講演名 2011-01-27
Processing burden reduction of a large-scale data compression by employing averaged compressed sensing
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抄録(和)
抄録(英) Compressed sensing has been drawn much attention during last few years as an eminent solution for ill-posed inverse problem. However, compressed sensing is inherently vulnerable for the large-scale data process due to its time-consuming reconstruction process. Therefore, this paper pursues a solution to solve the scalability problem in compressed sensing. This paper proposes an averaged compressed sensing method which reduces the signal processing burden in dealing with large-scale data as follows: First, large-scale data which is sampled over Nyquist rate is divided into multiple groups. Then, conventional measurement is conducted for each group with identical measurement matrix. Second, multiple measurements data is averaged. Alternatively, the averaging process can be conducted prior to the measurement process. Experiments verify the practical validity of the proposed method.
キーワード(和)
キーワード(英) Compressed sensing / Compressive sampling / Flexible wireless system / L1-norm minimization
資料番号 SR2010-69
発行日

研究会情報
研究会 SR
開催期間 2011/1/20(から1日開催)
開催地(和)
開催地(英)
テーマ(和)
テーマ(英)
委員長氏名(和)
委員長氏名(英)
副委員長氏名(和)
副委員長氏名(英)
幹事氏名(和)
幹事氏名(英)
幹事補佐氏名(和)
幹事補佐氏名(英)

講演論文情報詳細
申込み研究会 Software Radio(SR)
本文の言語 ENG
タイトル(和)
サブタイトル(和)
タイトル(英) Processing burden reduction of a large-scale data compression by employing averaged compressed sensing
サブタイトル(和)
キーワード(1)(和/英) / Compressed sensing
第 1 著者 氏名(和/英) / Doohwan LEE
第 1 著者 所属(和/英)
NTT Network Innovation Laboratories, NTT Corporation
発表年月日 2011-01-27
資料番号 SR2010-69
巻番号(vol) vol.110
号番号(no) 398
ページ範囲 pp.-
ページ数 6
発行日