• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.21-28
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• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in an under-sampled (i.e., under Nyquist rate) representation. Specially, a block compressed sensing with Smoothed Projected Landweber (BCS-SPL) framework is one of the most widely used schemes. Currently, a variety of BCS-SPL schemes have been actively studied. However, when restoring, block sizes have effects on the reconstructed visual qualities, and in this paper, both a basic scheme of BCS-SPL and several modified schemes of BCS-SPL with structured measurement matrix are analyzed for the effects of the block sizes on the performances of reconstructed image qualities. Through several experiments, it is shown that a basic scheme of BCS-SPL provides superior performance in block size 4.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.460-462
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• 1998

객체 지향 방법론을 이용한 소프트웨어의 개발은 최근에 대중적인 개념으로 자리를 잡고 있다. 이러한 객체 지향 개념은 전통적인 구조화 개발 방법론과 많은 차이점을 가지고 있다. 따라서 소프트웨어의 품질 특성을 평가하는 메트릭스도 객체 지향 개념에 맞게 재정의되는 연구들이 많이 이루어지고 있다. 이 논문에서는 이러한 메트릭스를 품질 측정 척도의 관점에서 객체 지향의 핵심 개념으로 평가하였다. 그리고 이를 바탕으로 품질 척도로서의 기능을 잘 발휘하는 메트릭스를 선별 적용하고, 적용한 결과를 사용자에게 해석하여주며, 코딩 스타일도 함께 검증하여서 사용자에게 실질적인 도움이 될 수 있도록 도구를 설계 및 구현하였다.