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A Dynamic Transformer Network for Vehicle Detection

IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Fluid Antennas-Enabled Multiuser Uplink: A Low-Complexity Gradient Descent for Total Transmit Power Minimization

arXiv

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arXiv 2023年

作者： Hu, Guojie Wu, Qingqing Xu, Kui Ouyang, Jian Si, Jiangbo Cai, Yunlong Al-Dhahir, Naofal The College of Communication Engineering Rocket Force University of Engineering Xi’an710025 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The College of Communications Engineering The Army of Engineering University Nanjing210007 China The Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China The Integrated Service Networks Lab of Xidian University Xi’an710100 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

We investigate multiuser uplink communications from multiple single-antenna users to a base station (BS), which is equipped with multiple fluid antennas (FAs) and adopts zero-forcing receivers to decode multiple signals. We aim to optimize antennas’ positions at the BS, to minimize the total transmit power of all users subject to the minimum rate requirement. After applying transformations, we show that the problem is equivalent to minimizing the sum of each eigenvalue’s reciprocal of a matrix, which is a function of all antennas’ positions at the BS. Subsequently, the projected gradient descent (PGD) method is utilized to find a locally optimal solution. In particular, different from the latest related work, we exploit the eigenvalue decomposition to successfully derive a closed-form gradient for the PGD, which facilitates the practical implementation greatly. We demonstrate by simulations that via careful optimization for all antennas’ positions in our proposed design, the total transmit power of all users can be decreased significantly as compared to competitive benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Antennas

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AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET/CT for detection of cardiac sarcoidosis

引用

Journal of Nuclear Cardiology 2025年 48卷 102195页

作者： Miller, Robert JH. Shanbhag, Aakash Marcinkiewicz, Anna M. Struble, Helen Gransar, Heidi Hijazi, Waseem Fujito, Hidesato Kransdorf, Evan Kavanagh, Paul Liang, Joanna X. Builoff, Valerie Dey, Damini Berman, Daniel S. Slomka, Piotr J. Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Center of Radiological Diagnostics National Medical Institute of the Ministry of the Interior and Administration Warsaw Poland

Background: [18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) plays a central role in diagnosing and managing cardiac sarcoidosis. We propose a fully automated pipeline for quantification of [18F]FDG PET activity using deep learning (DL) segmentation of cardiac chambers on computed tomography (CT) attenuation maps and evaluate quantitative approaches based on this framework. Methods: We included consecutive patients undergoing [18F]FDG PET/CT for suspected cardiac sarcoidosis. DL segmented left atrium, left ventricle (LV), right atrium, right ventricle, aorta, LV myocardium, and lungs from CT attenuation scans. CT-defined anatomical regions were applied to [18F]FDG PET images automatically to quantify target to background ratio (TBR), volume of inflammation (VOI) and cardiometabolic activity (CMA) using full sized and shrunk segmentations. Results: A total of 69 patients were included, with mean age of 56.1 ± 13.4 and cardiac sarcoidosis present in 29 (42 %). CMA had highest prediction performance (area under the receiver operating characteristic curve [AUC] .919, 95 % confidence interval [CI] .858 – .980) followed by VOI (AUC .903, 95 % CI .834 – .971), TBR (AUC .891, 95 % CI .819 – .964), and maximum standardized uptake value (AUC .812, 95 % CI .701 – .923). Abnormal CMA (≥1) had a sensitivity of 100 % and specificity 65 % for cardiac sarcoidosis. Lung quantification was able to identify patients with pulmonary abnormalities. Conclusion: We demonstrate that fully automated volumetric quantification of [18F]FDG PET for cardiac sarcoidosis based on CT attenuation map-derived volumetry is feasible, rapid, and has high prediction performance. This approach provides objective measurements of cardiac inflammation with consistent definition of myocardium and background region. © 2025 American Society of Nuclear Cardiology

关键词： Cardiac sarcoidosis Deep learning Hypermetabolism Positron emission tomography Quantification

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Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

引用

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Xinjia Chen Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin China Department of Engineering Technology Northwestern State University of Louisiana Natchitoches LA USA Communications Research Centre Canada Ottawa ON Canada

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users’ preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user’s on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems.

