Dissertations / Theses on the topic 'Fusion distances'

Les Systèmes d'Information Géographique (SIG) gèrent des informations complexes localisées dans un espace géographique. Durant la dernière décennie, ces systèmes ont vu l'émergence d'une part des SIG en trois dimensions et plus particulièrement des modèles numériques de terrain (MNT) et d'autre part de l'intégration de données temporelles. Les travaux de recherche en informatique présentés dans ce manuscrit s'attaquent à ces deux thématiques et sont appliqués au domaine maritime. En termes de MNT, ces recherches ont essentiellement porté sur l'intégration de MNT terrestre et maritime. Une méthode de fusion générant un MNT continu a été définie. Elle est basée sur l'appariement d'éléments caractéristiques linéaires (crêtes, talwegs, lignes de rupture de pente). Elle emploie des distances linéaires discrètes introduites par Fréchet. Ces distances fournissent des vecteurs d'appariement autorisant la fusion de MNT à l'aide d'une technique de déformation élastique. En ce qui concerne les SIG spatio-temporels, ces travaux concernent les déplacements d'objets mobiles et plus particulièrement les navires. Dans le cadre de la navigation maritime, les SIG ont une place prépondérante via les aides à la navigation, les systèmes de surveillance du trafic et les simulateurs de formation. Dans ce cadre, un nouveau modèle de représentation des déplacements, la vue relative, a été formalisé. Ce formalisme s'appuie sur la perception humaine de l'espace spatio-temporel et des objets mobiles. Cette représentation est complémentaire de la représentation absolue. Les déplacements relatifs vis-à-vis d'un objet référent sont ainsi plus facilement analysables. Les évolutions des distances et des vitesses relatives sont mieux perçues. Parallèlement, une simulation des activités de navires à l'aide de systèmes multi-agents a été conçue. Les déplacements simultanés d'un grand nombre de navires dans un environnement maritime sont ainsi simulés. Elle prend en compte les différentes activités concurrentes des objets mobiles et les contraintes géographiques. Qui plus est, elle intègre des raisonnements à base de patrons pour reproduire les raisonnements humains. Finalement, à partir des bases de données historiques des positions d'objets mobiles dans un environnement ouvert, des techniques d'extractions des trajectoires et de fouille de données spatio-temporelles ont été réalisées. Elles permettent pour chaque itinéraire et chaque type d'objets de définir des routes types spatio-temporelles et de détecter à la volée des comportements inhabituels. Ces travaux, bien qu'appliqués au domaine maritime, sont génériques. Les méthodes de fusion de MNT peuvent être appliquées à tous types de MNT ayant des zones de recouvrement. De même, les travaux de modélisation, d'analyses et de simulation sur les objets mobiles sont adaptables à tous les déplacements d'objets mobiles dans des espaces ouverts.

[Formulae and special characters can only be approximated here. Please see the pdf version of the abstract for an accurate reproduction.] This thesis is a new proposal for analysing spatial point patterns in spatial statistics using the outputs of popular techniques of (classical, non-spatial, multivariate) cluster analysis. The outputs of a chosen hierarchical algorithm, named fusion distances, are applied to investigate important spatial characteristics of a given point pattern. The fusion distances may be regarded as a missing link between the fields of spatial statistics and multivariate cluster analysis. Up to now, these two fields have remained rather separate because of fundamental differences in approach. It is shown that fusion distances are very good at discriminating different types of spatial point patterns. A detailed study on the power of the Monte Carlo test under the null hypothesis of Complete Spatial Randomness (the benchmark of spatial statistics) against chosen alternative models is also conducted. For instance, the test (based on the fusion distance) is very powerful for some arbitrary values of the parameters of the alternative. A new general approach is developed for analysing a given point pattern using several graphical techniques for exploratory data analysis and inference. The new strategy is applied to univariate and multivariate point patterns. A new extension of a popular strategy in spatial statistics, named the analysis of the local configuration, is also developed. This new extension uses the fusion distances, and analyses a localised neighbourhood of a given point of the point pattern. New spatial summary function and statistics, named the fusion distance function H(t), area statistic A, statistic S, and spatial Rg index, are introduced, and proven to be useful tools for identifying relevant features of spatial point patterns. In conclusion, the new methodology using the outputs of hierarchical clustering algorithms can be considered as an essential complement to the existing approaches in spatial statistics literature.

