StudyQA — Master: Networks and Distributed Systems

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Description Contents Requirements

Do you find satellites, collaborating robots, safety-critical networks between cars and fighting cyber terrorism interesting? Do you want to master advanced technology and obtain a strong technical competence profile, while also learning about team work? And would you like to combine theory with practice? Then you should consider the Master in Networks and Distributed Systems (NDS).

NDS is a master in engineering, with several specialized courses in computer networks, network modeling and performance analysis methods, network planning and management, fault-tolerance and reliability study in the field of distributed systems.

The courses give you a broad foundation within subjects such as:

Traffic theory and modelling
Wired and wireless networks
Security and management.

In the projects, you will work in-depth with problems of your own choice, often in close cooperation with researchers and/or industrial partners.

You will get familiar with project-based learning, and in addition to the technical content, you will also learn about project management and planning. Moreover, we focus on systems design, where you will design a distributed system to solve a real existing problem. Part of the system will also be implemented and tested.

Networks and Distributed Systems at Aalborg University is a two-year master's programme (120 ECTS). Students can also choose to pursue one or two semesters. The programme has strong focus on problem-based learning. The courses and projects are primarily aimed at gaining knowledge on applying methods: analytical, numerical and experimental for analysing, designing and testing of networks and distributed systems.

Each semester has a theme which is supported by the offered courses and the student project.

1st semester

Communication Networks and Ambient Intelligence (elective)

This course covers the techniques needed to understand and analyse modern data communication networks, Analysis and design of communication networks; network architectures and related Internet protocols. Ambient intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people. This course also provides an overview of Human-Computer Interaction in which people are surrounded by intelligent and intuitive interfaces embedded in everyday objects around them.

The workload is designed in such a way as to properly combine the theoretical knowledge gained with practical hands-on experience. You will be required to work on mini projects in groups as part of the course. The projects are designed so that you may gain skills on network models and architectures, selected technologies and tools relevant for monitoring, simulation and emulation.

Stochastic Processes

This course introduces you to stochastic processes starting with definitions of a stochastic process, White-Sense Stationary (WSS) processes, Auto Regressive Moving Average (ARMA) processes, Markov models and Poisson point processes.

The course will be accompanied by mini projects to properly combine the theoretical knowledge gained with practical hands-on experience. You will learn how to simulate stochastic processes through the courses and the mini projects. You will gain the appropriate engineering intuition of the basic concepts and results related to stochastic processes that allow for a particular engineering problem involving randomness to design an appropriate model, derive solutions, assess the performance of these solutions and possibly modify the model.

Distributed Real Time Systems

This course is aimed at explaining how and why distributed systems work. You can find such systems for example in cars, production lines or even whole factories where different sensors and controllers need to exchange information in real time. In order to do this, there are several specialised types of networks, or buses as they are called. These will be explained in detail during the course, and you will be provided with the theoretical tools needed to analyse the system off-line. In addition, you will be presented with a set of computer simulators which will help you design and manage a distributed real time network. You will also learn about reliability concepts.

Wireless PHY/MAC fundamentals

A course description will soon be posted.

2nd Semester

Course descriptions for the 2nd semester are pending.

3rd Semester

Systems of Systems / Complex Systems

This course introduces you to methodologies for designing a system of systems in terms of designing the properties of the individual systems as well as their interconnecting behaviour, establishing the system of systems. A systematic approach to the design of network architectures and local behaviour rules which together constitute systems of systems that are optimal with respect to objectives formulated at a macroscopic level will be presented.

Through mini projects, the course will give an introduction to the opportunities that complex systems provide in research and in applications. Several approaches to the study of complex systems will be described, basic concepts will be introduced and implications for the study of different systems will be discussed.

Machine Learning (elective)

The course gives a comprehensive introduction to machine learning which is a field concerned with learning from examples and has roots in computer science, statistics and pattern recognition. The objective is realised by presenting methods and tools proven valuable and by addressing specific application problems.

Projects within the course will enable you to apply the taught methods to solve concrete engineering problems and will give you competencies in analysing a given problem and identifying appropriate machine learning methods to solve the problem.

Non-Linear Control Systems (elective)

The course comprises an introduction to nonlinear control systems, and it discusses the notions of stability such as stability in Lyapunov sense, asymptotic, and exponential stability. Moreover, the course puts forward tests for checking if a system is stable based on behaviour of a so-called Lyapunov function.

The focus of the course and projects within this course is on geometric methods: observability and controllability tests based on Lie algebras and feedback linearisation. Feedback linearisation is a pure geometrical method that helps to find a certain map which translates a nonlinear system into a linear one. The course introduces nonlinear techniques within observer design and sensor fusion as an extended Kalman filter, an unscented Kalman filter and particle filters. Last but not least, the elements of hybrid control will be introduced; herein, the notion of a hybrid automaton, bisimulation, formal verification of control and hybrid systems, stability and control of switched systems.

Project example, 3rd semester: Performance analysis and network planning The project work was based on an existing information processing problem where a distributed system and/or communication system comprises a part of the solution. An initial design may be conducted and made subject to analysis or analysis may be performed on an existing design. Emphasis may be put solely on the communication facility and the associated network planning. Choice of parameters, methods and tools for the analysis must be chosen depending on the problem to be solved.