Desenvolvimento de método para inferência de características físicas da água associadas às variações espectrais. Caso de Estudo: Reservatório de Itupararanga/SP

Pós-graduação em Ciências Cartográficas - FCT

Inferência estatística clássica para a confiabilidade de rede de coautoria com enfoque nos vértices

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Detection and Modeling of Boundary Layer Height

This thesis tackles the problem of the automated detection of the atmospheric boundary layer (BL) height, h, from aerosol lidar/ceilometer observations. A new method, the Bayesian Selective Method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in an statistically optimal way different sources of information. Firstly atmospheric stratification boundaries are located from discontinuities in the ceilometer back-scattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneus physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice. The BSM algorithm has been tested for four months of continuous ceilometer measurements collected during the BASE:ALFA project and is shown to realistically diagnose the BL depth evolution in many different weather conditions. Then the BASE:ALFA dataset is used to investigate the boundary layer structure in stable conditions. Functions from the Obukhov similarity theory are used as regression curves to fit observed velocity and temperature profiles in the lower half of the stable boundary layer. Surface fluxes of heat and momentum are best-fitting parameters in this exercise and are compared with what measured by a sonic anemometer. The comparison shows remarkable discrepancies, more evident in cases for which the bulk Richardson number turns out to be quite large. This analysis supports earlier results, that surface turbulent fluxes are not the appropriate scaling parameters for profiles of mean quantities in very stable conditions. One of the practical consequences is that boundary layer height diagnostic formulations which mainly rely on surface fluxes are in disagreement to what obtained by inspecting co-located radiosounding profiles.

Detection of Internal Defects in Concrete Members Using Global Vibration Characteristics

Rock-pocket and honeycomb defects impair overall stiffness, accelerate aging, reduce service life, and cause structural problems in hardened concrete members. Traditional methods for detecting such deficient volumes involve visual observations or localized nondestructive methods, which are labor-intensive, time-consuming, highly sensitive to test conditions, and require knowledge of and accessibility to defect locations. The authors propose a vibration response-based nondestructive technique that combines experimental and numerical methodologies for use in identifying the location and severity of internal defects of concrete members. The experimental component entails collecting mode shape curvatures from laboratory beam specimens with size-controlled rock pocket and honeycomb defects, and the numerical component entails simulating beam vibration response through a finite element (FE) model parameterized with three defect-identifying variables indicating location (x, coordinate along the beam length) and severity of damage (alpha, stiffness reduction and beta, mass reduction). Defects are detected by comparing the FE model predictions to experimental measurements and inferring the low number of defect-identifying variables. This method is particularly well-suited for rapid and cost-effective quality assurance for precast concrete members and for inspecting concrete members with simple geometric forms.

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.

Adaptive sensing for target tracking applications

In a statistical inference scenario, the estimation of target signal or its parameters is done by processing data from informative measurements. The estimation performance can be enhanced if we choose the measurements based on some criteria that help to direct our sensing resources such that the measurements are more informative about the parameter we intend to estimate. While taking multiple measurements, the measurements can be chosen online so that more information could be extracted from the data in each measurement process. This approach fits well in Bayesian inference model often used to produce successive posterior distributions of the associated parameter. We explore the sensor array processing scenario for adaptive sensing of a target parameter. The measurement choice is described by a measurement matrix that multiplies the data vector normally associated with the array signal processing. The adaptive sensing of both static and dynamic system models is done by the online selection of proper measurement matrix over time. For the dynamic system model, the target is assumed to move with some distribution and the prior distribution at each time step is changed. The information gained through adaptive sensing of the moving target is lost due to the relative shift of the target. The adaptive sensing paradigm has many similarities with compressive sensing. We have attempted to reconcile the two approaches by modifying the observation model of adaptive sensing to match the compressive sensing model for the estimation of a sparse vector.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

Stereological Modelling of Random Particles

Stochastic models for three-dimensional particles have many applications in applied sciences. Lévy–based particle models are a flexible approach to particle modelling. The structure of the random particles is given by a kernel smoothing of a Lévy basis. The models are easy to simulate but statistical inference procedures have not yet received much attention in the literature. The kernel is not always identifiable and we suggest one approach to remedy this problem. We propose a method to draw inference about the kernel from data often used in local stereology and study the performance of our approach in a simulation study.

