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HDR Imaging with One-Bit Quantization

HDR Imaging with One-Bit Quantization

引用

sensor array and multichannel signal processing workshop

作者： Arian Eamaz Farhang Yeganegi Mojtaba Soltanalian University of Illinois Chicago Chicago IL USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Modulo sampling and dithered one-bit quantization frame-works have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and sensors. Modulo sampling, with its high-resolution approach utilizing modulo ADCs, offers an unlimited dynamic range, while dithered one-bit quantization offers cost-efficiency and reduced power consumption while operating at elevated sampling rates. Our goal is to explore the synergies between these two techniques, leveraging their unique advantages, and to apply them to non-bandlimited signals within spline spaces. One noteworthy application of these signals lies in High Dynamic Range (HDR) imaging. In this paper, we expand upon the Unlimited One-Bit (UNO) sampling framework, initially conceived for bandlimited signals as proposed in [1], to encompass non-bandlimited signals found in the context of HDR imaging. We present a novel algorithm rigorously examined for its ability to recover images from one-bit modulo samples. Additionally, we introduce a sufficient condition specifically designed for UNO sampling to properly recover non-bandlimited signals within spline spaces. Our numerical results vividly demonstrate the effectiveness of UNO sampling in the realm of HDR imaging.

关键词： Image sensors Sufficient conditions Quantization (signal) Power demand Heuristic algorithms signal processing algorithms sensors

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Block Sparsity Based Channel Estimation for IRS-Assisted mmWave MIMO Systems

Block Sparsity Based Channel Estimation for IRS-Assisted mmW...

引用

sensor array and multichannel signal processing workshop

作者： Fang Guo Zhenhua Zhou Bin Liao Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong Province China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we consider the downlink channel estimation for the intelligent reflecting surface (IRS) assisted MIMO systems in the millimeter wave band. It is challenging to perform the channel estimation for the IRS-assisted wireless communication systems, due to the large amount of passive reflecting elements at the IRS. On account of the inherent sparsity of millimeter-wave channels, the sparse representation expressions of the channel between the base station (BS) and IRS and that between the IRS and user equipment (UE) are obtained separately. Furthermore, the cascaded channel is estimated by solving the BS-IRS and IRS-UE channels in an alternating way. To improve the estimation accuracy and reduce the pilot over-head, the algorithm of the block-structured orthogonal matching pursuit (BS-OMP) is devised to estimate the BS-IRS channel by exploiting its block sparsity. The simulation results show that the proposed method provides the higher estimation accuracy and consumes less pilot overhead by comparing with its counterparts.

关键词： Wireless communication Accuracy Simulation Millimeter wave technology Channel estimation Matching pursuit algorithms Estimation

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Block Successive Convex Approximation for Concomitant Linear DAG Estimation

Block Successive Convex Approximation for Concomitant Linear...

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sensor array and multichannel signal processing workshop

作者： Seyed Saman Saboksayr Gonzalo Mateos Mariano Tepper Dept. of Electrical and Computer Eng. University of Rochester Intel Labs

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

We develop a novel continuous optimization algorithm to recover latent directed acyclic graphs (DAGs) from observational (and possibly heteroscedastic) data adhering to a linear structural equation model (SEM). Our starting point is the recently proposed Concomitant Linear DAG Estimation (CoLiDE) framework, which advocates minimizing a sparsity-regularized convex score function augmented with a smooth, nonconvex acyclicity penalty. While prior work focused on score function design to jointly estimate DAG structure along with exogenous noise levels, optimization aspects were left unexplored. To bridge this gap, here we show that CoLiDE has a favorable structure amenable to optimization via a block successive convex approximation (BSCA) algorithm. We derive efficient, closed-form updates to refine the DAG adjacency matrix and noise variance estimates in a cyclic fashion. Although the acyclicity regularizer is devoid of a Lipschitz gradient and hence our approximation function is not a global upper bound of the original cost, a descent direction can be obtained via line search to yield a provably convergent sequence. Numerical tests showcase the superiority of the proposed BSCA iterations relative to the original (Adam-based) inexact block coordinate descent solver.

