing. Finally, for an improved feature extraction, a novel spectral-spatial attention module is integrated in the network. Extensive experiments on a synthetic and three real datasets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods. © 1980-2012 IEEE.

关键词： attention module autoencoder autonomous unmixing module deep denoising module Hyperspectral unmixing

Diversified and compatible web APIs recommendation in IoT

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arXiv 2021年

作者： Gong, Wenwen Wu, Huiping Wang, Xiaokang Zhang, Xuyun Wang, Yawei Chen, Yifei Khosravi, Mohammad R. Colleage of Information and Electrical Engineering China Agricultural University Beijing100000 China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shouguang262700 China Department of Computer Science St. Francis Xavier University AntigonishNS Canada Department of Computing Macquarie University SydneyNSW2122 Australia Department of Computer Engineering Persian Gulf University Bushehr*** Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz71557-13876 Iran

With the ever-increasing popularity of Internet of Things (IoT), massive enterprises are attempting to encapsulate their developed outcomes into various lightweight web APIs (application programming interfaces) that can be accessible remotely. In this situation, finding and composing a list of existing web APIs that can corporately fulfill the software developers' functional needs have become a promising way to develop a successful mobile app, economically and conveniently. However, the big volume of candidate IoT web APIs put additional burden on the app developers' web APIs selection decision-makings, since it is often a challenging task to simultaneously guarantee the diversity and compatibility of the finally selected a set of web APIs. Considering this challenge and latest successful applications of game theory in IoT, a Diversified and Compatible web APIs Recommendation approach, namely DivCAR, is put forward in this paper. First of all, to achieve API diversity, DivCAR employs random walk sampling technique on a pre-built "API-API" correlation graph to generate diverse "API-API" correlation subgraphs. Afterwards, with the diverse "API-API" correlation subgraphs, we model the compatible web APIs recommendation problem to be a minimum group Steiner tree search problem. Through solving the minimum group Steiner tree search problem, manifold sets of compatible and diverse web APIs ranked are returned to the app developers. At last, we design and enact a set of experiments on a real-world dataset crawled from ***. Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the web APIs recommendation diversity and compatibility. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

Diversified and compatible web APIs recommendation in IoT

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arXiv 2021年

关键词： Internet of things

H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation

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arXiv 2024年

作者： Rodrigues, Livia Bocchetta, Martina Puonti, Oula Greve, Douglas Londe, Ana Carolina França, Marcondes Appenzeller, Simone Iglesias, Juan Eugenio Rittner, Leticia Universidade Estadual de Campinas School of Electrical and Computer Engineering Brazil Massachusetts General Hospital Harvard Medical School United States Dementia Research Centre Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology University College London London United Kingdom Centre for Cognitive and Clinical Neuroscience Division of Psychology Department of Life Sciences College of Health Medicine Life Sciences Brunel University London United Kingdom Universidade Estadual de Campinas School of Medical Sciences Brazil Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

The hypothalamus is a small structure located in the center of the brain and is involved in significant functions such as sleeping, temperature, and appetite control. Various neurological disorders are also associated with hypothalamic abnormalities. Automated image analysis of this structure from brain MRI is thus highly desirable to study the hypothalamus in vivo. However, most automated segmentation tools currently available focus exclusively on T1w images. In this study, we introduce H-SynEx, a machine learning method for automated segmentation of hypothalamic subregions that generalizes across different MRI sequences and resolutions without retraining. H-synEx was trained with synthetic images built from label maps derived from ultra-high resolution ex vivo MRI scans, which enables finer-grained manual segmentation when compared with 1mm isometric in vivo images. We validated our method using Dice Coefficient (DSC) and Average Hausdorff distance (AVD) across in vivo images from six different datasets with six different MRI sequences (T1, T2, proton density, quantitative T1, fractional anisotrophy, and FLAIR). Statistical analysis compared hypothalamic subregion volumes in controls, Alzheimer’s disease (AD), and behavioral variant frontotemporal dementia (bvFTD) subjects using the Area Under the Receiving Operating Characteristic curve (AUROC) and Wilcoxon rank sum test. Our results show that H-SynEx successfully leverages information from ultra-high resolution scans to segment in vivo from different MRI sequences. Our automated segmentation was able to discriminate controls versus Alzheimer’s Disease patients on FLAIR images with 5mm spacing. H-SynEx is openly available at https://***/liviamarodrigues/hsynex. © 2024, CC BY.

