MATLAB Fundamentalsor equivalent experience using MATLAB, and a good understanding of signal processing theory, including linear systems, spectral analysis, and filter design
Day 1 of 2
Signals in MATLAB
Objective: Generate sampled and synthesized signals from the command line and visualize them. Create noise signals for a given specification. Perform signal processing operations like resampling, modulation, and correlation.
Creating discrete signals
Sampling and resampling
Performing resampling, modulation, and correlation
Generating streaming signals
Objective: Understand different spectral analysis techniques and the use of windowing and zero padding. Become familiar with the spectral analysis tools in MATLAB and explore nonparametric (direct) and parametric (model-based) techniques of spectral analysis.
Discrete Fourier transform
Windowing and zero padding
Power spectral density estimation
Using a spectrum analyzer in MATLAB
Linear Time Invariant Systems
Objective: Represent linear time-invariant (LTI) systems in MATLAB and compute and visualize different characterizations of LTI systems.
LTI system representations
Frequency and impulse response
Visualizing filter properties
Applying filters to finite and streaming signals
Day 2 of 2
Objective: Design filters interactively using the Filter Design and Analysis app. Design filters from the command line using filter specification objects.
Interactive filter design
Common filter design functions
Filter design with filter specification objects
Reducing filter delay
The Signal Analysis App
Objective: Learn to use a powerful all-in-one app for importing and visualizing multiple signals, performing spectral analysis on them, and designing and applying filters to the signals. Make simple statistical and cursor measurements on signals.
Browse signals and make simple measurements
Perform interactive spectral analysis
Design and apply filters to signals interactively
Objective: Understand principles of polyphase multirate filter design. Design multirate interpolating and decimating filters. Design multistage and narrow-band filters.
Downsampling and upsampling
Noble identities and polyphase FIR structures
Polyphase decimators and interpolators
Design multistage and interpolated FIR filters
Adaptive Filter Design
Objective: Design adaptive filters for system identification and noise cancellation.
Basics of adaptive filtering
Perform system identification
Perform noise cancellation
Improve adaptive filter efficiency
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When you register for one of these courses, you can rely on the fact that it won't be canceled or rescheduled for any reason.
MATLAB and Simulink Course Schedule
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