The Master of Science in Intelligent Systems, Signals and Control provides the essential competencies and technological skills in the interlinked areas of signal processing, dynamic systems and automatic control, to help you advance and shape this industry. These thematic areas are the crucial building blocks of modern methodologies applied for the design, development, implementation and analysis of complex smart systems.
Throughout this course, you will learn how to take a transdisciplinary approach to look out for, and capture, the similarities and structures of the dynamics of different systems and processes, how to extract useful information from typically large amounts of data generated by such systems, and how to optimise and control their behaviour. The course will cover the relevant theories, design methodologies and implementation techniques within the areas of system modelling, signal processing, dynamics and systems, automatic control, computer vision, machine learning and artificial intelligence.
Robotics is an interdisciplinary field which applies knowledge from multiple engineering areas to create robotic systems that are able to perform complex tasks autonomously. This M.Sc. provides knowledge in a number of areas that are crucial for the development of smart robotic systems. These include system modelling, automatic control, machine learning, signal processing and computer vision.
Automation and Control is about creating systems and machines that operate automatically to perform some desired action. Application examples include control of driverless cars, autopilot systems, and control of robotic arms for manufacturing. In this course, you will learn how to design and create reliable, smarter and more autonomous controllers for these complex and challenging systems.
DSP is at the core of modern-day electronic systems which either acquire signals from their sensors or receive them through communication channels. DSP algorithms process these signals to denoise, filter, enhance, compress, model or extract information. A solid DSP programme is provided in this M.Sc. covering introductory and advanced signal processing methods, machine learning and computer vision.
Biomedical engineering is an interdisciplinary field which applies engineering to medicine and biology. The M.Sc. provides the opportunity to apply principles of signal and image analysis, and system modelling to data collected from the human body, such as brain and muscle signals, eye movements, and thermal images, to extract biomarkers for clinical diagnosis or human-machine interfacing.
AI enables machines to interact intelligently with their environment. It aims to simulate natural human intelligence, such as the ability of humans to interpret the data around them, make decisions based on that data and learn from their outcomes. Approaches include the use of mathematical models to represent signals and systems, statistical methods for model learning, as well as the ability to analyse visual input.
The integration of these technologies in industry leads to many smart systems which can be used in a large number of sectors such as healthcare, transportation, manufacturing, ICT, environment and many others.
This course will be delivered 100% online so that you may follow the course at your own pace. The course is designed to be completed in 12 months if followed on a full-time basis and in 36 months if followed on a part-time basis. Each study unit will consist of asynchronous lectures, forum discussions and will be assessed completely by assignments delivered between the months of October and June. At the end of the taught units, you will be asked to conduct a dissertation under the supervision of one of your tutors. While the project work can be carried out remotely, with remote access to the necessary resources, you will also have the opportunity to carry out the dissertation project in our labs if you wish to do so!
You can meet your future lecturers here and the support staff here.
The suggested roadmap for following the course on a full-time basis can be found here [PDF].
For further information, please contact us on msc-sce.eng@um.edu.mt.
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