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signal Reconstruction From Quantized Noisy Samples of the Discrete Fourier Transform

IEEE TRANSACTIONS ON signal processing 2022年 70卷 2093-2104页

作者： Goyal, Mohak Kumar, Animesh Indian Inst Technol Dept Elect Engn Mumbai 400076 Maharashtra India Stanford Univ Dept Management Sci & Engn Stanford CA 94305 USA Twitter Toronto ON Canada

In this paper, we present two variations of an algorithm for signal reconstruction from one-bit or two-bit noisy observations of the discrete Fourier transform (DFT). The one-bit observations of the DFT correspond to the sign of its real part, whereas, the two-bit observations of the DFT correspond to the signs of both its real and imaginary parts. We focus on images for analysis and simulations, thus using the sign of the 2D-DFT. This choice of the class of signals is inspired by previous works on this problem. For our algorithm, we show that the expected mean squared error (MSE) in signal reconstruction is asymptotically proportional to the inverse of the sampling rate. The samples are affected by additive zero-mean noise of a known distribution. We solve this signal estimation problem by designing an algorithm that uses contraction mapping, based on the Banach fixed point theorem. Numerical tests with four benchmark images are provided to show the effectiveness of our algorithm. Various metrics for image reconstruction quality assessment such as PSNR, SSIM, ESSIM, and MS-SSIM are employed. On all four benchmark images, our algorithm outperforms the state-of-the-art in all of these metrics by a significant margin.

关键词： signal processing algorithms Discrete Fourier transforms signal reconstruction Noise measurement Sparse matrices Image reconstruction Compressed sensing Denoising single-bit sampling contraction mapping companding quantization image processing

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Graphical Inference in Linear-Gaussian State-Space Models

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4757-4771页

作者： Elvira, Victor Chouzenoux, Emilie Univ Edinburgh Sch Math Edinburgh EH8 9YL Midlothian Scotland Alan Turing Inst London NW1 2DB England Univ Paris Saclay INRIA Cent Supelec Ctr Vis Numer F-1190 Gif Sur Yvette France

State-space models (SSM) are central to describe time-varying complex systems in countless signal processing applications such as remote sensing, networks, biomedicine, and finance to name a few. Inference and prediction in SSMs are possible when the model parameters are known, which is rarely the case. The estimation of these parameters is crucial, not only for performing statistical analysis, but also for uncovering the underlying structure of complex phenomena. In this paper, we focus on the linear-Gaussian model, arguably the most celebrated SSM, and particularly in the challenging task of estimating the transition matrix that encodes the Markovian dependencies in the evolution of the multi-variate state. We introduce a novel perspective by relating this matrix to the adjacency matrix of a directed graph, also interpreted as the causal relationship among state dimensions in the Granger-causality sense. Under this perspective, we propose a new method called GraphEM based on the well sounded expectation-maximization (EM) methodology for inferring the transition matrix jointly with the smoothing/filtering of the observed data. We propose an advanced convex optimization solver relying on a consensus-based implementation of a proximal splitting strategy for solving the M-step. This approach enables an efficient and versatile processing of various sophisticated priors on the graph structure, such as parsimony constraints, while benefiting from convergence guarantees. We demonstrate the good performance and the interpretable results of GraphEM by means of two sets of numerical examples.

关键词： Time series analysis Mathematical models Sparse matrices Numerical models Kalman filters signal processing algorithms State-space methods State-space modeling graphical inference sparsity proximal methods primal-dual algorithms Kalman filtering EM algorithm

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Byzantine-Resilient Resource Allocation Over Decentralized Networks

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4711-4726页

作者： Wang, Runhua Liu, Yaohua Ling, Qing Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Pazhou Lab Guangzhou 510300 Guangdong Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China

This paper considers the resource allocation problem over a decentralized multi-agent network and at presence of Byzantine agents. Compared with its centralized counterpart, a decentralized algorithm enjoys better scalability when the network is large-scale, but is more vulnerable when some of the agents are malicious and send wrong messages during the optimization process. We utilize the classical Byzantine attack model to describe these malicious actions, and propose a novel Byzantine-resilient decentralized resource allocation algorithm, abbreviated as BREDA. At each iteration of BREDA, each honest agent receives messages from its neighbors, uses coordinate-wise trimmed mean (CTM) to aggregate these messages, and then updates its local primal and dual variables with gradient descent and ascent, respectively. Theoretical analysis indicates that BREDA converges to a neighborhood of an optimal solution. Numerical experiments demonstrate the resilience of BREDA to various Byzantine attacks.

