• 대한전자공학회논문지SP
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• 제46권5호
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• pp.8-14
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• 2009

본 논문에서는, 컴프레시브 센싱(compressive sensing, CS)에서 양자화된 측정을 사용하여 CS 복구(reconstruction)를 하는 경우에 일반화된 양자화 제한(generalized quantization constraint, GQC) 집합을 사용하여 convex 최적화를 수행하는 방법을 제안하였다. 제안한 GQC에서는 기존의 양자화 제한 집합의 크기를 조정할 수 있도록 하였으며, 균일 스칼라 양자기를 사용한 CS 복구의 모의실험을 통하여 m/klogn > 2 인 CS 문제에서, 기존의 QC 방법에 비하여 CS 복구의 에러에서 3.4-3.6dB의 성능 개선을 얻을 수 있었다.

• Journal of Communications and Networks
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• 제15권5호
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• pp.443-456
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• 2013

Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist sampling theorem. CS has recently gained a lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately, courtesy of CS. This article gives a brief background on the origins of this idea, reviews the basic mathematical foundation of the theory and then goes on to highlight different areas of its application with a major emphasis on communications and network domain. Finally, the survey concludes by identifying new areas of research where CS could be beneficial.

• 한국위성정보통신학회논문지
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• 제8권4호
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• pp.130-134
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• 2013

본 논문에서는 3D 방송의 기본적인 원리를 설명하고 3D 방송을 CS 기술을 적용하여 데이터 용량을 줄이는 방식을 제안한다. 샘플링 이론과 CS 기술의 차이점을 설명하고 개념과 동작원리를 설명한다. 최근 제안된 CS 센싱의 복원 알고리즘인 AMP(Approximate Message Passing)와 CoSaMP(Compressive Sampling Matched Pursuit)를 소개하고 이를 이용하여 이미지 데이터를 압축 복원하여 비교한다. 두 알고리즘의 계산시간을 비교하여 낮은 복잡도를 갖는 알고리즘을 판단한다.

• 한국항행학회논문지
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• 제14권1호
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• pp.80-86
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• 2010

압축 센싱 (Compressive Sensing, CS)은 많은 응용분야에서 유망한 기술로 널리 연구되고 있다. 압축 센싱 이론에 의하면 어떤 특별한 기저에서 성긴 신호 (sparse signal)이라는 것이 알려졌다면 이 신호는 전통적인 방법이 사용하는 샘플 수보다 훨씬 적은 샘플로 최적화 과정을 통해 복원이 가능하다는 것이다. 본 논문에서는 이러한 압축 센싱 기술을 균일한 선형 배열로 구성된 다중 입출력 레이더 시스템에 적용하고자 한다. 특별히 압축 센싱 기술을 사용하여 DOA (direction-of-arrival)을 찾는 문제를 고찰하고 그 성능을 전통적인 적응형 다중 입출력 기법의 성능과 비교한다. 모의 실험을 통해 압축 센싱 방법은 전통적인 적응형 다중 입출력 기법에 비해 훨씬 적은 샘플로 비슷한 성능을 보임을 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1618-1637
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• 2018

By means of compressive sensing (CS) technique, this paper considers the collection of sensor data with spatiotemporal correlations in wireless sensor networks (WSNs). In energy-constrained WSNs, one-dimensional CS methods need a lot of data transmissions since they are less applicable in fully exploiting the spatiotemporal correlations, while the Kronecker CS (KCS) methods suffer performance degradations when the signal dimension increases. In this paper, an appropriate sensing matrix as well as an efficient sensing method is proposed to further reduce the data transmissions without the loss of the recovery performance. Different matrices for the temporal signal of each sensor node are separately designed. The corresponding energy-efficient data gathering method is presented, which only transmitting a subset of sensor readings to recover data of the entire WSN. Theoretical analysis indicates that the sensing structure could have the relatively small mutual coherence according to the selection of matrix. Compared with the existing spatiotemporal CS (CS-ST) method, the simulation results show that the proposed efficient measurement method could reduce data transmissions by about 25% with the similar recovery performance. In addition, compared with the conventional KCS method, for 95% successful recovery, the proposed sensing structure could improve the recovery performance by about 20%.

