樱花动漫

INT2002ZUT: English for Publications

This module develops your academic writing skills for postgraduate-level research. You will learn to write two distinct genres of academic writing: a critical literature review that synthesizes research in your field, and a research article that presents your own analysis and arguments. Through a structured writing process incorporating peer review and tutor feedback, you will develop your ability to engage critically with sources, construct persuasive arguments, and communicate complex ideas effectively in writing appropriate for postgraduate study.

INT2003ZUT: Oral English for Academia

This module develops your spoken English and listening skills for postgraduate-level academic study in university. Through collaborative group presentations, critical engagement with academic lectures, and participation in academic discussions, you will build the confidence and competence needed for postgraduate study. The module emphasizes synthesis of multiple sources, critical response to academic content, and development of intercultural communication skills essential for successful collaboration in academic environments. Assessment includes both group and individual components, ensuring comprehensive development of oral academic skills.

COMM004ZUT: Multivariable State-Space Control

Control theory is concerned with forcing the measured outputs of a system to follow a desired reference command, through the manipulation of certain input variables to the system whilst facing its uncertain knowledge and external disturbances. A powerful concept in this field is the notion of feedback – whereby the measured outputs of the system are compared in real-time with the reference signal, and the errors are processed to compute updates of the manipulated system inputs. Control systems are often a `hidden technology’ and exist all around us and are often a key aspect of many of the devices and products we rely upon: aircraft, communications devices, robots, chemical plants, space exploration, motors and drives, and land-vehicles.

The aim of this course is to introduce the concept of a state-space system, and how such a representation can be used for the systematic development of control laws for multivariable systems. You will learn how to create state-space models from other representations (such as transfer functions and higher order differential equations), and how to analyse the properties of state-space systems. The key notion of controllability will be described, and different paradigms will be introduced to provide systematic ways of designing feedback controls laws, including so-called observer-based strategies.

COMM006ZUT: Dissertation

You will be required to solve a research or industrially-related practical problem based on the topics learned within, but not exclusive to, the MSc programme you are registered for. The project work will lead to a major piece of work (dissertation) of approximately 15,000 words (max. 80 pages, including references and appendices) that involves project planning, analytical, experimental or empirical results and their interpretation, showing how the goals of the project have been met. You will receive a list of potential projects and will be required to express your two preferences. Alternatively, you can discuss your own dissertation ideas with the module leader / potential supervisor (academic staff) with a view to explore if they offer required technical rigour and research challenge. You will be encouraged to discuss your preferences with relevant academic staff before we allocate projects. As part of the research project, you are expected to undertake a considerable amount of self-study. There is no formal taught component in the module, apart from suggested regular meetings with the supervisor.

ENSM024ZUT: Data-Centric Engineering

The module will introduce you to mathematical foundations and state-of-the-art methods in probabilistic modelling, Bayesian analysis, and probabilistic machine learning.

The next decade will see a step changes in data-driven technology, impacting all aspects of engineering and industry. By exploiting data being generated presents enormous engineering opportunities to transform both system design and control. This module focuses on the logic, algorithms, and frameworks that are essential to tackle real-world data and the grand challenges of modern data-driven engineering applicable to the domains such as materials, patient-specific medicine, virtual prototyping, and sustainability.

The module aims at providing you skills and knowledge in mathematical foundations and advanced methods for data-centric engineering at the frontiers of current applied research, providing tools and mindset needed for the applications of contents from other modules in the field of Electronic Information engineering.

ENSM026ZUT: Network Engineering, Modelling and Management

This module has been designed to develop your knowledge of power electronics and power systems, data acquisition and automation. The future grid will see more integration of renewable energy sources (RES) and, thus, it is vital to understand power electronics, which is the enabling technology for integrating RES to the Grid. You will also learn the basic skills for modelling and analyzing the power network. The objective of this module is to consolidate and further develop your core knowledge and understanding of electrical power systems engineering, particularly the issues associated with the connection of renewable energy projects to electricity distribution grids. Additionally, this module will allow you to gain hands-on experience in the practical aspects of data acquisition and control and associated software in the context of power electronic converters. You will have ‘hands-on’ interaction with sensors, data acquisition systems and control equipment. You will be working in small groups on a chosen project to select sensor components data loggers, and actuators to assemble a data logging and control system for your project that could then be deployed ‘externally’. This is theoretical and practical course using lectures and laboratory-based exercises, and resulting in individual design and group design exercises.

ENSM028ZUT: Nonlinear Control

In this module, you will learn why some Engineering systems are better modelled as nonlinear equations. Whilst linear systems are better understood from a mathematical perspective (often yielding analytic solutions) and have been extensively studied and used as a platform for the design of a wide range of linear control strategies, many real engineering systems are nonlinear and cannot be approximated well by linear ones (except around limited operational points). In this module, you will look at methods to analyse nonlinear systems and will introduce some state-of-the-art techniques for developing practical nonlinear control strategies for such systems.

