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Energy-efficient distributed estimation by utilizing a nonlinear amplifier

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Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation,

Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.

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Date Created
2013

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Exploring Students' Engagement With and Construction of Community

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Sense of Community is related to numerous positive outcomes for university students. The purpose of this study was to explore sense of community amongst low income students who received a last dollar scholarship. This study also sought to understand how

Sense of Community is related to numerous positive outcomes for university students. The purpose of this study was to explore sense of community amongst low income students who received a last dollar scholarship. This study also sought to understand how students define community and how they interact with communities from their past (before university), present (since they started college), and how they envision their future community involvement after graduation. Through purposive sampling, six low income Arizona State University students were selected based on similar characteristics. The scholarship that they belong to selects them based on financial need, integrity, and prolonged commitment to community service. Using a qualitative narrative inquiry, I interviewed participants about their understanding and experiences with communities. Interviews were audio recorded and transcribed verbatim for analysis. Based on the analysis, I identified three major themes: community as construction, community as nonlinear, and community as intersectional. Drawing from participants' definitions and experiences of community, I argue that community is a construction. In other words, individuals create their own constructions of community, and their actions vary based on that construction. Participants also experience their communities intersectionally, that is individual's experience their communities as coexisting and through multiple community perspectives, rather than as a single stand-alone entity. Finally, community does not exist as part of a linear time paradigm. Instead community is experienced in terms of relevance to the individual in creating meaning from that community. In addition to the above themes, I also examined participant perspectives of ASU as a community. Based on this research, I recommend that a platform be provided for students to engage in a dialogue about their understanding of community and interactions with communities. Moreover, I suggest researchers utilize intersectionality, constructionism, and non-linear time to frame future research on sense of community. This research is significant because it helps us understand student engagement, and offers a framework through which universities can provide students an opportunity to better understand their own sense of community.

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Date Created
2017-05