Signal Sampling and Reconstruction

Signal sampling and reconstruction is a widely utilized technique in the field of digital signal processing. In signal processing, collected signals are sampled and represented in discrete form. To reconstruct the original continuous signal, interpolation of the sampled signal is required. This process can be accomplished using interpolation formulas, which are mathematical expressions designed to estimate signal values between sampled points. The key algorithm often involves sinc-function-based reconstruction or polynomial interpolation methods like Lagrange or spline interpolation.

In code implementation, this typically requires using functions such as numpy's interp() in Python or interp1() in MATLAB, along with proper handling of sampling rates to satisfy the Nyquist criterion. Therefore, signal sampling and reconstruction technology forms an indispensable component of digital signal processing, ensuring signal integrity and accuracy by properly recovering continuous signals from discrete samples.