UMH - ADVANCED APPLICATIONS IN SIGNAL PROCESSING

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ADVANCED APPLICATIONS IN SIGNAL PROCESSING

Course

Code: 2964

Degree: Master's in Telecommunications Engineering

School of Engineering of Elche

Year: Year 2 of Master's in Telecommunications Engineering

Semester: Fall

Type: Elective

Language: English

ECTS credits: 4.5

Lecture: 2

Laboratory: 2.5

Hours: 112.5

Directed: 39

Shared: 3.5

Autonomous: 70

Subject matter: Electives

Module: Elective

Department: Communications Engineering

Area: SIGNAL THEORY AND COMMUNICATIONS

Course instructors are responsible for the course content descriptions in English.

Description

Applications of advanced signal processing techniques in various fields of engineering and communications: bioengineering, audiovisual, remote sensing, and man-machine interfaces.

Faculty

Name	Coordinator	Lecture	Laboratory
GIMENO BLANES, FRANCISCO JAVIER	■	■	■
RODRIGUEZ MARTINEZ, ALBERTO		■	■

Professional interest

The signal processing and its applications are part of the current date of one of the disciplines with the greatest poyection and development in the coming years. The broad application from bio-engineering, pattern recognition to financial analysis makes this knowledge a fundamental tool for the future of Telecommunications engineers.

Competencies and learning outcomes

General competencies

Ability to plan, calculate and design products, processes and facilities in all areas of telecommunications engineering.
Capacity for mathematical modeling, calculation and simulation technology centers and engineering companies, particularly in research, development and innovation in all areas related to the Telecommunications Engineering and related multidisciplinary fields.
Ability to implement the acquired knowledge and resolve problems in new or unfamiliar contexts and broader mulitidisciplinares, being able to integrate knowledge.
Ability to understand the ethical responsibility and professional ethics of the activity of the profession of telecommunications engineer.
Ability to learn to communicate (oral and written) findings, and the knowledge and rationale underpinning these, to public-skilled and unskilled in a clear and unambiguous.
Possess skills for self-directed and autonomous lifelong learning.
Knowledge, understanding and ability to implement the necessary legislation in the exercise of the profession of Telecommunications Engineer.

Objectives (Learning outcomes)

Learning advanced techniques of digital filtering.
Learning advanced spectral analysis techniques. Spectral analysis of random signals.
Learning advanced techniques of time to frequency analysis. Split-Spectrum Systems, Wavelet Transform and Hilbert-Huang Transform.
Knowledge and understanding of the nature and origin of biomedical signals.
Knowledge and understanding of digital processing techniques for biomedical signals and bioengineering.
Basic algorithm development for biomedical signal processing (EEG, ECG, etc.).

Teaching units

Basic bibliography

Complementary bibliography

Gimeno Blanes, Francisco Javier. García Alberola, Arcadio / Amigó García, J.M. / Rojo Álvarez, José Luis . "Detección anticipada y mecanismos del síncope vasovagal en la prueba de basculación mediante tratamiento digital de señales". Elche Universidad Miguel Hernandez de Elche 2009.

Software

Anaconda V4.4
MATLAB2012B

Methodology and grading

Methodology

Case studies: Learning through the analysis of actual or simulated cases in order to interpret and resolve them by employing various alternative solution procedures.
Lecture: Pass on knowledge and activate cognitive processes in students, encouraging their participation.
Problem-based learning: Develop active learning strategies through problem solving that promote thinking, experimentation, and decision making in the student.
Project-based learning: Realization of a project to solve a problem, applying acquired learning and promoting abilities related to planning, design, performing activities, and reaching conclusions.
Solving exercises and problems: Exercise, test, and apply previous knowledge through routine repetition.

Grading

CONTINUOUS ASSESSMENT
The assessment system will be continuous assessment, with a high participation of students in classes. Attending lectures as an essential part of the learning process will be assessed. Likewise, the students will, work, practices and projects that will be evaluated.

Grading system of continuous assessment will consist of the following sections and weights:
20% Development work requested by the teacher
20% Projects by students
60% Laboratories

OFFICIAL ASSESSMENT:
Students who do not carry out continuous assessment have the option off later submitted to final examination of theory and practical problems.
Assessment and Final Grading:

30% theoretical choice exam
30% Test problems
40% Test Laboratory

Assessment test characteristics

There is no test for the access to the course

Correction criteria

In the exams, the calculation errors will penalize half of the question while the concept errors completely invalidate their score. Two calculation errors in the same question completely invalidate the score.

Additional requirements

The use of books, forms, computers, or electronic devices in general is not allowed in the exam. Only the use of a programmable calculator that will be checked and reset by the teacher at the time of access to the exam will be allowed.

Faculty: ADVANCED APPLICATIONS IN SIGNAL PROCESSING