ACS6124 Multisensor and Decision Systems

Module Description (subject to change)

The ability to use data and information from multiple sources and make informed decisions based on that data is key to many applications, e.g. manufacturing, aerospace, robotics, finance and healthcare. Through effective use of multisensory data and decision making we can reduce uncertainty, improve robustness and reliability, enhance efficiency and ultimately improve the performance of systems. In this module students will develop an in depth knowledge and understanding of multisensor and decision systems and the underlying mathematics and algorithms. Students will develop their confidence in solving complex problems requiring the application of multisensory and decision techniques to a wide variety of applications.

Credits: 15 (Spring semester)

Module Leader

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Dr A R Mills
Email: a.r.mills@sheffield.ac.uk
Amy Johnson Building

If you have any questions about the module please talk to me during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask questions.

Outside of lectures please contact me via email, or drop in to see me.

Other teaching staff

Learning Outcomes

Learning Outcomes

By the end of the module students will be able to:

  • Explain the importance of and need for multisensor and decision systems in a wide range of industrial and research applications, the relative merits and limitations of adopting such systems compared to other state-of-the-art solutions;[SM1fl, SM3fl]
  • Describe and explain the main components, architectures and design issues in multisensor and decision systems and future technological challenges and opportunities;[EA1fl]
  • Select and use appropriate architectures, algorithmic, computational and experimental tools (including those from the research literature) to provide innovative solutions to complex, unfamiliar, open-ended multisensor and decision systems subject to a variety of technological constraints;[EA3fl, D1fl]
  • Demonstrate creative and critical thinking in providing and evaluating solutions to complex multisensor and decision systems and effectively communicate and analyse such solutions;[EP3m,D2f1,D7m]
  • Effectively present appropriate design methodology, analysis and critical evaluation of solutions and any limitations and constraints of such solutions in the form of a technical report to a standard that a suitably qualified person could follow and use to obtain similar findings.[D6m]
Syllabus

1. Introduction to multisensor and decision systems for monitoring
2. Signal tracking and multisensor data compression
3. Kalman filtering for multisensor data fusion
4. Statistical decision theory
5. Decision systems for change detection
6. Decision systems for diagnosis
7. Introduction to decision systems for design
8. Decision trees
9. Value of information
10. Design of experiments
11. Multi-objective optimization and decision-making
12. Multi-disciplinary optimization and decision-making

Teaching Methods

Learning and Teaching Methods

NOTE: This summary of teaching methods is representative of a normal Semester. Owing to the ongoing disruption from Covid-19, the exact method of delivery will be different in 2020/21.

24 hours of taught lectures (12 x 2 hour lecture slots)
+ 4 hours guest lecture = 28 hours of lecutures
+ 4 hours tutorials
+ 3 x 4 hour labs = 12 hours of laboratory

Teaching Materials

Learning and Teaching Materials

All required materials will be available via Blackboard (MOLE).

Assessment

Assessment

The module will be assessed by:

  1. A 2 hour exam worth 70% assessing in depth knowledge of motivations, theoretical background and technical concepts of topics in multisensor and decision systems. (learning outcomes 1-3, 5)
  2. 2 laboratory reports (worth 15% each) explaining the design methodology, reasoning and tools to the solution of 2 complex open-ended multisensor and decision problems together with a critical evaluation of the presented solutions. (learning outcomes 3-5)

Students will have the opportunity to resit. The module resit mark will be based on the resit exam plus the original assignment marks (from the course work).

Feedback

Feedback

Written feedback returned via Blackboard (MOLE) for all laboratory assignments.

Verbal feedback will be received during the laboratories, tutorials and lectures.

You will have an opportunity to view marked exam scripts once exam results have been confirmed by the Faculty and released to students. The date of this review session will be announced by the Departmental Office.

You will be able to look at the exam paper and a sample solution on the Blackboard (MOLE) page for this module. The paper and solution will be available after the exam period.

Student Evaluation

Student Evaluation

The department has a standard procedure for collecting student feedback on every taught module and communicating the results and staff comments back to the students.

You can view the latest Department response to the survey feedback here.

Recommended Reading

Recommended Reading

• H. V. Poor. "An introduction to signal detection and estimation." Springer-Verlag, 1988.
• F. Gustafsson and F. Gustafsson. "Adaptive filtering and change detection." New York, Wiley, 2000.
• R. Isermann. "Fault-diagnosis systems: an introduction from fault detection to fault tolerance." Springer Science & Business Media, 2006.
• Goodwin P. & Wright, G. 2004. Decision analysis for management judgment. 3rd Edition, Wiley.
• Forrester A., Sobester A., Keane A. 2008. Engineering design via surrogate modelling: A practical guide. Wiley