Computer Science and Electronic Engineering BSc (Hons)

This module explores the design and development of computer software (applications) from two perspectives:

1. The implementation of software using programming code, in a high level statically typed, object-oriented programming language, such as Java.
2. The user-centred design approach to software design, considering human factors, user experience, usability, and prototyping.

The programming aspect covers a range of topics including

• The syntax and semantics of a programming language
• Different types of data, including collections
• Control flow - conditional, iterative and recursive
• Data input and output (CLI and file based)
• Object-orientation, including class design and inheritance
• Unit testing
• Basic refactoring

The human aspect considers topics that include

• User-centred design
• Human factors
• Usability
• Accessibility
• User experience
• Low and high-fidelity prototyping
• Evaluation

This module provides a strong foundation in the key areas of computing, i.e., mathematics, modern computing systems, cybersecurity, and professional skills. It equips students with the essential knowledge needed to tackle real-world computing problems and prepare them for advanced computing studies.

The mathematical aspect covers a range of topics that include:

• Set theory
• Geometric and trigonometric problem solving
• Vectors
• Linear, quadratic, fractional, and simultaneous equations
• Matrices
• Logarithmic functions
• Simple differential equations
• Introduction to complex numbers

The computing systems considers topics that include:

• Computer architecture - Hardware, software, and peripherals
• Basic computing mathematics - Number systems, binary operations, probability
• Logic gates and Boolean Algebra
• Machine Code and Assembly Language
• Compilers and Linkers
• Data Formats
• Operating Systems and File Management

The cybersecurity component addresses areas including:

• Cybersecurity Concepts
• The Human Weakness in Cybersecurity
• Cybersecurity Vulnerabilities
• Cybersecurity Exploitation
• Cryptography and Information Security
• Cybersecurity Auditing Methods
• Cybersecurity Defensive Strategies
• Programming for Cybersecurity with Python
• Ethical Hacking and Penetration Testing Lifecycle

The professional skills aspect encompasses subjects including:

• Employment & Employability
• Curriculum Vitae
• Role of Professional Bodies
• Security Obligations, Ethics and Law
• Copyright and IPR Concepts
• Report Writing and Referencing
• Proposal and Business Plans
• Social Networks and Collaboration
• Data Collection Methods
• Reflection

This module introduces fundamental concepts in database systems, emphasising their importance in modern computing. Students will gain theoretical and practical skills in designing, implementing, and querying relational databases, while also exploring alternative database management systems (DBMS), such as NoSQL and distributed databases. Through hands-on experience with tools like SQL and exposure to NoSQL systems, students will learn to choose the right database solution for different use cases.

The aims of this module are:

• Understand the role and importance of databases in computer systems and applications.
• Explain core database concepts, including schemas, tables, keys, and relationships.
• Design normalised database schemas using Entity-Relationship (ER) modelling.
• Implement relational databases in a DBMS.
• Write SQL queries for data definition, manipulation, and retrieval.
• Compare and contrast relational databases with alternative database models such as NoSQL and distributed systems.
• Explore use cases for NoSQL databases, including document stores, key-value stores, and graph databases.
• Understand the challenges and benefits of distributed databases, including replication and scalability.
• Awareness of basic techniques for database optimisation and indexing.
• Discuss data security, privacy, and ethical considerations in database design and use.

The module focuses on the application of computer programming, and related technologies, in solving everyday problems. In this process, problems and challenges will be analysed, leading to the selection and deployment of tools and techniques in response.

As a problem-based learning strategy is employed, the syllabus of the module is fluid from occurrence-to-occurrence. However, central themes and foundational concepts that will be covered are:

• Computational thinking (decomposition, abstraction, algorithm design, pattern recognition)
• Collaborative software development
• Solution evaluation
• Project portfolios

You now have the opportunity to pick an optional module to learn a new language or build on your existing language skills as part of your degree. You can choose:

• Subsidiary Language for Beginners (choice of German, Italian or Spanish)
• French: Intermediate Language Development
• Spanish: Intermediate Language Development
• Chinese: Intermediate Language Development
• German: Communication in Practice
• French: Communication in Practice
• Spanish: Communication in Practice

