Immanuel Manohar

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Signals and Systems

LEARNING OBJECTIVES

In this course, you'll learn how to look at signals, what you can find using signals, how to look at systems mathematically. How and where all this applies in real life. These are the main course objectives:

    1. Understand the basics on how signals and systems are viewed, the mathematics behind them.
      • Time domain, Frequency domain, Transforms (Laplace, Z and Fourier). 
    2. Using MATLAB to see visually and  process various signals.
    3. Having fun learning about an interesting field.

REQUIRED MATERIALS

  1. Matlab App Associated with the course can be found here
  2. Lecture notes can be accessed here: This is not a standalone material
  3. "Signals and Systems" by Alan V. Oppenheim - any edition -> This book is great! do get it if possible. 
  4. The following books could be used for references: 
  5. "Signals and Systems" by Alan V. Oppenheim - any edition -> This book is great! do get it if possible. 
  6. "Digital Signal Processing - A practical approach" by Emmanuel C Ifeachor and Barrie W. Jervis
  7. "Signals and Systems: A MATLAB® Integrated Approach" by Oktay Alkin
  8. "Digital Signal Processing" by John G. Proakis, Dimitris K Manolakis.

Past quiz and exams

Selected Projects are listed below:

Project 1: AM/FM Radio

A problem with its roots in early 1900's and wide spread use in both psychology and statistics. Here I look into the perturbation properties of factor analysis and help determine better algorithms and approaches. Also, S&P has no factors that can give a one day ahead prediction

Project 2: Music Transcription

While this is a cool decomposition, it's power and elegance is often overlooked in current research. People prefer Singular Value Decomposition (SVD) because of its simplicity, but how close does QR decomposition come to capture SVD details? QR is multiple times faster and has orders of complexity less than SVD.

Project 3: ECG Signal Processing

Features are extracted from images (Eg: Scale Invariant Feature Transform (SIFT)) to form basis of image recognition. Which features are best?, which are redundat? What does Non-negative Matrix Factorization (NMF) and Independant Component Analysis (ICA) have to say about extracted features?

Project 4: Scalograms

Advances in Wireless Networks have now enabled massive data transfer speeds, with its advance, the queues at routers and at network nodes are huge. Here we develop good queuing models which help in optimal solutions to data transfer over multiple hops