Despite a general consensus regarding anthropogenic global climate change across the international scientific community, many of the major greenhouse gas producers in the world, especially the United States, are hesitant to implement strict emissions regulations. According to some prominent atmospheric scientists, such as James Hansen and Michael Mann, if industrialized countries continue to produce carbon emissions at current rates, an irreversible planetary tipping point of raising temperatures 2°C above pre-industrial levels could be reached in less than 40 years. Societies have a wealth of information from the natural sciences to understand the climate problem and currently possess the technological means to address it. But substantial regulatory policies have not been implemented, clean energy technologies have not been established as the primary energy source, and widespread behavioral changes needed to create sustainable societies have not been fostered. This dissertation seeks to understand why the preponderance of scientific evidence surrounding climate change has not produced a sea change of public perceptions of the climate change problem consistent with the dire projections of climate science. It is grounded in four interrelated questions: (1) What are the prevalent discourses of climate change and to which institutions can these be attached? (2) How do suburban residents understand climate change? (3) Since electricity is a major link between suburban lifestyles and climate change, how does knowledge of climate change compare with knowledge of electricity production and consumption? (4) In what ways do institutional discourses of climate change connect to the viewpoints of suburban consumers? These questions were explored through a case study carried out in a neighborhood in the city of Tampa, Florida. Forty-six semi-structured, face-to-face interviews were conducted to understand perceptions related to climate change, suburban consumption, and environmental conservation. The interviews compiled information pertaining to personal knowledge and representations of socio-ecological relationships. The findings indicate that most relationships or connections to the natural world in general, and climate change in particular, are produced by the arrangements and processes of capital accumulation as experienced in everyday practices. Suburban residents seemed disconnected from or ignorant about how their everyday consumption is related to climate change. Based on ideological formations, as manifest in institutional discourses and material practices, suburban residents accept the social processes and spatial forms that they inhabit as being the only possible options for suburban living.

<div>Millimeter-wave (mmWave) bands have become the most promising candidate for enlarging the usable radio spectrum in future wireless networks such as 5G. Since frequent and location-specific blockages are expected for mmWaves, the challenge is understanding the propagation characteristics of mmWave signals and accordingly predicting the channel state information. This research direction has garnered great attention worldwide from industry, academia, and government. However, the majority of current research on mmWave communications has focused on urban areas with high population densities, with very few measurement campaigns in suburban and rural environments. These environments are extremely important for future wireless applications in areas including residential welfare, digital agriculture, and transportation. To fill in this research gap, we developed broadband mmWave channel sounding systems and carried out intensive measurement campaigns at 28 GHz, covering clear line-of-sight as well as non-line-of-sight scenarios over buildings and foliage clutters, to fully characterize the mmWave propagation in suburban and rural environments.</div><div><br></div><div>Moreover, the accuracy provided by traditional statistical models is insufficient for next-generation wireless networks with higher-frequency carriers, because they are unable to predict abrupt channel changes caused by site-specific blockages. To overcome this issue, we explored the possibility of utilizing site-specific geographic features such as buildings and trees in improving mmWave propagation models. A new channel modeling methodology highlighting site-specific parameter evaluation based on easily obtainable data sources (e.g., LiDAR) was proposed for accurate, fast, and automated channel state predictions. Accordingly, an overall root mean square error (RMSE) improvement of 11.79 dB was achieved in a one-building blockage scenario and a regional RMSE improvement of over 20 dB was observed in a coniferous forest. This approach also enables channel simulations for large-scale system performance evaluation, demonstrating a powerful and promising approach for planning and tuning future wide-area wireless networks. The simulation results showed that network densification alone is not enough for closing the digital gap, especially with mmWaves because of the impractical number of required towers. They also backed up supplementary solutions including private data relays, e.g., via drones and portable towers.</div>

Made available in DSpace on 2014-12-17T14:55:23Z (GMT). No. of bitstreams: 1 RonaldoAM.pdf: 2082162 bytes, checksum: 2dd11d30b856b23cf429317a1e68bfbe (MD5) Previous issue date: 2006-06-12 / In this dissertation new models of propagation path loss predictions are proposed by from techniques of optimization recent and measures of power levels for the urban and suburban areas of Natal, city of Brazilian northeast. These new proposed models are: (i) a statistical model that was implemented based in the addition of second-order statistics for the power and the altimetry of the relief in model of linear losses; (ii) a artificial neural networks model used the training of the algorithm backpropagation, in order to get the equation of propagation losses; (iii) a model based on the technique of the random walker, that considers the random of the absorption and the chaos of the environment and than its unknown parameters for the equation of propagation losses are determined through of a neural network. The digitalization of the relief for the urban and suburban areas of Natal were carried through of the development of specific computational programs and had been used available maps in the Statistics and Geography Brazilian Institute. The validations of the proposed propagation models had been carried through comparisons with measures and propagation classic models, and numerical good agreements were observed. These new considered models could be applied to any urban and suburban scenes with characteristic similar architectural to the city of Natal / Nesta tese novos modelos de predi??o de perda de percurso de propaga??o s?o propostos a partir de t?cnicas de otimiza??es recentes e de medi??es de n?veis de pot?ncias obtidas para as ?reas urbana e suburbana de Natal, cidade do Nordeste Brasileiro. Estes novos modelos s?o: (i) um modelo estat?stico que foi implementado baseado na adi??o de estat?sticas de 2a. ordem para a pot?ncia e a altimetria do relevo ao modelo de perdas lineares; (ii) um modelo com redes neurais artificiais que usou o treinamento do algoritmo backpropagation, a fim de obter a equa??o de perdas de propaga??o; (iii) um modelo baseado na t?cnica dos trajetos aleat?rios, que considera a aleatoriedade da absor??o e do caos do meio ambiente e que seus par?metros desconhecidos para a equa??o de perdas de propaga??o s?o determinados atrav?s de uma rede neural. A digitaliza??o do relevo das ?reas urbanas e suburbanas de Natal foi realizada atrav?s do desenvolvimento de programas computacionais espec?ficos e foram usados os mapas existentes no Instituto Brasileiro de Geografia e Estat?stica. As valida??es dos modelos propostos de predi??o de perda de propaga??o foram realizadas atrav?s de compara??es com medidas e modelos cl?ssicos de propaga??o, obtendo-se boas concord?ncias num?ricas. Estes novos modelos poder?o ser aplicados a qualquer cen?rio urbano e suburbano com caracter?sticas arquitet?nicas semelhantes ? cidade de Natal