Biometric recognition is an advanced technology that employs physical features (such as fingerprint, iris and face capture) and behavioural features (such as gait, signature and voice) to identify people. Biometric features are reliable and valid ways to describe the unique properties of individuals, but there are often rigorous requirements on the position and characteristics of devices used for data acquisition. Since biometric features can be difficult to capture at a distance, soft biometric features, such as height, weight, skin colour and gender, have received much attention. Although the uniqueness of soft biometric features is not as intuitively obvious as traditional biometric features, numerous experiments have demonstrated that the desired recognition accuracy can be achieved by using different soft biometric features. This thesis will propose state-of-the-art multimodal biometric fusion techniques to improve recognition performance of soft biometrics. The first contribution of this thesis is to estimate fusion performance based on three types of soft biometrics - face, body and clothing. Feature level and score level fusion strategies will be employed to measure and analyse the influence of fusion on soft biometric recognition. The second key contribution of this research is that the analysis of the influence of distance on soft biometric traits and an exploration of the potency of recognition using fusion at varying distances have been performed. A new soft biometric database, containing images of the human face, body and clothing taken at three different distances, was created and used to obtain face, body and clothing attributes. First, this new database was constructed to explore the suitability of each modality at a distance: intuitively, the face is suitable for near field identification, and the body becomes optimal when the subject is further away. The new dataset is used to explore the potential of face, body and clothing for human recognition using fusion. In this section, some novel fusion techniques on different levels (feature, score and rank level) are proposed to improve soft biometric recognition performance. A Supervised Generalised Canonical Correlation (SG-CCA) methodology is proposed to fuse the soft biometric features. The proposed SG-CCA is numerically validated to be the best fusion method compared with other multi-modal fusion methods. An SVM-weighted Likelihood Ratio Test (SVM-LRT) method is proposed for score level fusion. The experimental results demonstrate that SVM-LRT-based fusion significantly outperforms the single-mode recognition. A novel joint density distribution-based rank-score fusion is also proposed to combine rank and score information. Analysis using the new soft biometric database demonstrates that recognition performance is significantly improved by using the new methods over single modalities at different distances.

Object detection is a classical computer vision task, encountered in many practical applications such as robotics and autonomous driving. The latter involves serious consequences of failure and a multitude of challenging demands, including high computational efficiency and detection accuracy. Distant objects are notably difficult to detect accurately due to their small scale in the image, consisting of only a few pixels. This is especially problematic in autonomous driving, as objects should be detected at the earliest possible stage to facilitate handling of hazardous situations. Previous work has addressed small objects via use of feature pyramids and super-resolution techniques, but the efficiency of such methods is limited as computational cost increases with image resolution. Therefore, a trade-off must be made between accuracy and cost. Opportunely though, a common characteristic of driving scenarios is the predominance of distant objects in the centre of the image. Thus, the full-frame image can be downsampled to reduce computational cost, and a crop can be extracted from the image centre to preserve resolution for distant vehicles. In this way, short- and long-range images are generated. This thesis investigates the fusion of such images in a convolutional neural network, particularly the fusion level, fusion operation, and spatial alignment. A novel framework — DetSLR — is proposed for the task and examined via the aforementioned aspects. Through adoption of the framework for the well-established SSD detector and MobileNetV2 feature extractor, it is shown that the framework significantly improves upon the original detector without incurring additional cost. The fusion level is shown to have great impact on the performance of the framework, favouring high-level fusion, while only insignificant differences exist between investigated fusion operations. Finally, spatial alignment of features is demonstrated to be a crucial component of the framework.

Covariance Matrix in target tracking algorithms has a critical role at multi- sensor track fusion systems. This matrix reveals the uncertainty of state es- timates that are obtained from diferent sensors. So, many subproblems of track fusion usually utilize this matrix to get more accurate results. That is why this matrix should be interchanged between the nodes of the multi-sensor tracking system. This thesis mainly deals with analysis of approximations of the covariance matrix that can best represent this matrix in order to efectively transmit this matrix to the demanding site. Kullback-Leibler (KL) Distance is exploited to derive some of the representations for Gaussian case. Also com- parison of these representations is another objective of this work and this is based on the fusion performance of the representations and the performance is measured for a system of a 2-radar track fusion system.

