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Microsystems, Electronics and Acoustics
ISSN 2523-4447 · e‑ISSN 2523-4455 Open Access · CC BY-SA 4.0
Vol. 31 · Issue 1 · 2026 Apr 27, 2026 Review articles

Software Support for the Higher Mathematics Course at the Technical University: Part 3. GNU Octave

DV
Dmytro Voitsikh National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Ukraine
DH
Danylo Hryn National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Ukraine
MH
Mariia Hryniuk National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Ukraine
OB
PhD, Assoc. Prof. Oleksii Viktorovych Bogdanov Corresponding National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Ukraine
YB
PhD, Assoc. Prof. Yurii Pavlovych Butsenko National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Ukraine
OB
PhD, Assoc. Prof. Olena Ivanivna Balina Kyiv National University of Construction and Architecture Ukraine
IB
PhD, Assoc. Prof. Iryna Srhiivna Bezklubenko Kyiv National University of Construction and Architecture Ukraine
Pages359022.1-359022.10 PublishedApr 27, 2026 LicenseOpen Access
VOL 31 · ISS 1 · 2026 View issue

Abstract

This paper explores a methodological approach to modernizing the higher mathematics curriculum for engineering students, with a specific focus on the needs of “G5 Electronics, electronic communications, instrumentation and radio engineering” specialty. The study addresses the growing challenge posed by the "black box" paradigm, in which students rely on automated online calculators and artificial intelligence tools without comprehending the underlying mathematical logic or algorithmic structures. To counteract this trend, the authors propose integrating GNU Octave, an open-source computational environment, as a primary tool to bridge the gap between abstract theory and professional engineering practice. The article demonstrates the practical implementation of GNU Octave across three critical mathematical domains: Integral Calculus, emphasizing symbolic and numerical methods for signal processing applications; Multivariable Functions, focusing on 3D visualization techniques (using meshgrid and surf) for modeling spatial physical fields and potential distributions; Differential Equations, utilizing numerical solvers like ode45 to simulate transient processes in electronic circuits. The results suggest that transitioning from simplified automated tools to script-based computational modeling fosters a conscious mastery of mathematical concepts. This approach ensures that future engineers develop the analytical and programming competencies needed for complex system optimization and professional modeling in modern electronics.

Keywords

References

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#Educational technology #Hybrid learning #Electronic learning #Engineering education
p. 328241.1-328241.7
10.20535/2523-4455.mea.328241

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