This course provides the basic toolkit for any signal processing application - the abstraction of signals and systems, from the point of view of analysis and characterization. ↓ More info below. ↓
Take this course free on edX: https://www.edx.org/course/signals-systems-part-1-iitbombayx-ee210-1x-1#!
ABOUT THIS COURSE
We encounter signals and systems extensively in our day-to-day lives, from making a phone call, listening to a song, editing photos, manipulating audio files, using speech recognition softwares like Siri and Google now, to taking EEGs, ECGs and X-Ray images. Each of these involves gathering, storing, transmitting and processing information from the physical world. This course will equip you to deal with these tasks efficiently by learning the basic mathematical framework of signals and systems.
This course is divided into two parts. In the first part (EE210.1x), we explore the various properties of signals and systems, characterization of Linear Shift Invariant Systems, convolution and Fourier Transform. Building on that, in the 2nd part (EE210.2x) we will deal with the Sampling theorem, Z-Transform, discrete Fourier transform and Laplace transform. The contents of the first part are prerequisites for doing this part. Ideas introduced in this course will be useful in understanding further electrical engineering courses which deal with control systems, communication systems, power systems, digital signal processing, statistical signal analysis and digital message transmission. The concepts taught in this course are also useful to students of other disciplines like mechanical, chemical, aerospace and other branches of engineering and science.
What you'll learn
- How to unite abstractions for several kinds of systems, to draw a common system description
- How to identify properties that this system has or does not have
- How to deal with an important class of systems namely, linear shift invariant systems
- How to represent and analyze signals and systems in the Fourier domain