The adaptiveness provides uniquely identifying and monitoring the learner&rsquo
s learning activities according to his/her respective profile. The adaptive intelligent learning management systems (AILMS) help a wide range of students to achieve their learning goals effectively by delivering knowledge in an adaptive or individualized style through online learning settings. This study presents a multi-agent system, called MODA, developed to provide adaptiveness in learning management systems (LMS). A conceptual framework for adaptive learning systems is proposed for this purpose. The framework is based on the idea that adaptiveness is the best matching between the learner profile and the course content profile. The learning styles of learners and the content type of learning material are used to match the learner to the most suitable content. The thesis covers the pedagogical framework applied in MODA, the technical and multi-agent architectures of MODA, the TCP-IP based protocol providing communication between MODA and LMS, and a sample application of the system to an open source learning management system, OLAT. The study also discusses the possibilities of future interests.

The purpose of Language Identification (LID) is to identify a specific language from a spoken utterance, automatically. Language-specific characteristics are always associated with different languages. Most existing LID approaches utilise a statistical modelling process with common acoustic/phonotactic features to model specific languages while avoiding any language-specific knowledge. Great successes have been achieved in this area over past decades. However, there is still a huge gap between these languageindependent methods and the actual language-specific patterns. It is extremely useful to address these specific acoustic or semantic construction patterns, without spending huge labour on annotation which requires language-specific knowledge. Inspired by this goal, this research focuses on the language-feature dependency. Several practical methods have been proposed. Various features and modelling techniques have been studied in this research. Some of them carry out additional language-specific information without manual labelling, such as a novel duration modelling method based on articulatory features, and a novel Frequency-Modulation (FM) based feature. The performance of each individual feature is studied for each of the language-pair combinations. The similarity between languages and the contribution in identifying a language by using a particular feature are defined for the first time, in a quantitative style. These distance measures and languagedependent contributions become the foundations of the later-presented frameworks ?? language-dependent weighting and hierarchical language identification. The latter particularly provides remarkable flexibility and enhancement when identifying a relatively large number of languages and accents, due to the fact that the most discriminative feature or feature-combination is used when separating each of the languages. The proposed systems are evaluated in various corpora and task contexts including NIST language recognition evaluation tasks. The performances have been improved in various degrees. The key techniques developed for this work have also been applied to solve a different problem other than LID ?? speech-based cognitive load monitoring.

