Computer Engineering is a dynamic and collaborative degree programme; combining academic thought with practical application. Computer Engineers make the impossible possible. They challenge conventional processes and look beyond what exists towards what comes next.
From everyday systems, like games consoles and mobile phones to advanced systems for surveillance and medical devices, the modern world is made possible by the devices you will be taught to understand and develop during the Computer Engineering undergraduate programme. Additionally, Computer Engineering is one of the few research-led degrees in Queen’s which includes the design of both electronic hardware and software. As a CE graduate you can not only design the physical hardware but also write the software to run it.
Through our diverse network of industry links you begin learning from prospective employers from day one. Industry placements, company-sponsored hackathons and project challenges are a core part of the curriculum and vastly improve our graduate employability rates.
Electrical and Electronic Engineering at Queen’s is ranked 9th in the UK overall and 1st in the UK for Graduate Prospects in the Times and Sunday Times Good University Guide 2020.
Computer Engineering highlights
World Class Facilities
The School of Electronics, Electrical Engineering and Computer Science has a world-class reputation for research and provides excellent facilities, including access to major new research centres in Secure Information Technologies (CSIT), Electronics, Communications and Information Technology (ECIT) and Sonic Arts (SARC).
Career Development
Graduates in Computer Engineering are in high demand, with many developing careers in software, electronics or roles that combine both. Additionally, there are excellent, well-paid career prospects across a wide spectrum of positions: design; research; development; production; marketing and sales in industries such as avionics and space; telecommunications and broadcasting; connected health and medical electronics; consumer electronics and gaming; computing and software; embedded systems, smart networks and electronic security.
Industry Links
Our students are constantly given the opportunity to put theory into practice. We regularly consult a large number of employers including, for example, Civica and Sensata Technologies, who provide sponsorship for our students as well as NIE Networks who are members of the employer liaison panel for the course.
Further Study Opportunities
Further study is also an option open to Computer Engineering graduates. Students can choose from a wide range of Masters programmes as well as a comprehensive list of research topics.
Student Experience
Scholarships: students may be eligible for scholarships, eg the Sensata Technologies Scholarship, NIE Networks Scholarship and the Civica Scholarship. For further information, visit the School Website.
This four-year extended engineering degree has been established to provide a good supply of well-qualified engineers with an appropriate blend of engineering knowledge and skills in business practice and management. There is a core component of entrepreneurship, giving key insights into company creation. All of the material in the first two years is common with the BEng degree.
The programme contains the following themes which may change due to technology and industry needs:
Stage 1
May include topics such as:-
Embedded Systems
Mathematics
Analogue & Digital Electronics
Computer Architecture
Procedural & Object-Oriented Programming
Stage 2
May include topics such as:-
Embedded Systems
Professional Engineering Practice
Data Structures & Algorithms
Mathematics
Electric Circuits
Digital Electronics
Signals & Systems
Control
Communications Systems
Artificial Intelligence
Cyber-security
Stage 3
May include topics such as:-
Engineering Entrepreneurship
Advanced Electronics
Networks & Communication Protocols
Control Systems Engineering
Signal Processing
Communications Systems Engineering
Connected Health
Concurrent Programming
Machine Learning
Data Analysis
Cyber-Security
Stage 4
May include topics such as:-
Individual Technical Project
Algorithms
High-Performance Computing
Machine Learning
Wireless Communications Systems
Intelligent Systems
Cyber-Physical Systems
Wireless Sensor Systems
Custom Computer Engineering
People teaching you
EEECS E: eeecs@qub.ac.uk
T: +44 (0)28 9097 4669
Contact Teaching Hours
Medium Group Teaching
6 (hours maximum) 6 hours of practical classes, workshops or seminars each week
Large Group Teaching
9 (hours maximum) 9 hours of lectures
Personal Study
24 (hours maximum) 22-24 hours studying and revising in your own time each week, including some guided study using handouts, online activities etc
Learning and Teaching
The School has a world class reputation for research and provides excellent facilities, including access to major new research centres in Secure Information Technologies, Electronics, Communications and Information Technology and Sonic Arts. A number of modules on the course are closely linked to the research expertise of these centres and evolve and change rapidly to reflect some of the current, emerging and exciting developments in the field.
At Queen’s, we aim to deliver a high quality learning environment that embeds intellectual curiosity, innovation and best practice in learning, teaching and student support to enable student to achieve their full academic potential.
The MEng Computer Engineering provides a range of learning experiences which enable students to engage with subject experts, develop attributes and perspectives that will equip them for life and work in a global society and make use of innovative technologies and a world class library that enhances their development as independent, lifelong learners. Examples of the opportunities provided for learning on this course are:
E-Learning technologies
Information associated with lectures and assignments is often communicated via a Virtual Learning Environment (VLE) called Queen’s Online. A range of e-learning experiences are also embedded in the degree through, for example: interactive group workshops in a flexible learning space; IT and statistics modules; podcasts and interactive web-based learning activities; opportunities to use IT programmes associated with design in practicals and project- based work etc.
Lectures
Introduce basic information about new topics as a starting point for further self-directed private study/reading. Lectures also provide opportunities to ask questions, gain some feedback and advice on assessments (normally delivered in large groups to all year group peers).
Personal Tutor
Undergraduates are allocated a Personal Tutor who meets with them on several occasions during the year to support their academic development.
Practicals
Where you will have opportunities to develop technical skills and apply theoretical principles to real-life or practical contexts
Self-directed study
This is an essential part of life as a Queen’s student when important private reading, engagement with e-learning resources, reflection on feedback to date and assignment research and preparation work is carried out.
Seminars/tutorials
Significant amounts of teaching are carried out in small groups (typically 10-20 students). These provide an opportunity for students to engage with academic staff who have specialist knowledge of the topic, to ask questions of them and to assess their own progress and understanding with the support of peers. You should also expect to make presentations and other contributions to these groups.
Supervised projects
In final year, you will be expected to carry out a significant piece of research on a topic or practical methodology that you have chosen. You will receive support from a supervisor who will guide you in terms of how to carry out your research and will provide feedback to you on at least 2 occasions during the write up stage.
Assessment
Details of assessments associated with this course are outlined below:
The way in which you are assessed will vary according to the Learning objectives of each module. Some modules are assessed solely through project work or written assignments. Others are assessed through a combination of coursework and end of semester examinations. Details of how each module is assessed are shown in the Student Handbook which is provided to all students during their first year induction
Feedback
As students progress through their course at Queen’s they will receive general and specific feedback about their work from a variety of sources including lecturers, module co-ordinators, placement supervisors, personal tutors, advisers of study and peers. University students are expected to engage with reflective practice and to use this approach to improve the quality of their work. Feedback may be provided in a variety of forms including:
Feedback provided via formal written comments and marks relating to work that you, as an individual or as part of a group, have submitted
Face to face comment. This may include occasions when you make use of the lecturers’ advertised “office hours” to help you to address a specific query.
Placement employer comments or references
Online or emailed comment
General comments or question and answer opportunities at the end of a lecture, seminar or tutorial.
Pre-submission advice regarding the standards you should aim for and common pitfalls to avoid. In some instances, this may be provided in the form of model answers or exemplars which you can review in your own time.
Feedback and outcomes from practical classes.
Comment and guidance provided by staff from specialist support services such as, Careers, Employability and Skills or the Learning Development Service.
Once you have reviewed your feedback, you will be encouraged to identify and implement further improvements to the quality of your work.
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
1. Introduction to Computer Programming using Python
2. Introduction to Embedded Systems Programming using Arduino C
3. Introduction to Microcontroller Electronics
4. Introduction to Printed Circuit Board Design
Learning Outcomes
On successful completion of the course the student will:
• Understand the basic structure of a computer program, using both Python and the C programming languages.
• Understand the basic structure of an MCU (Microcontroller Unit)
• Understand how to develop software for an MCU.
• Understand how basic analogue and digital interface circuits are designed for an MCU.
• Understand how to develop event-driven ISR (Interrupt Service Routines).
• Understand how Printed Circuit Boards (PCBs) are designed and constructed.
Skills
Skills
The skills developed by the students during this course are as follows:
• How to use an IDE (Integrated Development Environment) for developing simple software programs.
• Understand how to edit, compile and test/debug simple programs.
• Design simple programming routines to carry out real-world tasks.
• Understand how to design simple embedded systems to solve real-world problems.
• Use a PCB design tool to design a basic Printed Circuit Board (PCB).
Linear Time Invariant Systems:
• Discrete and continuous time signals and systems.
• Simple signal arithmetic and manipulation
• Transformations of independent variables
• Properties of systems, including linearity, time invariance, stability, memory, causality
• Linear-time invariant (LTI) systems, convolution and impulse response
• Basic electronic data capture
• Fundamental data representation and manipulation
• Basic programming skills – program creation and execution
• Fundamental programming constructs: variables, structures, loops, conditionals.
