Current site scale stormwater management designs typically include multiple distributed stormwater best management practices (BMPs), necessary to meet regulatory objectives for nutrient removal and groundwater recharge. Selection of the appropriate BMPs for a particular site requires consideration of contributing drainage area characteristics, such as soil type, area, and land cover. Other physical constraints such as karst topography, areas of highly concentrated pollutant runoff, etc. as well as economics, such as installation and operation and maintenance cost must be considered. Due to these multiple competing selection criteria and regulatory requirements, selection of optimal configurations of BMPs by manual iteration using conventional design tools is not tenable, and the resulting sub-optimal solutions are often biased. This dissertation addresses the need for an objective BMP selection optimization tool through definition of an objective function, selection of an optimization algorithm based on defined selection criteria, development of cost functions related to installation cost and operation and maintenance cost, and ultimately creation and evaluation of a new software tool that enables multi-objective user weighted selection of optimal BMP configurations. A software tool is developed using the nutrient and pollutant removal logic found in the Virginia Runoff Reduction Method (VRRM) spreadsheets. The resulting tool is tested by a group of stormwater professionals from the Commonwealth of Virginia for two case studies. Responses from case study participants indicate that use of the tool has a significant impact on the current engineering design process for selection of stormwater BMPs. They further indicate that resulting selection of stormwater BMPs through use of the optimization tool is more objective than conventional methods of design, and allows designers to spend more time evaluating solutions, rather than attempting to meet regulatory objectives. / Ph. D.

Electroencephalogram (EEG) is a well known, and well used method for studying brain activity, and it's possibilities have lately stretched into the car industry, were it's capabilities of detecting sleepiness in drivers are currently being put to the test. When performing EEG signal analysis on the brain, standardized signal bands exists that are characteristic to specific states of mind, such as when a driver is feeling sleepy. However, EEG as a method for studying the brain has major problems. The signal contains a lot of information that can be redundant or irrelevant, and the result is easily influenced and deviant by other parameters, that can cause incorrectness and inaccuracy in the final prediction and classification of the signal frame. One of the important methods for reducing this inaccuracy of EEG, and also reducing the computational cost of the diagnose, is feature selection. Finding key features in the signal, that can support a reliable diagnosis of a specific state of mind, is of great importance. Especially since learning systems, incorrectly predicting or interpreting a signal in the classification stage, can lead to incorrect triggering of safety features in futuristic cars, such as cruiser control. There are many existing feature selection algorithms available, and features that has been tried in different research project. The goal of this research was to help gather more accurate inputs from EEG, through an optimization study, and to increase the reliability of EEG. And by doing so, hopefully improve safety systems in cars, that in turn could help preventing sleepiness-related accidents on roads in the future. This was realized through a study of features, and feature selection algorithms. By determining key features that could distinguish sleepiness from a signal, as well as performing accuracy tests for different feature selection algorithms, the motivation for an optimal selection, based on the used parameters, could be made. However limited this research was, it concluded that Information Gain as a method for selecting features, was the most accurate algorithm, and that some features were better to use then others, such as Huguchi's fractal dimension, and the Hjorth complexity.

