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This specialist option of the MSc Computational and Software Techniques in Engineering has been developed to deliver qualified engineers to the highest standard into the emerging field of digital signal and image processing who are capable of contributing significantly to this increased demand for both real-time and off-line systems operating over a range of mobile, embedded and workstation platforms.


  • Start dateSeptember
  • DurationOne year full-time, two-three years part-time
  • DeliveryTaught modules 45%, Group project 5%, Individual research project 50%
  • QualificationMSc
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

Developed for students interested in software development within the wide spectrum of industries in which digital signal processing and/or digital image processing plays a significant role. Suitable for candidates from a broad range of engineering backgrounds, including aeronautical, automotive, mechanical and electrical engineering in addition to the more traditional computational sciences background, who wish to both develop and complement their existing skill-set in this new area. Part-time students have a flexible commencement date.

Why this course?

This option of the MSc in Computational and Software Techniques in Engineering aims to develop your skill-base for the rapidly expanding engineering IT industry sector, not only in the UK but all over the world.  Graduates in this option have the opportunity to pursue a wide range of careers embracing telecommunications, the automotive industry, medical imaging, software houses and industrial research where demand for skills is high.

This course additionally forms part of the ESTIA (Ecole Supérieure des Technologies Industrielles Avancées) Cranfield MSc programme which gives ESTIA students the opportunity to study this degree based either at Cranfield University or ESTIA in Bidart, South-West France.

Cranfield University is very well located for visiting part-time students from all over the world, and offers a range of library and support facilities to support your studies.  This enables students from all over the world to complete this qualification whilst balancing work/life commitments.

Informed by Industry

The course is directed by an industrial advisory panel who meet twice a year to ensure that it provides the right mix of hands-on skills and up-to-date knowledge suitable for the wide variety of applications that this field addresses.

A number of members also attend the annual student thesis presentations which take place at the end of July.  This provides a good opportunity for students to meet key employers.

The Industry Advisory Panel includes:

  • Dr Adam Vile, Excelian
  • Mr Darren Baldwin, Excelian
  • Mr Matthew Breach, Ultra Electronics Sonar Systems
  • Mr Nigel Sedgewick, Selex
  • Dr Sanjiv Sharma, Airbus UK
  • Dr Steve King, Rolls Royce
  • Dr Julian Turnbull, AV
  • Mr Jon Loach, FACTSET
  • Prof David Emerson  (Scientific Computing, STFC Daresbury )
  • Dr Stuart Barnes (Software Engineer, Cambridge).

Course details

The course consists of 12 core modules, including a group design project, plus an individual research project. 

The course is delivered via a combination of structured lectures, tutorial sessions and computer based workshops. Mathematical and computational methods form the basis of the specialist modules, covering the theory and application of DSIP algorithms for the analysis, interpretation and processing of data in diverse fields such as computer vision, robotics, vibro-acoustic condition monitoring, medical diagnosis, remote sensing and data visualisation. This set of specialist modules are designed to provide students with the programming techniques necessary to develop, maintain and use core DSIP solution software over a wide range of industrial settings.

Group project

The group project which takes place in the spring is designed to provide you with invaluable experience of delivering a project within an industry structured team. The project allows you to develop a range of skills including learning how to establish team member roles and responsibilities, project management, delivering technical presentations and gaining experience of working in teams that include members with a variety of expertise and often with members who are based remotely.

Part-time students are encouraged to participate in a group project as it provides a wealth of learning opportunities. However, an option of an individual dissertation is available if agreed with the Course Director.

Recent Group Projects include:

  • Real-time Robotic Sensing
  • Automatic Video Surveillance
  • Face Recognition Systems
  • Applied Digital Signal Processing for Gear Box Analysis
  • Vibro-acoustic Analysis of Turbine Blades.

Individual project

The individual research project allows you to delve deeper into an area of specific interest. It is very common for industrial partners to put forward real world problems or areas of development as potential research project topics. In general you will begin to consider the research project after completing 3-4 modules - it then runs concurrently with the rest of your work.

For part-time students it is common that their research thesis is undertaken in collaboration with their place of work.

