ECE551^{Fall 2017}

Welcome to ECE551, a graduate level course in digital signal processing, also known as DSP-II. In this class we will cover advanced concepts in digital signal processing (e.g geometry and algebra of signals and systems spaces, approximations, filtering etc.). The course prerequisites are undergraduate level probability and DSP (ECE313 and ECE310 or their equivalents). Background in Linear Algebra and Calculus is strongly recommended.

• Course Syllabus
• Final Projects
• Prof. Bresler's project guidelines for ECE 513 (Vector space signal processing)

Homework will be given on a weekly basis.

• Submission Instruction in this link
• Homework 1 (due Sep. 11th) index_vector module , solution
• Homework 2 (due Sep. 27th), solution
• Homework 3 (due Oct. 1st, noon), solution
• Sample problem collection for midterm #1, Midterm #1 2015 , Midterm #1 2016
• Homework 4 (due Oct. 18th), solution
• Homework 5 (due Nov. 4th), solution
• Homework 6 (due Nov. 13th) , solution
• Sample problem collection for midterm #2, Midterm #2 2014, (disregard problem 3), Midterm #2 2016 ,
• Homework 7 (due Dec. 9th)

• Week #1: Lecture #1 slides (Monday), lecture #2 slides (Wednesday), Python notebook and audio file
• Week #3: Lecture #4 slides, complementary notes for Monday lecture (lecture #4), audio projections notebook and an example pulse, Radon transform notebook
• Week #3: Scribbles from the Radon lecture (to complement the Python notebook)
• Week #4: Lecture #6 slides (Monday), lecture #7 slides (Wednesday)
• Week #5: Lecture #8 slides (Monday), lecture #9 slides (Wednesday), Deconvolution notebook (with blazing fast gradient descent using FFT convolutions), Downsampling + aliasing demo (from my ECE 310 offering)
• Week #6: Notes on sound source localization via Prony's method
• Week #8: Lecture #12 slides (Monday), lecture #13 slides (Wednesday)
• Week #9: Lecture #14 slides (Monday), lecture #15 slides (Wednesday), Sampling and interpolation notebook
• Week #10: Lecture #16 slides (Monday), lecture #17 slides (Wednesday), Estimation notebook
• Week #11: Lecture #18 slides (Monday), lecture #19 slides (Wednesday), Wiener filtering notebook
• Week #12: Lecture #19 slides (Monday), Approximation by polynomials notebook
• Weeks #13, #14: Lecture #19 slides (Monday) Splines notebook

• Our textbook: "Foundations of Signal Processing" by Vetterli, Kovačević, and Goyal.
• Download Anaconda (Scientific Python). Python 3.6 is preferred.
• Download TA supplementary notes #1
• Download TA supplementary notes (probability theory)
• Download TA supplementary notes (Adaptive Filtering and LMS Algorithm) theory