Communications Systems:
• Overview of communication systems, electromagnetic spectrum
• Gain, Attenuation, Decibels and their use
• Analogue modulation: Amplitude Modulation (AM) Frequency Modulation (FM)
• Digital Modulation ASK, FSK, PSK
• Radio Receivers: Superheterodyne Receivers, Software defined radios, Filters
• Radio transmitters
• Noise, understanding N = kTB
• Transmission Lines
• Antennas
• Link Design Equation
• Propagation – Line of sight, Multipath Effects
• Optical Communications – eg Fibre “broadband”
• Introduction to Secure Communications
Learning Outcomes
On successful completion of this module, students will be able to:
• Understanding of the forms of continuous and discrete-time signals.
• Understanding the nature of transformations of a signal’s independent variable.
• Comprehensive understanding of the nature of fundamental signals, specifically the
• discrete-time impulse and continuous-time exponential.
• Understanding of the nature of LTI systems.
• The ability to analyse LTI systems to determine any one of input, output or system response, given knowledge of the other two.
• Manipulate practical electronic data via software.
• Appreciation of communications systems used in a wide range of applications, eg mobile comms, satellite, aviation, emergency services, telemetry etc.
• Understand how information is conveyed wirelessly from transmitter to receiver including modulation, antennas and propagation
• Understanding of transmission lines and landline based comms systems, such as fibre “broadband”
• Ability to design a basic analogue or digital wireless comms system including link design equations
• Practically measure communication system components and links
Skills
• Discrete and continuous time signals and system description and transformation.
• Properties of systems, including linearity, time invariance, stability, memory, causality
• Linear-time invariant (LTI) systems, convolution and impulse response
• Basic signal capture, analysis and manipulation in software.
• Practical measurement skills of RF time domain and frequency domain
• Ability to specify, setup and measure a basic wireless communications system
This module introduces the fundamentals of object-oriented programming. Real-world problems and exemplar code solutions are examined to encourage effective data modelling, code reuse and good algorithm design. Fundamental OO programming concepts including abstraction, encapsulation, inheritance and polymorphism are practically reviewed through case studies, with an emphasis on testing and the use of code repositories for better management of code version control.
Learning Outcomes
Students must be able to:
• Demonstrate knowledge, understanding and the application of the principles and application of object-oriented design, to include:
o Abstraction, encapsulation, inheritance and polymorphism
• Demonstrate knowledge of static data modelling techniques (through UML)
• Demonstrate knowledge, understanding and the application of the principles and application of object extensibility and object reuse.
• Demonstrate knowledge, understanding and the application of more advanced programming concepts, to include:
o Recursion
o Searching and sorting
o Basic data structures
• Demonstrate knowledge, understanding and the application of testing, in particular, unit and integration testing.
• Apply good programming standards in compliance with the relevant codes of practice and versioning tools being employed e.g. naming conventions, comments and indentation
• Analyse real-world challenges in combination with OO programming concepts to write code in an effective way to solve the problem.
Skills
KNOWLEDGE & UNDERSTANDING: Understand fundamental theories of object-oriented programming
INTELLECTUAL AND PRACTICAL:
• Be able to design, develop and test programs, which meet functional requirements expressed in English.
• Programs designed, developed and tested will contain a combination of some or all of the features as within the Knowledge and Understanding learning outcomes.
Differential Equations:
Fundamentals; 1st Order Methods; 2nd Order Methods
Integration:
Fundamentals; Integrating functions of functions; Integration functions of linear functions; Integration by parts; Integration by substitution; Integration by Reduction Formula; Applications
Sequences and Series:
Fundamentals; Convergence and Limits; Tests of Convergence; Power Series Properties; Limits for Indeterminate Solutions; L’Hopitals’s Rule;
Function Approximation:
Fundamentals; imiting Indeterminate Analytical Functions; Taylor’s and Maclaurin’s Series; Compositie Series Approximations; Accuracy Limitations
Learning Outcomes
• Understanding of the concept and forms of, and motivation for complex numbers.
• The ability to represent complex numbers in Cartesian, exponential and graphical forms.
• The ability to perform fundamental arithmetic operations on complex numbers.
• The ability to measure the modulus and argument of a complex number.
• The ability to use complex arithmetic to represent the roots of any number.
• Understanding of the concept of vector arithmetic.
• The ability to manipulate high-dimensional mathematical objects and apply fundamental arithmetic operations thereon.
• An understanding of the form and concepts behind manipulation of matrices.
• The ability to perform fundamental arithmetic operations on matrices.
• The ability to transform matrices.
• The ability to exploit matrices for the solution of linear algebraic equations.
• The ability to perform matrix triangularisation and inversion.
• The ability to use matrix triangularisation, matrix inverse and matrix determinants to solve systems of simultaneous equations.
• Differentiation of simple, parameteric and logarithmic functions
• 1st and 2nd order differential equations
• Integration of functions of functions, functions of linear functions, by parts, substitution or reduction
• Sequences and series
• Functional approximation
Skills
• Formulation and analysis of arithmetic problems including complex numbers.
• The ability to derive the roots of any number.
• Formulation and manipulation of high-dimensional mathematical objects.
• Formulation and solution of high-dimensional linear algebraic problems using matrix arithmetic.
The course develops the basics of logic components and digital circuits, and how they are implemented in real digital hardware platforms. The learning outcomes will address the following topics about the creation of digital systems:
o Number systems (Binary, Octal, Hexadecimal)
o Basic Gates in digital systems
o Combinational logic design
o Karnaugh maps (k-maps)
o Digital circuit minimisation via Boolean Algebra
o Digital circuit minimisation via Quine-McCluskey
o Sequential logic design
o Flip-flops
o Timing considerations
o Digital hardware technologies
o Multiple outputs and ROMs
o Field Programmable Gate Array (FPGA) technology
o Digital Counters
o Finite State Machines
o Computer architecture fundamentals
o Instruction Set Architecture (ISA)
o Verilog Hardware description Language (HDL)
Learning Outcomes
The module will provide a sound understanding of digital system design illustrated through practical digital hardware circuit design and programming skills using Verilog HDL programming. After the completion of this module you will be able to:
• Design, implement and analyse the operation of both combinational and
sequential logic circuit based on a system specification
• Understand the concept of cost in digital technologies and techniques to
learn techniques to minimize the cost
• Understanding architecture systems architecture and design
• Program and Design a digital design on Field Programmable Gate Array
(FPGA) technology
Skills
During this course of this module you will acquire the following key skills:
• How to model, analyse, optimize and implement digital systems
• Fundamentals of computer architecture
• Problem solving Programming a real Field Programmable Gate Array
(FPGA) system
• ICT skills
• HDL programming skills
Lectures:
1. Introduction to Fundamental Components (R, L, C)
2. Circuit Elements and Sources
3. Electric Circuit Laws and Theorems
4. AC and DC Circuit Analysis
5. Phasor Representation
6. Frequency Response of Simple Circuits
7. Basic amplifiers and system concepts
8. Feedback systems and operational amplifiers (Op Amp)
9. Diode characteristics and circuits – analysis and applications
10. Bipolar junction transistor (BJT)
Design project:
• Design of a DC Power Supply
Learning Outcomes
On completion of this module, a student will have achieved the following learning outcomes commensurate with module classification:
• Understand fundamentals of electric circuits, AC/DC circuit theorems, analysis techniques
• Understand phasor representation of alternating voltages and currents
• Acquire a practical understanding of the course material through a range
of lab experiments
• Understand analogue electronic devices and analogue circuits
• Develop an understanding of the experimental design and analysis of
electrical power supplies, design and test methodologies,
• Demonstrate analysis and interpretation of circuit results
• Develop a fundamental understanding of electronics principles needed for
analogue circuit design
• Develop a practical understanding of the different roles of electronic
devices in simple analogue and digital electronic circuits and systems.
Skills
Skills developed by students during this module are as follows:
General:
• Analysis of simple DC and AC electric circuits
Laboratory & Design Project:
• Development and analysis of a simple DC power supply circuit
• Measurement of key characteristics of electrical and electronic systems
• Debugging of electronic systems
• Testing of electronic systems
• Use of laboratory instruments
• Use of electrical/electronic engineering principles to develop solutions
• Presentation of technical engineering information clearly and concisely in
written form
• Analysis of simple analogue circuits
• Use of electrical/electronic engineering principles to develop circuit
solutions
1. Periodic functions, Fourier series and Fourier coefficients.
2. Vector/matrix notations and operations.
3. Fundamental theorem of linear systems, solving linear systems.
4. Orthogonality, eigenvalues, eigenvectors, eigendecomposition, and QR decomposition.
5. Multivariate functions, partial derivatives, chain rule.
6. Multivariate integration.
7. Multivariate optimisation: unconstrained optimisation and constrained optimisation.
8. Basic Probability Concepts and Common Probability Distributions.
9. Sampling, Parameter Estimation, Statistical Inference.
Learning Outcomes
Learning Outcomes
Fundamental understanding of the modern engineering mathematics, probability and statistics, basic theoretical concepts, methods, with application to the problems of analysis and modelling in electronic communications, microwave engineering and design of electronic components, circuits and systems.
Understand basic probability concepts, expectation and some of the most common probability distributions encountered in engineering. Understand different concepts related to sampling and data analysis. Build an appreciation of some of the different types of parameter estimation. Develop an understanding of the principles of statistical inference including hypothesis testing.
Skills
Skills
• Matrix algebra, analysis and modelling of linear systems.
• Fourier analysis
• Optimisation theory
• Multivariate calculus
• Probability and statistical inference
• Computational statistics.