关键词： Resource management signal processing algorithms Heuristic algorithms Cost function Costs Lagrangian functions Convergence Resource allocation decentralized multi-agent network Byzantine-resilience

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Subspace Segmentation by Low Rank Representation via the Sparse-Prompting Quasi-Rank Function

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IEEE ACCESS 2022年 10卷 55753-55765页

作者： Li, Haiyang Wang, Yusi Zhang, Qian Xing, Zhiwei Lin, Shoujin Peng, Jigen Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Xian Polytech Univ Sch Sci Xian 710048 Peoples R China Zhongshan MLTOR CNC Technol Co Ltd Zhongshan 528400 Peoples R China

In this paper, a general optimization formulation is proposed for the subspace segmentation by low rank representation via the sparse-prompting quasi-rank function. We prove that, with the clean data from independent linear subspaces, the optimal solution to our optimization formulation not only is the lowest rank but also forms a block-diagonal matrix, which implies that it is reasonable to use any sparse-prompting quasi-rank function as the measure of the low rank in subspace clustering. With the data contaminated by Gaussian noise and/or gross errors, the alternating direction method of multipliers is applied to solving it and every sub-optimization problem has a closed-form optimal solution when the band restricted thresholding operator induced by its corresponding sparse-prompting function has an analytic expression, in which the gross errors part is replaced with the sparse-prompting matrix function. Finally, taking a specific sparse-prompting function, the fraction function, we conduct a series of simulations on different databases to get the performance of our algorithm tested, and experimental results show that our algorithm can obtain lower clustering error rate and higher value of evaluation indicators ACC, NMI and ARI than other state-of-the-art subspace clustering algorithms on different databases.

关键词： Sparse matrices Minimization Databases Clustering algorithms Gaussian noise signal processing algorithms Optimization Band restricted thresholding operator face clustering low rank representation motion segmentation sparse-prompting quasi-rank function subspace segmentation

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Tens to Hundreds of Mrad/s Fast Polarization Tracking and Demultiplexing Using Angle-Plus-Phase Algorithm in Stokes Space

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2022年第5期14卷 1页

作者： Pan, Yan Yan, Lianshan Yi, Anlin Jiang, Lin Pan, Wei Luo, Bin Lau, Alan Pak Tao Southwest Jiaotong Univ Ctr Informat Photon & Commun Sch Informat Sci & Technol Chengdu 610031 Peoples R China Peng Cheng Lab Shenzhen 518052 Peoples R China Hong Kong Polytech Univ Dept Elect Engn Kowloon 100 HKD Hong Kong Peoples R China

A fast polarization tracking and demultiplexing algorithm in polarization-division-multiplexing (PDM) systems is proposed and demonstrated. The scheme utilizes two optimizing parameters in Stokes Space, including the polarization angle and the phase information. In addition to its transparency to modulation formats, the angle-plus-phase algorithm (APA) could provide fast convergence up to >100-Mrad/s polarization tracking speed. Compared to common polarization demultiplexing algorithms such as constant modulus algorithm (CMA) and linear Kalman filter (LKF), experimental results show that the polarization tracking capability of the proposed APA algorithm is significantly improved without sacrificing the transmission performance. Specifically, compared to CMA, the tracking speed can be improved from 9-Mrad/s to 160-Mrad/s for PDM quadrature phase shift keying (PDM-QPSK) signals. Furthermore, compared to CMA and LKF, APA has the best tracking performance, and the tracking speed can be reached to 34-Mrad/s for PDM 16 quadrature amplitude modulation (PDM-16QAM) signal.

