Material de segunda unidad del curso de Arquitectura de Computadoras.

Made available in DSpace on 2014-06-11T19:26:47Z (GMT). No. of bitstreams: 0 Previous issue date: 2001-11Bitstream added on 2014-06-13T18:55:12Z : No. of bitstreams: 1 pellison_at_me_botfca.pdf: 865523 bytes, checksum: 45905d1e1ffa2ddf25b178c1bed38c6f (MD5) / A drenagem do solo tem por objetivo a remoção por meios artificiais, do excesso de água acumulado, tornando o solo mais aproveitável ou adequado às atividades do homem. A ação de irrigação e drenagem subterrânea, com base em parâmetros hidrodinâmicos do solo, poderá, sem dúvida, levar a erros grosseiros. Para evitar tais erros, sempre que possível, utilizam-se dados sobre as condições obtidas em experimentos de campo, sobre as condições de flutuação do nível freático. A instrumentação para medida do nível freático à distância, cujos dados possam ser prontamente usados para controle automático da irrigação, poderá assegurar a produção econômica no curto prazo e, no longo prazo, a proteção ambiental. Com o avanço tecnológico na área de automação voltada às atividades agrícolas, nesta pesquisa propõe-se o desenvolvimento de um projeto para automatização de controle do nível freático da várzea, através de poços de observação instalados nesta área, que pertencente ao Departamento de Engenharia Rural da FCA, utilizando-se de um Controlador Lógico Programável e sistemas de transmissão de dados. Esta proposta fornece condições de avaliar o controle do nível do lençol freático em situações de sub-irrigação e drenagem controlada permitindo o manejo e controle racional do uso de água, evitando desperdícios, com armazenamento do excedente. / The soil drainage aims for the removal of the accumulated excess of water by artificial means, turning the soil more profitable or adapted to human’s activities. The irrigation system and the subsurface drainage together, in a meaning of hydrodynamic parameters of the soil, do can lead to erros. To correct such errors, when possible, is necessary to use data obtained from field experiments under flotation conditions of the water table level. The instrumentation for measurement of the water table at distance, whose data can be ready used for automated irrigation control, can assure the economic production at a short period and, at a long period, the enviromental protection. With the tecnological progress in the automation area turned to agricultural activities, this work aims for the design of an automated project to control the floodplain (varzea) water table, through observation wells installed in that area which belong to the Rural Engineering Department of the Faculty of Agronomic Sciences, using the Programmable Logical Controller and data transmissions systems. This proposal provides conditions to evaluate the water table at subsurface and controlled drainage conditions, allowing the management and rational control of water use, avoiding water wastage with storage of the surplus.

Made available in DSpace on 2015-08-20T17:09:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-11-11. Added 1 bitstream(s) on 2015-08-20T17:26:54Z : No. of bitstreams: 1 000846221.pdf: 1635996 bytes, checksum: 3433248530a7bdc61a9bdbec1086772b (MD5) / A geração eficiente de implicantes primos é um fator importante na fase de cobertura dos mintermos em métodos de minimização de funções booleanas. Este trabalho apresenta uma versão aprimorada do método denominado de Clause-Column Table, utilizado na geração de implicantes primos. Neste novo algoritmo adicionou-se o teorema da adjacência e um novo critério de parada. Estas modificações evitaram a geração de termos nulos e iterações desnecessárias que ocorriam no algoritmo original. O algoritmo original e o aprimorado foram implementados em linguagem C e comparados. O método Clause-Column Table Aprimorado também foi comparado com o método Quine-McCluskey e Expander. Os resultados comprovaram que a versão aprimorada gera menos iterações que a versão original, e que na maioria das funções analisadas evitou-se a geração de termos nulos. Ao comparar com o método de Quine-McCluskey e o Expander comprovou-se que o método Clause-Column Table Aprimorado é superior na geração dos implicantes primos, pois em alguns casos elimina aqueles que não são necessários para a cobertura da função. De posse dos implicantes primos o problema de cobertura dos mintermos foi formulado como um problema de programação linear inteira 0 e 1, em que a solução se abre a todos os avanços ocorridos na área de programação linear visando a obtenção de uma solução mínima / Efficient generation of prime implicants is an important factor in the coverage phase of minterms in minimization's methods of Boolean functions. This research presents an improved version of the method called Clause-Column Table, used to generate prime implicants. In this new algorithm was added to the adjacency theorem and a new stopping criterion. These modifications prevented the generation of null terms and unnecessary iterations that occurred in the original algorithm. The original and improved algorithms were implemented in C language and compared. The Clause-Column Table Improved method was compared with the Expander and Quine-McCluskey method. The results proved that the improved version generates fewer iterations than the original version, and that in most functions analyzed it was avoided the generation of null terms. Comparing Quine-McCluskey method and the Expander it was proved that the Clause-Column Table Enhanced method is superior in the generation of prime implicants, since in some cases eliminates those who are not required to cover the function. In ownership of the prime implicants the cover problem of minterms was formulated as an integer linear programming problem of 0 and 1, where the solution is open to all advances in the area of linear programming in order to obtain a minimal solution

Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD- heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem.

Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD- heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem.

Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD- heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem.