We describe heterogeneous multi-CPU and multi-GPU implementations of Jacobi's iterative method for the 2-D Poisson equation on a structured grid, in both single- and double-precision. Properly tuned, our best implementation achieves 98% of the empirical streaming GPU bandwidth (66% of peak) on a NVIDIA C1060. Motivated to find a still faster implementation, we further consider "wildly asynchronous" implementations that can reduce or even eliminate the synchronization bottleneck between iterations. In these versions, which are based on the principle of a chaotic relaxation (Chazan and Miranker, 1969), we simply remove or delay synchronization between iterations, thereby potentially trading off more flops (via more iterations to converge) for a higher degree of asynchronous parallelism. Our relaxed-synchronization implementations on a GPU can be 1.2-2.5x faster than our best synchronized GPU implementation while achieving the same accuracy. Looking forward, this result suggests research on similarly "fast-and-loose" algorithms in the coming era of increasingly massive concurrency and relatively high synchronization or communication costs.

The work describes techniques for solving systems of linear and differential equations. It explains the definition of conversion from system of linear to system of differential equations. The method of the elementary transmission and the transform algorithm are presented. Both of methods are demonstrated on simply examples and properties of conversion are shown. The work compares fast and accurate solutions of methods and algorithm. For computing examples and solving experiments following programs were used: TKSL and TKSL/C. The program TKSL/C was enriched with the graphic user interface which makes the conversion of systems and computing results easier.

<p>Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density.</p><p>In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis.</p><p>Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems.</p><p>However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images.</p><p>Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.</p>

Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density. In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis. Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems. However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images. Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.

Made available in DSpace on 2016-06-02T20:29:25Z (GMT). No. of bitstreams: 1 5508.pdf: 3679937 bytes, checksum: b0fcf6b5bde2860e350a71f742a54627 (MD5) Previous issue date: 2013-08-28 / Financiadora de Estudos e Projetos / The development of this work aims to aims to show the importance of the application of problem situations for the learning of equations and systems of first degree equations with two variables by students of the 8th grade level in paper and pencil environment and observation of two-pan balance. The proposal is to show the application of solving problem which involves first-degree equations with one unknown and systems of first-degree equation with two unknowns, in order to encourage the students to think, state the solution of the problem, try to overcome the learning difficulties, facing challenges which requires a great effort and dedication and find out by themselves the best strategy which must be used in order to solve the problem and, after this, compare and present the algorithms defined for the solution of these concepts. It is also presented the solution of these problems using the twopan balance method, the addition and substitution method, as well as the method of solving equations and equation systems with two unknowns. / O desenvolvimento deste trabalho tem como objetivo mostrar a importância da aplicação de situações-problemas para a aprendizagem de equações e sistemas de equações do primeiro grau com duas variáveis por alunos do 8° ano do ensino fundamental em ambiente papel e lápis e observação de balança de dois pratos. A proposta é mostrar a aplicação de resolução de problemas que envolva equações do primeiro grau com uma incógnita e sistema de equações do primeiro grau com duas incógnitas, a fim de incentivar os alunos a pensarem, encaminharem a solução do problema, tentarem superar as dificuldades de aprendizagem, enfrentarem desafios que exijam grande esforço e dedicação e descubram por si só a melhor estratégia que deve ser utilizada para o problema ser resolvido e, em seguida, comparar e apresentar os algoritmos definidos para a resolução destes conceitos. É apresentada também, a resolução destes problemas usando o método da balança de dois pratos, o método da adição e o método da substituição como método de resolução de equação e sistemas de equações com duas incógnitas.

科学研究費補助金 研究種目:総合研究(A) 課題番号:04302008 研究代表者:三井 斌友 研究期間:1992-1994年度