关键词： Upper bound Numerical analysis signal processing algorithms Estimation Approximation algorithms Mathematical models Topology

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Optimal Ratio Between Coherent and Orthogonal signals in Sparse MIMO Radar

Optimal Ratio Between Coherent and Orthogonal Signals in Spa...

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sensor array and multichannel signal processing workshop

作者： Helin Sun Joseph Tabrikian Hagit Messer Hongyuan Gao School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva ISRAEL College of Information and Communication Engineering Harbin Engineering University Harbin CHINA School of Electrical Engineering Tel Aviv University Tel Aviv ISRAEL

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

This paper addresses the problem of spatial waveform design for collocated multiple-input multiple-output (MIMO) radar systems with sparse antenna arrays. The use of sparse arrays allows to obtain narrower beams and therefore higher angular resolution and accuracy. However, if the spatial waveform is not designed properly, the resulting transmit-receive beam-pattern may suffer from significant sidelobes or ambiguity, which can strongly degrade the estimation performance. The Bayesian Cramer-Rao bound (BCRB), which is commonly used for waveform design, may produce inappropriate results as it considers only local errors and ignores the effect of sidelobes and ambiguity. To overcome this limitation, we propose using the arbitrary test-point transformation Weiss-Weinstein bound (AT-WWB) that was recently proposed, as an optimization criterion. This bound is a simpler and tighter version of the Weiss-Weinstein bound (WWB). This bound is derived for collocated MIMO radar and is minimized with respect to the ratio between coherent and orthogonal signals. The proposed method is demonstrated via simulations, and compared to optimization schemes using the BCRB and the WWB. It is shown that the spatial waveform optimized by AT- WWB exhibits superior performance in terms of direction-of-arrival estimation accuracy.

关键词： Direction-of-arrival estimation Accuracy array signal processing MIMO radar Simulation Estimation Spatial resolution

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Weight-Constrained Nested arrays with $w(1) = w(2) = 0$ for Reduced Mutual Coupling

Weight-Constrained Nested Arrays with $w(1) = w(2) = 0$ for ...

引用

sensor array and multichannel signal processing workshop

作者： Pranav Kulkarni P. P. Vaidyanathan Department of Electrical Engineering 136-93 California Institute of Technology Pasadena CA USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

It is well-known that appropriately designed sparse arrays can identify $\mathcal{O}(N^array)$ directions of arrivals (DOAs) using $N$ sensors through the difference coarray domain. Many sparse arrays have been proposed in the past decade that also reduce the impact of mutual coupling by reducing the coarray weights at small lags. In this paper, we propose weight-constrained nested arrays (WCNA) that have coarray weights satisfying either $w(1)=0$ or $w(1)=w(2)=0$ . These arrays are obtained by appropriately expanding a nested array and augmenting it with several additional sensors. We prove that the proposed arrays contain $\mathcal{O}(N^array)$ contiguous lags in their difference coarrays. Although the uniform segment in the coarray is not centered around 0, appropriately modified coarray-MUSIC can be applied to estimate the DOAs. We demonstrate through Monte-Carlo simulations that such arrays are robust to the presence of strong mutual coupling and can perform better than many other sparse arrays proposed in the literature when the mutual coupling is present. The proposed arrays are also observed to have longer uniform segments in the difference coarray than that of CADiS arrays (having $w(1)= 0$ ) and thus can identify more DOAs than CADiS arrays using coarray-MUSIC.

关键词： Couplings Mutual coupling Direction-of-arrival estimation Monte Carlo methods array signal processing Conferences sensors

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Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

Learning on Transformers is Provable Low-Rank and Sparse: A ...

