Dissertations / Theses on the topic 'Spectral mixtures'
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Ajohani, Maha. "SPECTRAL PHASOR ANALYSIS ON ABSORBANCE SPECTRA FOR QUANTIFYING THE CONTENT OF DYE MIXTURES." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1464191406.
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Vlack, Yvette A. "A Diffuse Spectral Reflectance Library of Clay Minerals and Clay Mixtures within the VIS/NIR Bands." Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1227006436.
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Luo, Zhaohui. "GC/FT-ICR Mass Spectral Analysis of Complex Mixtures: A Multidimensional Approach for Online Gas Phase Basicity Measurements." Fogler Library, University of Maine, 2006. http://www.library.umaine.edu/theses/pdf/LuoZX2006.pdf.
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Raksuntorn, Nareenart. "Unsupervised spectral mixture analysis for hyperspectral imagery." Diss., Mississippi State : Mississippi State University, 2009. http://library.msstate.edu/etd/show.asp?etd=etd-04192009-142516.
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Levi, Di Leon Rémi. "Etude théorique et expérimentale de l'absorption par CO2 et H2O dans le domaine infrarouge à température élevée." Châtenay-Malabry, Ecole centrale de Paris, 1986. http://www.theses.fr/1986ECAP0026.
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Un code de calcul raie par raie des propriétés radiatives de mélanges gazeux hétérogènes et anisothermes CO2, H2O, CO, N2, o a été construit a partir de données spectroscopiques, applicable de 300 à 2500 K de 0,1 à 50 ATM, et dans tout le domaine infrarouge. Réalisation d'un montage de mesure de la transitivité d'un gaz a température élevée
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Parra, Vásquez Gabriel Enrique. "Spectral mixture kernels for Multi-Output Gaussian processes." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/150553.
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Magíster en Ciencias de la Ingeniería, Mención Matemáticas Aplicadas.
Ingeniero Civil MatemáticoMulti-Output Gaussian Processes (MOGPs) are the multivariate extension of Gaussian processes (GPs \cite{Rasmussen:2006}), a Bayesian nonparametric method for univariate regression. MOGPs address the multi-channel regression problem by modeling the correlation in time and/or space (as scalar GPs do), but also across channels and thus revealing statistical dependencies among different sources of data. This is crucial in a number of real-world applications such as fault detection, data imputation and financial time-series analysis. Analogously to the univariate case, MOGPs are entirely determined by a multivariate covariance function, which in this case is matrix valued. The design of this matrix-valued covariance function is challenging, since we have to deal with the trade off between (i) choosing a broad class of cross-covariances and auto-covariances, while at the same time (ii) ensuring positive definiteness of the symmetric matrix containing these scalar-valued covariance functions. In the stationary univariate case, these difficulties can be bypassed by virtue of Bochner's theorem, that is, by building the covariance function in the spectral (Fourier) domain to then transform it to the time and/or space domain, thus yielding the (single-output) Spectral Mixture kernel \cite{Wilson:2013}. A classical approach to define multivariate covariance functions for MOGPs is through linear combinations of independent (latent) GPs; this is the case of the Linear Model of Coregionalization (LMC \cite{goo1997}) and the Convolution Model \cite{Alvarez:2008}. In these cases, the resulting multivariate covariance function is a function of both the latent-GP covariances and the linear operator considered, which usually results in symmetric cross-covariances that do not admit lags across channels. Due to their simplicity, these approaches fail to provide interpretability of the dependencies learnt and force the auto-covariances to have similar structure. The main purpose of this work is to extend the spectral mixture concept to MOGPs: We rely on Cram\'er's theorem \cite, the multivariate version of Bochner's theorem, to propose an expressive family of complex-valued square-exponential cross-spectral densities, which, through the Fourier transform yields the Multi-Output Spectral Mixture kernel (MOSM). The proposed MOSM model provides clear interpretation of all the parameters in spectral terms. Besides the theoretical presentation and interpretation of the proposed multi-output covariance kernel based on square-exponential spectral densities, we inquiry the plausibility of complex-valued t-Student cross-spectral densities. We validate our contribution experimentally through an illustrative example using a tri-variate synthetic signal, and then compare it against all the aforementioned methods on two real-world datasets.
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Stuttle, Matthew Nicholas. "A gaussian mixture model spectral representation for speech recognition." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620077.
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Raman, Pujita. "Speaker Identification and Verification Using Line Spectral Frequencies." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/52964.
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State-of-the-art speaker identification and verification (SIV) systems provide near perfect performance under clean conditions. However, their performance deteriorates in the presence of background noise. Many feature compensation, model compensation and signal enhancement techniques have been proposed to improve the noise-robustness of SIV systems. Most of these techniques require extensive training, are computationally expensive or make assumptions about the noise characteristics. There has not been much focus on analyzing the relative importance, or speaker-discriminative power of different speech zones, particularly under noisy conditions.
In this work, an automatic, text-independent speaker identification (SI) system and speaker verification (SV) system is proposed using Line Spectral Frequency (LSF) features. The performance of the proposed SI and SV systems are evaluated under various types of background noise. A score-level fusion based technique is implemented to extract complementary information from static and dynamic LSF features. The proposed score-level fusion based SI and SV systems are found to be more robust under noisy conditions.
In addition, we investigate the speaker-discriminative power of different speech zones such as vowels, non-vowels and transitions. Rapidly varying regions of speech such as consonant-vowel transitions are found to be most speaker-discriminative in high SNR conditions. Steady, high-energy vowel regions are robust against noise and are hence most speaker-discriminative in low SNR conditions. We show that selectively utilizing features from a combination of transition and steady vowel zones further improves the performance of the score-level fusion based SI and SV systems under noisy conditions.Master of Science
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Gurden, Stephen P. "Deconvolution of vapour-phase mid-infrared mixture spectra of organic solvents using chemometrics." Thesis, University of Bristol, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337176.
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Kressler, Florian. "The Integration of Remote Sensing and Ancillary Data." WU Vienna University of Economics and Business, 1996. http://epub.wu.ac.at/4256/1/WSG_RR_0896.pdf.
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Obtaining up-to-date information concernmg the environment at reasonable costs is a
challenge faced by many institutions today. Satellite images meet both demands and thus
present a very attractive source of information.
The following thesis deals with the comparison of satellite images and a vector based land use
data base of the City of Vienna. The satellite data is transformed using the spectral mixture
analysis, which allows an investigation at a sub-pixel level. The results of the transformation
are used to determine how suitable this spectral mixture analysis is to distinguish different
land use classes in an urban area. In a next step the results of the spectral mixture analysis of
two different images (recorded in 1986 and 1991) are used to undertake a change detection.
The aim is to show those areas, where building activities have taken place. This information
may aid the update of data bases, by limiting a detailed examination of an area to those areas,
which show up as changes in the change detection.
The proposed method is a fast and inexpensive way of analysing large areas and highlighting
those areas where changes have taken place. lt is not limited to urban areas but may easily be
adapted for different environments. (author's abstract)Series: Research Reports of the Institute for Economic Geography and GIScience
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Khiry, Manal Awad. "Spectral Mixture Analysis for Monitoring and Mapping Desertification Processes in Semi-arid Areas in North Kordofan State, Sudan." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2007. http://nbn-resolving.de/urn:nbn:de:swb:14-1187262045124-42224.
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Multi-temporal remotely sensed data (MSS, TM and ETM+)were used for monitoring and mapping the desertification processes in North Kordofan State, Sudan.A liear mixture model (LMM) was adopted to analyse and the desertification proccesses by using the image endmembers. interpretation of ancillary data and field observation was adopted to verfiy the role of human impacts in the temporal changes in the study area. The findings of the study proved the powerfull of remotely sensed data in monitoring and mapping the desertification processes and come out with valuable recommendations which could contribute positively in reducing desert encroachment in the area.
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Accioly, Luciano Jose de Oliveira. "Applying Spectral Mixture Analysis (SMA) For Soil Information Extraction On The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Data." Diss., The University of Arizona, 1997. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_1997_400_sip1_w.pdf&type=application/pdf.
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Pasero, Carolina, dipartimento di Biotecnologie, Chimica e. Farmacia, and Università degli studi di Siena. "A SERENDIPITOUS DISCOVERY THAT LED TO THE IDENTIFICATION OF ALKYL-GUANIDINE OLIGOMERS AS DEVELOPABLE ANTIBACTERIAL AGENTS." Doctoral thesis, Università di Siena, 2018. http://hdl.handle.net/11365/1047317.
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During the three years of my PhD I was involved in the synthesis of alkyl-guanidine oligomers as perspective antibacterial agents.
The research of new antibacterial drugs currently represents a cutting-adge field in medicinal chemistry, since the phenomenon of multidrug resistance (MDR) is growing very fast worldwide. MDR has been in fact defined by the World Health Organization as one of the three major threat to mankind in the 21st century, endangering the efficacy of the treatments. For this reason, both the prevention from the infections and the divulgation of awareness, as well as the help from different research area, academic and industrial, are urgently needed.
My PhD thesis is divided into two main parts: the first derives from a serendipitous discovery, that trigged the beginning of the project. It was devoted to the resolution of a multicomponent mixture of oligomers, spontaneously originated, through the design and synthesis of all the possible hypothesized structures.
Based on this study we have been able to identify a good candidate for next investigations, which were carried out in the second part of my work.
This latter part is a more basic example of extension of a chemical class of compounds. Starting from the identified scaffold, some structural modifications have been performed in order to analyse some preliminary structure-activity relationships.
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Wang, Yunli. "Mass Spectrum Analysis of a Substance Sample Placed into Liquid Solution." Thesis, North Dakota State University, 2011. https://hdl.handle.net/10365/28881.
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Mass spectrometry is an analytical technique commonly used for determining elemental composition in a substance sample. For this purpose, the sample is placed into some liquid solution called liquid matrix. Unfortunately, the spectrum of the sample is not observable separate from that of the solution. Thus, it is desired to distinguish the sample spectrum. The analysis is usually based on the comparison of the mixed spectrum with the one of the sole solution. Introducing the missing information about the origin of observed spectrum peaks, the author obtains a classic set up for the Expectation-Maximization (EM) algorithm. The author proposed a mixture modeling the spectrum of the liquid solution as well as that of the sample. A bell-shaped probability mass function obtained by discretization of the univariate Gaussian probability density function was proposed or serving as a mixture component. The E- and M- steps were derived under the proposed model. The corresponding R program is written and tested on a small but challenging simulation example. Varying the number of mixture components for the liquid matrix and sample, the author found the correct model according to Bayesian Information Criterion. The initialization of the EM algorithm is a difficult standalone problem that was successfully resolved for this case. The author presents the findings and provides results from the simulation example as well as corresponding illustrations supporting the conclusions.
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Wang, Yujia. "Assessing Bald Cypress (Taxodium distichum) Tree Dynamic Change in USF Forest Preserve Area Using Mixture-Tuned Matched Filtering and Multitemporal Satellite Imagery." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7375.
