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

Parameshwaran Ramalingam1, Abolfazl Mehbodniya2, Julian L Webber3

  • 1Department of ECE, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641048, Tamilnadu, India.

Computational Intelligence and Neuroscience
|January 20, 2022
PubMed
Summary

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This summary is machine-generated.

This study introduces a novel subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) method to efficiently compress large telemetric data. The approach enhances compression rates and reduces processing time, improving data handling capabilities.

Area of Science:

  • Data Science
  • Computer Engineering
  • Signal Processing

Background:

  • Telemetric data is large, posing storage and transmission challenges.
  • Existing lossless data compression (LDC) algorithms struggle with the unique characteristics of telemetric data.
  • Efficient compression is crucial for managing large volumes of telemetric information.

Purpose of the Study:

  • To develop a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach.
  • To increase compression rates and decrease compression time for telemetric data.
  • To enhance the computing capabilities in data compression.

Main Methods:

  • Developed two models: one for subsampled averaged telemetry data preprocessing and another for balanced recurrent neural lossless data compression (BRN-LDC).

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  • Implemented subsampling and averaging with an adjustable sampling factor during preprocessing.
  • Utilized a balanced compression interval (BCI) for data encoding based on probability measurements.

    • Main Results:

    • The balancing-based LDC approach effectively reduces compression time.
    • The proposed SB-RNLDC model demonstrates improved dependability in data compression.
    • Experimental results show enhanced computing capabilities compared to existing methodologies.

    Conclusions:

    • The SB-RNLDC approach offers a significant advancement in telemetric data compression.
    • The method provides a practical solution for managing large telemetric datasets.
    • This research contributes to more efficient and reliable data handling in telemetry.