• Title, Summary, Keyword: Compressed sensing

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Compressed Sensing Based Dynamic MR Imaging: A Short Survey (Compressed Sensing 기법을 이용한 Dynamic MR Imaging)

  • Jung, Hong;Ye, Jong-Chul
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.5
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    • pp.25-31
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    • 2009
  • The recently developed sampling theory, "compressed sensing" is gathering huge interest in MR reconstruction area because of its feasibility of high spatio-temporal resolution of dynamic MRI which has been limited in conventional methods based on Nyquist sampling theory. Since dynamic MRI usually has high redundant information along temporal direction, this can be very sparsely represented in most of cases. Therefore, compressed sensing that exploits the sparsity of unknown images can be effectively applied in most of dynamic MRI. This review article briefly introduces currently proposed compressed sensing based dynamic MR imaging algorithms and other methods exploiting sparsity. By comparing them with conventional methods, you may have insight how the compressed sensing based methods can impact nearly every area of clinical dynamic MRI.

Adaptive Adjustment of Compressed Measurements for Wideband Spectrum Sensing

  • Gao, Yulong;Zhang, Wei;Ma, Yongkui
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.1
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    • pp.58-78
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    • 2016
  • Compressed sensing (CS) possesses the potential benefits for spectrum sensing of wideband signal in cognitive radio. The sparsity of signal in frequency domain denotes the number of occupied channels for spectrum sensing. This paper presents a scheme of adaptively adjusting the number of compressed measurements to reduce the unnecessary computational complexity when priori information about the sparsity of signal cannot be acquired. Firstly, a method of sparsity estimation is introduced because the sparsity of signal is not available in some cognitive radio environments, and the relationship between the amount of used data and estimation accuracy is discussed. Then the SNR of the compressed signal is derived in the closed form. Based on the SNR of the compressed signal and estimated sparsity, an adaptive algorithm of adjusting the number of compressed measurements is proposed. Finally, some simulations are performed, and the results illustrate that the simulations agree with theoretical analysis, which prove the effectiveness of the proposed adaptive adjusting of compressed measurements.

Compressed Sensing of Low-Rank Matrices: A Brief Survey on Efficient Algorithms (낮은 계수 행렬의 Compressed Sensing 복원 기법)

  • Lee, Ki-Ryung;Ye, Jong-Chul
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.5
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    • pp.15-24
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    • 2009
  • Compressed sensing addresses the recovery of a sparse vector from its few linear measurements. Recently, the success for the vector case has been extended to the matrix case. Compressed sensing of low-rank matrices solves the ill-posed inverse problem with fie low-rank prior. The problem can be formulated as either the rank minimization or the low-rank approximation. In this paper, we survey recently proposed efficient algorithms to solve these two formulations.

Reconstruction of Magnetic Resonance Phase Images using the Compressed Sensing Technique (압축 센싱 기법을 이용한 MRI 위상 영상의 재구성)

  • Lee, J.E.;Cho, M.H.;Lee, S.Y.
    • Journal of Biomedical Engineering Research
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    • v.31 no.6
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    • pp.464-471
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    • 2010
  • Compressed sensing can be used to reduce scan time or to enhance spatial resolution in MRI. It is now recognized that compressed sensing works well in reconstructing magnitude images if the sampling mask and the sparsifying transform are well chosen. Phase images also play important roles in MRI particularly in chemical shift imaging and magnetic resonance electrical impedance tomography (MREIT). We reconstruct MRI phase images using the compressed sensing technique. Through computer simulation and real MRI experiments, we reconstructed phase images using the compressed sensing technique and we compared them with the ones reconstructed by conventional Fourier reconstruction technique. As compared to conventional Fourier reconstruction with the same number of phase encoding steps, compressed sensing shows better performance in terms of mean squared phase error and edge preservation. We expect compressed sensing can be used to reduce the scan time or to enhance spatial resolution of MREIT.

