Compressed sensing (CS) is an innovative approach of signal processing that facilitates sub-Nyquist processing of bio-signals, such as a neural signal, electrocardiogram (ECG), and electroencephalogram (EEG). This strategy can be used to lower the data rate to realize ultra-low-power performance, As the count of recording channels increases, data volume is increased resulting in impermissible transmitting power. This thesis work presents the implementation of a CMOS-based front-end design with the CS in the standard 180 nm CMOS process. A novel pseudo-random sequence generator is proposed, which consists of two different types of D flip-flops that are used for obtaining a completely random sequence. This thesis work also includes the (reverse electrowetting-on-dielectric) REWOD based energy harvesting model for self-powered bio-sensor which utilizes the electrical energy generated through the process of conversion of mechanical energy to electrical energy. This REWOD based energy harvesting model can be a good alternative to battery usage, particularly for the bio-wearable applications. The comparative analysis of the results generated for voltage, current and capacitance of the rough surface model is compared to that of results of planar surface REWOD.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1833492 |
| Date | 08 1900 |
| Creators | Kakaraparty, Karthikeya Anil Kumar |
| Contributors | Ifana, Mahbub, Namuduri, Kamesh, Guturu, Parthasarathy |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | viii, 42 pages, Text |
| Rights | Public, Kakaraparty, Karthikeya Anil Kumar, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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