Coursebooks 2016-2017

PDF
 

Statistical signal and data processing through applications

COM-500

Lecturer(s) :

Ridolfi Andrea

Language:

English

Summary

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

Content

1. Fundamentals of Statistical Signal Processing : Signals and systems from the deterministic and stochastic point of view.

2. Models, Methods, and algorithms :Parametric and non-parametric signal models (wide sense stationary, Gaussian, Markovian, auto regressive and white noise signals); Linear prediction and estimation (orthogonality principale and Wiener filter); Maximum likehood estimation and Bayesian a priori.

3. Statistical Signal Processing Tools for Spread Spectrum wireless transmission :Coding and decoding of information using position of pulses (annihilating filter approach); Avoiding interference with GPS(spectral mask and periodogram estimation); Spectrum estimation for classical radio transmissions (estimating frequencies of a harmonic signal).

4. Statistical Signal Processing Tools for the Analysis of Neurobiological Signals :Identification of spikes (correlation-bases methods); Characterization of multiple state neurons (Markovian models and maximum likelihood estimation); Classifying firing rates of neuron (Mixture models and the EM algorithm); Principal Component Analysis.

5. Statistical Signal Processing Tools for Echo cancellation :Adaptive filtering (least mean squares and recursive least squares).

Keywords

Statistical tools, spectral analysis, prediction, estimation, annihilating filter, mixture models, principal component analysis, stochastic processes, adaptive filtering, mathematical computing language (Matlab 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

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

Learning Outcomes

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

Teaching methods

Ex cathedra with exercises, numerical examples, computer session.

Expected student activities

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

Assessment methods

Resources

Bibliography

Background texts

More advanced texts

 

Ressources en bibliothèque
Notes/Handbook

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15   INM202 
15-16    
16-17   INM202 
17-18    
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

legend

  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German