Electricity and Magnetism

• Basic definitions of current, voltage and power.
• Principles of electricity: voltage and current sources, resistors, capacitors.
• Principles of magnetism: magnetic field, magnetic forces, inductors.
• DC circuits: Ohm’s Law, Kirchhoff’s Laws, Norton and Thevenin equivalent circuits with voltage and current source transformations. Steady state and transient (switching) DC circuit analysis. Analysis by nodal and loop methods. Superposition Theorem. Power.
• AC Circuits: Sinusoidal signals, amplitude, frequency and phase. Complex notation, Ohm’s Law, Kirchhoff’s Law and Norton and Thevenin equivalent circuits generalised to impedance and admittance. Steady state AC circuit analysis and phasor diagrams. Frequency response for RC, RL and RLC circuits. Resonance for RLC circuits. Superposition Theorem and Single Phase AC power.
• Measurement and Test Equipment: Meter Loading effects for voltage and current measurement. Use of multimeters, oscilloscopes, power supplies and signal generators in the laboratory.

Analogue Electronics

• Semiconductors: Semiconductor doping, electron and hole transport. The device physics of a p-n junction in forward and reverse bias.
• Diodes: diode equation, graphical/load line analysis, diode models. Zener and light emitting diodes. Diode circuits including rectifier, peak sample, power rectifier, clamps and regulator.
• Bipolar Junction Transistors (BJT): BJT structure, basic BJT operation, BJT characteristics and parameters, BJT amplifiers and BJT switching applications.
• Field effect transistors (FET): FET characteristics and parameters, FET biasing, FET amplifiers and FET switching applications.
• Operational amplifiers: Concept of an ideal operational amplifier and its practical realisation, design of inverting, non-inverting, summing and differential amplifiers.

Digital Electronics

• Number systems: decimal numbers, binary numbers, decimal-to-binary conversion, binary arithmetic, 2’s complements of binary numbers, hexadecimal numbers.
• Logic gates: the Inverter, AND, OR, NAND, NOR, Exclusive-OR and Exclusive-NOR.
• Logic fundamentals: Boolean algebra and logic simplification, Karnaugh map, De Morgan’s law.
• Combinational logic analysis: basic combinational logic circuits, implementing combinational logic, the universal property of NAND and NOR gates, combinational logic using NAND and NOR gates.
• Basic sequential logic circuits: Latches, S-R Flip-Flop, D Flip-Flop, J-K Flip Flop, registers.

This is an experiential learning opportunity that incorporates, 20 teaching contact hours/lectures to prepare for the150 contract hours where L5 students can use all their skills learned to date on an actual real-world (external business) client driven project, working in teams and produce an artefact. 

Students are also expected to undertake around 30 hours of self study.

This module not only gives them enhanced skills but the opportunity to work for a real client thus giving them a valuable CV and LInkedIn entry as work experience that can contribute to their employability portfolio. 

Students will collaborate in teams and produce full client documentation alongside a reflection of their expereince and this all give some much needed contemplation of their skills to date and how to use them. 

This module provides a structured, university-level work placement for 4, 5 or 7 weeks as one continuous block / period with a placement provider (i.e. industry apprioprate sector). It is designed to enhance your professional skills in a real-world job setting.

The placement can either be organised by you or with support from university staff.

All work placements within this module must be university-level; this means:

• Undertaking high-skilled work commensurate with level 5 study (e.g. report writing, attending meetings, delivering presentations, producing spreadsheets, writing content on webpages, social media, marketing services/products etc)
• Physically placed (albeit part of it can be hybrid) within an employer setting in one continuous block / period for 4, 5 or 7 weeks for a minimum of 140-147 hours over the course of the entire work placement

Where applicable, your existing part-time employer can be approached/used as the placement provider, if the high-skilled work.

• criterion above is fulfilled for the full duration of the placement.
• All quality assurances/agreements provided by the University are adhered to, by you and the employer.

The work placement context may not necessarily, reflect your degree discipline per se, but rather, it will give you an enriched experience to enhance your professional skills in a real-world job setting.

