logo-polimi
Loading...
Degree programme
Programme Structure
Show/Search Programme
Course Details
Save Document
Degree Programme
Read Degree Programme
Quantitative data
Faculty
Infrastructures
International context
Customized Schedule
Your customized time schedule has been disabled
Enable
Search
Search a Professor
Search a Course
Search a Course (system prior D.M. n. 509)
Search Lessons taught in English

Glossary
Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Educational activities
CSimilar or integrative activities
Language
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Context
Academic Year 2019/2020
School School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (476) Electronics Engineering
Track PSS - ELECTRONICS ENGINEERING
Programme Year 1

Course Details
ID Code 052471
Course Title ADVANCED DIGITAL SIGNAL PROCESSING
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 10.0
Semester First Semester
Course Description The course deals with fundamentals of the estimation theory, statistical signal processing and time-data analytics using an application-driven approach with several interdisciplinary engineering examples from audio/video and digital communications, vibration analysis, imaging and remote sensing, GPS and navigation systems. Goal is to gain practice on the following key-topics: algebra for signal processing and estimation theory, fundamentals of the estimation theory (BLUE, MLE, CRB, MMSE, MAP), parameter tracking and Kalman filtering, and adaptive LMS/RLS filtering. Spectral analysis (AR/MA/ARMA) and high-resolution methods for line spectra and array processing. Detection theory, pattern and feature detection/classification, and supervised/unsupervised classification methods. Exercises are on theoretical aspects and practical cases with the use of Matlab software and Montecarlo simulation. During the semester there are 3 interactive exercises on practical cases by students organized in working groups.
Scientific-Disciplinary Sector (SSD)
Educational activities SSD Code SSD Description CFU
C
ING-INF/03
TELECOMMUNICATIONS
10.0
Innovative teaching The course includes  1.0  credits in Innovative Teaching as follows:
  • Blended Learning & Flipped Classroom

Schedule, add and removeAlphabetical groupProfessorLanguageCourse details
From (included)To (excluded)
--AZZZZSpagnolini Umberto
manifesti v. 3.5.10 / 3.5.10
Area Servizi ICT
01/10/2023