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IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

ASBSO: An improved brain storm optimization with flexible search length and memory-based selection

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arXiv 2021年

作者： Yu, Yang Gao, Shangce Wang, Yirui Cheng, Jiujun Todo, Yuki The Faculty of Engineering University of Toyama Toyama930-8555 Japan Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Shanghai200092 China The School of Electrical and Computer Engineering Kanazawa University Kanazawa-shi920-1192 Japan

Brain storm optimization (BSO) is a newly proposed population-based optimization algorithm which uses a logarithmic sigmoid transfer function to adjust its search range during the convergent process. However, this adjustment only varies with the current iteration number, and lacks of flexibility and variety which makes a poor search efficiency and robustness of BSO. To alleviate this problem, an adaptive step length structure together with a success memory selection strategy are proposed to be incorporated into BSO. This proposed method, adaptive step length based on memory selection BSO, namely ASBSO, applies multiple step lengths to modify the generation process of new solutions, thus supplying a flexible search according to corresponding problems and convergent periods. The novel memory mechanism which is capable of evaluating and storing the degree of improvements of solutions is used to determine the selection possibility of step lengths. A set of 57 benchmark functions are used to test ASBSO's search ability, and four real-world problems are adopted to show its application value. All these test results indicate the remarkable improvement in solution quality, scalability and robustness of ASBSO. Copyright © 2021, The Authors. All rights reserved.

关键词： Swarm intelligence

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

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arXiv 2022年

作者： Xu, Junshen Moyer, Daniel Grant, P. Ellen Golland, Polina Iglesias, Juan Eugenio Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital BostonMA United States Harvard Medical School BostonMA United States Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London United Kingdom Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Boston United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion. Copyright © 2022, The Authors. All rights reserved.

关键词： Brain

A codebook-driven approach for low-light image enhancement

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Xu Wu Xianxu Hou Zhihui Lai Jie Zhou Ya-nan Zhang Witold Pedrycz Linlin Shen College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou 215400 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 China School of Computer Science Sichuan Normal University Chengdu 610065 China The Department of Electrical & Computer Engineering University of Alberta Alberta T6G 1H9 Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations; (2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging discrete codebook priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. The code can be obtained from https://***/csxuwu/CodeEnhance or https://***/*** .

关键词： Image coding

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