Lectures in course MICRO-311(a): Signals and systems II (for MT)

Explores the link between DTFT, Z-transforms, and Fourier transforms, vector analogy, and discrete signals.

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

Explores categorization of signals, linear systems, LID, convolution, and impulse response.

Explores discrete signal spaces, non-Euclidean norms, and the distinction between bounded and unrestricted signals.

Explores convolution stability, LID operators, and the Z-transform properties.

Explores properties and stability of signals and systems, including causal systems and compositional stability in convolution.

Explores Z-transform analysis, system properties, and signal functions in signals and systems.

Explores Z transform properties, ROC, inversion, and signal support behavior.

Covers difference equations, z-transform method, system equivalence, and stability criteria.

Explores roots verification, stability, reproduction modeling, Z transform, and LTI systems realization.

Explores feasible LID systems, transfer functions, stability analysis, and IIR filter implementation.

Explores signal processing, Fourier transform, eigenvectors, and parallel implementation in linear systems.

Explores the properties of the Discrete-Time Fourier Transform in signal processing.

Explores discrete signals, Fourier transform, modulation, convolution, DFT properties, and signal periodicity.

Explores the FFT algorithm, cost analysis, and digital filters in frequency analysis.

Explores Fourier Transform, FFT algorithms, DFT implementation cost, and frequency division for efficient computation.

Explores digital filter synthesis, emphasizing frequency response approximation and pole-zero placement.

Explores frequency response, group propagation time, and linear phase filters in signals & systems.

Explores filter synthesis methods and the role of random variables in measurement systems.

Explores conditional probabilities, independence, characteristic functions, and optimal detectors for signal detection.