• 한국위성정보통신학회논문지
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• 제9권3호
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• pp.59-63
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• 2014

본 논문에서는 3D 방송의 기본적인 원리를 설명하고 압축 센싱(Compressed Sensing) 기술을 적용하여 3D 방송의 데이터 용량을 줄이는 방식을 제안한다. 샘플링 이론과 압축 센싱 기술의 차이점을 설명하고 개념과 동작원리를 설명한다. 최근 제안된 압축 센싱의 복원 알고리즘인 SS-CoSaMP(Single-Space Compressive Sampling Matched Pursuit) 와 CoSaMP(Compressive Sampling Matched Pursuit)를 소개하고 이를 이용하여 데이터를 압축 복원하여 정확도를 비교한다. 두 알고리즘의 다양한 이미지 복원을 수행하고 계산시간을 비교한다. 결론적으로 낮은 복잡도를 갖는 3D 방송에 적합한 알고리즘을 판단한다.

• 한국위성정보통신학회논문지
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• 제10권3호
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• pp.57-61
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• 2015

본 논문에서는 3D 방송의 기본적인 원리를 설명하고 실감영상 CS 기술을 적용하여 데이터 용량을 줄이는 방식을 제안한다. 샘플링 이론과 CS 기술의 차이점을 설명하고 개념과 동작원리를 설명한다. 압축 센싱의 복원 알고리즘인 SS-CoSaMP(Single-Space Compressive Sampling Matched Pursuit) 와 AMP(Approximate Message Passing)를 소개하고 이를 이용하여 이미지 데이터를 압축 복원하여 비교한다. 계산시간을 비교하여 낮은 복잡도를 갖는 알고리즘을 판단한다.

• Journal of the Optical Society of Korea
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• 제18권2호
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• pp.146-150
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• 2014

We propose an experimental holographic imaging scheme combining compressive sensing (CS) theory with digital holography in phase-shifting conditions. We use the Mach-Zehnder interferometer for hologram formation, and apply the compressive sensing (CS) approach to the holography acquisition process. Through projecting the hologram pattern into a digital micro-mirror device (DMD), finally we will acquire the compressive sensing measurements using a photodiode. After receiving the data of two holograms via conventional communication channel, we reconstruct the original object using certain signal recovery algorithms of CS theory and hologram reconstruction techniques, which demonstrated the feasibility of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권4호
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• pp.703-719
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• 2011

In multi-user wireless communication systems, adaptive modulation and scheduling are promising techniques for increasing the system throughput. However, a mass of wireless recourse will be occupied and spectrum efficiency will be decreased to feedback channel quality indication (CQI) of all users in every subcarrier or chunk for adaptive orthogonal frequency division multiplexing (OFDM) systems. Thus numerous limited feedback schemes are proposed to reduce the system overhead. The recently proposed compressive sensing (CS) theory provides a new framework to jointly measure and compress signals that allows less sampling and storage resources than traditional approaches based on Nyquist sampling. In this paper, we proposed two novel CQI feedback schemes based on general CS and subspace CS, respectively, both of which could be used in a wireless OFDM system. The feedback rate with subspace CS is greatly decreased by exploiting the subspace information of the underlying signal. Simulation results show the effectiveness of the proposed methods, with the same feedback rate, the throughputs with subspace CS outperform the discrete cosine transform (DCT) based method which is usually employed, and the throughputs with general CS outperform DCT when the feedback rate is larger than 0.13 bits/subcarrier.

• Journal of electromagnetic engineering and science
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• 제17권3호
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• pp.113-119
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• 2017

The application of compressive sensing (CS) to a radar imaging system based on a frequency-scanned microstrip leaky wave antenna is investigated. First, an analytical model of the system matrix is formulated as the basis for the inversion algorithm. Then, $L_1-norm$ minimization is applied to the inverse problem to generate a range-azimuth image of the scene. Because of the antenna length, the near-field effect is considered in the CS formulation to properly image close-in targets. The resolving capability of the combined frequency-scanned antenna and CS processing is examined and compared to results based on the short-time Fourier transform and the pseudo-inverse. Both simulation and measurement data are tested to show the system performance in terms of image resolution.