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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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