Embedded Sensing for Acoustic Classification, Activity Recognition and Localisation

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Copyright: Wei, Bo
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Abstract
Embedded sensing aims to use low-cost computing, sensing and communication components to realise various sensing tasks. Embedded sensing has been successfully used in different applications. Large amounts of sensing data contain abundant information, but create huge overheads for the resource-limited embedded nodes. Furthermore, interference from other sources also brings noise, which decreases the sensing performance. In this thesis, we apply Sparse Approximation-based Classification method (SAC) and electronically switched directional (ESD) antennas to address the challenges of the limited amount of resources in embedded systems and interference. Three different problems are addressed. The first problem is to reduce the overhead of real-time classification on Acoustic Sensor Networks (ASNs). The main challenges of in-network classification in ASNs include effective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based featureless, low computational cost, and noise resilient framework for in-network classification in ASNs, which makes the computation feasible to be performed on resource constrained ASN platforms. The second problem is to make radio-based device-free activity recognition robust to radio frequency interference (RFI). Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. We investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We propose a number of SAC-based fusion methods to mitigate the impact of RFI and improve the location-oriented activity recognition performance. The third problem is to reduce the impact of multipath propagation for radio tomographic imaging (RTI) . RTI enables device-free localisation of people and objects in many challenging environments and situations. However, the localisation accuracy of RTI suffers from complicated multipath propagation behaviours in radio links. We propose to use inexpensive and energy efficient ESD antennas to improve the localisation accuracy of RTI. We implement a directional RTI system to understand how directional antennas can be used to improve RTI localisation accuracy.
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Author(s)
Wei, Bo
Supervisor(s)
Chou, Chun Tung
Hu, Wen
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Publication Year
2015
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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download public version.pdf 4.74 MB Adobe Portable Document Format
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