• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.409-416
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• 2017

In this paper, we consider a framework of compressed sensing over finite fields. One measurement sample is obtained by an inner product of a row of a sensing matrix and a sparse signal vector. A recovery algorithm proposed in this study for sparse signals based probabilistic decoding is used to find a solution of compressed sensing. Until now compressed sensing theory has dealt with real-valued or complex-valued systems, but for the processing of the original real or complex signals, the loss of the information occurs from the discretization. The motivation of this work can be found in efforts to solve inverse problems for discrete signals. The framework proposed in this paper uses a parity-check matrix of low-density parity-check (LDPC) codes developed in coding theory as a sensing matrix. We develop a stochastic algorithm to reconstruct sparse signals over finite field. Unlike LDPC decoding, which is published in existing coding theory, we design an iterative algorithm using probability distribution of sparse signals. Through the proposed recovery algorithm, we achieve better reconstruction performance as the size of finite fields increases. Since the sensing matrix of compressed sensing shows good performance even in the low density matrix such as the parity-check matrix, it is expected to be actively used in applications considering discrete signals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.149-152
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• 1998

정보통신 기술이 급격히 발달함에 따라 영상처리 및 압축 기술에 대한 중요성이 대두되고 있다. 현재 정지 압축 영상 표준인 JPEG과 동영상 압축 표준인 MPEG에서는 통계적 특성에 기반한 DCT 방법을 이용하여 압축을 수행하고 있다. 최근에는 웨이브렛 변환을 이용한 영상신호 처리 및 압축에 관한 연구가 활발히 이루어지고 있는데, 이는 기존의 DCT 방법과는 달리 속도와 압축률이 뛰어나며 블록화 현상(Blocking Effect)이 발생하지 않기 때문이다. 또한 웨이브렛 자체가 함수이고 이러한 함수에 다른 스케일과 해상도를 적용하는것이므로, 영상을 확대하거나 축소하더라도 이미지에 손상을 주지 않고 복원할 수 있다. 스테레오 이미지는 사람의 시각에서 물체를 보는 것처럼, 카메라에서 같은 장면을 약간의 차이를 두어 찍은 것이다. 따라서 오른쪽과 왼쪽의 두 이미지로 나누어지게 되는데, 이 두 이미지 사이에서 공통 부분이 많다는 특징을 가지게 된다. 따라서 두 이미지의 공통 부분을 찾아 내고, 이를 이용하여 압축을 할 수 있다면 압축률을 높일 수 있다. 본 연구에서는 웨이브렛 변환을 이용하여 스테레오 이미지에서의 공통 부분을 찾고 영상을 효율 岵막\ulcorner 압축하는 방법에 대하여 연구한다.

• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.49-60
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• 2002

Receive dynamic focusing with an array transducer can provide near optimum resolution only in the vicinity of transmit focal depth. A customary method to increase the depth of field is to combine several beams with different focal depths, with an accompanying decrease in the frame rate. In this Paper. we Present a simultaneous multiple transmit focusing method in which chirp signals focused at different depths are transmitted at the same time. These chirp signals are mutually orthogonal in a sense that the autocorrelation function of each signal has a narrow mainlobe width and low sidelobe levels. and the crossorelation function of any Pair of the signals has values smaller than the sidelobe levels of each autocorrelation function. This means that each chirp signal can be separated from the combined received signals and compressed into a short pulse. which is then individually focused on a separate receive beamformer. Next. the individually focused beams are combined to form a frame of image. Theoretically, any two chirp signals defined over two nonoverlapped frequency bands are mutually orthogonal In the present work. however, a tractional overlap of adjacent frequency bands is permitted to design more chirp signals within a given transducer bandwidth. The elevation of the rosscorrelation values due to the frequency overlap could be reduced by alternating the direction of frequency sweep of the adjacent chirp signals We also observe that the Proposed method provides better images when the low frequency chirp is focused at a near Point and the high frequency chirp at a far point along the depth. better lateral resolution is obtained at the far field with reasonable SNR due to the SNR gain in Pulse compression Imaging .