Indiana University-Purdue University Indianapolis (IUPUI) / Indoor tracking is a challenging problem. The level of accepted error is on a much smaller scale than that of its outdoor counterpart. While the global positioning system has become omnipresent, and a widely accepted outdoor tracking system it has limitations in indoor environments due to loss or degradation of signal. Many attempts have been made to address this challenge, but currently none have proven to be the de-facto standard. In this thesis, we introduce the concept of opportunistic tracking in which tracking takes place with whatever sensing infrastructure is present – static or mobile, within a given indoor environment. In this approach many of the challenges (e.g., high cost, infeasible infrastructure deployment, etc.) that prohibit usage of existing systems in typical application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges do still exist when it comes to provide an accurate positional estimate of an entities location in an indoor environment, namely: sensor classification, sensor selection, and multi-sensor data fusion. We propose an enhanced tracking framework that through the infusion of QoS-based selection criteria of trust and reliability we can improve the overall accuracy of the tracking estimate. This improvement is predicated on the introduction of learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly identified and evaluated based upon their specific behavioral characteristics through performance evaluation. This in-depth evaluation of sensors provides the basis for improving the sensor selection process. A side effect of obtaining this improved accuracy is the cost, found in the form of system runtime. This thesis provides a solution for this tradeoff between accuracy and cost through an optimization function that analyzes this tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an object as it moves indoors. We demonstrate that through this improved sensor classification, selection, data fusion, and tradeoff optimization we can provide an improvement, in terms of accuracy, over other existing indoor tracking systems.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O processo de envelhecimento com bem-estar é um processo de aprendizagem contínua que busca diariamente sentido e renovação existencial. Este estudo objetivou apresentar a perspectiva teórica da psicologia do desenvolvimento no curso de vida, lifespan, que vê o desenvolvimento como processo que se estende por toda a vida, em diálogo com idosos moradores em bairros de periferia de Porto Alegre. Este modelo apóia uma visão diluída da idade cronológica e não está ligado a etapas normativas, sendo determinado por aspectos biológicos, socioculturais, históricos e pessoais. Segundo ele, o desenvolvimento humano ocorre em ritmo e velocidade diferentes para cada idoso. Envelhecer satisfatoriamente neste modelo prevê a utilização de três processos: a seleção, a otimização e a compensação. Estas são estratégias de escolha de metas e de ações para concretizá-las que impulsionam o idoso em suas capacidades de resistência, de plasticidade, de flexibilidade, estratégias de gerenciamento para uma vida com bem-estar. Assim, este estudo utilizou a pesquisa qualitativa e o referencial fenomenológico quando entrevistou 30 idosos, entre 62 e 82 anos, que se reúnem em grupos de convivência ou são atendidas em Unidades Básicas de Assistência à Saúde da Associação Hospitalar Moinhos de Vento. Todos são moradores das vilas Morro da Cruz e Ilha da Pintada. A maioria são mulheres, 68% residem junto a familiares, dividindo pequenas moradias, 44% ou são analfabetos ou têm dificuldade de leitura. O número de filhos é elevado entre os idosos de menor escolaridade: até 14 filhos. Na autopercepção de seu envelhecimento, a maioria entende que mudou para uma situação melhor, mais alegre, menos cansativa, apesar de quase a totalidade relatar uma ou mais patologias e co-morbidades. O estudo levantou o uso das estratégias SOC: seleção, otimização e compensação no cotidiano dos idosos, revelando a importância das escolhas para a maioria dos idosos, apesar das muitas restrições causadas pela depauperação que vivem em termos socioeconômicos e culturais. Quando podem escolher e otimizar alguma ação significativa de socialização, de trabalho ou de aprendizagem, demonstram mais alegria e satisfação de vida. Quando necessitam compensar algo, eles são verdadeiros mestres, pois a situação de carência sempre esteve presente em seu meio. Compreender a velhice como um constante recomeçar, proporcionando espaços com maiores possibilidades de concretização de escolhas na idade avançada, é conceder cidadania aos idosos. / The getting old process with wellness is a continous learning process with daily search for life sense and existencial renovation. This dissertation discusses the perspective of the so-called lifespan development theory, which understands human development as a process that takes place throughout life. It does so by establishing a dialog with elderly people who live in two neighborhoods located in the outskirts of Porto Alegre, the capital of the state of Rio Grande do Sul, Brazil. The lifespan development model advocates a diluted view of chronological age and is not coupled with normative stages. It is determined by biological, sociocultural, historical and personal aspects. According to this model, human development occurs at a different speed and rhythm for each elderly person. Growing old in a satisfactory manner implies the use of three processes: selection, optimization and compensation. These are strategies for choosing goals and actions designed to achieve them that stimulate the elderly in their capacity for resistance, plasticity and flexibility. They are management strategies for a life with well-being. The author made use of qualitative research and the phenomenological approach when interviewing 30 senior citizens between 62 and 82 years of age who get together to socialize in groups or receive health care services at stations maintained by the Moinhos de Vento Hospital Association. They all live in the neighborhoods called Morro da Cruz and Ilha Pintada. Most of them are women, 68% live with family members, sharing small houses, 44% are illiterate or can hardly read. The number of children is high among the elderly with less education: up to 14 children. According to their own perception of their aging process, most of them think that they are in a better situation now. They feel happier and less tired, although almost all of them report one or more pathologies and comorbidities. The study examined the use of selection, optimization and compensation strategies by the elderly in their daily lives, showing the importance of the choices made by most of them, in spite of the constraints imposed on them by the poverty they experience in socioeconomic and cultural terms. When they are able to choose and optimize some meaningful action related to socializing, working or learning, they show more happiness and satisfaction with their lives. They are masters at compensating because their situation of deprivation is part and parcel of their life experience. Understanding aging as a constant new beginning that opens up possibilities of making choices in old age is a contribution toward turning the elderly into citizens.