A STUDY OF ILLNESS AND RESERVOIRS ASSOCIATED WITH GIARDIA LAMBLIA IN RURAL EGYPT

A longitudinal investigation of the health effects and reservoirs of Giardia lamblia was undertaken in forty households located in a rural Nile Delta region of Egypt. Stool specimens obtained once weekly for six months from two to four year old children were cyst or trophozoite-positive in 42 percent of the 724 examined. The mean duration of excretion in all but one Giardia-negative child was seven and one-half weeks with a range of one to 17 weeks. Clinical symptoms of illness were frequently observed within a month before or after Giardia excretion in stool of children, but a statistical inference of association was not demonstrated.^ Seventeen percent of 697 specimens obtained from their mothers was Giardia-positive for a mean duration of four weeks and a range of one to 18 weeks. Mothers were observed to excrete Giardia in stool less frequently during pregnancy than during lactation.^ Nine hundred sixty-two specimens were collected from 13 species of household livestock. Giardia was detected in a total of 22 specimens from cows, goats, sheep and one duck. Giardia cysts were detected in three of 899 samples of household drinking water.^ An ELISA technique of Giardia detection in human and animal stool was field tested under variable environmental conditions. The overall sensitivity of the assay of human specimens was 74 percent and specificity was 97 percent. These values for assay of animal specimens were 82 percent and 98 percent, respectively.^ Surface antigen studies reported from the NIH Laboratory of Parasitic Diseases show that antigens of three Egyptian human isolates are different from each other and from most other isolates against which they were tested.^ The ubiquity of human and animal fecal contamination combined with estimates of ill days per child per year in this setting are substantial arguments for the introduction of a suggested mass parasite control program to intervene in the cyclical transmission of agents of enteric disease. ^

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^

Metodología para la optimización de la base de datos de líneas límite del Instituto Geográfico Nacional

Esta metodología se ha desarrollado en el marco de un proyecto que es el objeto del Convenio Específico de Colaboración entre el Instituto Geográfico Nacional y la Escuela de Topografía de la Universidad Politécnica de Madrid relativo a la investigación, desarrollo, formación y difusión de conocimientos en el campo de las tecnologías de la información geográfica (TIG) para la investigación y desarrollo de la tecnología y metodología adecuada para la optimización de la información de la Base de Datos de Líneas Límite de la Dirección General del Instituto Geográfico Nacional. El fin fundamental del mismo es desarrollar una metodología para mejorar la precisión de la Base de Datos de Líneas Límite que tiene el Instituto Geográfico Nacional. La exigencia actual de calidad y seguridad en la descripción geométrica de las líneas límite obliga a optimizar dicha descripción mediante la aplicación de nuevas tecnologías no existentes en el momento del levantamiento, y al diseño de metodologías adecuadas que, minimizando los tiempos y costes de ejecución, consideren asimismo los distintos agentes que participan en España en la definición de las líneas límite. Para desarrollar dicha metodología será necesario en primer lugar digitalizar la información de los cuadernos de campo y las actas de deslinde existentes en el Instituto Geográfico Nacional, para que sea un trabajo abordable desde las tecnologías actuales; posteriormente, volcar la información referente a las líneas límite sobre ortofotografías a escala 1:5.000, a partir de los datos de los cuadernos de campo digitalizados. Se propondrá un nuevo sistema de gestión, tratamiento y almacenamiento de las líneas límite, con información sobre su linaje (origen de datos, precisión), así como el formato de salida de las propias líneas límite. Para controlar la calidad de la metodología propuesta, se deberá validar la misma mediante un estudio teórico de lamedida de rendimientos y precisiones y su verificación mediante toma de datos en campo. Particularmente, se llevará a cabo dicha validación en un conjunto de 140 líneas límite de 36 municipios de la provincia de Ávila y Segovia (los comprendidos en las hojas 556 y 457 del Mapa Topográfico Nacional 1:50.000). Una vez contrastada la metodología y efectuados los oportunos procesos de refinamiento, se redactarán las conclusiones de todo el proyecto, que englobarán las recomendaciones de trabajo y las precisiones resultantes, los rendimientos de los diferentes procesos y los costes que se generen mediante el empleo de la nueva metodología. ABSTARCT: This paper introduces the development of a methodology for the optimisation of the municipal boundaries database of the Instituto Geográfico Nacional. This project has arisen as part of a collaboration agreement between the Instituto Geográfico Nacional and the Escuela de Topografía of the Universidad Politécnica de Madrid which seeks to promote research, development and training in Geographic Information Technologies. Current quality requirements demand the use of new technologies to improve the accuracy of the geometrical description of municipal boundaries. These technologies didn’t exist when the municipal boundaries were first drawn up. Besides, it is convenient to design an appropriate methodology that minimises both costs and time employed. The two main steps in the process are: first, the conversion of all the available data (fixing boundary minutes and field survey notebooks) into digital format in order to make possible their integration in a CAD system; and second, the display and visual overlay of these digital data over an 1:5000 orthophotography of the study area, to identify the boundary monuments. A new system will be proposed to manage, process and storage municipal boundaries information, including its lineage; an output format for these data will be designed as well. In addition, a quality control will be designed to audit this scheme using Data Analysis and Statistical Inference techniques. Moreover, GPS technology will be used to get some boundary monuments co-ordinates to check the results of the proposed methodology. The complete scheme will be tested in a study area corresponding to Ávila and Segovia provinces comprising 140 boundary segments from 36 municipalities.

