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A realization of classification success in multi sensor data fusion


Masnan, Maz Jamilah and Zakaria, Ammar and Md Shakaff, Ali Yeon and Mahat, Nor Idayu and Hamid, Hashibah and Subari, Norazian and Mohamad Saleh, Junita (2012) A realization of classification success in multi sensor data fusion. In: Principal Component Analysis - Engineering Applications. InTech, Croatia, pp. 1-24. ISBN 978-953-51-0182-6

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Abstract

The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991).

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Quantitative Sciences
Depositing User: Dr. Nor Idayu Mahat
Date Deposited: 16 Apr 2017 08:38
Last Modified: 16 Apr 2017 08:38
URI: http://repo.uum.edu.my/id/eprint/21573

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