Modules

• Year 1

  Core Modules

  Embedded Systems (20 credits)

  ×

  Embedded Systems

  Overview

  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).

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Stage/Level

  1

  Credits

  20

  Module Code

  ECS1001

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Signals and Communications (20 credits)

  ×

  Signals and Communications

  Overview

  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

  Coursework

  60%

  Examination

  20%

  Practical

  20%

  Stage/Level

  1

  Credits

  20

  Module Code

  ELE1057

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Object Oriented Programming (20 credits)

  ×

  Object Oriented Programming

  Overview

  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.

  Coursework

  50%

  Examination

  20%

  Practical

  30%

  Stage/Level

  1

  Credits

  20

  Module Code

  CSC1029

  Teaching Period

  Spring

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Mathematics 1 (20 credits)

  ×

  Mathematics 1

  Overview

  Complex Arithmetic:
  1. Complex numbers: fundamentals, modulus and argument, Argand diagrams.
  2. Complex forms: cartesian, exponential, conversions between forms, conjugation
  3. Arithmetic: addition/subtraction, multiplication, division, exponentiation
  4. DeMoivre’s theorem
  
  Linear Algebra:
  1. Vector arithmetic: concept, high-dimensional objects and arithmetic operations.
  2. Matrices: fundamentals, notation, determinants, transposition
  3. Matrix arithmetic: addition/subtraction, multiplication, division, inversion, triangularisation.
  4. Linear equations: solution by Gaussian Elimination, Cramer’s Rule, Matrix Inversion.
  
  Differentiation:
  Fundamentals; Curve Sketching; Product and Chain Rules; Parametric Differentiation; Logarithmic Differentiation; Parital Differentiation
  
  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.

  Coursework

  50%

  Examination

  50%

  Practical

  0%

  Stage/Level

  1

  Credits

  20

  Module Code

  ELE1012

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Digital Systems (20 credits)

  ×

  Digital Systems

  Overview

  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

  Coursework

  60%

  Examination

  40%

  Practical

  0%

  Stage/Level

  1

  Credits

  20

  Module Code

  ECS1005

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Fundamentals of Electric Circuits (20 credits)

  ×

  Fundamentals of Electric Circuits

  Overview

  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

  Coursework

  20%

  Examination

  65%

  Practical

  15%

  Stage/Level

  1

  Credits

  20

  Module Code

  ECS1006

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

• Year 2

  Core Modules

  Mathematics (20 credits)

  ×

  Mathematics

  Overview

  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.

  Coursework

  20%

  Examination

  80%

  Practical

  0%

  Stage/Level

  2

  Credits

  20

  Module Code

  ELE2035

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Data Structures and Algorithms (20 credits)

  ×

  Data Structures and Algorithms

  Overview

  • 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.

  Coursework

  50%

  Examination

  0%

  Practical

  50%

  Stage/Level

  2

  Credits

  20

  Module Code

  CSC2059

  Teaching Period

  Autumn

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Employability Skills and Placement Preparation (0 credits)

  ×

  Employability Skills and Placement Preparation

  Overview

  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.

  Coursework

  0%

  Examination

  0%

  Practical

  100%

  Stage/Level

  2

  Credits

  0

  Module Code

  ELE2037

  Teaching Period

  Autumn

  Duration

  12 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Embedded Systems 2 (20 credits)

  ×

  Embedded Systems 2

  Overview

  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.
  
   

  Coursework

  0%

  Examination

  60%

  Practical

  40%

  Stage/Level

  2

  Credits

  20

  Module Code

  ELE2025

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Optional Modules

  Systems Security and Cryptography (20 credits)

  ×

  Systems Security and Cryptography

  Overview

  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).

  Coursework

  0%

  Examination

  100%

  Practical

  0%

  Stage/Level

  2

  Credits

  20

  Module Code

  CSC2056

  Teaching Period

  Spring

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Introduction to Artificial Intelligence and Machine Learning (20 credits)

  ×

  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.

