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Split-Radix Algorithm for the Discrete Hirschman Transform

IEEE signal processing LETTERS 2022年 29卷 199-203页

作者： Xue, Dingli DeBrunner, Linda DeBrunner, Victor Huang, Zhen Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Florida State Univ Dept Elect & Comp Engn Tallahassee FL 32310 USA

With the best basis function that compactly describes a discrete-time signal, the Discrete Hirschman Transform (DHT) has been proved to perform better than the Discrete Fourier Transform (DFT) in terms of high resolution and computational complexity. It is reasonable to develop fast algorithms for the DHT computation since the DHT has applied to multiple signal processing applications. In this letter, we propose a split-radix DHT (SRDHT) including mathematical decomposition and comparison of computation complexity. The SRDHT is computationally superior to the DFT and performs more efficiently than our previously developed radix-2/-4 DHTs, with further reduced arithmetic operations. We regard this proposed SRDHT as a more attractive candidate to compute the DHT for those existing and future Hirschman-based applications.

关键词： signal processing algorithms Discrete Fourier transforms Shape Arithmetic Hardware Convolution Computational complexity DHT HOT Hirschman split-radix FFT algorithm complexity signal processing convolution

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Joint Sensor Selection, Beamforming and Phase Control in Reconfigurable Intelligent Surface Aided IoT Fusion Networks

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第2期11卷 401-405页

作者： Zhou, Yuyan Liu, Yang Li, Ming Wu, Qingqing Wang, Hao Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Univ Macau Dept Elect & Comp Engn Macau Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

In this letter, we investigate the power minimization problem in an Internet of Things (IoT) sensor network assisted by reconfigurable intelligent surface (RIS). Different from the existing literature, we consider both transmit and non-transmit energy consumption, which leads to a joint sensor selection, beamforming design and RIS phase configuration problem. Based on the cutting-the-edge majorization minimization (MM) and penalty duality decomposition (PDD) frameworks, we develop two solutions which can achieve nearly identical performance as that of exhaustive search method. Extensive numerical results verify the great benefit in power saving via sensor selection and the deployment of RIS device.

关键词： Internet of Things Power demand Array signal processing signal to noise ratio Minimization Surface waves signal processing algorithms Reconfigurable intelligent surface (RIS) group-sparsity IoT sensor network

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A Reversible Jump MCMC in Bayesian Blind Deconvolution With a Spherical Prior

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IEEE signal processing LETTERS 2022年 29卷 2372-2376页

作者： Traulle, Benjamin Bidon, Stephanie Roque, Damien Univ Toulouse ISAE SUPAERO F-31055 Toulouse France

Blind deconvolution (BD) is the recovery of a scene of interest convolved with an unknown impulse response function. Within a Bayesian context, prior distributions are assigned to both unknowns to regularize the BD problem. A common approach is to use Gaussian priors. Nonetheless, the latter do not alleviate the so-called scale ambiguity that prevents from efficiently sampling the joint posterior distribution and building appropriate estimators. In this paper, we instead use a Von-Mises prior that removes scale ambiguity and we focus on the design of a sampling scheme of the joint posterior. The latter may exhibit multiple modes. We propose accordingly a reversible jump Markov chain Monte Carlo method that prevents samples from lingering in local modes. Compared to state-of-the-art techniques, the algorithm shows an improved within- and between-mode mixing property with synthetic data. This paves the way for the design of Bayesian estimators naturally deprived of scale ambiguity.

关键词： Bayes methods signal processing algorithms Deconvolution Space exploration Monte Carlo methods Markov processes Convolution Blind deconvolution multimodal posteriors RJMCMC algorithm scale ambiguity von Mises-Fisher prior

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Average Performance of OMP and Thresholding Under Dictionary Mismatch

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IEEE signal processing LETTERS 2022年 29卷 1077-1081页

作者： Pali, Marie-Christine Ruetz, Simon Schnass, Karin Univ Innsbruck Dept Math A-6020 Innsbruck Austria

This paper studies the performance of OMP in comparison to Thresholding in the case in which only a perturbed version of the generating dictionary is known. Both theory and numerical simulations show that simple Thresholding is a viable alternative to OMP in applications where only a perturbed version of the generating dictionary is given, such as dictionary learning.

