Both the maximum agreement forest problem and the multicut on trees problem are NP-hard, thus cannot be solved efficiently if P /=NP. The maximum agreement forest problem was motivated in the study of evolution trees in bioinformatics, in which we are given two leaf-labeled trees and are asked to find a maximum forest that is a subgraph of both trees. The multicuton trees problem has applications in networks, in which we are given a forest and a set of pairs of termianls and are asked to find a cut that separates all pairs of terminals. We develop combinatorial and algorithmic techniques that lead to improved parameterized algorithms, approximation algorithms, and kernelization algorithms for these problems. For the maximum agreement forest problem, we proceed from the bottommost level of trees and extend solutions to whole trees. With this technique, we show that the maxi- mum agreement forest problem is fixed-parameterized tractable in general trees, resolving an open problem in this area. We also provide the first constant ratio approximation algorithm for the problem in general trees. For the multicut on trees problem, we take a new look at the problem through the eyes of vertex cover problem. This connection allows us to develop an kernelization algorithm for the problem, which gives an upper bound of O(k3) on the kernel size, significantly improving the previous best upper bound O(k6). We further exploit this connection to give a parameterized algorithm for the problem that runs in time O∗ (1.62k), thus improving the previous best algorithm of running time O∗ (2k). In the protein complex prediction problem, which comes directly from the study of bioinformatics, we are given a protein-protein interaction network, and are asked to find dense regions in this graph. We formulate this problem as a graph clustering problem and develop an algorithm to refine the results for identifying protein complexes. We test our algorithm on yeast protein- protein interaction networks, and we show that our algorithm is able to identify complexes more accurately than other existing algorithms.

A single-chip, hybrid, heterogeneous, and dynamic shared memory multiprocessor architecture is being developed which may be used for real-time and non-real-time applications. This architecture can execute any application described by a dataflow (process flow) graph of any topology; it can also dynamically reconfigure its structure at the node and processor architecture levels and reallocate its resources to maximize performance and to increase reliability and fault tolerance. Dynamic change in the architecture is triggered by changes in parameters such as application input data rates, process execution times, and process request rates. The architecture is a Hybrid Data/Command Driven Architecture (HDCA). It operates as a dataflow architecture, but at the process level rather than the instruction level. This thesis focuses on the development, testing and evaluation of a new graphic software (hdca) developed to first do a static resource allocation for the architecture to meet timing requirements of an application and then hdca simulates the architecture executing the application using statically assigned resources and parameters. While simulating the architecture executing an application, the software graphically and dynamically displays parameters and mechanisms important to the architectures operation and performance. The new graphical software is able to show system and node level dynamic capability of the HDCA. The newly developed software can model a fixed or varying input data rate. The model also allows fault tolerance analysis of the architecture.

This thesis focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances.

Sensor failures or oversupply in wireless sensor networks (WSNs), especially initial random deployment, create both spare sensors (whose area is fully covered by other sensors) and sensing holes. We envision a team of robots to relocate sensors and improve their area coverage. Existing algorithms, including centralized ones and the only localized G-R3S2, move only spare sensors and have limited improvement because non-spare sensors, with area coverage mostly overlapped by neighbour sensors, are not moved, and additional sensors are deployed to fill existing holes. We propose a localized algorithm, called Localized Ant-based Sensor Relocation Algorithm with Greedy Walk (LASR-G), where each robot may carry at most one sensor and makes decision that depends only on locally detected information. In LASR-G, each robot calculates corresponding pickup or dropping probability, and relocates sensor with currently low coverage contribution to another location where sensing hole would be significantly reduced. The basic algorithm optimizes only area coverage, while modified algorithm includes also the cost of robot movement. We compare LASR-G with G-R3S2, and examine both single robot and multi robots scenarios. The simulation results show the advantages of LASR-G over G-R3S2.

The task assignment problem is one of the fundamental combinatorial optimization problems. It has been extensively studied in operation research, management science, computer science and robotics. Task assignment problems arise in various applications of multi-robot systems (MRS), such as environmental monitoring, disaster response, extraterrestrial exploration, sensing data collection and collaborative autonomous manufacturing. In these MRS applications, there are realistic constraints on robots and tasks that must be taken into account both from the modeling perspective and the algorithmic perspective. From the modeling aspect, such constraints include (a) Task group constraints: where tasks form disjoint groups and each robot can be assigned to at most one task in each group. One example of the group constraints comes from tightly-coupled tasks, where multiple micro tasks form one tightly-coupled macro task and need multiple robots to perform each simultaneously. (b) Task deadline constraints: where tasks must be assigned to meet their deadlines. (c) Dynamically-arising tasks: where tasks arrive dynamically and the payoffs of future tasks are unknown. Such tasks arise in scenarios like searchrescue, where new victims are found dynamically. (d) Robot budget constraints: where the number of tasks each robot can perform is bounded according to the resource it possesses (e.g., energy). From the solution aspect, there is often a need for decentralized solution that are implemented on individual robots, especially when no powerful centralized controller exists or when the system needs to avoid single-point failure or be adaptive to environmental changes. Most existing algorithms either do not consider the above constraints in problem modeling, are centralized or do not provide formal performance guarantees. In this thesis, I propose methods to address these issues for two classes of problems, namely, the constrained linear assignment problem and constrained generalized assignment problem. Constrained linear assignment problem belongs to P, while constrained generalized assignment problem is NP-hard. I develop decomposition-based distributed auction algorithms with performance guarantees for both problem classes. The multi-robot assignment problem is decomposed into an optimization problem for each robot and each robot iteratively solving its own optimization problem leads to a provably good solution to the overall problem. For constrained linear assignment problem, my approaches provides an almost optimal solution. For constrained generalized assignment problem, I present a distributed algorithm that provides a solution within a constant factor of the optimal solution. I also study the online version of the task allocation problem with task group constraints. For the online problem, I prove that a repeated greedy version of my algorithm gives solution with constant factor competitive ratio. I include simulation results to evaluate the average-case performance of the proposed algorithms. I also include results on multi-robot cooperative package transport to illustrate the approach.