  Coursework

  60%

  Examination

  40%

  Practical

  0%

  Stage/Level

  2

  Credits

  20

  Module Code

  CSC2062

  Teaching Period

  Spring

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Digital Systems (20 credits)

  ×

  Digital Systems

  Overview

  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

  Coursework

  60%

  Examination

  40%

  Practical

  0%

  Stage/Level

  2

  Credits

  20

  Module Code

  ECS2039

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Signals and Control (20 credits)

  ×

  Signals and Control

  Overview

  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

  Coursework

  50%

  Examination

  50%

  Practical

  0%

  Stage/Level

  2

  Credits

  20

  Module Code

  ELE2038

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Electronics and Circuits (20 credits)

  ×

  Electronics and Circuits

  Overview

  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

  Coursework

  30%

  Examination

  70%

  Practical

  0%

  Stage/Level

  2

  Credits

  20

  Module Code

  ELE2041

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Communications (20 credits)

  ×

  Communications

  Overview

  Course Contents
  
  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

  Coursework

  25%

  Examination

  75%

  Practical

  0%

  Stage/Level

  2

  Credits

  20

  Module Code

  ELE2040

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

• Year 3

  Core Modules

  Engineering Entrepreneurship (20 credits)

  ×

  Engineering Entrepreneurship

  Overview

  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.
  Business development: team development; product development; marketing approaches; financial planning.

  Learning Outcomes

  General:
  Report writing.
  Business presentation.
  Assimilation of business practices.
  Generation of product.
  
  Specific:
  Ability to pitch business concept.

  Skills

  Skills General:
  Presentational skills.
  Development of business acumen.
  Business plan creation.
  Team-working.
  Self-assessment.
  Creativity.

  Coursework

  70%

  Examination

  0%

  Practical

  30%

  Stage/Level

  3

  Credits

  20

  Module Code

  ELE3043

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Project 3 (40 credits)

  ×

  Project 3

  Overview

  The project is normally an investigation or design study of various branches of electrical and electronic engineering. The project originators typically endeavour 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 ability to conduct a substantial project over an extended period, perceive the nature of engineering problems or product specifications; to acquire and develop necessary skills and to plan and execute a suitable programme of work, including a final report.

  Skills

  Ability to apply general principles and design or analytical techniques to the solution of engineering problems. Such solutions may require investigative, practical or design skills or a combination of the three. Originality is encouraged.

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Stage/Level

  3

  Credits

  40

  Module Code

  ELE3001

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Optional Modules

  Video Analytics and Machine Learning (20 credits)

  ×

  Video Analytics and Machine Learning

  Overview

  • 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.

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Stage/Level

  3

  Credits

  20

  Module Code

  CSC3067

  Teaching Period

  Autumn

  Duration

  12 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Control Systems Engineering (20 credits)

  ×

  Control Systems Engineering

  Overview

  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

  Coursework

  30%

  Examination

  70%

  Practical

  0%

  Stage/Level

  3

  Credits

  20

  Module Code

  ELE3042

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  Yes

  Core/Optional

  Optional

  Malware Analysis (20 credits)

  ×

  Malware Analysis

  Overview

  • 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.

  Coursework

  0%

  Examination

  0%

  Practical

  100%

  Stage/Level

  3

  Credits

  20

  Module Code

  CSC3059

  Teaching Period

  Spring

  Duration

  12 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Advanced Electronics (20 credits)

  ×

  Advanced Electronics

  Overview

  • 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

  Coursework

  50%

  Examination

  50%

  Practical

  0%

  Stage/Level

  3

  Credits

  20

  Module Code

  ELE3046

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  Yes

  Core/Optional

  Optional

  Concurrent Programming (20 credits)

  ×

  Concurrent Programming

  Overview

  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.

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Stage/Level

  3

  Credits

  20

  Module Code

  CSC3021

  Teaching Period

  Autumn

  Duration

  12 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Signal Processing and Communications (20 credits)

  ×

  Signal Processing and Communications

  Overview

  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

  Coursework

  15%

  Examination

  85%

  Practical

  0%

  Stage/Level

  3

  Credits

  20

  Module Code

  ELE3041

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  Yes

  Core/Optional

  Optional

  Connected Health (20 credits)

  ×

  Connected Health

  Overview

  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

  Coursework

  30%

  Examination

  70%

  Practical

  0%

  Stage/Level

  3

  Credits

  20

  Module Code

  ECS3003

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Networks and Communications Protocols (20 credits)

  ×

  Networks and Communications Protocols

  Overview

  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

  Coursework

  20%

  Examination

  80%

  Practical

  0%

  Stage/Level

  3

  Credits

  20

  Module Code

  ELE3040

  Teaching Period

  Full Year

  Duration

  24 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Deep Learning (20 credits)

  ×

  Deep Learning

  Overview

  • 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.

  Coursework

  60%

  Examination

  40%

  Practical

  0%

  Stage/Level

  3

  Credits

  20

  Module Code

  CSC3066

  Teaching Period

  Spring

  Duration

  12 weeks

  Pre-requisite

  No

  Core/Optional

  Optional