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Wetlands are the most important and valuable ecosystems on Earth. They are called “kidneys of the Earth”. Vegetation change detection is necessary to understand the condition of a wetland and to support ecosystem sustainable management and utilization. It has been a great challenge to estimate vegetation (including bald cypress trees) coverage of the wetland because it is difficult to access directly. Satellite remote sensing technology can be one important feasible method to map and monitor changes of wetland forest vegetation and land cover over large areas. Remote sensing mapping techniques have been applied to detect and map vegetation changes in wetlands. To address spectral mixture issues associated with moderate resolution remote sensing images, many spectral mixture methods have been developed and applied to unmix the mixed pixels in order to accurately map endmembers (e.g., different land cover types and different materials within pixels) fractions or abundance. Of them, Mixture Tuned Matched Filtering (MTMF) is an advanced spectral unmixing method that has attracted many researchers to test it for mapping land cover types including mapping tree species with medium or coarse remote sensing image data. MTMF is a partial unmixing method that suppresses background noise and estimates the subpixel abundance of a single target material. In this study, to understand impacts of anthropogenic (e.g., urbanization) and natural forces/climate change on the bald cypress tree dynamic change, the bald cypress trees cover change in University of South Florida Forest Preserve Area was mapped and analysed by using MTMF tool and multitemporal Landsat imagery over 30 years from 1984 to 2015. To evaluate the MTMF’s performance, a tradition spectral unmixing method, Linear Spectral Unmixing (LSU), was also tested. The experimental results indicate that (1) the bald cypress tree cover percentage in the study area has generally increased during the 30 years from 1984 to 2015, but over the time period from 1994 to 2005, the bald cypress tree cover percentage reduced; (2) MTMF tool outperformed the LSU method in mapping the change of the bald cypress trees over the 30 years to demonstrate its powerful capability; and (3) there potentially exists an impact of human activities on the change of the bald cypress trees although a further quantitative analysis is needed in the future research.
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Weng, Fenqing. "Mapping and Assessing Urban Impervious Areas Using Multiple Endmember Spectral Mixture Analysis: A Case Study in the City of Tampa, Florida." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4253.
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The advance in remote sensing technology helps people more easily assess urban growth. In this study, the utility of multiple endmember spectral mixture analysis (MESMA) is examined in a sub-pixel analysis of Landsat Thematic Mapper (TM) imagery to map urban physical components in Tampa, FL. The three physical components of urban land cover (LC): impervious surface, vegetation and soil, were compared using the proposed MESMA with a traditional spectral mixture analysis (SMA). MESMA decomposes each pixel to address the heterogeneity of urban LC characteristic by allowing the number and types of endmembers to vary on a per pixel basis. This study generated 642 spectral mixture models of 2-, 3-, and 4-endmembers for each pixel to estimate the fractions of impervious surface, vegetation, soil, and shade in the study area with a constraint of lowest root mean square error (RMSE). A comparative analysis of the impervious surface areas (ISA) mapped with MESMA and SMA demonstrated that MESMA produced more accurate results of mapping urban physical components than those by SMA. With the multiyear Landsat TM data, we quantified sub-pixel %ISA and the %ISA changes to assess urban growth in the City of Tampa, Florida during the past twenty years. The experimental results demonstrate that the MESMA approach is effective in mapping and monitoring urban land use/land cover changes using moderate-resolution multispectral imagery at a sub-pixel level.
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Xiao, Ying. "New tools for unsupervised learning." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52995.
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In an unsupervised learning problem, one is given an unlabelled dataset and hopes to find some hidden structure; the prototypical example is clustering similar data. Such problems often arise in machine learning and statistics, but also in signal processing, theoretical computer science, and any number of quantitative scientific fields. The distinguishing feature of unsupervised learning is that there are no privileged variables or labels which are particularly informative, and thus the greatest challenge is often to differentiate between what is relevant or irrelevant in any particular dataset or problem.
In the course of this thesis, we study a number of problems which span the breadth of unsupervised learning. We make progress in Gaussian mixtures, independent component analysis (where we solve the open problem of underdetermined ICA), and we formulate and solve a feature selection/dimension reduction model. Throughout, our goal is to give finite sample complexity bounds for our algorithms -- these are essentially the strongest type of quantitative bound that one can prove for such algorithms. Some of our algorithmic techniques turn out to be very efficient in practice as well.
Our major technical tool is tensor spectral decomposition: tensors are generalisations of matrices, and often allow access to the "fine structure" of data. Thus, they are often the right tools for unravelling the hidden structure in an unsupervised learning setting. However, naive generalisations of matrix algorithms to tensors run into NP-hardness results almost immediately, and thus to solve our problems, we are obliged to develop two new tensor decompositions (with robust analyses) from scratch. Both of these decompositions are polynomial time, and can be viewed as efficient generalisations of PCA extended to tensors.
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Westerlund, Annie M. "Computational Study of Calmodulin’s Ca2+-dependent Conformational Ensembles." Licentiate thesis, KTH, Biofysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234888.
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Ca2+ and calmodulin play important roles in many physiologically crucial pathways. The conformational landscape of calmodulin is intriguing. Conformational changes allow for binding target-proteins, while binding Ca2+ yields population shifts within the landscape. Thus, target-proteins become Ca2+-sensitive upon calmodulin binding. Calmodulin regulates more than 300 target-proteins, and mutations are linked to lethal disorders. The mechanisms underlying Ca2+ and target-protein binding are complex and pose interesting questions. Such questions are typically addressed with experiments which fail to provide simultaneous molecular and dynamics insights. In this thesis, questions on binding mechanisms are probed with molecular dynamics simulations together with tailored unsupervised learning and data analysis. In Paper 1, a free energy landscape estimator based on Gaussian mixture models with cross-validation was developed and used to evaluate the efficiency of regular molecular dynamics compared to temperature-enhanced molecular dynamics. This comparison revealed interesting properties of the free energy landscapes, highlighting different behaviors of the Ca2+-bound and unbound calmodulin conformational ensembles. In Paper 2, spectral clustering was used to shed light on Ca2+ and target protein binding. With these tools, it was possible to characterize differences in target-protein binding depending on Ca2+-state as well as N-terminal or C-terminal lobe binding. This work invites data-driven analysis into the field of biomolecule molecular dynamics, provides further insight into calmodulin’s Ca2+ and targetprotein binding, and serves as a stepping-stone towards a complete understanding of calmodulin’s Ca2+-dependent conformational ensembles.QC 20180912
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Loum, Mor Absa. "Modèle de mélange et modèles linéaires généralisés, application aux données de co-infection (arbovirus & paludisme)." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS299/document.
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Nous nous intéressons, dans cette thèse, à l'étude des modèles de mélange et des modèles linéaires généralisés, avec une application aux données de co-infection entre les arbovirus et les parasites du paludisme. Après une première partie consacrée à l'étude de la co-infection par un modèle logistique multinomial, nous proposons dans une deuxième partie l'étude des mélanges de modèles linéaires généralisés. La méthode proposée pour estimer les paramètres du mélange est une combinaison d'une méthode des moments et d'une méthode spectrale. Nous proposons à la fin une dernière partie consacrée aux mélanges de valeurs extrêmes en présence de censure. La méthode d'estimation proposée dans cette partie se fait en deux étapes basées sur la maximisation d'une vraisemblanceWe are interested, in this thesis, to the study of mixture models and generalized linear models, with an application to co-infection data between arboviruses and malaria parasites. After a first part dedicated to the study of co-infection using a multinomial logistic model, we propose in a second part to study the mixtures of generalized linear models. The proposed method to estimate the parameters of the mixture is a combination of a moment method and a spectral method. Finally, we propose a final section for studing extreme value mixtures under random censoring. The estimation method proposed in this section is done in two steps based on the maximization of a likelihood
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Miyajima, Chiyomi, Yoshihiro Nishiwaki, Koji Ozawa, Toshihiro Wakita, Katsunobu Itou, Kazuya Takeda, and Fumitada Itakura. "Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification." IEEE, 2007. http://hdl.handle.net/2237/9623.
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Pedrassoli, Julio Cesar. "Habitação e sensoriamento remoto: uma análise da expansão urbana na RMSP por meio de imagens de satélite aplicando o modelo linear de mistura espectral." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/8/8136/tde-13022017-124413/.
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O objetivo desta pesquisa é analisar, em um período de 30 anos, a expansão urbana do Município de São Paulo e dos municípios da Região Metropolitana de São Paulo (RMSP) a partir do uso de imagens orbitais obtidas pela série Landsat nos anos de 1986, 1989, 2000, 2006, 2010 e 2015, aplicando o Modelo Linear de Mistura Espectral (MLME) com uso de membros de referência globais. Os dados obtidos com o uso do MLME foram associados às informações de produção habitacional formal e informal, considerando os empreendimentos imobiliários verticais, as favelas e os loteamentos irregulares (desde 1985 até o ano de 2015), possibilitando a compreensão do comportamento espacial da produção habitacional dessa região ao longo do período analisado. Para cada cena Landsat, foram calculadas as imagens relacionadas à fração Substrato (alto albedo), à fração Vegetação (biomassa) e à fração Sombra (baixo albedo). Durante o desenvolvimento do trabalho, é proposta uma metodologia de modelagem das mudanças do uso e ocupação do solo associados à produção habitacional, partindo da correlação temporal entre as frações. Cada imagem fração foi agregada em três níveis de escala: a escala da própria imagem (pixel de 30m), a escala intraurbana (setores censitários e distritos) e a escala regional (limites da RMSP) o que possibilitou, na escala intraurbana, a associação das informações extraídas do MLME aos dados de produção habitacional e aos censos demográficos de 1991, 2000 e 2010. Os resultados do modelo demonstram que as áreas de produção habitacional verticalizada, no período analisado, são detectadas pelo modelo como uma correlação negativa entre as frações substrato e sombra, indicando que a produção de sombra dos empreendimentos é acompanhada de uma diminuição da detecção da fração substrato, enquanto nas áreas de habitação precárias (favelas e loteamentos irregulares), a mudança detectada pelo modelo é de correlação negativa entre as frações vegetação e substrato e também entre as frações sombra e substrato, sendo indicativo do aumento do substrato para ambos os casos, ao longo do tempo. Os resultados também demonstram alta fidelidade posicional e possibilidade de aplicação geral do modelo proposto para outras categorias de uso do solo, indicando frentes para generalização da técnica aqui proposta.The objective of this research is to analyze, in a period of 30 years, the urban sprawl of Sao Paulo city and the municipalities of São Paulo Metropolitan Region (SPMR) using orbital images obtained by Landsat series in 1986, 1989, 2000, 2006, 2010, and 2015, applying the Linear Spectral Mixture Model (LSMM) and the use of global reference endmembers. The data obtained using the LSMM were associated to information on formal and informal housing production, considering the vertical real estate development, the slums and the informal neighborhoods (from 1985 to 2015), making possible to understand the housing production in the region in the time period analyzed. For each Landsat scene, image fractions were calculated in relation to the Substrate fraction (high albedo), Vegetation Fraction (biomass) and Shadow fraction (low albedo). In the development of the work, is proposed a methodology to modeling the changes in land use/land cover associated with the housing production through the temporal correlation between the fractions. Each image fraction was aggregated at three levels of scale: the scale of the image itself (30m pixel), the intra urban scale (census tracts and districts) and regional scale (limits of the SPMR) allowing, in the intra urban range, the association of information extracted from the LSMM, the housing production and the census demographic data from 1991, 2000 and 2010. The model results show that the vertical housing production areas, in the analyzed period, are detected by a negative correlation between substrate and shadow fractions, showing that the shadow increase in these areas are accompanied by a decrease in the substrate fraction detection, while in areas of precarious housing (slums and informal neighborhoods) the model detects a negative correlation between vegetation ad substrate fractions and also between the substrate and shadow fractions, indicating the increase of substrate for both cases over time. The results also show high positional fidelity and possibility of a general application of the proposed model for other categories of land use/land cover changes, showing the possibility of generalization of the technique proposed here.
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Velho, Luiz Felipe. "Análise da temperatura de superfície e da ocupação urbana no município de Porto Alegre." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/131947.
