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IEEE Open Journal of the Communications Society 2025年 6卷 4083-4100页

作者： Lancho, Alejandro Weiss, Amir Lee, Gary C. F. Jayashankar, Tejas Kurien, Binoy G. Polyanskiy, Yury Wornell, Gregory W. Universidad Carlos III de Madrid Signal Theory and Communications Leganés28911 Spain Gregorio Marañón Health Research Institute Madrid28007 Spain Bar-Ilan University Ramat Gan5290002 Israel Institute for Infocomm Research Communications and Networks Fusionopolis 138632 Singapore Massachusetts Institute of Technology Electrical Engineering and Computer Science Department CambridgeMA02139 United States MIT Lincoln Laboratory LexingtonMA02421 United States

We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset for data-driven analyses of RF signal problems. Specifically, we adopt a simplified signal model for developing and analyzing interference rejection algorithms. For this signal model, we introduce a set of carefully chosen deep learning architectures, incorporating key domain-informed modifications alongside traditional benchmark solutions to establish baseline performance metrics for this intricate, ubiquitous problem. Through extensive simulations involving eight different signal mixture types, we demonstrate the superior performance (in some cases, by two orders of magnitude) of architectures such as UNet and WaveNet over traditional methods like matched filtering and linear minimum mean square error estimation. Our findings suggest that the data-driven approach can yield scalable solutions, in the sense that the same architectures may be similarly trained and deployed for different types of signals. Moreover, these findings further corroborate the promising potential of deep learning algorithms for enhancing communication systems, particularly via interference mitigation. This work also includes results from an open competition based on the RF Challenge, hosted at the 2024 IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP'24). © 2020 IEEE.

关键词： Matched filters

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Beam Scanning for Integrated Sensing and Communication in IRS-aided mmWave Systems 24

Beam Scanning for Integrated Sensing and Communication in IR...

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24th IEEE International Workshop on signal processing Advances in Wireless Communications, SPAWC 2023

作者： Li, Renwang Shao, Xiaodan Sun, Shu Tao, Meixia Zhang, Rui Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Shanghai Jiao Tong University Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9781665496261

This paper investigates an intelligent reflecting surface (IRS) aided millimeter-wave integrated sensing and communication (ISAC) system. Specifically, based on the passive beam scanning in the downlink, the IRS finds the optimal beam for reflecting the signals from the base station to a communication user. Meanwhile, the IRS estimates the angle of a nearby target based on its echo signal received by the sensing elements mounted on the IRS (i.e., semi-passive IRS). We propose an ISAC protocol for achieving the above objective via simultaneous (beam) training and sensing (STAS). Then, we derive the achievable rate of the communication user and the Cramer-Rao bound (CRB) of the angle estimation for the sensing target in closed-form. The achievable rate and CRB exhibit different performance against the duration of beam scanning. Specifically, the average achievable rate initially rises and subsequently declines, while the CRB monotonically decreases. Consequently, the duration of beam scanning should be carefully selected to balance communication and sensing performance. Simulation results have verified our analytical findings and shown that, thanks to the efficient use of downlink beam scanning signal for simultaneous communication and target sensing, the STAS protocol outperforms the benchmark protocol with orthogonal beam training and sensing. © 2023 IEEE.

关键词： Millimeter waves

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Extended Version of "New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI"

arXiv

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arXiv 2023年

作者： Lobos, Rodrigo A. Chan, Chin-Cheng Haldar, Justin P. The Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

This is an unabridged version of a journal manuscript that has been submitted for publication [1]. (Due to length restrictions, we were forced to remove substantial amounts of content from the version that was submitted to the journal, including more detailed theoretical explanations, additional figures, and a more comprehensive bibliography. This content remains intact in this version of the document). Sensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In the first part of this work, we present a novel theoretical derivation of subspace-based sensitivity map estimation based on a linear-predictability/structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with distinct theory that may be more intuitive for some readers. In the second part of this work, we propose and evaluate a set of computational acceleration approaches (collectively known as PISCO) that can enable substantial improvements in computation time (up to ∼100× in the examples we show) and memory for subspace-based sensitivity map estimation. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI

arXiv

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arXiv 2024年

作者： Liu, Yunsong Mandal, Debdut Liao, Congyu Setsompop, Kawin Haldar, Justin P. Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Departments of Radiology and Electrical Engineering Stanford University StanfordCA94305 United States

We introduce a new algorithm to solve a regularized spatial-spectral image estimation problem. Our approach is based on the linearized alternating directions method of multipliers (LADMM), which is a variation of the popular ADMM algorithm. Although LADMM has existed for some time, it has not been very widely used in the computational imaging literature. This is in part because there are many possible ways of mapping LADMM to a specific optimization problem, and it is nontrivial to find a computationally efficient implementation out of the many competing alternatives. We believe that our proposed implementation represents the first application of LADMM to the type of optimization problem considered in this work (involving a linear-mixture forward model, spatial regularization, and nonnegativity constraints). We evaluate our algorithm in a variety of multiparametric MRI partial volume mapping scenarios (diffusion-relaxation, relaxation-relaxation, relaxometry, and fingerprinting), where we consistently observe substantial (∼3×-50×) speed improvements. We expect this to reduce barriers to using spatially-regularized partial volume compartment mapping methods. Further, the considerable improvements we observed also suggest the potential value of considering LADMM for a broader set of computational imaging problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Geometry of Random Sparse Arrays

