Lectures in course EE-350: Signal processing

Signal Processing: Linear Filters and Estimation

Covers applying concepts to build linear filters and perform estimation.

Digital Filters: Structure and Implementation

Explores digital filters, their implementation, characteristics, and realizations, with examples for illustration.

Digital Filters: Structure and Implementation

Covers the structure and implementation of digital filters.

Mesh Filters: Structures and Parameters

Explores the construction and parameters of mesh filters in signal processing.

Filter Structures: Part 2

Explores the geometric interpretation of filter responses and the challenges of achieving causal filters in real-time signal processing.

RIF Filters: Characteristics and Synthesis

Explores the characteristics and synthesis of RIF filters, including windowing operations and linear phase filters.

Finite Impulse Response Filters: Part 2

Explores the design of Finite Impulse Response filters and the optimization methods for control in signal processing.

Digital Filters: RII Filters - Part 2

Explores RII filters, analog-to-digital filter conversion, stability, and filter construction.

Digital Signal Processing: Filters RII - Part 2

Explores aliasing, sampling, equivalence of analog and digital signals, and digital filter design.

Digital Filters: Integration Equivalence

Covers the integration equivalence method for converting analog filters to digital ones.

Digital Filters: RII Filters - Part 2

Covers digital RII filters, elliptical and Chebyshev filters, frequency transformations, and filter realization.

Digital Filters: Phase Response and Stability

Explores phase response constraints, minimum phase systems, and digital filter design.

Linear Prediction and Estimation

Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.

Estimation and Linear Prediction - Part 2

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

Linear Estimation and Prediction: Part 2

Covers the estimation and prediction of random signals in linear systems.

Linear Prediction and Filtering: Part 2

Explores linear prediction, prediction coefficients, mean squared error minimization, and the Levinson-Durbin algorithm in signal processing.

Linear Estimation and Prediction

Explores linear estimation, Wiener filters, and optimal prediction in signal processing.

Signal Processing: Basics and Spectral Analysis

Covers the basics of signal processing, linear estimation, and digital filters.

Signal Processing: Basic Definitions and Signal Typology

Covers the basic definitions and typology of signals, including analog and digital signals.

Signal Processing: Basic Definitions and Properties

Explores basic signal definitions, properties, and digital signal processing techniques.