关键词： Image segmentation

High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models

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arXiv 2024年

作者： Rodrigues, Livia Bocchetta, Martina Puonti, Oula Greve, Douglas Londe, Ana Carolina França, Marcondes Appenzeller, Simone Rittner, Leticia Iglesias, Juan Eugenio Massachusetts General Hospital Harvard Medical School United States Universidade Estadual de Campinas School of Electrical and Computer Engineering Brazil Dementia Research Centre Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology University College London London United Kingdom Centre for Cognitive and Clinical Neuroscience Division of Psychology Department of Life Sciences College of Health Medicine and Life Sciences Brunel University London United Kingdom Universidade Estadual de Campinas School of Medical Sciences Brazil Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or shape analysis. Automated segmentation facilitates the study of brain structures in larger cohorts when compared with manual segmentation, which is time-consuming. However, the development of most automated methods relies on large and manually annotated datasets, which limits the generalizability of these methods. Recently, new techniques using synthetic images have emerged, reducing the need for manual annotation. Here we provide HELM, Hypothalamic ex vivo Label Maps, a dataset composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres, which can be used to develop segmentation methods using synthetic data. The label maps are obtained with a combination of manual labels for the hypothalamic regions and automated segmentations for the rest of the brain, and mirrored to simulate entire brains. We also provide the pre-processed ex vivo scans, as this dataset can support future projects to include other structures after these are manually segmented. © 2024, CC BY.

关键词： Magnetic resonance imaging

Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

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Noncontact Doppler Radar-based Heart Rate Detection on the S...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiuxia Shi Bin Hu Fuze Tian Qinglin Zhao Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University School of Computer and Communication Lanzhou University of Technology Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.

关键词： Biomedical measurement Noise cancellation time measurement Doppler radar Recording Doppler effect Biosensors

Variable petri nets for mobility

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arXiv 2021年

作者： Ding, Zhijun Yang, Ru Cui, Puwen Zhou, MengChu Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Department of Computer Science and Technology Tongji University Shanghai201804 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Mobile computing systems, service-based systems and some other systems with mobile interacting components have recently received much attention. However, because of their characteristics such as mobility and disconnection, it is difficult to model and analyze them by using a structure-fixed model. This work proposes a new Petri net model called Variable Petri Net (VPN) for modeling and analyzing these systems. The definition, firing rule, and related analysis technology of VPN are introduced in detail. In a VPN, the possible interaction interfaces are abstracted as a new kind of places called virtual places, and the occurrences of (dis)connections are described by new functions, which makes it appropriate to describe the component collaboration in systems and realize the scalability and pluggability of systems. Moreover, to overcome the shortcoming that markings cannot reflect link capability of a system, VPNs add a constraint function along with a marking to represent a complete system configuration. Several examples are used to demonstrate the newly proposed model and method. Copyright © 2021, The Authors. All rights reserved.

关键词： Embedded systems

State Permutation Control in Non-Hermitian Multiqubit Systems with Suppressed Non-Adiabatic Transitions

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arXiv 2025年

作者： Arkhipov, Ievgen I. Lewalle, Philippe Nori, Franco Özdemir, Şahin K. Whaley, K. Birgitta Joint Laboratory of Optics Palacký University Institute of Physics of CAS Faculty of Science Palacký University 17. listopadu 12 Olomouc771 46 Czech Republic Department of Chemistry University of California BerkeleyCA94720 United States Berkeley Center for Quantum Information and Computation BerkeleyCA94720 United States Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Saitama Wako-shi351-0198 Japan Quantum Information Physics Theory Research Team Quantum Computing Center RIKEN Wakoshi Saitama351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109-1040 United States Department of Electrical and Computer Engineering Saint Louis University St. LouisMO63103 United States

Non-Hermitian systems have been at the focus of intense research for over a decade, partly due to their nontrivial energy topology formed by intersecting Riemann manifolds with branch points known as exceptional points (EPs). This spectral property can be exploited, e.g., to achieve controlled state permutations that are necessary for implementing a wide class of classical and quantum information protocols. However, the imaginary spectra of typical non-Hermitian systems lead to instabilities and breakdown of adiabaticity, which impedes the practical use of EP-induced energy topologies in quantum information protocols that explicitly rely on symmetric state flips. On the other hand, techniques that help to suppress non-adiabatic transitions in non-Hermitian systems have also been developed, but these advances have so far been limited to single qubits. In this work, we address this long-standing problem by introducing a model of interacting qubits governed by an effective non-Hermitian Hamiltonian that hosts EPs and possesses a completely real energy spectrum. We demonstrate that such non-Hermitian Hamiltonians enable realization of permutation groups in the multi-qubit eigenspace. Our findings indicate that, contrary to previous beliefs, non-Hermiticity can be utilized to achieve controlled state permutations in time-modulated multiqubit systems, thus paving the way for the advancement and development of novel quantum information protocols. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning

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arXiv 2025年

作者： Zhao, Changyuan Zhang, Ruichen Wang, Jiacheng Niyato, Dusit Sun, Geng Du, Hongyang Li, Zan Jamalipour, Abbas Kim, Dong In College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computer Science and Technology Jilin University 130012 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments validate that this integrated GenAI framework outperforms traditional interpolation methods and deep learning baselines by achieving 57.35% and 88.68% reconstruction error reduction, respectively. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments. © 2025, CC BY.

关键词： Deep reinforcement learning