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Compressive spectral method for the simulation of the water waves [article]

Cihan Bayindir
2015 arXiv   pre-print
Utilizing a spectral method, it is shown that by using a smaller number of spectral components compared to the classical spectral method with a high number of components, it is possible to simulate the  ...  Signals with majority of the components zero, are known as the sparse signals.  ...  The root-mean-Comparison of the energy spectra of the classical spectral method and the proposed compressive spectral method for linear waves with N = 2048, M = 256.Again by means of an inverse FFT, it  ... 
arXiv:1512.06286v1 fatcat:mc63xjv4nfcyxm7wvmzh4yvj5a

Maximum Feasible Subsystem Algorithms for Recovery of Compressively Sensed Speech

Fereshteh Fakhar Firouzeh, John W. Chinneck, Sreeraman Rajan
2020 IEEE Access  
The goal in signal compression is to reduce the size of the input signal without a significant loss in the quality of the recovered signal.  ...  We present three new algorithms based on solutions for the MAXimum Feasible Subsystem problem (MAX FS) that improve upon the state of the art in recovery of compressed speech signals: more highly compressed  ...  MAX FS SOLUTION ALGORITHMS FOR SPARSE RECOVERY Finding a sparse solution to a linear system can be cast as an instance of MAX FS [10] : find a MAX FS solution for the system x = y, x = 0 where only constraints  ... 
doi:10.1109/access.2020.2990155 fatcat:fvqv45mlird3pafngufhr55yvy

Proposed Algorithms Based on Accelerated Quantized Iterative Hard Thresholding for Compressed Sensing

Mohamed Meliek, et.al.
2016 International Journal of Computing and Digital Systems  
Compressive sampling (CS) is a signal recovery technique that can effectively recover a sparse signal using fewer measurements than its dimension.  ...  Extensive matlab simulation programs are executed to simulate the performance of the three proposed schemes. In addition, they are compared with the related ones.  ...  Compressive Sensing overcomes these issues by using linear sampling operators that combine sampling and compression in a single step which reduces the number of measurements required.  ... 
doi:10.12785/ijcds/050604 fatcat:hoxpbe7yojfdrikgld2iwqxvb4

SPARSE REPRESENTATION THEORY AND ITS APPLICATION FOR FACE RECOGNITION

Yongjiao Wang, Chuan Wang, Lei Liang
2015 International Journal on Smart Sensing and Intelligent Systems  
Discrimination performance by using the sparse representation can also be applied to the face recognition, and any test sample can be expressed as a linear span of the all training samples.  ...  Experimental results show that face recognition method based on sparse representation is comparable to others.  ...  By the sparse representation and compressed sensing theory, If the vector is sparse enough, L0 norm sparse solution problem can be converted to solve the L1 norm problem.  ... 
doi:10.21307/ijssis-2017-751 fatcat:pmththpavvbvxou7t254of74iy

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

Yajie Sun, Yanqing Yuan, Qi Wang, Lihua Wang, Enlu Li, Li Qiao
2019 Computers Materials & Continua  
According to the principles of the compressive sensing and signal processing method, non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.  ...  The signal processing problem has become increasingly complex and demand high acquisition system, this paper proposes a new method to reconstruct the structure phased array structural health monitoring  ...  CMC, vol.58, no.2, pp.409-420, 2019 practice and innovation training project of Jiangsu province (Grant No. 201710300218), and the PAPD.  ... 
doi:10.32604/cmc.2019.03642 fatcat:tvtn2msaibemre3vbnf3udcff4

Sensing Matrix Design via Capacity Maximization for Block Compressive Sensing Applications [article]

Richard Obermeier, Jose Angel Martinez-Lorenzo
2018 arXiv   pre-print
In this paper, we present a novel method, based upon capacity maximization, for designing sensing matrices with enhanced block-sparse signal reconstruction capabilities.  ...  In many CS applications, such as electromagnetic imaging, practical limitations on the measurement system prevent one from generating sensing matrices in this fashion.  ...  General Linear System In the final example, the design algorithm was tested against a general linear system. y = Ax.  ... 
arXiv:1803.08186v1 fatcat:rbti5ithwrbh5im3yse7xah5ni

ECG denoising and compression by sparse 2D separable transform with overcomplete mixed dictionaries

A. Ghaffari, H. Palangi, M. Babaie-Zadeh, C. Jutten
2009 2009 IEEE International Workshop on Machine Learning for Signal Processing  
To do this we use 2DSL0 algorithm [8] , as a fast method to obtain the sparsest solution of an underdetermined system of linear equations.  ...  To do this we use 2DSL0 algorithm [8] , as a fast method to obtain the sparsest solution of an underdetermined system of linear equations. Remark 3.  ... 
doi:10.1109/mlsp.2009.5306223 fatcat:irqxakezf5gpfjibms76lauwra

Compression of ECG Signal Based on Compressive Sensing and the Extraction of Significant Features

Mohammed M. Abo-Zahhad, Aziza I. Hussein, Abdelfatah M. Mohamed
2015 International Journal of Communications, Network and System Sciences  
CS provides a new approach concerned with signal compression and recovery by exploiting the fact that ECG signal can be reconstructed by acquiring a relatively small number of samples in the "sparse" domains  ...  Based on the fact that these signals can be approximated by a linear combination of a few coefficients taken from different basis, an alternative new compression scheme based on Compressive Sensing (CS  ...  When does a unique solution exist to the set of linear equations y x = Φ ? Generally, a solution might exist for M N ≤ , i.e., for a determined or over-determined system.  ... 
doi:10.4236/ijcns.2015.85013 fatcat:wepjh7ehaffjrgflkktuxsdgpq

