Checking date: 26/04/2023


Course: 2023/2024

Applications of digital signal processing to transfer
(15396)
Bachelor in Telecommunication Technology Engineering (2010 Study Plan) (Plan: 238 - Estudio: 252)


Coordinating teacher: MARTÍNEZ OLMOS, PABLO

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
The students are expected to have basic knowledge of - principles of signal processing - probability and statistics.
Objectives
Specific technical skills: - Ability to design algorithms that address the classical problems of signal estimation and detection, and statistical model learning. Subject to complexity and performance constraints. - Ability to analyze and assess the complexity and performance of signal processing methods. - Ability to design, integrate and assess complex systems that involve various digital signal processing modules. - Ability to use numerical analysis and data processing software. General skills: - Ability to analyze and summarize problems. - Practical application of theoretical knowledge. - Problem solving. - Team work.
Description of contents: programme
The content of the course has been designed to first give a practical introduction to methods of artificial intelligence and , second , the application of the concepts learned in real problems oriented field of bioengineering and engineering data . This course seeks to strengthen the profile of the future telecommunications engineer with concepts and tools in high demand . Unit 1. Supervised Learning: Unit 1.1. Regression Unit 1.2. Classification Unit 2. Unsupervised Learning Unit 2.1. Clustering Unit 2.2. Dimensionality Reduction Unit 3.Applications: Unit 3.1. Non-linear Dimensionality Reduction. Spectral Clustering. Unit 3.2. Text-mining and automatic document classification. Unit 3.3. Introduction to Deep Learning Using Tensor Flow
Learning activities and methodology
Lectures (3 ECTS): Contents: basic theory and methods of signal processing in communications. Methodology: classical lecture with use of slides, videos and white/blackboard. Lab projects (3 ECTS): Contents: implementation of algorithms in for the simulation and assessment of digital transmission systems. Methodology: use of numerical software packages (matlab/octave) to study the performance of algorithms and systems.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Calendar of Continuous assessment
Basic Bibliography
  • Alan Oppenheim, Alan S. Willsky. Signal and Systems. Pearson, Prentice Hall.
  • Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer.
  • David Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press. 2012
  • S. Haykin. Adaptive Filter Theory. Prentice Hall. 2001
  • Steven Kay. Fundamentals of Statistical Signal Processing. Prentice Hall.
  • Steven Kay. Fundamentals of Statistical Signal Processing. Prentice Hall.

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