Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains ix, 68 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 40-41).

Thesis (Ph.D.)--McMaster University, 2003. / Advisor: Zhi-Quan (Tom) Luo. Includes bibliographical references (leaves 214-217). Also available via World Wide Web.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 101-103).

Thesis (Ph.D.)--University of Colorado at Boulder, 2002. / Director: Lucy Y. Pao. Includes bibliographical references.

Savannah ecosystems play a prominent role in the global carbon (C) cycle, yet fluxes are poorly quantified, and the key processes regulating vegetation dynamics are uncertain. Insight is particularly deficient in southern Africa’s miombo woodlands, a woody savannah that is home to over 100 million people. This biome is heavily disturbed, with widespread deforestation and degradation associated with agriculture, charcoal and timber extraction, and frequent fires from anthropogenic sources. In this thesis I combine plot inventory data with remote sensing and modelling techniques to improve our understanding of the miombo woodland C cycle. Using a network of forest inventory plots, I characterise floristic and functional diversity in a savannah-forest mosaic in southeastern Tanzania. Divergent vegetation structures are associated with variation in fire frequency, water supply, and soil chemo-physical properties. Corresponding differences are noted in fire resilience, water-use, and nutrient acquisition plant functional traits, suggesting that multiple interrelated environmental filters act to assemble heterogeneous tree communities. Re-inventory of forest plots was used to quantify key aspects of the woody C cycle. Tree growth rates are slow, calling for careful management of woodland resources, and significantly reduced where stems were damaged. Stem mortality is rare, though elevated in the smallest trees and where damage was recorded. Contemporary strategies to incentivise the conservation of miombo woodland ecosystems, such as the REDD+ programme of the United Nations, advocate payments for sustaining ecosystem services such as C sequestration. I report on a pilot REDD+ project aiming to reduce woodland degradation from frequent high intensity fires in southeastern Tanzania. Model simulations suggest that woody biomass is being gradually lost from the region, and that setting early season fires has the potential to reverse this trend. Realising substantial changes in C storage requires a demanding reduction to late fire frequency, and uncertainty in model predictions remains high. I quantify the C cycle of southern African woodlands by combining observational data with a diagnostic C cycle model under a model-data fusion framework. Model outputs show substantial variation in primary production, C allocation patterns, and foliar and canopy traits, which are associated with differences in woody cover, fire, and precipitation properties. C cycle dynamics correspond poorly to conventional land cover maps, indicating they may be unsuited to upscaling measurements and models of the terrestrial C cycle.

Multi-spectral imagery provides a robust and low-cost dataset for assessing wetland extent and quality over broad regions and is frequently used for wetland inventories. However in forested wetlands, hydrology is obscured by tree canopy making it difficult to detect with multi-spectral imagery alone. Because of this, classification of forested wetlands often includes greater errors than that of other wetlands types. Elevation and terrain derivatives have been shown to be useful for modelling wetland hydrology. But, few studies have addressed the use of LiDAR intensity data detecting hydrology in forested wetlands. Due the tendency of LiDAR signal to be attenuated by water, this research proposed the fusion of LiDAR intensity data with LiDAR elevation, terrain data, and aerial imagery, for the detection of forested wetland hydrology. We examined the utility of LiDAR intensity data and determined whether the fusion of Lidar derived data with multispectral imagery increased the accuracy of forested wetland classification compared with a classification performed with only multi-spectral image. Four classifications were performed: Classification A - All Imagery, Classification B - All LiDAR, Classification C - LiDAR without Intensity, and Classification D - Fusion of All Data. These classifications were performed using random forest and each resulted in a 3-foot resolution thematic raster of forested upland and forested wetland locations in Vermilion County, Illinois. The accuracies of these classifications were compared using Kappa Coefficient of Agreement. Importance statistics produced within the random forest classifier were evaluated in order to understand the contribution of individual datasets. Classification D, which used the fusion of LiDAR and multi-spectral imagery as input variables, had moderate to strong agreement between reference data and classification results. It was found that Classification A performed using all the LiDAR data and its derivatives (intensity, elevation, slope, aspect, curvatures, and Topographic Wetness Index) was the most accurate classification with Kappa: 78.04%, indicating moderate to strong agreement. However, Classification C, performed with LiDAR derivative without intensity data had less agreement than would be expected by chance, indicating that LiDAR contributed significantly to the accuracy of Classification B.