• Data structures: Stacks, Lists, Queues, Trees, Hash tables, Graphs, Sets and Maps
• Algorithms: Searching, Sorting, Recursion (with trees, graphs, hash tables etc.)
• Asymptotic analysis of algorithms
• Programming languages representation and implementation
Learning Outcomes
• Demonstrate understanding of the operation and implementation of common data structures and algorithmic processes (including stacks, lists, queues, trees, hash tables, graphs, sets and maps, alongside searching, sorting and recursion algorithms).
• Select, implement and use data structures and searching, sorting and recursive algorithms to model and solve problems.
• Perform asymptotic analysis of simple algorithms.
• Demonstrate understanding of the fundamentals of programming languages representation, implementation and execution.
Skills
Problem solving by analysis, solution design and application of techniques (e.g. suitable data structures, algorithms, and implementation in C++). Precision and conciseness of expression. Rigour in thought.
This module will prepare students for placement and graduate employment by developing an awareness of the business environment and the issues involved in successful career management combined with the development of key transferrable skills such as problem solving, communication and team working. Students will build their professional practice and ability to critically self-reflect to improve their performance.
Lectures will include: Introduction to placement requirements, CV building, local, national and international options, interview skills, assessment centres, placement approval, health and safety and wellbeing. Interactive workshops will focus on interview skills and team work.
This module will be delivered in-house by EEECS Careers & Placement Team.
Learning Outcomes
• To prepare students to compete effectively for placement and graduate employment in industry;
• Become more aware of their career aspirations and how to achieve them;
• Develop knowledge of undergraduate and graduate opportunities both locally, nationally and internationally;
• To develop and demonstrate a range of transferrable skills including communication skills, presentation, group working and problem solving;
• To develop professional skills in critical reflection of self and others feeding into improvements.
Skills
• Equips students with a clear understanding of placement requirements and the placement approval process.
• Develop practical experience of communication skills, presentation skills and team working skills.
• Gain a wider understanding of the business environment and the opportunities available within the Engineering and IT sectors.
• Manage own career decision making.
1. Introduction to Microcontrollers for Embedded Systems
2. Interfacing Sensors for Microcontrollers
3. Design Project for Microcontroller based Hardware
Learning Outcomes
Learning Outcomes
On successful completion of the course the student will:
• Understand the process of programming microcontrollers.
• Understand the basic hardware structure of a microcontroller.
• Understand analogue and digital interface circuits for microcontrollers.
• Understand how to interface sensors to microcontrollers.
• Understand how to design and construct a microcontroller hardware project.
Skills
The skills developed by the students during this course are as follows:
• How to use an IDE (Integrated Development Environment) for developing microcontroller software.
• Understand how to edit, compile and test/debug simple programs.
• Understand how to design simple embedded systems to solve real-world problems.
• Develop communication skills for working in a team.
• Develop project management skills for working in a team.
This module will cover fundamental concepts in cyber security and systems security. By the end of this module students should grasp the core principles of secure information system design, be aware of the current threats and challenges to the security of information systems, data, and services, and understand the application of cryptographic algorithms for confidentiality, integrity, and authentication.
• Security and Vulnerability
• Modern cryptography concepts and application
o Confidentiality, integrity and availability
o Symmetric cryptography and Public key cryptography
o Authentication, digital signatures and access control
o Use of cryptography in information systems
• Introduction to secure information system design
• Threats and challenges in cyber security
o Human threats/social engineering
o Physical layer attacks
• System protection technologies and countermeasures
Learning Outcomes
• Identify and analyse the current threats and challenges to the security of information systems, data, and services,
• Evaluate system protection technologies and methods,
• Apply knowledge of cryptographic algorithms to provide confidentiality, integrity, and authentication.
Skills
Problem solving, communication skills, time management, practical skills (including a base understanding of cryptography and challenges in cyber security).
Introduction to Artificial Intelligence and Machine Learning
Overview
• Concepts of artificial intelligence and machine learning.
• Fundamentals of supervised and unsupervised learning
• Fundamentals of experimental settings and hypothesis evaluation
• The concept of feature selection
• Evaluation in machine learning
o Type I and Type II errors
o Confusion matrices
o ROC and CMC curves
o Cross validation
• Linear and non-linear function fitting
o Linear Regression
o Kernels
• Classification models:
o Nearest Neighbour
o Naïve Bayes
o Decision Trees
• Clustering models:
o k-Means
o hierarchical clustering
o Anomaly detection
Learning Outcomes
• Knowledge and understanding of techniques and selected software relevant to the field of artificial intelligence.
• Ability to identify techniques relevant to particular problems in artificial intelligence and data analysis.
• Ability to discuss and provide reasonable argumentation using artificial intelligence and machine learning concepts.
• Ability to identify opportunities for software solutions in artificial intelligence and data analysis.
• Ability to solve specific data analysis problems using techniques of artificial intelligence and machine learning.
Skills
Problem and data analyses, design of logical and statistical models, application of computational techniques, understanding results.
This course will cover the design of complex digital systems based on the skills that developed in ECS1005. The course will include addressing the implementation of both combinational and sequential circuits. A significant number of technical design exercises and a project for the applications in the real digital world will be also included. The module will be delivered into two parts in two semesters, theoretical-based (first semester) and application-based (second semester) contents.
The first part (theoretical based) will deliver the following contents:
• Technologies
o Processors, GPU, AI processor
o Programmable logic devices
o Application specific integrated circuit (ASIC)
o Field programmable gate arrays (FPGAs)
• Hardware Description Language
o Development of Verilog source code
o Verilog simulation
o Logic synthesis
• Multiple-output Circuits
o Combinational logic revision
o Minimisation of multiple-output circuits, Petricks Method
o Tabular determination of multiple output
• Sequential Circuits
o Synchronous sequential systems
o Finite state machine analysis
o Moore and Mealy models
o State reduction techniques
• Fault Detection/Design for Testability
o Faults, controllability, and observability
o Fault detection
o Design for testability
The second part (application-based), the FPGA based labs with Verilog HDL, will enhance the digital design concepts in the students understanding, via a hands-on approach. This will include 5 structured labs introducing the students to the basic language constructs for modelling both the combinational and sequential elements of a digital design, as well as the optimization strategies for an efficient design by benchmarking in terms of the recourse usage and the operating frequency of an algorithm on an FPGA device. The methodology to communicate with the basic interfaces of the FPGA board will also be undertaken
The contents are as follows:
• Introduction to Xilinx Vivado Design Tool
• Simulating a design
• Constraints and TCL scripts
• Synthesize and Implementation
• Debugging a design
• Generating and downloading a bitstream onto a demo board
• Analysing Vivado reports
Learning Outcomes
The module will provide a comprehensive understanding and application of digital system design through practical digital hardware circuit design and programming skills using Verilog HDL programming. After the completion of this module, students will be able to:
• Design, implement and analyse complicated digital circuits
• Simulate, synthesis and implement practical circuits
• Design and implement a digital circuit design on FPGAs
• Analyse the performance of a digital circuit design through a design tool’s report.
Skills
During this course of this module, students will acquire the following key skills:
• How to design, verify, synthesis and analysis a design.
• Problem solving/debugging a real FPGA system
• ICT skills
• HDL programming skills
Signals: First, we will introduce Continuous Time (CT) and Discrete Time (DT) signals, their mathematical representations, and their classifications (power, energy, periodic, aperiodic, odd & even, etc). Following, we will cover transformations to signal independent variables: shifting, time-reversal, time scaling etc. Once the basic signal concept is covered, we will investigate building blocks for signal analysis: We will introduce CT and DT exponential signals (real and imaginary) and their sinusoidal representations in complex basis. Finally, we will cover unit impulse and step signals & their applications. Next, we will introduce the systems concept: We will talk about CT and DT systems, we will interconnect CT and DT systems, and we will understand the concepts of memory, time-reversal, inversion, causality, stability, time invariance and linearity with respect to CT and DT systems. We will also cover system algebra and block diagram representation of series, parallel and feedback type CT and DT systems. Once the basic system concept is covered, we will be ready to discuss Linear Time-Invariant (LTI) systems concept: We will understand the nature of LTI systems, we will cover, in detail, the convolution theory and its application to CT & DT signals, understand the concept of convolution sum representation of DT systems and convolution integral representation of CT systems, learn the properties of LTI systems (i.e. commutativity, distributivity, associativity, memory, inevitability, causality, stability). Finally, we will learn how to calculate the output and impulse response of CT systems using linear constant coefficient differential equations (and of DT systems using linear coefficient difference equations). At this point, we will have developed a comprehensive understanding of signals and systems in time-domain. Next, we will learn about Fourier transform: We will understand the nature and purpose of the Fourier Transform, we will investigate the restrictions on the applicability of Fourier transform analysis, and we will cover the convergence properties of the Fourier transform. We will then investigate the properties of the Fourier Transform (linearity, time-reversal, time-shift, time-scaling, Parseval’s relation, etc). We will, then, understand and apply the Fourier transform duality between multiplication and convolution. Finally, we will cover Fourier transform analysis of CT and DT LTI systems. Next, we will learn the sampling theory: We will understand how sampled signals are derived, investigate the frequency spectra of sampled signals, understand what is meant by “Nyquist rate” and “aliasing”, and finally, understand the effect of sampling rate in signal reconstruction. Next, we will cover Z-transform: We will derive the Z-transform representation of a DT signal and understand the concept of region of convergence in z-transform. Following, we will cover the properties of the Z-transform (linearity, Z-domain scaling, accumulation, differentiation, etc). We will apply Z-domain analysis to determine properties of LTI systems, to determine difference equation representations of DT LTI systems, and finally, to derive DT LTI system block diagrams.
