👉 Signal computing is a paradigm that focuses on the efficient processing of signals, particularly in digital systems, by treating them as continuous-time signals rather than discrete events. This approach leverages the principles of continuous mathematics, such as differential equations and complex analysis, to model and manipulate signals in a more natural and often more efficient manner. Signal computing aims to optimize the design of digital systems by exploiting the inherent properties of continuous signals, such as smoothness and differentiability, to reduce computational complexity and improve performance. Techniques like the Fast Fourier Transform (FFT) and other algorithms for signal processing are fundamental in this field, enabling rapid transformations between the time and frequency domains to filter, compress, and analyze signals with greater precision and speed. This methodology is widely used in various applications, including telecommunications, image processing, and audio analysis, where the ability to handle continuous signals effectively is crucial.