On étudie dans cette thèse des méthodes de localisation coopérative de robots mobiles sans utilisation de mesures extéroceptives relatives, comme des angles ou des distances entre robots. Les systèmes de localisation considérés sont basés sur des mesures de radionavigation sur des balises fixes ou des satellites. Pour ces systèmes, on observe en général un écart entre la position observée et la position réelle. Cet écart systématique (appelé biais) peut être dû à une mauvaise position de la balise ou à une différence entre la propagation réelles des ondes électromagnétiques par rapport aux conditions standard utilisées pour établir les modèles d’observation. L’influence de ce biais sur la localisation des robots est non négligeable. La coopération et l’échange de données entre les robots (estimations des biais, estimations des positions et données proprioceptives) est une approche qui permet de corriger ces erreurs systématiques. La localisation coopérative par échange des estimations est sujette aux problèmes de consanguinité des données qui peuvent engendrer des résultats erronés, en particulier trop confiants. Lorsque les estimations sont utilisées pour la navigation autonome à l’approche, on doit éviter tout risque de collision qui peut mettre en jeu la sécurité des robots et des personnes aux alentours. On doit donc avoir recours à un mécanisme d’intégrité vérifiant que l’erreur commise reste inférieure à une erreur maximale tolérable pour la mission. Dans un tel contexte, il est nécessaire de caractériser des domaines de confiance fiables contenant les positions des robots mobiles avec une forte probabilité. L’utilisation des méthodes ensemblistes à erreurs bornées est considérée alors comme une solution efficace. En effet, ce type d’approche résout naturellement le problème de consanguinité des données et fournit des domaines de confiance fiables. De surcroît, l’utilisation de modèles non-linéaires ne pose aucun problème de linéarisation. Après avoir modélisé un système coopératif de nr robots avec des mesures biaisées sur des balises, une étude d’observabilité est conduite. Deux cas sont considérés selon la nature des mesures brutes des observations. En outre, des conditions d’observabilité sont démontrées. Un algorithme ensembliste de localisation coopérative est ensuite présenté. Les méthodes considérées sont basées sur la propagation de contraintes sur des intervalles et l’inversion ensembliste. La coopération est effectuée grâce au partage des positions estimées, des biais estimés et des mesures proprioceptives.L’échange des estimations de biais permet de réduire les incertitudes sur les positions des robots. Dans un cadre d’étude simple, la faisabilité de l’algorithme est évaluée grâce à des simulations de mesures de distances sur balises en utilisant plusieurs robots. La coopération est comparée aux méthodes non coopératives. L’algorithme coopératif ensembliste est ensuite testé sur des données réelles en utilisant deux véhicules. Les performances de la méthode ensembliste coopérative sont enfin comparées avec deux méthodes Bayésiennes séquentielles, notamment une avec fusion par intersection de covariance. La comparaison est conduite en termes d’exactitude et d’incertitude
In this work, we study some cooperative localization issues for mobile robotic systems that interact with each other without using relative measurements (e.g. bearing and relative distances). The considered localization technologies are based on beacons or satellites that provide radio-navigation measurements. Such systems often lead to offsets between real and observed positions. These systematic offsets (i.e, biases) are often due to inaccurate beacon positions, or differences between the real electromagnetic waves propagation and the observation models. The impact of these biases on robots localization should not be neglected. Cooperation and data exchange (estimates of biases, estimates of positions and proprioceptive measurements) reduce significantly systematic errors. However, cooperative localization based on sharing estimates is subject to data incest problems (i.e, reuse of identical information in the fusion process) that often lead to over-convergence problems. When position information is used in a safety-critical context (e.g. close navigation of autonomous robots), one should check the consistency of the localization estimates. In this context, we aim at characterizing reliable confidence domains that contain robots positions with high reliability. Hence, set-membership methods are considered as efficient solutions. This kind of approach enables merging adequately the information even when it is reused several time. It also provides reliable domains. Moreover, the use of non-linear models does not require any linearization. The modeling of a cooperative system of nr robots with biased beacons measurements is firstly presented. Then, we perform an observability study. Two cases regarding the localization technology are considered. Observability conditions are identified and demonstrated. We then propose a set-membership method for cooperativelocalization. Cooperation is performed by sharing estimated positions, estimated biases and proprioceptive measurements. Sharing biases estimates allows to reduce the estimation error and the uncertainty of the robots positions. The algorithm feasibility is validated through simulation when the observations are beacons distance measurements with several robots. The cooperation provides better performance compared to a non-cooperative method. Afterwards, the cooperative algorithm based on set-membership method is tested using real data with two experimental vehicles. Finally, we compare the interval method performance with a sequential Bayesian approach based on covariance intersection. Experimental results indicate that the interval approach provides more accurate positions of the vehicles with smaller confidence domains that remain reliable. Indeed, the comparison is performed in terms of accuracy and uncertainty

