Checking date: 19/04/2023

Course: 2023/2024

Advanced signal processing
Master in Advanced Communications Technologies (Plan: 436 - Estudio: 278)

Coordinating teacher: RAMIREZ GARCIA, DAVID

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
STUDENTS ARE EXPECTED TO HAVE COMPLETED The student should have basic knowledge of - probability theory and statistics - linear algebra.
The main objective of this course is that the student is able to extract relevant information contained in the signals under study with the aid of statistical signal processing tools. To achieve this, the student will study advanced techniques of: - Random signals analysis - Estimation - Detection
Skills and learning outcomes
Description of contents: programme
DESCRIPTION OF CONTENTS: PROGRAMME · Parameter estimation - Method of moments - Maximum likelihood - Bayesian estimation · Signal Estimation - MMSE estimation - Linear estimation and prediction - Optimal and adaptive filtering · Hypothesis testing - Wald tests - Likelihood ratio methods - Bayesian tests
Learning activities and methodology
LEARNING ACTIVITIES AND METHODOLOGY The course is imparted in specific rooms and laboratories for the Master Program. It will include: - Lectures for the presentation, development and analysis of the contents of the course. - Practical sessions for the resolution of individual problems and practical projects in the laboratory. - A project for each part of the course. - Seminars for discussion with reduced groups of students
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Calendar of Continuous assessment
Basic Bibliography
  • Murphy, K.P.. Machine Leaning. A probabilistic perspective. MIT Press. 2012
  • C. P. Robert, G. Casella. Monte Carlo Statistical Methods. Springer. 2004
  • H. Stark, J. W. Woods. Probability and Random Processes with Applications to Signal Processing. Prentice Hall. 2002
  • L. Wasserman. All of Statistics. Springer. 2013
  • Poor, V. An Introduction to Signal Detection and Estimation. Springer. 1994
Additional Bibliography
  • Barber, D. Bayesian Reasoning and Machine Learning. Cambridge University Press. 2012
  • Bishop, C.M.. Pattern Recognition and Machine Learning. Springer. 2006

The course syllabus may change due academic events or other reasons.