Results 1  10
of
30
Distributed compressed sensing
, 2005
"... Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algori ..."
Abstract

Cited by 84 (21 self)
 Add to MetaCart
Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multisignal ensembles that exploit both intra and intersignal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We study in detail three simple models for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. We establish a parallel with the SlepianWolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In two of our three models, the results are asymptotically bestpossible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays.
Application of Kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series
, 2006
"... ..."
Linear Time Universal Coding and Time Reversal of Tree Sources via FSM Closure
 IEEE Trans. Inform. Theory
, 2004
"... Tree models are efficient parametrizations of finitememory processes, offering potentially significant model cost savings. The information theory literature has focused mostly on redundancy aspects of the universal estimation and coding of these models. In this paper, we investigate representations ..."
Abstract

Cited by 13 (2 self)
 Add to MetaCart
Tree models are efficient parametrizations of finitememory processes, offering potentially significant model cost savings. The information theory literature has focused mostly on redundancy aspects of the universal estimation and coding of these models. In this paper, we investigate representations and supporting data structures for finitememory processes, as well as the major impact these structures have on the computational complexity of the universal algorithms in which they are used. We first generalize the class of tree models, and then define and investigate the properties of the finite state machine (FSM) closure of a tree, which is the smallest FSM that generates all the processes generated by the tree. The interaction between FSM closures, generalized context trees, and classical data structures such as compact suffix trees brings together the informationtheoretic and the computational aspects, leading to an implementation in linear encoding/decoding time of the semipredictive approach to the Context algorithm, a lossless universal coding scheme in the class of tree models. An optimal context selection rule and the corresponding context transitions are computationally not more expensive than the various steps involved in the implementation of the BurrowsWheeler transform (BWT) and use, in fact, similar tools. We also present a reversible transform that displays the same "context deinterleaving" feature as the BWT but is naturally based on an optimal context tree. FSM closures are also applied to an investigation of the effect of time reversal on tree models, motivated in part by the following question: When compressing a data sequence using a universal scheme in the class of tree models, can it make a difference whether we read the sequence from...
Impact of Data Compression on Energy Consumption of WirelessNetworked Handheld Devices
 in Proceedings of the 23rd IEEE International Conference on Distributed Computing Systems (ICDCS’03
, 2003
"... We investigate the use of data compression to reduce the battery consumed by handheld devices when downloading data from proxy servers over a wireless LAN. To make a careful tradeoff between the communication energy and the overhead to perform decompression, we experiment with three universal lossl ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
We investigate the use of data compression to reduce the battery consumed by handheld devices when downloading data from proxy servers over a wireless LAN. To make a careful tradeoff between the communication energy and the overhead to perform decompression, we experiment with three universal lossless compression schemes, using a popular handheld device in a wireless LAN environment and we find interesting facts.
Universal Codes as a Basis for Time Series Testing
 Statistical Methodology
, 2006
"... We suggest a new approach to hypothesis testing for ergodic and stationary processes. In contrast to standard methods, the suggested approach gives a possibility to make tests, based on any lossless data compression method even if the distribution law of the codeword lengths is not known. We apply t ..."
Abstract