Recent Individual Research Projects include:

  • Vision Systems for Real Time Driver Assistance
  • Pattern Recognition for Vibration Analysis
  • Image Stabilisation for UAV Video Footage
  • Presenting Driver Assistance Information Using Augmented Reality
  • Real-time Object Tracking for Intelligent Surveillance Systems
  • 3D Stereo Vision Systems for Robotics and Vehicles.


Taught modules 45%, Group project 5%, Individual research project 50%


Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory modules and (where applicable) some elective modules affiliated with this programme which ran in the academic year 2018–2019. There is no guarantee that these modules will run for 2019 entry. All modules are subject to change depending on your year of entry.

Compulsory modules
All the modules in the following list need to be taken as part of this course

Computational Methods

Module Leader
  • Dr Irene Moulitsas

    The module aims to provide an understanding of a variety of computational methods for integration, solution of differential equations and solution of linear systems of equations.


    The module explores numerical integration methods; the numerical solution of differential equations using finite difference approximations including formulation, accuracy and stability; matrices and types of linear systems, direct elimination methods, conditioning and stability of solutions, iterative methods for the solution of linear systems.

Intended learning outcomes On successful completion of this module a student should be able to:
1. Implement and use numerical integration methods.
2. Use appropriate techniques to formulate numerical solutions to differential equations.
3. Evaluate properties of numerical methods for the solution of differential equations.
4. Choose and implement appropriate methods for solving differential equations.
5. Evaluate properties of systems of linear equations.
6. Choose and implement appropriate methods for solving systems of linear equations.
7. Evaluate the behaviour of the numerical methods and the numerical solutions.

C++ Programming

Module Leader
  • Dr Irene Moulitsas

    Object oriented programming (OOP) is the standard programming methodology used in nearly all fields of major software construction today, including engineering and science and C++ is one of the most heavily employed languages. This module aims to answer the question ‘what is OOP’ and to provide the student with the understanding and skills necessary to write well designed and robust OO programs in C++. Students will learn how to write C++ code that solves problems in the field of computational engineering, particularly focusing on techniques for constructing and solving linear systems and differential equations. Hands-on programming sessions and assignment series of exercises form an essential part of the course.
    An introduction to the Python language is also provided.

    • The OOP methodology and method, Classes, abstraction and encapsulation;
    • Destructors and memory management, Function and operator overloading, Inheritance and aggregation, Polymorphism and virtual functions, Stream input and output;
    • Templates, Exception handling, The C++ Standard Library and STL.
Intended learning outcomes

On successful completion of this module a student should be able to:

1. Apply the principles of the object oriented programming methodology - abstraction, encapsulation, inheritance and aggregation - when writing C++ programs.
2. Create robust C++ programs of simple to moderate complexity given a suitable specification.
3. Use the Standard Template Library and other third party class libraries to assist in the development of C++ programs.
4. Solve a range of numerical problems in computational engineering using C++.
5. Use development environments and associated software engineering tools to assist in the construction of robust C++ programs.
6. Evaluate existing C++ programs and assess their adherence to good OOP principles and practice.

Computer Graphics

Module Leader
  • Dr Karl Jenkins

    Computer graphics is a key element in the effective presentation and manipulation of data in engineering software. The aim of this module is to provide an in depth overview of the mathematical and software principles behind 2D and 3D visualisation, the viewing pipeline, and practical implementation in the widely used OpenGL graphics library. Representative GUI based 2D and 3D OpenGL applications using the Windows environment are used. Reference is also made to the programming model employed in OpenGL-ES, the version of OpenGL created for embedded devices and the basis for Android and iPhone apps. Hands-on exercises and an assignment supplement the learning process.

    • Mathematical principles behind 2D and 3D visualisation, Matrix transformations, The viewing pipeline, Modelling, viewing and projection, OpenGL graphics library, GLSL and shader programming.
    • Development of CG applications using OpenGL, GLSL and Qt, UI
    • WebGL, OpenGL-ES.

