Lossless data compression algorithm for satellite packet telemetry data

2013 ◽  
Author(s):  
Li Guojun ◽  
Shi Jian ◽  
Zhang Running
Keyword(s):  
Data Compression ◽  
Compression Algorithm ◽  
Telemetry Data ◽  
Lossless Data Compression

Study on Data Compression Algorithm Based on Semantic Analysis

2013 ◽  
Vol 842 ◽  
pp. 712-716
Author(s):  
Qi Hong ◽  
Xiao Lei Lu
Keyword(s):  
Data Compression ◽  
Semantic Analysis ◽  
Traditional Approach ◽  
Basic Element ◽  
Compression Algorithm ◽  
Huffman Coding ◽  
Text Compression ◽  
C Language ◽  
Lossless Data Compression ◽  
Compression Coding

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.

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

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.

scholarly journals Hybrid indexes for repetitive datasets

2014 ◽  
Vol 372 (2016) ◽  
pp. 20130137 ◽  
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.

scholarly journals Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Parameshwaran Ramalingam ◽  
Abolfazl Mehbodniya ◽  
Julian L. Webber ◽  
Mohammad Shabaz ◽  
Lakshminarayanan Gopalakrishnan
Keyword(s):  
Data Compression ◽  
Research Work ◽  
High Compression Ratio ◽  
Telemetry Data ◽  
Compression Time ◽  
Short Processing Time ◽  
Final Output ◽  
Lossless Data Compression ◽  
Association Data ◽  
Probability Measurement

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

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

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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