Clearing 2025

Our clearing vacancies will be live from the evening of Wednesday, 13 August. In the meantime, you can read our clearing guide on the clearing pages.

Master of Engineering (MEng)

MEng Electrical and Electronic Engineering with Industrial Experience

*This course is now closed for applications for 2025 entry.

  • Duration: 5 years
  • Year of entry: 2025
  • UCAS course code: H601 / Institution code: M20
  • Key features:
  • Industrial experience
  • Scholarships available
  • Accredited course

Full entry requirementsHow to apply

Course unit details:
Multi-Sensor Signal Processing & Imaging

Course unit fact file
Unit code EEEN44441
Credit rating 15
Unit level Level 4
Teaching period(s) Semester 2
Available as a free choice unit? No

Overview

  • Introduction to sensor fusion for multi-sensor systems and tomography imaging.
  • Hard-field tomography: x-ray, gamma-ray, optical and terahertz tomography. Sensing principles. Introduction to algorithms for hard-field reconstruction. Software design. Hardware design: sources, detectors and measurement “train”. State-of-the-art in hard-field process tomography. Multi-sensor, multi-modality approach. 
  • Soft-field tomography: electrical impedance and capacitance tomography, electromagnetic tomography. Sensing techniques. Hardware design: sensors, measuring circuits and calibration. Soft-field reconstruction and software design, applications. State-of-the-art in soft-field process tomography. 
  • MRI principles, imaging methods and applications. 

Aims

The programme unit aims to:

  • Introduce students to theoretical and practical fundamentals of signal and data processing for multi-sensor systems, addressing in detail the underlying principles, measurement techniques, image reconstruction and system design the example case of tomography imaging. 
  • Introduce students to the various aspects of multi-sensor data fusion for tomography imaging applications: medical, industrial, security. 
  • Enable students to undertake simple designs of multi-sensor and tomography imaging systems. 
  • Prepare students for Research and Development (R&D), academic or other employment in this area

Learning outcomes

On successful completion of the course, a student will be able to:

ILO 1: Perform back projection image reconstructions for soft field and hard field.

ILO 2: Perform conceptual designs of simple multi-sensor tomography systems.

ILO 3: Analyse the industrial needs and match them with existing technology; identify the needs for development of new technology and the basic drivers and developments in industrial multi-sensor systems.

ILO 4: Define the inverse problem in a particular imaging scenario as an approach to sensor fusion.

ILO 5: Identify the motivation for multi-sensor fusion and the value of tomography imaging.

ILO 6: Describe the physical and engineering principles of instrumentation for hard-field and soft field imaging.

Teaching and learning methods

Lectures, Pre-recorded videos, Tutorials, Practical Work/ Laboratory and Private Study

Assessment methods

Method Weight
Written exam 70%
Written assignment (inc essay) 30%

Feedback methods

.

Recommended reading

Kak, Avinash C. (Avinash Carl),Principles of computerized tomographic imaging, IEEE Engineering in Medicine and Biology Society. Conference. Type: Book ISBN: 0879421983 Available At: Main Library Blue Area Floor 3 616.0757 KAK

Computed tomography fundamentals, system technology, image quality, applications

Kalender, Willi A. Type: Book ISBN: 9783895786440 Edition: 3rd ed.

Measurement Science and Technology, Volume 17, Number 8, August 2006

K.J. Binns, P.J. Lawrenson, C.W. Trowbridge, The analytical and numerical solution of electric and magnetic fields: Wiley 1992

Jianming Jin, The finite element method in electromagnetics: John Wiley & Sons 3rd ed. (2014)

Study hours

Scheduled activity hours
Lectures 24
Practical classes & workshops 6
Tutorials 9
Independent study hours
Independent study 111

Teaching staff

Staff member Role
Wuqiang Yang Unit coordinator
Anthony Peyton Unit coordinator

Return to course details