Control: We will introduce the concept of a dynamical system: a mathematical abstraction of a physical, chemical, biological, economic, or other entity where we study the evolution of certain variables in time. We will use first principles of science and engineering to build dynamical systems and write them in a state space representation. Taylor’s theorem will allow us to approximate nonlinear dynamical systems. Next, we shall introduce the Laplace transform and its inverse that offer a systematic approach for solving linear differential equations and lay the theoretical foundations for a structured study of linear dynamical systems. This will allow us to describe linear dynamical systems using the transfer function – a complex function – and study the dynamical properties of first and second-order systems. At that point we will be ready to introduce the concept of bounded-input bounded-output (BIBO) stability, state Routh’s stability criterion and design BIBO-stable PID controllers. Lastly, we study the dynamic characteristics of linear systems upon sinusoidal excitation, introduce the celebrated Bode plots and revisit the problem of stability using frequency-based criteria.
Coursework:
1. Coursework assignment on signals and systems
2. Group coursework assignment on control and estimation theory
Labs:
1. Lab 1: Autonomous driving (lane keeping control) lab
2. Lab 2: Design of an inverted pendulum using system linearisation and PID controller design
Learning Outcomes
C1: Science and Mathematics
LO: Develop and apply analytical solutions to complex signals and systems related problems, covering LTI systems, Fourier analysis, sampling, and Z-transform.
Teaching: Lectures, tutorials
Assessment (in descending order of importance): Exam, Signals Coursework
LO: Gain a comprehensive understanding of numerical modelling and practical design of signals and communications systems and their engineering.
Teaching: Lectures, tutorials
Assessment: Signals Coursework
LO: Understand the basic components of a feedback control system and their role
Teaching: Lectures
Assessment (in descending order of importance): Exam, Control Coursework, Labs
LO: Model dynamical systems in the time and complex frequency domains
Teaching: Lectures, Control Labs
Assessment (in descending order of importance): Exam, Control Coursework, Labs
LO: Use the Laplace transform and its inverse to solve initial value problems
Teaching: Lectures, Control Labs
Assessment (in descending order of importance): Exam, Control Coursework, Labs
LO: Use Taylor’s approximation theorem to linearise dynamical systems at an equilibrium point
Teaching: Lectures, Control Labs
Assessment (in descending order of importance): Exam, Labs, Control Coursework
C2: Problem Analysis
LO: Formulating and analysing complex problems to reach substantiated conclusions.
Teaching: Lectures, Tutorials
Assessment (in descending order of importance): Exam, Signals Coursework, Control Coursework, Labs
LO: Evaluating data and equations using engineering principles and numerical frameworks.
Teaching: Lectures, Tutorials
Assessment (in descending order of importance: Exam, Signals coursework, Control Coursework, Labs
LO: Evaluating and processing data analytically
Teaching: Lectures, Tutorials
Assessment (in descending order of importance): Exam, Signals coursework
LO: Use first principles of physics and engineering to describe real-life dynamical systems in the form of ODEs/IDEs while choosing appropriate frames of reference and simplifying assumptions.
Teaching: Lectures
Assessment (in descending order of importance): Exam, Control Coursework
LO: Analyse the behaviour of dynamical systems, their impulse, step and frequency response characteristics and their limit behaviour at infinite time with special emphasis on first and second order systems.
Teaching: Lectures
Assessment (in descending order of importance): Exam, Control Coursework
C3: Analytics Tools and Techniques
LO: Apply computational techniques using numerical simulations to study complex signal models.
Teaching: Lectures, Tutorials.
Assessment: Signals Coursework.
LO: Develop analytical techniques to solve problems related to LTI systems, Fourier analysis, Nyquist sampling, and Z-Transform.
Teaching: Lectures, Tutorials
Assessment (in descending order of importance): Exam, Signals Coursework
LO: Use appropriate stability criteria (such as Routh’s tabulation, Bode’s criterion or other) to tell whether a given system is stable in the BIBO sense
Teaching: Lectures, Control Labs
Assessment (in descending order of importance): Exam, Control Coursework
C5: Design
LO: Use appropriate stability criteria (such as Routh’s tabulation, Bode’s criterion or other) to tell whether a given system is stable in the BIBO sense.
Teaching: Lectures, Labs
Assessment (in descending order of importance): Exam, Control Coursework
LO: Design PID controllers to achieve certain performance criteria such as desired stability margins, or poles with an adequately negative real part
Teaching: Lectures, Control Labs
Assessment (in descending order of importance): Exam, Control Coursework
C6: Integrated/Systems approach
LO: Develop an appreciation of the system abstraction to model interconnected dynamical systems and feedback loops
Teaching: Lectures
Assessment (in descending order of importance): Exam, Control Coursework
C12: Practical and Workshop Skills
LO: Use a numerical platform to simulate signals, systems and their analysis in the time domain, frequency domain and Z-domain.
Teaching: Lectures
Assessment: Signals Coursework
LO: Use Python to simulate dynamical systems and design control systems
Teaching: Labs
Assessment (in descending order of importance): Labs
C13: Materials, equipment, technologies, and processes
LO: Select and apply appropriate ways to numerically model signals and systems using a mathematical modelling environment.
Teaching: Lectures (practical examples)
Assessment: Signals Coursework
LO: Develop and apply appropriate analytical solutions to all aspects of signals and systems, from linear operations applied to continuous time and discrete time signals to Fourier transform and Z-transform.
Teaching: Lectures, Tutorials
Assessment (in descending order of importance): Exam, Signals Coursework
C15: Engineering and project management
LO: Ability to manage an engineering project, involving planning, distribution of tasks, collaborative development using technologies such as git, collaborating using issue trackers, etc.
Teaching: Lectures
Assessment (in descending order of importance): Control Coursework (Group assignment)
C16: Teamwork
LO: Ability to function effective as a member of a team (punctuality, responsibility, discipline, clear communication with other team members) tasked with the design of a control system
Teaching: Lectures
Assessment (in descending order of importance): Control Coursework (Group assignment)
C17: Communication
LO: Reporting of analytical and numerical results (in both Signals and Control). Through these activities, the students will learn communicating effectively on all aspects regarding signals and systems.
Teaching: Lectures (Q&A) and tutorials.
Assessment: Coursework assignments (Signals and Control)
Skills
Upon completion of this module, the students will be able to
1. Combine continuous-time and discrete-time signals
2. Manipulate fundamental signals, specifically discrete-time impulse, and continuous-time exponential
3. Convolve two signals
4. Analyse LTI systems to determine any one of input, output, or system response, given knowledge of the other two
5. Analyse systems in time-domain and frequency-domain, and the relationship between these two domains
6. Analyse systems in Z-domain
7. Design feedback control systems for linear SISO continuous-time systems using PID controllers
8. Solve engineering problems by breaking down the original problem into simple tasks, troubleshooting, debugging, and brainstorming
9. Collaborate with your fellow colleagues – perhaps the most valuable non-technical skills are collegiality and teamwork
10. Use appropriate software to simulate dynamical systems and perform symbolic computations
Part 1 (Circuits)
• System Equation
• Linear circuits
• Circuit Theorems and Methods of Circuit Analysis
o Mesh analysis
o Nodal analysis
o Thévenin’s theorem
• Two-port networks
o Admittance parameters
o Impedance parameters
o Hybrid parameters
o Transmission parameters
• Laplace transform in circuit analysis
• SPICE simulation software (LTspice, Qucs-spice)
Part 2 (Electronics)
• Linear Operational amplifiers: Basic operation, models, active Filters
• Non - Linear Operational amplifiers: oscillators and waveform generators
• Semiconductor diode: diode models, Zener Diodes, applications in DC power supplies, circuit analysis techniques, circuit design
• Bipolar transistor: internal current components, current gain, common configuration and basic equations, large/small signal model, bias circuits
• Bipolar transistor applications: Switching transistor, constant current source, regulated dc power supply, amplifiers, differential amplifier, frequency response of amplifiers
• FET transistors: small signal model, bias circuits, appreciation of differences between FET and bipolar transistor
• FET transistor applications: amplifiers including frequency response
Learning Outcomes
Part 1 (Circuits)
• Be able to apply Kirchhoff’s current law and Kirchhoff’s voltage law and Ohm’s law to solve electric circuits including node and loop analysis
• Understand the concepts of linearity
• Know how to analyze electric circuits using the principle of supernode and supermesh
• Understand when and how to use a source transformation
• Know how to analyze electric circuits containing dependent sources
• Be able to calculate a Thévenin equivalent circuit for a linear circuit
• Know how to calculate admittance, impedance, hybrid, and transmission parameters for two-port networks
• Be able to convert between admittance, impedance, hybrid, and transmission parameters
• Understand the interconnection of two-port networks to form more complicated networks
• Know how to combine capacitors and inductors in series and parallel
• Know how to calculate impedance and admittance for our basic circuit elements: R, L, C
• Be able to combine impedances and admittances in series and parallel
• Know how to use the Laplace transform to analyze transient circuits
Part 2 (Electronics)
• Understand and apply semiconductor device models
• Produce equivalent circuits of operational amplifiers, diodes and transistors
• Apply linear circuit techniques such as Thévenin theorem, Kirchhoff’s current law and potential divider rule, to semiconductor equivalent circuits
• Analyse and design analogue circuits containing components such as operational amplifiers, diodes and transistors geared towards specific applications
• Understand the real life specifications of semiconductor devices and circuits and how to produce designs within certain practical constraints
• Derive transfer function equations for semiconductor circuits including frequency response
Skills
• Numeric
• Problem solving theoretical circuit designs
• Analyse & design analogue circuits using op-amps, diodes and transistors
• Understand the operation of semiconductor devices.
