EE 701 Digital Signal Processing and Filtering Instructor: Dr. Ghazi Al Sukkar Dept. of Electrical Engineering The University of Jordan 1

2 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, 3 rd Edition Alan V. Oppenheim, Ronald W. Schafer  Grading Assignments- 10% Project (term paper)- 20% Midterm Exam- 30% Final Exam- 40%  Note: send me an (in the subject EE701) to assign you the term paper.

3 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  Digital Signal Processing, R.A. Roberts and C.T. Mullis. Addison- Wesley,  Real-Time Digital Signal Processing, Sen M. Kuo and Bob H. Lee, Wiley,  Optimum Signal Processing, S. Orfanidis, MacMillan, 1988.

Course Website 4

5 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

6 Outline Topic 6FIR 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

7 Where is DSP

8 DSP is Everywhere  Sound applications Compression, enhancement, special effects, synthesis, recognition, echo cancellation,…enhancement 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,…

9 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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11 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,…

12 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

13 Limitations of Analog Signal Processing  Accuracy limitations due to Component tolerances Undesired nonlinearities  Limited repeatability due to Tolerances Changes in environmental conditions  Temperature  Vibration

14 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

15 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/DDSPD/A analog signal analog signal digital signal

16 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

17 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

18 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