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Point source extraction is critical to proper analysis of images containing point sources obtained by focal plane array cameras. Two popular methods of extracting the intensity of a point source are aperture photometry and point spread function fitting. Digital aperture photometry encompasses procedures utilized to extract the intensity of an imaged point source. It has been used by astronomers in various forms for calculating stellar brightness. It is also useful for doing analysis of data associated with other unresolved radiating objects. The various aperture photometry methods include the two-aperture method, aperture correction, and growth curve method. The growth curve method utilizes integrated irradiance within an aperture versus growing aperture size. Signal to noise ratio, imperfect backgrounds, moving and off centered targets, and noise structure are just a few of the items that can cause problems with point source extraction. This thesis presents a study of how best to apply the growth curve method. Multiple synthetic image sets were produced to replicate real world data. The synthetic images contain a Gaussian target of known intensity. Noise was added to the images, and various image related parameters were altered. The growth curve method is then applied to each data set using every reasonable aperture size combination to calculate the target intensity. It will be shown that for different types of data, the most optimal application of the growth curve method can be determined. An algorithm is presented that can be applied to all data sets that fall within the scope of this study will be presented.

An excessive curve of Spee (COS) is a common orthodontic finding, particularly in patients with a deep overbite (OB). The purpose of this analysis was to evaluate COS leveling and OB correction in patients treated with IncognitoTM, a customized lingual appliance system. Pre- and post-treatment cephalometric radiographs were compared for 34 patients with a deep OB and excessive COS treated with IncognitoTM. The mean pre- and post-treatment COS was 1.78 mm (SD: 0.36 mm) and 0.37 mm (SD: 0.41 mm) respectively, indicating a significant amount of leveling (-1.41 mm, SD: 0.49 mm, p<0.001). The mean pre- and post-treatment OB was 5.80 mm (SD: 1.26 mm) and 2.91 mm (SD: 0.86 mm) respectively, demonstrating a significant reduction in OB (-2.89 mm, SD 1.27 mm, p<0.001). COS and OB correction was accomplished by incisor proclination, and a greater (but not significantly different) amount of mandibular incisor intrusion versus premolar and molar extrusion.

Remittances significantly affect recipient households’ behavior. Using data from the Jamaican Survey of Living Conditions and the Jamaican Labor Force Survey ( 2001-2007), this dissertation explores the effects of remittances on labor market participation and household expenditures. Jamaica’s proximity to the United States and its diaspora of educated individuals shapes an economy largely dependent on remittances, thus providing an interesting case study. First, we investigate whether remittances alter labor market behavior of married women in remittance-receiving households located in Jamaica. Remittances, the wife’s education, and wages are all treated as endogenous when estimating labor market participation and hours worked. Unlike other studies, we find that after instrumenting for remittances, the outside income has no significant effect on the supply of labor. Second, we assess the extent that remittances alter the consumption pattern of recipient households in Jamaica. Classical theory predicts that total income affects household consumption decisions, but developments in behavioral economics suggest otherwise. The disaggregation of income streams and consumption expenditures provide us with unique insight into household behavior and in particular, spending on items such as food, schooling, and vices. Using Engel curve estimation and the two-part fractional response models, we find that the source of income significantly affects the shares of income spent in specific consumption categories. Recipients, for example, generally spend more of their income on schooling and home production and less at the grocery store. These findings suggest important implications should government look to tax or restrict the flow of remittances.

Probabilistic decline curve analysis (PDCA) methods have been developed to quantify uncertainty in production forecasts and reserves estimates. However, the application of PDCA in shale gas reservoirs is relatively new. Limited work has been done on the performance of PDCA methods when the available production data are limited. In addition, PDCA methods have often been coupled with Arp’s equations, which might not be the optimum decline curve analysis model (DCA) to use, as new DCA models for shale reservoirs have been developed. Also, decline curve methods are based on production data only and do not by themselves incorporate other types of information, such as volumetric data. My research objective was to integrate volumetric information with PDCA methods and DCA models to reliably quantify the uncertainty in production forecasts from hydraulically fractured horizontal shale gas wells, regardless of the stage of depletion. In this work, hindcasts of multiple DCA models coupled to different probabilistic methods were performed to determine the reliability of the probabilistic DCA methods. In a hindcast, only a portion of the historical data is matched; predictions are made for the remainder of the historical period and compared to the actual historical production. Most of the DCA models were well calibrated visually when used with an appropriate probabilistic method, regardless of the amount of production data available to match. Volumetric assessments, used as prior information, were incorporated to further enhance the calibration of production forecasts and reserves estimates when using the Markov Chain Monte Carlo (MCMC) as the PDCA method and the logistic growth DCA model. The proposed combination of the MCMC PDCA method, the logistic growth DCA model, and use of volumetric data provides an integrated procedure to reliably quantify the uncertainty in production forecasts and reserves estimates in shale gas reservoirs. Reliable quantification of uncertainty should yield more reliable expected values of reserves estimates, as well as more reliable assessment of upside and downside potential. This can be particularly valuable early in the development of a play, because decisions regarding continued development are based to a large degree on production forecasts and reserves estimates for early wells in the play.