The performance of communication and storage systems can be improved if the data being sent or stored has certain patterns and structure. In particular, some benefit if the frequency of the symbols is balanced. This includes magnetic and optical data storage devices, as well as future holographic storage systems. Significant research has been done to develop techniques and algorithms to adapt the data (in a reversible manner) to these systems. The goal has been to restructure the data to improve performance while keeping the complexity as low as possible. In this thesis, we consider balancing binary sequences and present its application in holographic storage systems. An overview is given of different approaches, as well as a survey of previous balancing methods. We show that common compression algorithms can be used for this purpose both alone and combined with other balancing algorithms. Simplified models are analyzed using information theory to determine the extent of the compression in this context. Simulation results using standard data are presented as well as theoretical analysis for the performance of the combination of compression with other balancing algorithms. / Graduate

The thesis introduces a new technique that is an alternative method instead of applying deltas to literal file sequentially to retrieve a version from a repository. To my best knowledge / this is the first investigation about delta combination for copy/insert instruction type with many experimental results and conclusions. The thesis proves that the delta combination eliminates unnecessary I/O process for intermediate versions when delta application is considered, therefore reduces I/O time. Deltas are applied to literal sequentially to generate the required version in the classical way. Replace algorithm combines delta files which would be applied in delta application as combined delta, and applies it to literal to generate the required one. Apply runs in O (size (D)) time where D is the destination file and size (D) is its size. To retrieve nth version in a chain where 1st version is literal, it requires n-1 time apply. Replace algorithm runs in O (i + c * log2 n) time where i is the total length of all inserts, c is the total length of all copies in destination delta, and n is the number of instructions in source delta. To retrieve the same nth version, it requires n-2 time replace and one apply.

No description available.

This thesis identifies some problems, the genetic algorithm (GA) is facing in the area of vehicle routing and proposes various methods to address those problems. Those problems arise from the unavailability of suitable chromosomal representation and evaluation schemes of GA for the Vehicle Routing Problem (VRP). The representation and evaluation schemes already in use have problems of high computational cost, illegal chromosomes (chromosomes not representing a legal tour) and wrong fitness assignment (fitness not truly representing chromosome genetic makeup). These problems are addressed by several proposed new schemes, namely the Self Imposed Constraints Evaluation scheme, the Contour and Reverse Contour Evaluation schemes and the Order Skipping Evaluation scheme, which are specifically tailored for various objectives, problems and situations. Apart from this, a methodology, which has previously being used in other meta-heuristics, is incorporated into GA i.e., the independent application of GA on various sub-localities of the problem. We call this GA, a Localized Genetic Algorithm (LGA). LGA is an iterative procedure between optimization and controlled de-optimization. The procedure of controlled de-optimization is also novel. It brings the solution into a new search space while controlling its cost effectively. LGA is introduced with various search techniques, i.e. intensive, extensive and selective, the use of which depends on the problem size and the availability of computational resources. Furthermore, search reduction techniques (Fitness Approximation Methods) are also introduced into the LGA, which has enabled the LGA to be applied to large scale problems. Due to the implementation of those proposals, LGA is the first GA-driven approach to be applied to very large scale CVRP problems of up to 1200 customers, i.e. datasets presented by Feiyue in 2005 and large scale VRPTW problems of up to 1000 customers, datasets presented by Gehring and Homberger in 1999. Lastly, a standard unit for computational comparison, i.e., Bellman's Evaluation Units BEUs, is also introduced to facilitate computational comparisons for future researchers. LGA has shown promising results on CVRP and VRPTW problems. It is flexible and also has the potential to be extended to not only other vehicle routing problems, but also to other ordering problems.

Made available in DSpace on 2014-06-11T19:27:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-02-23Bitstream added on 2014-06-13T19:26:10Z : No. of bitstreams: 1 oliveira_de_me_sjrp.pdf: 1040006 bytes, checksum: 88dd8fa849febafe8d0aa9bf32892235 (MD5) / O principal objetivo deste trabalho é apresentar, discutir as qualidades e desempenho e provar a convergência de um método iterativo para a solução numérica do problema de autovalores de uma matriz, que chamamos de Método Assemelhado ao de Francis (MAF). O método em questão distingue-se do QR de Francis pela maneira, mais simples e rápida, de se obter as matrizes ortogonais Qk, k = 1; 2. Apresentamos, também, uma comparação entre o MAF e os algoritmos QR de Francis e LR de Rutishauser. / The main purpose of this work is to presente, to discuss the qualities and performance and to prove the convergence of an iterative method for the numerical solution of the eigenvalue problem, that we have called the Método Assemelhado ao de Francis (MAF)þþ. This method di ers from the QR method of Francis by providing a simpler and faster technique of getting the unitary matrices Qk; k = 1; 2; We present, also, a comparison analises between the MAF and the QR of Francis and LR of Rutishauser algorithms.