No description available.

A busca por vantagem competitiva é a realidade das empresas na atual economia global. A formação de redes de inovação representa uma forma colaborativa de desenvolver inovações e adquirir vantagem competitiva para atingir novos mercados. Nesses ambientes a gestão constitui-se em um desafio e diversas abordagens podem ser aplicadas na busca pelo sucesso. Esta pesquisa tem o objetivo de identificar e sistematizar como a abordagem de gestão ágil, fundamentada na flexibilidade e adaptabilidade, pode ser aplicada nesses ambientes. A metodologia de pesquisa compreende um estudo bibliográfico para embasamento teórico da temática proposta e uma pesquisa-ação para coletar dados em campo. A análise e discussão do cruzamento entre as evidências teóricas e os dados práticos visa propor uma modelagem de empresas para apoiar a aplicação da gestão ágil em ambientes de redes de inovação auto-organizadas. A modelagem desenvolvida foi guiada pela metodologia Enterprise Knowledge Development-Change Management Method (EKD-CMM) conforme a For Enterprise Modeling (4EM). O resultado da pesquisa identifica os benefícios e limitações observados na aplicação prática da mesma e contribui para ampliar a compreensão dos elementos envolvidos no processo de gestão de redes de inovação auto-organizadas, utilizando a abordagem ágil. / The race for competitive advantage is the business reality in today\'s global economy. Innovation networks formation is a collaborative way to develop innovations and acquire competitive advantage to reach new markets. Management is a challenge in these environments and different approaches can be applied in the pursuit of success. This research aims to identify and systematize how agile management approach, based on flexibility and adaptability, can be applied in these environments. The research methodology comprises a bibliographical study to present the theoretical background in the field of interest and one action research to data collection. The analysis and discussion of the intersection between theoretical background and field practical data aims to propose an enterprise modeling to support the application of agile management in self-organized innovation network environments. The modeling development was guided by the Enterprise Knowledge Development-Change Management Method (EKD-CMM) based on For Enterprise Modeling (4EM) methodology. The research result identifies the benefits and limitations observed in its pratical implementation and contributes to expand the understanding of the elements involved in the management process of self-organizing innovation networks using agile management approach.

Sistemas de análise de gestos têm se destacado por suas contribuições para a interação entre humanos, humanos e máquinas, e humanos e ambiente. Nessa interação, a gesticulação natural é vista como parte do sistema linguístico que suporta a comunicação, e qualquer sistema de informação que objetiva usar interação para suporte à decisão deveria ser capaz de interpretá-la. Essa interpretação pode ser realizada por meio da segmentação das fases do gesto. Para resolver essa tarefa, o estabelecimento de uma representação de dados eficiente para os gestos é um ponto crítico. A representação escolhida e sua associação a técnicas de análise podem ou não favorecer a solução sob implementação. Neste trabalho, formas de representação de gestos são submetidas aos algoritmos de reconhecimento de padrões MLP e SOM para elaborar um ambiente propício à identificação das representações mais discriminantes, quais aspectos as diferentes representações descrevem com eficiência, e como elas podem ser combinadas para melhorar a segmentação das fases do gesto. Para construção das representações multidimensionais são usados aspectos espaciais e temporais combinados com a normalização dos dados e a aplicação do filtro wavelet na busca pela representação mais discriminante para o reconhecimento das fases do gesto. Ambos os algoritmos alcançaram bons resultados com o uso dos aspectos temporais. O MLP conseguiu classificar todas as fases do gesto em configurações de representação contendo dados sobre todos os membros monitorados. O SOM apresentou boa capacidade para formar grupos contendo dados de uma mesma fase do gesto mesmo com o uso de poucas características na construção da representação, porém não foi possível identificar a proposta de uma nova fase do gesto com o aprendizado não supervisionado / Gestures analysis systems have stood out for their contributions to the interaction between humans, humans and machines, and humans and environments. In this interaction, natural gesticulation is seen as part of a linguistic system that supports the communication, and all information systems aiming at the use of such an interaction in making decisions should be able to interpret it. Such an interpretation can be carried out through the gesture phases segmentation. In order to solve this task, the establishment of an efficient data representation for gestures is a critical issue. The chosen representation as well as its combination with techniques for analysis can or can not favor the solution being developed. In this work, different forms representation for gestures are applied to pattern recognition algorithms MLP and SOM to create an adequate environment to identify the more discriminative representations, which