Introduction to Computing in Engineering: ES-2

ES-2 is a course for mainly first-year Tufts engineering students.  Starting in 2015 the course is being redesigned to focus more on the Matlab programming language, and on gaining an understanding of basic numerical methods and their use in engineering applications. At the end of the course:

1) You should have a solid introduction to programming concepts. While the course focuses on the Matlab programming language, the course is intended to teach skills that are transferable across computer languages.
2) You will have been exposed to good R&D programming practices, and should understand how to write clearly structured and well-commented code.
3) You will have gained an introduction to numerical methods that are broadly useful in modeling and data analysis, and have applied these tools to real engineering problems.

Here is an draft of the topics covered: schedule_draft


Digital Signal Processing:  EE-125

I have taught this Fall semester class since 2011.  The course description is as follows:

Discrete signals and systems, digital simulation of analog systems. Z transforms, recursion equations, finite ordersystems. Fourier transforms, line spectra and Fourier series, discrete Fourier series and Fast Fourier Transforms (FFT). Sampling and interpolation, mean square approximations. Non-recursive and recursive filters. Selected topics on algorithms, design and applications of digital signal processing.

The class includes six Matlab projects which have the goal of letting students use DSP concepts in a more applied setting.  In past years, projects have included simulation of reverberation in concert halls (via circular convolution), analysis of biomedical signals, and spectral analysis of ocean acoustic data.

I have a lot of fun with this class.  If you are considering taking it but are not sure, please feel free to contact me.

Linear Systems:  EE-102

I taught this course in Spring 2012.  The course is a prerequisite for EE-125 and introduces transform theory and linear systems theory.