The analysis of visibility between two points on the earth's terrain is a common use of GIS software. Most commercial GIS software packages include the ability to generate a viewshed, or a map of terrain surrounding a particular location that would be visible to an observer. Viewsheds are often generated using "bare-earth" Digital Elevation Models (DEMs) derived from the process of photogrammetry. More detailed models, known as Digital Surface Models (DSMs), are often generated using Light Detection and Ranging (LIDAR) which uses an airborne laser to scan the terrain. In addition to having greater accuracy than photogrammetric DEMs, LIDAR DSMs include surface features such as buildings and trees. This project used a visibility algorithm to predict visibility between observer and target locations using both photogrammetric DEMs and LIDAR DSMs of varying resolution. A field survey of the locations was conducted to determine the accuracy of the visibility predictions and to gauge the extent to which the presence of surface features in the DSMs affected the accuracy. The use of different resolution terrain models allowed for the analysis of the relationship between accuracy and optimal grid size. Additionally, a series of visibility predictions were made using Monte Carlo methods to add random error to the terrain elevation to estimate the probability of a target's being visible. Finally, the LIDAR DSMs were used to determine the linear distance of terrain along the lines-of-sight between the observer and targets that were obscured by trees or bushes. A logistic regression was performed between that distance and the visibility of the target to determine the extent to which a greater amount of vegetation along the line-of-sight impacted the target's visibility. / Master of Science

In recent years there has been large increase in the amount of digital mapping data of landscapes and urban environments available through satellite imaging. This digital information can be used to develop wind flow simulators over large cities or regions for various purposes such as pollutant transport control, weather forecasts, cartography and other topographical analysis. It can also be used by architects for city planning or by game programmers for virtual reality and similar applications. But this data is massive and contains a lot of redundant information such as trees, cars, bushes, etc. For many applications, it is beneficial to reduce these huge amounts of data through elimination of unwanted information and provide a good approximate model of the original dataset. The resultant dataset can then be utilized to generate surface grids suitable for CFD purposes or can be used directly for real-time rendering or other graphics applications. Digital Elevation Model, DEM, is the most basic data type in which this digital data is available. It consists of a sampled array of elevations for ground positions that are regularly spaced in a Cartesian coordinate system. The purpose of this research is to construct and test a simple and economical prototype which caters to image procesing and data reduction of DEM images through noise elimination and compact representations of complex objects in the dataset. The model is aimed at providing a synergy between resultant image quality and its size through the generation of various levels of detail. An alternate approach using the concepts of standard deviation helps in achieving the desired goal and the results obtained by testing the model on Salt Lake City dataset verify the claims. Thus, this thesis is aimed at DEM image processing to provide a simple and compact representation of complex objects encountered in large scale urban environment datasets and reduce the size of the dataset to accommodate efficient storage, computation, fast transmission across networks and interactive visualization.

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The mammalian palate develops early in embryogenesis by way of a carefully orchestrated series of temporally and spatially regulated signaling events. Molecular signaling pathways that have been proven to be vital to the process of palatogenesis include TGF-βs, BMPs, FGFs, EGF, and Wnts. The absence of connective tissue growth factor (CTGF/CCN2) has been shown previously to cause failure of proper palatogenesis, i.e. cleft palate. However, the details about the phenotype of this model of cleft palate were scarce. Additionally, CCN2 is known to interact with TGF-βs, BMPs, FGFs, EGF, and Wnts, though information on how these pathways were impacted in the developing palate lacking CCN2 were also not available. In Chapters 2 and 3, through our use of gross specimen and histological examination combined with cell and organ culture, we produced the most detailed characterization of the CCN2 knockout (KO) model of cleft palate with identification of negatively affected signaling pathways that lead to the clefting phenotype. Collection and examination of gross and histological sections revealed at 100% penetrance of cleft palate in which development is impaired around the phase of palatal shelf elevation. Organ culture also revealed that when artificially apposed, the CCN2 KO model system also suffers from a fusion deficit. Finally, utilizing cells isolated from the developing palates, we found a reduction in proliferation, adhesion, and spreading with an enhanced migratory ability. Addition of recombinant CCN2 was able to rescue cell spreading but not proliferation. CCN2 as an immobilized substrate did not rescue adhesive ability. Decreased adhesion and spreading in the KO cells are attributed to the inability of the KO cells to activate Rac1 and RhoA. Examination of gene expression differences by mRNA-sequencing and qRT-PCR revealed numerous gene expression alterations between the wild type (WT) and the KO palates, most notably FGF4 and EGFR. Addition of FGF4 or EGF to cell culture was unable to promote increased proliferation in the KO cells while producing a response in the WT cells. Examination of downstream signaling revealed highly amplified and prolonged ERK1/2 signaling in the FGF4 treated palate cells indicating that FGF signaling is significantly altered in the absence of CCN2. Treatment of the cells with EGF produced a response proportional to EGFR expression differences indicating that EGFR signaling is not impacted beyond the receptor protein levels. The link between EGFR protein levels and FGF mediated ERK1/2 activation is a protein called Spry2. We found greatly reduced Spry2 mRNA levels in the KO palates and upon FGF4 stimulation at 24 hours of exposure indicating that in the absence of CCN2, proper inhibition of FGF signaling and EGFR degradation is negatively altered. Collectively, the data demonstrate that CCN2 is vital to palatogenesis by impacting proliferation, shelf elevation, and shelf fusion through increased FGF signaling and reduced EGFR signaling resulting partially from reduced Spry2 activity. / Biomedical Sciences

