This lecture overviews discrete-time Signals and Systems topics. Discrete-time signals are presented: periodic signals, delta signal, unit step signal, exponential signal, trigonometric signals, complex exponential signal. Linear Shift-Invariant (LSI) systems are then presented in detail. 1D convolution and correlation, their properties and several examples are coming next. Finally, FIR and IIR systems are overviewed and… Continue reading Discrete-time Signals and Systems
Archives: Resources
Resource description
Continuous-time Signals and Systems
This lecture overviews continuous-time Signals and Systems topics. Continuous-time signals are presented: periodic signals, delta function, unit step signal, exponential signal, trigonometric signals, complex exponential signal. Linear Time-Invariant (LTI) continuous-time systems are then presented in detail. 1D convolution and correlation, their properties and several examples are coming next. Finally, LTI system description by differential equations… Continue reading Continuous-time Signals and Systems
Introduction to Signals and Systems
This lecture overviews Signals and Systems. 1D signals, 2D signals (images), 3D signals (videos, medical volumes) are presented. Multichannel signals come next. Special signals, e.g., Proteomic sequences, DNA sequences, text sequences are overviewed. Graph signals are presented. Signal processing and signal analysis are defined and their differences from other related disciplines are explained.
Robust Statistics
This lecture overviews Robust Statistics that has many applications in Data Analytics and Digital Signal Processing and Analysis. It covers the following topics in detail: Outliers. Measures of Robustness: Sensitivity Curve (SC), The Influence Function, Breaking Point. Robust Estimators: L – Estimators, M – Estimators, S – Estimators.
Statistical Detection
This lecture overviews Statistical Detection that has many applications in Machine Learning, Signal Analysis and Statistical Communications. It covers the following topics in detail: Signal or Noise Decision, Cost Function, Likelihood Ratio Test, MAP Detector, Neyman-Pearson Hypothesis Testing.
Probability Theory
This lecture overviews Probability Theory that has many applications in a multitude of scientific and engineering disciplines, notably in Pattern Recognition and Machine Learning. It covers the following topics in detail: Probability Space, Bayes theorem. One random variable, cumulative probability functions, probability density functions, expectation operators, mean, variance, functions of random variables, normal, uniform, Laplacian… Continue reading Probability Theory