关键词： Demultiplexing signal processing algorithms Stokes parameters Fitting Polarization Kalman filters Silicon Digital signal processing optical fiber communication polarization division multiplexing fast polarization tracking

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The Submerged Part of the AI-Ceberg

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IEEE signal processing MAGAZINE 2022年第5期39卷 10-17页

作者： Couillet, Romain Trystram, Denis Menissier, Thierry Univ Grenoble Alpes Grenoble Informat Lab F-38400 St Martin Dheres France Univ Grenoble Alpes Grenoble Inst Philosophy F-38400 St Martin Dheres France

This article discusses the contradiction between the exploding energy demand of artificial intelligence (AI) and the information and communication (ICT) industry as a whole and the parallel strong request for energy sobriety imposed by the need to mitigate the impact of climate change and the anticipated collapse of civilization as we know it. Under the form of an open reflection on the goods and evils of AI, the article raises the suggestion of a drastic change in the AI paradigm, more in phase with the vital obligation to design a more resilient society.

关键词： Social factors Climate change Information and communication technology Artificial intelligence signal processing algorithms Deep learning Neural networks Resilience

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A Synchro-Set-Aided Breadth-First Sphere Decoder for Polar-Coded MIMO Systems

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 6200-6215页

作者： Zhou, Huayi Deng, Xiangyun Cai, Yiqian Shen, Yifei Yang, Minhua Gross, Warren J. You, Xiaohu Zhang, Chuan Southeast Univ LEADS Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 211189 Peoples R China Southeast Univ Purple Mt Labs Nanjing 211189 Peoples R China McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0G4 Canada

The joint optimization of multiple-input-multiple-output (MIMO) detection and polar decoding has become a research hotspot for future communication systems. The error-correction performance of the separate detection and decoding (SDD) is far from the Shannon capacity, which cannot meet the requirements of communication scenarios such as ultra-reliable and low latency communications (URLLC). The existing joint detection and decoding (JDD) using breadth-first sphere decoding (BFSD) improves the reliability over SDD but still has a huge performance loss on low-rate codes. In this paper, JDD using synchro-set-aided BFSD (SA-BFSD) is proposed to greatly improve the error-correction performance for polar-coded MIMO systems. We first propose a method to generate the symbol synchro sets through the concept of frozen symbols, then refine the symbol synchro sets based on the characteristics analysis of the channel matrix. We optimize the enumerating order of the symbols and reduce the enumerating levels. The frame error rate (FER) and the bit error rate of the proposed algorithms are significantly improved especially for the low-rate codes. The proposed SA-BFSD JDD achieves an up to 7.8 dB performance gain over BFSD at FER =10(-3). The hardware architecture of the polar JDD scheme is first implemented with SMIC 40-nm CMOS technology for MIMO systems. The reliability and latency of SA-BFSD are much better than those of the existing works, which could meet the requirements of URLLC.

关键词： Codes Bit error rate Symbols signal processing algorithms Ultra reliable low latency communication Performance gain Hardware Joint detection and decoding K-best algorithm polar codes sphere decoding URLLC

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Learning in Restless Bandits Under Exogenous Global Markov Process

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 5679-5693页

作者： Gafni, Tomer Yemini, Michal Cohen, Kobi Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel Princeton Univ Princeton NJ USA Stanford Univ Stanford CA USA Univ Illinois Champaign IL USA Univ Calif Davis Dept Elect & Comp Engn Davis CA USA

We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribution of each arm. Under each global state, the rewards process of each arm evolves according to an unknown Markovian rule, which is non-identical among different arms. At each time, a player chooses an arm out of $N$ arms to play, and receives a random reward from a finite set of reward states. The arms are restless, that is, their local state evolves regardless of the player's actions. Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time $t$ the arm that maximizes the expected immediate value. The objective is to develop an arm-selection policy that minimizes the regret. To that end, we develop the Learning under Exogenous Markov Process (LEMP) algorithm. We analyze LEMP theoretically and establish a finite-sample bound on the regret. We show that LEMP achieves a logarithmic regret order with time. We further analyze LEMP numerically and present simulation results that support the theoretical findings and demonstrate that LEMP significantly outperforms alternative algorithms.

