Information technology. Adaptive lossless data compression algorithm (ALDC)

As a lossless data compression coding, Huffman coding is widely used in text compression. Nevertheless, the traditional approach has some deficiencies. For example, same compression on all characters may overlook the particularity of keywords and special statements as well as the regularity of some statements. In terms of this situation, a new data compression algorithm based on semantic analysis is proposed in this paper. The new kind of method, which takes C language keywords as the basic element, is created for solving the text compression of source files of C language. The results of experiment show that the compression ratio has been improved by 150 percent roughly in this way. This method can be promoted to apply to text compression of the constrained-language.

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USE OF NEW EFFICIENT LOSSLESS DATA COMPRESSION METHOD IN TRANSMITTING ENCRYPTED BAPTISTA SYMMETRIC CHAOTIC CRYPTOSYSTEM DATA

Jurnal Teknologi ◽

10.11113/jt.v78.8976 ◽

2016 ◽

Vol 78 (6-4) ◽

Author(s):

Muhamad Azlan Daud ◽

Muhammad Rezal Kamel Ariffin ◽

S. Kularajasingam ◽

Che Haziqah Che Hussin ◽

Nurliyana Juhan ◽

...

Keyword(s):

Data Compression ◽

Data Transmission ◽

Compression Algorithm ◽

Huffman Coding ◽

Compression Method ◽

Compression Technique ◽

Chaotic Dynamical System ◽

Fast Encoding ◽

Compression Mechanism ◽

Lossless Data Compression

A new compression algorithm used to ensure a modified Baptista symmetric cryptosystem which is based on a chaotic dynamical system to be applicable is proposed. The Baptista symmetric cryptosystem able to produce various ciphers responding to the same message input. This modified Baptista type cryptosystem suffers from message expansion that goes against the conventional methodology of a symmetric cryptosystem. A new lossless data compression algorithm based on theideas from the Huffman coding for data transmission is proposed.This new compression mechanism does not face the problem of mapping elements from a domain which is much larger than its range.Our new algorithm circumvent this problem via a pre-defined codeword list. The purposed algorithm has fast encoding and decoding mechanism and proven analytically to be a lossless data compression technique.

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Hybrid indexes for repetitive datasets

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2013.0137 ◽

2014 ◽

Vol 372 (2016) ◽

pp. 20130137 ◽

Cited By ~ 16

Author(s):

H. Ferrada ◽

T. Gagie ◽

T. Hirvola ◽

S. J. Puglisi

Keyword(s):

Dna Sequencing ◽

Data Compression ◽

Simple Technique ◽

Upper Bounds ◽

Compression Algorithm ◽

Original Text ◽

Human Genomes ◽

Lossless Data Compression

Advances in DNA sequencing mean that databases of thousands of human genomes will soon be commonplace. In this paper, we introduce a simple technique for reducing the size of conventional indexes on such highly repetitive texts. Given upper bounds on pattern lengths and edit distances, we pre-process the text with the lossless data compression algorithm LZ77 to obtain a filtered text, for which we store a conventional index. Later, given a query, we find all matches in the filtered text, then use their positions and the structure of the LZ77 parse to find all matches in the original text. Our experiments show that this also significantly reduces query times.

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A Lossless Data Compression Algorithm for Wireless Sensor Networks Based on Linear Regression Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.2441 ◽

2011 ◽

Vol 403-408 ◽

pp. 2441-2444

Author(s):

Hong Zhi Lu ◽

Xue Jun Ren

Keyword(s):

Linear Regression ◽

Regression Model ◽

Data Compression ◽

Linear Regression Model ◽

Compression Algorithm ◽

Sensor Data ◽

Simple Linear Regression ◽

Compression Algorithms ◽

One Dimensional ◽

Lossless Data Compression

According to the theory of simple linear regression model, this paper designed a lossless sensor data compression algorithm based on one-dimensional linear regression model. The algorithm computes the linear fitting values of sensor data’s differences and fitting residuals, which are input to a normal distribution entropy encoder to perform compression. Compared with two typical lossless compression algorithms, the proposed algorithm indicated better compression ratios.

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