Reza Mohammadkhani, PhD Email: Mohammadkhani@gmail.com

University of Kurdistan, Iran. WWW: eng.uok.ac.ir/mohammadkhani

Digital Signal Processing

Spring 2015

Course Details2

� Objectives:� Establish a background in Digital Signal Processing Theory

� Develop skills for implementation of DSP algorithms

� Textbook:A. V. Oppenheim and R. W. Schafer, Discrete-Time Signal

Processing, 3rd Edition, Prentice Hall, 2009

� Grading Policy� Midterm exam: 35%

� Final exam: 35%

� Homeworks: 10%

� Projects: 20%

Course Outline (1)3

� Introduction to Digital Signal Processing

� Discrete Time-Signals and Systems (Ch.2)

� The z-Transform (Ch.3)

� Sampling of Continuous-Time Signals (Ch.4)

� Analysis of LTI Systems (Ch.5)

� Structures for Discrete-Time Systems (Ch.6)

Course Outline (2)4

� Filter Design Techniques (Ch.7)

� The Discrete Fourier Transform (Ch.8)

� Computation of DFT

� Fourier Analysis of Signals Using DFT

� Parametric Signal Modeling

� Discrete Hilbert Transforms

Introduction5

DSP is everywhere6

Why learn DSP?7

� Impacts all aspects of modern life

� Communications (wireless, internet, GPS,…)

� Control and monitoring (cars, machines,…)

� Multimedia (mp3, video, cameras, restoration…)

� Health (medical devices, imaging …)

� Military (Radar, Sonar,… )

Example I: Sound applications8

� Compression, enhancement, special effects,

synthesis, recognition, echo cancellation,…

� Cell Phones, MP3 Players, Movies, Text-to-speech,…

Example II: Tomography9

Image from UC Berkeley lecture notes.

Example III: MRI (Magnetic Resonance Imaging)

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k-space (Raw Data) Image

Discrete Fourier transform

Example IV: functional MRI11

� Sensitivity to blood oxygenation - response to brain activity

� Convert from one signal to another

Taking fMRI further12

� fMRI decoding : “Mind Reading”

Gallant Lab, UC Berkeley

� Interpretation of signals

Signal Processing?13

� Convert one signal to another

� Filter, generate command, …

� Humans: the most advanced signal processors� speech and pattern recognition, speech synthesis,…

� Most real-world signals are analog

� They are continuous in time and amplitude

� Convert to voltage or currents using sensors and transducers

� Analog circuits process these signals using� Resistors, Capacitors, Inductors, Amplifiers,…

� Analog signal processing examples� Audio processing in FM radios

� Video processing in traditional TV sets

Limitations of Analog Signal Processing14

� Accuracy limitations due to

� Component tolerances

� Undesired nonlinearities

� Limited repeatability due to

� Tolerances

� Changes in environmental conditions (Temperature,…)

� Sensitivity to electrical noise

� Difficulty of implementing certain operations

� Nonlinear operations

� Time-varying operations

� Difficulty of storing information

Digital Signal Processing15

� Represent signals by a sequence of numbers

� Sampling or analog-to-digital conversions

� Process these numbers with a digital processor

� Digital signal processing

� Reconstruct analog signal from processed numbers

� Reconstruction or digital-to-analog conversion

� Inherently discrete (example?)

A/D DSP D/Aanalogsignal

analogsignal

digital signal

digital signal

Advantages of DSP16

� Flexibility

� System/implementation does not age

� “Easy” implementation

� Reusable hardware

� Sophisticated processing

� Process on a computer

� (Today) Computation is cheaper and better

References17

� Miki Lustig, EE123 Digital Signal Processing, Lecture

notes, Electrical Engineering and Computer Science,

UC Berkeley, CA, 2012. Available at:http://inst.eecs.berkeley.edu/~ee123/fa12/

� Güner Arslan, EE351M Digital Signal Processing,

Lecture notes, Dept. of Electrical and Computer

Engineering, The University of Texas at Austin, 2007.

Available at:www.ece.utexas.edu/~arslan/351m.html

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