Digital Signal Processing and Analysis Web Lecture Module

DESCRIPTION

Digital signals are ubiquitous and have many applications in several disciplines:

1. Digital Media, Social Media (music, voice signals),
2. Biomedical Signal Analysis and Diagnosis,
3. Bioinformatics
4. Autonomous cars, drones, marine vessels, robots
5. Big Data Analytics,
6. Internet and Communications (music broadcasting, streaming).
7. Scientific signal acquisition of any sort, e.g., Environment Sensing, Geophysical Prospecting.

This CVML Web Module focuses on Digital Signal Processing and Analysis and their applications. Digital filter design will be presented first, followed by Digital Filter Structures. Error analysis in digital filters is important in many applications. Adaptive Digital Filters are also presented, as they play an important role in Autonomous Systems. Spectral Signal Analysis is important for  power spectrum estimation.

Digital filter structure.

LECTURE LIST

1. FIR Filter Design
2. Digital Filter Structures
3. Fast 1D Convolution Algorithms
4. Error Analysis in Digital Filters
5. Adaptive Digital Filters
6. Spectral Signal Analysis
7. Hidden Markov Models

CVML WEB LECTURE MODULE SCHEDULE

This module has been designed to be mastered within 1 month (or less), if you have proper background (at least early undergraduate student in an EE, ECE, CS, CSE or any Engineering or Exact Sciences Department).
We propose that you follow the above mentioned  Lecture order. You may want to skip few Lectures that might not be of immediate interest to you for later study.

On average you can study 4 lectures per week. The related effort is as follows:
1) Lecture pdf study and filling the related understanding questionnaire: 1-2 hours per lecture (on average, depending on your background)
2) Tutorial exercise (if available): 1/2 hour on average (more if you do not have theoretical skills). We strongly recommend to try solve them yourself, before resorting to the existing solution.
3) Programming exercise (if available): 3-4 hours on average (more if you do not have good programming skills). We strongly recommend to try program them yourself, before resorting to the existing code.

The following lectures are accompanied by programming or tutorial exercises:

1. Fast 1D Convolution Algorithms (1 Programming Exercise)