The design of radiofrequency circuits and subsystems by means of electromagnetic and circuit simulators requires solving global optimization problems, often of noisy and costly functions. We will review the better-known heuristic methods, such as simulated annealing, genetic algorithms and evolution strategies. The use of surrogate models will be also dealt with by reviewing the space mapping technique. All these topics will be illustrated by practical projects, where students will optimize different radiofrequency subsystems or components.

This course is a comprehensive approach to Radio Frequency systems and the technologies involved. It is not a design course for active and passive components or circuits. It is a top-down approach that begins with the specifications of a system and finishes with the selection of the appropriate technology, components, and circuits to meet these specifications. An in-depth understanding of the variety of components in the market, their specifications and limitations is basic for a successful design of complex RF systems.

In this course a solid formation on advanced concepts in antennas technology is given. It covers both theoretical and practical aspects. Commercial or own software will be used for the design and simulation of antennas. The course is based on PBL concept.

In this course it is described in deep the elements needed, hardware and software, for the implementation of digital communication systems. Digital communications fundamentals are given as well the advanced concepts on digital signal processing to correct practical problems in the transmission channel.

We present a unified view of array processing, modern beam forming and MIMO systems including Array processing. Adaptive antennas. Implementation issues related to MIMO communications, Single user MIMO communications, Multiple user MIMO communications, Multiple user / Multiple cells MIMO communications, Massive MIMO, Virtual MIMO and wireless sensor networks.

This course focuses on communication systems that can be attacked in a hostile environment. Electronic measures and countermeasures for communications. The first issues are robust transmission methods, and then extend to intelligent attack resistant techniques.

A number of practical sessions will be carried out on antenna measurement, materials characterization, calibration of vector network analyzers, active components measurement, Electromagnetic compatibility etc.

The Final Master Thesis offers a great opportunity to closely collaborate with your favorite professor on advanced topics to be agreed with him/her. Located in the second semester with 12 ECTS, at that time you will have the global perspective about which are the more challenging topics or more demanded by local or international companies you desire to deepen. After an initial discussion with your academic advisor about your preferences, he/she will put in contact with the most suitable researcher among our professors who will lead your final stage before obtaining the final degree. You will have the chance to access to the professional laboratories in our department and get involved in our research or our technological transfer projects. Finally you will have to elaborate an extended report describing your achievements and make an oral presentation defending your results in front of other professors experts in the field. In the end, you will have gained full professional maturity to continue your career in academia or high technological companies with unique capabilities.

This course covers the fundamentals of the optimization of functions of continuous variables, considering both analytical and algoritmic aspects. Emphasis is made on techniques based on Lagrange and Karush-Kuhn-Tucker multipliers for constrained optimization. All the topics are motivated with concrete problems derived from practical applications.

This course uses most of the topics already covered in Optimization Fundamentals, but now focuses on the specific problems that arise with big data. Although we will provide the theoretical fundaments of the techniques developed, we will emphasize several key case studies in big data applications. We will distinguish three main blocks: 1) Parallelization of simple problems such as linear equations, matrix inversion and nonlinear problems. 2) Distributed optimization and statistical learning, covering network optimization techniques such as consensus, diffusion and gossip. 3) Parallelization of massive data optimization using coordinate descent-based methods.

This course is an introduction to the theory and practice of time series analysis, providing statistical tools to analyze random data that are ordered in time.

This course aims to help students to gain a global perspective of all the courses in this Track. Students will learn to critically assess the value of different scientific and technological approaches to derive knowledge from data in real-world applications. Case studies and debates are addressed over a set of conferences bringing together leading experts in different sectors: Cognitive Radio and Networks, Future Internet Services, Social Networks and Multimedia Analytics, Internet-of-Things, Machine-to-Machine, Smart Cities, Smart Grids, Biomedical Applications, Biometrics and Forensics, Financial Services, Robotic systems... 8 Signal Processing for Big Data This course addresses new challenges of signal processing when applied on large-scale data, including processing algorithms for large-scale sparse data, as the Sparse Fourier transform, discrete signal processing on graphs (DSPG), and the use of Tensors for analyzing Big Data.

In this lab students will consolidate their knowledge of theories and fundamental background received along the first semester of the Master. Particular attention is given to experimental work on Big Data platforms and tools (Hadoop, Spark, H2O, R, Python, Scala,...) applied over practical large-scale problems. Several real use cases are considered, mainly working with financial time series, sensor signals, and speech and audio signals.

This subject aims at presenting the most relevant techniques and methodologies to analyze multimedia collections at large scale. Several use cases are studied: large-scale content search and retrieval, automatic classification and content understanding, and hybrid (content-based and collaborative) multimedia recommendation systems.

This course presents a selection of the most recent and relevant techniques for massively processing, transmitting and storing images and video. It addresses new concepts as new signal sampling frameworks, Compressive Sensing, Random projections, and introduces innovative practical use cases such as the intuitive visualization of high dimensional data and applications in the computer vision field.

The goal of this course is to develop biologically-inspired approaches to Machine Learning, including Artificial Neural Networks, Deep Neural Networks and Swarm Systems. The course also introduces concepts and applications of System of System Engineering and Simulation of Intelligent Systems.

This course presents the motivation, fundamentals and different existing methods for solving Reinforcement Learning problems. Several practical use cases related to distributed communication and sensor networks are considered along the course.

The Final Master Thesis offers a great opportunity to closely collaborate with your favorite professor on advanced topics to be agreed with him/her. Located in the second semester with 12 ECTS, at that time you will have the global perspective about which are the more challenging topics or more demanded by local or international companies you desire to deepen. After an initial discussion with your academic advisor about your preferences, he/she will put in contact with the most suitable researcher among our professors who will lead your final stage before obtaining the final degree. You will have the chance to access to the professional laboratories in our department and get involved in our research or our technological transfer projects. Finally you will have to elaborate an extended report describing your achievements and make an oral presentation defending your results in front of other professors experts in the field. In the end, you will have gained full professional maturity to continue your career in academia or high technological companies with unique capabilities.