Intended learning outcomes On successful completion of this module a student should be able to:
1. Apply the principles of the viewing pipeline to compute device coordinates from a suitable ‘world coordinate system’ model.
2. Use the mathematical basis behind 2D/3D modelling and viewing to solve visualisation problems in OpenGL.
3. Understand, write and use basic shader programs using GLSL
4. Create simple interactive computer graphics based applications using OpenGL, GLSL (the shading language) and Qt.
5. Evaluate the major differences between the different version of OpenGL.

Management for Technology

    The importance of technology leadership in driving the technical aspects of an organisations products, innovation, programmes, operations and strategy is paramount, especially in today’s turbulent commercial environment with its unprecedented pace of technological development. Demand for ever more complex products and services has become the norm.  The challenge for today’s manager is to deal with uncertainty, to allow technological innovation and change to flourish but also to remain within planned parameters of performance.  Many organisations engaged with technological innovation struggle to find engineers with the right skills.  Specifically, engineers have extensive subject/discipline knowledge but do not understand management processes in organisational context.  In addition, STEM graduates often lack interpersonal skills.
    • Engineers and Technologists in organisations: The role of organisations and the challenges facing engineers and technologies.
    • People management: Understanding you. Understanding other people. Working in teams. Dealing with conflicts.
    • The Business Environment: Understanding the business environment; identifying key trends and their implications for the organisation.
    • Strategy and Marketing: Developing effective strategies; Focusing on the customer; building competitive advantage; The role of strategic assets.
    • Finance: Profit and loss accounts. Balance sheets. Cash flow forecasting.Project appraisal.
    • New product development: Commercialising technology. Market drivers. Time to market. Focusing technology. Concerns.
    • Business game: Working in teams (companies), students will set up and run a technology company and make decisions on investment, R&D funding, operations, marketing and sales strategy.
    • Negotiation: Preparation for Negotiations. Negotiation process. Win-Win solutions.
    • Presentation skills: Understanding your audience. Focusing your message. Successful presentations. Getting your message across.
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Recognise the importance of teamwork in the performance and success of organisations with particular reference to commercialising technological innovation.
  • Operate as an effective team member, recognising the contribution of individuals within the team, and capable of developing team working skills in themselves and others to improve the overall performance of a team.
  • Compare and evaluate the impact of the key functional areas (strategy, marketing and finance) on the commercial performance of an organisation, relevant to the manufacture of a product or provision of a technical service.
  • Design and deliver an effective presentation that justifies and supports any decisions or recommendations made
  • Argue and defend their judgements through constructive communication and negotiating skills.

Signal Analysis

Module Leader
  • Dr Zeeshan Rana

    The aim of this module is to provide students with the necessary mathematical basis and skills for the study of Computer and Machine Vision.

    • Revision of complex algebra
    • Important generalised functions
    • Series representation of period signals
    • Fourier analysis and the Fourier transforms
    • Convolution and correlation
    • The Sampling theorem
    • The Z transform
    • Probability and statistics: discrete, continuous and special distributions, sampling and estimation, significant tests.

Intended learning outcomes On successful completion of this module a student should be able to critically evaluate and apply concepts of:
1. Complex algebra.
2. Generalised functions, in particular the Dirac Delta function, and the Sampling property as the means for identifying their behaviour.
3. Fourier analysis and Fourier series representing a periodic function.
4. Fourier transform of a continuous function.
5. Convolution and Correlation and associated theorems.
6. Z transform for causal functions.
7. Basic elements of probability and statistics, as necessary for the analysis of signals and images.

Digital Signal Processing

Module Leader
  • Dr Yifan Zhao

    Digital signal processing, a major technology in almost all modern hi-tech applications and products, is at the heart of mobile phones, communications and vibro-acoustical condition Monitoring. The aim of this course is to provide an industry oriented course covering not only the theoretical aspects of classical and advanced time-frequency DSP but also the solid implementation aspects of the subject for students wishing to pursue a career in such areas as communications, speech recognition, bio-medical engineering, acoustics, vibrations, radar and sonar systems and multimedia.

