Lectures in course MICRO-311(b): Signals and systems II (for SV)

Explores statistical properties, signal representation, discrete signals, and binary signals.

Explores the canonical representation of discrete signals as a linear combination of dispersed pulses and other related concepts.

Explores discrete signals, linear systems, categorization examples, and convolution properties in signal processing.

Explores the properties and norms of discrete signals in signal processing.

Explores convolution, stability, causal systems, linear systems, and BIBO stability in Signals & Systems II.

Explores the convolution of signals, injectivity, inverse operators, stability, Young inequality, Z-transform, and product property.

Explores Z-transform, system analysis, and signal properties in signal processing.

Explores Z-transform inversion, convergence, and signal properties in discrete time.

Explores difference equations, impulse response, BIBO stability, and inverse operators in signal processing.

Explores characteristic polynomials, stability conditions, homogeneous solutions, and transfer functions in difference equations.

Explores feasible LID systems, rational transfer functions, stability, causality, and implementation parameters in computing.

Explores IIR filter implementations, canonical vs. non-canonical forms, stability, DTFT properties, and eigenvectors in LID systems.

Explores the properties of the discrete Fourier transform and its applications in signal processing, emphasizing DFT reversal and DTFT resampling.

Explores DTFT properties, frequency response analysis, and the ideal low-pass filter in signal processing.

Explores digital filters' implementation, cyclic convolution, FFT-based filtering, and the importance of filtering in signal processing.

Explores DTFT, DFT, FFT properties, and the cost analysis of fast algorithms.

Explores the theory and design of linear phase filters, emphasizing the importance of achieving optimal filter performance.

Explores the synthesis of digital filters from analog filters using the bilinear transformation method and Chebyshev's criterion.

Explores random variables, probability densities, Gaussian distribution, and conditional probabilities in measurement systems.

Explores conditional probabilities, characteristic functions, and optimal detectors in signals and systems.