The Level 5, 40-credit modules require a basic foundation of knowledge of your chosen language e.g. GCSE or equivalent,  a Level 4 module in the same language or equivalent previous learning. This module includes an optional placement abroad, such as an intensive course at a partner university. You can choose:

• Advanced Language Development and Global Sustainability (choice of German, French or Spanish)
• Upper Intermediate Language Development and Global Employability (choice of Chinese, French or Spanish)
• Post Beginner Language Development and Global Cultures (choice of German, Italian or Spanish)

Embedded Systems

Interfacing Systems Computers

• The computer as a controlling element for electro-mechanical devices.
• Interfacing between control system and the real world.
• Hardware/software trade-off, communication between devices, multiplexing.

Digital and Analogue circuits

• Concepts of ADC and DAC; CMOS and TTL logic interfacing, A/D and D/A conversion, Phase-locked loops, Pseudo-random bit sequences and noise generation, input/output, and data communications.
• Input and output impedance loading. Driving inductive loads. Isolated operational amplifiers. Bridge circuits.
• Practical applications and designs of DC and AC circuits.

Actuators and sensors

• Characteristics, interfacing techniques and applications for common actuators and sensors.
• Instrumentation classification and characteristics, measurement systems, errors, calibration, processing and signal conditioning elements, common sensor used in mechanical systems and data converters.
• Algorithmic development and implementation of computer programs.
• Sensors; Sensed quantities, Sensor types, principles and uses, measurement of position, velocity, acceleration and force using analogue and digital circuits.
• Actuators; Basic principles; Hydraulic systems; Pneumatic systems; Electrical systems.

Controller Design

1. Introduction to the Design Process and Servomechanism feedback control principle introducing actuators, sensors and physical limitations.

2. Root-locus Design and Relative Degree (or System Type) and their use in actuation subsystem selection.

3. P, PI, PI+D and PID controller design with Root Locus and their relationship with Zeigler-Nichols tuning methods.

4. Introduction to State-Space Controller design methods, including full-state and partial state feedback, stability, relationship to root-locus design and inverse dynamics for single-input and single-output systems.

5. Final Value Theorem in State-Space and Disturbances in State-Space Design.

6. Introduction to Digital Controller Design using Discrete-time State Space Methods.

7. Introduction to Z Transforms, Discrete-time transfer functions, discrete-time poles and zeros, stability and sampling rate selection.

8. Introduction to Frequency Response Methods, Nyquist and Bode plots and Gain and Phase Margins.

9. Time Delay modelling and compensation (e.g. Smith’s Predictor) and its implementation in discrete-time control.

10. Consideration of Actuator Power Saturation and use of anti-windup algorithms with all types of digital controller (e.g. Hanus Algorithm)

Computer Simulation

Computer workshops will be provided for hands-on experience in design of control systems and computer interfaces using an industry standard National Instruments Labview & MATLAB and Simulink packages. Essential features of the software will be introduced through a series of example applications.

Embedded Systems Design with Non-Configurable Processors

• Definition and classification of embedded systems.
• Embedded systems basics: tools, resources and real time issues.
• The processing units of embedded systems.
• Architectures and instruction sets of microprocessors.                       
• Use of assembly language for embedded applications.
• Digital signal processors (DSP) implemented with embedded systems.
• Advanced serial communications: serial communication protocols, Bluetooth, Universal Serial Bus (USB) and Ethernet.
• Control systems and communicating control data over the controller area network (CAN).           

Configurable Processor Design in Embedded System-on-Chip (SOC)

• Advanced architecture of field programmable gate array (FPGA).
• Very High Speed Integrated Circuit Hardware Description Language (VHDL) programming techniques, simulation and test-bench design.
• Concept of a finite state machine (FSM) with data path (FSMD) and the introduction of stored program control to design a simple reduced instruction set computing (RISC) processor.
• Design technology of a soft core RISC processor with hardware description language (HDL).
  • Identification of behavioural and Register Transfer Level (RTL) descriptions.
  • De-coupling of data and control paths.
  • Data path and control unit design.
  • Hierarchical design: libraries, Generics, Generate and instantiation.
  • Design and construction of re-useable modules Evaluation of the simple processor and its comparison with existing commercial soft cores processors such as ARM, MIPS and NIOSII.
  • Concept of intellectual property (IP) in embedded processor design.
  • Features of the Altera NIOSII 32-bit RISC processor incluiding its architecture, insruction set, assembly language and VHDL implementation.
  • Implementation, testing and evaluation of a multi-core NIOSII embedded system on an Alterra DE1-SOC FPGA evaluation board.
  • Use of signed, fixed point and floating point arithmetic in applications including a digital signal processing system.