• Understand “real world” applications of electronic circuits
• Problem solving, troubleshooting, debugging and measurement skills through lab activities
Part 1 (Electromagnetics and Antennas)
• Fundamentals on Electromagnetics
• Waves
• Radiation - Fundamentals on Antennas
• Antennas
• Antenna arrays
Part 2 (Wireless Communication)
• Information Theory
• Noise
• Basedband
• Error detection
• Passband
Learning Outcomes
Part 1 (Electromagnetics and Antennas)
• Have a strong grasp of the fundamental concepts of electromagnetic theory, principles, and applications.
• Physical understanding of propagating waves.
• Understand fundamentals of electromagnetic radiation and antennas.
• Design and test linear and/or loop antennas.
• Design and test uniform planar antenna arrays.
Part 2 (Wireless Communication)
• Understand the fundamentals of Information Theory.
• Perform communications system Noise calculations.
• Understand the basic principles in Baseband communication.
• Implement selected source coding and error detection schemes.
• Select appropriate digital modulation schemes for given application demands and constraints.
• Understanding of basic building blocks for wave propagation in wireless communication.
Skills
• Understand the physical behind electromagnetic fields and waves
• Analyse & design basic antennas and uniform antenna arrays
• Understand fundamentals of wireless communication
• Understand the operation of modern, digital communication systems
• Problem solving, troubleshooting, debugging and measurement skills through CW assignments and lab activities
Lectures:
Introduction to enterprise; student example pitches; overview of startup process;
Intellectual property overview; funding opportunities; business consultancy approaches; importance of branding.
Self-working:
Product development: derivation of product; creation of product specification; ethical and standards consideration, creation of prototype.
Business development: team development; product development; marketing approaches; financial planning.
Development of a challenging application: idea generation; application study; market study.
Learning Outcomes
General:
Report writing.
Business presentation.
Assimilation of business practices.
Generation of business ideas and products.
Specific:
Ability to pitch business concept.
Product development.
(Tested by business pitch and plan)
Skills
General:
Presentational skills.
Development of business acumen.
Business plan creation.
Team-working.
Self-assessment.
Creativity.
Lectures:
1. PRELIMINARIES:
Feedback control, poles and zeros, time domain specifications, Routh stability, discretisation, sampling time and resolution, analogue vs. digital control, s-plane and z-plane.
2. CONTROLLER DESIGN
a. ROOT LOCUS DESIGN: Evans rules, compensator design, applications
b. FREQUENCY DOMAIN DESIGN: Bode plots, compensator design, applications
c. PID CONTROL (analogue and discrete): Zieglar-Nichols tuning method, applications
d. DIRECT DESIGN METHOD, discrete-time design, Method of Ragazzini
e. FREQUENCY RESPONSE BASED DESIGN, Bode plots, w-plane, applications
3. IMPLEMENTATION ISSUES:
Digital simulation, hardware/software limitations, practical issues (aliasing, missing or corrupt data, chattering and deadbands).
4. MATLAB and Simulink tutorials for computer assisted control system design (CACSD).
Design project:
1. Lego Mindstorms-based modelling and control of a physical system
2. Design and implement a control system on the Mindstorms-based physical system based on given specifications such as overshoot, settling time etc.
3. Demonstration and presentation of the final design.
Learning Outcomes
General:
After the completion of this module you will be able to:
• Understand classical (analogue) control systems.
• Understand computer-based (digital) feedback control methods.
• Analyse and design simple feedback control systems to meet given performance specifications.
• Gain a good understanding of implementation issues.
Design Project/Laboratories:
• Practical understanding of modelling and controller design of a physical system.
• Importance of desired specifications.
• Practical understanding of software implementation using Matlab/Simulink.
• Hands-on experience of designing and implementing a real-time control system with application to robotics.
Skills
General:
• Understanding of analogue and digital feedback control
• Problem solving
• Use of MATLAB software tools
• Importance of practical issues in converting theory into practice
Design project:
• Implementation and testing of control systems with application to Robotics
• Simulation programming
• Sensor measurements and use of sensors
• Use of control engineering principles to develop working solutions
• Presentation of technical engineering information clearly and concisely in oral and written form
• Cyber Security Overview
• Malware Analysis in Virtual Machines
• Basic dynamic analysis
• X86 Disassembly
• IDA Pro
• Recognising C Code Constructs in Assembly
• Malware Types
• Analyzing Malicious Window Programs
• Covert Malware Launching
• Malware Behaviour and Signatures
• Machine learning for malware detection
Learning Outcomes
Students should be able to:
- Ability to perform advanced static analysis
- Ability to perform basic dynamic analysis
- Understand the different types of malware and understand their behaviour
- Understand how automated malware detection works
Skills
Problem analysis, Problem solving. Rigour in thought. Ability to work individually or as part of a team. Demonstrate increased communication, library, research, time management and organisational skills.
• Overview of generic machine learning pipelines
• Deep learning
o Feedforward neural networks
o Regularisation for deep learning
o Optimisation for training deep models
o Convolutional networks
o Auto-encoders
o Recurrent Networks
o Siamese Neural Network
• Evolving learned models
o Active Learning
o Transfer Learning
o Incremental Learning
• Applications of deep learning
Learning Outcomes
Be able to:
• Explain when and how machine learning is useful in industry, public institutions and research.
• Know and apply state-of-art deep learning techniques.
• Demonstrate the ability to understand and describe the underlying mathematical framework behind these operations.
• Design and develop original deep learning pipelines applied to a variety of problems
• Formulate and evaluate novel hypothesis
• Analyse an application problem, considering its suitability for applying deep learning, and propose a sensible solution
• Evaluate the performance of proposed deep learning solutions through rigorous experimentation
• Analyse quantitative results and use them to refine initial solutions
• Communicate finding effectively and in a convincing manner based on data, and compare proposed systems against existing state-of-art solutions
Skills
Problem solving. Self and independent learning. Research. Working with others and organisational skills. Critical analysis. Quantitative evaluation. Mathematical and logical thinking.
• Overview of imaging and video systems and generic machine learning pipelines
• Pattern recognition problems: Verification, detection and identification
• Data pre-processing:
o Image enhancement: Normalisation. Point Operations, Brightness and contrast.
o Filtering and Noise reduction. Convolution
• Classification
o Support Vector Machines (SVM).
o Boosting and ensemble of classifiers
o RF
o Neural networks.
o Deep Learning.
• Vision-specific Feature extraction:
o Simple features
o Gradients and Edge extraction
o Colour Extraction and colour histograms
o SIFT
o Histogram of Gradients HoG
• Unsupervised learning:
o Clustering and Bag of Words for vision
o Self-organised maps
• Segmentation, tracking and post processing
o Brightness segmentation
o Motion detection; Background modelling and subtraction; Optical Flow
o Template Matching
o Tracking: Kalman Filter, Particle Filter and tracking by detection
o Introduction to time series analysis
• Dimensionality reduction techniques and latent spaces.
o The curse of dimensionality
o Principal component analysis (PCA).
o Linear discriminant analysis (LDA).
• Introduction to Deep Learning
• GPU acceleration for video processing.
• Applications:
o Video Surveillance
o Cyber-physical security
o Medical imaging
o Secure corridors.
o Pose estimation.
o Biometrics
o Face detection
o Human behaviour analysis.
Learning Outcomes
Be able to:
• Explain when and how machine learning and computer vision is useful in industry, public institutions and research.
• Know and apply a range of basic computer vision and machine learning techniques.
• Demonstrate the ability to understand and describe the underlying mathematical framework behind these operations.
• Design and develop machine learning pipelines applied to computer vision applications
• Formulate and evaluate hypothesis
• Evaluate the performance of proposed machine learning solutions through rigorous experimentation
• Analyse quantitative results and use them to refine initial solutions
• Communicate finding effectively and in a convincing manner based on data, and compare proposed systems against existing solutions
Skills
Problem solving. Self and independent learning. Research. Working with others and organisational skills. Critical analysis. Quantitative evaluation. Mathematical and logical thinking.
Concurrent Programming Abstraction and Java Threads, the Mutual Exclusion Problem, Semaphores, Models of Concurrency, Deadlock, Safety and Liveness Properties. Notions are exemplified through a selection of concurrent objects such as Linked Lists, Queues and Hash Maps. Principles of graph analytics, experimental performance evaluation, application of concurrent programming to graph analytics.