关键词： Dictionaries Matching pursuit algorithms signal processing algorithms Perturbation methods Machine learning Atomic measurements Sparse representation Sparse approximation orthogonal matching pursuit perturbed dictionary average case analysis dictionary learning thresholding

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A Convex Optimization Approach For NLOS Error Mitigation in TOA-Based Localization

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IEEE signal processing LETTERS 2022年 29卷 677-681页

作者： Wu, Huafeng Liang, Linian Mei, Xiaojun Zhang, Yuanyuan Shanghai Maritime Univ Merchant Marine Coll Shanghai 201306 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 2Y2 Canada

This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem. In this case, we convert the RTLS problem into a semidefinite program (SDP), and then obtain the solution of the original problem by solving a generalized trust region subproblem (GTRS). The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know. The outperformance of the proposed method is demonstrated in the simulations compared with other state-of-the-art techniques.

关键词： Location awareness Mathematical models Computational complexity Optimization Noise measurement signal processing algorithms Measurement uncertainty Non-line-of-sight (NLOS) time-of-arrive (TOA) target localization regularized total least square (RTLS) wireless sensor network

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Fast and Accurate Spectrum Estimation via Virtual Coarray Interpolation Based on Truncated Nuclear Norm Regularization

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IEEE signal processing LETTERS 2022年 29卷 169-173页

作者： Fan, Qing Liu, Yu Yang, Tao Peng, Hao Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China

Compared with the Nyquist sampling scheme, the coprime sampling scheme reduces the requirement of sampling rate significantly and simultaneously shows high degrees of freedom. Actually, the information received by the sampler is not fully utilized due to the non-uniformity of derived second-order virtual coarray. In addition, the virtual signals corresponding to the non-uniform virtual coarray may be contaminated by noise, which could affect the accuracy of spectrum estimation. In this letter, we propose a novel virtual coarray interpolation method for the inexact Toeplitz covariance matrix based on truncated nuclear norm regularization. The missing elements and exact covariance matrix can be obtained simultaneously by solving the covariance matrix completion problem that contains the error term and non-Toeplitz errors propagation control term. Simulation results demonstrate the superiorities of the proposed spectrum estimation method in terms of accuracy and computational complexity.

关键词： Covariance matrices Interpolation Spectral analysis Estimation Direction-of-arrival estimation Simulation signal processing algorithms Virtual coarray interpolation spectrum estimation coprime sampling truncated nuclear norm

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Speech Time-Scale Modification With GANs

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IEEE signal processing LETTERS 2022年 29卷 1067-1071页

作者： Cohen, Eyal Kreuk, Felix Keshet, Joseph Bar Ilan Univ Dept Comp Sci IL-5290002 Ramat Gan Israel

While listening to spoken content, it is often desired to vary the speech rate while preserving the speaker's timbre and pitch. To date, advanced signal processing techniques are used to address this task, but it still remains a challenge to maintain a high speech quality at all time-scales. Inspired by the success of speech generation using Generative Adversarial Networks (GANs), we propose a novel unsupervised learning algorithm for time-scale modification (TSM) of speech, called ScalerGAN. The model is trained using a set of speech utterances, where no time-scales are provided. The ScalerGAN algorithm is composed of a generator that gets as input speech with the desired rate and outputs a time-adjusted speech;a discriminator that works on various spectrum scales;and a decoder that converts the time-adjusted signal back to the original rate to maintain consistency. Using an A/B test and conditional A/B test, human listeners were asked to compare ScalerGAN with other state-of-the-art TSM methods. The results showed that the speech quality of ScalerGAN outperforms all other methods.

