NOISE IMMUNITY IN DATA COMPRESSION INFORMATION AND MEASUREMENT SYSTEMS

2021 ◽  
Vol 75 (3) ◽  
pp. 100-107
Author(s):  
B.-B.S. Yesmagambetov ◽  
Keyword(s):  
Data Compression ◽  
Noise Immunity ◽  
Observation Interval ◽  
Measuring System ◽  
Parametric Methods ◽  
Measurement Systems ◽  
Huge Data ◽  
The Difference ◽  
Compressed Data ◽  
Non Parametric

When processing huge data streams in information systems, individual measurements or whole groups of measurements can be distorted or lost due to various reasons. Recovery of compressed data during transmission on communication channels is accompanied by errors related to distortion of information and service parts of messages due to presence of interference in transmission channel. To these errors are added errors caused by quantization of the transmitted implementations by level and time sampling. Research on methods of increasing noise immunity both during transmission and during recovery of measured data is an urgent task in the design of information and measurement systems. The article considers non-parametric methods of estimating probabilistic characteristics of random processes. A distinctive feature of non-parametric methods is the ranking of data measured at the observation interval. It is shown that ranking of data on transmitting side of information-measuring system enables correction of errors and failures based on strict monotony of ranked number of codes. Also, the error of recovery of continuous implementations taking into account distortions of compressed data in the communication channel was investigated. The obtained results indicate that the use of complex compression algorithms is impractical, since the difference in the error in the restoration of non-stationary messages between the simplest algorithm and the rather difficult one becomes negligible. The article presents the results of estimating recovery errors for various data compression methods.

ExypnoSteganos - A smarter approach to steganography

2021 ◽  
pp. 1-12
Author(s):  
Gaurav Sarraf ◽  
Anirudh Ramesh Srivatsa ◽  
MS Swetha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Building Block ◽  
Communication Techniques ◽  
Systems Security ◽  
Learning Technique ◽  
Critical Problems ◽  
Sporadic Cases ◽  
Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression

2010 ◽  
Vol 56 (4) ◽  
pp. 351-355
Author(s):  
Marcin Rodziewicz
Keyword(s):  
Data Compression ◽  
Channel Coding ◽  
Data Stream ◽  
Noisy Channels ◽  
Error Resiliency ◽  
Joint Source Channel Coding ◽  
Lossless Data Compression ◽  
Compressed Data ◽  
Source Channel Coding

Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression Limitations on memory and resources of communications systems require powerful data compression methods. Decompression of compressed data stream is very sensitive to errors which arise during transmission over noisy channels, therefore error correction coding is also required. One of the solutions to this problem is the application of joint source and channel coding. This paper contains a description of methods of joint source-channel coding based on the popular data compression algorithms LZ'77 and LZSS. These methods are capable of introducing some error resiliency into compressed stream of data without degradation of the compression ratio. We analyze joint source and channel coding algorithms based on these compression methods and present their novel extensions. We also present some simulation results showing usefulness and achievable quality of the analyzed algorithms.

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