Thesis (M.S.)--Michigan State University. School of Packaging, 2006. / Title from PDF t.p. (viewed on Nov. 20, 2008) Includes bibliographical references (p. 92-94). Also issued in print.

Thesis (Ph.D.)--University of Alabama at Birmingham, 2008. / Title from PDF title page (viewed on Sept. 17, 2009). Includes bibliographical references (p. 93-97).

Thesis (M.S.)--Ohio University, November, 2005. / Title from PDF t.p. Includes bibliographical references (p. 71-74)

In this thesis we present LaSLAT, a sensor network algorithm thatsimultaneously localizes sensors, calibrates sensing hardware, andtracks unconstrained moving targets using only range measurementsbetween the sensors and the target. LaSLAT is based on a Bayesian filter, which updates a probabilitydistribution over the quantities of interest as measurementsarrive. The algorithm is distributable, and requires only a constantamount of space with respect to the number of measurementsincorporated. LaSLAT is easy to adapt to new types of hardware and newphysical environments due to its use of intuitive probabilitydistributions: one adaptation demonstrated in this thesis uses amixture measurement model to detect and compensate for bad acousticrange measurements due to echoes.We also present results from a centralized Java implementation ofLaSLAT on both two- and three-dimensional sensor networks in whichranges are obtained using the Cricket ranging system. LaSLAT is ableto localize sensors to within several centimeters of their groundtruth positions while recovering a range measurement bias for eachsensor and the complete trajectory of the mobile.

Table of corrections inserted opposite table of contents. Bibliography: p.191-212. Investigates calibration for errors of a synthetic aperture in Inverse Synthetic Aperture Radar and Multiple Pass Synthetic Aperture Radar. Both are reviewed as the problems of array processing and are solved from the point of array calibration.

Made available in DSpace on 2014-06-11T19:23:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-12-13Bitstream added on 2014-06-13T18:09:47Z : No. of bitstreams: 1 catalao_ab_me_ift.pdf: 811288 bytes, checksum: d4e34c59801bd92233bc9f26884a19ab (MD5) / Neste trabalho estudamos a calibração de opções de câmbio no mercado brasileiro utilizando o processo estocástico proposto por Heston [Heston, 1993], como uma alternativa ao modelo de apreçamento de Black e Scholes [Black e Scholes,1973], onde as volatilidades implícitas de opções para diferentes preços de exercícios e prazos são incorporadas ad hoc. Comparamos dois métodos de apreçamento: o método de Carr e Madan [Carr e Madan, 1999], que emprega transfomada rápida de Fourier e função característica, e expansão assintótica para baixos valores de volatilidade da variância. Com a nalidade de analisar o domínio de aplicabilidade deste método, selecionamos períodos de alta volatilidade no mercado, correspondente à crise subprime de 2008, e baixa volatilidade, correspondente ao período subsequente. Adicionalmente, estudamos a incorporação de swaps de variância para melhorar a calibração do modelo / In this work we study the calibration of forex call options in the Brazilian market using the stochastic process proposed by Heston [Heston, 1993], as an alternative to the Black and Scholes [Black e Scholes,1973] pricing model, in which the implied option volatilities related to di erent strikes and maturities are incorporated in an ad hoc manner. We compare two pricing methods: one from Carr and Madan [Carr e Madan, 1999], which uses fast Fourier transform and characteristic function, and asymptotic expantion for low values of the volatility of variance. To analyze the applicability of this method, we select periods of high volatility in the market, related to the subprime crisis of 2008, and of low volatility, correspondent to the following period. In addition, we study the use of variance swaps to improve the calibration of the model

Public health practice and quality of medical care rely heavily on the accuracy, precision, and robustness of risk prediction models. Health care providers use risk prediction models to assess a patient’s risk of developing an event during a specified time frame given the patient’s specific characteristics, and subsequently recommend a course of treatment or preventative action. In public health research, risk prediction models are often constructed with common statistical modeling techniques, such as logistic regression for binary outcomes or Cox proportional hazard regression for time-to-event outcomes, and the performance of the model is assessed through internal or external validation, or some combination. Model validation requires statistical and clinical significance and satisfactory baseline or improvement in model calibration and discrimination: calibration quantifies how close predictions are to observed outcomes while discrimination quantifies the model’s ability to distinguish correctly between events and nonevents. Measures for evaluating these qualities include (but are not limited to) Brier score, calibration-in-the-large, proportion of variation (R2), sensitivity and specificity, area under the receiver operating characteristic curve (AUC), discrimination slope, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision theory analytic measures such as net benefit and relative utility. Among these measures exist several interrelationships under certain assumptions, and their estimation and interpretation is an active area of research. The first two parts of this thesis focus on studying the empirical distributions and improving confidence interval (CI) estimation of ∆AUC, NRI, and IDI for both binary event data and time-to-event data. Through data simulation and the comparison of several CI types derived with bootstrapping techniques, we make recommendations for proper estimation in future work and apply our recommendations to real-life Framingham Heart Study data. The third part of this thesis summarizes the many interrelationships and possible redundancies among the measures listed, extends theoretical formulas assuming normal variables for ∆AUC, NRI, and IDI from nested models to non-nested models and to Brier score, and explores the impact of varying discrimination and calibration assumptions on Yates’ and Sanders’ decomposed versions of Brier score through simulation. Lastly, overall conclusions and future directions are presented at the end.

