Course unit details:
Multi-Sensor Signal Processing & Imaging
| Unit code | EEEN64441 |
|---|---|
| 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 |
| 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
- Digital image processing by Gonzalez, Rafael C. Pearson Education Limited, 2018.
- Industrial tomography: systems and applications by Wang, Mi. Woodhead Publishing, 2022.
- Principles of computerized tomographic imaging by Kak, Avinash C. Society for Industrial and Applied Mathematics, 2001.
- Digital image processing, global edition [electronic resource] by Gonzalez, Rafael C. Pearson, 2018.
- Computed tomography fundamentals, system technology, image quality, applications by Kalender, Willi A. Publicis Pub, 2011.
Study hours
| Scheduled activity hours | |
|---|---|
| Lectures | 24 |
| Practical classes & workshops | 9 |
| Tutorials | 6 |
| Independent study hours | |
|---|---|
| Independent study | 111 |
Teaching staff
| Staff member | Role |
|---|---|
| Anthony Peyton | Unit coordinator |
| Wuliang Yin | Unit coordinator |