The estimation of software project attributes, such as size, is important for software project resource planning and process control. However, research regarding software attribute modeling, such as size, effort, and cost, are high-level and static in nature. This research defines a new operation-level software project attribute that describes the operational characteristic of a software project. The result is a measurement based on the s-curve parameter that can be used as a control variable for software project management. This result is derived from modeling the count of artifact instances created by the software engineering process, which are stored by software tools. Because of the orthogonal origin of this attribute in regard to traditional static estimators, this s-curve based software attribute can function as an additional indicator of software project activities and also as a quantitative metric for assessing development team capability.

The objective of this dissertation is to develop a suitable statistical methodology for functional data analysis. Modern advanced technology allows researchers to collect samples as functional which means the ideal unit of samples is a curve. We consider each functional observation as the resulting of a digitized recoding or a realization from a stochastic process. Traditional statistical methodologies often fail to be applied to this functional data set due to the high dimensionality. Functional hypothesis testing is the main focus of my dissertation. We suggested a testing procedure to determine the significance of two curves with order restriction. This work was motivated by a case study involving high-dimensional and high-frequency tidal volume traces from the New York State Psychiatric Institute at Columbia University. The overall goal of the study was to create a model of the clinical panic attack, as it occurs in panic disorder (PD), in normal human subjects. We proposed a new dimension reduction technique by non-negative basis matrix factorization (NBMF) and adapted a one-degree of freedom test in the context of multivariate analysis. This is important because other dimension techniques, such as principle component analysis (PCA), cannot be applied in this context due to the order restriction. Another area that we investigated was the estimation of functions with constrained restrictions such as convexification and/or monotonicity, together with the development of computationally efficient algorithms to solve the constrained least square problem. This study, too, has potential for applications in various fields. For example, in economics the cost function of a perfectly competitive firm must be increasing and convex, and the utility function of an economic agent must be increasing and concave. We propose an estimation method for a monotone convex function that consists of two sequential shape modification stages: (i) monotone regression via solving a constrained least square problem and (ii) convexification of the monotone regression estimate via solving an associated constrained uniform approximation problem.

Voltage sags are short duration of voltage reduction caused by system faults, overloads and starting of large motors. Voltage sags are the main causes of trips of various sensitive equipment. In order to understand the voltage-tolerance performance of some process control equipment, this thesis presents test results of some sensitive equipment (such as computers, AC contactors, high intensity discharge lamps and programmable logic controller) and provides their voltage tolerance curves. A number of magnitudes and durations recommended by IEC 61000- 4- 11 are used to perform the tests. With the performance information in hands, power quality requirements of different types of equipment and customer, and area of vulnerability for sensitive loads could be estimated.

The H2/air micro PEMFC stacks were designed and fabricated in-house through MEMS (Micro-Electro-Mechanical System) technology with deep UV lithography manufacturing processes (SU-8 photoresist) and micro electroforming manufacturing processes to construct a novel metallic bipolar plate. The effect of different operating parameters on micro PEMFC stacks performance was experimentally investigated for serpentine flow-field configuration. Experiments were conducted through a serious laboratory experiments with different operating conditions of temperature (25oC, 35 oC and 50 oC), anode backpressures (97kPa, 153kPa, 207kPa) as well as anode humidifier temperature (25oC, 35 oC and 50 oC). Experimental results are presented in the form of polarization VI curves and PI curves under above operating conditions. The influence of the aforementioned parameters was presented and discussed.

In the smiling curve theory, many enterprises in Taiwan product merchandise by OEM (Original Equipment Manufacturing) model become the one which has lowest additional value. It seems that there is no future of Taiwan's OEM industry. It suggests that the enterprises in Taiwan should make the transition to the research or own brands. But, Can the smiling curve theory be really suitable for the industries of OEM unconditionally? ODM (Own Designing and Manufacturing) should subdivide into two models, passive ODM and active ODM. Passive ODM is receiving requirements of brand owners and then designs detail functions of product and processes of manufacture. Active ODM is made a product of open model and provides it for brand owners. When the product of open model is accepted by customer, the manufacturer can deliver the product with little lead time. In Passive ODM model, the brand owner should take the risk of marketing. But in active ODM model, the manufacturer takes marketing risk, too. It is more difficult for manufacturer to know the market rather than to manufacture. By using simulation gets two results: 1.When the manufacturer doesn't know the market, it is high risk to make the transition to active ODM or OBM (Own Branding and Manufacturing). 2.It is more safety to make the transition to passive ODM and the manufacturer still can shorten the cycle of new product development, get more orders and improve the ability of the enterprise.