aspect the different representations describe with more efficiency, and how they can be combined in order to improve gesture phases segmentation. To construct the multidimensional representations we use spatial and temporal aspects combined with the normalization of the data and the application of the wavelet filter in the search for the most discriminating representation for the recognition of the gesture phases. Both algorithms achieved good results with the use of temporal aspects. MLP was able to classify all gesture phases using representation settings containing data about all monitored members. SOM presented good ability to form groups containing data of the same gesture phase even with the use of few characteristics in the construction of the representation, but it was not possible to identify the proposal of a new gesture phase with unsupervised learning

A construção e ampliação de rodovias gera impactos significativos ao meio ambiente. Os principais impactos ao meio biótico são a supressão de vegetação, redução da riqueza e abundância de espécies de fauna como decorrência da fragmentação de habitats e aumento dos riscos de atropelamento de animais silvestres e domésticos. O objetivo geral do trabalho foi identificar padrões espaciais nos atropelamentos de fauna silvestre por espécie (nome popular) utilizando ferramentas de análise espacial e machine learning. Especificamente, buscou-se compreender a relação entre atropelamentos de animais silvestres e variáveis que representam características de uso e cobertura do solo e caracterização da rodovia, tais como formação florestal, corpos d\'água, silvicultura, áreas edificadas, velocidade máxima permitida, volume de tráfego, entre outras. Os atropelamentos de fauna silvestre foram analisados por espécie atropelada, a fim de identificar os padrões espaciais dos atropelamentos específicos para cada espécie. As ferramentas de análise espacial empregadas foram a Função K - para determinar o padrão de distribuição dos registros de atropelamento de fauna, o Estimador de Densidade de Kernel - para gerar estimativas de densidade de pontos sobre a rodovia, a Análise de Hotspots - para identificar os trechos mais críticos de atropelamento de fauna e, por fim, o Self-Organizing Maps (SOM), um tipo de rede neural artificial, que reorganiza amostras de dados n-dimensionais de acordo com a similaridade entre elas. Os resultados das análises de padrões pontuais foram importantes para entender que os pontos de atropelamento possuem padrões de distribuição espacial que variam por espécie. Os eventos ocorrem espacialmente agrupados e não estão homogeneamente distribuídos ao longo da rodovia. De maneira geral, os animais apresentam trechos de maior intensidade de atropelamento em locais distintos. O SOM permitiu analisar as relações entre múltiplas variáveis, lineares e não-lineares, tais como são os dados ecológicos, e encontrar padrões espaciais distintos por espécie. A maior parte dos animais foi atropelada próxima de fragmentos florestais e de corpos d\'água, e distante de cultivo de cana-de-açúcar, silvicultura e área edificada. Porém, uma parte considerável das mortes de animais dos tipos com maior número de atropelamentos ocorreu em áreas com paisagem diversificada, incluindo alta densidade de drenagem, fragmentos florestais, silvicultura e áreas edificadas. / The construction and expansion of roads cause significant impacts on the environment. The main potential impacts to biotic environment are vegetation suppression, reduction of the abundance and richness of species due to forest fragmentation and increase of animal (domestic and wildlife) vehicle collisions. The general objective of this work was to identify spatial patterns in wildlife-vehicle collisions individually per species by using spatial analysis and machine learning. Specifically, the relationship between wildlife-vehicle collisions and variables that represent land use and road characterization features - such as forests, water bodies, silviculture, sugarcane fields, built environment, speed limit and traffic volume - was investigated. The wildlife-vehicle collisions were analyzed per species, in order to identify the spatial patterns for each species separately. The spatial analysis tools used in this study were K-Function - to determine the distribution pattern of roadkill, Kernel Density Estimator (KDE) - to identify the location and intensity of hotspots and hotzones. Self-Organizing Maps (SOM), an artificial neural network (ANN), was selected to reorganize the multi-dimensional data according to the similarity between them. The results of the spatial pattern analysis were important to perceive that the point data pattern varies between species. The events occur spatially clustered and are not uniformly distributed along the highway. In general, wildlife-vehicle collsions have their hotzones in different locations. SOM was able to analyze the relationship between multiple variables, linear and non-linear, such as ecological data, and established distinct spatial patterns per each species. Most of the wildlife was run over close to forest area and water bodies, and distant from sugarcane, silviculture and built environments. But a considerable part of the wildlife-vehicle collisions occurred in areas with diverse landscape, including high density of water bodies, silviculture and built environments.

