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arXiv 2024年

作者： Chen, Xianzhe Ren, Hong Pan, Cunhua Peng, Zhangjie Zhi, Kangda Liu, Yong Xi, Xiaojun Armada, Ana Garcia Wang, Cheng-Xiang The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China The Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai200234 China The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The Wireless Technology Laboratory Huawei Technologies Co. Ltd. Shanghai201206 China The Department of Signal Theory and Communications University Carlos III of Madrid Leganes28911 Spain The National Mobile Communications Research Laboratory School Information of Science and Engineering Southeast University Nanjing210096 China The Pervasive Communication Research Center Purple Mountain Laboratories Nanjing211111 China

This paper investigates a reconfigurable intelligent surface (RIS)-aided wideband massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with low-resolution analog-to-digital converters (ADCs). Frequency-selective Rician fading channels are considered, and the OFDM data transmission process is presented in time domain. This paper derives the closed-form approximate expression of the uplink achievable rate, based on which the asymptotic system performance is analyzed when the number of the antennas at the base station and the number of reflecting elements at the RIS grow to infinity. Besides, the power scaling laws of the considered system are revealed to provide energy-saving insights. Furthermore, this paper proposes a gradient ascent-based algorithm to design the phase shifts of the RIS for maximizing the minimum user rate. Finally, numerical results are presented to verify the correctness of analytical conclusions and draw insights. Copyright © 2024, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios

arXiv

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arXiv 2024年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China Department of Automation Tsinghua University Beijing100084 China School of Intelligent Systems Science and Engineering Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics Jinan University Zhuhai519070 China

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge- and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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Monaural Speech Enhancement Using a Multi-Branch Temporal Convolutional Network

SSRN

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SSRN 2022年

作者： Zhang, Qiquan Qian, Xinyuan Nicolson, Aaron Wang, Chenxu Paliwal, Kuldip K. Department of Electrical and Computer Engineering National University of Singapore Singapore Australian e-Health Research Centre CSIRO Australia School of Information and Electrical Engineering Harbin Institute of Technology Weihai264209 China Signal Processing Laboratory Griffith University BrisbaneQLD4160 Australia

Deep learning has achieved substantial improvement on single-channel speech enhancement. The performance of multi-layer perceptrons (MLPs)-based methods is limited by the ability to capture the long-term contextual information. The recurrent neural networks (RNNs), e.g., long short-term memory (LSTM) model, are able to capture the long-term temporal dependencies, but suffer from the high latency and the complex training process. Recently, temporal convolution networks (TCNs), employing 1-D dilated convolution and residual learning, achieves gained considerable success in speech enhancement. In this paper we propose a novel TCN model that employs multi-branch structure, called multi-branch TCN (MB-TCN), for monaural speech enhancement. The MB-TCN exploits split-transform-aggregate design, which is expected to obtain strong representational power with higher parameter efficiency. The MB-TCN model applies 1-D causal dilated CNN in each branch to expand receptive fields for capturing long-term temporal contextual information. Our extensive experimental investigation suggests that MB-TCNs outperform the residual LSTMs (ResLSTMs), TCNs, and the CNNs with dense aggregations (DenseNet) in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our proposed MB-TCN model demonstrates highly competitive performance to multiple state-of-the-art speech enhancement methods in terms of five widely used objective metrics. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Detection of White Matter Hyperintensities in Magnetic Resonance Imaging by Hyperspectral Subpixel Detection

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IEEE Access 2024年 12卷 111992-112007页

作者： Yung-Chieh Chang Chein-I Chang Yen-Chieh Ouyang Jyh-Wen Chai Wen-Hsien Chen Kuan-Jung Pan Hsin-Che Wang Clayton Chi-Chang Chen Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and Technology Taichung Taiwan Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Medical Research Center for Quantitative Imaging in Medicine (CQUIM) Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images generally associated with Alzheimer’s disease (AD) and cognitive decline. Finding WMHs of AD poses a great challenge for diagnosis. This paper interprets a brain MR image as a hyperspectral image so that a well stablished hyperspectral subpixel detection algorithm, constrained energy minimization (CEM), is applicable to solving the WMHs detection problem at mixed pixel and subpixel level. To resolve nonlinear mixing in detecting WMHs nearby boundaries, a nonlinear CEM, called kernel CEM (KCEM) is also developed. Since CEM is a hyperspectral technique without taking spatial correlation into account, CEM was also extended to iterative CEM (ICEM) by including spatial filters to capture spatial information for WMHs detection. This paper combines ICEM and KCEM to derive a new WHMs detection algorithm, iterative KCEM (IKCEM) to improve ICEM and KCEM on WMHs detection. To evaluate the WMHs detection performance, two criteria, Dice similarity index (DSI) and 3D ROC analysis are used as evaluation tools. In order to show the superiority of IKCEM, two commonly used software packages, statistical parametric mapping (SPM)-based algorithms, SPM-lesion growth algorithm (SPM-LGA) and SPM-lesion prediction algorithm (SPM-LPA) are implemented for validation and comparison.

