I spend a majority of my work day using Matlab and writing Matlab code. I work on a variety of projects that involve advanced signal processing and high-fidelity simulations that are all written in Matlab. I also use Matlab for all my day-to-day analysis efforts. This page has a variety of Matlab examples that you may find useful.
The code used in these examples can be download in the Members Only portion of the website
Matlab Examples - The Unit Step Function
Running Time: 4:03
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.
Matlab Examples - The Unit Ramp Function
Running Time: 4:12
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.
Matlab Examples - Linear Combination of Unit Steps and Ramps
Running Time: 4:40
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.
The Eigenvalue Power Method Algorithm in Matlab
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.