Graduate Thesis Or Dissertation
 

Compressive Sensing for Low Power Sensor Design

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/hm50tv96d

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  • Recent sensor System-on-Chips (SoC) have enabled significant advances in energy-efficiency by incorporating various micro-powered building blocks. Unfortunately, most of these sensor systems do not address the high power cost associated with data storage and transmission, which in some cases vastly exceeds the power consumed by the rest of the SoC. In recent years, Compressive-Sensing (CS) has been proposed as a method to accomplish significant sensor data compression, achieving compression rates up to 10x depending on the signal sparsity. This work addresses conventional CS issues including non-adaptive compression rate and offers a solution. First, a feasibility study is conducted to investigate the sparsity variance of some biomedical signals. Then an adaptive CS framework is proposed, to adjust the compression rate based upon the input signal’s sparsity on-the-fly. Thirdly, a CS framework is proposed, the reconstruction of which is aided by statistics collection. It is demonstrated how to fuse sensor data and statistics information together to improve the signal-to-error ratio (SER) of reconstruction. A test chip fabricated in TSMC 65-nm technology to implement the algorithm in a SoC incorporating statistics collection block in order to improve performance of the CS algorithm. The final portion of this research devoted to study emerging application of time-of-flight cameras for depth measurement. These devices generate a 3 Dimensional (3D) point cloud that basically includes 3D details of objects in front of them. A framework to apply CS to 3D point cloud data is presented. Finally it demonstrates how the idea of adaptive CS can be used for 3D point cloud data compression.
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  • 2017-08-16 to 2019-02-21

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