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You searched for +publisher:"Georgia Tech" +contributor:("Kang, Sung H."). Showing records 1 – 2 of 2 total matches.

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1. Blanco, Andres Arturo. Fast-waking and low-voltage thermoelectric and photovoltaic CMOS chargers for energy-harvesting wireless microsensors.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

The small size of wireless microsystems allows them to be deployed within larger systems to sense and monitor various indicators throughout many applications. However, their small size restricts the amount of energy that can be stored in the system. Current microscale battery technologies do not store enough energy to power the microsystems for more than a few months without recharging. Harvesting ambient energy to replenish the on-board battery extend the lifetime of the microsystem. Although light and thermal energy are more practical in some applications than other forms of ambient energy, they nevertheless suffer from long energy droughts. Additionally, due to the very limited space available in the microsystem, the system cannot store enough energy to continue operation throughout these energy droughts. Therefore, the microsystem must reliably wake from these energy droughts, even if the on-board battery has been depleted. The challenge here is waking a microsystem directly from an ambient source transducer whose voltage and power levels are limited due to their small size. Starter circuits must be used to ensure the system wakes regardless of the state of charge of the energy storage device. The purpose of the presented research is to develop, design, simulate, fabricate, test and evaluate CMOS integrated circuits that can reliably wake from no energy conditions and quickly recharge a depleted battery. Since the battery is depleted during startup, the system must use the low voltage produced by the energy harvesting transducer to transfer energy. The presented system has the fastest normalized wake time while reusing the inductor already present in the battery charger for startup, therefore, minimizing the overall footprint of the system. Advisors/Committee Members: Rincon-Mora, Gabriel A. (advisor), Ayazi, Farrokh (committee member), Mukhopadhyay, Saibal (committee member), Wang, Hua (committee member), Kang, Sung H. (committee member).

Subjects/Keywords: Energy harvesters; Low power circuits; Low voltage starters

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Blanco, A. A. (2017). Fast-waking and low-voltage thermoelectric and photovoltaic CMOS chargers for energy-harvesting wireless microsensors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58707

Chicago Manual of Style (16th Edition):

Blanco, Andres Arturo. “Fast-waking and low-voltage thermoelectric and photovoltaic CMOS chargers for energy-harvesting wireless microsensors.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 24, 2019. http://hdl.handle.net/1853/58707.

MLA Handbook (7th Edition):

Blanco, Andres Arturo. “Fast-waking and low-voltage thermoelectric and photovoltaic CMOS chargers for energy-harvesting wireless microsensors.” 2017. Web. 24 Mar 2019.

Vancouver:

Blanco AA. Fast-waking and low-voltage thermoelectric and photovoltaic CMOS chargers for energy-harvesting wireless microsensors. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/1853/58707.

Council of Science Editors:

Blanco AA. Fast-waking and low-voltage thermoelectric and photovoltaic CMOS chargers for energy-harvesting wireless microsensors. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58707

2. Cecen, Ahmet. Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data.

Degree: PhD, Computational Science and Engineering, 2017, Georgia Tech

The direct influence of spatial and structural arrangement in various length scales to the performance characteristics of materials is a core premise of materials science. Spatial correlations in the form of n-point statistics have been shown to be very effective in robustly describing the structural features of a plethora of materials systems, with a high number of cases where the obtained futures were successfully used to establish highly accurate and precise relationships to performance measures and manufacturing parameters. This work addresses issues in calculation, representation, inference and utilization of spatial statistics under practical considerations to the materials researcher. Modifications are presented to the theory and algorithms of the existing convolution based computation framework in order to accommodate deformed, irregular, rotated, missing or degenerate data, with complex or non-probabilistic state definitions. Memory efficient personal computer oriented implementations are discussed for the extended framework. A universal microstructure generation framework with the ability to efficiently address a vast variety of geometric or statistical constraints including those imposed by spatial statistics is assembled while maintaining scalability, and compatibility with structure generators in literature. Advisors/Committee Members: Kalidindi, Surya R. (advisor), Song, Le (committee member), Garmestani, Hamid (committee member), Chau, Duen H. (committee member), Kang, Sung H. (committee member).

Subjects/Keywords: Materials; Informatics; Data science; Image processing; Spatial statistics; Texture synthesis; Microstructure generator

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cecen, A. (2017). Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58723

Chicago Manual of Style (16th Edition):

Cecen, Ahmet. “Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 24, 2019. http://hdl.handle.net/1853/58723.

MLA Handbook (7th Edition):

Cecen, Ahmet. “Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data.” 2017. Web. 24 Mar 2019.

Vancouver:

Cecen A. Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/1853/58723.

Council of Science Editors:

Cecen A. Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58723

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