Cited by 12 (6 self)
 Add to MetaCart
We suggest a new approach to hypothesis testing for ergodic and stationary processes. In contrast to standard methods, the suggested approach gives a possibility to make tests, based on any lossless data compression method even if the distribution law of the codeword lengths is not known. We apply this approach to the following four problems: goodnessoffit testing (or identity testing), testing for independence, testing of serial independence and homogeneity testing and suggest nonparametric statistical tests for these problems. It is important to note that practically used socalled archivers can be used for suggested testing.
Universal codes as a basis for nonparametric testing of serial independence for time series
, 2005
"... ..."
PPM performance with BWT complexity: A fast and effective data compression algorithm
 Proceedings of the IEEE
, 2000
"... This paper introduces a new data compression algorithm. The goal underlying this new code design is to achieve a single lossless compression algorithm with the excellent compression ratios of the Prediction by Partial Mapping (PPM) algorithms and the low complexity of codes based on the Burrows Whee ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
This paper introduces a new data compression algorithm. The goal underlying this new code design is to achieve a single lossless compression algorithm with the excellent compression ratios of the Prediction by Partial Mapping (PPM) algorithms and the low complexity of codes based on the Burrows Wheeler Transform (BWT). Like the BWTbased codes, the proposed algorithm requires worst case O(n) computational complexity and memory; in contrast, the unboundedcontext PPM algorithm, called PPM 3, requires worst case O(n 2) computational complexity. Like PPM 3, the proposed algorithm allows the use of unbounded contexts. Using standard data sets for comparison, the proposed algorithm achieves compression performance better than that of the BWTbased codes and comparable to that of PPM 3. In particular, the proposed algorithm yields an average rate of 2.29 bits per character (bpc) on the Calgary corpus; this result compares favorably with the 2.33 and 2.34 bpc of PPM5 and PPM 3 (PPM algorithms), the 2.43 bpc of BW94 (the original BWTbased code), and the 3.64 and 2.69 bpc of compress and gzip (popular Unix compression algorithms based on Lempel–Ziv (LZ) coding techniques) on the same data set. The given code does not, however, match the best reported compression performance—2.12 bpc with PPMZ9—listed on the Calgary corpus results web page at the time of this publication. Results on the Canterbury corpus give a similar relative standing. The proposed algorithm gives an average rate of 2.15 bpc on the Canterbury corpus, while the Canterbury corpus web page gives average rates of 1.99 bpc for PPMZ9, 2.11 bpc for PPM5, 2.15 bpc for PPM7, 2.23 bpc for BZIP2 (a popular BWTbased code), and 3.31 and 2.53 bpc for compress and gzip, respectively. Keywords—Burrows Wheeler Transform, lossless source coding, prediction by partial mapping algorithm, suffix trees, text compression. I.
An O(n) semipredictive universal encoder via the BWT
 IEEE Trans. Inform. Theory
, 2004
"... We provide an O(N) algorithm for a nonsequential semipredictive encoder whose pointwise redundancy with respect to any (unbounded depth) tree source is O(1) bits per state above Rissanen’s lower bound. This is achieved by using the Burrows Wheeler transform (BWT), an invertible permutation transfo ..."
Abstract

Cited by 8 (2 self)
 Add to MetaCart
We provide an O(N) algorithm for a nonsequential semipredictive encoder whose pointwise redundancy with respect to any (unbounded depth) tree source is O(1) bits per state above Rissanen’s lower bound. This is achieved by using the Burrows Wheeler transform (BWT), an invertible permutation transform that has been suggested for lossless data compression. First, we use the BWT only as an efficient computational tool for pruning context trees, and encode the input sequence rather than the BWT output. Second, we estimate the minimum description length (MDL) source by incorporating suffix tree methods to construct the unbounded depth context tree that corresponds to the input sequence in O(N) time. Third, we point out that a variety of previous source coding methods required superlinear complexity for determining which tree source state generated each of the symbols of the input. We show how backtracking from the BWT output to the input sequence enables to solve this problem in O(N) worstcase complexity.
Unifying Text Search And Compression  Suffix Sorting, Block Sorting and Suffix Arrays
, 2000
"... Today many electronic documents are available such as articles of newspapers, dictionaries, books, DNA sequences, etc. and they are stored in databases. We also have many documents on the Internet and have many email documents. Therefore, fast queries on such huge amount of documents and their comp ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
Today many electronic documents are available such as articles of newspapers, dictionaries, books, DNA sequences, etc. and they are stored in databases. We also have many documents on the Internet and have many email documents. Therefore, fast queries on such huge amount of documents and their compression to reduce costs for storing or transferring them are important. In this thesis, a unified method for improving efficiency of search and compression for huge text data is proposed. All search methods and compression methods used in this thesis are related to a data structure called suffix array. The suffix array is a text search data structure and it is used in a text compression method called block sorting. Both are promising search method and compression method and there are many studies on the methods. Now a data structure called inverted file is used for queries from huge amount of documents. Though it is widely used, query unit is a document in order to reduce disk space to sto...