Advanced Embedded Systems
(
ELE11120
)
The module content will focus on three key areas of embedded systems. The first section will explore the selection and application of embedded system components, including microcontrollers, sensors, actuators, and communication interfaces, while considering real-world constraints. The second section will critically evaluate the accuracy, precision, and environmental influences on analytical sensor elements within embedded applications, addressing challenges such as sensor drift, noise, and calibration. The final section will examine the integration of intelligent instrumentation into industrial and environmental systems, highlighting the role of real-time processing, adaptive control, and embedded AI in enhancing automation and system performance.
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Automation and Robotics
(
MEC11107
)
This module explores automation and robotics in industry. This module covers the kinematics modeling of robotic arms and different controllers for robotic arms. The module also includes the use of industry-level programming tools and simulators, as well as the control of physical robots. You will engage with both physical and simulated robots to solve manipulation and navigation tasks in industrial settings. Practical sessions will utilize Siemens Melfa Basic, Festo Infodicatic graphical language, and MATLAB to provide hands-on experience.
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Introduction to Human-Robot Interaction
(
CSI11102
)
With the growing presence of automation, machine learning, and personal assistants, the engagements with intelligent machines are ubiquitous (i.e., Yuksel, Collisson, Czerwinski, 2017, Brooks, 2019). The module is focused on introducing Human-Robot Interaction as a relevant and important field of study within current computing and User Experience trends. It aims to create an understanding of the relationships between humans and robots, to facilitate smooth integration and better experience during interaction in both, private and public contexts. This multidisciplinary research area draws from Human-Computer Interaction, Cognitive Psychology, understanding of Robotics and Artificial Intelligence.
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IoT and Autonomous Systems
(
ELE11118
)
Internet of Things (IoT) is an emerging network of physical objects that connects various sensors, software, and other technologies to the Internet. The aim of this module is to learn about design and development of IoT systems, including embedded architecture of IoT, technologies and autonomous systems. On this module you will learn concepts, practical aspects and applications of sensors, wireless technologies, robotic systems, data collection (and related processing technologies) for remote control of objects. The following topics will be covered: Introduction to Internet of Things, basic concepts and the state of the art technology used in IoT systems. Transducers and sensors Drones and Autonomous Underwater Vehicles (AUV) Autonomous systems sub-components : Sensors, motion control and intelligent decision making (i.e. an introduction to Artificial Intelligence and Machine Learning Techniques).
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MSc Project
(
ENG11100
)
The student will learn about important elements of project management, such as planning, control, cost, problem solving skills, report writing and defend the outcome during a viva session. The project is normally completed during 13 weeks of full time research or part time equivalent, 26 weeks.
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Mechatronic Systems
(
MEC11114
)
In this module, you will learn the mechatronic design of process/product, by considering the synergistic of various engineering disciplines. Such approach encourages one to consider system design as a whole from the beginning, especially the system integration consideration.The topics that cover in the module include mechatronic design process, definitions of Mechatronics, advantages of microprocessor, system control, product and process design applications and advantages of mechatronic design approach. In the design of a product or process, you will also learn conceptual design, user requirement specification, embodiment design, standards, safety regulations, selection of measurement system, controller hardware and software, actuator system, signal conditioning, human-machine interface, design of application program and integration of components.
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Modelling and Computation for Smart Places
(
ELE11117
)
This module equips students with advanced skills in computational and analytical techniques essential for addressing complex engineering challenges in the design and optimization of smart places. Students will develop a solid foundation in mathematical modelling, programming, and numerical methods, enabling them to devise and evaluate solutions for real-world engineering problems.The module begins by introducing a comprehensive suite of computational and analytical methods, fostering a deep understanding of their theoretical underpinnings and practical applications. Through hands-on programming exercises, students will learn to implement these methods effectively, bridging the gap between conceptual knowledge and real-world practice.Mathematical modelling will play a central role, with a focus on translating complex engineering scenarios into solvable mathematical representations. Students will engage with case studies relevant to smart infrastructure, transportation systems, sustainable energy, and urban planning to hone their problem-solving skills.A significant emphasis will be placed on numerical methods, empowering students to select, implement, and critically evaluate the performance of algorithms for solving engineering problems. The module will guide students in applying these methods to optimize processes, analyze data, and simulate dynamic systems, ensuring solutions are robust, efficient, and applicable to the evolving demands of smart places.By the end of this module, students will have cultivated a holistic skillset in computational modelling and numerical problem-solving, preparing them to tackle interdisciplinary engineering challenges with confidence and innovation.
