Computational frameworks for context-aware hybrid sensor fusion. (2nd January 2016)

• Record Type: Journal Article
• Authors: Biswas, Pratik K.; Moon, Sangwoo; Qi, Hairong; Dey, Anind K.
• Is Part Of: International journal of image and data fusion. Volume 7:Number 1(2016)
• Page Start: 83
• Page End: 102
• Publication Date: 2016-01-02
• Subjects: context -- context-aware applications -- context toolkit -- feature extraction -- risk minimisation -- projection -- independence maximisation -- classifier -- pattern recognition system; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2015.1086825
• Languages: English
• ISSNs: 1947-9832

Abstract

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.