Overview

Prerequisites

• MA1113

Learning Outcomes

• Given a continuous signal, determine the effects of sampling in time.
• Be able to define the minimum sampling frequency to avoid aliasing when discretizing a continuous signal
• Given a dynamic system, determine if it is Linear Time Invariant (LTI);
• Given a LTI system, compute the response by convolution;
• Given a periodic signal, determine its Fourier Series expansion;
• Given a continuous time signal, compute its Fourier Transform;
• Given a continuous time signal, compute the Fourier Transform using the tables and the properties;
• Given a system, characterize it in terms of its frequency response;
• Apply the properties of the Fourier Transform to basic modulation and demodulation problems;
• Develop the ability to recognize and characterize simple discrete random processes in the time domain.
• Learn to be able to fully characterize stationary discrete random processes from a second moment viewpoint in the time domain and frequency domains. Develop an understanding for Gaussian white noise for 1- and N-dimensional random vectors.
• Learn to characterize random signals from a second moment viewpoint as they are processed through discrete linear systems. Also learn to characterize simple linear transformations for continuous signals in time and frequency domains.
• Learn how to deal with uncertainty in estimated parameters via confidence intervals.

Other Information

Ability to program in MATLAB is required.