In order to produce a more current inventory of forest estimates along with change estimates, the Forest Inventory Analysis (FIA) program has moved to an annual system in which 20% of the permanent plots in a state are surveyed. The previous system sampled permanent plots in 10-year intervals by sampling states sequentially in a cycle (Wayman 2001, USDA FIA). The move to an annual assessment has introduced the use satellite technology to produce forest estimates. Wayman et al (2001) researched the effectiveness of satellite technology in relation to aerial photo-interpretation, finding the satellite method to do an adequate job, but reporting over-estimations of forest area. This research extends the satellite method a step further, introducing the use of ancillary data in post-classification. The US Forest Service has well-defined definitions of forest and nonforest land-use in its (FIA) program. Using these definitions as parameters, post-classification techniques were developed to improve forest area estimates from the initial spectral classification. A goal of the study was to determine the accuracy of using readily available ancillary data. US Census data, TIGER street files, and local tax parcel data were used. An Urban Mask was created based on population density to mask out Forested pixels in a classified image. Logistic Regression was used to see if population density, street density, and land value were good predictors of forest/nonforest pixels. Research was also conducted on accuracy when using contiguity filters. The current filter used by the Virginia Department of Forestry (VDoF) was compared to functions available in ERDAS Imagine. These filters were applied as part of the post-classification techniques. Results show there was no significant difference in map accuracies at the 95% confidence interval using the ancillary data with filters in a post-classification sort. However, the use of ancillary data had liabilities depending on the resolution of the data and its application in overlay. / Master of Science

A better understanding of ecosystem processes requires accurate estimates of forest biomass and structure on global scales. Recently, there have been demonstrations of the ability of remote sensing instruments, such as radar and lidar, for the estimation of forest parameters from spaceborne platforms in a consistent manner. These advances can be exploited for global forest biomass accounting and structure characterization, leading to a better understanding of the global carbon cycle. The popular techniques for estimation of forest parameters from radar instruments in particular, use backscatter intensity, interferometry and polarimetric interferometry. This dissertation analyzes the accuracy of biomass and structure estimates over temperate forests of the North-Eastern United States. An empirical approach is adopted, relying on ground truth data collected during field campaigns over the Harvard and Howland Forests in 2009. The accuracy of field biomass estimates, including the impact of the diameter-biomass allometry is characterized for the field sites. Full waveform lidar data from two LVIS field campaigns of 2009 over the Harvard and Howland forests is analyzed to assess the accuracy of various lidar-biomass relationships. Radar data from NASA JPL's UAVSAR is analyzed to assess the accuracy of the backscatter-biomass relationships with a theoretical radar error model. The relationship between field biomass and InSAR heights is explored using SRTM elevation and LVIS derived ground topography. Temporal decorrelation, a major factor affecting the accuracy of repeat-pass InSAR observations of forests is analyzed using the SIR-C single-day repeat data from 1994. Finally, PolInSAR inversion of heights over the Harvard and Howland forests is explored using UAVSAR repeat-pass data from the 2009 campaign. These heights are compared with LVIS height estimates and the impact of temporal decorrelation is assessed.

Advanced manufacturing machinery is a corner stone of essential industries of technologicallydeveloped societies. Their accuracy permits the production of complexproducts according to tight geometric dimensions and tolerances for high efficiency,interchangeability and sustainability. The accuracy of advanced manufacturingmachinery can be quantified by the performance measure of positioning accuracy.Positioning accuracy measures the closeness between a commanded and an attainedposition on a machine tool or industrial manipulator, and it is ruled by lawsof physics in classical mechanics and thermodynamics. These laws can be applied tomodel how much the machinery deflects due to gravity, expands due to a change intemperature and how much and how long it vibrates due to process forces; hence,one can quantify how much the accuracy decreases. Thus, to produce machinerywith ever higher accuracy and precision one can design machines which deflect,expand and vibrate less or one can understand and model the actual behaviour ofthe machinery to compensate for it.This licentiate thesis uses physics-based modelling to quantify the positioningaccuracy of machine tools and industrial robots. The work investigates the potentialincrease in positioning accuracy because of the simultaneous modelling of the kinematics,static deflections, vibrations and thermo-elasticity as a lumped-parametermodel of the machinery. Consequently the models can be used to quantify thechange of the accuracy throughout the workspace.The lumped parameter models presented in this work require empirical modelcalibration and validation. The success of both, calibration and validation, dependson the availability of the right measurement instruments, as these need to be ableto capture the actual positioning accuracy of machinery. This thesis focuses on theimportance of measurement instruments in industry and metrology and creates acatalogue of requirements and trends to identify the features of the measurementinstruments required for the factories of the future. These novel measurement instrumentsshall be able to improve model calibration and validation for an improvedoverall equipment effectiveness, improved product quality, reduced costs, improvedsafety and sustainability as a result of physics-based modelling and measurementof advanced manufacturing machinery.

