Course
PostgraduateSemester
Sem. ISubject Code
AVD612Subject Title
Computational Methods for Signal ProcessingSyllabus
Real Time DSP Module: Fixed point representation of signals and introducing fixed point tool box in Matlab. Learning the impact of number representations in signal processing applications-IIR, FIR filter design in C- implementation of signal processing algorithms by applying quantization techniques and analyzing using Mat lab and DSP processor. Audio signal processing-echo cancellation, stream processing, block processing and vector processing of signals using DSP processor application to adaptive filtering, FFT, DCT, Wavelet using DSP processor. Learning code optimization techniques-pipelining and scheduling and implementation using TMS processor. Introducing OMAP and implementing simple image processing algorithms in DaVinci processor using real time systems. Mini project using DSP processors-DSP system design.
Python module: Basics of programming in python, variables and data types, arithmetic and logical operations, functions and flow control. Debugging python. Scientific computing in Python using numpy and scipy libraries. Linear algebraic functions, optimization, probability and random processes in Python. Object oriented programming in Python. Signals and systems simulation in Python, SVD using python, filtering, convolution, and autocorrelation using python, image and video processing in Python. Introduction to Pandas as a data processing library. Overview of other useful libraries in Python. Basic machine learning algorithms in Python using sci kit library.
Mini project in Python: Using Python for simulation study of a signal processing (audio or video processing system), or implementation of a machine learning system (e.g. recommendation engine) in Python.
Text Books
Same as Reference
References
1. A practical introduction to programming and problem-solving. Attaway, Stormy, Matlab Butterworth- Heinemann, 2013.
2. Essential MATLAB for engineers and scientists, Attaway, Stormy, Valentine, Daniel T., and Brian Hahn. Matlab Academic Press, 2016.
3. Digital signal processing using MATLAB: a problem-solving companion. Ingle, Vinay K., and John G. Proakis. Cengage Learning, 2016.
4. Contemporary communication systems using MATLAB, Proakis, John G., Masoud Salehi, and Gerhard Bauch. Cengage Learning, 2012.
5. Principles of Digital Communications System Simulation, Sam Shanmugam, Tranter, Pearson Education, India.
6. A brain-friendly guide. O'Reilly , Barry, Paul. Head first Python: Media, Inc., 2016.
7. Think Python: How to think like a computer scientist, Downey, Allen B. Green Tea Press, 2012.
8. A primer on scientific programming with Python Langtangen, Hans Petter. Springer.
9. Machine Learning with PyTorch and Scikit-Learn, Raschka, Sebastian, Yuxi Liu, and Vahid Mirjalili. 2022.
10. Other online resources.
Course Outcomes (COs):
CO1: Understand basic programming constructs in python - use of variables, arithmetic and logical operations, functions, control flow to create simple programs in python
CO2: Apply and understand and programming techniques for scientific computing in Python. Understand and apply functions from standard scientific computing libraries such as Numpy and Scipy.
CO3: Analyze user requirements, apply programming techniques to create a standalone Python software. Evaluate performance of the software, analyze performance tradeoffs
CO4: Implementation of DSP algorithm using number representations for real time application
CO5: Evaluate different processor architectural development specifically made for DSP applications and gaining hands on experience on real time experiments.
CO6: Ability to design and implement computational intensive DSP system using DSP hardware and analyze and evaluate the performance in terms of speed and power