White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Power Efficient Data Compression Hardware for Wearable and Wireless Biomedical Sensing Devices

Dai, Chengliang (2016) Power Efficient Data Compression Hardware for Wearable and Wireless Biomedical Sensing Devices. PhD thesis, University of York.

[img]
Preview
Text (PDF)
thesis 2.pdf
Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.

Download (50Mb) | Preview

Abstract

This thesis aims to verify a possible benefit lossless data compression and reduction techniques can bring to a wearable and wireless biomedical device, which is anticipated to be system power saving. A wireless transceiver is one of the main contributors to the system power of a wireless biomedical sensing device, and reducing the data transmitted by the transceiver with a minimum hardware cost can therefore help to save the power. This thesis is going to investigate the impact of the data compression and reduction on the system power of a wearable and wireless biomedical device and trying to find a proper compression technique that can achieve power saving of the device. The thesis first examines some widely used lossy and lossless data compression and reduction techniques for biomedical data, especially EEG data. Then it introduces a novel lossless biomedical data compression technique designed for this research called Log2 sub-band encoding. The thesis then moves on to the biomedical data compression evaluation of the Log2 sub-band encoding and an existing 2-stage technique consisting of the DPCM and the Huffman encoding. The next part of this thesis explores the signal classification potential of the Log2 sub-band encoding. It was found that some of the signal features extracted as a by-product during the Log2 sub-band encoding process could be used to detect certain signal events like epileptic seizures, with a proper method. The final section of the thesis focuses on the power analysis of the hardware implementation of two compression techniques referred to earlier, as well as the system power analysis. The results show that the Log2 sub-band is comparable and even superior to the 2-stage technique in terms of data compression and power performance. The system power requirement of an EEG signal recorder that has the Log2 sub-band implemented is significantly reduced.

Item Type: Thesis (PhD)
Academic Units: The University of York > Computer Science (York)
Depositing User: Mr Chengliang Dai
Date Deposited: 07 Sep 2016 14:48
Last Modified: 07 Sep 2016 14:48
URI: http://etheses.whiterose.ac.uk/id/eprint/13847

You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.

Actions (repository staff only: login required)