31323 Statistical Change Detection in Fault Diagnosis and Signal Processing
Statistisk detektion i fejldiagnose og signalanalyse
Point( ECTS )
Taught under open university
The course will take place during the period June - September. Lecture days will be determined after hearing the participants.
Scope and form:
Study group where participants present material.
Duration of Course:
[The course is not following DTUs normal Schedule]
Date of examination:
Decide with teacher, Decide with teacher
Type of assessment:
General course objectives:
To make participants able to work with change detection in signals using stringent mathematical methods at an international level.
A student who has met the objectives of the course will be able to:
know several probability distribution functions of importanc to engineering applications
describe detectors based on Neuman-Pearson assumption
Describe detectors based on Bayes hypothesis
desctibe detectors for deterministic signals, including matched filters
Design detectors for random signals and understand their properties
describe algorithms for single or combined hypothesis testing
design detectors for signals in white or coloured noise
understand how detectors are designed for known or unknown signals
Important probability density functions, statistical decision theory 1, deterministic signals, random signals, statistical decision theory 2, deterministic signals with unknown parameters, random signals with unknown parameters, unknown noise parameters, nongaussian noise, model change detection, vector extensions.
S. M. Kay: Fundamentals of Statistical Signal Processing - volume 2: Detection theory.
The course is inteded for ph.d students in the areas of automation, signal processing, communication, informatics and mathematical modelling where detection of signals in noise is essential.
Green challenge participation:
Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More infor
, Building 326, room 114, Ph. (+45) 4525 3565
, Building 322, room 126, Ph. (+45) 4525 3356
31 Department of Electrical Engineering
01 Department of Applied Mathematics and Computer Science
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Last updated: 24. april, 2013