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Efficient Parameters for Compressed Sensing recovery algorithms

WIRELESS PERSONAL COMMUNICATIONS 2017年第3期94卷 1715-1736页

作者： Shalaby, Wafaa A. Saad, Waleed Shokair, Mona Dessouky, Moawad Menoufia Univ Elect & Elect Commun Engn Dept Fac Elect Engn Menoufia Egypt

Compressed sensing CS has been an effective research area which it plays an efficient role in many applications such as cognitive radio, imaging, radar and many other applications. The main part of CS system is to recover an input signal by using minimum number of samples than used by conventional (Nyquist) sampling. In this paper, the minimum number of measurements and the number of sparsity level used to reconstruct the signal with minimum error, computational complexity and time will be optimized. Moreover, different recovery algorithms such as convex optimization, greedy algorithms, iterative hard thresholding and hard thresholding pursuit algorithms are used for optimal recovery of sparse signal from a small number of linear measurements. Furthermore, the effect of using CS as denosing will be investigated. Then comparisons study between CS as denoising and conventional denoising process will be made to reduce the effect of noise on the signal.

关键词： Compressive sensing Sub-Nyquist sampling recovery algorithms Denoising

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EFFECT OF MULTIPATH CHANNEL MODELS TO THE recovery algorithms ON COMPRESSED SENSING IN UWB CHANNEL ESTIMATION

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Journal of Electronics(China) 2013年第3期30卷 254-260页

作者： Nguyen ThanhSon Guo Shuxu Chen Haipeng College of Electronic Science and Engineering Jilin University

Multipath arrivals in an Ultra-WideBand (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely the signal sparsity of the impulse response of the UWB channel that is exploited in this work aiming at UWB channel estimation based on Compressed Sensing (CS). However, these multipath arrivals mainly depend on the channel environments that generate different sparse levels (low-sparse or high-sparse) of the UWB channels. According to this basis, we have analyzed the two most basic recovery algorithms, one based on linear programming Basis Pursuit (BP), another using greedy method Orthogonal Matching Pursuit (OMP), and chosen the best recovery algorithm which are suitable to the sparse level for each type of channel environment. Besides, the results of this work is an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.

关键词： Compressed Sensing (CS) Ultra-WideBand (UWB) system recovery algorithms Multipath channel

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Constraint-specific recovery network for solving airline recovery problems

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COMPUTERS & OPERATIONS RESEARCH 2010年第6期37卷 1014-1026页

作者： Eggenberg, Niklaus Salani, Matteo Bierlaire, Michel Ecole Polytech Fed Lausanne ENAC OR TRANSP CH-1015 Lausanne Switzerland

In this paper, we consider the recovery of an airline schedule after an unforeseen event called disruption, making the planned schedule infeasible. We present a modeling framework that allows the consideration of operational constraints within a Column Generation (CG) scheme. We introduce the general concept of recovery network, generated for each individual unit of the problem, and show how unit-specific constraints are modeled using resources. We fully illustrate the concept by solving the Aircraft recovery Problem (ARP) with maintenance planning, we give some insights into applying the model to the Passenger recovery Problem (PRP) and we present computational results on real data. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Airline scheduling recovery algorithms Column generation

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Split-Process-Merge Technique-Based Algorithm for Accelerated recovery of Compressively Sensed Images and Videos

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WIRELESS PERSONAL COMMUNICATIONS 2021年第1期118卷 93-108页

作者： Florence Gnana Poovathy, J. Sankararajan, Radha Vellore Inst Technol Dept ECE Chennai CampusVandalur Kelambakkam Rd Chennai 600127 Tamil Nadu India SSN Coll Engn Dept ECE Old Mahabalipuram Rd Chennai 603110 Tamil Nadu India

Greater compression ratio can be achieved while compressing images and videos by using the technique called compressive sensing or compressed sensing (CS). In CS, sparsity of the signal is exploited in order to achieve high compression. CS-based compression converts the images and video frames to a set of m < n measurements, which are transmitted to the receiver where the images and video frames are recovered by an efficient reconstruction algorithm with minimum error. Many reconstruction procedures solve least squares problem iteratively to recover the original signal, which ultimately increases the algorithmic complexity and runtime. In this work, split, process and merge technique for image and video reconstruction is proposed, which surpasses the adversities of the iterative algorithms, and provides better reconstruction performance in terms of perception and objective measures. Even with small number of measurements, the peak to signal noise ratio obtained using split and merge technique is above 30 dB, and the structural similarity is above 97% with an accuracy of 98% for both images and videos. The total runtime of the proposed algorithm is around 1.5 s and 9 s for images and videos respectively, which is small compared to iterative algorithms. Thus, the proposed split and merge technique based compressive sensing recovery algorithm proves to perform better in terms of speed and reconstruction quality.