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A urbanização modifica a superfície, promovendo a troca da cobertura natural por materiais de construção. As áreas urbanas, além do solo impermeável, têm a presença de edifícios, que alteram a rugosidade da superfície, a velocidade e a direção dos ventos e provocam o sombreamento da superfície, bloqueando a incidência da energia solar. Assim, analisar a geometria de ocupação da cidade é importante para o entendimento do clima urbano e para o planejamento da cidade. O sensoriamento remoto é uma importante forma de obtenção de informações das áreas urbanas, contudo é preciso considerar a heterogeneidade deste ambiente e a mistura espectral existente nos dados satelitais. Dessa forma, o modelo linear de mistura espectral apresenta-se como importante método de extração de informações dos ambientes urbanos. O objetivo deste trabalho é identificar áreas com padrão horizontal e com padrão vertical de ocupação urbana, em Porto Alegre, e relacionar essa característica geométrica com valores de temperatura de superfície. Para tanto, imagens do sensor TM do satélite Landsat 5, adquiridas entre 1984 e 2009 foram utilizadas, bem como dados censitários, dados meteorológicos e modelos gerados por varredura laser. A partir das imagens TM foram geradas três imagens fração: solo, sombra e vegetação. A fração solo foi utilizada na identificação de áreas de ocupação horizontal e de expansão urbana, e a fração sombra foi utilizada na identificação de áreas verticalizadas. Utilizando as mesmas imagens, obtiveram-se os valores de temperatura de superfície. As áreas com ocupação horizontal, caracterizadas por moradias em casas, apresentaram baixos valores de sombra e altos valores de solo. As áreas verticalizadas apresentaram altos valores de sombra e baixos valores de solo. Os resultados extraídos das imagens fração têm similaridade com dados de artigos científicos e com os dados da varredura laser. A temperatura de superfície, em Porto Alegre, mostrou forte correlação com dados meteorológicos, e se caracteriza por valores mais altos nas áreas urbanizadas e mais baixos onde a ocupação é rarefeita. Nas áreas urbanizadas, maiores valores de temperatura de superfície são encontrados nas regiões com padrão de ocupação horizontal, enquanto os menores valores são encontrados nas regiões verticalizadas. A metodologia escolhida gerou resultados compatíveis com outros dados de uso e ocupação do solo, provenientes de diferentes fontes, e contribui com características da área urbana e do clima urbano da cidade de Porto Alegre, informações essas escassas nos principais bancos de dados acadêmicos.The urbanization modifies the landscape, promoting changes from natural to man-made environment. Besides the impermeable soil, the urban areas have a lot of buildings, that changes the surface roughness, the wind speed and direction and also are responsible for shading the surface, blocking the incidence of solar energy. Analysing the city occupation geometry is important to understanding of the urban climate behaviour, and naturally the city planning. Remote sensing is a very important tool to get information about the urban areas, but is necessary to consider the heterogeneity of this environment and the existing spectral mixing in satellite data. Based on this, the linear model of spectral mixing can be classified as an important method of information extraction from urban environments. The goal of this research is to identify areas with horizontal and vertical patterns of urban occupation in the city of Porto Alegre – Brazil and relate this geometric characteristic with values of surface temperature. Therefore, images of the TM sensor of the Landsat 5 satellite were used (during the 1984-2009 period) and also the census data, meteorological data and models generated by laser scanning. Three fraction images were generated based on TM images: soil, shade and vegetation. The soil fraction was used for the identification of the areas with horizontal occupation and urban expansion, and the shadow fraction was used to identify verticalized areas. Based on the same images the surface temperature was obtained. Areas with horizontal occupation, mostly represented by houses, presented low shading values and high soil values. On the other hand, verticalized areas presented high shading values and low soil values. These results, obtained from the images fraction, are similar with the results from scientific papers and data from laser scanning. In Porto Alegre, the surface temperature indicated strong correlation with meteorological data, and was characterized by higher values in urbanized areas and lower values where the occupation is least intense. In urban areas, higher values of temperature are found in areas with horizontal occupation pattern, while the lowest values are found in verticalized regions. Furthermore, it is possible to suggest that the chosen methodology lead to conclusions that are consistent with other data of land use and occupation from different sources. Contributing with some information about characteristics of the urban area and urban climate of the city of Porto Alegre, which are usually not well documented in academic databases.
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Oprisan, Ana. "Fluctuations, Phase Separation and Wetting Films near Liquid-Gas Critical Point." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/435.
Full textAbstract:
Gravity on Earth limits the study of the properties of pure fluids near critical point because they become stratified under their own weight. Near the critical point, all thermodynamic properties either diverge or converge and the heating and cooling cause instabilities of the convective flow as a consequence of the expansibility divergence. In order to study boiling, fluctuation and phase separation processes near the critical point of pure fluids without the influence of the Earth's gravity, a number of experiments were performed in the weightlessness of Mir space station. The experimental setup called ALICE II instrument was designed to suppress sedimentation and buoyancy-driven flow. Another set of experiments were carried out on Earth using a carefully density matched system of deuterated methanolcycloxexane to observe critical fluctuations directly. The set of experiments performed on board of Mir space station studied boiling and wetting film dynamics during evaporation near the critical point of two pure fluids (sulfur hexafluoride and carbon dioxide) using a defocused grid method. The specially designed cell containing the pure fluid was heated and, as a result, a low contrast line appeared on the wetting film that corresponded to a sharp change in the thickness of the film. A large mechanical response was observed in response to the cell heating and we present quantitative results about the receding contact lines. It is found that the vapor recoil force is responsible for the receding contact line. Local density fluctuations were observed by illuminating a cylindrical cell filled with the pure fluid near its liquid- gas critical point and recorded using a microscope and a video recorder. Microscopic fluctuations were analyzed both in sulfur hexafluoride and in a binary mixture of methanol cyclohexane. Using image processing techniques, we were able to estimate the properties of the fluid from the recorded images showing fluctuations of the transmitted and scattered light. We found that the histogram of an image can be fitted to a Gaussian relationship and by determining its width we were able to estimate the position of the critical point. The characteristic length of the fluctuations corresponding to the maximum of the radial average of the power spectrum was also estimated. The power law growth for the early stage of the phase separation was determined for two different temperature quenches in pure fluid and these results are in agreement with other experimental results and computational simulations.
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Chu, Shuyu. "Change Detection and Analysis of Data with Heterogeneous Structures." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78613.
Full textAbstract:
Heterogeneous data with different characteristics are ubiquitous in the modern digital world.
For example, the observations collected from a process may change on its mean or variance.
In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical
modeling and analysis challenging.
Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging.
This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.Ph. D.
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Caliot, Cyril. "Modélisation et simulation de l'émission énergétique et spectrale d'un jet réactif composé de gaz et de particules à haute température issus de la combustion d'un objet pyrotechnique." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2006. http://tel.archives-ouvertes.fr/tel-00271463.
Full textAbstract:
Les travaux réalisés durant la thèse s'inscrivent dans une problématique scientifique liée à l'étude des transferts radiatifs. Plus particulièrement, l'application de cette étude est la télédétection infrarouge d'un écoulement diphasique réactif et turbulent à haute température. Cette étude a pour objectif la modélisation et la simulation du rayonnement infrarouge émis par cet écoulement et re¸cu par un détecteur. Pour développer un outil de simulation numérique de la signature infrarouge d'un jet de gaz et de particules à haute température, les espèces majoritaires qui sont responsables de l'émission du rayonnement ont été identifiées lors d'expérimentations. Les campagnes expérimentales ont permis la construction de bases de données concernant les gaz (CO2-CO-H2O) et les particules (oxydes métalliques) présents dans le jet. Connaissant la nature des gaz et des particules, le calcul de leurs propriétés radiatives doit être réalisé. Cette étape est nécessaire puisque ces propriétés caractérisent l'émission de rayonnement par le jet et elles doivent être connues pour résoudre l'équation de transfert radiatif. Pour les gaz, un code de calcul raie par raie de spectres synthétiques a été développé. De plus, pour diminuer le temps de calcul d'une signature infrarouge, il est préférable d'utiliser des modèles spectraux de bandes étroites. Le modèle de télédétection infrarouge est un modèle spectral utilisant des k(coefficient d'absorption)-distributions sous l'hypothèse des k-corrélés avec l'approximation d'un gaz unique pour le mélange associée à l'hypothèse des gaz fictifs. Les paramètres de ce modèle (CKFG-SMG), ont été tabulés et validés dans l'étude. En ce qui concerne les propriétés radiatives des nuages de particules sphériques, le modèle de Mie est utilisé car il est valable pour les gammes de fractions volumiques rencontrées. Pour tester l'influence de la diffusion, une étude de sensibilité à la diffusion a été réalisée. En effet, nous avons quantifié l'erreur commise sur le flux émis par différentes couches si les processus de diffusion du rayonnement sont négligés. Cette étude a montré que l'influence de la diffusion peut être négligée dans le cadre de notre étude. La modélisation de la signature infrarouge du jet diphasique réactif issu de la combustion du matériau pyrotechnique, nécessite la connaissance des températures et des concentrations en gaz et particules, en tous les points du jet. Ce jet diphasique réactif a été simulé à l'aide du logiciel Fluent. De plus, une interface graphique a été développée qui recrée la scène optronique en se servant des profils aérothermochimiques du jet diphasique et des données concernant la position du détecteur. De cette fa¸con, un outil de simulation numérique de la signature infrarouge du jet (SIRJET) a été développé qui inclue un modèle de transfert radiatif (lancer de rayon) ainsi que les paramètres tabulés (gaz et particules) du modèle spectral de télédétection infrarouge (CK, CKFG, CK-SMG, CKFG-SMG). Enfin, une confrontation est présentée entre une mesure et le résultat d'une simulation de la signature infrarouge d'un jet diphasique à haute température.
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Amaral, Cibele Hummel do. "Sensoriamento remoto hiperespectral e definição de espécies indicadoras aplicados à geobotânica no bioma cerrado." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/44/44137/tde-27042015-105259/.
