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EE 701Digital Signal Processing and Filtering
Instructor: Dr. Ghazi Al SukkarDept. of Electrical EngineeringThe University of JordanEmail: [email protected]
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Course Details Objective
Establish a background in Digital Signal Processing Theory Design and implementation of DSP algorithms
Text Book Discrete-Time Signal Processing, Prentice Hall, 3rd Edition Alan V. Oppenheim, Ronald W. Schafer
Grading Assignments - 10% Project (term paper) - 20% Midterm Exam - 30% Final Exam - 40%
Note: send me an email (in the subject EE701) to assign you the term paper.
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Useful References Digital Signal processing, by John Proakis and Dimitris G. Monalakis. Digital Signal Processing, A computer based approach, third edition, by
Sanjit K. Mitra. Digital Signal Processing, Sanjit H. Mitra. Mc Graw Hill, 3rd Ed. 2006 Digital Signal Processing, R.A. Roberts and C.T. Mullis. Addison-Wesley,
1996. Real-Time Digital Signal Processing, Sen M. Kuo and Bob H. Lee, Wiley,
2001. Optimum Signal Processing, S. Orfanidis, MacMillan, 1988.
Course Websitehttp://www2.ju.edu.jo/sites/Academic/ghazi.alsukkar/
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Course Outline Topic 1 Discrete-Time Signals and
Systems + LTI Topic 2 The Z-Transform Topic 3 Sampling of Continuous Time
Signals Topic 4 Transform Analysis of Linear
Time-Invariant Systems Topic 5 Structures of Discrete-Time
Systems
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Outline Topic 6 FIR and IRR Discrete Filter
Design Techniques Topic 7 The Discrete Fourier
Transform (DFT) Topic 8 Computation of the Discrete
Fourier Transform Topic 9 Applications of Digital Signal
Processing
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Where is DSP
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DSP is Everywhere Sound applications
Compression, enhancement, special effects, synthesis, recognition, echo cancellation,…
Cell Phones, MP3 Players, Movies, Dictation, Text-to-speech,…
Communication Modulation, coding, detection, equalization, echo
cancellation,… Cell Phones, dial-up modem, DSL modem, Satellite
Receiver,… Automotive
ABS, GPS, Active Noise Cancellation, Cruise Control, Parking,…
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DSP Application Medical
Magnetic Resonance, Tomography, Electrocardiogram,…
Military Radar, Sonar, Space photographs, remote
sensing,… Image and Video Applications
DVD, JPEG, Movie special effects, video conferencing,…
Mechanical Motor control, process control, oil and mineral
prospecting,…
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Signal Processing Humans are the most advanced signal processors
speech and pattern recognition, speech synthesis,… We encounter many types of signals in various
applications Electrical signals: voltage, current, magnetic and
electric fields,… Mechanical signals: velocity, force, displacement,… Acoustic signals: sound, vibration,… Other signals: pressure, temperature,…
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Signal processing 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
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Limitations of Analog Signal Processing
Accuracy limitations due to Component tolerances Undesired nonlinearities
Limited repeatability due to Tolerances Changes in environmental conditions
Temperature Vibration
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Cont.. Sensitivity to electrical noise Inflexibility to changes Limited dynamic range for voltage
and currents Difficulty of implementing certain
operations Nonlinear operations Time-varying operations
Difficulty of storing information
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Digital Signal Processing Represent signals by a sequence of numbers
Sampling or analog-to-digital conversions Perform processing on these numbers with a digital
processor Digital signal processing
Reconstruct analog signal from processed numbers Reconstruction or digital-to-analog conversion
A/D DSP D/Aanalogsignal
analogsignal
digital signal
digital signal
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Input vs. Output
Analog input – analog output Digital recording of music
Analog input – digital output Touch tone phone dialing
Digital input – analog output Text to speech
Digital input – digital output Compression of a file on computer
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Advantages and disadvantages of Digital Signal Processing Advantages
Accuracy can be controlled by choosing word length Repeatable Sensitivity to electrical noise is minimal Dynamic range can be controlled using floating point
numbers Flexibility can be achieved with software implementations Non-linear and time-varying operations are easier to
implement Digital storage is cheap Digital information can be encrypted for security Price/performance and reduced time-to-market
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Cont..
Disadvantages Sampling causes loss of information A/D and D/A requires mixed-signal
hardware Limited speed of processors!!! Quantization and round-off errors