Fully adaptive gaussian mixture metropolis-hastings algorithm

Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an efficient adaptive Metropolis-Hastings algorithm to draw samples from generic multimodal and multidimensional target distributions. The proposal density is a mixture of Gaussian densities with all parameters (weights, mean vectors and covariance matrices) updated using all the previously generated samples applying simple recursive rules. Numerical results for the one and two-dimensional cases are provided.

An adaptive population importance sampler

Monte Carlo (MC) methods are widely used in signal processing, machine learning and communications for statistical inference and stochastic optimization. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this work, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples generated. The cloud of proposals is adapted by learning from a subset of previously generated samples, in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error and robustness to initialization.

Metodología para la determinación de los parámetros de planificación de los nodos e infraestructuras logísticas en un territorio, considerando en su planificación el impacto sobre los diferentes componentes territoriales

Esta tesis presenta el diseño y la aplicación de una metodología que permite la determinación de los parámetros para la planificación de nodos e infraestructuras logísticas en un territorio, considerando además el impacto de estas en los diferentes componentes territoriales, así como en el desarrollo poblacional, el desarrollo económico y el medio ambiente, presentando así un avance en la planificación integral del territorio. La Metodología propuesta está basada en Minería de Datos, que permite el descubrimiento de patrones detrás de grandes volúmenes de datos previamente procesados. Las características propias de los datos sobre el territorio y los componentes que lo conforman hacen de los estudios territoriales un campo ideal para la aplicación de algunas de las técnicas de Minería de Datos, tales como los ´arboles decisión y las redes bayesianas. Los árboles de decisión permiten representar y categorizar de forma esquemática una serie de variables de predicción que ayudan al análisis de una variable objetivo. Las redes bayesianas representan en un grafo acíclico dirigido, un modelo probabilístico de variables distribuidas en padres e hijos, y la inferencia estadística que permite determinar la probabilidad de certeza de una hipótesis planteada, es decir, permiten construir modelos de probabilidad conjunta que presentan de manera gráfica las dependencias relevantes en un conjunto de datos. Al igual que con los árboles de decisión, la división del territorio en diferentes unidades administrativas hace de las redes bayesianas una herramienta potencial para definir las características físicas de alguna tipología especifica de infraestructura logística tomando en consideración las características territoriales, poblacionales y económicas del área donde se plantea su desarrollo y las posibles sinergias que se puedan presentar sobre otros nodos e infraestructuras logísticas. El caso de estudio seleccionado para la aplicación de la metodología ha sido la República de Panamá, considerando que este país presenta algunas características singulares, entra las que destacan su alta concentración de población en la Ciudad de Panamá; que a su vez a concentrado la actividad económica del país; su alto porcentaje de zonas protegidas, lo que ha limitado la vertebración del territorio; y el Canal de Panamá y los puertos de contenedores adyacentes al mismo. La metodología se divide en tres fases principales: Fase 1: Determinación del escenario de trabajo 1. Revisión del estado del arte. 2. Determinación y obtención de las variables de estudio. Fase 2: Desarrollo del modelo de inteligencia artificial 3. Construcción de los ´arboles de decisión. 4. Construcción de las redes bayesianas. Fase 3: Conclusiones 5. Determinación de las conclusiones. Con relación al modelo de planificación aplicado al caso de estudio, una vez aplicada la metodología, se estableció un modelo compuesto por 47 variables que definen la planificación logística de Panamá, el resto de variables se definen a partir de estas, es decir, conocidas estas, el resto se definen a través de ellas. Este modelo de planificación establecido a través de la red bayesiana considera los aspectos de una planificación sostenible: económica, social y ambiental; que crean sinergia con la planificación de nodos e infraestructuras logísticas. The thesis presents the design and application of a methodology that allows the determination of parameters for the planning of nodes and logistics infrastructure in a territory, besides considering the impact of these different territorial components, as well as the population growth, economic and environmental development. The proposed methodology is based on Data Mining, which allows the discovery of patterns behind large volumes of previously processed data. The own characteristics of the territorial data makes of territorial studies an ideal field of knowledge for the implementation of some of the Data Mining techniques, such as Decision Trees and Bayesian Networks. Decision trees categorize schematically a series of predictor variables of an analyzed objective variable. Bayesian Networks represent a directed acyclic graph, a probabilistic model of variables divided in fathers and sons, and statistical inference that allow determine the probability of certainty in a hypothesis. The case of study for the application of the methodology is the Republic of Panama. This country has some unique features: a high population density in the Panama City, a concentration of economic activity, a high percentage of protected areas, and the Panama Canal. The methodology is divided into three main phases: Phase 1: definition of the work stage. 1. Review of the State of the art. 2. Determination of the variables. Phase 2: Development of artificial intelligence model 3. Construction of decision trees. 4. Construction of Bayesian Networks. Phase 3: conclusions 5. Determination of the conclusions. The application of the methodology to the case study established a model composed of 47 variables that define the logistics planning for Panama. This model of planning established through the Bayesian network considers aspects of sustainable planning and simulates the synergies between the nodes and logistical infrastructure planning.