Course Details

Subject {L-T-P / C} : EE6143 : Signal Transforms {3-0-0 / 3}
Subject Nature : Theory
Coordinator : Dr. Supratim Gupta

Syllabus

Introduction to signal transforms: Orthonormal vector and functional space and variations Metric Preservation: Parseval’s Theorem in orthonormal space Hilbert Transform, Hilbert-Huang Transform Fourier transform, Fourier series, Generalized Fourier transform property and convolution theorem Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), Short Time Fourier Transform (STFT) Wavelet Transform: Method & Properties, Multi-resolution analysis, M Band QMF filter banks Quantization and coding of transform coefficients Frame Theory: Signal approximation & compressed sampling, Parseval’s Theorem in redundant space, Curvelet transform & beyond: Method & Properties.

Course Objectives

  1. To make the students adept to visualizing the domain of signal transforms
  2. To make the students adept to represent signals in compact transform domain
  3. To make the students adept to develop algorithms to map from signal to other transform domain
  4. To make the students adept to implement the algorithm in software/Hardware

Course Outcomes

• The student will be aware and able to visualize the domain of signal transforms
• The student will be able to represent signals in the compact transform domain
• The student will be able to develop algorithms to map from signal to other transform domain
• The student will be able to implement the algorithm in software/Hardware

Essential Reading

  1. Truong Nguyen & Gilbert Strang, Wavelets and Filter Banks, Wellesley-Cambridge Press , Latest Ed.
  2. K. R. Rao and P. Yip, Discrete Cosine Transform: Algorithms, Advantages, Applications, Academic Press , Latest Ed.

Supplementary Reading

  1. Khalid Sayood, Introduction to Data Compression, Elsevier , 2011 or Latest Ed.
  2. Gilbert Strang, Linear Algebra and Its Applications, Nelson Engineering , 2007 or Latest Ed.