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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| Published in: | Computational Intelligence and Neuroscience Vol. 2022; pp. 1 - 10 |
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| Language: | English |
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Hindawi
10.01.2022
Hindawi Limited John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Webber, Julian L. Shabaz, Mohammad Ramalingam, Parameshwaran Gopalakrishnan, Lakshminarayanan Mehbodniya, Abolfazl |
| AuthorAffiliation | 3 Graduate School of Engineering Science, Osaka University, Osaka, Japan 5 Department of Computer Science Engineering, Chandigarh University, Ajitgarh, Punjab, India 2 Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City, Kuwait 6 Department of ECE, National Institute of Technology, Tiruchirappalli, India 1 Department of ECE, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641048, Tamilnadu, India 4 Arba Minch University, Arba Minch, Ethiopia |
| AuthorAffiliation_xml | – name: 4 Arba Minch University, Arba Minch, Ethiopia – name: 1 Department of ECE, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641048, Tamilnadu, India – name: 3 Graduate School of Engineering Science, Osaka University, Osaka, Japan – name: 5 Department of Computer Science Engineering, Chandigarh University, Ajitgarh, Punjab, India – name: 6 Department of ECE, National Institute of Technology, Tiruchirappalli, India – name: 2 Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City, Kuwait |
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| Cites_doi | 10.1371/journal.pone.0206521 10.1109/access.2020.3015493 10.1155/2015/795353 10.1155/2012/539638 10.1016/j.patrec.2020.04.006 10.5194/gmd-12-4099-2019 10.36478/jeasci.2019.831.836 10.1109/TNNLS.2019.2910073 10.1109/JSTSP.2020.2969554 10.1016/j.ress.2019.04.011 10.1109/ets50041.2021.9465460 10.1007/s11390-019-1921-0 10.1109/TCSVT.2018.2881040 10.1002/cpe.4283 10.1109/access.2018.2872778 10.1109/tkde.2011.79 10.1007/s10462-019-09760-1 10.1155/2019/8981240 10.1109/access.2020.3037254 10.1109/tifs.2011.2119314 10.1155/2019/8616215 10.1155/2012/471857 |
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| Copyright | Copyright © 2022 Parameshwaran Ramalingam et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Parameshwaran Ramalingam et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Parameshwaran Ramalingam et al. 2022 |
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| References | 22 23 25 26 L. Luo (24) 2019; 125 H. Yan (17) 2019; 20 10 12 13 14 16 19 X. Delaunay (18) 2018; 12 D. Tao (11) 2018; 13 1 2 3 4 5 6 7 A. Guerra (15) 2018; 13 8 9 20 21 38124854 - Comput Intell Neurosci. 2023 Dec 13;2023:9830360 |
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| SubjectTerms | 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 |
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| Title | Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning |
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