Software engineering projects that utilize inappropriate pathfinding algorithms carry a significant risk of poor runtime performance for customers. Using social network theory, this experimental study examined the impact of algorithms, frameworks, and map complexity on elapsed time and computer memory consumption. The 1,800 2D map samples utilized were computer random generated and data were collected and processed using Python language scripts. Memory consumption and elapsed time results for each of the 12 experimental treatment groups were compared using factorial MANOVA to determine the impact of the 3 independent variables on elapsed time and computer memory consumption. The MANOVA indicated a significant factor interaction between algorithms, frameworks, and map complexity upon elapsed time and memory consumption, F(4, 3576) = 94.09, p < .001, h2 = .095. The main effects of algorithms, F(4, 3576) = 885.68, p < .001, h2 = .498; and frameworks, F(2, 1787) = 720,360.01, p .001, h2 = .999; and map complexity, F(2, 1787) = 112,736.40, p < .001, h2 = .992, were also all significant. This study may contribute to positive social change by providing software engineers writing software for complex networks, such as analyzing terrorist social networks, with empirical pathfinding algorithm results. This is crucial to enabling selection of appropriately fast, memory-efficient algorithms that help analysts identify and apprehend criminal and terrorist suspects in complex networks before the next attack.

The fast-developing industry of electric vehicles is growing, and so is the driver community, which puts pressure on the electric charging grid. The purpose of this thesis is to simplify for the drivers of electric cars to charge their cars during trips. The research questions investigated are” How do the technologies and algorithms behind navigation APIs differ from each other?” and “What information is provided by the charging station APIs and how do they collect data about new stations?”. Information for the thesis was collected by reading and analyzing both documentation and previous work, as well as by conducting experiments. The study was limited to purely electric vehicles. We created an application to conduct experiments on the API combination Mapbox and Open Charge Map, we call it ChargeX. We compare, TomTom, Tesla, Plugshare, Google Maps and ChargeX. The most common shortest-path algorithms are Dijkstra’s, A* and Bidirectional A*. They provide reasonable solutions to the shortest path problem. The algorithms can be improved by considering traffic flow, travel time and distance between origin and destination and apply it as weights on the edges. What has the largest impact on the final route is the choice of charging stations. The algorithm for picking charging stations can be optimized in several ways for example by considering real time availability information of the charging stations, prioritize highways, calculate the temperature and altitude impact on the battery or prioritize faster chargers such as superchargers for Tesla.

La synthèse de la parole par corpus (sélection d'unités) est le sujet principal de cette thèse. Tout d'abord, une analyse approfondie et un diagnostic de l'algorithme de sélection d'unités (algorithme de recherche dans le treillis d'unités) sont présentés. L'importance de l'optimalité de la solution est discutée et une nouvelle mise en œuvre de la sélection basée sur un algorithme A* est présenté. Trois améliorations de la fonction de coût sont également présentées. La première est une nouvelle façon – dans le coût cible – de minimiser les différences spectrales en sélectionnant des séquences d'unités minimisant un coût moyen au lieu d'unités minimisant chacune un coût cible de manière absolue. Ce coût est testé pour une distance sur la durée phonémique mais peut être appliqué à d'autres distances. Notre deuxième proposition est une fonction de coût cible visant à améliorer l'intonation en se basant sur des coefficients extraits à travers une version généralisée du modèle de Fujisaki. Les paramètres de ces fonctions sont utilisés au sein d'un coût cible. Enfin, notre troisième contribution concerne un système de pénalités visant à améliorer le coût de concaténation. Il pénalise les unités en fonction de classes reposant sur une hiérarchie du degré de risque qu'un artefact de concaténation se produise lors de la concaténation sur un phone de cette classe. Ce système est différent des autres dans la littérature en cela qu'il est tempéré par une fonction floue capable d'adoucir le système de pénalités pour les unités présentant des coûts de concaténation parmi les plus bas de leur distribution. / This PhD thesis focuses on the automatic speech synthesis field, and more specifically on unit selection. A deep analysis and a diagnosis of the unit selection algorithm (lattice search algorithm) is provided. The importance of the solution optimality is discussed and a new unit selection implementation based on a A* algorithm is presented. Three cost function enhancements are also presented. The first one is a new way – in the target cost – to minimize important spectral differences by selecting sequences of candidate units that minimize a mean cost instead of an absolute one. This cost is tested on a phonemic duration distance but can be applied to others. Our second proposition is a target sub-cost addressing intonation that is based on coefficients extracted through a generalized version of Fujisaki's command-response model. This model features gamma functions modeling F0 called atoms. Finally, our third contribution concerns a penalty system that aims at enhancing the concatenation cost. It penalizes units in function of classes defining the risk a concatenation artifact occurs when concatenating on a phone of this class. This system is different to others in the literature in that it is tempered by a fuzzy function that allows to soften penalties for units presenting low concatenation costs.