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.

La motivation de cette thèse de Doctorat est de modéliser quelques problèmes biologiques avec des systèmes et des équations de réaction-diffusion. La thèse est divisée en sept chapitres: 1. On modélise des ions de calcium et des protéines dans une épine dendritique mobile (une microstructure dans les neurones). On propose deux modèles, un avec des protéines qui diffusent et un autre avec des protéines fixées au cytoplasme. On démontre que le premier problème est bien posé, que le deuxième problème est presque bien posé et qu'il y a un lien continu entre les deux modèles. 2. On applique les techniques du Chapitre 1 pour un modèle d'infection virale et réponse immunitaire dans des cellules cultivées. On propose comme avant deux modèles, un avec des cellules qui diffusent et un autre avec des cellules fixées. On démontre que les deux problèmes sont bien posés et qu'il y a un lien continu entre les deux modèles. On Žtudie aussi le comportement asymptotique et la stabilité des solutions pour des temps larges, et on fait des simulations dans Matlab. 3. Dans le Chapitre 3 on montre que la croissance a deux effets positives dans la formation de motifs ou patterns. Le premier est un effet anti-explosion (anti-blow-up) car les solutions sur un domaine croissant explosent plus tard que celles sur un domaine fixé, et si la croissance est suffisamment rapide alors elle peut même empêcher l'explosion. Le deuxième est un effet stabilisant car les valeur propres sur un domaine croissant ont des parties réelles plus petites que celles sur un domaine fixé. 4. On étend la définition de front progressif à des variétés et on en étudie quelques propriétés. 5. On étudie des front progressifs sur la droite réelle. On démontre qu'il y a deux fronts progressifs qui se déplacent dans des directions opposées et qu'ils se bloquent mutuellement, générant ainsi une solution stationnaire non-triviale. Cet exemple montre que pour des modèles à diffusion non-homogène les fronts progressifs ne sont pas nécessairement des invasions. 6. On étudie des fronts progressifs sur la sphère. On démontre que pour des sous-domaines de la sphère avec des conditions aux limites de Dirichlet le front progressif est toujours bloqué, tandis que pour la sphère complète le front peut ou bien invahir ou bien être bloqué, tout en fonction des conditions initiales. 7. On étudie un problème elliptique aux valeurs propres nonlinéaires. Sur la sphère de dimension 1 on démontre l'existence de multiples solutions non-triviales avec des techniques de bifurcation. Sur la sphère de dimension n on utilise les mêmes arguments pour dŽmontrer l'existence de multiples solutions non-triviales à symétrie axiale, i.e. qui ne dépendent que de l'angle vertical.

