FROM THEORY TO PRACTICE: THE APPLICATION OF WAVELET TRANSFORM IN REAL-TIME ENGINEERING PROCESSES

FROM THEORY TO PRACTICE: THE APPLICATION OF WAVELET TRANSFORM IN REAL-TIME ENGINEERING PROCESSES

The Wavelet Transform has emerged as a pivotal analytical tool in modern engineering, adept at offering a nuanced approach to time-frequency analysis of signals across diverse applications. This paper delves into the integration of wavelet transforms into real-time engineering processes, highlighting their advantages over traditional Fourier methods for handling non-stationary signals. We begin with a foundational review of wavelet transform theory, emphasizing its capacity for localized analysis through both continuous and discrete forms. Practical implementations of various wavelet families, such as Haar, Daubechies, and Morlet, are explored, underscoring their benefits in computational efficiency, signal fidelity, and real-time applicability. We particularly focus on their applications in Air Quality Monitoring and heart disease analysis, showcasing how wavelet-based systems enhance performance in these critical areas. The integration of wavelet transforms with machine learning algorithms is also examined, illustrating pioneering advancements in predictive analytics and automated systems. This study not only bridges theoretical concepts with empirical applications but also sets the stage for future innovations in real-time signal processing, particularly in environmental and biomedical engineering.