Abstract
AbstractThe 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.
Funder
Council of Scientific and Industrial Research, India
DST-SERB: IMPRINT-2
Publisher
Springer Science and Business Media LLC
Reference35 articles.
1. Ugurlu, Y. Measuring the impact of virtual instrumentation for teaching and research. In 2011 IEEE Global Engineering Education Conference (EDUCON), 4–6 April 2011 152–158 (2011). https://doi.org/10.1109/EDUCON.2011.5773129.
2. Adeagbo, A., Ofoegbu, E., Dada, T. & Adegboye, L. Development of a micro-controller based automation system for residential use. N. Y. Sci. J. 14(8), 46–52 (2021).
3. Amine, B. M., Zohra, C. F., Ilyes, H., Lahcen, A. & Tayeb, A. Smart home automation system based on Arduino. IAES Int. J. Robot. Autom. 7(4), 215 (2018).
4. Dymora, P. & Paszkiewicz, A. Performance analysis of selected programming languages in the context of supporting decision-making processes for industry 4.0. Appl. Sci. 10(23), 8521 (2020). https://www.mdpi.com/2076-3417/10/23/8521.
5. Mahmoud, M. S., Sabih, M. & Elshafei, M. Using OPC technology to support the study of advanced process control. ISA Trans. 55, 155–167. https://doi.org/10.1016/j.isatra.2014.07.013 (2015).