Checking date: 10/07/2020

Course: 2020/2021

Advanced signal processing
Study: Master in Advanced Communications Technologies (278)

Coordinating teacher: MIGUEZ ARENAS, JOAQUIN

Department assigned to the subject: Department of Signal and Communications Theory

Type: Electives
ECTS Credits: 6.0 ECTS


Students are expected to have completed
STUDENTS ARE EXPECTED TO HAVE COMPLETED The student should have basic knowledge of - probability theory and statistics - linear algebra.
Competences and skills that will be acquired and learning results.
COMPETENCES AND SKILLS THAT WILL BE ACQUIRED AND LEARNING RESULTS. - Acquisiton of knowledge and skills that provide with a background of creativity in the development and application of ideas, often within a research context. - Ability to apply acquired knowledge and to solve problems under novel or almost novel situations or within broader (multidisciplinar) contexts related with Signal Processings - Acquisition of skills for learning in an autonomous and continuated manner. - Systematic comprehension of signal processing as a discipline of study and of the research skills and methods related with Signal Processing - Ability to perform a critical analysis and synthesis of new and complex ideas. - Ability to study and review scientific and technical documents about signal processing - Ability to capture a deep view of the state-of-the-art in signal processing technology, as well as to forecast the near future in the field - Ability to carry out an original work in a specific signal processing topic, including its presentation and discussion with other scientists - Application of math, statistics and science to signal processing problems - Ability to design and carry out experiments, as well as to analize and interpret their outcome - Deep knowledge of advanced signal processing techniques such as linear filtering, adaptive filters, stochastic filtering in dynamical systems, and their application - Ability to solve estimation and prediction problems in dynamic systems, including state space models and stochastic filters design. - Deep understanding of adaptive algorithms, including steepest descend, least squares and non-linear versions. Ability to efficiently apply those algorithms in adaptive signal processing problems.
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 filtering · Model-based signal Processing - Markov chains and processes - State space models · Hypothesis testing and classification - 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 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
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 and the academic weekly planning may change due academic events or other reasons.