This course serves as a bridge to understanding the relationship between the Analog world and its Discrete-Time representation in digital computers. Digital Signal processing is covered with emphasis on the relationship between continuous-time and discrete-time systems in the time and frequency domains. Practical aspects of digital filter design and implementation structures are discussed and the use of stochastic models to model quantization effects in digital signal processing is introduced. Real world applications of Digital Signal Processing are analyzed to provide insight into the application of these techniques. Clarity in important DSP concepts is provided using MatLab for exercises and experiments in digital signal processing techniques.
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Instructor: |
Jeffrey N. Denenberg |
Email: |
jeffd@cse.bridgeport.edu |
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Pre-requisites: |
EE 603 - Discrete and Continuous Systems I (or equivalent) |
Phone: |
(203) 268-1021 |
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Textbook: |
“Introduction to Signal Processing,” Orfanidis, Prentice-Hall, 1996, ISBN # 0‑13‑209172‑0 |
SW: |
MatLab, Simulink, Signal Processing Toolbox, DSP Blockset, Wavelet Toolbox |
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Supplement: |
“Mastering DSP Concepts Using MatLab,” Ambardar and Borghesani, Prentice-Hall, 1997, ISBN # 0‑13‑534976‑1 |
Exams: |
Two (~4th&9th
wk) - 30% ea. |
Topics:
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1. |
Signals and Systems: Review |
1.1 - 1.2, Notes |
(0.5 weeks) |
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2. |
Sampling of signals in the time and frequency domain |
1.3 - 1.7, Notes |
(1 week) |
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3. |
Quantization |
2.1 - 2.4 |
(0.5 weeks) |
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4. |
Discrete-Time Systems |
c3 |
(1 week) |
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Exam 1 |
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5. |
FIR filters and Convolution |
c4 |
(1 week) |
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6. |
The Z-Transform |
c5 |
(1 week) |
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7. |
Transfer Functions |
c6 |
(1 week) |
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8. |
Digital filter implementation structures |
c7 |
(1 week) |
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Exam 2 |
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9. |
Efficient computation of the DFT: the FFT algorithms |
c9 |
(1 week) |
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10. |
FIR digital filter design |
c10 |
(1 week) |
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11. |
IIR digital filter design |
11.1 - 11.4 |
(1 week) |
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12. |
Topics in current digital signal processing
applications |
c8, Notes |
(1 week) |
Course Goals
|
1. |
Develop an understanding of the relationship between discrete-time signals/systems and their real-world, analog counterparts. |
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2. |
Be prepared to analyze and design digital filters to process these signals. |
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3. |
Be familiar with the breadth of applications of DSP in today’s world. |
Estimated ABET category content as estimated by faculty member who prepared this course description:
Engineering Science: 2 credits or 67% Engineering Design: 1 credit or 33%
Prepared by Jeffrey N. Denenberg Date: March 2, 1998
Classroom Experiences in Digital Signal
Processing
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Demonstrations |
Purpose |
Notes |
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Introduction to DSP |
Underscore the effectiveness and breadth of real-world DSP applications |
Speech Spectrograms, Speech Modeling, Speech Compression
and |
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Aliasing |
Demonstrate the effects of aliasing on real signals |
Aliasing shown visually in both time and frequency domains, audible effects are heard |
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Minimum Phase Filters |
Derive equivalent minimum phase filters from linear phase prototypes |
Introduces the importance of signal delay in filter designs |
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Quantization Noise |
Relate classroom mathematics to the audible (and visual) effects of quantization. |
Quantization is shown visually in both the time and frequency domains, audible effects are heard |