AN ABSTRACT OF THE THESIS OF Olanrewaju Ari Adeniyi for the Master of Science degree in Mechanical Engineering and Energy Processes presented June 2012, at Southern Illinois University Carbondale Title: FUSION OF ULTRASONIC C-SCAN DATA WITH FINITE ELEMENT ANALYSIS Major Professor: Dr. Tsuchin Philip Chu Ultrasonic testing is a highly valued method in the field of Non-destructive testing (NDT). It is an engineering tool that allows for non-invasive testing and evaluation. It is used widely in the aerospace industry to determine the integrity of complex materials without the use of destructive measures. This method of testing can be utilized to provide multitude of parameters such as material properties and thicknesses. It can also be used to test for discrepancies in test specimen such as voids, impurities, delamination and other defects that could degrade the integrity of a structure. The problem is that this method is limited in the area of evaluation of end results. Results are generated in the form of data images and are evaluated for quality or quantitative image assessment. Simulation models are created from an image, which causes low accuracy of analysis. The integration of Ultrasonic C-scan data with Finite Element Analysis (FEA) addresses these issues. It allows for models to be generated from Ultrasonic C-scan data, which provides the means to conduct accurate FEA simulations. The fusion of Ultrasonic C-scan data with computational methods, such as FEA, allows tested materials to be subjected to loading conditions that may be experienced in actual use. The results from FEA analysis can provide localized stress and strain fields generated from the loading conditions. The success of this analysis relies on the ability to generate high quality C-scan data to create accurate CAD data models. The generation of high quality scans will produce vital analysis information such as material properties, thickness, voids, surface inclusions and other critical deformities, all which will be used to generate a CAD analysis. With the ultrasonic data generated, finite element analysis can be utilized to further evaluate tested specimen. This technique has been applied to an isotropic aluminum block standard and an anisotropic Carbon Fiber Reinforced Polymer sample, both with known defects.

Submitted by LUIS FERNANDO CASTILHO MASCHI null (maschibr@yahoo.com.br) on 2018-03-27T13:55:29Z No. of bitstreams: 1 SUMARIZAÇÃO DE DADOS NO NODO POR PARÂMETROS.pdf: 1425727 bytes, checksum: 7815d75156e3306a56b50c9922887e5d (MD5) / Rejected by Elza Mitiko Sato null (elzasato@ibilce.unesp.br), reason: Solicitamos que realize correções na submissão seguindo as orientações abaixo: Problema 01) O ano descrito na CAPA e na FOLHA DE ROSTO deverá ser o ano de entrega da dissertação na Seção Técnica de Pós-Graduação. Problema 02) A FICHA CATALOGRÁFICA (Obrigatório pela ABNT NBR14724) deve ser a elaborada pela Biblioteca, na sua ficha falta número do CDU, palavras-chave. Problema 03) Falta a data na FOLHA DE APROVAÇÃO, que é a data efetiva da defesa. Problema 04) Faltam as palavras-chave no abstracts e no resumo. Estou encaminhando anexo um modelo das páginas pré-textuais. Lembramos que o arquivo depositado no repositório deve ser igual ao impresso. Agradecemos a compreensão on 2018-03-27T17:55:34Z (GMT) / Submitted by LUIS FERNANDO CASTILHO MASCHI null (maschibr@yahoo.com.br) on 2018-05-03T13:41:48Z No. of bitstreams: 2 SUMARIZAÇÃO DE DADOS NO NODO POR PARÂMETROS.pdf: 1425727 bytes, checksum: 7815d75156e3306a56b50c9922887e5d (MD5) SUMARIZAÇÃO DE DADOS NO NODO POR PARÂMETROS.pdf: 1342697 bytes, checksum: b2357258f4e04e3d3eafb7b6023c58ad (MD5) / Rejected by Elza Mitiko Sato null (elzasato@ibilce.unesp.br), reason: Solicitamos que realize correções na submissão seguindo as orientações abaixo: Anexar um arquivo só; data da defesa por extenso (28 de fevereiro de 2018); tirar folha em branco antes da dedicatória; colocar o Campus de Rio Claro na natureza da pesquisa. Agradecemos a compreensão. on 2018-05-04T21:30:57Z (GMT) / Submitted by LUIS FERNANDO CASTILHO MASCHI null (maschibr@yahoo.com.br) on 2018-05-11T20:01:50Z No. of bitstreams: 1 SUMARIZAÇÃO DE DADOS NO NODO POR PARÂMETROS.pdf: 1342185 bytes, checksum: 30fe378e25bc78d274d13f711fb3d38c (MD5) / Approved for entry into archive by Paula Torres Monteiro da Torres (paulatms@sjrp.unesp.br) on 2018-05-11T22:36:59Z (GMT) No. of bitstreams: 1 maschi_lf_me_sjrp_int.pdf: 1342185 bytes, checksum: 30fe378e25bc78d274d13f711fb3d38c (MD5) / Made available in DSpace on 2018-05-11T22:36:59Z (GMT). No. of bitstreams: 1 maschi_lf_me_sjrp_int.pdf: 1342185 bytes, checksum: 30fe378e25bc78d274d13f711fb3d38c (MD5) Previous issue date: 2018-02-28 / Com o surgimento da Internet das Coisas, por meio de bilhões de objetos ou dispositivos inseridos na Internet, gerando um volume de dados nunca antes imaginado. Este trabalho propõe uma maneira de coletar e processar dados locais através da tecnologia de fusão de dados chamada de sumarização de dados. A principal característica desta proposta é a fusão local de dados, através de parâmetros fornecidos pela aplicação e/ou base de dados, garantindo a qualidade dos dados coletados pelo nodo do sensor. Nos testes, um nodo sensor com a técnica proposta, aqui identificada como Sumarização de Dados no Nodo por Parâmetros (SDNP), realiza a sumarização de dados e posteriormente é comparado com outro nodo que realizou uma gravação contínua dos dados coletados. Foram criados dois conjuntos de nós para estes testes, um com um nodo de sensor que analisou a luminosidade de salas de aula, que neste caso obteve uma redução de 97% no volume de dados gerados, e outro conjunto que analisou a temperatura dessas salas, obtendo uma redução de 80% no volume de dados. Através desses testes, verificou-se que a sumarização de dados local no nodo pode ser usada para reduzir o volume de dados gerados, diminuindo assim o volume de mensagens geradas pelos ambientes IoT. / With the advent of the Internet of Things, billions of objects or devices are inserted into the global computer network, generating and processing data in a volume never before imagined. This work proposes a way to collect and process local data through the data fusion technique called summarization. The main feature of the proposal is the local data fusion through parameters provided by the application, ensuring the quality of data collected by the sensor node. In the tests, the sensor node was compared when performing the data summary with another that performed a continuous recording of the collected data. Two sets of nodes were created, one with a sensor node that analyzed the luminosity of the room, which in this case obtained a reduction of 97% in the volume of data generated, and another set that analyzed the temperature of the room, obtaining a reduction of 80 % in the data volume. Through these tests, it has been verified that the local data fusion at the node can be used to reduce the volume of data generated, consequently decreasing the volume of messages generated by IoT environments.