    • Discrete-time signals and systems
    • The correlation of discrete-time signals
    • The discrete Fourier transform
    • The power spectral density
    • The short time Fourier transform
    • The wavelet transform
    • Classical and adaptive digital filtering

Intended learning outcomes On successful completion of this module a student should be able to:
1. Understand and be able to apply the concepts of discrete time signals and systems and correlation of discrete time signals
2. Understand and be able to apply the concept, properties and application of the classical discrete Fourier transform
3. Understand and be able to apply the concepts, properties and application of the non-parametric and parametric estimates of the classical power spectral density
4. Understand and be able to apply the fundamental principles of advanced time-frequency signal processing
5. Understand the concept, properties and application of the advanced time-frequency technique, the short time Fourier transform
6. Understand and be able to apply the concept, properties and application of the advanced time-frequency technique, the wavelet transform
7. Understand and be able to apply the concept, properties and application of digital filtering, including FIR and IIR filters.

Machine Learning

Module Leader
  • Dr Irene Moulitsas
    The aim of this module is to provide students with the necessary knowledge and understanding for the application of machine learning techniques to real world industrial problems within the domain of digital signal and image processing and beyond.
    • Machine Learning Theory & Methodology
    • Decision Tree Classifiers
    • Instance Based Learning
    • Bayesian Classification
    • Genetic Algorithms
    • Ant Colony Optimisation
    • Neural Networks
    • Support Vector Machines
Intended learning outcomes On successful completion of this module a student should be able to:
1. Apply a range of machine learning techniques to solve industrial problems within the domain of computer and machine vision.
2. Evaluate the application of machine learning approaches to a wider set of data mining and classification type problems.
3. Using a provided implementation, plan machine learning analysis on suitable forms of computer and machine vision data.
4. Explain the concepts and operation of a range of machine learning algorithms in order to facilitate re-implementation in a software programming environment with which they are already familiar.
5. Solve machine learning computer vision problems through interactive learning workshops.

Advanced Graphics

Module Leader
  • Dr Karl Jenkins

    High performance computer graphics are used in many areas of software application development, and are fundamental to games, entertainment, CAD and scientific visualisation. The aim of this module is to introduce students to the advanced techniques used in the generation of computer graphics. Building on the basic methods of the Introductory course, students will learn how to generate more realistic effects, such as the use of lighting and surface details to create realistic representations of computer generated graphical objects and display them to the screen.

    Surfaces and Tessellation, Geometric and Raster Algorithms, Light, Illumination and Shading, Texture Mapping, Bump Mapping, Displacement Mapping, Environment Mapping, Introduction to Virtual Reality.
Intended learning outcomes On successful completion of this module a student should be able to:
1. Understand the concepts, underlying principles and operation of a range of advanced computer graphics algorithms and techniques;
2. Optimize the graphics pipeline by implementing surface algorithms, such as surface tessellation and rendering, leading to real-time performance;
3. Understand the models of interaction between light and materials, as well as being able to demonstrate a practical capability of implementing such methods;
4. Implement algorithms using the OpenGL graphics library and GLSL and apply these techniques to solving a specific problem in computer graphics.

Applications of Computer Vision for Robotics (Group Project)


    The low-level and mid-level visual understanding achievable using various digital image processing techniques allow us to tackle the Artificial Intelligence problem of artificial visual sensing – computer vision (also termed 'robot vision'). By developing these techniques further we can apply image processing to a number of different visual inspection and understanding tasks within the realm of science and engineering. Here we investigate applied digital image processing in the form of computer vision – the automated interpretation and understanding of visual information. The digital signal application area focuses on the use of vibroacoustics for condition monitoring.

    • Geometric Object Recognition (industrial)
    • Principle Component Analysis Based Object Recognition (industrial and faces)
    • 3D object recognition and sensing – range data and stereo vision
    • Object motion detection, scene change detection and object tracking approaches
    • Robotic Control
Intended learning outcomes

On successful completion of this module a student should be able to:

1. Apply, describe and critically evaluate the concept and limitations of computer vision for Robotics.
2. Describe, implement and evaluate a computer vision system according to basic application requirements and specifications.
3. Apply, describe and critically evaluate the basic concepts of object recognition.
4. Apply, describe and critically evaluate a range of computer vision applications in Robotics.

