Signal Processing: Basic Definitions and Properties

Related lectures (30)

Page 3 of 3

Covers Analog-Digital and Digital-Analog conversion, including sampling, quantization, Flash converter, weighted current generation, and R2R network.

Explores quantization and coding of digital signals, discussing uniform quantization, error analysis, and signal-to-quantization noise ratio.

Explores neural signals, EMG control, muscle synergies, and HD-EMG for prosthetic devices.

Covers the generation of signals with noise, sampling, and conversion to digital.

Explores sampling, signal representation, point transformations, and signal vector spaces in the context of signals and systems.

Explores the sampling of pure sinusoids, emphasizing the Nyquist theorem and its practical implications.

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

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

Covers the sampling theorem, impulse train sampling, bandlimited signals, and the Nyquist rate.

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