The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.
A Practical Approach to Introducing Electrical Undergraduates to Artificial Neural Networks
Abstract
Artificial intelligence techniques are increasingly being applied to a variety of problems in the field of electrical engineering, and consequently universities worldwide are paying more attention to training electrical engineers in applying neural networks (ANNs) in practice. As part of the above training, an introductory practical project on ANNs has been offered as an elective to the undergraduates in the author's department. The project introduces a single layer ANN model consisting of two neurons. The algorithm, the hardware implementation and the test procedure concerning one of the neurons, are described. Then the students apply the same techniques for the other neuron and combine the two circuits to form the final ANN hardware. The agreement of the results conducted by the theoretical models with those obtained by the constructed circuits, allow the students to become familiar with ANN hardware techniques and realise that they present a practical and efficient solution.
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“Another driving factor is the importance of equipping students with the skills needed to fill the workforce gap [22]-[24], [29], [31], [34], [38], [39], [41], [43].”
From Integrating AI in engineering education: A comprehensive review and student-informed module design for U.K. students (2025) · cites this paper as [31]
Citations
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Hao, Y., Liu, Y., Liu, B., Amarantidis, G., & Ghannam, R. (2025) · IEEE Transactions on Education · doi:10.1109/te.2025.3536105 · Cited by 32
“Another driving factor is the importance of equipping students with the skills needed to fill the workforce gap [22], [23], [24], [29], [31], [34], [38], [39], [41], [43].”
Artificial Intelligence in Engineering Education in the Case of Self-driving Vehicle Curriculum
Zhou, R., Zhi, P., Xu, X., Liu, Z., Li, X., & Zhou, Q. (2022) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc55140.2022.9922348 · Cited by 8
Project-based learning of Feedforward Neural Networks with a physical pendulum
Ramírez-Martínez, A., López-González, L., Chong-Quero, J. E., Cervantes-Culebro, H., & Cruz-Villar, C. A. (2023) · World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) · doi:10.1109/weef-gedc59520.2023.10344302
Metadata
- Authors
- Halkiadis, I. S.
- Affiliations
- Department of Electrical Engineering, Technological Institution of Chalkida, Psahna, Greece
- Corresponding author
- Halkiadis, I. S.
- Venue
- International Journal of Electrical Engineering & Education
- Publisher
- SAGE Publications
- Published
- 2009-04
- Volume
- 46
- Issue
- 2
- Pages
- 121-132
- ISSN
- 0020-7209 (ISSN-L)
- 2050-4578
- OpenAlex
- W2313776762
- Cited by
- 2
- References
- 26
- Citations by year
- 2023: 1 · 2022: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Electrical Engineering Education (publishedVersion)
- Topics
- Social Sciences › Education › Engineering Education and Pedagogy
- Social Sciences › Education › Experimental Learning in Engineering
- Social Sciences › Education › Undergraduate Neuroscience Education and Research
- Keywords
- artificial neural networks, neural network hardware, single-layer neural network, two-neuron network, electrical engineering education
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