No / Radio Frequency Identification (RFID) technology has been widely applied in many scenarios such as inventory control, supply chain management due to its superior properties including fast identification and relatively long interrogating range over barcode systems. It is critical to efficiently identify the unknown tags because these tags can appear when new tagged objects are moved in or wrongly placed. The state-of-the-art Basic Unknown tag Identification Protocol-with Collision-Fresh slot paring (BUIP-CF) protocol can first deactivate all the known tags and then collect all the unknown tags. However, BUIP-CF protocol investigates an ALOHA-like technique and causes too many tag responses, which results in low efficiency. This paper proposes a Fast Unknown tag Identification (FUI) protocol which investigates an indicator vector to label the unknown tags with a given accuracy and removes the time-consuming tag responses in the deactivation phase. FUI also adopts the classical Enhanced Dynamic Framed Slotted ALOHA (EDFSA) protocol to collect the labeled unknown tags. We then investigate the optimal parameter settings to maximize the performance of the proposed FUI protocol. Extensive simulation experiments are conducted to evaluate the performance of the proposed FUI protocol and the experimental results show that it considerably outperforms the state-of-the-art protocol.

A diagnostic test identifies patients according to their disease status. Different meta-analytic models for diagnostic test accuracy studies have been developed to synthesize the sensitivity and specificity of the test. Because of the likely correlation between the sensitivity and specificity of a test, modeling the two parameters using a bivariate model is desirable. Historically, the logit transformation has been used to model sensitivity and specificity pairs from multiple studies as a bivariate normal. In this thesis, we propose two transformations, the arcsine square root and the Freeman-Tukey double arcsine transformation, in the context of a bivariate random-effects model to meta-analyze diagnostic test accuracy studies. We evaluated the performance of the three transformations (the commonly used logit and the proposed transformations) using an extensive simulation study in terms of bias, root mean square error and coverage probability. We illustrate the methods using three real data sets. The simulation study results showed that, for smaller sample size and higher values of sensitivity and specificity, the proposed transformations are less biased, have smaller root mean square error and better coverage probability than the standard logit transformation regardless of the number of studies. On the other hand, for large sample sizes, the usual logit transformation is less biased and has better coverage probability regardless of the true values of sensitivity, specificity and number of studies. However, when the sample size is large, the logit transformation has better root mean square error for moderate and large number of studies. The point estimates of the two parameters, sensitivity & specificity, from the methods using the three real data sets follow patterns similar to those reported by our simulation. / Thesis / Master of Science (MSc)

