Back to Search
Start Over
Computational frameworks for context-aware hybrid sensor fusion.
- Source :
-
International Journal of Image & Data Fusion . Mar2016, Vol. 7 Issue 1, p83-102. 20p. - Publication Year :
- 2016
-
Abstract
- This paper proposes inexpensive, specialised, computational frameworks that automate and integrate context-aware sensing, data aggregation, information extraction and understanding and qualitative decision making through intelligent algorithms. Its contributions are spread across context-aware data collection and aggregation, hybrid feature extraction incorporating both supervised and unsupervised approaches, and decision-based information fusion. It provides a toolkit that makes it easier for applications to use context. It presents a hybrid feature extraction framework based on two diverse optimisation problems in aspects of risk and independence to extract features resulting in higher classification performance. It combines a context-aware multi-sensor data collection model and a “Feature Input Feature Output (FeI-FeO)” based fusion model with an intelligent classifier to create a “Feature Input Decision Output (FeI-DeO)” based pattern recognition system, which can classify targets by eliminating redundant contexts. The proposed frameworks achieve context-sensitive information fusion with higher accuracy, less energy consumption and greater fault tolerance in resource-constrained environments with data collected from distributed sensors. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DATA mining
*PATTERN recognition systems
*DISTRIBUTED sensors
Subjects
Details
- Language :
- English
- ISSN :
- 19479832
- Volume :
- 7
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- International Journal of Image & Data Fusion
- Publication Type :
- Academic Journal
- Accession number :
- 113744543
- Full Text :
- https://doi.org/10.1080/19479832.2015.1086825