Full textAbstract:
A Geobotânica por sensoriamento remoto é uma técnica de obtenção de informações geológicas indiretas em ambientes cobertos por vegetação e apresenta grandes perspectivas por sua capacidade de otimizar trabalhos de campo e gerar possíveis alvos a serem examinados. O objetivo deste estudo foi realizar a discriminação espectral, em escala de folha e de copa, de espécies arbóreas neotropicais associadas localmente a diferentes formações e fácies geológicas, bem como defini-las remotamente como indicadoras geológicas na Estação Ecológica de Mogi-Guaçu, São Paulo, Brasil. Dados obtidos em 70 unidades amostrais, como texturais de solos e sedimentos, químicos de solos, de nível do freático, altitudinais (modelo digital de terreno),fitossociológicos e fisiológicos/estruturais de vegetação (índices hiperespectrais de vegetação), foram minerados e analisados através da técnica de quantização vetorial Self-Organizing Maps (SOM). Inga veraWilld. subsp. affinis (DC.) T.D. Penn (INVE) e Calophyllum brasiliense Cambess. (CABR) mostraram-se associadas à planície de inundação, incluindo meandros abandonados (Depósitos Aluvionares), com amplo domínio das frações argila e silte nos sedimentos. Qualea grandifloraMart. (QUGR) e Tabebuia ochracea(Cham.) Standl. (TAOC) foram identificadas apenas nas colinas e platôs da Formação Aquidauana,com altas porcentagens das frações areia fina, média e grossa, e escasso silte. Cedrela fissilisVell. (CEFI) e Zeyheria tuberculosa(Vell.) Bur. (ZETU) demonstraram estar associadas a uma fácies aflorante da Formação Aquidauana, com distinta presença das frações de areia grossa e muito grossa, além da baixa porcentagem das frações silte e areiafina. As cinco primeiras espécies tiveram dados bioquímicos e espectrais (400-2.500 nm, FieldSpec 3 Hi-Res) coletados, em escala de folha, tanto no período chuvoso quanto noseco. Seus espectros foram classificados através da técnica Multiple Endmember Spectral Mixture Analysis(MESMA). As espécies foram bem discriminadas em ambos os momentos sazonais, nessa escala de trabalho. Dentre os melhores resultados por intervalo espectral, as exatidões do produtor e do usuário não foram inferiores a 87,5%. Esse sucesso mostrou estar intimamente ligado à alta variabilidade bioquímica observada em suas folhas. As variações intra e interespecíficas em compostos bioquímicos puderam ser correlacionadas às suas variações espectrais. A discriminação espectral em escala de copa foi realizada com dois membros-finais (MF) via MESMA para CEFI, INVE e QUGR. Os pixels das imagenspré-processadas do sistema de sensores aeroportados ProSpecTIR-VS (530-2.532 nm, 1 m de resolução espacial) foram modelados por três MF: MF da classe de espécie-alvo, MF de outras classes de vegetação e sombra fotométrica. A falta de comissão espectrale a relativa baixa omissão espectral atingidas por QUGR na modelagem com dois MF, que incluiu outras classes de vegetação, refletiu em um mapeamento satisfatório de sua fração espectral. As tendências em distribuição dessa espécie indicaram claramente as colinas e platôs da Formação Aquidauana na área estudada.Geobotany via remote sensing is a technique for obtaining indirect geological information in vegetated areas and presents great perspectives by its capability for field work optimization and target generation to be evaluated afterwards. The aim of this research was to perform the spectral discrimination of Neotropical tree species (at leaf and crown levels) which are locally associated to geological facies and formations in the Mogi-Guaçu Ecological Station, in southeastern Brazil. Data from 70 sampling units, such as soils and sediments texture, soils chemistry, groundwater level, elevation (digital terrain model), plant sociology and vegetation physiology/structure (hyperspectral vegetation indices), were mined and analyzed through the vetorial quantization method called Self-Organizing Maps (SOM; Kohonen, 1982). Inga veraWilld. subsp. affinis(DC.) T.D. Penn (INVE) and Calophyllum brasiliense Cambess. (CABR) demonstrated to be associated to the floodplain, including paleochannels (Alluvial Deposits sequence), with clayey-silty sediments. Qualea grandifloraMart. (QUGR) and Tabebuia ochracea(Cham.) Standl. (TAOC) were sampled on hills and plateaus of the Aquidauna Formation, which stood out for higher fine, medium and coarse sand contents and lower silt content. Cedrela fissilisVell. (CEFI) and Zeyheria tuberculosa(Vell.) Bur. (ZETU) showed be associated to one outcrop facies of the Aquidauna Formation, with distinctive presence of coarse and very coarse sandas well as lower silt and very fine sand contents. Biochemical and spectral (400-2.500 nm, FieldSpec Hi-Res 3) data were collected from the leaves of the first five species, during both rainy and dry seasons. Their spectra were classified through Multiple Endmember Spectral Mixture Analysis(MESMA). All target species were well discriminated at leaf scale. Considering the best classification results per spectral range, user\'s and producer\'s accuracies were always higher than 87,5%. These results seem to be linked to the great biochemical variability of their leaves. The intra and interspecific variability of biochemical compounds were correlated with spectral variability. The spectral discrimination at crown scale was performed with two endmembers (EM) via MESMA for CEFI, INVE e QUGR. The 1-m pixels of the preprocessed ProSpecTIR-VS images (530-2.532 nm) were modeled by three EM: EM of the target species, EM of other vegetation classes, and photometric shade. The QUGRclass achieved a relatively lower spectral omission and had no spectra erroneously assigned to its class in the two EM classification, which included other vegetation classes. This classification result was reproduced in the three EM image unmixing. The distribution tendency of that species clearly indicated the hills and plateaus of the Aquidauana Formation in the study area.
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Pandini, D. "STUDI PER LA MODELLIZZAZIONE DELLA RIFLETTANZA SPETTRALE NEGLI STRATI PITTORICI." Doctoral thesis, Università degli Studi di Milano, 2012. http://hdl.handle.net/2434/168396.
Full textAbstract:
Nowadays a lot of physical techniques are available in order to have information about an historical painting. They are able to know which chemical elements are present in the paint layer, or they are also able to show the pattern under the colored layer, but there is not a non-destructive technique able to study the artist technique of painting or the historical pigments used in order to obtain the particular nuance we can observe.
In this thesis we study an evolution of the VIS-NIR-spectroscopic technique with these goals. In particular we start from a preliminary historical study of the artist’s pigments available for paintings and, starting from the colorimetric technique till the spectrophotometetric technique we create a representative pigments Database and we study a new method for pigment grindings identification, pigment’s mixture recognition and pigments layer technique studies.
The international method for color measurements provides the use of colorimeter but the sizes of these instruments don’t allow to perform measurements in any cases. Applications in the field of cultural heritage like as pigment characterizations on statues or ceramic and sometimes paintings don’t allow the use of integrating sphere. So we study the applicability of optical fiber for the realization of optical fiber remote probes. The use of optical fibre is consolidated for spectroscopy measurements (in particular for UV-VIS-NIR spectroscopy), but it may introduce an error in the detection of the spectra and, in consequence, a further error in the definition of the color of the sample analyzed. The first goal of this work was therefore the evaluation of compatibility of CIE colorimetric results obtained using Fiber Optic Reflectance Spectroscopy (FORS) and those values obtained with a standard colorimeter and then we extend the use of remote probe in NIR range for the measure of the pigments reflectance spectra.
After the test of the remote probe setup we have studied spectra obtained for well-defined mixtures of the most important artist’s pigments with the barium sulphate white pigment, the same used like standard sample for color measurements. These mixture simulate the different desaturation degree of the main color like in the earlier paintings techniques before Renaissance age. In order to have the best numerical characterization we fit the main behavior of the spectra using two analytical models: the Gaussian function (with 4 free parameters) and the sigmoidal function (with 4 free parameters): the comparison of their parameters allow to define the change of the spectra for different concentration of each colored pigment in the white one when thy were mixed in oil or without oil (as in oil renaissance paint technique and in the tipical affresco Middleage paint technique). Determination of weight mixture Pigment to white were: pigment pure, 1:1, 1:2, 1:5, 1:10, 1:20, 1:50. The pigments analyzed with sigmoidal fit were: Cadmium Red , Lacca rossa (Red Laquel), Chromium Orange, Cadmium Yellow, Naple Yellow (with Lead), Green Chromium oxide, Cobalt Blue, Cobalt Violet and Ultramarine Green. Pigments studied using Gaussian fit were: Green Chromium oxide, Cobalt Violet, Ultramarine Green, Verderame (Copper Green), Azurite, Ultramarine blue (artificial), Lapislazuli, Green earth, Malachite Green, Zinc Yellow and Cinabrum.
The next step is the research of a model able to study mixtures of two different colored pigments. The best historical model able to describe the interaction light-pigment was the Kubelka-Munk theory. In this model the Absorbtion (K) and Scattering (S) coefficients are needed. Here we study a method to obtain this coefficient from the reflectance spectra of the pigments and the spectra of their mixtures with barium sulphate and we use the same method in order to obtain the spectra of a mixture of two different colored pigment in an oil-paint layer.
The change of the color due to the presence of the oil is finest and the normal remote probes (optical fiber) setup is not enough to solve the differences, like it is not able for study the effect of the different grindings of the pigment’s powders. We realized an high-definition setup for reflectance measurements without optical fibers and a Fianium white LASER source (collaboration wit AMOLF-Fom institute, Amsterdam) creating also samples with characteristics nearest the Kubelka-Munk request. Using this high sensitivity setup we obtain the reflectance spectra for all the selected pigments and we study the difference of color due to the different grindings of the powder for the azurite and verderame pigments.
We were able also to study color differences of specific paint techniques of ancient painters: the “Italian” Renaissance technique based on the mixtures of color pigments and the “Flamish” technique based on the overlapping of translucent glaze of different color in order to obtain the deep in the paint and the famous sfumato.
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Phythian, Mark. "Speaker identification for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36079/3/__qut.edu.au_Documents_StaffHome_StaffGroupR%24_rogersjm_Desktop_36079_Digitised%20Thesis.pdf.
Full textAbstract:
A major application of Speaker Identification (SI) is suspect identification by voice. This thesis investigates techniques that can be used to improve SI technology as applied to suspect identification. Speech Coding techniques have become integrated into many of our modern
voice communications systems. This prompts the question - how are automatic speaker identification systems and modern forensic identification techniques affected by the introduction of digitally coded speech channels? Presented in this thesis are three separate studies investigating the effects of speech coding and compression on current speaker recognition techniques. A relatively new Spectral Analysis technique - Higher Order Spectral Analysis
(HOSA) - has been identified as a potential candidate for improving some aspects of forensic speaker identification tasks. Presented in this thesis is a study investigating the application of HOSA to improve the robustness of
current ASR techniques in the presence of additive Gaussian noise. Results from our investigations reveal that incremental improvements in each of these aspects related to automatic and forensic identification are achievable.
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Pradella, Lorenzo. "A data-driven prognostic approach based on AR identification and hidden Markov models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Find full textAbstract:
In this work a data-driven prognostic approach based on AutoRegressive (AR) estimation and hidden Markov models (HMMs) is addressed. In particular, the approach is capable of achieving Prognostic and Health Management (PHM) tasks such as real time detection and Remaining Useful Life (RUL) estimation. The approach can be seen as composed of a training part (offline)
and an exploitation part (online). The offline part relies upon the use of a scalar health indicator coming from the system identification field: the Itakura Saito (IS) spectral distance. In particular, raw acceleration data, gathered in an unsupervised framework from the machine, are modeled by AR processes and then transformed into IS. Then, HMMs are used to map such IS signals into a finite number of parameters. Moreover, in the training procedure of HMMs, a left-to-right clustering of unsupervised data, based on Mixture of Gaussians (MOG) distribution is proposed.
During the online exploitation a simulation of a running signal is tested against trained ones in order to carry out PHM tasks in real time.
Simulations have been performed using a public benchmark available in ”NASA prognostic data repository”. It contains run-to-failure tests on bearings, on which acceleration signals
are gathered. In particular the gathering experiment simulates an industry application, under constant operating conditions.
Results of simulations, performed on real time data, validate the proposed prognostic approach and make the combined use of IS an HMMs a reliable way in achieving PHM goals.
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Dušil, Lubomír. "Automatické rozpoznávání logopedických vad v řečovém projevu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218161.
Full textAbstract:
The thesis is aimed at an analysis and automatic detection of logopaedic defects in speech utterance. Its objective is to facilitate and accelerate the work of logopaedists and to increase percentage of detected logopaedic defects in children of the youngest possible age followed by the most successful treatment. It presents methods of speech work, classification of the defects within individual stages of child development and appropriate words for identification of the speech defects and their subsequent remedy. After that there are analyses of methods of calculating coefficients which reflect human speech best. Also classifiers which are used to discern and determine whether it is a speech defect or not. Classifiers exploit coefficients for their work. Coefficients and classifiers are being tested and their best combination is being looked for in order to achieve the highest possible success rate of the automatic detection of the speech defects. All the programming and testing jobs has been conducted in the Matlab programme.
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Nam, Anne-Marie. "Contribution de la RMN 13C à l’analyse des huiles végétales, huiles essentielles et résines (Olea europaea, Pinus halepensis et Cedrus atlantica)." Thesis, Corte, 2014. http://www.theses.fr/2014CORT0001/document.
