The following courses are available for 2016/2017. Students may choose any combination of three courses.
Data Processing and ModellingLecturers: Armando J. Pinho, Pétia Georgieva, Telmo Reis Cunha Aims: This course is to gather knowledge in advanced areas of Data Driven Modelling and it consists of the following three teaching modules: A) Data compression and data complexity, B) Machine Learning, and C) Nonlinear System Identification. A) Data compression and data complexity Module Summary: This module aims at providing a brief overview of the information theory behind discrete source coding and the corresponding data compression algorithms. Data compression is among the most important research areas in modern computing. In fact, representing information efficiently is a key requirement in virtually every system, including those handling text, audio, image or video, but also other types of information, such as DNA sequences. The main objective of data compression is to represent messages with less bits than those originally used. However, lossless data compression also plays an important role in measuring the complexity of data. This is a less known use of data compression algorithms, but that is gaining increasing interest, for example for assessing the complexity of DNA sequences, for measuring the similarity between images, or for discovering the authorship of a certain written text. In this module, we will address and explore this important connection. Training outcomes: Knowledge of important aspects of data compression and data complexity and their relations with information theory and Kolmogorov complexity. Program: 1. Basic concepts of information theory
2. Variablelength codes 3. Universal source codes
4. Data compression and data complexity
B) Machine Learning Module Summary: Machine Learning (ML) is a subfield of artificial intelligence that is concerned with the design, analysis, implementation, and applications of programs that learn from examples or experience. Learning from data is commercially and scientifically important. ML consists of methods and respective software that extract automatically interesting knowledge (patterns, models, relationships) in large databases of sometimes chaotic and redundant information. ML is a databased knowledgediscovering process that has the potential not only to analyze events in retrospect but also to predict future events or important alterations. It is a top field in computer science with growing job options. Training outcomes: This module will provide an introduction in the field of Machine Learning. Upon successful completion of the module, students will have a general understanding of ML algorithms and their use in big data mining. Students will have designed and implemented several ML algorithms. Students will also be able to identify, formulate and solve ML problems that arise in practical applications. Students will have knowledge of the strengths and weaknesses of different ML algorithms (relative to the characteristics of the application domain) and be able to adapt or combine some of the key elements of existing ML algorithms to design new algorithms as needed. Program: I. Fundamentals of Machine Learning
II. Machine Learning applications
C) Nonlinear System Identification Module Summary: This module discusses stateoftheart tools used to construct mathematical representations of the input output behavior of a nonlinear dynamic system (behavioral models), a process commonly known as System Identification. Achieving mathematical representations of system behavior is fundamental for many different system analyses such as simulation, control, design, sensitivity analysis, and others. This module is divided in two main parts. The first one, after introducing the basic concepts of System Identification, is focused on the main model topologies and structures that are used for modeling linear and nonlinear dynamic systems, where the latter receives the strongest emphasis. Universal approximator models – polynomial filters and artificial neural networks – are thoroughly analyzed. Beyond the presentation and support of each model, issues such as model complexity, implementation, predictive ability, accuracy, robustness and simulation efficiency are thoroughly addressed. The second part is dedicated to model parameter extraction methodologies, with the presentation of the most important methods used to calculate the parameters of linear timeinvariant, nonlinear static and nonlinear dynamic models. Parametric and nonparametric system identification approaches are explored through the presentation of time and frequencydomain methods. Nonlinear optimization, correlation analysis and leastsquares estimation methods are presented in detail in this part. Practical applications of system identification techniques to a set of testcases (of distinct nature) are presented along with the theory throughout the module, illustrating the concepts lectured in the module classes. Training outcomes: At the end of this module the students should:
Program: 1. Basics on System Identification Theory
2. Model Structures for TimeInvariant Systems
3. Model Extraction and Validation
Mobile Communication SystemsLecturers: Atílio Gameiro (coordinator), António Navarro Learning outcomes:
Syllabus: Component One I. Introduction and basic principles
II. Wireless Channel
III. Principles of OFDM and DVBT/T2 Systems
IV. Advanced channel coding for modern wireless systems
V. Multiple access schemes
VI. Link and multilink design concepts for fading channels
Component Two This component will tackle topics that represent trends and emerging techniques in the field of wireless / mobile communication systems. The topics for this component will be proposed at the beginning of the course will be assigned as homework for student research and presentation. Eventually some topics can be presented through guest lectures. Examples of possible topics are:
Optical CommunicationsLecturers: Mário Lima (Coordinator), António Teixeira, Luís Pessoa, Henrique Salgado Aims: The course aims to provide the students with the fundamentals related to optical communication systems and networks, presenting nowadays scenarios (core and access), and foreseeing next generation optical networks (NGN). It discusses several issues covering in a first part the principles of optoelectronics and fiber optics operation, followed by optical networks aspects, namely related to access passive optical networks (NGAPONs): standards, design and installation. The students will be able to receive complementary laboratory formation, by performing some experiments related to the course topics. Program: 1. Optical communication systems fundamentals
2. Next generation optical networks
RF Circuits and SubsystemsLecturers: José Machado da Silva, Nuno Borges Carvalho, Paulo Pereira Monteiro Aims: This course is focused on RF electronic circuits and subsystems and is intended to complement the basic undergraduate knowledge on telecommunications electronics of PhD students, as RF systems are the basis of all wireless systems. These currently include all types of mobile communication systems, Bluetooth, Zigbee and WiFi, and will be the basis for future solutions on 5G communications, especially Internet of Things, white space technologies and M2M. Prerequisites: It is expected that students have already undergraduate knowledge of electromagnetism, electromagnetic wave propagation in guided media and electronic circuits. Summary of the expected learning outcomes at the end of the UC:
Wireless Networks and ProtocolsLecturers: Adriano Moreira (coordinator), Manuel Ricardo, Rui Aguiar Aims: Wireless Networks and Protocols (WNP) is a course for students aimed at specializing in the mobile communications theme of MAPTele. The WNP course has two main objectives: Students should be able to: 1. Describe the evolution path of the wireless and mobile communications systems; Syllabus: 1. Introduction to Wireless Networks and Protocols: a) Overview; b)History; c)Standards and market issues; d)Evolution and trends. 2. Fundamentals of wireless communications: a) Transmission; b) Wireless data links and medium access control; c; Networking; d) Mobility concepts; e) Research issues. 3. Telecommunications systems: a) GSM; b) GPRS; c) UMTS; d) LTE; e) TETRA; f) Broadcast and satellite. 4. IEEE wireless data networks: a) WPAN; b) WLAN; c) WMAN. 5. Convergence and interoperability: a) Evolution of 3GPP networks; b) Wireless mesh networks; c) Research issues. 6. Quality of service: a) Characterization and models; b) Case studies: 3GPPQoS, IEEEQoS, IPQoS; c) Research issues. 7. Support for services and applications: a) Web services components; b) Services and applications platforms; c) Research issues. 8. Authentication and access control: a) Fundamentals of Authentication and Access Control; b) Characterization and models; c) Case studies: 3GPP, 802.1x, 3GPP; d) Research issues.

Edition 2016/2017 >