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You searched for subject:(Singular value decompositions). Showing records 1 – 3 of 3 total matches.

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KTH

1. Fransson, Erik Johannes Aagaard. Building and Evaluating a 3D Scanning System for Measurementsand Estimation of Antennas and Propagation Channels.

Degree: Electromagnetic Engineering, 2012, KTH

Wireless communications rely, among other things, on theunderstanding of the properties of the radio propagationchannel, the antennas and their interplay. Adequate measurementsare required to verify theoretical models and togain knowledge of the channel behavior and antenna performance.As a result of this master thesis we built a 3D fieldscanner measurement system to predict multipath propagationand to measure antenna characteristics. The 3Dscanner allows measuring a signal at the point of interestalong a line, on a surface or within a volume in space. In orderto evaluate the system, we have performed narrowbandchannel sounding measurements of the spatial distributionof waves impinging at an imaginary spherical sector. Datawas used to estimate the Angle-of-Arrivals (AoA) and amplitudeof the waves. An estimation method is presented tosolve the resulting inverse problem by means of regularizationwith truncated singular value decomposition. The regularizedsolution was then further improved with the helpof a successive interference cancellation algorithm. Beforeapplying the method to measurement data, it was testedon synthetic data to evaluate its performance as a functionof the noise level and the number of impinging waves. Inorder to minimize estimation errors it was also required tofind the phase center of the horn antenna used in the channelmeasurements. The task was accomplished by directmeasurements and by the regularization method, both resultsbeing in good agreement.

Subjects/Keywords: 3D field scanner; radio propagation channel; virtual array; MIMO; Angle-of-Arrival; regularization; inverse problem; L-curve; truncated singular value decompositions; successive interference cancellation; horn antenna; phase center.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Fransson, E. J. A. (2012). Building and Evaluating a 3D Scanning System for Measurementsand Estimation of Antennas and Propagation Channels. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104802

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Fransson, Erik Johannes Aagaard. “Building and Evaluating a 3D Scanning System for Measurementsand Estimation of Antennas and Propagation Channels.” 2012. Thesis, KTH. Accessed May 07, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104802.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Fransson, Erik Johannes Aagaard. “Building and Evaluating a 3D Scanning System for Measurementsand Estimation of Antennas and Propagation Channels.” 2012. Web. 07 May 2021.

Vancouver:

Fransson EJA. Building and Evaluating a 3D Scanning System for Measurementsand Estimation of Antennas and Propagation Channels. [Internet] [Thesis]. KTH; 2012. [cited 2021 May 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104802.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Fransson EJA. Building and Evaluating a 3D Scanning System for Measurementsand Estimation of Antennas and Propagation Channels. [Thesis]. KTH; 2012. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104802

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universidade do Estado do Rio de Janeiro

2. Mauro de Amorim. Decomposição aleatória de matrizes aplicada ao reconhecimento de faces.

Degree: Master, 2013, Universidade do Estado do Rio de Janeiro

Métodos estocásticos oferecem uma poderosa ferramenta para a execução da compressão de dados e decomposições de matrizes. O método estocástico para decomposição de matrizes estudado utiliza amostragem aleatória para identificar um subespaço que captura a imagem de uma matriz de forma aproximada, preservando uma parte de sua informação essencial. Estas aproximações compactam a informação possibilitando a resolução de problemas práticos de maneira eficiente. Nesta dissertação é calculada uma decomposição em valores singulares (SVD) utilizando técnicas estocásticas. Esta SVD aleatória é empregada na tarefa de reconhecimento de faces. O reconhecimento de faces funciona de forma a projetar imagens de faces sobre um espaço de características que melhor descreve a variação de imagens de faces conhecidas. Estas características significantes são conhecidas como autofaces, pois são os autovetores de uma matriz associada a um conjunto de faces. Essa projeção caracteriza aproximadamente a face de um indivíduo por uma soma ponderada das autofaces características. Assim, a tarefa de reconhecimento de uma nova face consiste em comparar os pesos de sua projeção com os pesos da projeção de indivíduos conhecidos. A análise de componentes principais (PCA) é um método muito utilizado para determinar as autofaces características, este fornece as autofaces que representam maior variabilidade de informação de um conjunto de faces. Nesta dissertação verificamos a qualidade das autofaces obtidas pela SVD aleatória (que são os vetores singulares à esquerda de uma matriz contendo as imagens) por comparação de similaridade com as autofaces obtidas pela PCA. Para tanto, foram utilizados dois bancos de imagens, com tamanhos diferentes, e aplicadas diversas amostragens aleatórias sobre a matriz contendo as imagens.

Stochastic methods offer a powerful tool for performing data compression and decomposition of matrices. These methods use random sampling to identify a subspace that captures the range of a matrix in an approximate way, preserving a part of its essential information. These approaches compress the information enabling the resolution of practical problems efficiently. This work computes a singular value decomposition (SVD) of a matrix using stochastic techniques. This random SVD is employed in the task of face recognition. The face recognition is based on the projection of images of faces on a feature space that best describes the variation of known image faces. These features are known as eigenfaces because they are the eigenvectors of a matrix constructed from a set of faces. This projection characterizes an individual face by a weighted sum of eigenfaces. The task of recognizing a new face is to compare the weights of its projection with the projection of the weights of known individuals. The principal components analysis (PCA) is a widely used method for determining the eigenfaces. This provides the greatest variability eigenfaces representing information from a set of faces. In this dissertation we discuss the quality of…

Advisors/Committee Members: Gustavo Mendes Platt, Luiz Mariano Paes de Carvalho Filho, Carlos Eduardo de Souza, Ricardo Fabbri, Francisco Duarte Moura Neto.

