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Browsing by Author "Jia, Tao"

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    Collaborative Position Location for Wireless Networks in Harsh Environments
    Jia, Tao (Virginia Tech, 2010-03-18)
    Position location has become one of the more important tasks for improving communication and networking performance for future commercial wireless systems. It is also the enabling technology for many control and sensing applications envisioned by the wireless sensor networks (WSN). Despite its meaningfulness and many algorithms being developed in the past several years, position location in harsh propagation environments remains to be a challenging issue, due mainly to the lack of sufficient infrastructure support and the prominent phenomenon of non-line-of-sight (NLOS) signal propagation. Recently, adopting the concept of collaborative position location has attracted much research interest due to its potential in overcoming the abovementioned two difficulties. In this work, we approach collaborative position location from two different angles. Specifically, we investigate the optimal performance of collaborative position location, which serves as a theoretical performance benchmark. In addition, we developed a computationally efficient algorithm for collaborative position location and incorporated an effective NLOS mitigation method to improve its performance in NLOS-dense environments. Overall, our work provides insight into both theoretical and practical aspects of collaborative position location.
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    Stochastic Modeling of Gene Expression and Post-transcriptional Regulation
    Jia, Tao (Virginia Tech, 2011-07-21)
    Stochasticity is a ubiquitous feature of cellular processes such as gene expression that can give rise to phenotypic differences for genetically identical cells. Understanding how the underlying biochemical reactions give rise to variations in mRNA/protein levels is thus of fundamental importance to diverse cellular processes. Recent technological developments have enabled single-cell measurements of cellular macromolecules which can shed new light on processes underlying gene expression. Correspondingly, there is a need for the development of theoretical tools to quantitatively model stochastic gene expression and its consequences for cellular processes. In this dissertation, we address this need by developing general stochastic models of gene expression. By mapping the system to models analyzed in queueing theory, we derive analytical expressions for the noise in steady-state protein distributions. Furthermore, given that the underlying processes are intrinsically stochastic, cellular regulation must be designed to control the`noise' in order to adapt and respond to changing environments. Another focus of this dissertation is to develop and analyze stochastic models of post-transcription regulation. The analytical solutions of the models proposed provide insight into the effects of different mechanisms of regulation and the role of small RNAs in fine-tunning the noise in gene expression. The results derived can serve as building blocks for future studies focusing on regulation of stochastic gene expression.