Integration of mathematical signal analysis methods into the professional training of students at technical universities

Abstract

The article discusses the pedagogical aspects of integrating mathematical methods of signal analysis into the training of technical university students in the context of modern energy-efficient technologies and Internet of Things (IoT) systems. The relevance of the study is due to the growing demands on engineers to be able to work with large amounts of real data, perform analytical processing, and make informed engineering decisions in the field of computer-integrated heating control systems for “smart homes.” Particular attention is paid to the problem of insufficient practical orientation of teaching mathematical disciplines in technical education and the gap between theoretical knowledge and real engineering tasks.

The purpose of the article is to justify and experimentally verify a step-by-step methodology for teaching student’s mathematical methods of analyzing heating parameters based on real IoT data using Fourier and wavelet transforms. The work applies competency-based, interdisciplinary, and practice-oriented approaches to teaching. The proposed methodology involves a sequential transition from the analysis of time temperature signals to spectral and time-frequency analysis, with the subsequent use of the results obtained to optimize the operation of heating systems.

The article defines a list of key mathematical methods necessary for analyzing heating parameters in IoT systems and establishes their connection with specific laboratory work. A complete cycle of classes based on real or near-real data from a “smart home” is described. Considerable attention is paid to the physical and engineering interpretation of the spectral characteristics of temperature signals, which contributes to the formation of systematic engineering thinking in students.

The pedagogical effectiveness of the methodology is confirmed by the results of an experimental study using quantitative indicators: levels of professional competence, results of educational testing, and assessment of practical skills. The data obtained indicate a statistically significant increase in the level of student training, an improvement in the quality of mastery of mathematical methods, and the ability to apply them to solve real engineering problems in the field of energy-efficient IoT systems.