This module offers an introduction to Bayesian networks by means of a worked example of computing a bayesian network from a joint probability distribution (JPD).
An overview of the convergence of the DTFT.
This module will take the ideas of sampling CT signals further by examining how such operations can be performed in the frequency domain and by using a computer.
An overview of the DTFT and Convolution.
This module covers the fundamentals of Discrete-Fourier Transformations.
Definitions and basic algorithms for Fourier analysis for discrete-time signals.
Fourier representation for infinite-length, finite-energy, discrete-time signals.
Review of the discrete time fourier transform.
Details the discrete-time fourier transform.
The module will provide analysis and examples of how a continuous-time signal is converted to a digital signal and processed.
This module focus on examining reconstruction relations.
The FFT, an efficient way to compute the DFT, is introduced and derived throughout this module.
An overview of the concepts behind the Impulse/Dirac Distribution.
You will investigate the effects of windowing and zero-padding on the Discrete Fourier Transform of a signal, as well as the effects of data-set quantities and weighting windows used in Power Spectral Density estimation.
This module looks at writing our DTFS equations in matrix form to make calculations and displaying the basis easier.
Este modulo trata con la traslación de problemas de tiempo continuo a problemas de tiempo discreto.
Two algorithms to detect the fundamental frequency of a signal: one in the time domain (Autocorrelation) and one in the frequency domain (Harmonic Product Spectrum / HPS)
An overview of the relationship between the DFT and the DTFT.
This module deals with translating continuous time problem into discrete time
" This class teaches the fundamentals of signals and information theory with emphasis on modeling audio/visual messages and physiologically derived signals, and the human source or recipient. Topics include linear systems, difference equations, Z-transforms, sampling and sampling rate conversion, convolution, filtering, modulation, Fourier analysis, entropy, noise, and Shannon's fundamental theorems. Additional topics may include data compression, filter design, and feature detection. The undergraduate subject MAS.160 meets with the two half-semester graduate subjects MAS.510 and MAS.511, but Assignments and Labs differ."
