Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Spectroscopy of Electroproduced Light to Medium Mass Lambda Hypernuclei

The E01-011 experiment at Jefferson Laboratory (JLab) studied light-to-medium mass Λ hypernuclei via the AZ + e → [special characters omitted] + e' + K+ electroproduction reaction. Precise measurement of hypernuclear ground state masses and excitation energies provides information about the nature of hyperon-nucleon interactions. Until recently, hypernuclei were studied at accelerator facilities with intense π+ and K- meson beams. The poor quality of these beams limited the resolution of the hypernuclear excitation energy spectra to about 1.5 MeV (FWHM). This resolution is not sufficient for resolving the rich structure observed in the excitation spectra. By using a high quality electron beam and employing a new high resolution spectrometer system, this study aims to improve the resolution to a few hundred keV with an absolute precision of about 100 keV for excitation energies. In this work the high-resolution excitation spectra of [special characters omitted], and [special characters omitted] hypernuclei are presented. In an attempt to emphasize the presence of the core-excited states we introduced a novel likelihood approach to particle identification (PID) to serve as an alternative to the commonly used standard hard-cut PID. The new method resulted in almost identical missing mass spectra as obtained by the standard approach. An energy resolution of approximately 400–500 keV (FWHM) has been achieved, an unprecedented value in hypernuclear reaction spectroscopy. For [special characters omitted] the core-excited configuration has been clearly observed with significant statistics. The embedded Λ hyperon increases the excitation energies of the 11B nuclear core by 0.5–1 MeV. The [special characters omitted] spectrum has been observed with significant statistics for the first time. The ground state is bound deeper by roughly 400 keV than currently predicted by theory. Indication for the core-excited doublet, which is unbound in the core itself, is observed. The measurement of [special characters omitted] provides the first study of a d-shell hypernucleus with sub-MeV resolution. Discrepancies of up to 2 MeV between measured and theoretically predicted binding energies are found. Similar disagreement exists when comparing to the [special characters omitted] mirror hypernucleus. Also the core-excited structure observed between the major s-, p- and d-shell Λ orbits is not consistent with the available theoretical calculations. In conclusion, the discrepancies found in this study will provide valuable input for the further development of theoretical models.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Spatial Articulations of Race, Desire, and Belonging in Western North Carolina

The sociocultural mythology of the South homogenizes it as a site of abjection. To counter the regionalist discourse, the dissertation intersects queer sexualities with gender and race and focuses on exploring identity and spatial formation among Black lesbian and queer women. The dissertation seeks to challenge the monolith of the South and place the region into multiple contexts and to map Black geographies through an intentional intersectional account of Black queer women. The dissertation utilizes qualitative research methods to ascertain understandings of lived experiences in the production of space. The dissertation argues that an idea of Progress has been indoctrinated as a synonym for the lgbtq civil rights movement and subsequently provides an analysis of progress discourses and queer sexualities and political campaigns of equality in the South. Analyses revealed different ways to situate progress utilizing the public contributions of three Black women interviewed for the dissertation. Moreover, the dissertation utilizes six Black queer and lesbian women to explain the multifarious nature of identities and their construction in place. Black queer and lesbian women produce spaces that deconstruct the normativity of stasis and physicality, and the dissertation explores the consequential realities of being a body in space. These consequences are particularly highlighted in the dissertation by discussions of the processes of racialization in the bounded and unbounded senses of space and place and the impacts of religious institutions, specifically Christianity. The dissertation concluded that no space is without complication. Other considerations should be made in the advancement of alleviating oppression deeply embedded in United States landscapes. Black women’s geographies offer epistemological and ontological renderings that enrich analyses of space, place, and landscape. The dissertation also concludes that Black women’s bodies represent sites for the production of geographic knowledge through narrating their spaces of material trajectories of interlocking, multiscalar lives.