Full textAbstract:
Les produits issus de la biomasse végétale connaissent depuis quelques années un succès grandissant dans de nombreuses industries (aromathérapie, cosmétique, parfums, etc.). L’objectif de notre étude était, d’une part de contribuer au développement des méthodes d’identification et de quantification des constituants des mélanges naturels par RMN 13C et RMN 1H (par exemple le squalène dans l’huile d’olive), et d’autre part, de contribuer à la caractérisation chimique de deux conifères introduits en Corse, le pin d’Alep (huile essentielle) et le cèdre de l’Atlas (huile essentielle, huile pyrolytique et résine).La première partie concerne la quantification du squalène présent dans l’huile d’olive. En effet, ce triterpène linéaire participe à la stabilité oxydative de l’huile d’olive et joue un rôle important dans la réduction des risques de certains cancers. Il s’agissait de mettre au point un protocole expérimental basé sur la RMN 1H ou 13C en utilisant un appareil de routine (9,4 Tesla). La quantification par RMN 13C s’est avérée fiable et elle a été appliquée à la quantification du squalène dans 25 échantillons d’huile d’olive de Corse.La seconde partie de nos travaux concerne la caractérisation chimique de l’huile essentielle de pin d’Alep introduit dans trois stations de Corse (Capo di Feno, Saleccia et Tre Padule de Suartone). L’analyse détaillée de l’huile essentielle de cônes, par combinaison des techniques chromatographiques et spectroscopiques, a permis d’identifier 48 composés. La composition chimique est largement dominée par les monoterpènes (Alpha-pinène et myrcène) et un sesquiterpène oléfinique ((E)-Béta-caryophyllène). La RMN 13C a permis d’identifier divers diterpènes, dont le 8,12-époxy-14-labdèn-13-ol. Ce composé est décrit pour la première fois dans l’huile essentielle de pin d’Alep. L’analyse de 15 échantillons d’huile essentielle de cônes a mis en évidence trois types de compositions : Alpha-pinène ; myrcène ; Alpha-pinène/myrcène. Enfin, l’analyse de 47 échantillons d’huile essentielle d’aiguilles, associée à un traitement statistique des données (ACP, AFD), a permis de mettre en évidence une variabilité chimique intraspécifique. Ainsi, les échantillons se répartissent en trois groupes selon leur teneur en (E)-Béta-caryophyllène (groupe I), en E-Béta-caryophyllène/Alpha-pinène/myrcène (groupe II) et en myrcène (groupe III).La dernière partie a été consacrée à l’étude de Cedrus atlantica. L’analyse détaillée d’une huile essentielle commerciale (du Maroc) a permis d’identifier 20 constituants, les composés majoritaires sont les Alpha-, Béta- et Gamma-himachalène. Par ailleurs, 7 composés absents de notre bibliothèque de données spectrales ont été identifiés par RMN 13C. Les compositions chimiques des huiles pyrolytiques artisanales du Maroc sont également dominées par les himachalènes et la (E)-Alpha-atlantone. Enfin, nous avons réalisé l’analyse qualitative et quantitative par RMN 13C, sans séparation préalable, de 28 échantillons de résine de C. atlantica récoltés dans les 5 forêts de Corse référencées par l’ONF (Bavella, Bonifato, Ospedale, Pineta et Vizzavona). A côté des acides résiniques, nous avons également identifié 3 lignanes (pinorésinol, laricirésinol et 9-acétate de laricirésinol). Pour l’ensemble de ces composés, nous avons mis en évidence trois types chimiques. Enfin, nous avons mis au point et validé un protocole expérimental de quantification des lignanes par RMN 1HNatural products isolated from plants acquired, in recent years, a growing success in many industries (aromatherapy, cosmetic, perfumes). The objective of our study was to contribute, on the one hand, to the development of methods involving 13C and 1H NMR for the identification and quantification of the components of natural mixtures, and on the other hand, to the chemical characterization of two conifers brought to Corsica, Aleppo pine (essential oil) and atlas cedar (essential oil, pyrolysis oil and resins).The first part concerned the quantification of squalene present in olive oil. Indeed, linear triterpene participated in the oxidative stability of olive oil and play an important role by decreasing some kind of cancers risks. It’s deal with to elaborate an experimental part based on NMR 1H and 13C by using routine apparatus (9.4 Tesla). Quantification by NMR 13C is reliable and have been applied to squalene quantification in 25 samples of Corsican olive oil.The second part of our work concerned the chemical characterization of the essential oil of Aleppo pine brought to Corsica in three different stations (Capo di Feno, Saleccia and Tre Padule de Suartone). A detailed analysis of cone essential oil, using the combination of chromatographic and spectroscopic techniques, leads to the identification of 48 compounds. Chemical composition is extensively dominated by monoterpens (Alpha-pinene and myrcene) and one olefinic sesquiterpen ((E)-Béta-caryophyllene). 13C NMR allowed to the identification of various diterpens, particularly the 8,12-epoxy-14-labden-13-ol. This compound is described for the first time in aleppo pine essential oil. Fifteen samples of cone essential oil were analysed allowing differentiating three kinds of compositions: Alpha-pinene; myrcene; Alpha-pinene/myrcene. Finally, analysis of 47 samples of needles essential oil, associated with statistical treatment of the results (PCA and PFA), suggested the occurrence of an intraspecific chemical variability. Samples are divided into three groups, based on their high contents in (E)-Béta-caryophyllene (groupe I), in (E)-Béta-caryophyllene/Alpha-pinene/myrcene (groupe II) and in myrcene (groupe III).The last part concerned the study of Cedrus atlantica. A detailed analysis of commercial essential oil leads to the identification of 20 constituents. Main compounds are Alpha-, Béta- and Gamma-himachalene. Moreover, seven compounds, not yet referenced in our home-made NMR spectral data library, were identified by NMR 13C. Chemical compositions of handwork pyrolysis oils from Morocco were also dominated by the himachalene’s and (E)--atlantone. Qualitative and quantitative analysis of 28 samples of Cedrus atlantica resins, obtained from the five Corsican forests referenced by the Office National des Forêts of Corsica (Bavella, Bonifato, Ospedale, Pineta and Vizzavona) have been done, without any step of chromatography. Beside resinic acids, we have been identified three lignans (pinoresinol, lariciresinol and lariciresinol-9-acetate). Qualitative and quantitative analysis of these compounds, leads to the occurrence of three types of chemical composition. Finally, we have developed and validated a method for quantification of lignans by 1H NMR
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George, Elizabeth Baby. "Hyperspectral remote sensing for soil property estimation in the context of spectral mixtures." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5995.
Full textAbstract:
The implementation of sustainable agricultural, hydrological, and environmental management entails an improved understanding of soil properties and its conditions at increasingly finer resolutions. In this context, the detailed reflectance spectra provided by hyperspectral sensors can be beneficially employed in acquiring information about the topsoil. The recent availability of hyperspectral data from newly launched sensors and the advancements in techniques for handling and analyzing hyperspectral images have given rise to a wealth of new research findings in soil monitoring and mapping. Though hyperspectral imaging spectroscopy has facilitated the mapping of soil properties at large scales with finer resolutions, it is limited to only bare soil pixels. This is because the presence of non-soil cover in the form of photosynthetic or non-photosynthetic vegetation in a pixel affects the reflectance spectra by causing a “spectral mixing effect”, which in turn would affect the performance of soil property estimation models. Hence it is essential to identify the bare soil pixels in a study area and address the issue of spectral mixtures prior to soil property mapping. In this context, the reported study aimed at addressing this issue of spectral mixtures to improve the knowledge base on topsoil properties, particularly clay content, in a study area characterized by heterogeneity in terms of soil type and fertility, area under cultivation, crop type and cropping system. This work focused on the following three objectives:
To analyze the effect of different spectral reduction strategies prior to endmember extraction in a linear spectral mixture model in terms of pixel reconstruction errors.
To propose a thresholding approach for bare soil identification using pixel soil fractions obtained from spectral unmixing prior to clay content mapping and compare it with the classic method of using spectral indices
To develop a novel soil fraction-based composite mapping approach to extend the spatial coverage of predicted clay maps.
Spectral mixture modelling is one of the most important techniques for classifying hyperspectral data at sub-pixel resolution and identifying spectrally pure endmembers for estimating their corresponding abundances is an important step in spectral unmixing. The application of spectral reduction techniques prior to endmember extraction for unmixing would optimize the process by increasing the sensitivity of the algorithms to the most distinctive and informative features of the dataset. The first part of the study compared different spectral reduction techniques prior to endmember extraction on six real hyperspectral datasets, including an Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) image over Indian sub-continent. Spectral reduction of the datasets were applied using both feature extraction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA), Minimum Noise Fraction (MNF), and a feature selection technique based on Partial Informational Correlation (PIC) measure, along with a no reduction case where all the spectral bands were used. A PIC based spectral reduction was employed for endmember extraction specifically in the context of spectral unmixing for the first time in this study. The locations of the endmembers were identified from these reduced datasets using four endmember extraction algorithms- Pixel Purity Index (PPI), N-FINDR, Automatic Target Generation Process (ATGP) and Vertex Component Analysis (VCA). The endmembers identified from the different combinations of spectral reduction and endmember extraction techniques were used for linear spectral unmixing of the original datasets. The performance of each of the combinations after unmixing were compared in terms of the pixel reconstruction errors and the computation times for each dataset. It was observed that the PIC based spectral reduction performed well in terms of reconstruction errors and computation times when combined with the N-FINDR endmember extraction algorithm. This approach could be recommended for spectral reduction in unmixing of datasets from heterogeneous study areas with similar endmember classes.
The second part of the study aimed to analyze the impact of bare soil pixel identification on clay content estimation using an airborne hyperspectral image. Two methods were tested for identifying the bare soil pixels (i) using a combination of two spectral indices, Normalized Difference Vegetation Index (NDVI) for identifying photosynthetic vegetation and Cellulose Absorption Index (CAI) for identifying non-photosynthetic vegetation and (ii) using soil fraction obtained from spectral unmixing using three endmembers: soil, photosynthetic and non-photosynthetic vegetation. The study used an AVIRIS-NG image and laboratory measured clay content of 272 soil samples acquired over an area of 300 sq. km, in the Gundlupet taluk, Karnataka, India. Two sets of bare soil pixels were identified using the two methods and partial least squares regression (PLSR) models were calibrated and validated to estimate the clay contents over the study area from each. The performances of the regression models and predicted clay content maps were analyzed and compared. It was observed that the PLSR model based on bare soil pixels identified by unmixing provided better performances (R2 of 0.61) than the one using spectral indices (R2 of 0.46) for validation, even though the area mapped was reduced by half (14.96% of the study area) as compared to the latter (30.31%). This study highlighted that an improvement in prediction performance comes at the cost of reduction in spatial coverage in mapping of clay content. Finally, the study also brought forth the need for studying other spectral perturbing factors such as rugosity (due to ploughing) which may explain the modest performances of the clay prediction models, even using a hyperspectral image characterized by high spatial and spectral resolutions.