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Network Security
(
CSN11111
)
The aim of this module is to develop a deep understanding and provide with hands-on implementation experience of network security concepts. A focus has been made on discussing networking fundamentals and implementation in first half of the module. Considerable content in second part of the module has been focused on Artificial Intelligence (AI) and Machine Learning (ML)-based solutions in the cybersecurity domain. Developing exemplar AI models in practical for achieving security measures allow students to professionally design, implement, and analyse AI-based cybersecurity strategies. An outline of the main areas includes:• Introduction to network security and challenges: It focuses on discussing various network threats and attacks that can compromise the network security. Discussion then diverts to network defence strategies, for example, perimeter and defence-in-depth.• Access Control and Authentication: This lecture focuses on introducing and discussing various types of traditional centre of gravity of computer security, the “Access Control”. The list of concepts in this lecture covers trust and identity, attacks, models – access control models, network device access control, AAA, Layer 2, device hardening.• Firewalls: In this part of module, a discussion around different types and existing technologies of firewall is presented. Students get an opportunity to implement and deploy the concepts, such as host-based or network-based firewall, static packet filtering, stateful packet filtering, and multilayer firewall in the lab environment. • Fundamentals of Cryptography and Remote Access VPN: Fundamentals of encryption, decryption, and authentication are touched before diving deeper in developing remote access and Virtual Private Networks (VPNs) for network security, while also covering types (L2, L3 and L4/5) and technologies (IPSec and SSL).• Artificial Intelligence and Machine Learning: Discussion here covers introduction to various learning techniques including supervised, unsupervised and reinforcement learning in terms of various application domains, i.e., classification, regression and clustering.• Introduction to machine learning models: Various AI models are introduced and implemented including neural network, linear regression, k-means clustering, support vector machine, random forest, decision tree, deep neural network.• Data-Driven Cybersecurity: Intrusion Detection Systems (IDS) vs Intrusion Detection and Prevention Systems (IDPS) are introduced in terms of types, alert monitoring and sensor tuning; behavioural analysis, in-line vs out-of-line IDS/IDPS. Practical provides an opportunity to applying AI techniques to real-world intrusion detection problems. • Relevant state-of-the-art research in the domain: Discussions around recent research advances in the fields of network security and cybersecurity.
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Research Skills and Project Management
(
MEC11117
)
This module aims to improve students' skills in two critical academic and professional areas: conducting comprehensive literature reviews and managing research projects.The first part of the module focuses on literature review techniques. Students will learn methods for identifying, evaluating, and synthesising scholarly sources to build a strong foundation for their research. Essential skills include searching and assessing various information sources, critically analysing and interpreting existing research, and identifying gaps in the literature. The module also teaches practical strategies for organising and writing a literature review that aligns with a specific research question or hypothesis.The second part of the module provides a comprehensive overview of research project management. It covers all project stages, from inception to completion, and equips students with essential skills in teamwork, information searching, and utilising various information sources. Additionally, students will learn project management principles, focusing on risk management, time management, and cost control.By the end of the module, students will be proficient in conducting thorough literature reviews and managing research projects effectively.
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Research Through Design
(
CSI11101
)
In this module you will be introduced to research approaches that can offer rich insights that impact the process of innovation and design, with a focus on how to design research. You will be encouraged to empathise and understand user needs in relation to day to day activities/tasks/experiences. You will be responsible for your learning through the planning stages of a project based on evidence and rigour to research and a target user group understanding. Through this research design, you will complete the module with a strong foundation for planning and completing research projects, including your MSc dissertation.
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Wireless Sensors and Mobile Communications
(
ELE11119
)
The module comprises of thematic areas covering the most essential aspects of wireless systems. Beginning with the presentation of the fundamental parts of wireless systems, a detailed analysis of each component will follow, namely, antennas, propagation channels, and digital modulation. A more detailed description of provisional topics can be seen below.A. Fundamentals of wireless systems: Major components, Block diagrams.B. Antennas: Fundamental antenna parameters, Antenna performance and design metrics, Antennas as part or wireless systems, Transmitting and receiving antennas for point-to-point communications and Friis transmission formula.C. Propagation channels: Propagation mechanisms, Path loss modelling (including Friis transmission formula), Shadowing statistical modelling, Multipath propagation stochastic modelling.D. Digital modulation: Baseband and passband signals, Nyquist theorems for signal interference and reconstruction, Digital modulation techniques, Demodulation and detection performance.
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* These are indicative only and reflect the course structure in the current academic year. Some changes may occur between now and the time that you study.