<p>This licentiate thesis work investigates two medium access control (MAC) methods, when used in traffic safety applications over vehicular <em>ad hoc</em> networks (VANETs). The MAC methods are carrier sense multiple access (CSMA), as specified by the leading standard for VANETs IEEE 802.11p, and self-organizing time-division multiple access (STDMA) as used by the leading standard for transponders on ships. All vehicles in traffic safety applications periodically broadcast cooperative awareness messages (CAMs). The CAM based data traffic implies requirements on a predictable, fair and scalable medium access mechanism. The investigated performance measures are <em>channel access delay</em>, <em>number of consecutive packet drops</em> and the <em>distance between concurrently transmitting nodes</em>. Performance is evaluated by computer simulations of a highway scenario in which all vehicles broadcast CAMs with different update rates and packet lengths. The obtained results show that nodes in a CSMA system can experience <em>unbounded channel access delays</em> and further that there is a significant difference between the best case and worst case channel access delay that a node could experience. In addition, with CSMA there is a very high probability that several <em>concurrently transmitting nodes are located close to each other</em>. This occurs when nodes start their listening periods at the same time or when nodes choose the same backoff value, which results in nodes starting to transmit at the same time instant. The CSMA algorithm is therefore both <em>unpredictable</em> and <em>unfair</em> besides the fact that it <em>scales badly</em> for broadcasted CAMs. STDMA, on the other hand, will always grant channel access for all packets before a predetermined time, regardless of the number of competing nodes. Therefore, the STDMA algorithm is <em>predictable</em> and <em>fair</em>. STDMA, using parameter settings that have been adapted to the vehicular environment, is shown to outperform CSMA when considering the performance measure <em>distance between concurrently transmitting nodes</em>. In CSMA the distance between concurrent transmissions is random, whereas STDMA uses the side information from the CAMs to properly schedule concurrent transmissions in space. The price paid for the superior performance of STDMA is the required network synchronization through a global navigation satellite system, e.g., GPS. That aside since STDMA was shown to be scalable, predictable and fair; it is an excellent candidate for use in VANETs when complex communication requirements from traffic safety applications should be met.</p>

El Raonament Basat en Casos (CBR) és un paradigma d'aprenentatge basat en establir analogies amb problemes prèviament resolts per resoldre'n de nous. Per tant, l'organització, l'accés i la utilització del coneixement previ són aspectes claus per tenir èxit en aquest procés. No obstant, la majoria dels problemes reals presenten grans volums de dades complexes, incertes i amb coneixement aproximat i, conseqüentment, el rendiment del CBR pot veure's minvat degut a la complexitat de gestionar aquest tipus de coneixement. Això ha fet que en els últims anys hagi sorgit una nova línia de recerca anomenada Soft-Computing and Intelligent Information Retrieval enfocada en mitigar aquests efectes. D'aquí neix el context d'aquesta tesi.Dins de l'ampli ventall de tècniques Soft-Computing per tractar coneixement complex, els Mapes Autoorganitzatius (SOM) destaquen sobre la resta per la seva capacitat en agrupar les dades en patrons, els quals permeten detectar relacions ocultes entre les dades. Aquesta capacitat ha estat explotada en treballs previs d'altres investigadors, on s'ha organitzat la memòria de casos del CBR amb SOM per tal de millorar la recuperació dels casos.La finalitat de la present tesi és donar un pas més enllà en la simple combinació del CBR i de SOM, de tal manera que aquí s'introdueixen les capacitats de Soft-Computing i de Knowledge Discovery de SOM en totes les fases del CBR per nodrir-les del nou coneixement descobert. A més a més, les mètriques de complexitat apareixen en aquest context com un instrument precís per modelar el funcionament de SOM segons la tipologia de les dades. L'assoliment d'aquesta integració es pot dividir principalment en quatre fites: (1) la definició d'una metodologia per determinar la millor manera de recuperar els casos tenint en compte la complexitat de les dades i els requeriments de l'usuari; (2) la millora de la fiabilitat de la proposta de solucions gràcies a les relacions entre els clústers i els casos; (3) la potenciació de les capacitats explicatives mitjançant la generació d'explicacions simbòliques; (4) el manteniment incremental i semi-supervisat de la memòria de casos organitzada per SOM.Tots aquests punts s'integren sota la plataforma SOMCBR, la qual és extensament avaluada sobre datasets provinents de l'UCI Repository i de dominis mèdics i telemàtics.Addicionalment, la tesi aborda de manera secundària dues línies de recerca fruït dels requeriments dels projectes on ha estat ubicada. D'una banda, s'aborda la definició de funcions de similitud específiques per definir com comparar un cas resolt amb un de nou mitjançant una variant de la Computació Evolutiva anomenada Evolució de Gramàtiques (GE). D'altra banda, s'estudia com definir esquemes de cooperació entre sistemes heterogenis per millorar la fiabilitat de la seva resposta conjunta mitjançant GE. Ambdues línies són integrades en dues plataformes, BRAIN i MGE respectivament, i són també avaluades amb els datasets anteriors. / El Razonamiento Basado en Casos (CBR) es