The limited utility of imaging spectroscopy for topsoil property mapping requires approaches for maximizing the bare soil coverage so as to extend the mapped area. In this regard, a novel soil fraction-based composite mapping approach for clay content estimation from a single AVIRIS-NG image over heterogenous crop fields was proposed as the last part of this study. The approach classified the image pixels according to their soil fraction determined by spectral unmixing and assigned specific regression models to each soil fraction class to estimate clay content along with the prediction uncertainty. The composite clay map gave modest performances with R_val^2 ranging from 0.53 to 0.63 for soil fraction thresholds varying from >0.3 to >0.7 respectively and showed correct spatial pattern irrespective of the soil fraction classes. In addition, the effect of the soil fraction threshold on clay content estimation was analyzed by comparing the performances of regression models built using bare soil pixels identified from varying soil fraction thresholds. It was observed that the model performance increases with adoption of higher soil fraction thresholds in terms of an increase in R_val^2 and decrease in RMSEP values. The potential mapped area in terms of clay content in this study ranged from 10.39% to 52.81% with a soil fraction threshold of >0.7 and >0.3 respectively, and so the compositing approach allowed an extension of the mapped surface by 42.42%. The proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions where a combination of the proposed soil fraction-based thresholding and multi-temporal image compositing could significantly increase both the extent of the mapped area and associated prediction performances.
The proposed studies on combination of spectral reduction and endmember extraction techniques prior to unmixing, the bare soil identification analyses and soil fraction-based composite clay mapping approach are envisaged as a premise for future researchers to develop on. The scope of its applicability may be broadened and refined with the inclusion of more sensors, soil properties and analysis techniques. The proposed framework may thus be extended to incorporate newer study areas.
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Chang, Chia-Yuan, and 張嘉元. "Verification Of Linear Spectral Mixture Analysis Using Brain Phantom." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/35574176493284843699.
Full textAbstract:
碩士東海大學
資訊工程學系
100
The technique of Linear Spectral Unmixing, which is widely used in remote sensing, has recently been applied to analyze MRI images. Linear Spectral Unmixing is a technique normally used in Hyperspectral images to sense target sub-pixels and classify materials accordingly. In remote sensing, the dimensions of an object of interest are usually smaller than ground resolution. Due to an increased difficulty in identifying objects of a smaller dimension, subpixel object sensing can thus be used to recognize an object. The computer simulation brain images and real brain MRI scans are usually analyzed in the experiments of applying Linear Spectral Unmixing in MRI. Although there exists ground truth on the computer simulation data of brain tissues, an image cannot be created by subpixels in computer simulation images. It is impractical to quantize a real human brain from MRI; as such, the quantization process is completed by a professional physician. Thus, it is not yet verified that whether the method of Linear Spectral Unmixing achieves the sense of subpixels. In this research, a phantom has been made and its subpixels from MRI obtained to verify the sense and quantization capability of subpixel objects by the approach of Linear Spectral Unmixing. Compared with the real ratio in the phantom, the method of Linear Spectral Unmixing is able to obtain the percentages of different tissues. It verifies that the approach is able to resolve the defect associated with traditional image processing methods – that is, an inability to sense subpixel – and result in the effective quantization of main brain tissues.
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Liu, Chun-You, and 劉俊佑. "Multivariate Curve ResolutionApplied to Separate Mixture Spectra." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/36236544572438739636.
Full textAbstract:
碩士國立臺灣大學
電機工程學研究所
96
The multivariate curve resolution- alternative least square, MCR-ALS method is applied to separate metal nanoparticles mixture spectra. The mixture spectra were got from three kinds of methods. First is using Gaussian function to simulate the mixture spectra. Second is using darkfield microscopy system to measure the scattering spectra of gold, sliver nanoparticles and gold nanorods ,and then to do numerical mixture. Third is to do experimental mixture of metal nanoparticles, and then measure the scattering mixture spectra. The constraints conditions, non-negativity and unimodality are used in alternative least square. The concentration metrics represent the quantification information which play an important role in biomedical microarray and diagnosis.
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DI, NUZZO Cinzia. "Model selection and mixture approaches in the spectral clustering algorithm." Doctoral thesis, 2022. http://hdl.handle.net/11570/3222428.
Full textAbstract:
The central aim of this thesis is the study of spectral clustering. Spectral clustering is a well-known method in machine learning, here, it is dealt with from a statistical point of view.
Spectral clustering methods have become very popular for finding non-convex clusters of data. These methods are based on the graph theory, where the data are represented by the vertices in an undirected graph and the edges are weighted by the similarities between pairs of units. In particular, the spectral approach is based on the properties of the pairwise similarity matrix coming from a suitable kernel function and then the clustering problem is reformulated as a graph partition problem.
The spectral clustering algorithm does not work directly on the raw data but on the embedded data in a suitable feature space having a smaller (and even a very smaller) dimension than the space of the original data. This implies that this algorithm is a handy approach for handling high-dimensional data. However, the spectral clustering algorithm runs once two features have been selected as input: the number of clusters and the similarity measure that describes the local neighborhood relationships between the units. The similarity measure depends on the choice of a suitable kernel function based on some hyper-parameter(s). Throughout this thesis, the selection of the number of clusters and the selection of the kernel function will be referred to the model selection, even if there is no underlying probabilistic assumption on the data and therefore the term ``model selection" should be interpreted in a broad sense.
In this framework, the thesis focuses on research on two significant topics: model selection and applications to different kinds of data sets.
As regards the first topic, in this thesis, two criteria for the selection of the number of clusters and of the kernel function with its hyper-parameter(s) have been proposed. The first criterion combines different approaches taking information from different graphical and geometrical features deriving from the embedded data, and for this reason, I refer to this approach as joint-graphical. Secondly, some results in the framework of an automatic approach for the selection of the hyper-parameter(s) of the spectral clustering algorithm through mixture models according to the maximum likelihood approach have been presented. In this context, the spectral clustering algorithm is based on two main steps: the first step concerns essentially a data transformation with a dimensionality reduction based on an embedding coming from a kernel function; the second step concerns an appropriate clustering algorithm of the embedded data. Usually, the latter step is performed using the $k$-means algorithm, here, mixture models have been taken into account showing to be more robust approaches with respect to the choice of the hyper-parameter(s) that is in general quite critical.
The second topic analyzed concerns applications of spectral clustering in different kinds of data sets: text data, mixed-type data and three-way data.
The thesis is organized as follows. Chapter 1 is devoted to summarize the main results of the spectral clustering literature. In Chapter 2 some practical issues about the selection of kernel function and its hyper-parameters as well as the number of clusters proposed in the literature are presented. Chapter 3 concerns the document clustering and the model-based clustering of the embedded data is introduced; moreover, a new suitable kernel function for text clustering is also proposed. Chapter 4 presents an application about the mixed-type data and some categorical kernel functions are also introduced. Chapter 5 and Chapter 6 present methodological contributions regarding the model selection. More precisely, in Chapter 5 a joint-graphical method to select both the number of clusters and the hyper-parameter in the kernel function is proposed; while Chapter 6 concerns the proposal of an automatic method for the selection of the hyper-parameter(s) in the kernel function via Gaussian mixture model according to the maximum likelihood approach. Chapter 6 presents also original application of the spectral clustering technique in three-way data clustering. In Chapter 7 some ideas for future research are presented. Further examples and comparisons of the spectral clustering algorithms and kernel functions are provided in the Appendix A. Finally, in Appendix B, model-based approach to clustering is summarized.
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Shih, Pao-Ming, and 施保名. "High Pressure Structures of Ionic Liquid/ DMSO Mixtures via Infrared Spectra." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/19281921272231052312.
Full text
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Shih, Chieh-Lang, and 施介嵐. "Analyzing the Master Imagery of Taiwan Area with Spectral Mixture Analysis." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/50472061168631210658.
Full textAbstract:
碩士國立交通大學
土木工程系所
92
For the hyperspectral images, its feature of a large number of channels and narrow bandwidths, makes it feasible for spectral mixture analysis schemes. The statistics based maximum likelihood classification scheme suffers from the Hughes phenomena with the high dimensional imagery. In this study, both the spectral un-mixing with user defined end members and matched filter approach with the spectral libraries are experimented with the Master data collected for the Taiwan area.
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Jian, Ting-Chen, and 陳建廷. "MS/MS Assisted Library Searching of Mixture's Electron Impact Mass Spectra." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/46816325812745167114.
Full text
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Chou, Jen-Hsiang, and 周仁祥. "Study on Multi-walled Carbon Nanotubes Characterization Using Raman Spectral Analysis and Gassian Mixture Modeling." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/rz4z48.
Full textAbstract:
博士國立臺北科技大學
電機工程系博士班
101
Microwave plasma chemical vapor deposition (MPCVD) is used to grow various types of diamond films and carbon nanotubes at various temperatures. However, only actual experience is sufficiently reliable to obtain satisfactory plasma using MPCVD. Therefore, this study uses a reflected power sensor as a reference parameter and a charge-coupled device to observe the plasma image. Manufacturing parameters—such as gas flow rate, input microwave power, working distance, deposition time, chamber pressure, and substrate temperature—were all fixed to grow multi-walled carbon nanotubes (MWCNTs). Independent variables were controlled by adjustments of E-H tuner positions along the x- and y- axes, which directly affect plasma conditions. MWCNT quality indices, MWCNT aspect ratios, and the ID/IG intensity ratio of the MWCNT Raman spectra are considered to grow better-quality MWCNTs using the self-assembled MPCVD system. This study uses empirical mode decomposition and Gaussian mixture modeling (GMM) to analyze functionalized MWCNT Raman spectra. Raman spectra data can be represented by a linear combination of a trend and several Gaussian functions that can be used to characterize MWCNT D and G band patterns. The performance of the method is compared using the floating Gaussian-Lorentz fitting method performance. The results show that the proposed method performs good decomposition for identifying D and G band patterns in MWCNTs. Plasma modeling and control issues are important for MPCVD systems. The tunable reflected microwave power of the MPCVD system is crucial for controlling plasma shape and position. However, modeling the tunable reflected power of microwave plasma is highly complex and is poorly understood. This study uses a 2D GMM to model the microwave power distribution corresponding to the adjustable electromagnetic field. The estimated modeling results show that microwave power data can be simplified as a linear combination of certain Gaussian functions that provide a predictable and controlled basis for real-time tuning of manufacturing parameters and plasma sharpening. The experiment results show that each E-H tuner position fabricates highly reproducible MWCNT films after GMM.
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Lu, Shih-Chieh, and 呂仕傑. "Applications of Schiff Base Salen Compounds to Detect Metal Ions by Cholesteric Liquid Crystal Mixtures and CD Spectra." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/33719697358199821445.
Full textAbstract:
碩士國立交通大學
材料科學與工程學系所
102
We synthesize a series of compounds with Schiff base Salen ligand which have different chirality, because Salen structure can complex metal ions, we dope these compounds to cholesteric liquid crystals and make them become a liquid crystal chemosensor. The chemosensors can detect metal ions by changing the pitch of cholesteric liquid crystal phases, when the wavelength of reflected light shift, we can see the color of cholesteric liquid crystal phases change by eyes. These liquid crystal mixtures have specific selectivity, only complexing lithium ions (Li+) can change the color of cholesteric liquid crystal phase. The compound has different chirality would make the wavelength of reflected light have different shiht. When two chiral dopants have the same chirality, the color of cholesteric liquid crystal phases would change from green to red, but when two chiral dopants have different chirality, the color of cholesteric liquid crystal phases would change from green to blue. We use FT-IR spectrum to identify the complex ligand of molecule functional groups and use CD spectra to measure molecule chirality.
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Khiry, Manal Awad [Verfasser]. "Spectral mixture analysis for monitoring and mapping desertification processes in semi-arid areas in North Kordofan State, Sudan / Manal Awad Khiry." 2007. http://d-nb.info/98584843X/34.
Full text
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Jafari, Reza. "Arid land condition assessment and monitoring using mulitspectral and hyperspectral imagery." 2007. http://hdl.handle.net/2440/41431.