Avec l’avènement du traitement distribué et l’utilisation accrue des services web inter et intra organisationnels alimentée par la disponibilité des connexions réseaux à faibles coûts, les données multisources partagées ont de plus en plus envahi les systèmes d’informations. Ceci a induit, dans un premier temps, le changement de leurs architectures du centralisé au distribué en passant par le coopératif et le fédéré ; et dans un deuxième temps, une panoplie de problèmes d’exploitation allant du traitement des incohérences des données doubles à la synchronisation des données distribuées. C’est le cas des bases de prospection marketing où les données sont enrichies par des fichiers provenant de différents fournisseurs.Nous nous intéressons au cadre particulier de construction de fichiers de prospection pour la réalisation de campagnes marketing B-to-B, tâche traitée manuellement par les experts métier. Nous visons alors à modéliser le raisonnement de brokers humains, afin d’optimiser et d’automatiser la sélection du « plan fichier » à partir d’un ensemble de données d’enrichissement multisources. L’optimisation en question s’exprimera en termes de gain (coût, qualité) des données sélectionnées, le coût se limitant à l’unique considération du prix d’utilisation de ces données.Ce mémoire présente une triple contribution quant à la gestion des bases de données multisources. La première contribution concerne l’évaluation rigoureuse de la qualité des données multisources. La deuxième contribution porte sur la modélisation et l’agrégation préférentielle des critères d’évaluation qualité par l’intégrale de Choquet. La troisième contribution concerne BrokerACO, un prototype d’automatisation et d’optimisation du brokering multisources basé sur l’algorithme heuristique d’optimisation par les colonies de fourmis (ACO) et dont la Pareto-optimalité de la solution est assurée par l’utilisation de la fonction d’agrégation des préférences des utilisateurs définie dans la deuxième contribution. L’efficacité du prototype est montrée par l’analyse de campagnes marketing tests effectuées sur des données réelles de prospection. / In Business-to-Business (B-to-B) marketing campaigns, manufacturing “the highest volume of sales at the lowest cost” and achieving the best return on investment (ROI) score is a significant challenge. ROI performance depends on a set of subjective and objective factors such as dialogue strategy, invested budget, marketing technology and organisation, and above all data and, particularly, data quality. However, data issues in marketing databases are overwhelming, leading to insufficient target knowledge that handicaps B-to-B salespersons when interacting with prospects. B-to-B prospection data is indeed mainly structured through a set of independent, heterogeneous, separate and sometimes overlapping files that form a messy multisource prospect selection environment. Data quality thus appears as a crucial issue when dealing with prospection databases. Moreover, beyond data quality, the ROI metric mainly depends on campaigns costs. Given the vagueness of (direct and indirect) cost definition, we limit our focus to price considerations.Price and quality thus define the fundamental constraints data marketers consider when designing a marketing campaign file, as they typically look for the "best-qualified selection at the lowest price". However, this goal is not always reachable and compromises often have to be defined. Compromise must first be modelled and formalized, and then deployed for multisource selection issues. In this thesis, we propose a preference-driven selection approach for multisource environments that aims at: 1) modelling and quantifying decision makers’ preferences, and 2) defining and optimizing a selection routine based on these preferences. Concretely, we first deal with the data marketer’s quality preference modelling by appraising multisource data using robust evaluation criteria (quality dimensions) that are rigorously summarized into a global quality score. Based on this global quality score and data price, we exploit in a second step a preference-based selection algorithm to return "the best qualified records bearing the lowest possible price". An optimisation algorithm, BrokerACO, is finally run to generate the best selection result.