In this dissertation, we focus on two related parts of a 3D face recognition system with wireless transportation. In the first part, the core components of the system, namely, the feature extraction and classification component, are introduced. In the feature extraction component, range images are taken as inputs and processed in order to extract features. The classification component uses the extracted features as inputs and makes classification decisions based on trained classifiers. In the second part, we consider the wireless transportation problem of range images, which are captured by scattered sensor nodes from target objects and are forwarded to the core components (i.e., feature extraction and classification components) of the face recognition system. Contrary to the conventional definition of being a transducer, a sensor node can be a person, a vehicle, etc. The wireless transportation component not only brings flexibility to the system but also makes the “proactive” face recognition possible. For the feature extraction component, we first introduce the 3D Morphable Model. Then a 3D feature extraction algorithm based on the 3D Morphable Model is presented. The algorithm is insensitive to facial expression. Experimental results show that it can accurately extract features. Following that, we discuss the generic face warping algorithm that can quickly extract features with high accuracy. The proposed algorithm is robust to holes, facial expressions and hair. Furthermore, our experimental results show that the generated features can highly differentiate facial images. For the classification component, a classifier based on Mahalanobis distance is introduced. Based on the classifier, recognition performances of the extracted features are given. The classification results demonstrate the advantage of the features from the generic face warping algorithm. For the wireless transportation of the captured images, we consider the location-based wireless sensor networks (WSN). In order to achieve efficient routing perfor¬mance, a set of distributed stateless routing protocols (PAGER) are proposed for wireless sensor networks. The loop-free and delivery-guaranty properties of the static version (PAGER-S) are proved. Then the performance of PAGER protocols are compared with other well-known routing schemes using network simulator 2 (NS2). Simulation results demonstrate the advantages of PAGER.

When a large number of base stations fail due to the breakdown of some transmission circuit in a mobile cellular network, base stations located in neighboring areas may take over those malfunctioned base stations and continue to provide the access service of mobile communications for users in surrounding areas, thereby reducing the area in which mobile communications are out of service. Therefore, if leased circuits in base stations could complete the route distribution configuration prior to the onset of malfunction, it could decrease the impact of circuit breakdown and traffic loss. Also, the efficiency would be improved if the circuit assignment personnel could complete the job when the leased lines are less, while avoiding reassignment in the future and enhancing the mobile communications operations. In this study, we use a graph structure to represent the present mobile cellular network and establish the route-selection strategies. We define the "Optimal Route Assignment" for a newly constructed base station, which refers to the route assignment that causes least impact on disconnection area when any circuit in the network is broken. We also propose to use A* algorithm for optimal route assignment. However, the computation for the optimal route is time consuming. Measures such as computation time and least hops are considered in designing other strategies for route assignment. These strategies are parametric and we carried out experiments by adjusting and controlling parameters using real routing data. The experimental results demonstrate that there is no single winner among the proposed strategies. We identify a number of best strategies for different operating regions.

Every knapsack problem may be relaxed to a cyclic group problem. In 1969, Gomory found the subadditive characterization of facets of the master cyclic group problem. We simplify the subadditive relations by the substitution of complementarities and discover a minimal representation of the subadditive polytope for the master cyclic group problem. By using the minimal representation, we characterize the vertices of cardinality length 3 and implement the shooting experiment from the natural interior point. The shooting from the natural interior point is a shooting from the inside of the plus level set of the subadditive polytope. It induces the shooting for the knapsack problem. From the shooting experiment for the knapsack problem we conclude that the most hit facet is the knapsack mixed integer cut which is the 2-fold lifting of a mixed integer cut. We develop a cutting plane algorithm augmenting cutting planes generated by shooting, and implement it on Wong-Coppersmith digraphs observing that only small number of cutting planes are enough to produce the optimal solution. We discuss a relaxation of shooting as a clue to quick shooting. A max flow model on covering space is shown to be equivalent to the dual of shooting linear programming problem.

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This dissertation is concerned with the problem of estimating travel time per links in urban context using sparsely sampled GPS data. One of the challenges in this thesis is use the sparsely sampled data. A part of this research work, i developed a digital map with its new geographic information system (GIS), dealing with map-matching problem, where we come out with an enhancement tecnique, and also the shortest path problem.The thesis research work was conduct within the project PUMAS, which is an avantage for our research regarding the collection process of our data from the real world field and also in making our tests. The project PUMAS (Plate-forme Urbaine de Mobilité Avancée et Soutenable / Urban Platform for Sustainable and Advanced Mobility) is a preindustrial project that has the objective to inform about the traffic situation and also to develop an implement a platform for sustainable mobility in order to evaluate it in the region, specifically Rouen, France. The result is a framework for any traffic controller or manager and also estimation researcher to access vast stores of data about the traffic estimation, forecasting and status.