Learning Outcomes
To understand the problems that are specific to concurrent programs and the means by which such problems can be avoided or overcome.
Skills
To model and to reason rigorously about the properties of concurrent programs; to analyse and construct concurrent programs in Java; to quantitatively analyse the performance of concurrent programs.
Course Contents • Discrete-time (DT) signals.
• Fourier analysis
• Discrete linear filters and adaptive filtering
• Laplace transform
• Stochastic signal processing and multipath fading channels
• Digital modulation and demodulation
• Channel coding
• OFDM
• Using signal processing to analyse the performance of communications systems
Learning Outcomes
After the completion of this module you will be able to:
• Have a strong grasp on the fundamental concepts and techniques pertaining to signals and communications systems, with an emphasis on the discrete-time domain, for further study in communications and signal processing.
• Design specific signal processing system models and algorithms.
• Perform statistical analysis and inferences on random signals
• Familiar with Matlab software in the simulation of DTFT and wireless communications systems
Skills
• Numeric analysis
• System design and problem solving
• How to construct and analyse discrete- time models
Connected Health is a model for healthcare delivery that uses technology to provide healthcare remotely. It is a rapidly evolving societal challenge. Innovative Information and Communication Technology is core to its success. This module examines the connected health concept with a focuses on the enabling technology.
• The evolution of Connected Health; Tele-health and medicine, Current trends and challenges.
• How fundamental electronics can be used to transduce medical markers from the human body.
• Personal health data networks (IEEE 11073); standards and regulation; Medical device approval study (MHRA/FDA).
• Electronic patient records, Digital Health Records, Data management (security, privacy). Data processing and analysis.
• Medical electronics and sensors; sensor analysis and design; sensor circuit theory and analysis; Invasive wireless implant sensors.
• Body sensors and personal area networks; Physiological measurement and monitoring; wireless sensor networks in healthcare applications.
• Biologically inspired sensing and data harvesting. For future applications.
The module is structured into four main topics: Topic 1: Evolution of Connected Health; Topic 2 Medical Biosignals and Sensing; Topic 3: Standards & Regulations inc. Industrial Case Study; Topic 4 Wireless Healthcare Technologies.
Learning Outcomes
After the completion of this module you will be able to:
Describe the recent evolution of connected health technologies.
• Understand the electronics and software requirements for selected connected health applications.
• Describe specific point of care sensor technologies and their role in physiological monitoring.
• Practical understanding of the different roles of electronic sensor devices
• Describe the need for standards and regulations in connected health.
• Design and analyse different electronic circuits for the analysis of raw medical biosignals
• Understand the communications and networking of wireless connected health devices.
Skills
• Problem solving
• Numeric
• Improving Own Learning and Performance
• Information and Communication Technology
Lectures/Practicals:
1. Overview of OSI layers
2. Error detection and correction, Cyclic Redundancy Checking
3. Forward error control, Viterbi coding/decoding
4. Layer 2 principles: ARQ schemes (Idle RQ, Continuous RQ, link
utilisation)
5. Queuing theory principles (Latency, Throughout, round-trip time,
utilisation, single-server queues, multi-server-queues)
6. MAC Layer (TDMA, OFDMA,CDMA, ALOHA, Carrier Sense Multiple
Access)
7. TCP/IP (Congestion control)
8. Principles of physical layer. PHY aspects of cellular and mobile radio
systems (Frequency reuse, Interference)
9. High spectrally-efficient techniques for cellular systems (DSSS,
Frequency-Hopping)
Coursework - Design Exercise:
1. Fading phenomena in mobile communication systems using MATLAB
2. Calculation of link margin and path-loss for different frequencies and
environments
3. Emulation of fading effects in MATLAB
Learning Outcomes
Have a strong grasp on the fundamental concepts of networks and communication protocols
Understand the concepts of error detection and control
Understand the principles of queuing theory and its applications on network protocols
Calculate the average throughout, latency and utilisation of single and multi-server queues
Describe the principles of the MAC layer and technologies associated with it
Describe the operation of TCP/IP protocols
Understand the fundamental concepts associated with the operation of mobile networks
Practical understanding of how mobile communication systems work
Determine the performance limits of mobile networks in MATLAB
Simulate fading distributions in MATLAB
Skills
Assimilation of error correction and control techniques, OSI layers, protocols of communications and networks, queuing theory
Ability to solve mathematical and conceptual questions individually
Ability to meet specific deadlines
Ability to simulate mobile systems in MATLAB
Presentation of technical engineering information clearly and concisely in written form
• Review device physics and small signal analysis
• Common second order effects
• Different transistor configurations and amplifier classes
• Differential pairs with active load
• Feed-back circuits
• Frequency response and gain-bandwidth product
• Filter design
• Basic noise analysis
Learning Outcomes
• Understand advance concepts of analog circuit design.
• Analyse circuits with multiple transistors and op-amps.
• Build complex circuits using transistors and amplifiers.
Skills
• Problem solving
• Circuit trouble shooting
• Analysis of complex analog circuits
• Simulation/computational modelling of analog circuits
Book Requirement
• Microelectronics Circuit Analysis and Design (4th Edition) by Donald A. Neamen
The project normally takes the form of an investigation or design study concerned with one of the various branches of electrical and electronic engineering. The project originator typically endeavours to ensure an element of design, manufacture and test in the project specification, even if the project is software-based. There are of necessity many variations on this theme.
Learning Outcomes
To develop the ability to conduct a substantial project over an extended period;to perceive the nature of engineering problems or product specifications; to acquire and develop the necessary skills and to plan and execute a suitable programme of work.
Skills
The ability to apply general principles and design or analytical techniques to the solution of engineering problems - which may require investigative, practical or design skills or a combination of all three. Originality is encouraged.
• Modelling and simulation of continuous-time and discrete-time systems, connections between them and discretisation methods
• Solutions, similarity transformations and special forms
• Reachability/controllability and observability/detectability
• Linear state and output feedback controllers, variations, separation principle
• State observers
• Lyapunov stability and related numerical methods
• Linear Quadratic Regulators and Model Predictive Control
• Invariance and constrained control, set-theoretic approaches
• Introduction to types of Hybrid systems, simulation and examples
• Switching systems; examples, analysis methods and control approaches
• Hybrid automata, solutions, Zeno behaviour, concepts in abstraction, bisimulation, model checking/ formal verification
Learning Outcomes
• Good knowledge of classic state-space (control and estimation) methods for control and estimation.
• Good knowledge of stability and safety analysis algorithms for dynamical systems.
• Good knowledge of modelling cyber-physical systems as hybrid systems.
• Know how to apply numerical methods for stability analysis and control design of general dynamical systems
Skills
• Modelling of complex dynamical systems in engineering.
• Advanced technical knowledge related to control design, estimator design, safety analysis for complex systems.
• Mathematical reasoning.
• Software/Programming skills.
The contents of the course include:
(i) Part 1: An introduction and overview to the various core aspects of robotics including:
• Kinematic: position and orientations, forward kinematic, and inverse kinematic.
• Dynamics and control system: Robot dynamics, Path planning and trajectory, Computed torque control, and Cartesian control.
(ii) Part 2: Fundamental concepts and architectures of
• Robot-vision system: vision sensors, issues of vision guided robotics, visual servoing.
• Force-based robot control: Stiffness control, hybrid position/force control, Impedance Control, and Admittance Control.
• Manufacturing robots: An overview of Robots in manufacturing, Robots for pick and place applications, Robots for finishing applications.
(iii) Part 3: Fundamental principles of intelligent techniques:
• Machine learning: Learning system model, Machine learning structure, Learning techniques, Reinforcement learning, Applications to Robotics.
• Neural network: Perceptron as a classififer, Adaline and speepest descent algorithm, Multilayer Perceptron (MLP) networks, Back propagation training algorithm, LMS algorithm.
• Fuzzy logics: Fuzzy logic and fuzzy sets, fuzzification and defuzzification, fuzzy control.
The module has a final written examination and two coursework (coursework 1: calculate kinematic and dynamic of robot, and coursework 2: design intelligent controller for robot). The coursework 1 accounts for 20% of the final mark, the coursework 2 accounts for 20% of the final mark, while the final exam contributes 60%.
Learning Outcomes
At the end of this module students will be able to:
1. Demonstrate a good understanding of robots’ structure, their dynamics, control system and associated sensor and actuator technology.
2. Demonstrate a good understanding of robot-vision system.
3. Design visual servicing controllers for robot-vision applications.
4. Understand how to design force-based control schemes for practical applications.
5. Design force-based controllers.
6. Understand how to build a robot system for a particular application in manufacturing.
7. Demonstrate a good understanding of machine learning, neural network and fuzzy logic.
8. Use machine learning for classification.
9. Use neural network for classification or approximation.
10. Design fuzzy logic controllers for robotic applications.
Skills
The module will give you good knowledge about robot kinematics and dynamics, actuators and sensors, experience of how to design a robot system and its controller for a particular application. Furthermore, you will have knowledge of a range of intelligent systems techniques and how to apply them for robotic applications.