Full textAbstract:
Arid lands cover approximately 30% of the earth’s surface. Due to the broadness, remoteness, and harsh condition of these lands, land condition assessment and monitoring using ground-based techniques appear to be limited. Remote sensing imagery with its broad areal coverage, repeatability, cost and time-effectiveness has been suggested and used as an alternative approach for more than three decades. This thesis evaluated the potential of different remote sensing techniques for assessing and monitoring land condition of southern arid lands of South Australia. There were four specific objectives: 1) to evaluate vegetation indices derived from multispectral satellite imagery for prediction of vegetation cover; 2) to compare vegetation indices and field measurements for detecting vegetation changes and assessing land condition; 3) to examine the potential of hyperspectral imagery for discriminating vegetation components that are important in land management using unmixing techniques; and 4) to test whether spatial heterogeneity in land surface reflectance can provide additional information about land condition and effects of management on land condition. The study focused on Kingoonya and Gawler Soil Conservation Districts that were dominated by chenopod shrublands and low open woodlands over sand plains and dunes. The area has been grazed predominately by sheep for more than 100 years and land degradation or desertification due to overgrazing is evident in some parts of the region, especially around stock watering points. Grazing is the most important factor that influences land condition. Four full scenes of Landsat TM and ETM+ multispectral and Hyperion hyperspectral data were acquired over the study area. The imagery was acquired in dry seasons to highlight perennial vegetation cover that has an important role in land condition assessment and monitoring. Slope-based, distance-based, orthogonal transformation and plant-water sensitive vegetation indices were compared with vegetation cover estimates at monitoring points made by state government agency staff during the first Pastoral Lease assessments in 1991. To examine the performance of vegetation indices, they were tested at two scales: within two contrasting land systems and across broader regional landscapes. Of the vegetation indices evaluated, selected Stress Related Vegetation Indices using red, nearinfrared and mid-infrared bands consistently showed significant relationships with vegetation cover at both land system and landscape scales. Estimation of vegetation cover was more accurate within land systems than across broader regions. Total perennial and ephemeral plant cover was predicted best within land systems (R2=0.88), while combined vegetation, plant litter and soil cryptogam crust cover was predicted best at landscape scale (R2=0.39). The results of applying one of the stress related vegetation indices (STVI-4) to 1991 TM and 2002 ETM+ Landsat imagery to detect vegetation changes and to 2005 Landsat TM imagery to discriminate Land Condition Index (LCI) classes showed that it is an appropriate vegetation index for both identifying trends in vegetation cover and assessing land condition. STVI-4 highlighted increases and decreases in vegetation in different parts of the study area. The vegetation change image provided useful information about changes in vegetation cover resulting from variations in climate and alterations in land management. STVI-4 was able to differentiate all three LCI classes (poor, fair and good condition) in low open woodlands with 95% confidence level. In chenopod shrubland and Mount Eba country only poor and good conditions were separable spectrally. The application of spectral mixture analysis to Hyperion hyperspectral imagery yielded five distinct end-members: two associated with vegetation cover and the remaining three associated with different soils, surface gravel and stone. The specific identity of the image end-members was determined by comparing their mean spectra with field reflectance spectra collected with an Analytical Spectral Devices (ASD) Field Spec Pro spectrometer. One vegetation end-member correlated significantly with cottonbush vegetation cover (R2=0.89), distributed as patches throughout the study area. The second vegetation end-member appeared to map green and grey-green perennial shrubs (e.g. Mulga) and correlated significantly with total vegetation cover (R2=0.68). The soil and surface gravel and stone end-members that mapped sand plains, sand dunes, and surface gravel and stone did not show significant correlations with the field estimates of these soil surface components. I examined the potential of a spatial heterogeneity index, the Moving Standard Deviation Index (MSDI), around stock watering points and nearby ungrazed reference sites. One of the major indirect effects of watering points in a grazed landscape is the development around them of a zone of extreme degradation called a piosphere. MSDI was applied to Landsat red band for detection and assessment of these zones. Results showed watering points had significantly higher MSDI values than non-degraded reference areas. Comparison of two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Perpendicular Distance vegetation index (PD54), which were used as reference indices, showed that the PD54 was more sensitive than NDVI for assessing land condition in this perennial-dominated arid environment. Piospheres were found to be more spatially heterogeneous in land surface reflectance. They had higher MSDI values compared to non-degraded areas, and spatial heterogeneity decreased with increasing distance from water points. The study has demonstrated overall that image-based indices derived from Landsat multispectral and Hyperion hyperspectral imagery can be used with field methods to assess and monitor vegetation cover (and consequently land condition) of southern arid lands of South Australia in a quick and efficient way. Relationships between vegetation indices, end-members and field measurements can be used to estimate vegetation cover and monitor its variation with time in broad areas where field-based methods are not effective. Multispectral vegetation indices can be used to assess and discriminate ground-based land condition classes. The sandy-loam end-member extracted from Hyperion imagery has high potential for monitoring sand dunes and their movement over time. The MSDI showed that spatial heterogeneity in land surface reflectance can be used as a good indicator of land degradation. It differentiated degraded from nondegraded areas successfully and detected grazing gradients slightly better than widely used vegetation indices. Results suggest further research using these remote sensing techniques is warranted for arid land condition assessment and monitoring in South Australia.http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1295218
Thesis (Ph.D.) -- School of Earth and Environmental Science, 2007
43
Jafari, Reza. "Arid land condition assessment and monitoring using mulitspectral and hyperspectral imagery." Thesis, 2007. http://hdl.handle.net/2440/41431.
Full textAbstract:
Arid lands cover approximately 30% of the earth’s surface. Due to the broadness, remoteness, and harsh condition of these lands, land condition assessment and monitoring using ground-based techniques appear to be limited. Remote sensing imagery with its broad areal coverage, repeatability, cost and time-effectiveness has been suggested and used as an alternative approach for more than three decades. This thesis evaluated the potential of different remote sensing techniques for assessing and monitoring land condition of southern arid lands of South Australia. There were four specific objectives: 1) to evaluate vegetation indices derived from multispectral satellite imagery for prediction of vegetation cover; 2) to compare vegetation indices and field measurements for detecting vegetation changes and assessing land condition; 3) to examine the potential of hyperspectral imagery for discriminating vegetation components that are important in land management using unmixing techniques; and 4) to test whether spatial heterogeneity in land surface reflectance can provide additional information about land condition and effects of management on land condition.
The study focused on Kingoonya and Gawler Soil Conservation Districts that were dominated by chenopod shrublands and low open woodlands over sand plains and dunes. The area has been grazed predominately by sheep for more than 100 years and land degradation or desertification due to overgrazing is evident in some parts of the region, especially around stock watering points. Grazing is the most important factor that influences land condition. Four full scenes of Landsat TM and ETM+ multispectral and Hyperion hyperspectral data were acquired over the study area. The imagery was acquired in dry seasons to highlight perennial vegetation cover that has an important role in land condition assessment and monitoring.
Slope-based, distance-based, orthogonal transformation and plant-water sensitive vegetation indices were compared with vegetation cover estimates at monitoring points made by state government agency staff during the first Pastoral Lease assessments in 1991. To examine the performance of vegetation indices, they were tested at two scales: within two contrasting land systems and across broader regional landscapes. Of the vegetation indices evaluated, selected Stress Related Vegetation Indices using red, nearinfrared and mid-infrared bands consistently showed significant relationships with vegetation cover at both land system and landscape scales. Estimation of vegetation cover was more accurate within land systems than across broader regions. Total perennial and ephemeral plant cover was predicted best within land systems (R2=0.88), while combined vegetation, plant litter and soil cryptogam crust cover was predicted best at landscape scale (R2=0.39).
The results of applying one of the stress related vegetation indices (STVI-4) to 1991 TM and 2002 ETM+ Landsat imagery to detect vegetation changes and to 2005 Landsat TM imagery to discriminate Land Condition Index (LCI) classes showed that it is an appropriate vegetation index for both identifying trends in vegetation cover and assessing land condition. STVI-4 highlighted increases and decreases in vegetation in different parts of the study area. The vegetation change image provided useful information about changes in vegetation cover resulting from variations in climate and alterations in land management. STVI-4 was able to differentiate all three LCI classes (poor, fair and good condition) in low open woodlands with 95% confidence level. In chenopod shrubland and Mount Eba country only poor and good conditions were separable spectrally.
The application of spectral mixture analysis to Hyperion hyperspectral imagery yielded five distinct end-members: two associated with vegetation cover and the remaining three associated with different soils, surface gravel and stone. The specific identity of the image end-members was determined by comparing their mean spectra with field reflectance spectra collected with an Analytical Spectral Devices (ASD) Field Spec Pro spectrometer. One vegetation end-member correlated significantly with cottonbush vegetation cover (R2=0.89), distributed as patches throughout the study area. The second vegetation end-member appeared to map green and grey-green perennial shrubs (e.g. Mulga) and correlated significantly with total vegetation cover (R2=0.68). The soil and surface gravel and stone end-members that mapped sand plains, sand dunes, and surface gravel and stone did not show significant correlations with the field estimates of these soil surface components.
I examined the potential of a spatial heterogeneity index, the Moving Standard Deviation Index (MSDI), around stock watering points and nearby ungrazed reference sites. One of the major indirect effects of watering points in a grazed landscape is the
development around them of a zone of extreme degradation called a piosphere. MSDI was applied to Landsat red band for detection and assessment of these zones. Results showed watering points had significantly higher MSDI values than non-degraded reference areas. Comparison of two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Perpendicular Distance vegetation index (PD54), which were used as reference indices, showed that the PD54 was more sensitive than NDVI for assessing land condition in this perennial-dominated arid environment. Piospheres were found to be more spatially heterogeneous in land surface reflectance. They had higher MSDI values compared to non-degraded areas, and spatial heterogeneity decreased with increasing distance from water points.
The study has demonstrated overall that image-based indices derived from Landsat multispectral and Hyperion hyperspectral imagery can be used with field methods to assess and monitor vegetation cover (and consequently land condition) of southern arid lands of South Australia in a quick and efficient way. Relationships between vegetation indices, end-members and field measurements can be used to estimate vegetation cover and monitor its variation with time in broad areas where field-based methods are not effective. Multispectral vegetation indices can be used to assess and discriminate ground-based land condition classes. The sandy-loam end-member extracted from Hyperion imagery has high potential for monitoring sand dunes and their movement over time. The MSDI showed that spatial heterogeneity in land surface reflectance can be used as a good indicator of land degradation. It differentiated degraded from nondegraded areas successfully and detected grazing gradients slightly better than widely used vegetation indices. Results suggest further research using these remote sensing techniques is warranted for arid land condition assessment and monitoring in South Australia.Thesis (Ph.D.) -- University of Adelaide, School of Earth and Environmental Sciences, 2007
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DULINA, ANASTASIIA. "Multivariate analysis in the study of the chemical processes." Doctoral thesis, 2019. http://hdl.handle.net/11573/1348225.
