👉 Multiplicative Harmonic (MH) math is a powerful technique used in various fields, including signal processing, control systems, and machine learning, to analyze and manipulate signals or systems by focusing on their frequency components. The core idea is that instead of working with the time-domain representation of a signal, which can be complex and sensitive to noise, MH math operates on the frequency-domain representation. This is achieved by computing the inverse Fourier transform of the product of the signal's power spectral density (PSD) with a set of complex exponentials, known as the MH basis functions. These basis functions are designed to capture the essential features of the signal across different frequencies, allowing for a more efficient and robust analysis. By concentrating on the most significant frequency components, MH math simplifies the representation of signals and systems, making it easier to design filters, estimate parameters, and perform other signal processing tasks. This approach is particularly useful in scenarios where computational efficiency and noise reduction are critical, such as in wireless communications and biomedical signal processing.