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Improved track-before-detect method for detecting range-spread targets in generalized Pareto clutter

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Science China(Information Sciences) 2019年第4期62卷 44-46页

作者： Jian XUE Shuwen XU Penglang SHUI National Lab of Radar Signal Processing Collaborative Innovation Center of Information Sensing and UnderstandingXidian University Department of Electrical and Computer Engineering Mcmaster University

Dear editor,High-resolution radars demonstrate a good targetdetection performance, especially for weak targets such as periscopes and small boats [1]. Methods for adaptive detection employed by high-resolution

关键词： TBD Improved track-before-detect method for detecting range-spread targets in generalized Pareto clutter SCR

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Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery

arXiv

引用

arXiv 2024年

作者： Sledge, Isaac J. Byrne, Dominic M. King, Jonathan L. Ostertag, Steven H. Woods, Denton L. Prater, James L. Kennedy, Jermaine L. Marston, Timothy M. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Naval Sea Systems Command United States The Applied Sensing and Processing Branch Naval Surface Warfare Center Panama CityFL United States The Littoral Acoustics and Target Physics Branch Naval Surface Warfare Center Panama CityFL United States The Division Head of the Sensing Sciences and Systems Division Naval Surface Warfare Center Panama CityFL United States The Applied Physics Laboratory University of Washington SeattleWA United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL United States The University of Florida United States

We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels, to uncover a set of semi-sparse, spatially-discriminative regions in each image. The classification uncertainty of each region is then evaluated. Those areas with the lowest uncertainties are then chosen to be weakly labeled segmentation seeds, at the pixel level, for the second part of the framework. Each of the seed extents are progressively resized according to an unsupervised, information-theoretic loss with structured-prediction regularizers. This reshaping process uses multi-scale, adaptively-weighted features to delineate class-specific transitions in local image content. Content-addressable memories are inserted at various parts of our framework so that it can leverage features from previously seen images to improve segmentation performance for related images. We evaluate our weakly-supervised framework using real-world CSAS imagery that contains over ten seafloor classes and ten target classes. We show that our framework performs comparably to nine fully-supervised deep networks. Our framework also outperforms eleven of the best weakly-supervised deep networks. We achieve state-of-the-art performance when pre-training on natural imagery. The average absolute performance gap to the next-best weakly-supervised network is well over ten percent for both natural imagery and sonar imagery. This gap is found to be statistically significant. © 2024, CC BY.

关键词： Semantic Segmentation

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Deep Learning-Enabled, Computed Tomography-Based Race- and Sex-Specific Epicardial Adipose Tissue Thresholds for Cardiovascular Risk Stratification

Deep Learning-Enabled, Computed Tomography-Based Race- and S...

引用

Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Buchwald, Mikolaj Shanbhag, Aakash Miller, Robert J.H. Zhang, Wenhao Michalowska, Anna M. Killekar, Aditya Lemley, Mark Kavanagh, Paul B. Fujito, Hidesato Liang, Joanna X. Builoff, Valerie Knight, Stacey Einstein, Andrew J. Miller, Edward J. Feher, Atilla Sinusas, Albert J. Chareonthaitawee, Panithaya Bullock-Palmer, Renee P. Di Carli, Marcelo F. Berman, Daniel S. Dey, Damini Slomka, Piotr J. Departments of Medicine Division of Artificial Intelligence in MedicineImaging and Biomedical Sciences Cedars-Sinai Medical Center Los AngelesCA United States Poznan Supercomputing and Networking Center Polish Academy of Sciences Poznan Poland Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Department of Cardiac Sciences University of Calgary CalgaryAB Canada Center of Radiological Diagnostics National Medical Institute The Ministry of the Interior and Administration Warsaw Poland Department of Cardiology Nihon University Itabashi Hospital Tokyo Japan Intermountain Medical Center Heart Institute MurrayUT United States Division of Cardiology Columbia University Irving Medical Center New York CityNY United States Section of Cardiovascular Medicine Department of Internal Medicine Yale University School of Medicine New HavenCT United States Department of Cardiovascular Medicine Mayo Clinic RochesterMN United States Department of Cardiology Deborah Heart and Lung Center Browns MillsNJ United States Division of Cardiovascular Medicine Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Cardiovascular Imaging Program Departments of Radiology and Medicine Division of Nuclear Medicine and Molecular Imaging Department of Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA United States

