Digital Signal Processing (DSP) has become over the years an important tool with applications in Electrical and Mechanical Engineering fields. DSP has penetrated many domains of applications, such as digital communications, medical imaging, audio & video systems, consumer electronics, robotics, remote sensing, finance etc. The Discrete-Time Signal Processing paradigm is a convenient setting to analyse the basic principles of DSP. At the end of this course, the students should be able to understand these basic principles, and apply fundamental algorithms and methods to analyse and design discrete-time systems for modern DSP applications. Though the course will focus on the study of theoretical concepts, methods and algorithms, the student will be confronted with application and implementation issues, through various examples and assignments requiring personal computer work including processing of real-world signals. ENGN 4537 Discrete Time Signal Processing builds directly on Signals & Systems Theory (ENGN2228 Signal Processing) by developing the students' understanding of the principles of advanced signal processing theory. Specific Topics include:

1. Review of signals & systems Theory (Discrete LTI systems, Convolution, Difference Equations).

2. z-transforms.

3. Sampling of continuous time signals (basic principles, changing sampling rate, pre-filtering to avoid aliasing and quantization errors).

4. Transform Analysis of LTI systems (frequency response of LTI, all pass systems, minimum phase systems)

5. IIR & FIR filters and filter design techniques.

6. Discrete Time Fourier Transform (DFT) and FFT algorithms.

7. Fourier analysis of real signals with DFT.

8. Overview of microprocessor architectures for DSP, implementation aspects of modern DSP algorithms.