https://scholars.lib.ntu.edu.tw/handle/123456789/428076
標題: | Compressive Sensing Matrix Design for Fast Encoding and Decoding via Sparse FFT | 作者: | Sung-Hsien Hsieh Chun-Shien Lu SOO-CHANG PEI |
關鍵字: | Convex optimization; Costs; Decoding; Fast Fourier transforms; Frequency domain analysis; Hardware; Linear transformations; Matrix algebra; Signal encoding; Signal sampling; Compressive sensing; Computation costs; Frequency domains; Gaussian random matrices; Hardware implementations; Sparsity; Transformation matrices; Transformed domain; Compressed sensing | 公開日期: | 2018 | 卷: | 25 | 期: | 4 | 起(迄)頁: | 591-595 | 來源出版物: | IEEE Signal Processing Letters | 摘要: | Compressive sensing (CS) is proposed for signal sampling below the Nyquist rate based on the assumption that the signal is sparse in some transformed domain. Most sensing matrices (e.g., Gaussian random matrix) in CS, however, usually suffer from unfriendly hardware implementation, high computation cost, and huge memory storage. In this letter, we propose a deterministic sensing matrix for collecting measurements fed into sparse fast Fourier transform (sFFT) as the decoder. Compared with the conventional paradigm with Gaussian random matrix at encoder and convex programming or greedy method at decoders, sFFT can reconstruct sparse signals with very low computation cost under the comparable number of measurements. But, the limitation is that the signal must be sparse in the frequency domain. We further show how to relax this limitation into any domains with the transformation matrix or dictionary being circulant. Experimental and theoretical results validate that the proposed method achieves fast sensing, fast recovery, and low memory cost. © 2012 IEEE. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/428076 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042689078&doi=10.1109%2fLSP.2018.2809693&partnerID=40&md5=56e6ef5c3a4236aa96ad1a0fd2585293 |
ISSN: | 10709908 | DOI: | 10.1109/lsp.2018.2809693 | SDG/關鍵字: | Convex optimization; Costs; Decoding; Fast Fourier transforms; Frequency domain analysis; Hardware; Linear transformations; Matrix algebra; Signal encoding; Signal sampling; Compressive sensing; Computation costs; Frequency domains; Gaussian random matrices; Hardware implementations; Sparsity; Transformation matrices; Transformed domain; Compressed sensing |
顯示於: | 電機工程學系 |
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