un paradigma de aprendizaje basado en establecer analogías con problemas previamente resueltos para resolver otros nuevos. Por tanto, la organización, el acceso y la utilización del conocimiento previo son aspectos clave para tener éxito. No obstante, la mayoría de los problemas presentan grandes volúmenes de datos complejos, inciertos y con conocimiento aproximado y, por tanto, el rendimiento del CBR puede verse afectado debido a la complejidad de gestionarlos. Esto ha hecho que en los últimos años haya surgido una nueva línea de investigación llamada Soft-Computing and Intelligent Information Retrieval focalizada en mitigar estos efectos. Es aquí donde nace el contexto de esta tesis.Dentro del amplio abanico de técnicas Soft-Computing para tratar conocimiento complejo, los Mapas Autoorganizativos (SOM) destacan por encima del resto por su capacidad de agrupar los datos en patrones, los cuales permiten detectar relaciones ocultas entre los datos. Esta capacidad ha sido aprovechada en trabajos previos de otros investigadores, donde se ha organizado la memoria de casos del CBR con SOM para mejorar la recuperación de los casos.La finalidad de la presente tesis es dar un paso más en la simple combinación del CBR y de SOM, de tal manera que aquí se introducen las capacidades de Soft-Computing y de Knowledge Discovery de SOM en todas las fases del CBR para alimentarlas del conocimiento nuevo descubierto. Además, las métricas de complejidad aparecen en este contexto como un instrumento preciso para modelar el funcionamiento de SOM en función de la tipología de los datos. La consecución de esta integración se puede dividir principalmente en cuatro hitos: (1) la definición de una metodología para determinar la mejor manera de recuperar los casos teniendo en cuenta la complejidad de los datos y los requerimientos del usuario; (2) la mejora de la fiabilidad en la propuesta de soluciones gracias a las relaciones entre los clusters y los casos; (3) la potenciación de las capacidades explicativas mediante la generación de explicaciones simbólicas; (4) el mantenimiento incremental y semi-supervisado de la memoria de casos organizada por SOM. Todos estos puntos se integran en la plataforma SOMCBR, la cual es ampliamente evaluada sobre datasets procedentes del UCI Repository y de dominios médicos y telemáticos.Adicionalmente, la tesis aborda secundariamente dos líneas de investigación fruto de los requeri-mientos de los proyectos donde ha estado ubicada la tesis. Por un lado, se aborda la definición de funciones de similitud específicas para definir como comparar un caso resuelto con otro nuevo mediante una variante de la Computación Evolutiva denominada Evolución de Gramáticas (GE). Por otro lado, se estudia como definir esquemas de cooperación entre sistemas heterogéneos para mejorar la fiabilidad de su respuesta conjunta mediante GE. Ambas líneas son integradas en dos plataformas, BRAIN y MGE, las cuales también son evaluadas sobre los datasets anteriores. / Case-Based Reasoning (CBR) is an approach of machine learning based on solving new problems by identifying analogies with other previous solved problems. Thus, organization, access and management of this knowledge are crucial issues for achieving successful results. Nevertheless, the major part of real problems presents a huge amount of complex data, which also presents uncertain and partial knowledge. Therefore, CBR performance is influenced by the complex management of this knowledge. For this reason, a new research topic has appeared in the last years for tackling this problem: Soft-Computing and Intelligent Information Retrieval. This is the point where this thesis was born.Inside the wide variety of Soft-Computing techniques for managing complex data, the Self-Organizing Maps (SOM) highlight from the rest due to their capability for grouping data according to certain patterns using the relations hidden in data. This capability has been used in a wide range of works, where the CBR case memory has been organized with SOM for improving the case retrieval.The goal of this thesis is to take a step up in the simple combination of CBR and SOM. This thesis presents how to introduce the Soft-Computing and Knowledge Discovery capabilities of SOM inside all the steps of CBR to promote them with the discovered knowledge. Furthermore, complexity measures appear in this context as a mechanism to model the performance of SOM according to data topology. The achievement of this goal can be split in the next four points: (1) the definition of a methodology for setting up the best way of retrieving cases taking into account the data complexity and user requirements; (2) the improvement of the classification reliability through the relations between cases and clusters; (3) the promotion of the explaining capabilities by means of the generation of symbolic explanations; (4) the incremental and semi-supervised case-based maintenance. All these points are integrated in the SOMCBR framework, which has been widely tested in datasets from UCI Repository and from medical and telematic domains. Additionally, this thesis secondly tackles two additional research lines due to the requirements of a project in which it has been developed. First, the definition of similarity functions ad hoc a domain is analyzed using a variant of the Evolutionary Computation called Grammar Evolution (GE). Second, the definition of cooperation schemes between heterogeneous systems is also analyzed for improving the reliability from the point of view of GE. Both lines are developed in two frameworks, BRAIN and MGE respectively, which are also evaluated over the last explained datasets.