The Constrained Shortest Path Problem (CSPP) consists of finding the shortest path in a graph or network that satisfies one or more resource constraints. Without these constraints, the shortest path problem can be solved in polynomial time; with them, the CSPP is NP-hard and thus far no polynomial-time algorithms exist for solving it optimally. The problem arises in a number of practical situations. In the case of vehicle path planning, the vehicle may be an aircraft flying through a region with obstacles such as mountains or radar detectors, with an upper bound on the fuel consumption, the travel time or the risk of attack. The vehicle may be a submarine travelling through a region with sonar detectors, with a time or risk budget. These problems all involve a network which is a discrete model of the physical domain. Another example would be the routing of voice and data information in a communications network such as a mobile phone network, where the constraints may include maximum call delays or relay node capacities. This is a problem of current economic importance, and one for which time-sensitive solutions are not always available, especially if the networks are large. We consider the simplest form of the problem, large grid networks with a single side constraint, which have been studied in the literature. This thesis explores the application of Constraint Programming combined with Lagrange Relaxation to achieve optimal or near-optimal solutions of the CSPP. The following is a brief outline of the contribution of this thesis. Lagrange Relaxation may or may not achieve optimal or near-optimal results on its own. Often, large duality gaps are present. We make a simple modification to Dijkstra’s algorithm that does not involve any additional computational work in order to generate an estimate of path time at every node. / We then use this information to constrain the network along a bisecting meridian. The combination of Lagrange Relaxation (LR) and a heuristic for filtering along the meridian provide an aggressive method for finding near-optimal solutions in a short time. Two network problems are studied in this work. The first is a Submarine Transit Path problem in which the transit field contains four sonar detectors at known locations, each with the same detection profile. The side constraint is the total transit time, with the submarine capable of 2 speeds. For the single-speed case, the initial LR duality gap may be as high as 30%. The first hybrid method uses a single centre meridian to constrain the network based on the unused time resource, and is able to produce solutions that are generally within 1% of optimal and always below 3%. Using the computation time for the initial Lagrange Relaxation as a baseline, the average computation time for the first hybrid method is about 30% to 50% higher, and the worst case CPU times are 2 to 4 times higher. The second problem is a random valued network from the literature. Edge costs, times, and lengths are uniform, randomly generated integers in a given range. Since the values given in the literature problems do not yield problems with a high duality gap, the values are varied and from a population of approximately 100,000 problems only the worst 200 from each set are chosen for study. These problems have an initial LR duality gap as high as 40%. A second hybrid method is developed, using values for the unused time resource and the lower bound values computed by Dijkstra’s algorithm as part of the LR method. The computed values are then used to position multiple constraining meridians in order to allow LR to find better solutions. / This second hybrid method is able to produce solutions that are generally within 0.1% of optimal, with computation times that are on average 2 times the initial Lagrange Relaxation time, and in the worst case only about 5 times higher. The best method for solving the Constrained Shortest Path Problem reported in the literature thus far is the LRE-A method of Carlyle et al. (2007), which uses Lagrange Relaxation for preprocessing followed by a bounded search using aggregate constraints. We replace Lagrange Relaxation with the second hybrid method and show that optimal solutions are produced for both network problems with computation times that are between one and two orders of magnitude faster than LRE-A. In addition, these hybrid methods combined with the bounded search are up to 2 orders of magnitude faster than the commercial CPlex package using a straightforward MILP formulation of the problem. Finally, the second hybrid method is used as a preprocessing step on both network problems, prior to running CPlex. This preprocessing reduces the network size sufficiently to allow CPlex to solve all cases to optimality up to 3 orders of magnitude faster than without this preprocessing, and up to an order of magnitude faster than using Lagrange Relaxation for preprocessing. Chapter 1 provides a review of the thesis and some terminology used. Chapter 2 reviews previous approaches to the CSPP, in particular the two current best methods. Chapter 3 applies Lagrange Relaxation to the Submarine Transit Path problem with 2 speeds, to provide a baseline for comparison. The problem is reduced to a single speed, which demonstrates the large duality gap problem possible with Lagrange Relaxation, and the first hybrid method is introduced. / Chapter 4 examines a grid network problem using randomly generated edge costs and weights, and introduces the second hybrid method. Chapter 5 then applies the second hybrid method to both network problems as a preprocessing step, using both CPlex and a bounded search method from the literature to solve to optimality. The conclusion of this thesis and directions for future work are discussed in Chapter 6.

