An editor has nominated this article for deletion. You are welcome to participate in the deletion discussion, which will decide whether or not to retain it. |
This article is an orphan, as no other articles link to it. Please introduce links to this page from related articles; try the Find link tool for suggestions. (October 2024) |
Do not use {{Draft article}} in mainspace Signal processing is a mathematical approach to manipulating signals for various applications, particularly in communication systems. This processing may involve the transfer of signals from a transmitter to a receiver, where the system is commonly referred to as a communication system. In such systems, signal processing includes tasks such as limiting the baseband signal in terms of frequency and amplitude, modulating the baseband signal with a carrier wave, demodulating the signal at the receiving end, enhancing the quality of weak received signals, and selecting a specific channel from multiple channels.
The complexity of signal processing can vary significantly, leading to the development of various algorithms aimed at reducing this complexity. One strategy for complexity reduction is the exploitation of repetitiveness in operations. Additionally, changing the domain of signal processing can also simplify the processing tasks. For instance, the original domain for speech signals is typically the time domain, represented in a time-amplitude format, whereas the original domain for images is the spatial domain, where intensity levels are represented as functions of two spatial coordinates.
However, the representation of a signal in its original domain is not always optimal for signal processing applications. The process of transforming a signal from one representation to another through mathematical transformations is known as a "transform." Depending on the application, the original domain may be more suitable for signal manipulation, while in other cases, the transform domain may provide advantages for processing. For non-stationary signals, time-frequency domain representations may offer the best approach for specific applications. This paper aims to discuss various transforms, time-frequency representations, and their advantages in signal processing applications.[1]