Reliability

•  Input/Output (I/O) synchronisation, bi-stable timing violations and meta-stability.
• Limitations of conventional synchronous system design.
• Asynchronous systems, critical and non-critical hazards.
• State assignments and asynchronous design using one-hot codes.

Students will undertake a large self-directed software project in a specialist topic of their choice with guidance and support from a dedicated academic supervisor.

The project will begin with an appraisal of said topic, usually through a literature review and/or a commercial assessment of viability. This will be followed by planning and creation of a practical software artefact covering an implementation lifecycle, making use of project management techniques.

Ethical issues will be explored, leading to required approval for quantitative and/or qualitative testing, with results then analysed and used to inform futher development and to draw conclusions against a hypothesis.

This module is introduces the theory and practice of network protocol design, maintenance and evalutation. We will build from first principles towards a professional, research and development approach to the subject. This will include topics such as:

• Routing
• Traffic engineering
• Distributed protocol design
• Use of discrete event simulation tools
• Evaluation and analysis of protocols
• Mobile and wireless networking
• Graph theory
• Network optimisation
• Computational complexity
• Software defined networking
• Information centric networking

The module combines relevant theoretical abstractions with essential practical networking approaches to build a strong profile of skills, abilities and knowledge for the successful student.

This module offers an in-depth exploration of artificial intelligence (AI) and its transformative role in the development of advanced software systems. It introduces key theoretical approaches and practical techniques for designing and deploying intelligent technologies, empowering you with the skills to build AI-driven solutions.

Key topics covered include:

• Introduction to Artificial Intelligence: Understanding the foundations of AI and its significance in modern software development.
• Theoretical Approaches to AI: Exploring algorithms and models that underpin intelligent systems, such as decision trees, neural networks, and reinforcement learning.
• Practical AI Implementation: Gaining hands-on experience with AI techniques, including machine learning, natural language processing, and computer vision, through coding exercises and projects.
• Designing and Deploying Intelligent Systems: Examining methods for building robust, scalable, and ethically sound AI technologies.
• AI in Various Domains: Critically evaluating how AI is applied across industries such as business, healthcare, education, law, government, and scientific research, along with the ethical and societal implications of these applications.

This module blends theory with practical application, equipping you to develop intelligent systems and critically assess their impact in a wide range of real-world contexts.

The Robotics module provides an introduction to the foundational principles of robotics, exploring the theoretical aspects that underpin the design, application, and ethical considerations of robotic systems.

You will begin by examining the fundamental question: What is a robot? This includes understanding the diverse applications of robots across industries and their role in society. The module also delves into the ethical implications of robotics, such as their impact on employment, privacy, and safety.

Key technical topics include an overview of mechatronics, which integrates mechanical, electronic, and computer engineering; sensors, which enable robots to perceive their environment; and control systems, which ensure robots can perform tasks accurately and autonomously.

The module is assessed in a practical project where you will design a simulated robot, applying the concepts learned to demonstrate your understanding of robotic systems.

The module covers a range of topics that include:

• Basic cloud computing concepts, advantages, and service delivery models.
• Identity and Access Management (IAM) for centrally managing access to cloud resources.
• Secure networking practices within the cloud environments.
• Design and implementation of highly available, and secure cloud architecture.
• Design and implementation of cloud resources such as Virtual servers, Databases, and storage solutions.
• Introduction to serverless architecture within the cloud environments.
• Application Data protection, both in transit and at rest, with in the cloud environments.
• Logging and Monitoring within the cloud.
• Incident Response Management within the cloud environments.

Recognition of the need to apply data science applications in organisations.

Establishment of correct selection and application of data science techniques (i.e. data shaping, model type selection, testing and application) in various organisational contexts. 

Application of common machine learning tools (e.g. logistic regression, non-linear model estimation, neural network) in a common development environment (e.g. R, Python, Scala) in preparation for the real world context.

Evaluation of the role of ethics in the application of data science techniques.