Subjects/Keywords: PROBABILIDADE E ESTATISTICA APLICADAS; Decomposição em valores singulares; Análise de componentes principais; Autofaces; Reconhecimento de faces; Decomposição aproximada de matrizes; Métodos estocásticos; Singular value decomposition; Principal component analysis; Eigenfaces; Face recognition; Approximate matrix decompositions; Stochastic methods

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Amorim, M. d. (2013). Decomposição aleatória de matrizes aplicada ao reconhecimento de faces. (Masters Thesis). Universidade do Estado do Rio de Janeiro. Retrieved from http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=4819 ;

Chicago Manual of Style (16th Edition):

Amorim, Mauro de. “Decomposição aleatória de matrizes aplicada ao reconhecimento de faces.” 2013. Masters Thesis, Universidade do Estado do Rio de Janeiro. Accessed May 07, 2021. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=4819 ;.

MLA Handbook (7th Edition):

Amorim, Mauro de. “Decomposição aleatória de matrizes aplicada ao reconhecimento de faces.” 2013. Web. 07 May 2021.

Vancouver:

Amorim Md. Decomposição aleatória de matrizes aplicada ao reconhecimento de faces. [Internet] [Masters thesis]. Universidade do Estado do Rio de Janeiro; 2013. [cited 2021 May 07]. Available from: http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=4819 ;.

Council of Science Editors:

Amorim Md. Decomposição aleatória de matrizes aplicada ao reconhecimento de faces. [Masters Thesis]. Universidade do Estado do Rio de Janeiro; 2013. Available from: http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=4819 ;


University of Texas – Austin

3. -1155-8213. Addressing challenges in modeling of coupled flow and poromechanics in deep subsurface reservoirs.

Degree: PhD, Engineering Mechanics, 2018, University of Texas – Austin

In coupled flow and poromechanics phenomena representing hydrocarbon production or CO₂ sequestration in deep subsurface non-fractured reservoirs, the spatial domain in which fluid flow occurs is usually much smaller than the spatial domain over which significant deformation occurs. The vertical extent of the poromechanical domain can be two orders of magnitude more than the characteristic thickness of the flow domain (reservoir). The lateral extent of the poromechanical domain should also be allowed to be substantially larger than that of the flow domain to enable the imposition of far-field boundary conditions on the poromechanical domain. The typical approach is to either impose an overburden pressure directly on the reservoir thus treating it as a coupled problem domain or to model flow on a huge domain with zero permeability cells to mimic the no flow boundary condition on the interface of the reservoir and the surrounding rock. The former approach precludes a study of land subsidence or uplift and further does not mimic the true effect of the overburden on stress sensitive reservoirs whereas the latter approach has huge computational costs. The flow domain requires an areal resolution fine enough to be able to capture the underlying nonlinearities in the multiphase flow equations. If the same grid resolution is employed for the poromechanical domain, the simulator would crash for lack of memory and computing resource. With that in mind, it is imperative to establish a framework in which fluid flow is resolved on a finer grid and poromechanical deformation is resolved on a coarse grid. In addition, the geometry of the flow domain necessitates the use of non-nested grids which allows for freedom of choice of the poromechanical grid resolution. Furthermore, to achieve the goal of rendering realistic simulations of subsurface phenomena, we cannot ignore the heterogeneity in flow and poromechanical properties, as well as the lack in accuracy of the poromechanical calculations if the grid for the poromechanics domain is too coarse. This dissertation is a rendition of how we invoke concepts in computational geometry, parallel computing, applied mathematics and convex optimization in designing and implementing algorithms that tackle all the aforementioned challenges. Advisors/Committee Members: Wheeler, Mary F. (Mary Fanett) (advisor), Landis, Chad (committee member), Huang, Rui (committee member), Balhoff, Matthew (committee member).

Subjects/Keywords: Fixed-stress split iterative scheme; Overlapping nonmatching hexahedral grids; Upscaling and downscaling; Singular value decompositions; Surface intersections; Delaunay triangulations; Mandel’s problem; Biot system; Heterogeneous poroelastic medium; Nested two-grid approach; Contraction mapping; Anisotropic poroelastic medium; Computational homogenization; Hanging nodes; Augmented Lagrangian

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-1155-8213. (2018). Addressing challenges in modeling of coupled flow and poromechanics in deep subsurface reservoirs. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2120

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-1155-8213. “Addressing challenges in modeling of coupled flow and poromechanics in deep subsurface reservoirs.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed May 07, 2021. http://dx.doi.org/10.26153/tsw/2120.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-1155-8213. “Addressing challenges in modeling of coupled flow and poromechanics in deep subsurface reservoirs.” 2018. Web. 07 May 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-1155-8213. Addressing challenges in modeling of coupled flow and poromechanics in deep subsurface reservoirs. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2021 May 07]. Available from: http://dx.doi.org/10.26153/tsw/2120.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-1155-8213. Addressing challenges in modeling of coupled flow and poromechanics in deep subsurface reservoirs. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://dx.doi.org/10.26153/tsw/2120

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

.