Introduction to Signal Processing, Lectures

• discuss the fundamental concepts of signal processing and solve problems related to them,
• use Matlab and computational methods to solve basic signal processing problems,
• solve the properties of a linear filter through its transfer function  and can design an FIR filter both by Matlab and manually using window  design method,
• implement the Fourier transform of a sequence both directly and using the FFT algorithm,
• desing a digital system to convert the sampling rate,
• design simple pattern recognition systems that use the signal processing tools for feature extraction.

Core content

• Basics of Matlab for signal processing
• Basics of digital signal processing: sampling theorem, discrete signals and systems, and the convolution operator.
• Analysis  of discrete signals and systems: discrete Fourier transform, FFT  algorithm, z-transform, transfer function and frequency response.
• Design of linear systems using the window desing method
• Multirate DSP: Decimation and Interpolation
• Fundamentals of machine learning. Applications of signal processing algorithms in pattern recognition.
• Applications. Visiting lectures from university or the industry.

Complementary knowledge

• Fourier transform, Fourier series and Discrete-time Fourier transform

Specialist knowledge

• Parks-McClellan algorithm

Prerequisites

Good knowledge of BSc level engineering mathematics.

Signals and Measurements course is a recommended prerequisite.

Material

• Type: Lecture handout
  
• Name: Introduction to Signal Processing
• Author: Sari Peltonen
  
• Exam material: Yes
• Language: English

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