This course objective is to comprehend and master advanced data processing techniques, particularly statistical treatment of signals with digital devices, including understanding and mastering spectral analysis and estimation techniques of real random signals, as well as estimation and detection theories; understanding and mastering advanced digital filtering techniques, particularly optimum and adaptive filtering, is another objective. Also addressed are advanced signal classification and regression techniques for use in artificial intelligence applications through data processing, which includes understanding and mastering machine learning techniques, such as neural networks and support-vector machines. Lastly, another course objective is to learn and understand techniques for processing large volumes of data from acquisition devices for statistical extraction (big data analytics).
Competencies and learning outcomes
- Autonomous learning capacity within the field of electronic engineering.
- Teamwork skills.
- Be able to manage, analyze, synthesize, and update information.
- Be able to apply the acquired knowledge and resolve problems in multidisciplinary environments.
- Ability to project, calculate, and design products and systems in the field of electronic engineering.
- Be able to implement, direct, and manage manufacturing projects and processes with electronic equipment and systems.
- Ability to conduct research and generate new ideas.
- Know how to communicate conclusions and the latest knowledge and rationale that support them with specialists and non-specialists in a clear and unambiguous manner.
- Ability to analyze distinct subsystems of an advanced electronic product and select the optimum device for its implementation.
- Analysis and processing ability with advanced signal techniques in both time and frequency domains.
- Be capable of evaluating and implementing advanced data analysis techniques.
Objectives (Learning outcomes)
- The general objective of the course is to provide additional training to that received throughout the degree studies, that consolidate the knowledge in digital signal processing, which allow the student to have a solid knowledge in these techniques for the subsequent use of advanced techniques for signal processing in different types of applications.
- Understanding and mastery of the techniques of estimation, decision / detection and classification of signals
- Understanding and mastery of the basic concepts of statistical estimation. Knowledge of the basic techniques used in the theory of esteem.
- Understanding and mastery of the basic concepts of statistical decision. Knowledge of the basic techniques used in the theory of detection.
- Understanding and mastering the design and analysis of optimal filters and adaptive filtering.
- Understanding and mastery of the basic concepts of classification and regression of signals.
- Understanding and mastery of basic machine learning methods. Neural Networks and Support Vector Machines.
- Kay, Steven M. 1951-. "Fundamentals of statistical signal processing Vol. III Practical algorithm development". Upper Saddle River, NJ Prentice Hall 2013.
- Kay, Steven M. 1951-. "Fundamentals of statistical signal processing Vol. 1 Estimation theory". Upper Saddle River, New Jersey (USA) Prentice Hall PTR reimp. 2013.
- Kay, Steven M. 1951-. "Fundamentals of statistical signal processing Vol. 2 Detection theory". Upper Saddle River, New Jersey (USA) Prentice Hall PTR reimp. 2013.
- Manolakis, Dimitris G. Ingle, Vinay K./Kogon, Stephen M. "Statistical and adaptive signal processing [electronic resource] : spectral estimation, signal modeling, adaptive filtering, and array processing /". Boston : Artech House, c2005.
- Proakis, John G. Manolakis, Dimitris G. "Digital signal processing Principles, algorithms and applications". Upper Saddle River (Estados Unidos) Prentice-Hall cop. 1996.