La radiothérapie est une des méthodes principales utilisée dans le traitement thérapeutique des tumeurs malignes. Pour améliorer son efficacité, deux problèmes essentiels doivent être soigneusement traités : la prédication fiable des résultats thérapeutiques et la segmentation précise des volumes tumoraux. La tomographie d’émission de positrons au traceur Fluoro- 18-déoxy-glucose (FDG-TEP) peut fournir de manière non invasive des informations significatives sur les activités fonctionnelles des cellules tumorales. Les objectifs de cette thèse sont de proposer: 1) des systèmes fiables pour prédire les résultats du traitement contre le cancer en utilisant principalement des caractéristiques extraites des images FDG-TEP; 2) des algorithmes automatiques pour la segmentation de tumeurs de manière précise en TEP et TEP-TDM. La théorie des fonctions de croyance est choisie dans notre étude pour modéliser et raisonner des connaissances incertaines et imprécises pour des images TEP qui sont bruitées et floues. Dans le cadre des fonctions de croyance, nous proposons une méthode de sélection de caractéristiques de manière parcimonieuse et une méthode d’apprentissage de métriques permettant de rendre les classes bien séparées dans l’espace caractéristique afin d’améliorer la précision de classification du classificateur EK-NN. Basées sur ces deux études théoriques, un système robuste de prédiction est proposé, dans lequel le problème d’apprentissage pour des données de petite taille et déséquilibrées est traité de manière efficace. Pour segmenter automatiquement les tumeurs en TEP, une méthode 3-D non supervisée basée sur le regroupement évidentiel (evidential clustering) et l’information spatiale est proposée. Cette méthode de segmentation mono-modalité est ensuite étendue à la co-segmentation dans des images TEP-TDM, en considérant que ces deux modalités distinctes contiennent des informations complémentaires pour améliorer la précision. Toutes les méthodes proposées ont été testées sur des données cliniques, montrant leurs meilleures performances par rapport aux méthodes de l’état de l’art
Radiation therapy is one of the most principal options used in the treatment of malignant tumors. To enhance its effectiveness, two critical issues should be carefully dealt with, i.e., reliably predicting therapy outcomes to adapt undergoing treatment planning for individual patients, and accurately segmenting tumor volumes to maximize radiation delivery in tumor tissues while minimize side effects in adjacent organs at risk. Positron emission tomography with radioactive tracer fluorine-18 fluorodeoxyglucose (FDG-PET) can noninvasively provide significant information of the functional activities of tumor cells. In this thesis, the goal of our study consists of two parts: 1) to propose reliable therapy outcome prediction system using primarily features extracted from FDG-PET images; 2) to propose automatic and accurate algorithms for tumor segmentation in PET and PET-CT images. The theory of belief functions is adopted in our study to model and reason with uncertain and imprecise knowledge quantified from noisy and blurring PET images. In the framework of belief functions, a sparse feature selection method and a low-rank metric learning method are proposed to improve the classification accuracy of the evidential K-nearest neighbor classifier learnt by high-dimensional data that contain unreliable features. Based on the above two theoretical studies, a robust prediction system is then proposed, in which the small-sized and imbalanced nature of clinical data is effectively tackled. To automatically delineate tumors in PET images, an unsupervised 3-D segmentation based on evidential clustering using the theory of belief functions and spatial information is proposed. This mono-modality segmentation method is then extended to co-segment tumor in PET-CT images, considering that these two distinct modalities contain complementary information to further improve the accuracy. All proposed methods have been performed on clinical data, giving better results comparing to the state of the art ones

Many wireless sensor network (WSN) applications depend on knowing the position of nodes within the network if they are to function efficiently. Location information is used, for example, in item tracking, routing protocols and controlling node density. Configuring each node with its position manually is cumbersome, and not feasible in networks with mobile nodes or dynamic topologies. WSNs, therefore, rely on localisation algorithms for the sensor nodes to determine their own physical location. The basis of several localisation algorithms is the theory that the higher the number of reference nodes (called “references”) used, the greater the accuracy of the estimated position. However, this approach makes computation more complex and increases the likelihood that the location estimation may be inaccurate. Such inaccuracy in estimation could be due to including data from nodes with a large measurement error, or from nodes that intentionally aim to undermine the localisation process. This approach also has limited success in networks with sparse references, or where data cannot always be collected from many references (due for example to communication obstructions or bandwidth limitations). These situations require a method for achieving reliable and accurate localisation using a limited number of references. Designing a localisation algorithm that could estimate node position with high accuracy using a low number of references is not a trivial problem. As the number of references decreases, more statistical weight is attached to each reference’s location estimate. The overall localisation accuracy therefore greatly depends on the robustness of the selection method that is used to eliminate inaccurate references. Various localisation algorithms and their performance in WSNs were studied. Information-fusion theory was also investigated and a new technique, rooted in information-fusion theory, was proposed for defining the best criteria for the selection of references. The researcher chose selection criteria to identify only those references that would increase the overall localisation accuracy. Using these criteria also minimises the number of iterations needed to refine the accuracy of the estimated position. This reduces bandwidth requirements and the time required for a position estimation after any topology change (or even after initial network deployment). The resultant algorithm achieved two main goals simultaneously: accurate location discovery and information fusion. Moreover, the algorithm fulfils several secondary design objectives: self-organising nature, simplicity, robustness, localised processing and security. The proposed method was implemented and evaluated using a commercial network simulator. This evaluation of the proposed algorithm’s performance demonstrated that it is superior to other localisation algorithms evaluated; using fewer references, the algorithm performed better in terms of accuracy, robustness, security and energy efficiency. These results confirm that the proposed selection method and associated localisation algorithm allow for reliable and accurate location information to be gathered using a minimum number of references. This decreases the computational burden of gathering and analysing location data from the high number of references previously believed to be necessary.
Thesis (PhD(Eng))--University of Pretoria, 2011.
Electrical, Electronic and Computer Engineering
unrestricted