Water drops that nucleate and grow over an evaporating polymer solution exposed to a current of moist air remain noncoalescent and self-assemble into close packed arrays. The hexagonally close packed, nearly monodisperse drops, eventually evaporate away, leaving a polymer film, with ordered array of pores. Meanwhile, typical breath figures or dew that form when moist air contacts cold surfaces involve coalescence-assisted growth of highly polydisperse, disordered array of water drops. This dissertation provides the first quantitative attempt aimed at the elucidation of the mechanism of the breath figure templated assembly of the ordered arrays of pores in polymer films. The creation and evolution of a population of close packed drops occur in response to the heat and mass fluxes involved in water droplet condensation and solvent evaporation. The dynamics of drop nucleation, growth, noncoalescence and self-assembly are modeled by accounting for various transport and thermodynamic processes. The theoretical results for the rate and extent of evaporative cooling and growth are compared with experiments. Further, the dissertation describes a rich array of experimental observations about water droplet growth, noncoalescence, assembly and drying that have not been reported in the published literature so far. The theoretical framework developed in this study allows one to rationalize and predict the structure and size of pores formed in different polymer-solvent systems under given air flow conditions. While the ordered arrays of water drops present an example of dynamics, growth and assembly of spherical particles, the study on colloidal gold nanorods focuses on the behavior of rodlike particles. A comprehensive set of theoretical arguments based on the shape dependent hydrodynamics of rods were developed and used for centrifugation-assisted separation of rodlike particles from nanospheres that are typical byproducts of seed mediated growth of nanorods. Since the efficiency of shape separation is assessed using UV-Vis-NIR spectroscopy and transmission electron microscopy (TEM), the present dissertation elucidates the shape dependent parameters that affect the optical response and phase behavior of colloidal gold nanorods. The drying of a drop of colloidal gold nanorods on glass slides creates coffee ring like deposits near the contact line, which is preceded by the formation of a liquid crystalline phase. The assemblies of rods on TEM grids are shown to be the result of equilibrium and non-equilibrium processes, and the ordered phases are compared with two dimensional liquid crystals. The methodology of pattern characterization developed in this dissertation is then used to analyze the structure of the exocuticle of iridescent beetle Chrysina gloriosa. The patterns were characterized using Voronoi analysis and the effect of curvature on the fractions on hexagonal order of tiles was determined. Further, these patterns were found to be analogous to the focal conic domains formed spontaneously on the free surface of a cholesteric liquid crystal. In summary, the dissertation provides the crucial understanding required for the widespread use of breath figure templated assembly as a method for manufacturing porous films, that requires only a drop of polymer solution (dilute) and a whiff of breath! Further, the dissertation establishes the physical basis and methodology for separating and characterizing colloidal gold nanorods. The dissertation also suggests the basis for the formation and structure of tiles that decorate the exoskeleton of an iridescent beetle Chrysina gloriosa.