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

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...

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Vydáno v:Computational Intelligence and Neuroscience Ročník 2022; s. 1 - 10
Hlavní autoři: Ramalingam, Parameshwaran, Mehbodniya, Abolfazl, Webber, Julian L., Shabaz, Mohammad, Gopalakrishnan, Lakshminarayanan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Hindawi 10.01.2022
Hindawi Limited
John Wiley & Sons, Inc
Témata:
Algorithms
Analysis
Clustering
Compression
Compression ratio
Data Compression
Data processing
Data transmission
Deep Learning
Energy efficiency
Ground stations
Information processing
Lossless equipment
Machine learning
Neural networks
Neural Networks, Computer
Preprocessing
Recurrent neural networks
Research Article
Sensors
Telemetry
ISSN:1687-5265, 1687-5273, 1687-5273
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Popis
Shrnutí: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.
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Academic Editor: Suneet Kumar Gupta
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/4886586