ISBN: (纸本)9781510685987

While the volume of epicardial adipose tissue (EAT) has been linked to various conditions and showed a prognostic value of cardiovascular events, an insufficient effort was put into proposing clear, race- and sex-specific thresholds of high-risk and low-risk EAT amounts. Also, previous works focused on the absolute volume of EAT in milliliters, adjusted to the body size of the patients, and none has proposed a relative measure of what is the amount of EAT for a given patient also in reference to the size of the patient’s heart. In this retrospective study, computed tomography attenuation correction (CTAC) scans from 20,587 patients (58% male;84% White, 13% Black, 3% Asian) from 6 sites were used. With the previously described deep learning (DL)-based method, EAT values per patient were calculated, and subsequently normalized using either the DL-obtained cardiac volume mask, body size, or both. The association between the percent of EAT in reference to the heart size and incident mortality or nonfatal myocardial infarction (MI) was evaluated with Cox models adjusted for age, sex, body mass index, race, cigarette smoking status, hypertension, diabetes, family history of coronary artery disease, dyslipidemia, and left ventricle size. In total, 4,133 events were observed over a period of 6-year follow-up. Our analysis showed that when the amount of EAT was normalized jointly for body surface area and heart size, the predictive effect of EAT was diminished (p=0.85). Patients with higher EAT density, however, were more likely to experience death or MI during follow-up (p © 2025 SPIE.

关键词： Risk assessment

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An information-theoretic approach for automatically determining the number of state groups when aggregating markov chains

arXiv

引用

arXiv 2021年

作者： Sledge, Isaac J. Príncipe, José C. Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama City United States Department of Electrical and Computer Engineering University of Florida Department of Biomedical Engineering University of Florida

A fundamental problem when aggregating Markov chains is the specification of the number of state groups. Too few state groups may fail to sufficiently capture the pertinent dynamics of the original, high-order Markov chain. Too many state groups may lead to a non-parsimonious, reduced-order Markov chain whose complexity rivals that of the original. In this paper, we show that an augmented value-of-information-based approach to aggregating Markov chains facilitates the determination of the number of state groups. The optimal state-group count coincides with the case where the complexity of the reduced-order chain is balanced against the mutual dependence between the original- and reduced-order chain dynamics. © 2021, CC BY.

关键词： Information theory

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Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Sledge, Isaac J. Príncipe, José C. Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States lab The Naval Sea Systems Command United States Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL32611 United States The University of Florida United States

In this paper, we consider the problem of adjusting the exploration rate when using value-of-information-based exploration. We do this by converting the value-of-information optimization into a problem of finding equilibria of a flow for a changing exploration rate. We then develop an efficient pathfollowing scheme for converging to these equilibria and hence uncovering optimal action-selection policies. Under this scheme, the exploration rate is automatically adapted according to the agent’s experiences. Global convergence is theoretically assured. We first evaluate our exploration-rate adaptation on the Nintendo GameBoy games Centipede and Millipede. We demonstrate aspects of the search process, like that it yields a hierarchy of state abstractions. We also show that our approach returns better policies in fewer episodes than conventional search strategies relying on heuristic, annealing-based exploration-rate adjustments. We then illustrate that these trends hold for deep, value-of-information-based agents that learn to play ten simple games and over forty more complicated games for the Nintendo GameBoy system. Performance either near or well above the level of human play is observed. © 2022, CC BY.

关键词： Reinforcement learning

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Locally Decodable Index Codes

Locally Decodable Index Codes

引用

作者： Natarajan, Lakshmi Prasad Krishnan, Prasad Lalitha, V. Dau, Hoang Department of Electrical Engineering Iit Hyderabad Kandi502285 India Signal Processing and Communications Research Center International Institute of Information Technology Hyderabad Hyderabad500032 India Discipline of Computer Science and Information Technology School of Science Rmit University MelbourneVIC3000 Australia

An index code for broadcast channel with receiver side information is locally decodable if each receiver can decode its demand by observing only a subset of the transmitted codeword symbols instead of the entire codeword. Local decodability in index coding is known to reduce receiver complexity, improve user privacy and decrease decoding error probability in wireless fading channels. Conventional index coding solutions assume that the receivers observe the entire codeword, and as a result, for these codes the number of codeword symbols queried by a user per decoded message symbol, which we refer to as locality, could be large. In this paper, we pose the index coding problem as that of minimizing the broadcast rate for a given value of locality (or vice versa) and designing codes that achieve the optimal trade-off between locality and rate. We identify the optimal broadcast rate corresponding to the minimum possible value of locality for all single unicast problems. We present new structural properties of index codes which allow us to characterize the optimal trade-off achieved by: vector linear codes when the side information graph is a directed cycle;and scalar linear codes when the minrank of the side information graph is one less than the order of the problem. We also identify the optimal trade-off among all codes, including non-linear codes, when the side information graph is a directed 3-cycle. Finally, we present techniques to design locally decodable index codes for arbitrary single unicast problems and arbitrary values of locality. © 1963-2012 IEEE.

关键词： Economic and social effects

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