Signal reconstruction is the process of recovering a signal from its sampled or distorted version. This is essential in various fields, such as telecommunications and audio processing, where the original signal needs to be restored for accurate interpretation. Techniques like interpolation and filtering are commonly used to fill in gaps or reduce noise in the data. In digital signal processing, Nyquist-Shannon sampling theorem plays a crucial role in ensuring that a signal can be accurately reconstructed from its samples. By sampling at a rate higher than twice the highest frequency of the signal, it becomes possible to retrieve the original waveform without losing important information.