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2015-07-17T15:18:49Z No. of bitstreams: 1 Wilson da Silva Lourenco.pdf: 1321079 bytes, checksum: ea090b0df77d0c04ef1dde30e7b41558 (MD5) / Made available in DSpace on 2015-07-17T15:18:49Z (GMT). No. of bitstreams: 1 Wilson da Silva Lourenco.pdf: 1321079 bytes, checksum: ea090b0df77d0c04ef1dde30e7b41558 (MD5) Previous issue date: 2015-02-26 / The network optimization problems (NOP) are common to several areas such as engineering, transport and telecommunications, and have been objects of intense research and studies. Among the classical NOP are the problems of Shortest Path (SPP), Max Flow (MFP) and Traveling Salesman (TSP), which are usually studied in undergraduate and graduate courses such as Industrial Engineering, Computer Science, Information Systems and Logistics, with the use of resources such as chalk and blackboard that hinder the teacher's work, in the sense of showing the functioning of algorithms that solve these problems while maintaining students' motivation for learning. In this context, it is proposed in this research, a computational tool, characterized as a Learning Object (OA) and called TASNOP - Teaching Algorithms for Solving Network Optimization Problems, whose purpose is to contribute to students' understanding about concepts from NOP and, mainly, the functioning of algorithms A*, Greedy Search and Dijkstra used for resolution of SPP, Ford-Fulkerson employed in the resolution of MFP and the Nearest Neighbor to solve the TSP. It is important to highlight that the proposed OA can be accessed through web and also employed in distance learning environments (DLE). Experiments conducted in 2014 with 129 students of Computer Science, from which 51 performed an exercise using the TASNOP and 78 without this tool, confirm that students who used the TASNOP performed better in solving the proposed exercise, corroborating the idea that the OA helped to improve their understanding about the algorithms discussed in this research. In addition, the 51 students who employed the TASNOP answered a questionnaire about it use and, the answers indicated that the TASNOP shows a potential to be used as a learning support tool. / Os problemas de otimização em redes (POR) são comuns a diversas áreas como engenharia, transportes e telecomunicações, e têm sido objetos de intensas pesquisas e estudos. Entre os POR clássicos estão os problemas de Caminho Mínimo (PCM), Fluxo Máximo (PFM) e Caixeiro Viajante (PCV), os quais normalmente são estudados em cursos de graduação e pós-graduação tais como Engenharia de Produção, Ciência da Computação, Sistemas de Informação e Logística, com a utilização de recursos como giz e lousa, o que dificulta o trabalho do professor, no sentido de mostrar o funcionamento dos algoritmos que solucionam esses problemas, mantendo a motivação dos alunos para a aprendizagem. Neste contexto, propõe-se nesta pesquisa, uma ferramenta computacional, caracterizada como um Objeto de Aprendizagem (OA) denominado TASNOP - Teaching Algorithms for Solving Network Optimization Problems, cuja finalidade é contribuir para compreensão dos alunos sobre conceitos de POR e, principalmente, sobre o funcionamento dos algoritmos A*, Busca Gulosa, e Dijkstra, usados para resolução do PCM, Ford-Fulkerson empregado na resolução de PFM e o algoritmo Vizinho mais Próximo para resolução do PCV. É importante ressaltar que o OA proposto pode ser acessado via web e, inclusive, ser acoplado em ambientes de ensino a distância (EaD). Experimentos realizados no ano de 2014 envolvendo 129 alunos do curso de Ciência da Computação, dos quais 51 resolveram um exercício com o uso do TASNOP e 78 sem o seu uso, permitiram verificar que os alunos que utilizaram o TASNOP obtiveram melhor desempenho na resolução do exercício proposto, corroborando a ideia de que o OA contribuiu para melhorar suas compreensões acerca dos algoritmos abordados nesta pesquisa. Em adição, os 51 alunos que usaram o TASNOP responderam a um questionário sobre o seu uso e, com base nessas respostas, ficou evidente o potencial do TASNOP como uma ferramenta de apoio ao ensino.

O Problema dos K Menores Caminhos é uma generalização do Problema do Menor Caminho, em que desejamos encontrar os K caminhos de menor custo entre dois vértices de um grafo. Estudamos e implementamos algoritmos que resolvem esse problema em grafos dirigidos, com peso nos arcos e que permitem apenas caminhos sem repetição de vértices na resposta. Comparamos seus desempenhos utilizando grafos do 9th DIMACS Implementation Challenge. Identificamos os pontos fortes e fracos de cada algoritmo, e propusemos uma variante híbrida dos algoritmos de Feng e de Pascoal. Essa variante proposta obteve desempenho superior aos algoritmos base em alguns grafos, e resultado superior a pelo menos um deles na grande maioria dos testes. / The K-Shortest Path Problem is a generalization of the Shortest Path Problem, in which we must find the K paths between two vertices in a graph that have the lowest costs. We study some K-Shortest Path Problem algorithms applied to weighted directed graphs, allowing only paths with no repeated vertices. We compare empirically implementation of some algorithms, using instance graphs from the 9th DIMACS Implementation Challenge. We identify the strengths and weaknesses of each algorithm, and we propose a hybrid version of Feng\'s and Pascoal\'s algorithms. This proposed variant achieve better perfomance compared to both base algorithms in some graphs, and it is better than at least one of them in most cases.

The problem of route planning in air transport (in public transport in general ) is similar to the shortest path problem. The main differences are the time dependency of the input graph and the multicriterial aspect of the path costs . The aim of this thesis was to create a complex system that would be able to load elementary flights from database and then respond to user requests with combined journeys made from single flights . It was achieved using two state of the art algorithms , CSA and RAPTOR , which were adapted for the flight graph. The experiments which were run on real world data showed massive speedup of the algorithms when using the proposed optimisations . The whole system was also tested against an existing proprietary solution .

The thesis is focused on solving the Sokoban game using artificial intelligence algorithms. The first part of the thesis describes the Sokoban game, state space and selected state space search methods. In the second part selected methods were implemented and graphic user interface was created in the Python environment. Comparative experiments were executed in the final part.