关键词： Hyperspectral imaging Magnetic resonance imaging Iterative methods Medical diagnostic imaging White matter Manuals Energy management Neuroimaging Brain modeling Alzheimer's disease Energy measurement

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Iterative Random Training Sampling Convolutional Neural Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2023年 61卷

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan Providence University Department of Computer Science and Information Management Taichung02912 Taiwan University of Maryland School of Medicine R. A. Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Convolutional neural network (CNN) has received considerable interest in hyperspectral image classification (HSIC) lately due to its excellent spectral-spatial feature extraction capability. To improve CNN, many approaches have been directed at exploring the infrastructure of its network by introducing different paradigms. This article takes a rather different approach by developing an iterative CNN that extends a CNN by including a feedback system to repeatedly process the same CNN in an iterative manner. Its idea is to take advantage of a recently developed iterative training sampling spectral-spatial classification (IRTS-SSC) that allows CNN to update its spatial information of classification maps through a feedback spatial fil- tering system via IRTS. The resulting CNN is called iterative random training sampling CNN (IRTS-CNN) with several unique features. First, IRTS-CNN combines CNN and IRTS-SSC into one paradigm, an architecture that has never been investigated in the past. Second, it implements a series of spatial filters to capture spatial information of classified data samples and further feeds this information back via an iterative process to expand the current input data cube for the next iteration. Third, it utilizes the expanded data cube to randomly reselect training samples and then to reimplement CNN iteratively. Last but not least, IRTS-CNN provides a general framework that can implement any arbitrary CNN as an initial classifier to improve its performance through an iterative process. Extensive experiments are conducted to demonstrate that IRTS-CNN indeed significantly improves CNN, specifically when only a small size of limited training samples is used. © 1980-2012 IEEE.

关键词： Iterative methods

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Integrating HL7 FHIR into Clinical Decision Support Systems: A Real-World Application with Pepper Humanoid Robot in Hospital During Doctor Visits

Integrating HL7 FHIR into Clinical Decision Support Systems:...

引用

International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Valentino Šafran Samo Horvat Bojan Ilijevec Igor Rober Roj Vojko Flis Izidor Mlakar Laboratory of Digital Signal Processing University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia Department of Thoracic Surgery University Medical Centre Maribor Maribor Slovenia Clinical Department of Abdominal and General Surgery University Medical Centre Maribor Maribor Slovenia Department of Nurses in Clinic for Surgery University Medical Centre Maribor Maribor Slovenia Clinical Department of Cardiac Surgery University Medical Centre Maribor Maribor Slovenia

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

This study explores the integration of HL7 Fast Healthcare Interoperability Resources (FHIR) into a Clinical Decision Support System (CDSS) utilizing a Pepper robot to enhance doctor visits in a hospital setting. We evaluate a real-world implementation where the Pepper robot acts as an interface for data collection from patients, clinical monitors, and hospital information systems. The CDSS leverages FHIR to seamlessly integrate this data and present it visually, aiming to improve clinical decision-making processes for doctors. Our research investigates the potential of this FHIR-based CDSS with a robot interface to optimize healthcare delivery and potentially improve patient outcomes.

关键词： Decision support systems Surveys Hospitals Service robots Soft sensors Transforms User experience Usability Monitoring Interoperability

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signal structure information-based data association for maneuvering targets with a convolutional Siamese network

Signal structure information-based data association for mane...

引用

IEEE International Conference on Radar

作者： Chang Gao Hongwei Liu Pramod K. Varshney Junkun Yan Penghui Wang National Laboratory of Radar Signal Processing Xidian University Xi'an China Department of Electrical Engineering and Computer Science Syracuse University NY USA

ISBN: (纸本)9781665468893

Traditional data association methods usually suffer performance degradation caused by model mismatch when target maneuvers occur. In addition, the signal structure information (SSI) available from the employed signal processing techniques is rarely used in data association due to the lack of appropriate tools. To overcome the model mismatch problem brought by the target maneuvers, we resort to a data driven method to design the data association algorithm. To make full use of the SSI, a convolutional neural network is used to transform the SSI into a feature space, where the similarity of the SSI between two observations can be calculated using the Euclidean distance. The resulting structure is the convolutional Siamese network (CSN). By comparing the similarity with a predetermined margin parameter, the decision about association can be made. Simulation results are presented to show that the use of SSI in the designed CSN can improve the association probability of maneuvering targets.

