Lectures related to Continuous-Time Stochastic Processes: Linear System

Wiener Filter for Discrete-Time Stochastic Processes

Covers the theory and application of the Wiener filter for discrete-time stochastic processes.

Linear Time Invariant Systems: Impulse Response and Convolution

Covers linear time invariant systems, focusing on impulse response and convolution.

Signal and System Analysis: Impulse Response and Convolution

Covers the analysis of LTI systems using impulse response and convolution.

Wiener Filtering: Theory and Applications

Covers the theory and applications of Wiener filtering in signal processing.

Signals and Systems: Convolution and Fourier Transform

Covers convolution and Fourier transforms in linear time-invariant systems.

Introduction to Signals and Systems: Course Overview

Introduces the EPFL course on Signals and Systems, covering course structure, evaluation, and key engineering concepts.

Neural Signals and Signal Processing

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

Signals and Systems: Sampling Theorem and Applications

Discusses the sampling theorem and its applications in signal processing.

Signals & Systems II: Roots, Equations, and Impulse Responses

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

Fourier Transform and Differential Equations

Discusses the Fourier transform and its application to solving differential equations, focusing on the wave equation and its transformations.

Neural Signals and Signal Processing

Delves into neural signals, GLM, ANOVA, brain mapping, connectivity, and multivariate methods.

Discrete Signals and Linear Systems

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

Laplace Transform Basics

Covers the Laplace transform basics, including examples of complex-valued poles and its application to LTI systems.

LTI Systems Analysis

Covers the analysis of LTI communication systems through Fourier transforms and system stability.

Z-Transforms: Poles and Zeros

Explores Z-Transforms, Poles, Zeros, and their applications in discrete-time systems and Linear Time-Invariant systems.

Continuous-time Fourier Transform

Covers the continuous-time Fourier transform, its properties, and applications to stable LTI systems.

Poisson Process: Impulsive Noise

Covers the Poisson process, focusing on impulsive noise and shot noise models.

Neural Signals and Signal Processing

Explores nuclear magnetic resonance, MRI principles, pulse sequences, image reconstruction, safety considerations, and volume normalization in brain imaging.

Estimation and Linear Prediction - Part 2

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

Neural Signals and Signal Processing: Hemodynamic Imaging

Explores hemodynamic imaging techniques to measure brain activity and understand brain structure and function.