Image Processing and Analysis

Module Leader
  • Dr Yifan Zhao
    The most powerful method of sensing available to humans is vision. In computing visual information is represented as a digital image. In order to process visual information in computer systems we need to know about processing digital images. Here we focus upon the task of low-level visual processing.
    • Image Applications
    • Image Representation
    • Image Capture Hardware
    • Image Sampling & Noise
    • Image Geometry & Locality, Processing Operations Upon Images
    • Camera Projection / Convolution Model
    • Image Transformation
    • Image Enhancement
Intended learning outcomes On successful completion of this module a student should be able to:
1. Apply, describe and critically evaluate common digital image representations.
2. Apply, describe and critically evaluate a range of local and global image transforms.
3. Apply, describe and critically evaluate image processing in the frequency domain.
4. Apply, describe and critically evaluate basic image feature extraction for simple image comparison tasks.
5. Apply, describe and critically evaluate techniques to counter noise in digital images
6. Apply, describe and critically evaluate basic feature-based image classification.

Computer Vision

Module Leader
  • Dr Zeeshan Rana
    Digital Image Processing allows us to process visual information in computer systems. By processing visual information we can develop automated visual interpretation and understanding – artificial vision, itself a large part of wider field of the Artificial Intelligence. In order to achieve this we must be able to extract high-level visual information such as edges and regions from images and additionally allow for the efficient storage of large amounts of visual data. Here we concentrate on mid-level visual interpretation and image compression.

    • Image Restoration
    • Image Compression
    • Image Feature Extraction and Processing
    • Image Segmentation
    • Basic Feature-based Classification Approaches
    • Stereo Vision and Object Tracking
Intended learning outcomes On successful completion of this module a student should be able to:
1. Apply, describe and critically evaluate the effects and impact of image compression.
2. Apply, describe and critically evaluate methods for image restoration (deblurring).
3. Apply, describe and critically evaluate feature post-processing approaches.
4. Apply, describe and critically evaluate basic feature-based image classification.
5. Understand and apply Stereo Vision.
6. Understand and apply Object Tracking approaches

Teaching team

Cranfield University is a leader in applied mathematics and computing applications, and you will be taught by experienced Cranfield academic staff. Our staff are practitioners as well as tutors, with clients that include: UK Ministry of Defence Home Office Scientific Development Branch Caterpillar Rolls-Royce Department of Transport (DfT). Our teaching team work closely with business and have academic and industrial experience. Knowledge gained working with our clients is continually fed back into the teaching programme, to ensure that you benefit from the very latest knowledge and techniques affecting industry. The course also includes visiting lecturers from industry who will relate the theory to current best practice. In recent years, our students have received lectures from industry speakers including: Jonathan Mckinnell, BBC R&D Mark Bernhardt, Waterfall Solutions Andy Lomas, Head of CG, Framestore.

Your career

The MSc in Computer and Machine Vision attracts enquiries from companies all over the world who wish to recruit high quality graduates. There is considerable demand for students with expertise in engineering software development and for those who have strong technical programming skills in industry standard languages and tools. Graduates of this course will be in demand by commercial engineering software developers, automotive, telecommunications, medical and other industries and research organisations, and have been particularly successful in finding long-term employment.

Some students may go onto degrees, on the basis of their MSc research project. Thesis topics are most often supplied by individual companies on in-company problems with a view to employment after graduation - an approach that is being actively encouraged by a growing number of industries.

A selection of companies that have recruited our graduates include:

  • BAE Systems
  • European Aeronautic Defence and Space Company (EADS)
  • Defence, Science and Technology Laboratory (Dstl)
  • Orange France
  • Microsoft
  • EDS Unigraphics
  • Delcam
  • GKN Technology
  • Logica
  • Oracle Consulting Services
  • National Power
  • Altran Technologies
  • Earth Observation Sciences Ltd
  • Oracle Consulting Services
  • Easams Defence Consultancy
  • Xyratex.

How to apply

Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.