• Title, Summary, Keyword: 압축센싱

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다중채널 압축센싱

  • Kim, Jong-Min;Lee, Ok-Gyun;Ye, Jong-Cheol
    • The Magazine of the IEIE
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    • v.38 no.1
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    • pp.44-49
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    • 2011
  • 다중채널 압축센싱(multi-channel compressive sensing) 문제는 0이 아닌 성분이 공통된 위치에 분포하는 벡터들을 복원하는 방법을 다루는 문제이며 레이다의 도착방향 추정 문제, 역산란 문제, 산란광 단층촬영과 같은 많은 실용적인 문제에 응용될 수 있다. 압축 센싱 문제는 성긴(sparse) 속성을 갖는 벡터를 상당히 높은 확률로 복원시킬 수 있음이 밝혀져 있다. 이로 인해 기존의 압축 센싱 방법이 다중채널 압축센싱에서도 많이 활용되어 왔으며, 측정 벡터의 개수가 적을 때에도 높은 확률로 입력 신호를 복원할 수 있다. 그러나, 측정 벡터의 개수가 많아질수록, 기존의 압축센싱 알고리즘을 이용했을 때의 성능은 복수신호분리 (MUSIC) 알고리즘과 같이 배열신호처리(array signal processing)에서 활용되는 방법을 적용했을 때보다 더 나쁜 특성을 보인다. 이러한 기존 방법의 문제점으로 인해 우리는 새로운 다중채널 압축센싱 알고리즘을 제시하고자 하며, 이는 기존의 압축센싱 이론과 배열 신호처리 알고리즘을 개별적으로 적용할 때 가지는 한계를 극복할 수 있게 해준다.

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차세대 통신 네트워크를 위한 압축센싱기술의 응용

  • Jeong, Bang-Cheol;Sin, Won-Yong
    • Information and Communications Magazine
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    • v.28 no.9
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    • pp.69-75
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    • 2011
  • 본고에서는 압축센싱(Compressed sensing) 기술의 개념과 동작원리를 소개하고 최근 제안된 Message Passing 기반의 복호암고리즘에 대하여 알아본다. Message Passing 기반의 복호알고리즘은 기존 최적화기반의 복호알고리즘보다 낮은 복잡도로 동작하면서도 뛰어난 성능을 갖는 것으로 알려져 있다. 또한, 신호처리 및 정보이론 분야에서 활발히 연구되고 있는 압축센싱 기술의 차세대 이동통신 시스템 응용의 가능성을 검토하고 최근 통신시스템을 위하여 제안된 압축센싱 기반의 알고리즘을 추가로 검토한다.

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.

Reconstructed Iimage Quality Improvement of Distributed Compressive Video Sensing Using Temporal Correlation (시간 상관관계를 이용한 분산 압축 비디오 센싱 기법의 복원 화질 개선)

  • Ryu, Joong-seon;Kim, Jin-soo
    • Journal of the Korea Industrial Information Systems Research
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    • v.22 no.2
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    • pp.27-34
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    • 2017
  • For The Purpose of Pursuing the Simplest Sampling, a Motion Compensated Block Compressed Sensing with Smoothed Projected Landweber (MC-BCS-SPL) has been Studied for an Effective Scheme of Distributed Compressive Video Sensing with all Compressed Sensing (CS) Frames. However, Conventional MC-BCS-SPL Scheme is Very Simple and so it Does not Provide Good Visual Qualities in Reconstructed Wyner-Ziv (WZ) Frames. In this Paper, the Conventional Scheme of MC-BCS-SPL is Modified to Provide Better Visual Qualities in WZ Frames. That is, the Proposed Agorithm is Designed in such a way that the Reference Frame may be Adaptively Selected Based on the Temporal Correlation Between Successive Frames. Several Experimental Results show that the Proposed Algorithm Provides Better Visual Qualities than Conventional Algorithm.

Analysis of Signal Recovery for Compressed Sensing using Deep Learning Technique (딥러닝 기술을 활용한 압축센싱 신호 복원방법 분석)

  • Seong, Jin-Taek
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.10 no.4
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    • pp.257-267
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    • 2017
  • Compressed Sensing(CS) deals with linear inverse problems. The theoretical results of CS have had an impact on inference problems and presented amazing research achievements in the related fields including signal processing and information theory. However, in order for CS to be applied in practical environments, there are two significant challenges to be solved. One is to guarantee in real time recovery of CS signals, and the other is that the signals have to be sparse. To this end, the latest researches using deep learning technology have emerged. In this paper, we consider CS problems based on deep learning and discuss the latest research results. And the approaches for CS signal reconstruction using deep learning show superior results in terms of recovery time and performance. It is expected that the approaches for CS reconstruction using deep learning shown in recent studies can not only raise the possibility of utilization of CS, but also be highly exploited in the fields of signal processing and communication areas.

