## Matlab Tutorial (15 videos)

Sampling a Sinusoid

5/15/14

Running Time: 4:54

This example considers impulse sampling of a sinusoid, but all analytic work is performed in Matlab.  The example demonstrates the concept of aliasing and how the aliased frequency can be computed. You can download the code used in this example in the Members Area of the site.

The Unit Step Function

2/14/15

Running Time: 4:03

This example plots several different unit step functions using a unitStep function written for Matlab.  This function can be used to easily generate different step functions as needed.  You can download the code used in this example in the Members Area of the site.

The Ramp Function

2/17/15

Running Time: 4:12

This example plots several different ramp functions using a "ramp" function written for Matlab.  This function can be used to easily generate different ramp functions as needed by specifying their start time and slope.  This simple function is often used in linear systems classes to construct other signals. You can download the code used in this example in the Members Area of the site.

Linear Combination of Unit Steps and Ramps

2/23/15

Running Time: 4:40

This example construct and plots a signal x(t) that is a linear combination of various time-shifted unit step and ramp functions.   Each component of the signal is constructed using the "ramp" and "unitStep" functions detailed in the previous videos.  The final signal is constructed by summing all components of the signal to form the final x(t).  This type of signal (i.e. a linear combination of unit steps and ramps) is often analyzed in undergraduate linear systems courses. You can download the code used in this example in the Members Area of the site.

The Eigenvalue Power Method Algorithm in Matlab

7/9/18

Running Time: 6:14

This video shows how to implement the eigenvalue power method algorithm in Matlab.  The script that's examined shows a plot of the matrix eigenvalue and eigenvector estimate as a function of algorithm iteration and how the estimated values converge to the true values of the matrix.

Different experiments are performed with different initial guesses for the algorithm and with matrices with different eigenvalues. As expected, as the difference between eigenvalues becomes smaller, the algorithm takes longer to converge.