This thesis deals with the challenge of autonomous navigation of the ExoMars rover. The absence of global positioning systems (GPS) in space, added to the limitations of wheel odometry makes autonomous navigation based on these two techniques - as done in the literature - an inviable solution and necessitates the use of other approaches. That, among other reasons, motivates this work to use solely visual data to solve the robot’s Egomotion problem. The homogeneity of Mars’ terrain makes the robustness of the low level image processing technique a critical requirement. In the first part of the thesis, novel solutions are presented to tackle this specific problem. Detection of robust features against illumination changes and unique matching and association of features is a sought after capability. A solution for robustness of features against illumination variation is proposed combining Harris corner detection together with moment image representation. Whereas the first provides a technique for efficient feature detection, the moment images add the necessary brightness invariance. Moreover, a bucketing strategy is used to guarantee that features are homogeneously distributed within the images. Then, the addition of local feature descriptors guarantees the unique identification of image cues. In the second part, reliable and precise motion estimation for the Mars’s robot is studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous Localisation And Mapping (VSLAM) is investigated, proposing enhancements and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation techniques are explored. Alternative photogrammetry reprojection concepts are tested. Lastly, data fusion techniques are proposed to deal with the integration of multiple stereo view data. Our robust visual scheme allows good feature repeatability. Because of this, dimensionality reduction of the feature data can be used without compromising the overall performance of the proposed solutions for motion estimation. Also, the developed Egomotion techniques have been extensively validated using both simulated and real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot motion estimation are introduced, presenting interesting benefits. The obtained results prove the innovative methods presented here to be accurate and reliable approaches capable to solve the Egomotion problem in a Mars environment. / © Cranfield University

As a consequence of the widespread use of computers coupled to high-quality printers and different types of papers, forgery, counterfeiting, change of wills, anonymous letter writing and felonious use of the documents have become serious problems. Forensic analysts are always seeking methods that can provide reliable information on whether a specimen collected at the crime scene is linked to the crime or to a source of known origin. Sensitive methods that can provide more detailed characterization of natural or man-made materials or even provide information not previously available to forensic examiners. Recent advances in rapid solid sampling of materials using laser ablation (LA) coupled to inductively coupled plasma mass spectroscopy (ICP-MS) have led to this analytical method to be regarded as the “gold standard” in the field of elemental analysis for trace level components in solids. Another, emerging, analytical technique that uses the same laser pulse to generate a plasma that can be interrogated with spectroscopy is laser induced break down spectroscopy (LIBS). The analysis of ink and paper is also possible because of the surface removal effect of laser interactions with the samples. In the present study, printing inks were analyzed using LIBS, LA-ICP-MS and both of them in tandem mode. In the tandem setup, the light generated during the relaxation of the excited species (LIBS) was used to create a spectral signature of the elements, and the mass-to-charge ratio of the ejected particles (ICP-MS) was used to create a mass spectrum. For a set of 319 printing ink samples, LA-ICP-MS alone provided discrimination greater than 99%. A subset of 43 printing inks, having a very similar elemental profile, was analyzed by tandem LIBS/LA-ICP-MS. The fusion of LIBS and LA-ICP-MS provided additional discrimination through the detection of elements like Ca, Si, Fe, and K by LIBS, that are difficult to detect and confirm using standalone ICP-MS because of the spectral interferences (isobaric and polyatomic) involved. The combination of these two sensors was found to minimize the individual limitations and provide a more complete and representative chemical characterization of printing inks.