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Performance comparison of instrument automation pipelines using different programming languages
The article presents a performance analysis of fully automated, in-house developed 2D ultrasound computerized tomography systems using different programming languages. The system is fully automated in four programming languages: LabVIEW, MATLAB, C and Python. It includes codes for sensors, instrumen...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10616098/ https://www.ncbi.nlm.nih.gov/pubmed/37903822 http://dx.doi.org/10.1038/s41598-023-45849-y |
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author | Kumar, Ankur Goswami, Mayank |
author_facet | Kumar, Ankur Goswami, Mayank |
author_sort | Kumar, Ankur |
collection | PubMed |
description | The article presents a performance analysis of fully automated, in-house developed 2D ultrasound computerized tomography systems using different programming languages. The system is fully automated in four programming languages: LabVIEW, MATLAB, C and Python. It includes codes for sensors, instruments interfacing, real-time control, synchronized data acquisition, simultaneous raw data processing and analysis. Launch performance, eight performance indices and runtime performance are used for the analysis. It is found that C utilizes the least processing power and executes fewer I/O processes to perform the same task. In runtime analysis (data acquisition and real-time control), LabVIEW (365.69 s) performed best in comparison to MATLAB (623.83 s), Python (1505.54 s), and C (1252.03 s) to complete the experiment without data processing. However, in the experiment with data processing, MATLAB (640.33 s) performed best in comparison to LabVIEW (731.91 s), Python (1520.01 s) and C (1930.15 s). Python performed better in establishing faster interfacing and RAM usage. The study provides a methodology to select optimal programming languages for instrument automation-related aspects to optimize the available resources. |
format | Online Article Text |
id | pubmed-10616098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106160982023-11-01 Performance comparison of instrument automation pipelines using different programming languages Kumar, Ankur Goswami, Mayank Sci Rep Article The article presents a performance analysis of fully automated, in-house developed 2D ultrasound computerized tomography systems using different programming languages. The system is fully automated in four programming languages: LabVIEW, MATLAB, C and Python. It includes codes for sensors, instruments interfacing, real-time control, synchronized data acquisition, simultaneous raw data processing and analysis. Launch performance, eight performance indices and runtime performance are used for the analysis. It is found that C utilizes the least processing power and executes fewer I/O processes to perform the same task. In runtime analysis (data acquisition and real-time control), LabVIEW (365.69 s) performed best in comparison to MATLAB (623.83 s), Python (1505.54 s), and C (1252.03 s) to complete the experiment without data processing. However, in the experiment with data processing, MATLAB (640.33 s) performed best in comparison to LabVIEW (731.91 s), Python (1520.01 s) and C (1930.15 s). Python performed better in establishing faster interfacing and RAM usage. The study provides a methodology to select optimal programming languages for instrument automation-related aspects to optimize the available resources. Nature Publishing Group UK 2023-10-30 /pmc/articles/PMC10616098/ /pubmed/37903822 http://dx.doi.org/10.1038/s41598-023-45849-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kumar, Ankur Goswami, Mayank Performance comparison of instrument automation pipelines using different programming languages |
title | Performance comparison of instrument automation pipelines using different programming languages |
title_full | Performance comparison of instrument automation pipelines using different programming languages |
title_fullStr | Performance comparison of instrument automation pipelines using different programming languages |
title_full_unstemmed | Performance comparison of instrument automation pipelines using different programming languages |
title_short | Performance comparison of instrument automation pipelines using different programming languages |
title_sort | performance comparison of instrument automation pipelines using different programming languages |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10616098/ https://www.ncbi.nlm.nih.gov/pubmed/37903822 http://dx.doi.org/10.1038/s41598-023-45849-y |
work_keys_str_mv | AT kumarankur performancecomparisonofinstrumentautomationpipelinesusingdifferentprogramminglanguages AT goswamimayank performancecomparisonofinstrumentautomationpipelinesusingdifferentprogramminglanguages |