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.

Content

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).

Learning Prerequisites

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

Resources

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).

More advanced texts

  • 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.

 

Ressources en bibliothèque

Notes/Handbook

  • Slides handouts;
  • Collection of exercises.

Websites

Moodle Link

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
  • 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

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22