Deep Learning Based Localization of Zigbee Interference Sources Using Channel State Information

As the field of Internet of Things (IoT) continues to grow, a variety of wireless signals fill the ambient wireless environment. These signals are used for communication, however, recently wireless sensing has been studied, in which these signals can be used to gather information about the surrounding space. With the development of 802.11n, a newer standard of WiFi, more complex information is available about the environment a signal propagates through. This information called Channel State Information (CSI) can be used in wireless sensing. With the help of Deep Learning, this work attempts to generate a fingerprinting technique for localizing a ZigBee interference source in the presence of 802.11

Doctor of Philosophy

dissertationThis dissertation explores three key facets of software algorithms for custom hardware ray tracing: primitive intersection, shading, and acceleration structure construction. For the first, primitive intersection, we show how nearly all of the existing direct three-dimensional (3D) ray-triangle intersection tests are mathematically equivalent. Based on this, a genetic algorithm can automatically tune a ray-triangle intersection test for maximum speed on a particular architecture. We also analyze the components of the intersection test to determine how much floating point precision is required and design a numerically robust intersection algorithm. Next, for shading, we deconstruct Perlin noise into its basic parts and show how these can be modified to produce a gradient noise algorithm that improves the visual appearance. This improved algorithm serves as the basis for a hardware noise unit. Lastly, we show how an existing bounding volume hierarchy can be postprocessed using tree rotations to further reduce the expected cost to traverse a ray through it. This postprocessing also serves as the basis for an efficient update algorithm for animated geometry. Together, these contributions should improve the efficiency of both software- and hardware-based ray tracers

Some extensions in continuous models for immunological correlates of protection

Detail of results of case-cohort investigation. (DOCX 41 kb

Phosphorylation of cardiac myosin binding protein C releases myosin heads from the surface of cardiac thick filaments

Cardiac myosin binding protein C (cMyBP-C) has a key regulatory role in cardiac contraction, but the mechanism by which changes in phosphorylation of cMyBP-C accelerate cross-bridge kinetics remains unknown. In this study, we isolated thick filaments from the hearts of mice in which the three serine residues (Ser273, Ser282, and Ser302) that are phosphorylated by protein kinase A in the m-domain of cMyBP-C were replaced by either alanine or aspartic acid, mimicking the fully nonphosphorylated and the fully phosphorylated state of cMyBP-C, respectively. We found that thick filaments from the cMyBP-C phospho-deficient hearts had highly ordered cross-bridge arrays, whereas the filaments from the cMyBP-C phospho-mimetic hearts showed a strong tendency toward disorder. Our results support the hypothesis that dephosphorylation of cMyBP-C promotes or stabilizes the relaxed/superrelaxed quasi-helical ordering of the myosin heads on the filament surface, whereas phosphorylation weakens this stabilization and binding of the heads to the backbone. Such structural changes would modulate the probability of myosin binding to actin and could help explain the acceleration of cross-bridge interactions with actin when cMyBP-C is phosphorylated because of, for example, activation of beta1-adrenergic receptors in myocardium

Aflatoxin regulations and global pistachio trade: Insights from social network analysis

Aflatoxins, carcinogenic toxins produced by Aspergillus fungi, contaminate maize, peanuts, and tree nuts in many regions of the world. Pistachios are the main source of human dietary aflatoxins from tree nuts worldwide. Over 120 countries have regulations for maximum allowable aflatoxin levels in food commodities. We developed social network models to analyze the association between nations' aflatoxin regulations and global trade patterns of pistachios from 1996-2010. The main pistachio producing countries are Iran and the United States (US), which together contribute to nearly 75% of the total global pistachio market. Over this time period, during which many nations developed or changed their aflatoxin regulations in pistachios, global pistachio trade patterns changed; with the US increasingly exporting to countries with stricter aflatoxin standards. The US pistachio crop has had consistently lower levels of aflatoxin than the Iranian crop over this same time period. As similar trading patterns have also been documented in maize, public health may be affected if countries without aflatoxin regulations, or with more relaxed regulations, continually import crops with higher aflatoxin contamination. Unlike the previous studies on maize, this analysis includes a dynamic element, examining how trade patterns change over time with introduction or adjustment of aflatoxin regulations. © 2014 Bui-Klimke et al