Fairness, Interpretability and Privacy in Machine Learning
Overview
• Machine Learning and Fairness: How ML algorithms could make unfair decisions
• The Political and Philosophical Underpinnings of ML Fairness
• Overview of some recent Fair AI algorithms
• Machine Learning and Interpretability: The case for explainable and transparent ML
• Overview of how some recent Interpretable AI algorithms, and how they operationalize interpretability
• Machine Learning and Privacy: How ML could (inadvertently) violate privacy
• Overview of recent advances in Privacy oriented ML
• Situating Fairness, Interpretability and Privacy within the broader ML ethics context
Learning Outcomes
Be able to:
• Understand the risks of socially applied ML along several ethics dimensions.
• Critically analyse ML algorithms with respect to fairness and deliberate on improvements.
• Critically analyse the interpretability of ML algorithms, and contemplate on improving their interpretability characteristics.
• Understand the privacy implications of ML algorithms, and develop pathways towards enhancing privacy.
• Communicate findings and decision making processes grounded on data and ethical principles.
Skills
Problem solving. Self and independent learning. Research. Working with others and organisational skills. Critical analysis. Quantitative evaluation. Mathematical and logical thinking.
Lectures and Schedule:
1. Introduction
2. Sensor Systems
3. Zigbee, 6LoWPAN
4. 802.11 MAC layer
5. Low Power WSN MACs
6. Reading week (Research for Presentation)
7. Presentations
8. IoT 802.11ah
9. Lab 1
10. Lab 2
11. Class Test
12. IoTLoRAWAN and other IoT
13. Rouiting for WSN
14. PowerManagement
15. Synchronization
16. Localization
16. Lab3
17. Lab4
18. Tutorials
20. Lab5 (Showcase)
Coursework:
1. Presentation (Research literature in Sensors and Presentation)
2. Class test (Semester 1)
3. Laboratory and Report (Semester 2)
Laboratory:
1. Lab 1. Sensor System Introduction.
2. Lab 2. Reading Sensor Data
3. Lab 3. RTC interfacing and data logging
4. Lab 4. Comparing sensor data and data analysis
Learning Outcomes
The following LOs are provided through examination, laboratory and coursework (class test and presentation) with significant overlapping of LOs across assessment elements.
Science and Mathematics LOs:
Wireless Sensors Systems: sub-systems and challenges (SM1m).
Throughput and delay calculations. Time synchronization and localization. Power consumption calculations (SM2m).
Understanding of telecommunications protocols. Understanding of PHY layer. Apply knowledge to wireless sensor technologies (SM3m).
Enabling technologies for the Internet of Things. Recent standardization activity on these new technologies (SM4m).
Understanding of random access principles, contention and contention resolution. Appreciation of the limitations that these impose on future high dense wireless networks. (SM5m).
Understanding of hardware architectures, sensor technology and communication architectures. Applying them effectively when designing a wireless sensor system in coursework/laboratory. (SM6m).
Engineering Analysis LOs:
Understanding of basic PHY layer principles such as coding and modulation. Impact on throughput. (EA1m).
Performance assessment of layer 2 technologies (throughput and delay). (EA2m).
Time synchronization protocols. Non determinism of communication latency. Calculation of delay and offset. Critical path. Limitation of some synchronization protocols. Alternative protocols. (EA3m).
Integrating different subsystems (sensors, microcontroller, communication sub-subsystem) to provide engineering solutions, data acquisition and analysis through laboratory challenge. (EA4m).
Investigation of emerging technologies that enable IoT. IEEE 802.11ah. LoRaWAN (EA5m).
Design LOs:
Literature review of sensors and comparison (presentation).Presentation on a scientic paper of their choice in this context. (EA6m) (Design D6m).
Design of a wireless sensor system in laboratory to address a specific challenge/need (e.g. pollution monitoring, light and temperature sensing, calibration and comparison of data sets). (D8m)
Economic, Social and Environmental Context LOs:
Study of subsystems and commercial sensors to provide a wireless sensor system solution in lab (ET2m).
Engineering Practice LOs:
knowledge of wireless technologies (ZigBee, WiFi). IoT technologies: IEEE 802.11ah, LoRaWAN. Extensive knowledge of sensors. (EP2m).
Research on sensors and sensor comparison for presentation. (EP4m).
Understanding of recent standards for IoT communication technologies: IEEE 802.11ah. (EP6m).
Appreciation of new developments in IoT (systems and platforms). (EP9m).
Consideration of commercial components and constraints in lab. (EP10m).
Understanding of different roles in a collaborative project. Initiative and personal responsibility for their individual role.(EP11m).
Skills
Effective communication of knowledge and ideas.
The ability to critically assess and design modern wireless communications systems and in particular wireless sensor networks and systems using data acquisition boards.
The ability to understand existing system architectures and standards in such a context.
Use embedded software to program arduino based systems and perform sensor data acquisition, data analysis.
Analysis and design of algorithms, complexity, n-p completeness; algorithms for searching, sorting; algorithms which operate on trees, graphs, strings. Database algorithms, B-tree and hashing, disk access, algorithms. Applications of algorithms
Learning Outcomes
To understand some of the principal algorithms used in Computer Science; to be able to analyse and design efficient algorithms to suit particular applications.
Skills
Analysis, design and implementation of efficient algorithms.
Lectures:
Wireless communication systems are a primary enabling technology in the realisation of a smarter connected society. This course provides a fundamental understanding of the concepts and techniques used in the design of modern wireless communication systems. In particular it covers the following topics:
• Cellular systems
• Propagation modelling
• Spectrum management
• Multiuser systems (multiple access, multiuser diversity techniques)
• Multiple antennas (e.g., maximal ratio combining, MIMO techniques)
• Multicarrier communications
• Multiuser scheduling techniques (e.g., greedy access, proportional fair)
• Cognitive radio techniques
Coursework:
The coursework will assess the following topics:
1. Wireless channel modelling
2. Modulation.
3. Performance analysis of wireless communication systems.
.
Learning Outcomes
General:
Students should acquire an understanding of mobile and cellular systems [SM1m]; demonstrate a knowledge of the wireless channel propagation characteristics; be able to examine the advanced concepts and techniques which allow modern wireless communication systems to be designed and assessed against a given operational specification [SM2m, SM3m, EA2m]; understand the future development of wireless systems and their limitations (including social and economic implications) [SM4m, EA5m, ET2m].
Coursework [SM6m, D6m, EA3m, EP11m]:
The coursework will develop a practical understanding of the different types of wireless channels encountered in wireless communication systems [SM6m, EA3m]. It will also familiarise the student with simple digital modulation and demodulation techniques and the performance analysis of wireless communication systems [EA3m]. It will further develop team-work and presentation skills through group-based project work and project presentation [D6m, EP11m].
Students will also have gained experience of Matlab functions useful for the wireless channel modelling, wireless system simulation, performance debug and test [EA3m, EA5m].
Skills
General:
Assimilation of lecture material, Matlab skills, system model and problem-solving skills as well as basic probability and random theories [SM2m].
Streaming workload modelling languages
* Algorithm optimisation schemes
* Handling of time in algorithm design
* Number systems and operations
* High Level Synthesis (HLS) technology for Field Programmable Gate Array (FPGA)
* Application partitioning for parallel processing platforms
* System optimisation
Learning Outcomes
Understand the design principles for design of heterogeneous hardware/software embedded digital signal processing (DSP) systems, in five specific areas: computing architectures, application modelling, parallel partitioning, scheduling and code generation, and implementation optimisation.
* In the area of computer architectures, students will be able to:
* The influence of number system on accuracy and cost of different number systems
* Handling time and dependency in custom architecture design
* Discriminate how behavioural expressions of a function translate to circuit architectures using High Level Synthesis (HLS) technology.
* In the area of application modelling and code generation, students will be able to:
* Analyse and compare dataflow languages for a given application
* Derive firing rules for dataflow actors
* Apply mathematical consistency checks to static dataflow models
* Appraise the implementation concerns of parallel processing algorithms
* Investigate constructive hierarchical and multi-stage partitioning algorithms
* Relate constructive and iterative partitioning algorithms
* Relate partitioning algorithms to achieve specific implementation goals
* Analyse dataflow models for deadlock
* Analyse the code and data memory requirements, throughput and efficiency of the resulting embedded schedules
* In the area of optimisation of custom systems, students will be able to:
* Outline the behaviour of system optimisation approaches.
* Contrast graph transformation techniques for optimisation of embedded dataflow schedules
* Transform embedded schedules for optimisation with respect to data memory, throughput and efficiency
* Relate advanced dataflow models for further optimisation with respect to a given criteria
* Illustrate retiming, folding and unfolding, hardware sharing for dedicated hardware optimisation
Skills
Assimilation of technical material
Critical thought in the design of resource-constrained computer designed problems
Application to practical data processing design examples
This module focuses on approaches to use multiple compute resources simultaneously to solve problems. Parallel programming is the use of closely located normally homogeneous computing resources such as multicore processors, high performance clusters or supercomputers to speed computation up through simultaneous execution. Distributed computing is the opposite end where multiple heterogenous systems with unreliable and/or slow communication links are used to spread workload.
This practically-oriented module will cover the theory and implementation of parallel and distributed systems using different programming techniques, environments and concepts.