This thesis describes a method for the recognition of objects in an unconstrained environment with a widely ranging illumination, imaged from unknown view points and complicated background. The general problem is simplified by placing specially designed patterns on the object that allows us to solve the pose determination problem easily. There are several key components involved in the proposed recognition approach. They include pattern detection, pose estimation, model acquisition and matching, searching and indexing the model database. Other crucial issues pertaining to the individual components of the recognition system such as the choice of the pattern, the reliability and accuracy of the pattern detector, pose estimator and matching and the speed of the overall system are addressed. After establishing the methodological framework, experiments are carried out on a wide range of both synthetic and real data to illustrate the validity and usefulness of the proposed methods. The principal contribution of this research is a methodology for Tagged Object Recognition (TOR) in unconstrained conditions. A robust pattern (calibration chart) detector is developed for off-the-shelf use. To empirically assess the effectiveness of the pattern detector and the pose estimator under various scenarios, simulated data generated using a graphics rendering process is used. This simulated data provides ground truth which is difficult to obtain in projected images. Using the ground truth, the detection error, which is usually ignored, can be analysed. For model matching, the Chamfer matching algorithm is modified to get a more reliable matching score. The technique facilitates reliable Tagged Object Recognition (TOR). Finally, the results of extensive quantitative and qualitative tests are presented that show the plausibility of practical use of Tagged Object Recognition (TOR). The features characterising the enabling technology developed are the ability to a) recognise an object which is tagged with the calibration chart, b) establish camera position with respect to a landmark and c) test any camera calibration and 3D pose estimation routines, thus facilitating future research and applications in mobile robots navigations, 3D reconstruction and stereo vision.

abstract: RF transmitter manufacturers go to great extremes and expense to ensure that their product meets the RF output power requirements for which they are designed. Therefore, there is an urgent need for in-field monitoring of output power and gain to bring down the costs of RF transceiver testing and ensure product reliability. Built-in self-test (BIST) techniques can perform such monitoring without the requirement for expensive RF test equipment. In most BIST techniques, on-chip resources, such as peak detectors, power detectors, or envelope detectors are used along with frequency down conversion to analyze the output of the design under test (DUT). However, this conversion circuitry is subject to similar process, voltage, and temperature (PVT) variations as the DUT and affects the measurement accuracy. So, it is important to monitor BIST performance over time, voltage and temperature, such that accurate in-field measurements can be performed. In this research, a multistep BIST solution using only baseband signals for test analysis is presented. An on-chip signal generation circuit, which is robust with respect to time, supply voltage, and temperature variations is used for self-calibration of the BIST system before the DUT measurement. Using mathematical modelling, an analytical expression for the output signal is derived first and then test signals are devised to extract the output power of the DUT. By utilizing a standard 180nm IBM7RF CMOS process, a 2.4GHz low power RF IC incorporated with the proposed BIST circuitry and on-chip test signal source is designed and fabricated. Experimental results are presented, which show this BIST method can monitor the DUT’s output power with +/- 0.35dB accuracy over a 20dB power dynamic range. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

Made available in DSpace on 2014-06-11T19:22:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-05-27Bitstream added on 2014-06-13T19:48:45Z : No. of bitstreams: 1 lopes_rf_me_prud.pdf: 5197639 bytes, checksum: 1c8da2ba222153e72f0e411a1ff89f69 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / A utilização de imagens adquiridas por sensores CCD de médio formato, em plataformas aéreas, é uma alternativa para a redução de custos em projetos de aerolevantamento. Porém, alguns inconvenientes, como a pequena área de cobertura e a limitação às bandas do visível restringem algumas aplicações. Para resolver estes problemas podem ser usados vários sensores CCD simultaneamente, sendo necessária uma etapa posterior de fusão e registro destas imagens. Neste trabalho foram utilizados dois sistemas compostos por múltiplas câmaras, o Sistema de Aquisição e Pós-Processamento de Imagens Tomadas com Câmaras Digitais (SAAPI) e o Sistema de Câmaras Fuji. O sistema SAAPI permite diferentes configurações entre os sensores que o compõem, podendo estar nos modos duplooblíquo ou nadiral à área fotografada. A metodologia desenvolvida no trabalho trata duas questões envolvidas com a configuração de sistemas de múltiplas câmaras: o registro entre imagens IR e RGB e a fusão entre imagens RGB oblíquas, tendo como objetivo o aumento da resolução espectral e geométrica da imagem final produzida. Para isto são utilizados processos como a retificação e reamostragem de imagens. Estes, por sua vez, necessitam dos dados de calibração do sistema de câmaras, pois os parâmetros de orientação interior e exterior (POI e POE) são imprescindíveis para corrigir os efeitos das distorções nas imagens e o efeito da inclinação do sistema de câmaras. Experimentos realizados com imagens áreas, utilizando a metodologia proposta, demonstram que a discrepância nas coordenadas de pontos comuns às imagens registradas foi menor que 1 pixel. Este resultado é dependente da qualidade da calibração do sistema de câmaras / The use of images acquired by medium format CCD sensors from aerial platforms, is a cost effective alternative for aerial surveying projects. However, some drawbacks, as small coverage area and limitation to the visible spectrum bands restrict some applications. To solve these problems multiple CCD sensors can be used simultaneously, requiring a later stage for registration and fusion of these images. In this study, two systems with multiple cameras, the System for Airborne Acquisition and Processing of Digital Images (SAAPI) and Fuji Camera System were used. The SAAPI system allows different configurations between the sensors that can be either double-oblique or nadir to the area photographed. The methodology developed in this study approached two issues involved with systems with multiple cameras: the registration between IR and RGB images and oblique RGB images fusion, aiming at increasing the geometric and spectral resolution of the final image to be produced. There were used processes such as rectification and images resampling. These processes require the previous camera and system calibration, because inner and exterior orientation parameters (IOP and EOP) are essential to correct the effects of distortions in the images and the effect of camera convergence within the system. Experiments performed with real terrestrial and aerial images using the proposed methodology, showed that the discrepancy in the coordinates of common points in the registered images were less than 1 pixel. This result is dependent on the quality of the system and camera calibration