Compressed Sensing-Based Multi-Layer Data Communication in Smart Grid Systems

  • Islam, Md. Tahidul;Koo, Insoo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.9
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    • pp.2213-2231
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    • 2013
  • Compressed sensing is a novel technology used in the field of wireless communication and sensor networks for channel estimation, signal detection, data gathering, network monitoring, and other applications. It plays a significant role in highly secure, real-time, well organized, and cost-effective data communication in smart-grid (SG) systems, which consist of multi-tier network standards that make it challenging to synchronize in power management communication. In this paper, we present a multi-layer communication model for SG systems and propose compressed-sensing based data transmission at every layer of the SG system to improve data transmission performance. Our approach is to utilize the compressed-sensing procedure at every layer in a controlled manner. Simulation results demonstrate that the proposed monitoring devices need less transmission power than conventional systems. Additionally, secure, reliable, and real-time data transmission is possible with the compressed-sensing technique.

A Study on the Reconstruction of a Frame Based Speech Signal through Dictionary Learning and Adaptive Compressed Sensing (Adaptive Compressed Sensing과 Dictionary Learning을 이용한 프레임 기반 음성신호의 복원에 대한 연구)

  • Jeong, Seongmoon;Lim, Dongmin
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37A no.12
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    • pp.1122-1132
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    • 2012
  • Compressed sensing has been applied to many fields such as images, speech signals, radars, etc. It has been mainly applied to stationary signals, and reconstruction error could grow as compression ratios are increased by decreasing measurements. To resolve the problem, speech signals are divided into frames and processed in parallel. The frames are made sparse by dictionary learning, and adaptive compressed sensing is applied which designs the compressed sensing reconstruction matrix adaptively by using the difference between the sparse coefficient vector and its reconstruction. Through the proposed method, we could see that fast and accurate reconstruction of non-stationary signals is possible with compressed sensing.

Cooperative Bayesian Compressed Spectrum Sensing for Correlated Signals in Cognitive Radio Networks (인지 무선 네트워크에서 상관관계를 갖는 다중 신호를 위한 협력 베이지안 압축 스펙트럼 센싱)

  • Jung, Honggyu;Kim, Kwangyul;Shin, Yoan
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38B no.9
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    • pp.765-774
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    • 2013
  • In this paper, we present a cooperative compressed spectrum sensing scheme for correlated signals in decentralized wideband cognitive radio networks. Compressed sensing is a signal processing technique that can recover signals which are sampled below the Nyquist rate with high probability, and can solve the necessity of high-speed analog-to-digital converter problem for wideband spectrum sensing. In compressed sensing, one of the main issues is to design recovery algorithms which accurately recover original signals from compressed signals. In this paper, in order to achieve high recovery performance, we consider the multiple measurement vector model which has a sequence of compressed signals, and propose a cooperative sparse Bayesian recovery algorithm which models the temporal correlation of the input signals.

Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction

  • Chen, Xingyi;Zhang, Yujie;Qi, Rui
    • Journal of Information Processing Systems
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    • v.15 no.2
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    • pp.410-421
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    • 2019
  • Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

Sparse reconstruction of guided wavefield from limited measurements using compressed sensing

  • Qiao, Baijie;Mao, Zhu;Sun, Hao;Chen, Songmao;Chen, Xuefeng
    • Smart Structures and Systems
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    • v.25 no.3
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    • pp.369-384
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    • 2020
  • A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l1-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

Compressed Sensing and the Applications of Wireless Communications (압축 감지 기술과 무선통신 응용)

  • Hwang, Dae-Sung;Kim, Dae-Sung;Choi, Jin-Ho;Ha, Jeong-Seok
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.5
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    • pp.32-39
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    • 2009
  • Compressed Sensing is a method to sample analog signals at a rate under the Nyquist rate. With this scheme, it is possible to represent signals with a relatively smaller number of measurements than that of the conventional sampling method, and the original signals are reconstructed with high probability from the acquired measurements using the linear programming. Compressed sensing allows measurement time and/or the amount of ADC (analog-to-digital converter) resources for the signal acquisitions to be reduced. In this paper, we presents the backgrounds of the compressed sensing, a way to acquire measurements from an analog signal with a random basis, and the signal recovery method. Also we introduce applications of compressed sensing in wireless communications.