关键词： Correlation Convolution Simulation Radar Euclidean distance Transforms Radar tracking

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The PHD/CPHD filter for Multiple Extended Target Tracking with Trajectory Set Theory and Explicit Shape Estimation

arXiv

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arXiv 2025年

作者： Cheng, Yuanhao Cao, Yunhe Yeo, Tat-Soon Jie, Fu Zhang, Wei National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore

In this paper, we propose two methods for tracking multiple extended targets or unresolved group targets with elliptical extent shape. These two methods are deduced from the famous Probability Hypothesis Density (PHD) filter and the Cardinality-PHD (CPHD) filter, respectively. In these two methods, Trajectory Set Theory (TST) is combined to establish the target trajectory estimates. Moreover, by employing a decoupled shape estimation model, the proposed methods can explicitly provide the shape estimation of the target, such as the orientation of the ellipse extension and the length of its two axes. We derived the closed Bayesian recursive of these two methods with stable trajectory generation and accurate extent estimation, resulting in the TPHD-E filter and the TCPHD-E filter. In addition, Gaussian mixture implementations of our methods are provided, which are further referred to as the GM-TPHD-E filter and the GM-TCPHD-E filters. We illustrate the ability of these methods through simulations and experiments with real data. These experiments demonstrate that the two proposed algorithms have advantages over existing algorithms in target shape estimation, as well as in the completeness and accuracy of target trajectory generation. Copyright © 2025, The Authors. All rights reserved.

关键词： Group theory

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A Novel Tri-Band Antenna Design for Wireless LAN Applications

A Novel Tri-Band Antenna Design for Wireless LAN Application...

引用

IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Sarosh Ahmad Hichem Boubakar Wael Jaafar Halim Yanikomeroglu Department of Signal Theory and Communications Universidad Carlos III de Madrid Spain Laboratory of Information Processing and Telecommunications (LTIT) University of Bechar Bechar Algeria Software and Information Technology Engineering Department Ecole de Technologie Supérieure QC Canada Systems and Computer Engineering Department Carleton University ON Canada

ISBN: (纸本)9781665490382

In this paper, we propose the design of a novel tri-band compact patch antenna. This antenna is resonant within frequencies 2.45 GHz, 3.5 GHz, and 5.8 GHz, making it compatible with wireless local area network (WLAN), world interoperability for microwave access (WiMAX), and Bluetooth. The flexible Roger RT5880 substrate with a thickness of 0.8 mm is used to construct the antenna, and a 50 Ω microstrip line is used as its feeding technique. The final structure has a small size of 30 × 20 × 0.8 mm 3 compared to state-of-the-art designs. Through simulations, we demonstrate the stable and satisfying performances of the novel antenna design over the three targeted frequency bands, thus favoring its use for integrated systems that operate within WLAN, WiMAX, and Bluetooth wireless bands.

关键词： Antenna measurements Microwave antennas Wireless LAN Bluetooth Patch antennas Resonant frequency Microstrip antennas

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Movable-Antenna-Array-Enabled communications with CoMP Reception

arXiv

引用

arXiv 2023年

作者： Hu, Guojie Wu, Qingqing Ouyang, Jian Xu, Kui 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 Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China The College of Communications Engineering The Army of Engineering University Nanjing210007 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 consider the movable-antenna (MA) array-enabled wireless communication with coordinate multi-point (CoMP) reception, where multiple destinations adopt the maximal ratio combination technique to jointly decode the common message sent from the transmitter equipped with the MA array. Our goal is to maximize the effective received signal-to-noise ratio, by jointly optimizing the transmit beamforming and the positions of the MA array. Although the formulated problem is highly non-convex, we reveal that it is fundamental to maximize the principal eigenvalue of a hermite channel matrix which is a function of the positions of the MA array. The corresponding sub-problem is still non-convex, for which we develop a computationally efficient algorithm. Afterwards, the optimal transmit beamforming is determined with a closed-form solution. In addition, the theoretical performance upper bound is analyzed. Since the MA array brings an additional spatial degree of freedom by flexibly adjusting all antennas' positions, it achieves significant performance gain compared to competitive benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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