Cross-border acquisitions (CBAs) have extensively been used by Multilatinas as a preferred entry mode in foreign markets, quickly providing access to resources, competencies and local intelligence without the burden of starting up a greenfield investment and bearing its associated risk to face the liability of foreignness. Using fixed-effects, generalized least square (FEGLS) regressions applied on a panel data sample of 602 CBA deals announced during the 1989-2011 period by 182 Multilatinas competing in 74 industries and headquartered in Argentina, Brazil, Chile, Colombia, Mexico, Peru and Venezuela, the impact of cultural and psychic distances (as perceived by investors) on the performance of these CBA deals (measured by acquirers\' shareholders\' short term announcement returns) was evaluated. Key emerging conclusions are that: a) the national cultural distance composite index, based on Hofstede\'s four dimensions seems to better predict investors\' reaction to CBA announcements in comparison with the other \"psychic\" distance concepts, such as the administrative and geographical distances between home and host countries; b) investors\' perceptions regarding the cultural dissimilarities between these countries are factored in their response as an anticipation of the expected difficulties that acquiring firms\' will have during the post-merger integration process, as predicted by the several theoretical streams that focus on the role of culture in M&A; c) due to the positive and significant moderating effect of the uncertainty avoidance dimension, investors seem also to perceive that acquirers from home countries characterized by high uncertainty avoidance scores will be able to better handle the challenges that they will face in the post-merger integration stage, to the extent that these firms, as recognized in the cross-cultural research literature, have been associated with a preference for organizational rules and procedures that increase the chances of a successful completion of the merger or acquisition deal; d) although its role is recognized in the cross-cultural literature, power distance levels have no significant moderating effect on the cultural distance-M&A performance relationship. The models are robust to varying lengths of event windows and to alternative measurements of cultural distance, such as those based on the framework developed by the GLOBE project (House, Hanges, Javidan, Dorfman, & Gupta, 2004) and on the country cultural cluster maps proposed by Ronen and Shenkar (2013). Limitations of this study are pointed out and future research directions are suggested in order to advance our knowledge and understanding of the antecedents of the performance of the cross-border acquisitions made by Multilatinas.
Aquisições internacionais têm sido extensivamente usadas por Multilatinas como um modo preferencial de entrada em mercados estrangeiros, permitindo um rápido acesso a recursos, competências e inteligência locais, sem as desvantagens tipicamente associadas a investimentos greenfield e os riscos inerentes ao passivo de ser estrangeiro (liability of foreignness). Utilizando modelos de regressão de efeitos fixos e estimadores de mínimos quadrados generalizados, aplicados a uma amostra de dados em painel referentes a 602 aquisições internacionais anunciadas durante o período de 1989-2011 por 182 Multilatinas competindo em 74 setores econômicos e sediadas na Argentina, Brasil, Chile, Colômbia, México, Peru e Venezuela, foram avaliados os impactos das distâncias culturais e psíquicas (tais como percebidas pelos investidores) sobre o desempenho destas aquisições (medido pelos retornos de curto prazo aos acionistas das empresas adquirentes). Da análise dos resultados emergem as seguintes conclusões: a) a distância cultural calculada com base nas quatro dimensões de Hofstede parece melhor prever a reação dos investidores aos anúncios das aquisições internacionais, em comparação com os outros conceitos de distância \"psíquica\", tais como as distâncias administrativa e geográfica entre os países de origem hospedeiro; b) a percepção dos investidores em relação às diferenças culturais entre estes países são aparentemente levadas em consideração, como uma antecipação das esperadas dificuldades que empresas adquirentes enfrentarão durante o processo de integração das empresas adquiridas, tal como previsto pelas várias correntes teóricas que focam o papel da cultura em transações de fusões e aquisições; c) devido ao efeito moderador positivo e estatisticamente significante da dimensão relacionada à aversão à incerteza (uncertainty avoidance), os investidores também parecem perceber que adquirentes localizados em países caracterizadas pela elevada pontuação nesta dimensão serão capazes de melhor lidar com os desafios que enfrentarão na fase de integração pós-aquisição, na medida em que estas empresas, conforme reconhecido pela literatura empírica, têm sido associadas a uma preferência por regras e processos organizacionais que permitam aumentar as chances de uma bem sucedida aquisição; d) embora o seu papel seja reconhecido na literatura pertinente, a dimensão tolerância ao poder (power distance) não apresenta efeito moderador significante sobre a relação distância cultural e desempenho das aquisições. Os modelos são robustos para diferentes durações das janelas de eventos, bem como a conceitos alternativos de distância cultural, tais como as baseadas no modelo proposto pelo projeto GLOBE (House et al., 2004) e pelos mapas de classificação cultural dos países desenvolvidos por Ronen e Shenkar (2013). Limitações desta tese são apontadas e direções futuras de pesquisa são sugeridas no sentido de avançar nosso conhecimento e entendimento dos fatores que efetivamente afetam o desempenho das aquisições internacionais realizadas pelas Multilatinas.

