翻訳と辞書 Words near each other ・ Verities & Balderdash ・ Veritism ・ Veriton ・ Veritone Minimum Phase Speakers ・ Veritrade ・ Verity ・ Verity (disambiguation) ・ Verity (statue) ・ Verity and the Shades ・ Verity Bargate ・ Verity Bargate Award ・ Verity Barton ・ Verity Birdwood ・ Verification condition generator ・ Verification of employment ・ Verification-based message-passing algorithms in compressed sensing ・ Verificationism ・ Verified Audit Circulation ・ Verified Carbon Standard ・ Verified-Accredited Wholesale Distributors ・ VeriFlux ・ Verifone ・ Verify in field ・ Verigar issue ・ Verige bridge ・ Verigy ・ Verija ・ Veriko Anjaparidze ・ Verila Glacier ・ Verilator Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Verification-based message-passing algorithms in compressed sensing ： ウィキペディア英語版 Verification-based message-passing algorithms in compressed sensing Verification-based message-passing algorithms (VB-MPAs) in compressed sensing (CS), a branch of digital signal processing that deals with measuring sparse signals, are some methods to efficiently solve the recovery problem in compressed sensing. One of the main goal in compressed sensing is the recovery process. Generally speaking, recovery process in compressed sensing is a method by which the original signal is estimated using the knowledge of the compressed signal and the measurement matrix.〔D. L. Donoho, A. Javanmard, and A. Montanari, “Information-theoretically optimal compressed sensing via spatial coupling and approximate message passing,” in Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on, 2012, pp. 1231–1235.〕 Mathematically, the recovery process in Compressed Sensing is finding the sparsest possible solution of an under-determined system of linear equations. Based on the nature of the measurement matrix one can employ different reconstruction methods. If the measurement matrix is also sparse, one efficient way is to use Message Passing Algorithms for signal recovery. Although there are message passing approaches that deals with dense matrices, the nature of those algorithms are to some extent different from the algorithms working on sparse matrices.〔〔Chandar, Venkat, Devavrat Shah, and Gregory W. Wornell. "A simple message-passing algorithm for compressed sensing." Information Theory Proceedings (ISIT), 2010 IEEE International Symposium on. IEEE, 2010.〕 == Overview == The main problem in recovery process in CS is to find the sparsest possible solution to the following under-determined system of linear equations $Ax\; =\; y$ where $A$ is the measurement matrix, $x$ is the original signal to be recovered and $y$ is the compresses known signal. When the matrix $A$ is sparse, one can represent this matrix by a bipartite graph $G=(V\_l\backslash cup\; V\_r,E)$ for better understanding.〔〔Indyk, Piotr. "Explicit constructions for compressed sensing of sparse signals." Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, 2008.〕〔Gilbert, Anna C., et al. "One sketch for all: fast algorithms for compressed sensing." Proceedings of the thirty-ninth annual ACM symposium on Theory of computing. ACM, 2007.〕〔Sarvotham, Shriram, Dror Baron, and Richard G. Baraniuk. "Sudocodes-Fast Measurement and Reconstruction of Sparse Signals." Information Theory, 2006 IEEE International Symposium on. IEEE, 2006.〕 $V\_l$ is the set of variable nodes in $G$ which represents the set of elements of $x$ and also $V\_r$ is the set of check nodes corresponding to the set of elements of $y$. Besides, there is an edge $e=(u,v)$ between $u\backslash in\; V\_l$ and $v\backslash in\; V\_r$ if the corresponding elements in $A$ is non-zero, i.e. $A\_\backslash neq\; 0$. Moreover, the weight of the edge $w(e)=A\_$.〔Y. Eftekhari, A. Heidarzadeh, A. H. Banihashemi, and I. Lambadaris, “Density evolution analysis of node-based verification-based algorithms in compressed sensing,” Information Theory, IEEE Transactions on, vol. 58, no. 10, pp. 6616–6645, 2012.〕 Here is an example of a binary sparse measurement matrix where the weights of the edges are either zero or one. $$ A = \left(iterative manner in order to efficiently find signal $x$. These messages are different for variable nodes and check nodes. However, the basic nature of the messages for all variable node and check nodes are the same in all of the verification based message passing algorithms.〔 The messages $\backslash mu^(v\_i):~V\_l\; \backslash mapsto\; \backslash mathbb\backslash times\; \backslash$ emanating from variable node $v\_i$ contains the value of the check node and an indicator which shows if the variable node is verified or not. Moreover, the messages $\backslash mu^(c\_i):~V\_r\; \backslash mapsto\; \backslash mathbb\backslash times\; \backslash mathbb^+$ emanating from check node $c\_i$ contains the value of the check node and the remaining degree of the check node in the graph.〔〔(【引用サイトリンク】first1=Seyed Mohammad Ebrhiam )〕 In each iteration, every variable node and check node produce a new message to be transmitted to all of its neighbors based on the messages that they have received from their own neighbors. This local property of the message passing algorithms enables them to be implemented as parallel processing algorithms and makes the time complexity of these algorithm so efficient.〔F. Zhang and H. D. Pfister, “On the iterative decoding of high-rate LDPC codes with applications in compressed sensing,” arXiv preprint (arXiv:0903.2232 ), 2009.〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Verification-based message-passing algorithms in compressed sensing」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.