引用

sensor array and multichannel signal processing workshop

作者： Hongkang Li Meng Wang Shuai Zhang Sijia Liu Pin-Yu Chen Dept. Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY USA Dept. Data Science New Jersey Institute of Technology Newark NJ USA Dept. Computer Science and Engineering Michigan State University East Lansing MI USA IBM Thomas J. Watson Research Center Yorktown Heights NY USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models. However, due to the technical challenges of the non-convex optimization caused by the complicated architecture of Transformers, the theoretical study of why these methods can be applied to learn Transformers is mostly elusive. To the best of our knowledge, this paper shows the first theoretical analysis of the property of low-rank and sparsity of one-layer Transformers by characterizing the trained model after convergence using stochastic gradient descent. By focusing on a data model based on label-relevant and label-irrelevant patterns, we quantify that the gradient updates of trainable parameters are low-rank, which de-pends on the number of label-relevant patterns. We also analyze how model pruning affects the generalization while improving computation efficiency and conclude that proper magnitude-based pruning has a slight effect on the testing performance. We implement numerical experiments to support our findings.

关键词： Training Analytical models Adaptation models Computational modeling Neurons Stochastic processes signal processing algorithms

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Robust Meta-Learning over Graphs with Graph Neural Networks

Robust Meta-Learning over Graphs with Graph Neural Networks

引用

sensor array and multichannel signal processing workshop

作者： Alireza Sadeghi Georgios B. Giannakis Department of Electrical and Computer Engineering University of Minnesota Dept. of ECE University of Minnesota Minneapolis MN USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Graph neural networks (GNNs) have well docu-mented success in various learning over graph problems, from drug discovery to recommender systems. These data hungry models however, are challenged in applications with limited available samples. Meta-learning paradigm provides principled methods to empower GNNs to learn rapidly from a few labeled data only, particularly beneficial for tasks with small datasets. Nonetheless, existing meta-learning frameworks for graphs encounter difficulties when the labeled nodes or links used during training are distant from those encountered during testing, rendering meta-learning in graph tasks challenging. In this context, current work leverages a suite of novel techniques to enhance generalization performance of learning over graphs with GNNs. A novel idea here is to further incorporate an extra adversarial update step during “adaptation” phase of conventional meta-learning algorithms. Additionally, our proposed method incorporates curvature information of the loss landscape during meta-updating to facilitate reliable knowledge transfer to downstream tasks with slightly different structures. The proposed methods are simple and with minimal computational costs on training process. Numerical experiments conducted on learning over graph problems validate the superiority of our algorithm over the existing counterparts, showcasing effectiveness when dealing with limited data resources.

关键词： Metalearning Training signal processing algorithms Rendering (computer graphics) Graph neural networks Reliability Task analysis

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Translation Identifiability-Guided Unsupervised Cross-Platform Super-Resolution for OCT Images

Translation Identifiability-Guided Unsupervised Cross-Platfo...

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sensor array and multichannel signal processing workshop

作者： Jiahui Song Sagar Shrestha Xueshen Li Yu Gan Xiao Fu School of Electrical Engineering and Computer Science Oregon State University Corvallis USA Department of Biomedical Engineering Stevens Insitute of Technology Hoboken USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Optical Coherence Tomography (OCT) is a non-invasive technique for obtaining detailed, cross-sectional images of coronary arteries. However, cost-effective OCT systems produce only low-resolution (LR) images. Unsupervised OCT super-resolution (OCT-SR) presents a cost-effective solution, eliminating the need for high-resolution (HR) systems or co-registered LR-HR image pairs. Existing unsupervised OCT-SR methods formulate the SR task as an image-to-image translation problem, and use CycleGAN as their backbone. However, CycleGAN is known to lack translation identifiability that can result in incorrect SR results. Existing methods often empirically combat this issue by using multiple regularization terms to incorporate expert-annotated side information, resulting in complicated learning losses and extensive annotations. This work proposes a translation identifiability-guided framework based on recent advances in unsupervised domain translation. Employing a diversified distribution matching module, our approach guarantees OCT translation identifiability under reasonable conditions using a simple and succinct learning loss. Numerical results indicate that our framework matches or surpasses the state-of-the-art (SOTA) baseline's performance while requiring considerably fewer resources, e.g., annotations, computation time, and memory.

