Course Syllabus
Course
Title:
Biomedical Digital Signal Processing
Class
Schedule:
Saturdays 6:45p - 8:15p
(Office Hours: Saturdays 5:45p - 6:45p)
Instructor: Yasser M. Kadah, Ph.D.
Textbooks:
1.
John R. Buck, Alan V. V. Oppenheim, Alan V.
Oppenheim, and Ronald W. Schafer,
Discrete-Time Signal Processing, 2nd ed., Prentice
Hall, 1998.
2.
C. Sidney
Burrus, James C. McClellan, et al., Computer-Based Exercises for
Signal Processing Using Matlab, Prentice Hall, 1993.
Grading
Policy:
Homeworks + Class Projects + Oral Exam 40%, Final Written Exam: 60%. Attendance
of at least 75% of the classes is mandatory for a passing grade.
Course
Contents:
- Introduction
- Overview
of digital signal processing (DSP).
- Overview
of biomedical applications of DSP.
- Discrete-Time
Signals and Systems
- Sampling
and discrete form of signals.
- Discrete
systems
- Convolution
- Difference
equations
- Discrete-Time
Fourier Analysis
- Review
of Continuous Fourier transform (CFT)
- Discrete-time
Fourier transform (DTFT).
- Frequency
domain analysis of linear time invariant systems
- Analysis
of sampling and reconstruction of analog signals.
- The
z-Transform
- Definition
and of forward z-transform
- Inversion
of z-transform
- Solution
of difference equations
- Sampling
- Representation of sampling in the frequency domain
-
Recovery of continuous domain signals from discrete samples
- Discrete-time
processing of continuous signals
- Quantization
errors and other sources of error.
- The
Discrete Fourier Transform (DFT)
- The
discrete Fourier series analysis
- Sampling
and reconstruction in the z-domain
- The
discrete Fourier transform (DFT)
- Linear
and circular convolution using DFT
- The
Fast Fourier Transform (FFT)
- Introduction
to Digital Filters
- FIR
vs. IIR filters
- Implementation/realization
structures
- FIR
Filter Design
- Properties
of linear phase FIR filters
- Window
design techniques
- Frequency
sampling design techniques
- Optimal
equi-ripple design technique
- IIR
Filter Design
- Characteristics
of classical analog filters
- Design
using transformation methods
- Applications
- Doppler
signal processing
- MR
sampling design