Objective

The course is designed for students to learn advanced digital signal processing techniques and their applications. Students have learnt random processes and basic digital signal processing in ELE3410. In this course, they will learn how to realize filters, design filters of a given frequency response, and design optimal statistical filters. Also, they will learn fast Fourier transform and estimate power spectral density. They will learn the DCT and time-frequency analysis. Finally, a multimedia standard that uses digital signal processing is explained as an application example.

Syllabus

• Introduction
• Filter Realization
• FIR and IIR Filter Design to satisfy a given frequency response
• Optimal FIR Statistical Filter
• Spectral Estimation
• Fast Fourier Transform (FFT)
• The Discrete Cosine Transform (DCT)
• Time-Frequency Analysis
• DSP for multimedia applications

Learning Outcome

By the end of the course, students should be able to

• Realize a filter in Canonic direct form, Cascade form and Parallel form using delays, multipliers and adders from its system function H(z) .
• Design FIR filters by Window method.
• Use Matlab to design IIR filters using impulse invariant method and bilinear transformation method.
• Choose between a FIR or IIR filter for an application.
• Design an optimal FIR filter for an application.
• Estimate the power spectral density of a random process from a sampled portion by Periodogram with a proper trade-off between estimation error and resolution.
• Compute DFT using the decimation-in-time FFT algorithms and decimation-in-frequency FFT algorithms.
• Compute the DCT directly or from the DFT.
• Have some understanding of Time-Frequency Representation, the uncertainty principle and Wavelet Transform.
• Understand how the JPEG image coding standard and the DCT work.

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