Thesis deals with data fusion and calibration of sensory system of Orpheus-X3 robot and EnvMap mapping robot. These robots are parts of Cassandra robotic system that is used for exploration of hazardous or inaccessible areas. Corrections of measured distances are determined for used laser scanners Velodyne HDL-64, Velodyne HDL-32 and range camera SwissRanger SR4000. Software MultiSensCalib has been created and is described. This software is used for determination of intrinsic parameters of heterogeneous cameras of the sensory head and for determination of mutual position and orientation of these sensors. Algorithm for data fusion of CCD camera stereo pair, thermal imager stereo pair and range camera is proposed. Achieved calibration and data-fusion parameters are evaluated by several experiments.

Le domaine maritime littoral constitue un milieu fragile, placé à l'interface Terre/Mer où s'exercent de nombreuses pressions issues des milieux terrestres, maritimes et atmosphériques. Les approches globales de Gestion Intégrée des Zones Côtières (GIZC) ont pour but de pallier les lacunes des politiques précédentes au niveau des interactions simultanées des différents acteurs du littoral. Cette approche requiert un Modèle Numérique de Terrain (MNT) littoral continu ainsi que des données maritimes et terrestres cohérentes entre elles. En raison d'acquisitions ou de productions différentes pour les MNT terrestres et maritimes, l'ensemble du relief n'est pas traité de manière ni homogène ni continue. De plus les techniques d'intégration des MNT actuelles entraînent des lissages des reliefs, voire la perte des éléments caractéristiques. Des outils et une nouvelle méthodologie d'intégration de MNT ont donc été définis. Les outils utilisent la distance de Fréchet pour calculer une distance maximale d'écartement entre les points homologues de deux lignes. La valeur de cette distance donne une mesure de ressemblance de forme qui sert de base à leur appariement ultérieur. La méthodologie de fusion proposée dans cette thèse se veut générique tout en s'appuyant sur les lignes caractéristiques spécifiques du paysage. Elle effectue un enchaînement de traitements répartis en trois étapes majeures : 1) transformations cartographiques et analyse des zones de recouvrement ; 2) segmentation et extraction de lignes caractéristiques ; 3) appariement et fusion à l'aide de déformations élastiques. Ces outils d'intégration ont été testés sur des données réelles en 2D pour la comparaison du trait de côte, puis validés sur des MNT simulés 3D.

Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.