关键词： Annotations Optical coherence tomography Superresolution Optical imaging Optical sensors Task analysis Optical signal processing

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Frank-Wolfe Algorithm for Simplicial and Nonnegative Component Analysis

Frank-Wolfe Algorithm for Simplicial and Nonnegative Compone...

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sensor array and multichannel signal processing workshop

作者： Jingzhou Hu Kejun Huang Department of Computer and Information Science and Engineering University of Florida Gainesville Florida

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we propose to solve simplicial and nonnegative component analysis problems using the Frank-Wolfe algorithm. Simplicial component analysis (SCA) is a blind source separation technique that is widely used in hyperspectral unmixing, which has an interesting geometric interpretation of finding the minimum volume enclosing simplex of a set of data points. A highly related but different problem is called nonnegative component analysis (NCA), which is a variant of the celebrated nonnegative matrix factorization. Both problems enjoy amiable identifiability guarantees under a mild assumption that the factor matrix with simplicial and nonnegative structures satisfies the sufficiently scattered condition in the probability simplex or the nonnegative orthant, respectively. Algorithm design for either formulations remains to be challenging. In this paper we propose to use the Frank-Wolfe algorithm to solve it. After a brief review of the Frank-Wolfe algorithm, we first provide an improved convergence analysis of it by employing the backtracking line search strategy for choosing step sizes, and show that for convex problems it converges to a global optimum at a linear rate if the step sizes can be lowerbounded. Notice that no strong convexity is necessary to achieve such linear rate, which is somewhat surprising. Despite the improved convergence rate analysis, the proof is surprisingly succinct and easy to understand. Then we show how it can be applied to SCA and NCA with similar implementations. We conclude the paper by showing its performance in numerical experiments comparing with some baseline algorithms.

关键词： Backtracking Reviews Conferences signal processing algorithms Search problems Blind source separation Numerical models

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Channel Estimation in Low-Resolution Near-Field Massive MIMO Systems

Channel Estimation in Low-Resolution Near-Field Massive MIMO...

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sensor array and multichannel signal processing workshop

作者： Ly V. Nguyen Duy H. N. Nguyen Italo Atzeni Antti Tölli A. Lee Swindlehurst Center for Pervasive Communications and Computing University of California Irvine CA USA Department of Electrical and Computer Engineering San Diego State University CA USA Centre for Wireless Communications University of Oulu Finland

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Massive multiple-input-multiple-output (MIMO) is a core technology of current and future wireless networks. However, the very large dimension of a massive antenna array can lead to radical changes in the electromagnetic fields near the array, and the classical far-field channel model is no longer accurate. Instead, the channel should be modeled under the assumption of near-field spherical wavefronts. Furthermore, the very large dimension of the arrays can also result in high power consumption and hardware complexity. A practical solution for this problem is to use low-resolution analog-to-digital converters (ADCs). It is therefore of significance to study the near-field channel estimation problem for MIMO systems implemented with low-resolution ADCs. We propose an efficient on-grid polar-domain channel estimation method which relies on the polar-domain sparsity of the near-field channels. We first reformulate the sparse low-resolution near-field maximum-likelihood channel estimation problem by exploiting an approximation of the cu-mulative distribution function of a normal random variable as a logistic activation function. We then develop an on-grid polar-domain channel estimation method based on the gradient descent approach and the polar-domain sparsity of the near-field channel. Finally, we apply the deep unfolding technique to optimize the performance of the proposed method and illustrate its efficiency via several simulation studies.

关键词： Maximum likelihood estimation Power demand Wireless networks Channel estimation signal processing algorithms Massive MIMO Random variables

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