Abstract
Working and learning remotely has been a rising and steadily increasing phenomenon. Of particular interest is the conducting of laboratory experiments over remote facilities. The ever-evolving electronic systems application requirements and the dynamic operating environments have necessitated the need for configurable systems. Internet of Things (IoT) is now driving the industrial revolution 4.0 and hence learning remotely is inevitable. In the same vein, configurable remote laboratories are an important aspect of the electronic engineering revolution. Configurability enables laboratory platforms to run with multiple sets of hardware and to design systems capable of handling future developments and plugins, bringing more support to students performing laboratory work during the Covid-19 era. This is a study to develop a relatively inexpensive, configurable, and cloud-based remote laboratory platform for electronic engineering students. The laboratory platform provides real-time interaction with two myRIO devices. The devices are used as the instructor’s hardware experimental setup and are in the faculty laboratory and functioning as IoT nodes. LabVIEW myRIO toolkit was used to design the finite state machines which contain the lab applications. Each device consists of a tri-state finite state machine with each state being a laboratory session. Six basic electronic engineering experiments were used, and students can access them from any internet-connected device through common browsers. The system can be configured to have more myRIO devices and more experiments per device.
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Acknowledgments
My heartfelt gratitude goes to NI Romania for their deep support in this research. I would also like to thank Prof.Dr. Doru URSUTIU, my supervisor, for all his efforts to secure the kits and for being helpful in the writing of this report.
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Chamunorwa, T., Ursuțiu, D., Samoilă, C., Hedesiu, H., Modran, H.A. (2023). Software Configurable Hardware-Based Remote Laboratory System. In: Auer, M.E., El-Seoud, S.A., Karam, O.H. (eds) Artificial Intelligence and Online Engineering. REV 2022. Lecture Notes in Networks and Systems, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-031-17091-1_2
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DOI: https://doi.org/10.1007/978-3-031-17091-1_2
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