关键词： Compressed sensing recovery algorithms SPMT Quality measuring parameters

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A stable recovery method for the Robin inverse problem

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MATHEMATICS AND COMPUTERS IN SIMULATION 2004年第4-5期66卷 367-383页

作者： Chaabane, S Elhechmi, C Jaoua, M LAMSIN ENIT Tunis 1002 Tunisia LAMSIN ENIT Sfax 3038 Tunisia

We consider the inverse problem of identifying a Robin coefficient by performing measurement on some part of the boundary. After turning the inverse problem to an optimisation one by using a Kohn and Vogelius cost function, we study the stability of this method and present some numericals experiments using synthetic data. (C) 2004 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： inverse problem Robin coefficient stability recovery algorithms

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Sparse Spectrum recovery of Streaming Signals Based on Multi-Resolution 79

Sparse Spectrum Recovery of Streaming Signals Based on Multi...

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IEEE 79th Vehicular Technology Conference (VTC-Spring)

作者： Li, Hang Wang, Xin Wang, Xing Guo, Wenbin Beijing Univ Posts & Telecommun Wireless Signal Proc & Network Lab Beijing 100088 Peoples R China Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9781479944828

In this paper, we consider the problem of multi-resolution analysis for sparse spectrum of streaming signals. The multi-resolution compressed sensing for streaming signals (MRCSSS) is proposed, which fully exploits the inner relationship between frequency resolution and sensing time based on the analog-to-information converter(AIC). Different from the existing algorithms, we deduce the correlation between frequency support of high-resolution and low-resolution spectrum. Then, the recovered frequency support of low-resolution spectrum is utilized to estimate the frequency support of high-resolution spectrum, which serves as a priori knowledge for a more efficient reconstruction of high-resolution spectrum. Simulation results have demonstrated the effectiveness of our algorithms.

关键词： Spectrum sensing multi-resolution recovery algorithms streaming signal

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A stable recovery method for the Robin inverse problem

A stable recovery method for the Robin inverse problem

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Workshop on Inverse Obstacle Problems

作者： Chaabane, S Elhechmi, C Jaoua, M LAMSIN ENIT Tunis 1002 Tunisia LAMSIN ENIT Sfax 3038 Tunisia

关键词： inverse problem Robin coefficient stability recovery algorithms

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A survey on compressive sensing techniques for cognitive radio networks

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PHYSICAL COMMUNICATION 2016年第0期20卷 61-73页

作者： Salandine, Fatima Kaabouch, Naima El Ghazi, Hassan Univ North Dakota Dept Elect Engn Grand Forks ND 58201 USA Natl Inst Posts & Telecommun STRS Lab Rabat Morocco

In cognitive radio, one of the main challenges is wideband spectrum sensing. Existing spectrum sensing techniques are based on a set of observations sampled by an analog/digital converter (ADC) at the Nyquist rate. However, those techniques can sense only one band at a time because of the hardware limitations on sampling rate. In addition, in order to sense a wideband spectrum, the band is divided into narrow bands or multiple frequency bands. Secondary users (SU) have to sense each band using multiple RF frontends simultaneously, which results in a very high processing time, hardware cost, and computational complexity. In order to overcome this problem, the signal sampling should be as fast as possible, even with high dimensional signals. Compressive sensing has been proposed as one of the solutions to reduce the processing time and accelerate the scanning process. It allows reducing the number of samples required for high dimensional signal acquisition while keeping the important information. Over the last decade, a number of papers related to compressive sensing techniques have been published. However, most of these papers describe techniques corresponding to one process either sparse representation, sensing matrix, or recovery. This paper provides an in depth survey on compressive sensing techniques and classifies these techniques according to which process they target, namely, sparse representation, sensing matrix, or recovery algorithms. It also discusses examples of potential applications of these techniques including in spectrum sensing, channel estimation, and multiple-input multiple-output (MIMO) based cognitive radio. Metrics to evaluate the efficiencies of existing compressive sensing techniques are provided as well as the benefits and challenges in the context of cognitive radio networks. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Cognitive radio network Spectrum sensing Compressive sensing Sparsity representation Sensing matrix recovery algorithms

