Checking date: 16/05/2019


Course: 2019/2020

Applications of signal processing
(8931)
Study: Master in Advanced Communications Technologies (278)
EPI


Coordinating teacher: GONZALEZ SERRANO, FRANCISCO JAVIE

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

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
Basic courses on Digital Signal Processing
Competences and skills that will be acquired and learning results.
BC9 Students should communicate their conclusions, knowledge and rationale to specialists and non-specialists in a clear and unambiguous way. BC10 Students should have the learning skills to enable them to continue studying in a autonomous or self-directed way. SC1 Ability to make critical analysis of technical and scientific documents in the field of Signal Processing and Communications. SC3 Ability to produce original research work in some specific fields of Signal Processing, including its preparation as a presentation, and its oral exposition and defense. SC4 Ability to apply the knowledge in mathematics, statistics and to different problems in signal acquisition, analysis, and processing, in communications, in bioengineering, etc... SC7 Know and master basic and advanced signal processing techniques (predictive models, spectral analysis, array processing) and its application in different environments (communications, bioengineering, imaging).
Description of contents: programme
Unit 1: Signal Prediction: Time-Series models - Linear Prediction: ARMA Models Unit 2: Multidimensional Signal Processing - Dimensionality Reduction: PCA, LDA, Autoencoders - Array Processing Unit 3: Selected Applications in Bioengineering, Communications and other sectors * Signal Processing for Brain Computer Interfaces * Acquisition: compress sensing * Dimensionality Reduction: face recognition. * ICA/Blind Source Separation. * Signal Processing in the Encrypted Domain.
Learning activities and methodology
Activity Code Activity Hours % Presential AF1 Lectures and exercises 36 20 AF2 Laboratory 6 3,3 AF3 Supervision of Students 14 7,7 AF4 Team Work 24 0 AF5 Individual work 100 0 TEACHING METHODS MD1: Classroom lectures, with the support of slide presentations, to provide the main concepts of the subject and the related bibliography. MD2: Critical reading of recommended texts: scientific and academic journal articles, conference proceedings, reports, and manuals, either for class discussion, or to extend and consolidate the knowledge of the subject. MD3: Resolution, individually or in group, of practical cases, problems, etc.. proposed by the teacher. MD4: Presentation and discussion of topics and practical cases related to the course syllabus. MD5: Preparation, individually or in group, technical reports.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • Alessandro Piva and Stefan Katzenbeisser (Guest Editors). Signal Processing in the Encrypted Domain. EURASIP Journal on Information Security, Hindawi. 2007
  • Francis Castanié (Editor). Spectral Analysis: Parametric and Non-Parametric Digital Methods. ISTE. 2006
  • Peter J. Brockwell, Richard A. Davis. Time Series: Theory and Methods. Springer. 28/04/2009
  • Petre Stoica and Randolph Moses. Spectral Analysis of Signals. PRENTICE HALL, Upper Saddle River, New Jersey. 2005
  • Pierre Comon (Editor), Christian Jutten (Editor). Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press. March 8, 2010

The course syllabus and the academic weekly planning may change due academic events or other reasons.