Sampling and Reconstruction

Related lectures (30)

Page 3 of 3

Explores analog-to-digital conversion using flash converters and weighted currents.

Explains signal sampling, the need for discrete sampling, errors, quantization, and choosing the right sampling period.

Covers the Fourier Transform, properties, periodic signals, and digital signals.

Explores frequency periodicity in the discrete Fourier transform for signal reconstruction.

Covers the principles of digital communication, focusing on the Nyquist Sampling Theorem and signal space dimension.

Explains the basics of Fourier transform and demonstrates its application through examples, including periodic functions and Fourier Transform Pairs.

Covers the basics of signals and systems in communication, including modulation, medical imaging, Fourier analysis, and biological systems.

Covers the basics of the Fourier transform and its applications in signal processing.

Covers the analysis of analog signals, complex impedance, and Fourier series in linear circuits.

Explores discrete Fourier transform, signal reconstruction, sampling interpretation, and periodic signal repetition.