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Clutter mitigation in sub-Nyquist radars using difference set codes

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IET RADAR SONAR AND NAVIGATION 2020年第3期14卷 487-494页

作者： Sohrabi, Ali Sabahi, Mohamad F. Parvaresh, Farzad Univ Isfahan Dept Elect Engn Esfahan Iran

In this study, the authors address the issue of clutter mitigation in monostatic pulse-Doppler radars, using sub-Nyquist sampling and compressed sensing (CS) recovery algorithms. Generally, clutter is modelled as a coloured random process and a whitening filter is employed to maximise the signal to clutter ratio. Here, the authors apply this approach to the case in which the received signal sampling rate is much smaller than the Nyquist rate. They also generalise the recently proposed sub-Nyquist method based on difference set (DS) codes, which uses very few samples to detect targets in the presence of only white Gaussian noise. By using the frequency-coded modulation waveform with the frequencies selected in accordance to the DS, the detection performance of the proposed method can be significantly improved compared with the previously proposed sub-Nyquist methods in the presence of clutter. Furthermore, the authors show that the proposed method can increase the target recovery resolution due to the features of the DS codes and the employed CS model.

关键词： radar clutter Gaussian noise filtering theory modulation coding random processes radar signal processing Doppler radar compressed sensing signal sampling clutter mitigation sub-Nyquist radars difference set codes monostatic pulse-Doppler radars sub-Nyquist sampling compressed sensing recovery algorithms coloured random process whitening filter clutter ratio received signal sampling rate Nyquist rate sub-Nyquist method white Gaussian noise frequency-coded modulation waveform target recovery resolution DS codes employed CS model

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A novel weighted compressive sensing using L1-magic recovery technique in medical image compression

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2019年第1期8卷 2-2页

作者： Alaa, Eman Elsaid Ashour, Amira S. Guo, Yanhui Kasem, Hossam M. Tanta Univ Fac Engn Dept Elect & Elect Commun Engn Tanta Egypt Univ Illinois Dept Comp Sci Springfield IL 62703 USA

Recent technological advancement in computing technology, communication systems, and machine learning techniques provides opportunities to biomedical engineers to achieve the requirements of clinical practice. This requires storage and/or transmission of medical images with the conservation of the medical information over the communication channel. Accordingly, medical compression is necessary for efficient channel bandwidth utilization. To solve the trade-off between the compression ratio and the preservation of significant information, compressed sensing (CS) can be used. During image recovery in CS, an optimization algorithm is used, such as greedy pursuit, convex relaxation, and Bayesian framework. In the present work, a convex relaxation optimization called L1-magic is employed, where the objective function can be relaxed to the nearest convex norm, i.e., l1-norm. In addition, the discrete cosine transform is used for recovery by transforming the image from time-to frequency-domain. To improve the medical image recovery, a weighted L1-magic is proposed using a threshold based on the image content, where high weight is given to the significant details in the image. Thus, the significant information in the image (values greater than the threshold) is multiplied by a weight factor according to the image characteristics for a successful recovery process. A comparative study of the proposed weighted L1-magic and orthogonal matching pursuit (OMP), one of the greedy algorithms, was conducted. Different metrics were measured, including the Structural Similarity Index Measure and Peak Signal-to-Noise Ratio (PSNR) to evaluate CS performance using the proposed weighted L1-magic as well as the weighted OMP and the principal component analysis (PCA) as a traditional compression method at different compression ratios (CR). The experimental results on diabetic retinopathy images dataset proved the superiority of the weighted L1-magic method, where for example as 0.4 CR, avera

关键词： Compressed sensing L1-magic Orthogonal matching pursuit Principal component analysis recovery algorithms

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