关键词： Spectrogram Generators signal processing algorithms Decoding Training Vocoders Time-domain analysis Deep neural networks generative adversarial networks speech synthesis time-scale modification

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Rule-based Sleep-Apnea detection algorithm 9

Rule-based Sleep-Apnea detection algorithm

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9th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)

作者： Pugliese, Luigi Guagnano, Michele Groppo, Sara Violante, Massimo Groppo, Riccardo Politecn Torino Turin Italy Sleep Advice Technol SAT Turin Italy

ISBN: (纸本)9798350336948

Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep disorder characterized by repeated episodes of breathing cessation during sleep. It affects the quality of life and can lead to severe health complications. Continuous monitoring of Heart Rate Variability (HRV) and oxygen saturation (SpO2) can provide valuable insights into the presence and severity of sleep apnea. The algorithm herein proposed aims to identify the presence of OSAS and then to highly accurately differentiate (Severe, Moderate or Low) its severity level. The algorithm was evaluated on an online dataset;at the end of the algorithm assessment, a correlation coefficient of 98.65% was reached.

关键词： OSAS OSA Obstructive Sleep Apnea Hypopnea IOT Smartwatch Algorithm Classification algorithms Sensors Electrocardiography signal processing algorithms Hardware

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A Sharp Analysis of Covariate Adjusted Precision Matrix Estimation via Alternating Projected Gradient Descent

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IEEE signal processing LETTERS 2022年 29卷 877-881页

作者： Lv, Xiao Cui, Wei Liu, Yulong Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Phys Beijing 100081 Peoples R China

In this letter, we present a sharp algorithmic analysis for alternating projected gradient descent which is used to solve the covariate adjusted precision matrix estimation problem in high-dimensional settings. By introducing a new analytical tool (the generic chaining), we remove the impractical resampling assumption used in the literature. The new analysis also demonstrates that this algorithm not only enjoys a linear convergence rate in the absence of convexity, but also attains the minimax rate with optimal order of sample complexity. Our results, meanwhile, reveal a time-data tradeoff in this problem. Numerical experiments are provided to verify our theoretical results.

关键词： signal processing algorithms Convergence Estimation Complexity theory Estimation error Linear regression Prediction algorithms Multivariate linear regression covariate adjusted precision matrix estimation alternating gradient descent

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Complex Video Completion Fusing Low-RankBackground and Deep Foreground Priors

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IEEE signal processing LETTERS 2022年 29卷 2737-2741页

作者： Wang, Jian-Li Huang, Ting-Zhu Zhao, Xi-Le Miao, Yu-Chun Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Recently, low-rank prior (LRP) has achieved success in tensor completion, thanks to its ability in capturing global correlations. However, since the presence of dynamic foreground breaks the low-rank assumption, LRP sometimes fails to work for complex videos. How to effectively capture the implicit low-rankness is a tricky challenge in complex video completion. To address this challenge, we propose a video completion model, which organically combines the respective merits of deterministic LRP and deep image prior. Specifically, we first decompose a complex video into the dynamic foreground and static background, and then characterize each component individually. For the dynamic foreground, instead of the hand-crafted prior, we consider the deep foreground prior expressed by U-Net architecture in an unsupervised manner, while we consider the tensor low-rank prior for the static background. Moreover, we develop an efficient alternating direction method of multipliers algorithm under the plug-and-play framework to tackle the resulting model. Different from the classic plug-and-play framework with a pre-trained and fixed network, our algorithm can adaptively update the parameters of network during iterations, which enables the network to have a more flexible expression ability. Experimental results verify that the proposed method can do better than some state-of-the-art tensor completion methods in complex video completion.

关键词： Adaptation models Tensors Correlation Heuristic algorithms signal processing algorithms Video surveillance Reliability Complex video completion deep foreground prior low-rank background prior plug-and-play

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