Introduction to Discrete-Time Signals and Systems
Discrete-time convolution sum and example
Lec-1 Discrete Time Signal and System
Lecture 11, Discrete-Time Fourier Transform | MIT RES.6.007 Signals and Systems, Spring 2011
Continuous-time and Discrete-time Signals
Lecture 19, Discrete-Time Sampling | MIT RES.6.007 Signals and Systems, Spring 2011
Lecture 10, Discrete-Time Fourier Series | MIT RES.6.007 Signals and Systems, Spring 2011
(ML 14.2) Markov chains (discrete-time) (part 1)
2. Discrete-Time (DT) Systems
Introduction to the DT Fourier Transform
Lecture 18, Discrete-Time Processing of Continuous-Time Signals | MIT RES.6.007 Signals and Systems
Lecture - 8 Discrete Time Fourier Transform
Discrete Time Fourier Series Example
Control Systems Engineering - Lecture 13 - Discrete Time and Non-linearity
Introduction to Discrete-Time Signals and Systems
Discrete-time convolution sum and example
Lec-1 Discrete Time Signal and System
Lecture 11, Discrete-Time Fourier Transform | MIT RES.6.007 Signals and Systems, Spring 2011
Continuous-time and Discrete-time Signals
Lecture 19, Discrete-Time Sampling | MIT RES.6.007 Signals and Systems, Spring 2011
Lecture 10, Discrete-Time Fourier Series | MIT RES.6.007 Signals and Systems, Spring 2011
(ML 14.2) Markov chains (discrete-time) (part 1)
2. Discrete-Time (DT) Systems
Introduction to the DT Fourier Transform
Lecture 18, Discrete-Time Processing of Continuous-Time Signals | MIT RES.6.007 Signals and Systems
Lecture - 8 Discrete Time Fourier Transform
Discrete Time Fourier Series Example
Control Systems Engineering - Lecture 13 - Discrete Time and Non-linearity
Signal Processing Tutorial: Discrete-Time Convolution Examples (Part 1 - Intro)
{Classification of Signals and Systems] Continuous-Time Signal to Discrete-Time Signal
DT Convolution-Simple Example Part 1
Lecture 23, Mapping Continuous-Time Filters to Discrete-Time Filters | MIT RES.6.007
Lec-3 Discrete Time Signal and System(Contd...)
04 - Discrete Time Fourier Transform
Digital Signal Processing 2: Discrete-Time System - Prof E. Ambikairajah
Lec-2 Discrete Time Signal and System(Contd...)
Tutorial: Discrete-time sinusoids
3. Elementary Signals in the Discrete Time Domain
Lecture-12 Representation of Discrete Time Convolution
Lecture - 38 Discrete - Time Systems (1)
Lecture - 8 Discrete Time Dynamical Systems
Lecture - 14 Discrete Time Systems in the Frequency Domain
Lecture 15, Discrete-Time Modulation | MIT RES.6.007 Signals and Systems, Spring 2011
18. Discrete-Time (DT) Fourier Representations
Lecture - 41 Discrete - Time Systems (4)
19. More About Fourier Transform of Discrete Time Signals
Lec-9 Relation Between Discrete Time and Continuous Signals
Discrete systems - English
EE603 Lecture 07 (9/15/14) - Random Sequences (Part 4)
How to find the Integration of Discrete-Time Signals with Simulink
Discrete-time Dynamical System 05 (Determinacy/Indeterminacy)
Discrete-time Dynamical System 04 (Diagonalization)
Aerospace Engineering Video Tutorial 14 Discrete time Optimal Control
Discrete-time Dynamical System 02 (Linear approximation)
Discrete-time Dynamical System 03 (Variable stacking)
Discrete-time Dynamical System 01
One-Layer Continuous-and Discrete-Time Projection Neural Networks | IEEE | IEEE projects 2014
Discrete-Time Signal Processing [With Access Code] (Prentice Hall Signal Processing) VIDEO - OFERTAS
Discrete Time Unit Step and Unit Impulse
Discrete time processing and continuous time signals | Digital Signal Processing
Discrete time signals | Digital Signal Processing
Discrete Fourier transform
Fundamental of Electrical Engineering: W7L1 Discrete Time Spectral Analysis
Fundamental of Electrical Engineering: W6L4 Discrete Time Signals and Systems
Mod-01 Lec-26 Discrete time dynamics (Part V)
Mod-01 Lec-18 Discrete time dynamics(Part II)
Mod-01 Lec-20 Discrete time dynamics (Part IV)
Mod-01 Lec-19 Discrete time dynamics (Part III)
Mod-01 Lec-17 Discrete time dynamics (Part I)
Signal & Systems- Lecture 34 - Properties of Discrete Time Fourier Transform (NPTEL)
Discrete time is the discontinuity of a function's time domain that results from sampling a variable at a finite interval. For example, consider a newspaper that reports the price of crude oil once every day at 6:00AM. The newspaper is described as sampling the cost at a frequency of 24 hours, and each number that's published is called a sample. The price is not defined by the newspaper in between the times that the numbers were published. Suppose it is necessary to know the price of the oil at 12:00PM on one particular day in the past; one must base the estimate on any number of samples that were obtained on the days before and after the event. Such a process is known as interpolation. In general, the sampling period in discrete-time systems is constant, but in some cases nonuniform sampling is also used.
Discrete-time signals are typically written as a function of an index n (for example, x(n) or xn may represent a discretisation of x(t) sampled every T seconds). In contrast to Continuous signal systems, where the behaviour of a system is often described by a set of linear differential equations, discrete-time systems are described in terms of difference equations. Most Monte Carlo simulations utilize a discrete-timing method, either because the system cannot be efficiently represented by a set of equations, or because no such set of equations exists. Transform-domain analysis of discrete-time systems often makes use of the Z transform.