지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
The mathematical foundations of the compressive sensing which goes against the common wisdom of data acquisition (the Nyquist-Shannon theorem) is reviewed. The compressive sensing asserts that one can reconstruct images or signals of interest accurately from a number of samples far smaller than the desired resolution of the the image (e.g., the number of pixels in the image). The compressive sensing has far reaching implications. It suggests the new data acquisition protocols that translates analog information to digital form with fewer sensors considered necessary.
목차
Ⅰ. Abstract
Ⅱ. Introduction
Ⅲ. Problem of Compressive Sensing
Ⅳ. Incoherent Sampling: Mathematical Principles
Ⅴ. Geometry of l₁ Minimization
Ⅵ. Stability Of Reconstruction Algorithm And Error Estimate
Ⅶ. Random Sensing Matrices
Ⅷ. Discussions and Implications
Acknowledgments
References
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UCI(KEPA) : I410-ECN-0101-2012-569-004063884