scholarly journals Sequential Adaptive Compressed Sampling via Huffman Codes

2011 ◽  
Vol 10 (3) ◽  
pp. 231-254
Author(s):  
Akram Aldroubi ◽  
Haichao Wang ◽  
Kourosh Zarringhalam
Keyword(s):  
Huffman Codes ◽  
Compressed Sampling

Synchronizing codewords of q-ary Huffman codes

1999 ◽  
Vol 197-198 (1-3) ◽  
pp. 637-655 ◽  
Author(s):  
S Perkins
Keyword(s):  
Huffman Codes

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

Compressed Sampling of Spectrally Sparse Signals Using Sparse Circulant Matrices

Frequenz ◽  
2014 ◽  
Vol 68 (11-12) ◽  
Author(s):  
Guangjie Xu ◽  
Huali Wang ◽  
Lei Sun ◽  
Weijun Zeng ◽  
Qingguo Wang
Keyword(s):  
Circulant Matrices ◽  
Noise Robustness ◽  
Sparse Signals ◽  
Measurement Matrix ◽  
Compressed Sampling ◽  
Random Measurement ◽  
Compressive Performance ◽  
Generating Sequence ◽  
Periodic Autocorrelation ◽  
Selection Of

AbstractCirculant measurement matrices constructed by partial cyclically shifts of one generating sequence, are easier to be implemented in hardware than widely used random measurement matrices; however, the diminishment of randomness makes it more sensitive to signal noise. Selecting a deterministic sequence with optimal periodic autocorrelation property (PACP) as generating sequence, would enhance the noise robustness of circulant measurement matrix, but this kind of deterministic circulant matrices only exists in the fixed periodic length. Actually, the selection of generating sequence doesn't affect the compressive performance of circulant measurement matrix but the subspace energy in spectrally sparse signals. Sparse circulant matrices, whose generating sequence is a sparse sequence, could keep the energy balance of subspaces and have similar noise robustness to deterministic circulant matrices. In addition, sparse circulant matrices have no restriction on length and are more suitable for the compressed sampling of spectrally sparse signals at arbitrary dimensionality.

A Huffman Codes Based Approach to Achieve Minimum Redundancy

2010 ◽  
Author(s):  
J. S. Bhullar ◽  
Parvinder S. Sandhu ◽  
Manish Gupta
Keyword(s):  
Huffman Codes

Structural Exploration of Water, Nitrate/Water, and Oxalate/Water Clusters with Basin-Hopping Method Using a Compressed Sampling Technique

2014 ◽  
Vol 118 (2) ◽  
pp. 508-516 ◽  
Author(s):  
Yi-Rong Liu ◽  
Hui Wen ◽  
Teng Huang ◽  
Xiao-Xiao Lin ◽  
Yan-Bo Gai ◽  
...  
Keyword(s):  
Water Clusters ◽  
Sampling Technique ◽  
Compressed Sampling ◽  
Basin Hopping

scholarly journals Low Complexity Cyclic Feature Recovery Based on Compressed Sampling

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhuo Sun ◽  
Jia Hou ◽  
Siyuan Liu ◽  
Sese Wang ◽  
Xuantong Chen
Keyword(s):  
Signal Reconstruction ◽  
Low Complexity ◽  
Computation Complexity ◽  
Memory Consumption ◽  
Compressed Sampling ◽  
Communication Signal ◽  
Recovery Performance ◽  
Recovery Approach ◽  
High Processing ◽  
Theoretical Analyses

To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing complexity. In this paper, we propose a new cyclic feature recovery approach based on the reconstruction of autocorrelation sequence from sub-Nyquist samples, which can reduce the computation complexity and memory consumption significantly, while the recovery performance remains well in the same compressive ratio. Through theoretical analyses and simulations, we conducted to show and verify our statements and conclusions.

scholarly journals Modified HuffBit Compress Algorithm – An Application of R

2018 ◽  
Vol 15 (3) ◽  
Author(s):  
Nahida Habib ◽  
Kawsar Ahmed ◽  
Iffat Jabin ◽  
Mohammad Motiur Rahman
Keyword(s):  
Dna Sequences ◽  
Binary Tree ◽  
Deoxyribonucleic Acid ◽  
Repetitive Sequences ◽  
Huffman Codes ◽  
R Language ◽  
Living Organisms ◽  
Encoding Method ◽  
Dna Sequence Compression ◽  
Sequence Compression

Abstract The databases of genomic sequences are growing at an explicative rate because of the increasing growth of living organisms. Compressing deoxyribonucleic acid (DNA) sequences is a momentous task as the databases are getting closest to its threshold. Various compression algorithms are developed for DNA sequence compression. An efficient DNA compression algorithm that works on both repetitive and non-repetitive sequences known as “HuffBit Compress” is based on the concept of Extended Binary Tree. In this paper, here is proposed and developed a modified version of “HuffBit Compress” algorithm to compress and decompress DNA sequences using the R language which will always give the Best Case of the compression ratio but it uses extra 6 bits to compress than best case of “HuffBit Compress” algorithm and can be named as the “Modified HuffBit Compress Algorithm”. The algorithm makes an extended binary tree based on the Huffman Codes and the maximum occurring bases (A, C, G, T). Experimenting with 6 sequences the proposed algorithm gives approximately 16.18 % improvement in compression ration over the “HuffBit Compress” algorithm and 11.12 % improvement in compression ration over the “2-Bits Encoding Method”.

Design and analysis of dynamic Huffman codes

1987 ◽  
Vol 34 (4) ◽  
pp. 825-845 ◽  
Author(s):  
Jeffrey Scott Vitter
Keyword(s):  
Huffman Codes
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