EHB 315E - Digital Signal Processing

Course Objectives

o Understand the concept of sampling of continuous-time signals to produce discrete-time signals and the importance and application of the Nyquist sampling theorem.
o Understand discrete Fourier transforms and be able to use them to characterize discrete-time signals.
o Understand z-transforms and be able to use them to characterize discrete-time signals.
o Understand discrete-time systems and the concepts of linearity, causality, and stability. Know how to characterize linear time-invariant discrete-time systems in the time domain through the use of convolution (impulse response) and difference equations. Know how to characterize linear time-invariant discrete-time systems in the frequency domain through the use of discrete-time Fourier transforms (frequency response) and z-transforms (transfer functions).
o Know how to represent discrete-time systems using block diagrams. Know how to determine the stability of discrete-time systems in both the time domain and frequency domain. Know techniques for implementation of discrete-time systems.
o Understand the basic concepts of infinite-impulse-response digital filters, finite-impulse-response digital filters. Know how to design finite- and infinite impulse-response filters. Understand the concept of the fast Fourier transform.

Course Description

Introduction to discrete-time systems, and digital signal processing. Discrete time linear systems,difference equations, discrete convolution, stability. Discrete-time Fourier transform,analog-to-digital and digital-to-analog conversion, örnekleme. z-transform.Discrete Fourier transform (DFT). Fast Fourier transform (FFT). Digital filter design and implementation. Fundamentals of statistical signal processing. Random processes and power spectrum. Wiener filter. Fundamentals of adaptive filtering. Steepest descent and LMS algorithms.