Core units:
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Mobile & Wireless Networks: Mobile and wireless networking technologies are ubiquitous nowadays. A key issue facing enterprises today is how to harness the potential of mobile and wireless technologies to enhance the operational effectiveness of the business and illuminate potential opportunities. This unit reviews the broad spectrum of relevant technologies (e.g., WiFi, ad hoc networks, cellular networks, etc.), examines their operating principles/protocols and relevant standards as well as the use of these networks in different context and scenarios. The unit also explores the main challenges faced in these networks as well as how they fit into the latest networking paradigms.
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Wireless, Sensor & Actuator Networks: You will explore the operating principles, standards, design and configuration of WSANs. Future and emerging paradigms – such as Mobile Crowdsensing Systems, and technologies such as Wireless Power Transfer in ad-hoc networks will also be addressed.
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Security and Privacy in IoT: Smart objects, IoT applications, and their enabling platforms are often vulnerable to security attacks and changing operating and context conditions that can compromise the security of some of their components (e.g., local sensors, network components, application-level components). They also generate, make use of and inter-relate massive personal data in ways that can potentially breach legal and privacy requirements. This unit will develop your capability to understand and produce security architectures of IoT-related networks and systems.
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Research Methods & Professional Issues: Research requires a structured and disciplined approach at all stages. We will help you to develop key research skills in many areas from project proposals and planning to critical analysis of research findings, academic writing, and dissemination. Ensuring you give due consideration to professional standards and ethical issues in research.
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Individual Masters Project: You will develop an understanding of the characteristics and implications inherent in the solution of a complex, real-world problem within the context of a substantial, independently-conducted piece of work.
Option units (choose two)
Option units (choose one semester 1)
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Cloud Computing: Cloud computing provides a secure infrastructure for systems integration, data processing and manipulation as well as storage. The concept of virtualisation including servers, storage and networks, as well as the cloud structure, services and deployment models will be discussed. Locally-based networks, IT resources/services and their migration to the cloud are elaborated and strategies for migration to the cloud will be explored.
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Neuronal Analysis: This unit focuses on the study and application of data analysis techniques in neuroscience. Such algorithms are either adapted or specifically designed for analysing neuronal activity in physiological and pathological brain states. The mathematical modelling of basic neuronal functions over the last century, inspired the development of sophisticated parallel data processors that imitate biological neurons and networks to a certain degree. A successful example is the widely used family of deep learning-based approaches. In parallel, methods from e.g., computational statistics, dynamical systems, partial differential equations and more recently machine learning, feedback to the neuroscience field; and provided valuable insights in the understanding of the nervous system. This unit discusses state-of-the-art analytical tools for identifying normal and altered behaviour of neuronal activity at multiple spatiotemporal scales, their applications and current limitations.
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Search & Optimisation: Search & optimisation techniques are employed in a vast number of areas, including medicine, defence, transporation, aerospace, and finance. Whether the goal is to improve the performance of a new drug, aircraft, traffic signal controllers, or investment decisions, stochastic optimisation algorithms can be employed by researchers and practitioners to design optimal, diverse, and pertinent solutions to many real-world problems.
Option units (choose one semester 2)
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Data Processing and Analytics: The unit aims to develop student knowledge and skills in the evolving areas of Data Modelling, Data Analytics and Big Data Analytics. Students develop an understanding of data design, implementation and use of data-driven systems. Moreover, they learn how to model data and big data, discover knowledge within the data and deal with the dimensionality of the data. Overall, they develop critical understanding of the methodologies and techniques to cope with Data and Big Data, i.e. data of high volume, high velocity and high variety, utilising the appropriate platform (e.g. Hadoop, Storm, Spark, MongoDB).
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Artificial Intelligence: The aim of this unit is to provide you with an introduction to the principles and techniques employed within the greater field and sub-fields of Artificial Intelligence (AI), and the skills and knowledge required to employ AI techniques to solve real-world and synthetic problems. We will approach AI from a computer-science perspective, with focus given to the challenges faced within the field, nature inspired algorithms, and their applications to complex real-world problems.
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Computer Vision: The field of Computer Vision has been developing rapidly over the last decade and became the cornerstone of many recent innovations across a number of domains and application areas, including state-of-the-art image classification (e.g. for use in search engines), object detection (e.g. in autonomous vehicles), semantic segmentation (e.g. in identification of tumours in x-ray images), biometrics (e.g. face verification) or automatic annotation of medical images, to name a few. There is a shortage of skilled individuals with deep understanding of the underlying principles and able to confidently apply them in order to solve real-world problems. This unit focuses on a number of Computer Vision techniques, ranging from traditional approaches established over many decades of research, to the latest state-of-the-art developments, together with appropriate tools. The unit entails a strong empirical element where Computer Vision models will be built and evaluated.
Please note that option units require minimum numbers in order to run and may only be available on a semester by semester basis. They may also change from year to year.
Programme specification
Programme specifications provide definitive records of the University's taught degrees in line with Quality Assurance Agency requirements. Every taught course leading to a BU Award has a programme specification which describes its aims, structure, content and learning outcomes, plus the teaching, learning and assessment methods used.
Whilst every effort is made to ensure the accuracy of the programme specification, the information is liable to change to take advantage of exciting new approaches to teaching and learning as well as developments in industry. If you have been unable to locate the programme specification for the course you are interested in, it will be available as soon as the latest version is ready.
