Checking date: 08/05/2018

Course: 2018/2019

Advanced signal processing
Study: Master in Multimedia and Communications (278)

Coordinating teacher: ARTES RODRIGUEZ, ANTONIO

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

Type: Electives
ECTS Credits: 6.0 ECTS


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.
- 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
+ Parameter Estimation - Bayesian Parameter Estimation - Risk-based Estimation - Nonrandom Parameter Estimation - Latent Variable Models + Hypothesis Testing and Signal Classification - Bayesian Hypothesis Testing, Neyman-Pearson, Composite Tests - Signal Classification - Asymptotic Performance + Signal Estimation - MMSE Estimation - Linear Estimation and Prediction - Adaptive Filtering + Model-Based Signal Processing - Markov Chains - Hidden Markov Models
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 (Units 1-2: optimal filtering; Units 3-4: adaptive algorithms). - 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
  • 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.