COM-500 / 6 credits

Teacher: Ridolfi Andrea

Language: English

## Summary

Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numerical exercises.

## Keywords

Statistical tools, spectral analysis, prediction, estimation, annihilating filter, mixture models, principal component analysis, stochastic processes, hidden Markov models, adaptive filtering, mathematical computing language (Matlab, Python, or similar).

## Required courses

Stochastic Models in Communications (COM-300), Signal Processing for Communications (COM-303).

## Recommended courses

Mathematical Foundations of Signal Processing (COM-514).

## Important concepts to start the course

Calculus, Algebra, Fourier Transform, Z Transform, Probability, Linear Systems, Filters.

## Learning Outcomes

By the end of the course, the student must be able to:

• Choose appropriate statistical tools to solve signal processing problems;
• Analyze real data using a mathematical computing language;
• Interpret spectral content of signals;
• Develop appropriate models for observed signals;
• Assess / Evaluate advantages and limitations of different statistical tools for a given signal processing problem;
• Implement numerical methods for processing signals.

## Teaching methods

Ex cathedra with exercises and numerical examples.

## Expected student activities

Attendance at lectures, completing exercises, testing presented methods with a mathematical computing language (Matlab, Python, or similar).

## Assessment methods

• 20% midterm
• 10% mini project
• 70% Final exam

## Supervision

 Office hours Yes Assistants Yes Forum Yes

## Bibliography

Background texts

• P. Prandoni, Signal Processing for Communications, EPFL Press;
• P. Bremaud, An Introduction to Probabilistic Modeling, Springer-Verlag, 1988;
• A.V. Oppenheim, R.W. Schafer, Discrete Time Signal Processing, Prentice Hall, 1989;
• B. Porat, A Course in Digital Signal Processing, John Wiley & Sons,1997;
• C.T. Chen, Digital Signal Processing, Oxford University Press;
• D. P. Bertsekas, J. N. Tsitsiklis, Introduction to Probability, Athena Scientific, 2002 (excellent book on probability).

• L. Debnath and P. Mikusinski, Introduction to Hilbert Spaces with Applications, Springer-Verlag, 1988;
• A.N. Shiryaev, Probability, Springer-Verlag, New York, 2nd edition, 1996;
• S.M. Ross, Introduction to Probability Models, Third edition, 1985;
• P. Bremaud, Markov Chains, Springer-Verlag, 1999;
• P. Bremaud, Mathematical Principles of Signal Processing, Springer-Verlag, 2002;
• S.M. Ross, Stochastic Processes, John Wiley, 1983;
• B. Porat, Digital Processing of Random Signals, Prentice Hall,1994;
• P.M. Clarkson, Optimal and Adaptive Signal Processing, CRC Press, 1993;
• P. Stoïca and R. Moses, Introduction to Spectral Analysis, Prentice-Hall, 1997.

## Notes/Handbook

• Slides handouts;
• Collection of exercises.

## In the programs

• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Statistical signal and data processing through applications
• Lecture: 3 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Statistical signal and data processing through applications
• Lecture: 3 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Statistical signal and data processing through applications
• Lecture: 3 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Statistical signal and data processing through applications
• Lecture: 3 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Statistical signal and data processing through applications
• Lecture: 3 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Statistical signal and data processing through applications
• Lecture: 3 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Statistical signal and data processing through applications
• Lecture: 3 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Exam form: Written (summer session)
• Subject examined: Statistical signal and data processing through applications
• Lecture: 3 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks

## Reference week

 Mo Tu We Th Fr 8-9 9-10 10-11 11-12 BC04 12-13 13-14 14-15 BC04 15-16 16-17 17-18 18-19 19-20 20-21 21-22

Thursday, 14h - 17h: Lecture BC04

Friday, 11h - 13h: Exercise, TP BC04