关键词： Markov processes Task analysis signal processing algorithms Pricing Heuristic algorithms Computational modeling Numerical models restless multi-armed bandit sequential learning sequential decision making

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Lamb Wave Wireless Communication Through Healthy and Damaged Channels With Symmetrical and Asymmetrical Steps and Notches

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2022年第7期69卷 2390-2399页

作者： Bahouth, Rudy Benmeddour, Farouk Moulin, Emmanuel Assaad, Jamal Univ Lille Univ Polytech Hauts France UMR IEMN 8520 Cent LilleCNRS F-59000 Lille France Inst Natl Sci Appl INSA F-59313 Le Mont Houy Valenciennes France

Data transmission through solid metallic channels is recommended in certain industries where no other options are proposed, such as nuclear, aerospace, and smart vehicles. In addition to the Faraday shielding effect of electromagnetic waves, another issue related to damage presence due to mechanical loads exists. Severe damage in the transmission channel leads to signal loss at the receiver. For this sake, ultrasonic guided waves, such as Lamb waves, maybe a good substitute since they can propagate through long distances in solid metallic structures. The scope of this work is to build a reliable, reproducible, and high data-rate wireless communication experimental platform, using ultrasonic guided waves, through healthy and damaged plates for industrial usage. The target is to compensate at first for the effect of dispersion, reverberation, scattering, and boundary reflections for the healthy plate. The novelty of this work falls within the performance analysis of the demodulation algorithm based on cross-correlation combined with binary phase-shift keying (BPSK), using a finite-element simulation through healthy and damaged plates with different depths of symmetrical and asymmetrical notches (SN and AN) and steps based on the bit error percentage (BEP). Furthermore, another contribution related to the impact of multiple reflections and mode conversions caused by symmetrical and asymmetrical steps and notches is taken into account. After this, numerical results are validated using an ultrasonic guided wave experimental platform. Results based on BEP analysis prove that the algorithm has successfully compensated for the effect of dispersion and boundary reflections for the healthy plate and multiple reflections and mode conversions for the damaged ones. A highly effective data rate of up to 350 kb/s can be reached even in the presence of severe damage in the transmission channel.

关键词： Dispersion Binary phase shift keying signal processing algorithms Demodulation Aluminum Finite element analysis Solids Asymmetrical notches binary phase-shift keying (BPSK) modulation bit error percentage (BEP) channel characterization cross-correlation symmetrical notches ultrasonic lamb waves

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Tear the Image Into Strips for Style Transfer

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 3978-3988页

作者： Huang, Yujie Liu, Yuhao Jing, Minge Zeng, Xiaoyang Fan, Yibo Fudan Univ Coll Microelect State Key Lab ASIC & Syst Shanghai 200000 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China ZTE Corp State Key Lab Mobile Network & Mobile Multimedia Shenzhen 518057 Peoples R China

Recently, Deep Convolutional Neural Networks (DCNNs) have achieved remarkable progress in computer vision community, including in style transfer tasks. Normally, most methods feed the full image to the DCNN. Although high-quality results can be achieved in this manner, several underlying problems arise. For one, with the increase in image resolution, the memory footprint will increase dramatically, leading to high latency and massive power consumption. Furthermore, these methods are usually unable to integrate with the commercial image signal processor (ISP), which processes the image in a line-sequential manner. To solve the above problems, we propose a novel ISP-friendly deep learning-based style transfer algorithm: SequentialStyle. A brand new line-sequential processing mode is proposed, where the image is torn into strips, and each strip is sequentially processed, contributing to less memory demand. We further propose a Spatial-Temporal Synergistic (STS) mechanism that decouples the previously simplex 2-D image style transfer into spatial feature processing (in-strip) and temporal correlation transmission (in-between strips). Compared with the SOTA style transfer algorithms, experimental results show that our SequentialStyle is competitive. Besides, SequentialStyle has less demand for memory consumption, even for the images whose resolutions are 4 k or higher.

关键词： Strips Correlation Pipelines signal processing algorithms Memory management Task analysis Image resolution Style Transfer Image signal Processor Deep Learning

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