Microelectronic Measuring Systems (MEMS) are being used to capture kinematic data in real-world environments. The benefits of using MEMS are their small size, relatively low cost (compared to an Optical Motion Capture System) and the ability to capture real-time data in almost any environment. The accuracy and precision of MEMS can be influenced by elements in their surrounding environment such as building materials (i.e., reinforced steel) and structural components (i.e., elevators). Recognizing the influence of the environment on MEMS output is important if the MEMS are to be used in real-world environments where subjects could navigate between various environments. MEMS can also be affected by dynamic motion therefore testing of the MEMS in the same conditions in which they are to be used will help to identify any issues prior to data collection. The overall purpose of this thesis was to determine if the outputs of four Shimmer 2r MEMS were accurate and precise enough in static and dynamic conditions to use in a future study to assess gait activities of daily living in individuals with a unilateral transtibial amputation. In order to understand the effect of the environment on the MEMS, accuracy and precision were assessed in a rural environment (to reduce the effect of building materials and structural components) as well as the clinical environment where they will eventually be used for research. The MEMS were also evaluated in static and dynamic conditions to better understand how motion affected accuracy and precision. The results of this study confirmed that the clinical environment affected the MEMS outputs. During the dynamic condition, the gyroscope output of one MEMS sensor was significantly different than the other devices indicating recalibration or possible exclusion from future studies. Prior to using MEMS in research, it is advisable to investigate the effects of the environment on the sensor outputs as well as assess the performance of the individual sensors. / Thesis / Master of Science Rehabilitation Science (MSc) / The overall objective of this thesis was to determine if four Shimmer 2r Microelectronic Measuring Systems (MEMS) were accurate and precise enough in static and dynamic conditions prior to their use in a future study to assess seven activities of daily living (including level walking, ramp walking and stairs) in individuals with a unilateral transtibial amputation in a clinical environment. To understand the effect the environment has on the MEMS, they were assessed in both a rural environment to reduce the effect of building materials, as well as the clinical environment where they will eventually be used for research. This study confirmed that the clinical environment affected the MEMS outputs, although these effects were deemed to be clinically insignificant for the intended purpose of these MEMS. Calibration as well as accuracy and precision assessment of MEMS should be executed in the conditions and environments in which they are to be utilized.

Methods featured are primarily conformal symplectic exponential time differencing methods, with a focus on families of methods, the construction of methods, and the features and advantages of methods, such as order, stability, and symmetry. Methods are applied to the problem of the damped harmonic oscillator. Construction of both exponential time differencing and integrating factor methods are discussed and contrasted. It is shown how to determine if a system of equations or a method is conformal symplectic with flow maps, how to determine if a method is symmetric by taking adjoints, and how to find the stability region of a method. Exponential time differencing Stormer-Verlet is derived and is shown as the example for how to find the order of a method using Taylor series. Runge-Kutta methods, partitioned exponential Runge-Kutta methods, and their associated tables are introduced, with versions of Euler's method serving as examples. Lobatto IIIA and IIIB methods also play a key role, as a new exponential trapezoid rule is derived. A new fourth order exponential time differencing method is derived using composition techniques. It is shown how to implement this method numerically, and thus it is analyzed for properties such as error, order of accuracy, and structure preservation.

Long-range demand planning and capacity management play an important role for policy makers and airline managers alike. Each makes decisions regarding allocating appropriate levels of funds to align capacity with forecasted demand. Decisions today can have long lasting effects. Reducing forecast errors for long-range range demand forecasting will improve resource allocation decision making. This research paper will focus on improving long-range demand planning and forecasting errors of passenger traffic in the U.S. domestic airline industry. This paper will look to build upon current forecasting models being used for U.S. domestic airline passenger traffic with the aim of improving forecast errors published by Federal Aviation Administration (FAA). Using historical data, this study will retroactively forecast U.S. domestic passenger traffic and then compare it to actual passenger traffic, then comparing forecast errors. Forecasting methods will be tested extensively in order to identify new trends and causal factors that will enhance forecast accuracy thus increasing the likelihood of better capacity management and funding decisions.

We compared several group and individual testing strategies in terms of cost and accuracy and then showed which one is more accurate while costing as little as possible for a specified prevalence rate. / Thesis / Master of Science (MSc)

This study investigated whether watching crime related television shows affected accuracy of psychological profiles for a sexual homicide offender. The television shows in the study were a fiction drama with a profiling element, a fiction drama without a profiling element, a nonfiction show with a profiling element, a nonfiction show without a profiling element, and a fiction drama with no crime element at all. Participants were 290 college students who watched a television show and then profiled a sexual homicide offender. High self-exposure to crime related television shows and experimental exposure to profiling related television shows were associated with greater accuracy for profiling certain aspects of the offender and/or offense. In addition, gender interacted with crime show viewing for certain types of profile accuracy.