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Approved for public release; distribution is unlimited. / An established cloud analysis routine has been modified for use in the Arctic. The separation of clouds from the snow and sea ice backgrounds is accomplished through a multispectral technique which utilizes VHRR channel 2 (visible), channel 3 (near infrared) and channel 4 (infrared) data. The primary means of cloud identification is based on a derived channel 3 reflectance image. At this wavelength, a significant contrast exists between liquid clouds and the arctic backgrounds, unlike in the standard visible and infrared images. The channel 3 reflectance is obtained by first using the channel 4 emission temperature to estimate the thermal emission component of the total channel 3 radiance. This thermal emission component is subsequently removed from the total radiance, leaving only the solar reflectance component available for analysis. Since many ice clouds do not exhibit a substantially greater reflectance is channel 3, the routine exploits differences in transmissive characteristics between channels 3 and 4 for identification. The routine was applied to six case studies which had been analyzed by three independent experts to establish 'ground truth'. Verification of the cloud analysis results, through a comparison to the subjective analyses, yielded impressive statistics. A success rate of 77.9% was obtained with an arguably small data base of 131 undisputed scenes / http://archive.org/details/objectivetechniq00barr / Lieutenant, United States Navy

As virtualization platforms or cloud computing have become more of a commodity, many more organizations have been utilizing them. Many organizations and technologies have emerged to fulfill those cloud needs. Cloud vendors provide similar services, but the differences can have significant impact on specific applications. Selecting the right provider is difficult and confusing because of the number of options. It can be difficult to determine which application characteristics will impact the choice of implementation. There has not been a concise process to select which cloud vendor and characteristics are best suited for the application requirements and organization requirements. This thesis provides a model that identifies crucial application characteristics, organization requirements and also characteristics of a cloud. The model is used to analyze the interaction of the application with multiple cloud platforms and select the best option based on a suitability score. Case studies utilize this model to test three applications against three cloud implementations to identify the best fit cloud implementation. The model is further validated by a small group of peers through a survey. The studies show that the model is useful in identifying and comparing cloud implementations with regard to application requirements.

Global economic trends suggest that there is a need to generate sustainable renewable energy to meet growing global energy demands. Solar energy harnessed by concentrated photovoltaic (CPV) systems has a potential for strong contributions to future energy supplies. However, as a relatively new technology, there is still a need for considerable research into the relationship between the technology and the solar resource. Research into CPV systems was carried out at the University of Ottawa’s Solar Cells and Nanostructured Device Laboratory (SUNLAB), focusing on the acquisition and assessment of meteorological and local solar resource datasets as inputs to more complex system (cell) models for energy yield assessment. An algorithm aimed at estimating the spectral profile of direct normal irradiance (DNI) was created. The algorithm was designed to use easily sourced low resolution meteorological datasets, temporal band pass filter measurement and an atmospheric radiative transfer model to determine a location specific solar spectrum. Its core design involved the use of an optical depth parameterization algorithm based on a published objective regression algorithm. Initial results showed a spectral agreement that corresponds to 0.56% photo-current difference in a modeled CPV cell when compared to measured spectrum. The common procedures and datasets used for long term CPV energy yield assessment was investigated. The aim was to quantitatively de-convolute various factors, especially meteorological factors responsible for error bias in CPV energy yield evaluation. Over the time period from June 2011 to August 2012, the analysis found that neglecting spectral variations resulted in a ~2% overestimation of energy yields. It was shown that clouds have the dominant impact on CPV energy yields, at the 60% level.

Global economic trends suggest that there is a need to generate sustainable renewable energy to meet growing global energy demands. Solar energy harnessed by concentrated photovoltaic (CPV) systems has a potential for strong contributions to future energy supplies. However, as a relatively new technology, there is still a need for considerable research into the relationship between the technology and the solar resource. Research into CPV systems was carried out at the University of Ottawa’s Solar Cells and Nanostructured Device Laboratory (SUNLAB), focusing on the acquisition and assessment of meteorological and local solar resource datasets as inputs to more complex system (cell) models for energy yield assessment. An algorithm aimed at estimating the spectral profile of direct normal irradiance (DNI) was created. The algorithm was designed to use easily sourced low resolution meteorological datasets, temporal band pass filter measurement and an atmospheric radiative transfer model to determine a location specific solar spectrum. Its core design involved the use of an optical depth parameterization algorithm based on a published objective regression algorithm. Initial results showed a spectral agreement that corresponds to 0.56% photo-current difference in a modeled CPV cell when compared to measured spectrum. The common procedures and datasets used for long term CPV energy yield assessment was investigated. The aim was to quantitatively de-convolute various factors, especially meteorological factors responsible for error bias in CPV energy yield evaluation. Over the time period from June 2011 to August 2012, the analysis found that neglecting spectral variations resulted in a ~2% overestimation of energy yields. It was shown that clouds have the dominant impact on CPV energy yields, at the 60% level.

<p dir="ltr">Accurate 3D point clouds are crucial in various fields, and the advancement of software algorithms has facilitated the reconstruction of 3D models from high-quality images. Notably, both single-camera and multi-camera systems have gained popularity in obtaining these images. While single-camera setups offer simplicity and cost-effectiveness, multi-camera systems provide a broader field of view and improved coverage. However, a crucial gap persists, a lack of direct comparison and comprehensive analysis regarding the quality of point clouds acquired from each system. This thesis aims to bridge this gap by evaluating the point cloud quality obtained from both single-camera and multi-camera systems, considering various factors such as lighting conditions, camera settings, and the stability of multi-camera setup in the 3D reconstruction process. Our research also aims to provide insights into how these factors influence the quality and performance of the reconstructed point clouds. By understanding the strengths and limitations of each system, researchers and professionals can make informed decisions when selecting the most suitable 3D imaging approach for their specific applications. To achieve these objectives, we designed and utilized a custom rig with three vertically stacked cameras, each equipped with a fixed camera lens, and maintained uniform lighting conditions. Additionally, we employed a single-camera system with a zoom lens and non uniform lighting conditions. Through noise analysis, our results revealed several crucial findings. The single-camera system exhibited relatively higher noise levels, likely due to non-uniform lighting and the use of a zoom lens. In contrast, the multi-camera system demonstrated lower noise levels, which can be attributed to well-lit conditions and the use of fixed lenses. However, within the multi-camera system, instances of significant instability led to a substantial increase in noise levels in the reconstructed point cloud compared to more stable conditions. Our noise analysis showed the multi-camera system preformed better compared to the single-camera system in terms of noise quality. However, it is crucial to recognize that noise detection also revealed the influence of factors like lighting conditions, camera calibration and camera stability of multi-camera systems on the reconstruction process.</p>