Full textAbstract:
The classical problem of the determination of the spectral components from a data set is very often a badly conditioned problem without univocal solutions. Very often one deals with experimental data which are a superposition, or mixture, of the pure spectra of the individual components and their associated proportions. When dealing with evolving systems such as chemical reactions several compounds are coexisting and if one collects UV-Vis or any other kind of absorption spectra they will be the overimposition of the singles spectra associated with the chemical species present in the reaction bath with an intensity that depends on their concentration profiles. In this case it is very important to be able to extract each single component that is the absorption spectrum associated with each species, and the concentration evolution with time. More than often, this decomposition is aimed at situations for which little a priori information is available. Several theoretical approaches have been developed to achieve this goal and the multivariate analysis (MA) is one of the most promising approach. The multivariate curve resolution (MCR) is the generic denomination of a family of MA methods meant to solve the mixture analysis problem that is able to provide a chemically meaningful additive bilinear model of pure contributions from the sole information of an original data matrix including a mixed measurement. MA methods are key-tools in order to extract the pure component information (pure component spectra and the concentration profiles) from the chemical mixture (spectroscopic) data. The problem is to compute: 1) the number of independent components s and 2) the pure component factors C (concentration profiles) and A (spectra). An actuality of the problem of the determination of the spectral components from a chemical-physical data is driven by its broad range of applications in situations where a reasonable approximation of the bilinear model, or any other fundamental basic equation that has the same mathematical structure, holds. Also, the bilinear model can be extended for the analysis of multiple data sets that are meant to connect different experiments together. The main goal of the thesis is to develop and apply an innovative MCR approach for the multivariate analysis of chemical systems. In particular, this method has been developed to have an ad hoc tool to be used for chemical reactions for which a combined UV-Vis and X-ray absorption (XAS) investigation is carried out. The final goal is to extract the UV-Vis and XAS spectra associated with all the intermediates that are formed during bimolecular chemical reactions occurring in the ms time range. In particular, in the first part of the thesis I have implemented the non negative matrix factorization (NMF) method to a newly written code, PyFitIt, that has been specifically developed for the multivariate analysis of XAS spectra. This method has been then applied to different sets of experimental data collected on different bimolecular chemical reactions in solution for which UV-Vis and XAS spectra were collected simultaneously. By using this new approach it was possible to extract, from the initial experimental data sets, both the XAS spectra and the concentration profiles of the intermediate species that were formed during the reaction. From the analysis of the X-ray absorption near edge structure (XANES) spectra it will be possible to determine the oxidation state of the absorption element, the nature of the short-lived intermediates that are formed during the reaction, and their three-dimensional structure with a picometric accuracy. To test the reliability of the entire procedure ten different systems have been investigated and the principal components and concentration profiles have been successfully extracted for all the investigated systems. The new procedure and code that has been developed in this thesis will be a valid tool that can be applied to different XAS data sets in the future.
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Kubečková, Jana. "Mapování vybraných druhů hornin vrcholových partií Krkonoš s využitím laboratorní a obrazové spektroskopie." Master's thesis, 2013. http://www.nusl.cz/ntk/nusl-323456.
Full textAbstract:
Laboratory and image spectroscopy for mapping of selected rocks in peak areas of the Krkonoše Mountains Abstract This thesis deals with geological mapping of selected rocks in peak areas of the Krkonoše Mountains. Four areas of interest were situated in two parts of Krkonše Mountains - on the west side it is the area of Vysoké kolo and Harrachovy kameny and on the east side there is the area of Sněžka and the area of Kozí hřbety. The main data were acquired by the hyperspectral sensor APEX. Ground spectral measurments of selected rocks and block fields were executed and the laboratory spectral measurments of geological samples and lichens were executed. Practical part aims at classification of rocks and lichens in selected areas using four classification methods: SAM, SID, MESMA and LSU. The spectral library is one of the outputs of this thesis. This spectral library contains the spectra of pure rocks and lichens and mixtured spectra of rocks and lichens. The output of this thesis is the comparation of used classification methods, the analysis of spatial and geological accuracy and evaluation of lichens influence on the classification results, spectral library and maps of classified rocks occurrence. Keywords: classification, block fields, hyperspectral data, spectral mixture, lichens, The Krkonoše Mountains
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Falk, Tiago. "Blind Estimation of Perceptual Quality for Modern Speech Communications." Thesis, 2008. http://hdl.handle.net/1974/1642.
Full textAbstract:
Modern speech communication technologies expose users to perceptual quality degradations that were not experienced earlier with conventional telephone systems. Since perceived speech quality is a major contributor to the end user's perception of quality of service, speech quality estimation has become an important research field. In this dissertation, perceptual quality estimators are proposed for several emerging speech communication applications, in particular for i) wireless communications with noise suppression capabilities, ii) wireless-VoIP communications, iii) far-field hands-free speech communications, and iv) text-to-speech systems.
First, a general-purpose speech quality estimator is proposed based on statistical models of normative speech behaviour and on innovative techniques to detect multiple signal distortions. The estimators do not depend on a clean reference signal hence are termed ``blind." Quality meters are then distributed along the network chain to allow for both quality degradations and quality enhancements to be handled. In order to improve estimation performance for wireless communications, statistical models of noise-suppressed speech are also incorporated.
Next, a hybrid signal-and-link-parametric quality estimation paradigm is proposed for emerging wireless-VoIP communications. The algorithm uses VoIP connection parameters to estimate a base quality representative of the packet switching network. Signal-based distortions are then detected and quantified in order to adjust the base quality accordingly. The proposed hybrid methodology is shown to overcome the limitations of existing pure signal-based and pure link parametric algorithms.
Temporal dynamics information is then investigated for quality diagnosis for hands-free speech communications. A spectro-temporal signal representation, where speech and reverberation tail components are shown to be separable, is used for blind characterization of room acoustics. In particular, estimators of reverberation time, direct-to-reverberation energy ratio, and reverberant speech quality are developed.
Lastly, perceptual quality estimation for text-to-speech systems is addressed. Text- and speaker-independent hidden Markov models, trained on naturally produced speech, are used to capture normative spectral-temporal information. Deviations from the models, computed by means of a log-likelihood measure, are shown to be reliable indicators of multiple quality attributes including naturalness, fluency, and intelligibility.Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-12-22 14:54:49.28
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Chatterjee, Saikat. "Rate-Distortion Performance And Complexity Optimized Structured Vector Quantization." Thesis, 2008. https://etd.iisc.ac.in/handle/2005/1056.
Full textAbstract:
Although vector quantization (VQ) is an established topic in communication, its practical utility has been limited due to (i) prohibitive complexity for higher quality and bit-rate, (ii) structured VQ methods which are not analyzed for optimum performance, (iii) difficulty of mapping theoretical performance of mean square error (MSE) to perceptual measures. However, an ever increasing demand for various source signal compression, points to VQ as the inevitable choice for high efficiency. This thesis addresses all the three above issues, utilizing the power of parametric stochastic modeling of the signal source, viz., Gaussian mixture model (GMM) and proposes new solutions. Addressing some of the new requirements of source coding in network applications, the thesis also presents solutions for scalable bit-rate, rate-independent complexity and decoder scalability.
While structured VQ is a necessity to reduce the complexity, we have developed, analyzed and compared three different schemes of compensation for the loss due to structured VQ. Focusing on the widely used methods of split VQ (SVQ) and KLT based transform domain scalar quantization (TrSQ), we develop expressions for their optimum performance using high rate quantization theory. We propose the use of conditional PDF based SVQ (CSVQ) to compensate for the split loss in SVQ and analytically show that it achieves coding gain over SVQ. Using the analytical expressions of complexity, an algorithm to choose the optimum splits is proposed. We analyze these techniques for their complexity as well as perceptual distortion measure, considering the specific case of quantizing the wide band speech line spectrum frequency (LSF) parameters. Using natural speech data, it is shown that the new conditional PDF based methods provide better perceptual distortion performance than the traditional methods.
Exploring the use of GMMs for the source, we take the approach of separately estimating the GMM parameters and then use the high rate quantization theory in a simplified manner to derive closed form expressions for optimum MSE performance. This has led to the development of non-linear prediction for compensating the split loss (in contrast to the linear prediction using a Gaussian model). We show that the GMM approach can improve the recently proposed adaptive VQ scheme of switched SVQ (SSVQ). We derive the optimum performance expressions for SSVQ, in both variable bit rate and fixed bit rate formats, using the simplified approach of GMM in high rate theory.
As a third scheme for recovering the split loss in SVQ and reduce the complexity, we propose a two stage SVQ (TsSVQ), which is analyzed for minimum complexity as well as perceptual distortion. Utilizing the low complexity of transform domain SVQ (TrSVQ) as well as the two stage approach in a universal coding framework, it is shown that we can achieve low complexity as well as better performance than SSVQ. Further, the combination of GMM and universal coding led to the development of a highly scalable coder which can provide both bit-rate scalability, decoder scalability and rate-independent low complexity. Also, the perceptual distortion performance is comparable to that of SSVQ.
Since GMM is a generic source model, we develop a new method of predicting the performance bound for perceptual distortion using VQ. Applying this method to LSF quantization, the minimum bit rates for quantizing telephone band LSF (TB-LSF) and wideband LSF (WB-LSF) are derived.
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Chatterjee, Saikat. "Rate-Distortion Performance And Complexity Optimized Structured Vector Quantization." Thesis, 2008. http://hdl.handle.net/2005/1056.
Full textAbstract:
Although vector quantization (VQ) is an established topic in communication, its practical utility has been limited due to (i) prohibitive complexity for higher quality and bit-rate, (ii) structured VQ methods which are not analyzed for optimum performance, (iii) difficulty of mapping theoretical performance of mean square error (MSE) to perceptual measures. However, an ever increasing demand for various source signal compression, points to VQ as the inevitable choice for high efficiency. This thesis addresses all the three above issues, utilizing the power of parametric stochastic modeling of the signal source, viz., Gaussian mixture model (GMM) and proposes new solutions. Addressing some of the new requirements of source coding in network applications, the thesis also presents solutions for scalable bit-rate, rate-independent complexity and decoder scalability.
While structured VQ is a necessity to reduce the complexity, we have developed, analyzed and compared three different schemes of compensation for the loss due to structured VQ. Focusing on the widely used methods of split VQ (SVQ) and KLT based transform domain scalar quantization (TrSQ), we develop expressions for their optimum performance using high rate quantization theory. We propose the use of conditional PDF based SVQ (CSVQ) to compensate for the split loss in SVQ and analytically show that it achieves coding gain over SVQ. Using the analytical expressions of complexity, an algorithm to choose the optimum splits is proposed. We analyze these techniques for their complexity as well as perceptual distortion measure, considering the specific case of quantizing the wide band speech line spectrum frequency (LSF) parameters. Using natural speech data, it is shown that the new conditional PDF based methods provide better perceptual distortion performance than the traditional methods.
Exploring the use of GMMs for the source, we take the approach of separately estimating the GMM parameters and then use the high rate quantization theory in a simplified manner to derive closed form expressions for optimum MSE performance. This has led to the development of non-linear prediction for compensating the split loss (in contrast to the linear prediction using a Gaussian model). We show that the GMM approach can improve the recently proposed adaptive VQ scheme of switched SVQ (SSVQ). We derive the optimum performance expressions for SSVQ, in both variable bit rate and fixed bit rate formats, using the simplified approach of GMM in high rate theory.
As a third scheme for recovering the split loss in SVQ and reduce the complexity, we propose a two stage SVQ (TsSVQ), which is analyzed for minimum complexity as well as perceptual distortion. Utilizing the low complexity of transform domain SVQ (TrSVQ) as well as the two stage approach in a universal coding framework, it is shown that we can achieve low complexity as well as better performance than SSVQ. Further, the combination of GMM and universal coding led to the development of a highly scalable coder which can provide both bit-rate scalability, decoder scalability and rate-independent low complexity. Also, the perceptual distortion performance is comparable to that of SSVQ.
Since GMM is a generic source model, we develop a new method of predicting the performance bound for perceptual distortion using VQ. Applying this method to LSF quantization, the minimum bit rates for quantizing telephone band LSF (TB-LSF) and wideband LSF (WB-LSF) are derived.