Semester I (Fall 2001) : Aug. 20, 2001 - Dec. 7, 2001.
 | ECE-430: Communication and Signal Processing. |
| ECE-415: Image Analysis and Machine Vision I. |
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CS-401: Computer Algorithms. |
Semester II (Spring 2002) : Jan 7, 2002 - May 4, 2002.
| ECE-530: Random Signal Analysis. |
| ECE-515: Image Analysis and Machine Vision II. |
| ECE-517: Image Processing. |
Semester III (Fall 2002): Aug. 26, 2003 - Dec. 14, 2002.
Semester IV (Spring 2003): Jan. 13, 2003 - May 11, 2003.
| ECE-418: Digital Signal Processing II. |
| MATH-425: Linear Algebra II. |
Semester V (Fall 2003): Aug. 25, 2003 - Dec. 12, 2003.
| CS-580: Query Processing in Database Systems. |
| STAT-522: Multivariate Statistical Analysis (Audit). |
Semester VI (Spring 2004): Jan. 13, 2004 - May 11, 2004.
| ECE-491: Object-oriented programming for computer engineers. |
| STAT-411: Statistical Theory (Audit). |
Semester VII (Fall 2004): Aug. 25, 2004 - Dec. 12, 2004.
| STAT-511: Advanced Statistical Theory I. |
Instructor: Dr. Rashid Ansari. (Home Page )
Text: R.D. Yates, D.J. Goodman. "Probability and Stochastic Processes: A friendly introduction for Electrical and Computer Engineers", (First Edition)John Wiley & Sons, 1999.
Course Description:
| Probability, basic notions. |
| Discrete & Continuous random variables. |
| Multiple random variables. |
| Transformation of random variables. |
| Expected values, moment generating functions. |
| Random Processes. |
| Autocorrelation, stationarity, power spectral density. |
| Noise, NMSE filtering. |
| Applications. |
Instructor: Dr. Jezekiel Ben-Arie ( Home Page )
Text: R.C. Gonzalez, R.E. Woods. "Digital Image Processing".
Instructor: Dr. Bhaskar DasGupta. ( Home Page )
Text: Thomas H. Corman, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. "Introduction to Algorithms" McGraw-Hill, ISBN 0-07-013151-1
Course Description:
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| Amortized Analysis (Chapter 17) |
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| Greedy algorithms |
| Graphs: Minimum Spanning Trees (Chapter 23) |
| Scheduling (Chapter 16.5) |
| Quick sorting (Chapter 7) |
| Merge sort (Chapter 2.3.1) |
| Binary search |
| Finding the median (Chapter 9) |
| The Knapsack problem (Chapter 16.2) |
| Chained matrix multiplication (Chapter 15.2) |
| Basic definitions and representations of graphs (Chapter 22) |
| Traversing trees (Chapter 12.1) |
| Depth-first search for directed and undirected graphs with applications (Chapters 22.3 22.4 22.5) |
| Minimum spanning tree algorithms: Kruskal and Prim's algorithm (Chapter 23) |
| Breadth-first search (Chapter 22.2) |
| Shortest paths (Chapter 24) |
| The complexity of sorting |
Instructor: Dr. Rashid Ansari. ( Home Page )
Text: "Probability, Random Variables and Stochastic Processes", A. Papoulis, S.U. Pillai, Fourth Edition.
Course Outline:
| Review of Probability. |
| Random Processes. |
| Autocorrelation, Stationarity. |
| Power Spectral Density. |
| Special Processes. |
| Processing with Linear Systems. |
| Processing with Non Linear Systems. |
Instructor: Dr. Jezekiel Ben-Arie ( Home Page )
Text: R.C. Gonzalez, R.E. Woods. "Digital Image Processing".
Instructor: Dr. Dan Schonfeld. ( Home Page )
Text: R.C. Gonzalez and R.E. Woods, Digital Image Processing, Prentice-Hall, 2002.
Course Description:
| Introduction |
| Image Transforms |
| Image Restoration |
| Image Reconstruction |
| Image Compression |
| Template Matching |
| Motion Estimation |
| Video Compressio |
Instructor: Dr. Rashid Ansari. (Home Page )
Text: Digital Communications, 2/e, by John Proakis, McGraw Hill, 1995.
Course Description:
| Review of random processes; source coding; channel coding |
| Digital modulation and demodulation |
| Synchronization and carrier recovery |
| Intersymbol interference - Viterbi algorithm |
| Fading multipath channels |
| Spread spectrum communications |
Instructor: Dr. Derong Liu ( Home Page )
Text: "Statistical Digital Signal Processing and Modeling" by Monson H. Hayes (New York: John Wiley, 1996)
ISBN: 0-471-59431-8Course Description:
| Discrete time random signals and properties |
| Signal modeling and forward and backward prediction |
| Wiener filter and properties |
| Linear Prediction and algorithms |
| Kalman filter and it extensions |
| Smoothing filters |
| Adaptive filters including LMS and RLS algorithms |
| Adaptive filter applications such as to: channel equalizing, echo canceling, etc |
| Adaptive filter convergence properties.
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Instructor: Daniel Graupe. (Home Page )
Text: .
Course Description:
Instructor: Richard G. Larson ( Home Page )
Text: D. E. Radford, Linear Algebra, (partial book draft).
Course Description:
| Inner Product Spaces |
| Least Squares Approximation |
| Eigenvalues and Eigenvectors |
| Powers of Diagonizable Matrices, Fibonacci Sequence |
| Transition Matrices |
| Geometric Transformations |
| Symmetric Matrices |
| Maps of Inner Product Spaces |
| Triangulizable Matrices, Cayley-Hamilton Theorem |
| Generalized Eigenspaces |
| Nilpotent Matrices |
| Jordan Canonical Form |
| Minimal Polynomial |
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