Geometry of Random Sparse Arrays

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Asilomar Conference on signals, Systems & computers

作者： Mehmet Can Hücümcnoğlu Robin Rajamäki Piya Pal Department of Electrical and Computer Engineering University of California San Diego Department of Signal Processing and Acoustics Aalto University

We consider random sparse arrays whose sensors are randomly placed on a grid of fixed size. Although deterministic sparse array geometries such as nested, coprime and their many variants have been extensively studied, less is known about difference/sum sets of random arrays. In this work, we analytically characterize the size of the contiguous segment of the so-called difference coarray of random sparse arrays. The difference coarray determines fundamental performance limits of sparse arrays and is therefore an essential object of study. Moreover, a large contiguous coarray is usually desired to guarantee unambiguous identification of many signal sources. We show that i.i.d. sampling schemes can be inadequate for guaranteeing a large contiguous coarray with high probability. Instead, one needs to design alternative random sampling schemes. We propose such a scheme and verify numerically that it yields random arrays with a difference coarray whose contiguous segment scales super-linearly with the expected number of sensors.

关键词： Geometry computers Sensor phenomena and characterization Apertures Random variables Standards Sensor arrays

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Independent Vector Extraction Constrained on Manifold of Half-Length Filters 31

Independent Vector Extraction Constrained on Manifold of Hal...

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31st European signal processing Conference, EUSIPCO 2023

作者： Koldovský, Zbyněk Čmejla, Jaroslav Adali, Tülay O'Regan, Stephen Acoustic Signal Analysis and Processing Group Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Czech Republic Department of Electrical and Computer Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Naval Surface Warfare Center Carderock Division West BethesdaMD United States

ISBN: (纸本)9789464593600

Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does not account for any relationships between parameters across the mixtures/frequencies. The separation proceeds jointly only through the source model, where statistical dependencies of sources across the mixtures are taken into account. In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies. This is achieved by constraining the set of feasible parameters to the manifold of half-length separating filters, which has a clear interpretation and application in frequency-domain speaker extraction. © 2023 European signal processing Conference, EUSIPCO. All rights reserved.

关键词： Extraction

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Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization

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IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

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Pros and Cons of Fixation and Smooth Pursuit Interaction in Assistive Technology: A Review

Pros and Cons of Fixation and Smooth Pursuit Interaction in ...

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International Conference on Science and Technology-computer (ICST)

作者： Muhammad Oka Bagus Wibowo Syukron Abu Ishaq Alfarozi Sunu Wibirama Department of Electrical and Information Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical and Information Engineering Signal Processing Laboratory Indonesia

Assistive technology implements various methods of interaction according to the needs of the user. Eye tracking is one of several interaction methods that detects gaze and trajectory of eye movements to control a computer system. During gaze-based interaction, events detection algorithm is implemented for classifying eye movements. Fixation and smooth pursuit are two most prominent eye movements used in assistive technology. Unfortunately, there is no study that thoroughly investigates advantages and disadvantages of fixation and smooth pursuit eye movement in assistive technology. To handle this research gap, we present a literature study to investigate implementations of fixation and smooth pursuit eye movement in various assistive technologies. We elaborate pros and cons of using fixation and smooth pursuit in their related technologies. Based on our review, we found that smooth pursuit is more useful to be implemented in public assistive technology than fixation. Smooth pursuit-based interaction does not require personal calibration process that takes significant amount of time. With our concluded results, our study may be used as a guidance for early career human-computer interaction researchers and front-end engineers to assess suitability of types of interaction in a gaze-based assistive technology.

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Direct Multipath-Based SLAM

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IEEE Transactions on signal processing 2025年

作者： Liang, Mingchao Leitinger, Erik Meyer, Florian University of California San Diego Department of Electrical and Computer Engineering La Jolla CA92093 United States Graz University of Technology Signal Processing and Speech Communication Laboratory Graz8010 Austria University of California San Diego Scripps Institution of Oceanography La Jolla CA92093 United States

Radio-based localization approaches that make use of reflections in the propagation environment to improve the accuracy and robustness of location estimates have a variety of potential applications in future wireless communication networks. Multipath-based simultaneous localization and mapping (SLAM) aims at estimating the position of agents and reflecting features in the environment by exploiting the relationship between the local geometry and multipath components (MPCs) in received radio signals. Existing multipath-based SLAM methods preprocess received radio signals using a channel estimator. The channel estimator lowers the data rate by extracting a set of dispersion parameters for each MPC. These parameters are then used as measurements for SLAM. Bayesian estimation for multipath-based SLAM is facilitated by the lower data rate. However, due to finite resolution capabilities limited by signal bandwidth, channel estimation is prone to errors and MPC parameters may be extracted incorrectly and lead to a reduced SLAM performance. We propose a multipath-based SLAM approach that directly uses received radio signals as inputs. A new statistical model that can effectively be represented by a factor graph is introduced. The factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that avoids data preprocessing by a channel estimator. Numerical results based on synthetic and real data in challenging single-input, single-output (SISO) scenarios demonstrate that the proposed method outperforms conventional methods in terms of localization and mapping accuracy. © 1991-2012 IEEE.

关键词： Mapping

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