ENSM030ZUT: Professional Ethics, Competence and Commercial Awareness

Today’s engineering professionals demonstrate a personal and professional commitment to society, to their profession, and to the environment. These principles are embedded in professional codes of conduct and mechanisms for self-regulation. Professional competence integrates knowledge, understanding, skills and values and is accrued through professional development. Health and Safety is addressed through a study of the mechanisms by which major failures usually occur. The purpose and benefit of professional bodies, the autonomy for a profession and a right to self-regulation are examined.

You will be given a basic grasp of accounting, with respect to using accounts as a tool for measuring and improving the financial health of a business. The investor’s perspective is also addressed. The skills obtained by you during this module are widely applicable and easily transferred to a wide range of industries and situations. Learning is based on seminar sessions with topics generally being discussed as a group.

ENSM031ZUT: Robotics and Automation

This module aims to develop your knowledge and understanding of robotics and automation. The use of robotics in society is increasing, with applications ranging from agriculture to manufacturing, with a growing interest in autonomous systems. The module will provide you with an appreciation of the basic concepts of robotics, simulation and modelling techniques and critical components of such complex robotic systems, introducing you to the fundamentals of robotic systems, including kinematics and dynamics, as applied to manipulators and mobile robots. The module will also review the actuators and sensors supporting robotic systems and their motion control. In addition, this module will cover various aspects of automation and the application of robotic platforms, in industry, particularly for mobile sensing.

You will learn planning tasks and design new automated systems with control and optimisation strategies. Scheduled tutorials and laboratory sessions aim to enhance your understanding of robotics and automation systems, their capability, planning/control, and fundamentals of robotic operating systems.

ZUTM001: Machine Learning

This module provides a comprehensive introduction to the?core principles and applications?of machine learning, covering fundamental concepts including?hypothesis space,?supervised/ unsupervised/semi-supervised learning paradigms, and?model selection methodologies?along with their implementation scenarios. Key algorithms encompass?supervised techniques?(linear/ nonlinear models, neural networks, support vector machines, kernel functions, Bayesian classification, and ensemble learning) and?unsupervised approaches?(clustering algorithms, principal component analysis, and anomaly detection). Through?integrating theoretical frameworks with practical implementation, you will master systematic methodologies for?tackling engineering problems?using machine learning algorithms and will develop proficiency in?strategically combining diverse algorithms?to enhance solution effectiveness for complex engineering challenges.

The main objective of the module is to provide you with specialised knowledge and critical understanding of common machine learning algorithms, including the main ideas and fundamental steps of these algorithms, and to deepen your understanding through programming exercises and typical application examples. The course focuses on cultivating your theoretical literacy in the fields of artificial intelligence and data mining, while also balancing hands-on practical skills, thereby laying a solid foundation for continuous learning in the future.

ZUTM003: Modern Signal Processing

The course aims to: 1) Introduce fundamental principles of statistical signal processing and adaptive signal processing; 2) Develop graduate students' theoretical research capabilities in modern signal processing; 3) Lay a solid foundation for future research projects. Students will learn key digital signal processing techniques, learn linear filter design methods, gain preliminary understanding of signal modeling and estimation approaches, and comprehend the principles of adaptive and adaptive signal processing. Additionally, the course explores cutting-edge theories and applications in signal processing, such as compressed sensing.

The primary objective of the Modern Signal Processing module is to equip students with specialized knowledge and a critical understanding of the core theories and advanced algorithms in random signal analysis and adaptive signal processing. This module is designed to build upon the foundations of digital signal processing, probability, and linear algebra, empowering you to tackle the challenges of random signals prevalent in modern engineering systems. An essential aim is to foster a deep comprehension of the philosophical shift from classical to modern signal processing paradigms, emphasizing how statistical methods and adaptive techniques provide powerful tools for extracting meaningful information from noisy, real-world data.

ZUTM005: Probability Theory and Stochastic Process

This module provides a comprehensive introduction to the core principles and applications of probability and random processes, establishing a rigorous foundation in fundamental concepts including axiomatic probability, random variables, distribution functions, and expectation theory. The curriculum extensively explores key stochastic processes such as stationary processes, Gaussian processes, Poisson processes, and Markov chains, along with their critical characterization through correlation functions and power spectral density. Through integrating theoretical frameworks with practical engineering analysis, you will master systematic methodologies for tackling uncertainty in electrical and computer systems and will develop proficiency in strategically modelling and analysing random signals, enabling you to design and optimize robust solutions for complex challenges in communications, signal processing, and networks.