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)
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
Dr C Genes
By the end of the module students will be able to:
1. Introduction to multisensor and decision systems for monitoring
Learning and Teaching Methods
24 hours of taught lectures (12 x 2 hour lecture slots)
Learning and Teaching Materials
All required materials will be available via Blackboard (MOLE).
The module will be assessed by:
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).
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.
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.
• H. V. Poor. "An introduction to signal detection and estimation." Springer-Verlag, 1988.