Topics covered will include:
• Basic concepts and terminology
• Parallel programming models
• Program and problem analysis
• Practical parallel programming and implementation of parallel code
• Distributed computing theory
• Data synchronisation methods
Learning Outcomes
To demonstrate understanding of:
• The principles underpinning effective and efficient parallel programs
• The principles underpinning effective and efficient distributed computing
• Implementation of parallel and distributed solutions in an efficient fashion
• Modern multi-threaded execution environments and software development architectures
Skills
Improving Own Learning and Performance, Problem Solving, planning and researching assignments, design and implementation of solutions
AAA including Mathematics and and at least one from Biology, Chemistry, Computing, Digital Technology, Electronics, Further Mathematics, Geography, ICT [not Applied ICT], Physics, Software Systems Development or Technology and Design.
A maximum of one BTEC/OCR Single Award or AQA Extended Certificate will be accepted as part of an applicant's portfolio of qualifications with a Distinction* being equated to a grade A at A-level.
Irish leaving certificate requirements
H2H2H3H3H3H3 including Higher Level grade H2 in Mathematics and a Science subject (see list under A-level requirements)
International Baccalaureate Diploma
36 points overall, including 6,6,6 at Higher Level, including Mathematics and a relevant Science
Graduate
A minimum of a 2:2 Honours Degree, provided any subject requirements are also met
Note
All applicants must have GCSE English Language grade C/4 or an equivalent qualification acceptable to the University.
Further information
Applicants for the MEng degree will automatically be considered for admission to the BEng degree if they are not eligible for entry to the MEng degree both at initial offer making stage and when results are received.
Option to transfer
Transfers between BEng and MEng may be possible at the end of Stage 2.
How we choose our students
Applications are dealt with centrally by the Admissions and Access Service rather than by the School of Electronics, Electrical Engineering and Computer Science. Once your application has been processed by UCAS and forwarded to Queen's, an acknowledgement is normally sent within two weeks of its receipt at the University.
Selection is on the basis of the information provided on your UCAS form. Decisions are made on an ongoing basis and will be notified to you via UCAS.
Applicants offering A-level/BTEC Level 3 qualifications must have, or be able to achieve, a minimum of six GCSE passes at grade B/6 or better to include Mathematics (minimum grade C/4 required in GCSE English Language). However, this profile may change from year to year depending on the demand for places. Selectors will also check that any specific entry requirements in terms of A-level subjects can be fulfilled.
Offers are normally made on the basis of three A-levels. Applicants repeating A-levels require BBC at the first attempt. Candidates are not normally asked to attend for interview.
Applicants offering two A-levels and one BTEC Subsidiary Diploma/National Extended Certificate (or equivalent qualification) will also be considered. Offers will be made in terms of the overall BTEC grade awarded. Please note that a maximum of one BTEC Subsidiary Diploma/National Extended Certificate (or equivalent) will be counted as part of an applicant’s portfolio of qualifications. The normal GCSE profile will be expected.
For applicants offering the Irish Leaving Certificate, please note that performance at Irish Junior Certificate (IJC) is taken into account. For last year’s entry, applicants for this degree must have had a minimum of 6 IJC grades B/Higher Merit. The Selector also checks that any specific entry requirements in terms of Leaving Certificate subjects can be satisfied.
Applicants offering BTEC Extended/National Extended Diplomas, Higher National Certificates and Higher National Diplomas are not normally considered for MEng entry but, if eligible, will be made a change course offer for the corresponding BEng programme.
Access course qualifications are not considered for entry to the MEng degree and applicants should apply for the corresponding BEng programme.
Subject to satisfactory academic performance during the first two years of the BEng course, it may be possible for students to transfer to the MEng programme at the end of Stage 2.
The information provided in the personal statement section and the academic reference together with predicted grades are noted but these are not the final deciding factors in whether or not a conditional offer can be made. However, they may be reconsidered in a tie break situation in August.
A-level General Studies and A-level Critical Thinking are not normally considered as part of a three A-level offer and, although they may be excluded where an applicant is taking four A-level subjects, the grade achieved could be taken into account if necessary in August/September.
If you are made an offer then you may be invited to a Faculty/School Visit Day, which is usually held during the second semester. This will allow you the opportunity to visit the University and to find out more about the degree programme of your choice; the facilities on offer. It also gives you a flavour of the academic and social life at Queen's.
If you cannot find the information you need here, please contact the University Admissions and Access Service (admissions@qub.ac.uk), giving full details of your qualifications and educational background.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
An IELTS score of 6.0 with a minimum of 5.5 in each test component or an equivalent acceptable qualification, details of which are available at: http://go.qub.ac.uk/EnglishLanguageReqs
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
International Students - Foundation and International Year One Programmes
INTO Queen's offers a range of academic and English language programmes to help prepare international students for undergraduate study at Queen's University. You will learn from experienced teachers in a dedicated international study centre on campus, and will have full access to the University's world-class facilities.
These programmes are designed for international students who do not meet the required academic and English language requirements for direct entry.
There is a shortage of electrical and electronic engineers, not only locally in Northern Ireland and the rest of the UK, but worldwide, so employment prospects are excellent. The employment rate for graduates of this degree from Queen's (2013) was 100% (percentage employed in a graduate level job within 6 months of graduating).
Overview
Studying for an Electrical and Electronic Engineering degree at Queen’s will assist you in developing the core skills and employment-related experiences that are valued by employers, professional organisations and academic institutions. Graduates from this degree at Queen’s are well regarded by many employers (local, national and international) and over half of all graduate jobs are now open to graduates of any discipline, including Electrical and Electronic Engineering.
Although the majority of our graduates are interested in pursuing careers in engineering significant numbers develop careers in a wide range of other sectors. The following is a list of the major career sectors (and some starting salaries) that have attracted our graduates in recent years:
Management Consultancy - £26-38,000;
Investment Banking - £34K
Accountancy - £30K
Fast Stream Civil Service - £26,500
Varied graduate programmes (Times Top 100 Graduate Recruiters/AGR, Association of Graduate Recruiters UK)
Employment Links
The School has links with a large number of employers, providing opportunities for summer and year-long placements, as well as projects. Currently there are more companies offering placement opportunities than there are students seeking placements, with the result that opportunities for placements and employment in Electrical and Electronic Engineering are excellent.
What employers say
We are highly committed to the renewal of engineering talent in Northern Ireland and through our engagement with QUB we have had the opportunity to engage with the highest calibre of students. Our talent pool is predominantly sourced from the Electrical and Electronic Engineering programme with recent graduates able to apply their university learning to practical, real-life projects from the outset, bringing a new level of skills to our workforce.
Northern Ireland Electricity
Degree Plus/Future Ready Award for extra-curricular skills
In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Degree Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.
1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.
2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
The tuition fees quoted above for NI and ROI are the 2024/25 fees and will be updated when the new fees are known. In addition, all tuition fees will be subject to an annual inflationary increase in each year of the course. Fees quoted relate to a single year of study unless explicitly stated otherwise.
Tuition fee rates are calculated based on a student’s tuition fee status and generally increase annually by inflation. How tuition fees are determined is set out in the Student Finance Framework.
Additional course costs
Students may wish to become a student member of BCS - The Chartered Institute for IT - at an annual cost of £20, or £30 for four years (subject to change).
All Students
Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.
Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.
Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.
If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.
Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.
There are also additional charges for graduation ceremonies, examination resits and library fines.
How do I fund my study?
There are different tuition fee and student financial support arrangements for students from Northern Ireland, those from England, Scotland and Wales (Great Britain), and those from the rest of the European Union.
Application for admission to full-time undergraduate and sandwich courses at the University should normally be made through the Universities and Colleges Admissions Service (UCAS). Full information can be obtained from the UCAS website at: www.ucas.com/students.
When to Apply
UCAS will start processing applications for entry in autumn 2025 from early September 2024.
The advisory closing date for the receipt of applications for entry in 2025 is still to be confirmed by UCAS but is normally in late January (18:00). This is the 'equal consideration' deadline for this course.
Applications from UK and EU (Republic of Ireland) students after this date are, in practice, considered by Queen’s for entry to this course throughout the remainder of the application cycle (30 June 2025) subject to the availability of places. If you apply for 2025 entry after this deadline, you will automatically be entered into Clearing.
Applications from International and EU (Other) students are normally considered by Queen's for entry to this course until 30 June 2025. If you apply for 2025 entry after this deadline, you will automatically be entered into Clearing.
Applicants are encouraged to apply as early as is consistent with having made a careful and considered choice of institutions and courses.
The Institution code name for Queen's is QBELF and the institution code is Q75.
Additional Information for International (non-EU) Students
Applying through UCAS
Most students make their applications through UCAS (Universities and Colleges Admissions Service) for full-time undergraduate degree programmes at Queen's. The UCAS application deadline for international students is 30 June 2025.
Applying direct
The Direct Entry Application form is to be used by international applicants who wish to apply directly, and only, to Queen's or who have been asked to provide information in advance of submitting a formal UCAS application. Find out more.
Applying through agents and partners
The University’s in-country representatives can assist you to submit a UCAS application or a direct application. Please consult the Agent List to find an agent in your country who will help you with your application to Queen’s University.