Study on Compressed Sensing of ECG/EMG/EEG Signals for Low Power Wireless Biopotential Signal Monitoring (저전력 무선 생체신호 모니터링을 위한 심전도/근전도/뇌전도의 압축센싱 연구)

  • Lee, Ukjun;Shin, Hyunchol
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.3
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    • pp.89-95
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    • 2015
  • Compresses sensing (CS) technique is beneficial for reducing power consumption of biopotential acquisition circuits in wireless healthcare system. This paper investigates the maximum possible compress ratio for various biopotential signal when the CS technique is applied. By using the CS technique, we perform the compression and reconstruction of typical electrocardiogram(ECG), electromyogram(EMG), electroencephalogram(EEG) signals. By comparing the original signal and reconstructed signal, we determines the validity of the CS-based signal compression. Raw-biopotential signal is compressed by using a psuedo-random matrix, and the compressed signal is reconstructed by using the Block Sparse Bayesian Learning(BSBL) algorithm. EMG signal, which is the most sparse biopotential signal, the maximum compress ratio is found to be 10, and the ECG'sl maximum compress ratio is found to be 5. EEG signal, which is the least sparse bioptential signal, the maximum compress ratio is found to be 4. The results of this work is useful and instrumental for the design of wireless biopotential signal monitoring circuits.

Computational performance and accuracy of compressive sensing algorithms for range-Doppler estimation (거리-도플러 추정을 위한 압축 센싱 알고리즘의 계산 성능과 정확도)

  • Lee, Hyunkyu;Lee, Keunhwa;Hong, Wooyoung;Lim, Jun-Seok;Cheong, Myoung-Jun
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.5
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    • pp.534-542
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    • 2019
  • In active SONAR, several different methods are used to detect range-Doppler information of the target. Compressive sensing based method is more accurate than conventional methods and shows superior performance. There are several compressive sensing algorithms for range-Doppler estimation of active sonar. The ability of each algorithm depends on algorithm type, mutual coherence of sensing matrix, and signal to noise ratio. In this paper, we compared and analyzed computational performance and accuracy of various compressive sensing algorithms for range-Doppler estimation of active sonar. The performance of OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), BPDN (CVX) (Basis Pursuit Denoising), LARS (Least Angle Regression) algorithms is respectively estimated for varying SNR (Signal to Noise Ratio), and mutual coherence. The optimal compressive sensing algorithm is presented according to the situation.

An Effective MC-BCS-SPL Algorithm and Its Performance Comparison with Respect to Prediction Structuring Method (효과적인 MC-BCS-SPL 알고리즘과 예측 구조 방식에 따른 성능 비교)

  • Ryug, Joong-seon;Kim, Jin-soo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.7
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    • pp.1355-1363
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    • 2017
  • Recently, distributed compressed video sensing (DCVS) has been actively studied in order to achieve a low complexity video encoder by integrating both compressed sensing and distributed video coding characteristics. Conventionally, a motion compensated block compressed sensing with smoothed projected Landweber (MC-BCS-SPL) has been considered as an effective scheme of DCVS with all compressed sensing frames pursuing the simplest sampling. In this scheme, video frames are separately classified into key frames and WZ frames. However, when reconstructing WZ frame with conventional MC-BCS-SPL scheme at the decoder side, the visual qualities are poor for temporally active video sequences. In this paper, to overcome the drawbacks of the conventional scheme, an enhanced MC-BCS-SPL algorithm is proposed, which corrects the initial image with reference to the key frame using a high correlation between adjacent key frames. The proposed scheme is analyzed with respect to GOP (Group of Pictures) structuring method. Experimental results show that the proposed method performs better than conventional MC-BCS-SPL in rate-distortion.

Channel State Information Feedback Scheme Based on Non-Convex Compressed Sensing for Massive MIMO Systems (거대 다중 안테나 시스템을 위한 넌컨벡스 압축센싱 기반채널 정보 피드백 기법)

  • Kim, Jung-Hyun;Kim, Inseon;Park, Jin Soo;Song, Hong-Yeop;Han, Sung Woo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.4
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    • pp.628-636
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    • 2015
  • In this paper, we propose a non-convex compressed sensing(NCCS)-based channel state information(CSI) feedback scheme for massive multiple-input multiple-output(MIMO) systems. Combining the random vector quantization(RVQ), the proposed scheme permits a transmitter to obtain CSI with acceptable accuracy under substantially reduced feedback load. Furthermore, it recovers CSI from fewer measurements than that of existing convex compressed sensing(CCS)-based schemes even if the measurements are inaccurate and incomplete. Simulation results show that the proposed scheme achieves higher throughput than both existing CCS-based feedback scheme and random vector quantization(RVQ) feedback scheme with the same feedback load.

A Compressive Sensing Based Imaging Algorithm Using Incoherent Measurements and DCT (저상관도 측정치와 DCT를 이용한 압축센싱 기반 영상 획득 알고리듬)

  • Kim, Seehyun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.10
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    • pp.1961-1966
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    • 2016
  • Compressive sensing has proved that a signal can be restored from less samples than the Nyquist rate. Reducing the required data rate is essential for a variety of fields including compression, transmission, and storage. It has been made lots of attempt to apply the compressive sensing theory into data intensive fields, such as image processing which needs to cover 4K and 8K pictures. In this paper, an image acquisition algorithm based on compressive sensing is proposed. It combines DCT, which can compact the energy of a image into a few coefficients, and the Noiselet transform, which is incoherent with DCT. The DCT coefficients represent the coarse structure of the images while the Noiselet information holds the fine details. Performance experiments with several images show that the proposed image acquisition algorithm not only outperforms the previous results, but also improves the reconstruction quality faster as the number of measurements increases.