Multiple target localization in WSNs using compressed sensing reconstruction based on ABC algorithm

Hao Feng, Junyan Chen, Lei Luo, H.F. Abdul Amir, A.M. Korsunsky, Z. Guo
2016 MATEC Web of Conferences  
In allusion to the application of compressed sensing theory in multi-target localization in wireless sensor networks, the paper proposes a method of sparse signal reconstruction based on artificial bee  ...  A mode of disturbances mutation operation for food source exploitation is designed to improve the search efficiency in high-dimensional solution space; it also improves the global search efficiency of  ...  ); Guangxi Colleges and Universities Key Laboratory of cloud computing and complex systems (No. 15209).  ... 
doi:10.1051/matecconf/20165907008 fatcat:av3nfwnm45cotoyluegmlinkg4

Utilizing Compressibility in Reconstructing Spectrographic Data, With Applications to Noise Robust ASR

Bengt J. BorgstrÖm, Abeer Alwan
2009 IEEE Signal Processing Letters  
The existence of sparse representations for spectrographic data motivates the spectral reconstruction solution to be posed as an optimization problem minimizing the 1-norm.  ...  In this letter we propose a novel algorithm for reconstructing unreliable spectrographic data, a method applicable to missing feature-based automatic speech recognition (ASR).  ...  Additionally, both techniques rely on the notion of compressibility, and specifically each method minimizes the 1 -norm of the given signal in a sparse domain.  ... 
doi:10.1109/lsp.2009.2016452 fatcat:jlbs6oy2fncphfi2zdezelhgly

Compressed dictionary learning for detecting activations in fMRI using double sparsity

Shuangjiang Li, Hairong Qi
2014 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)  
GLM where the BOLD signal may be regarded as a linear combination of a sparse set of brain activity patterns.  ...  we generate some synthetic BOLD signal, as shown in Fig. 3 We model the fMRI time series z of a particular voxel as a sparse linear combination of various stimuli and additive noise.  ...  We demonstrate the result comparison on activation detection using the GLM with a design matrix and the CDL with a learnt dictionary.  ... 
doi:10.1109/globalsip.2014.7032154 dblp:conf/globalsip/LiQ14 fatcat:5u3uhuf7q5ahzotuydxzhbiof4

Compressed sensing for denoising in adaptive system identification

Seyed Hossein Hosseini, Mahrokh G. Shayesteh
2012 20th Iranian Conference on Electrical Engineering (ICEE2012)  
We manipulate the transmitted pilot (input signal) and the received signal such that the weights of adaptive filter approach the compressed version of the sparse system instead of the original system.  ...  We propose a new technique for adaptive identification of sparse systems based on the compressed sensing (CS) theory.  ...  In this paper, we propose a new method, in which the adaptive filter identifies the compressed version of the sparse system h.  ... 
doi:10.1109/iraniancee.2012.6292545 fatcat:ya6pspgjrfgtrbw4c4bvie2aga

Preconditioning for Underdetermined Linear Systems with Sparse Solutions

Evaggelia Tsiligianni, Lisimachos P. Kondi, Aggelos K. Katsaggelos
2015 IEEE Signal Processing Letters  
In this paper, we consider an underdetermined linear system with sparse solutions and propose a preconditioning technique that yields a system matrix having the properties of an incoherent unit norm tight  ...  Performance guarantees for the algorithms deployed to solve underdetermined linear systems with sparse solutions are based on the assumption that the involved system matrix has the form of an incoherent  ...  Karin Schnass for providing us the code implementing the method in [8] .  ... 
doi:10.1109/lsp.2015.2392000 fatcat:ipg3klyh7zconiaowj6xx5oioa

Preface to the Special Issue on Sparse Approximate Solution of Linear Systems

Gitta Kutyniok, Allan Pinkus, Holger Rauhut, Vladimir Temiyakov
2014 Linear Algebra and its Applications  
journal homepage: www.elsevier.com/locate/laa Preface Special issue on sparse approximate solution of linear systems Recent years have witnessed an impressive research activity in the area of compressed  ...  The basic mathematical problem consists in solving an underdetermined linear system, i.e., y = Ax, A being an m × n matrix with m n, under certain a priori assumptions on the vector x.  ...  The very different article, entitled "Paved with good intentions: analysis of a randomized block Kaczmarz method", by Needell and Tropp shows, by comparison, the situation when solving overdetermined least-squares  ... 
doi:10.1016/j.laa.2013.06.019 fatcat:pgdhr5wjr5hntge466ytjmko44

Based on Compressed Sensing of Orthogonal Matching Pursuit Algorithm Image Recovery

Caifeng Cheng, Deshu Lin
2020 Journal on Internet of Things  
Compressive sensing theory mainly includes the sparsely of signal processing, the structure of the measurement matrix and reconstruction algorithm.  ...  Reconstruction algorithm is the core content of CS theory, that is, through the low dimensional sparse signal recovers the original signal accurately.  ...  Acknowledgement: This study was supported by the Yangtze University Innovation and Entrepreneurship Course Construction Project of "Mobile Internet Entrepreneurship".  ... 
doi:10.32604/jiot.2020.09116 fatcat:oddxy2cyjfclznvethwo7nrg5m
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