• TTA Journal
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• s.178
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• pp.124-127
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• 2018

(주)칩스앤미디어는 2003년 반도체 설계 자산(Semiconductor IP)생산을 시작으로 현재는 표준 영상 압축 기술인 MPEG 표준을 비롯하여 UHD 표준 압축 기술인 HEVC 등을 시장에 공급하고 있으며, 이미지 신호 처리(ISP) 및 CV IP 등의 시장 출시를 준비 중이다. HEVC의 개발 이후 ISO/IEC에서는 차세대 영상 압축 기술에 대한 표준화를 논의하고 있다. (주)칩스앤미디어는 표준 규격이 완료되기를 기다려 이를 구현하기보다, 한 발 더 앞서 행동하는 것이 현명하리라 판단했다. 직접 국제 표준을 제안함으로써 원천 특허를 확보하고자 한 것이다. TTA 자문서비스의 도움으로 (주)칩스앤미디어는 차세대 영상 압축 기술 표준 관련 특허출원에 성공했다.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.960-966
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• 2000

It is essential to develop the robust video transmission system over wireless channel, since the effect of the noise and the interference to compressed video may be fatal. In this paper, we have designed turbo code, which is adequate for the transmission of video and analyzed the performance of that. Especially, we have focused the rate compatible punctured turbo(RCPT) code, which has rate compatible(RC) property for unequal error protection(UEP). It has been shown through computer simulation that the turbo code with helical interleaver yields better performance than those with other interleavers when the size of the interleaver is not large.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.541-547
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• 2013

Range resolution is important performance parameter to distinguish a target accurately. The destination of modern radar systems, to overcome the limitations of existing analog radar systems, is to improve the range resolution of the distance with low transmission power. For that reason, the research on pulse compression techniques is briskly studying. In the Receiver, modulation system of transmitted signal which has used in pulse compression technique is divided PM and FM to distinguish a target. In this paper, We analysed and designed the pulse compression signal processing module using the Baker Code which is the one of PM method's.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.17-24
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• 1995

This paper presents a new compression method (or ECG data using iterated contractive transformations. The method represents any range of ECG signal by piecewise self-afrine fractal Interpolation (PSAFI). The piecewise self-afrine rractal model is used where a discrete data set is viewed as being composed of contractive arfine transformation of pieces of itself. This algorithm was evaluated using MIT/BIH arrhythmia database. PSAFI is found to yield a relatively low reconstruction error for a given compression ratio than conventional direct compression methods. The compression ratio achieved was 883.9 bits per second (bps) - an average percent rms difference (AFRD) of 5.39 percent -with the original 12b ECG samples digitized at 400 Hz.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.240-249
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• 2014

According to the compressive sensing theory, it is possible to perfectly reconstruct a signal only with a fewer number of measurements than the Nyquist sampling rate if the signal is a sparse signal which satisfies a few related conditions. From practical viewpoint for image applications, it is important to reduce its computational complexity and memory burden required in reconstruction. In this regard, a Block-based Compressive Sensing (BCS) scheme with Smooth Projected Landweber (BCS-SPL) has been already introduced. However, it still has the computational complexity problem in reconstruction. In this paper, we propose a method which modifies its stopping criterion, tolerance, and convergence control to make it converge faster. Experimental results show that the proposed method requires less iterations but achieves better quality of reconstructed image than the conventional BCS-SPL.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1452-1459
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• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in and under Nyquist rate representation. Generally, the measurement prediction usually works well with a small block while the quality of recovery is known to be better with a large block. In order to overcome this dilemma, conventional research works use a structural measurement matrix with which compressed sensing is done in a small block size but recovery is performed in a large block size. In this way, both prediction and recovery are made to be improved at same time. However, the conventional researches did not compare the performances of the structural measurement matrix, affected by the block size. In this paper, by expanding a structural measurement matrix of conventional works, their performances are compared with different block sizes. Experimental results show that a structural measurement matrix with $4{\times}4$ Hadamard transform matrix provides superior performance in block size 4.