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Cao, Yingqiu Kan, Edwin Chihchuan Lal, Amit Molnar, Alyosha Christopher 2018-10-23T13:36:14Z 2018-10-23T13:36:14Z 2018-08-30 ProQuest Submission ID: 11051 ProQuest Publication ID: 10928109 https://hdl.handle.net/1813/59769 https://doi.org/10.7298/X46T0JV7 10489867 proposed circuits can be combined with other techniques including high-Q impedance matching for input voltage boosting and hierarchical tandem stages for further improvement on operating conditions. With a Q=10 matching network, -27 dBm sensitivity and 22% efficiency can be achieved for about 0.5 V DC output to a 500 kΩ load at 570 MHz. On the other hand, the sensing variation can originate from the targeted biological sensing signal, which complicates both the sensor system design and the associated signal analysis. We will illustrate a spike-sorting method to reliably classify the enteric neural signals which have unique waveform features but large variation in magnitude, timing and duration. The proposed fastDTW spike classification algorithm provides improvements in accuracy and computational cost in comparison with Cross-correlation based template matching and PCA + k-means clustering without time warping. When appled to mouse ENS neurons in high noise and high variability environment, fastDTW successfully recognized spikes with variability is as large as 1.2 ms in width and a few milli-volt in magnitude. The captured waveform features are used for variation correlation analyses to better understand the operating principles of enteric nervous system. Although other variation sources can also affect the sensor system design, our approaches of device compensation based on operational feedback and signal tolerance based on time warping are able to give illustrations for sensor designers to successfully countermeasure uncontrollable variation sources. en_US Internet of Things Neuron network RF energy harvesting Spike sorting Electrical engineering Variation resolutions for CMOS sensing networks dissertation or thesis application/pdf Electrical and Computer Engineering Cornell University Doctor of Philosophy Ph. D., Electrical and Computer Engineering https://hdl.handle.net/1813/59810 Publication Cornell Theses and Dissertations Cao, Yingqiu 5893a6ea-7af3-41d7-abc6-04bcd26ab5df open.access open.access <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="0afbb305-e35a-4bcd-919b-d06c9ebda78d"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>en_US</Language> <Title>Variation resolutions for CMOS sensing networks</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018-08-30</PublicationDate> <DOI>https://doi.org/10.7298/X46T0JV7</DOI> <Authors> <Author> <DisplayName>Cao, Yingqiu</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <OrgUnit /> </Publisher> </Publishers> <Keyword>Internet of Things</Keyword> <Keyword>Neuron network</Keyword> <Keyword>RF energy harvesting</Keyword> <Keyword>Spike sorting</Keyword> <Keyword>Electrical engineering</Keyword> <Abstract>proposed circuits can be combined with other techniques including high-Q impedance matching for input voltage boosting and hierarchical tandem stages for further improvement on operating conditions. With a Q=10 matching network, -27 dBm sensitivity and 22% efficiency can be achieved for about 0.5 V DC output to a 500 kΩ load at 570 MHz. On the other hand, the sensing variation can originate from the targeted biological sensing signal, which complicates both the sensor system design and the associated signal analysis. We will illustrate a spike-sorting method to reliably classify the enteric neural signals which have unique waveform features but large variation in magnitude, timing and duration. The proposed fastDTW spike classification algorithm provides improvements in accuracy and computational cost in comparison with Cross-correlation based template matching and PCA + k-means clustering without time warping. When appled to mouse ENS neurons in high noise and high variability environment, fastDTW successfully recognized spikes with variability is as large as 1.2 ms in width and a few milli-volt in magnitude. The captured waveform features are used for variation correlation analyses to better understand the operating principles of enteric nervous system. Although other variation sources can also affect the sensor system design, our approaches of device compensation based on operational feedback and signal tolerance based on time warping are able to give illustrations for sensor designers to successfully countermeasure uncontrollable variation sources.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>