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Course: 2023/2024

Advanced signal processing

(18538)

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

EPI

Coordinating teacher: RAMIREZ GARCIA, DAVID

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives

ECTS Credits: 6.0 ECTS

Course: 1º

Semester: 1º

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.

Objectives

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

Link to document

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

SE1	Participation in class
SE2	Individual or team works made during the course
SE3	Final exam


Evaluation systems                         Minimum weighting (%)                   Maximum Weighting
 (%)
SE1                                             0                                                       20
SE2                                              0                                                    100
SE3                                              0                                                       60

% 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.