Lectures

You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

• Lecture-01
  About: Introduction: What is a signal and why this course?
  [slides]
  
• Lecture-02
  About: What is a model?
  [slides]
  
• Lecture-03
  About: Signal, Model, Representation, and Polynomials
  [slides]
  

  Readings:

  • Chapter 1: Foundations of Signal Processing, M. Vetterli, J. Kovacevic, V. K. Goyal
• Lecture-04
  About: Polynomial Representations
  [slides]
  

  Readings:

  • Chapter 2: Foundations of Signal Processing, M. Vetterli, J. Kovacevic, V. K. Goyal

  Viewing:

  • The Vector Space of Polynomials, Trefor Bazett
• Lecture-05
  About: Fourier Series Representations
  [slides]
  

  Readings:

  • Chapter 1: Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood
  • Chapter 4: Foundations of Signal Processing, M. Vetterli, J. Kovacevic, V. K. Goyal

  Codes:

  • Making sine waves
  • Estimate Fourier series
• Lecture-06
  About: Fourier Series Representations: Meeting Exponentials
  [board]
  

  Readings:

  • Chapter 2: Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood
• Lecture-07
  About: Fourier Series Representations: Moving to Periodized Signals
  [slides]
  

  Readings:

  • Chapter 2: Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood

  Code:

  • Rect(.) Fourier series
• Lecture-08
  About: Fourier Transform: Reperesentations for aperiodic signals
  [slides]
  

  Readings:

  • Chapter 2: Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood
• Lecture-09
  About: Fourier Transform/Inverse and Convolution
  [slides]
  

  Readings:

  • Chapter 3: Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood

  Demos:

  • Convolution Demo 1
  • Convolution Demo 2

  Viewings:

  • Anatomy of human ear
• Lecture-10
  About: Taking questions and discussion
  [board]
  
• Lecture-11
  About: Understanding nature as repository of continuous signals
  [slides]
  
• Lecture-12
  About: Sampling and Interpolation-I: Introduction
  [slides]
  
• Lecture-13
  About: Sampling and Interpolation-I: Dirac and Shah
  [slides]
  

  Readings:

  • Chapter 5: Sampling and Interpolation Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood
• Lecture-14
  About: Sampling and Interpolation-II: Dirac and Shah more
  [slides]
  

  • Chapter 4: Distributions Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood
• Lecture-15
  About: Sampling and Interpolation-III: Shanon Sampling and Interpolation
  [slides]
  

  • Chapter 5: Sampling and Interpolation Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood
• Lecture-18
  About: Sampling and Aliasing
  [slides]
  

  • Chapter 5: Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood
• Lecture-19
  About: Discrete Fourier Transform: Part-I
  [slides]
  

  • Chapter 6: Lecture Notes for EE 261 - The Fourier Transform and its Applications, Brad Osgood
• Lecture-20
  About: Discrete Fourier Transform: Part-II (As a NN)
  [slides]
  

  Demos:

  • Fourier transform as a Neural Network
• Lecture-21
  About: Potential Project Ideas-I
  [slides]
  
• Lecture-22
  About: Potential Project Ideas-II
  [other]
  

  Project:

  • Our Project Repository
• Lecture-23-24
  About: Discrete Fourier Transform: Part-IV
  [slides]
  

  Videos:

  • What is Fourier Transform

  Reading:

  • How the FFT Gained Acceptance by James W. Cooley
  • Gauss and the History of the Fast Fourier Transform by Michael T. Heideman, Don H. Johnson, C. Sidney Burrus.

  Code:

  • Computing DFT in python
  • Denosing with DFT
• Lecture-25
  About: 2-D Fourier transform: Application to Image analysis
  [slides]
  

  Reading:

  • 2-D Fourier Transform and applications by A. Zisserman.
  • 2-D Fourier Tranform with python by Stephen Gruppetta.

  Code:

  • 2-D FFT Images
  • Taj Mahal Image
• Lecture-26-27-28-29
  About: Time-Frequency Analysis: Bringing time into the frequency space
  [slides]
  

  Reading:

  • Theory of communication by Dennis Gabor.
  • Estimating and interpreting the instantaneous frequency of a signal-Part 1: Fundamentals by Boualem Boashash.

  Code:

  • Computing STFT
  • Audio sample
• Lecture-30-31
  About: Edit distance computation (DP), Dynamic Time Warping (DTW), and Clustering
  [slides]
  

  Reading:

  • Sec. 9 in Pattern Reocgnition and Machine Learning by C. Bishop.
  • Data clustering: a review by A. K. Jain, et al., 1999.
• Lecture-32
  About: Why reduce dimensions? Getting into PCA and LDA
  [board]
  

  Reading:

  • Chap. 12: PCA by C. Bishop.
  • Section 4.1.4 Fisher’s linear discriminant by C. Bishop.

  Code:

  • PCA
  • LDA and PCA
• Lecture-33-34
  About: Sparse representations
  [slides]
  

  Reading:

  • Basis Representations and Sparsity by Justin Romberg.
  • Atomic Decmpositions by Basis Pursuit by Chen, Donoho, and Saunders.