Signal Sampling and Reconstruction

In signal processing, signal sampling and reconstruction refers to the process of sampling known continuous signals and subsequently recovering the original signals from discrete samples. Sampling is the process of discretizing continuous signals at specific time intervals, while reconstruction involves converting discrete signals back into continuous form. This fundamental process plays a critical role in digital signal processing and communication systems, widely applied in audio, video, and other signal processing and transmission scenarios. Through proper sampling and reconstruction techniques, we can preserve essential information of the original signal while enabling subsequent processing and analysis. Key implementation aspects include:

Sampling typically employs analog-to-digital converters (ADCs) with carefully selected sampling rates following the Nyquist criterion (fs ≥ 2fmax). Reconstruction commonly utilizes digital-to-analog converters (DACs) with interpolation algorithms like zero-order hold, linear interpolation, or sinc interpolation for ideal reconstruction. Code implementations often involve functions like scipy.signal.resample() in Python or interp1() in MATLAB for signal reconstruction.

Therefore, mastering signal sampling and reconstruction principles is essential in signal processing, with practical implementations requiring attention to anti-aliasing filters, quantization parameters, and reconstruction filter design.