Lectures related to Spectral Density and Cross Power Signals

Stochastic Models for Communications

Covers stochastic models for communications, including stationarity, ergodicity, power spectral density, and Wiener filter.

Stochastic Processes: 2nd Order Analysis

Explores stochastic processes, stationarity, ergodicity, and Wiener filtering for image restoration.

Stochastic Processes: Second Order Analysis

Covers the analysis of second-order stochastic processes in communication systems and image restoration using the Wiener filter.

Estimation and Linear Prediction - Part 2

Explores power spectral density, Wiener-Khintchine theorem, ergodicity, and correlation estimation in random signals for signal processing.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Signals & Systems II: Complex Random Vectors and Stationarity

Explores complex random vectors, stationarity, ergodicity, and the analysis of signals.

Dynamical Approaches to Spectral Theory of Operators

Explores ergodic theory, spectral operators, and almost periodic factors in dynamical systems and spectral theory.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Time Series: Estimation and Spectral Representation

Explores time series estimation, spectral representation, and p-variate analysis in depth.

Signals & Systems II: Stationary Signals and Spectral Density

Explores stationary signals, spectral density, cross power, and Gaussian processes.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Continuous-Time Stochastic Processes: Ergodicity Examples

Covers examples of ergodicity in continuous-time stochastic processes, illustrating concepts such as ergodicity and random processes.

Efficiency of Sampling: Ergodicity and Autocorrelation Functions

Explores the efficiency of sampling in molecular dynamics, focusing on ergodicity and autocorrelation functions.

Ergodic properties of low complexity symbolic systems

Explores the influence of complexity on ergodic properties of symbolic systems, presenting the Curtis-Hedlund-Lyndon Theorem and constructions of minimal subshifts.

Linear Time Invariant Filters: Time Series

Explores estimation, ergodicity, sampling, and linear time invariant filters in time series analysis.

Introduction to Quantum Chaos

Covers the introduction to Quantum Chaos, classical chaos, sensitivity to initial conditions, ergodicity, and Lyapunov exponents.

Statistical Signal Processing Tools

Explores statistical signal processing tools for wireless communications, including spectral estimation and signal detection, classification, and adaptive filtering.

Correlations of the Liouville function

Explores correlations of the Liouville function along deterministic and independent sequences, covering key concepts and theorems.

Ergodic properties in exponential dynamics

Explores ergodic properties in exponential dynamics, emphasizing dense orbits and specific functions.