Information regarding our 2023/24 admissions cycle

Our 2023/24 postgraduate taught admissions cycle will open on Monday, 10 October. For most programmes, the application form will not open until this date.

MSc Communications and Signal Processing with Extended Research

Year of entry: 2023

Course unit details:
Multi-Sensor Signal Processing & Imaging

Course unit fact file
Unit code EEEN64442
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by
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.

 

 

Aims

This course unit detail provides the framework for delivery in the current academic year and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates.

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 the successful completion of the course, students will be able to: Developed Assessed
ILO1  

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.

X  
ILO2 Describe the physical and engineering principles of instrumentation for hard-field and soft field imaging. X X
ILO3 Perform conceptual designs of simple multi-sensor tomography systems. X X
ILO4 Define the inverse problem in a particular imaging scenario as an approach to sensor fusion. X X
ILO5 Perform back projection image reconstructions for soft field and hard field. X X
ILO6 Identify the motivation for multi-sensor fusion and the value of tomography imaging. X  

 

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%

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 9
Tutorials 9
Independent study hours
Independent study 108

Teaching staff

Staff member Role
Krikor Ozanyan Unit coordinator
Wuliang Yin Unit coordinator

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