Topographic analysis using digital elevation models (DEMs) has become commonplace in soil and hydrologic modeling and analysis and there has been considerable assessment of the effects of grid resolution on topographic metrics using DEMs of 10 m resolution or coarser. However, examining fine-scale (i.e., 1-10 m) soil and hydrological variability of headwater catchments may require higher-resolution data that has only recently become available, and both DEM accuracy and the effects of different high-resolution DEMs on topographic metrics are relatively unknown. This study has two principle research components. First, an error analysis of two high-resolution DEMs derived from light detection and ranging (LiDAR) data covering the same headwater catchment was conducted to assess the applicability of such DEMs for modeling fine-scale environmental phenomena. Second, one LiDAR-derived DEM was selected for computing topographic metrics to predict fine-scale functional soil units termed hydropedological units (HPUs). HPU development is related to topographic and surface/subsurface heterogeneity resulting in distinct hydrologic flowpaths leading to variation of soil morphological expression. Although the two LiDAR datasets differed with respect to data collection methods and nominal post-spacing of ground returns, DEMs interpolated from each LiDAR dataset exhibited similar error. Grid resolution affected DEM-delineated catchment boundaries and the value of computed topographic metrics. The best topographic metrics for predicting HPUs were the topographic wetness index, bedrock-weighted upslope accumulated area, and Euclidean distance from bedrock. Predicting the spatial distribution of HPUs may provide a more comprehensive understanding of hydrological and biogeochemical functionality of headwater systems. / Master of Science

No / The water permeability constant, (K-w), is one of the many important parameters that affect optimal design and operation of RO processes. In model based studies, e.g. within the RO process model, estimation of W-w is therefore important There are only two available literature correlations for calculating the dynamic K-w values. However, each of them is only applicable for a given membrane type, given feed salinity over a certain operating pressure range. In this work, we develop a time dependent neural network (NN) based correlation to predict K-w in RO desalination processes under fouling conditions. It is found that the NN based correlation can predict the K-w values very closely to those obtained by the existing correlations for the same membrane type, operating pressure range and feed salinity. However, the novel feature of this correlation is that it is able to predict K-w values for any of the two membrane types and for any operating pressure and any feed salinity within a wide range. In addition, for the first time the effect of feed salinity on Kw values at low pressure operation is reported. Whilst developing the correlation, the effect of numbers of hidden layers and neurons in each layer and the transfer functions is also investigated. (C) 2014 Elsevier B.V. All rights reserved.

Acute coronary syndrome (ACS) refers to a group of clinical conditions caused by myocardial ischemia including unstable angina, non-ST-segment elevation myocardial infarction (NSTEMI), and ST-segmcnt elevation myocardial infarction (STEMI). Appropriate and accurate diagnosis has life-saving implications and requires a quick but thorough evaluation of the patient's history, physical examination, electrocardiogram, radiographic studies, and cardiac biomarkers. The management of patients with suspected or confirmed ACS continues to evolve as new evidence from clinical trials is considered and as new technology becomes available to both primary care physicians and cardiologists. Low- and intermediate-risk patients have frequently been managed in a chest pain center or in the emergency department. While stress testing with or without radionuclide imaging is the most common evaluation method, a CT angiogram is sometimes substituted High-risk patients are often managed with an early invasive strategy involving left heart catheterization with a goal of prompt revascularization of at-risk, viable myocardium. With the increased availability of cardiac catheterization facilities, patients with STEMI are more commonly being managed with primary percutaneous coronary intervention, although thrombolysis is still used where such facilities are not immediately available. This article provides primary care physicians with a concise review of the pathophysiology, clinical evaluation, and management of ACS based on the best available evidence in 2008.

Tropical cyclone (TC)-spawned tornadoes in Florida were analyzed to determine patterns of occurrence based on storm and geographic features. Tornadoes were determined to be associated with a landfalling or nearby TC if a tornado occurred within 800 km of the TC’s center of circulation. TC-tornadoes were analyzed for patterns based on distance and angle from TC’s center, topographic influences, population biases, and influence based on time of landfall. Most TC-Tornadoes tend to occur more often before landfall than after. It was discovered that tornadoes have occurred in different areas with respect to the bearing from the center depending on the landfall location and time of landfall. It was also discovered that land use type, and elevation had little to do with TC-Tornado occurrence. The results do suggest some population bias. The findings will be a guide for operational meteorologists to aid in forecasting likely tornadogenesis from TCs.

Doctor of Philosophy / Department of Biological & Agricultural Engineering / Naiqian Zhang / Soil surface roughness is a major factor influencing soil erosion by wind and water. Studying surface roughness requires accurate Digital Elevation Model (DEM) data. A vehicle-based laser measurement system was developed to generate high-resolution DEM data. The system consisted of five units: a laser line scanner to measure the surface elevation, a gyroscope sensor to monitor the attitude of the vehicle, a real-time kinematic GPS to provide the geographic positioning, a frame-rail mechanism to support the sensors, and a data-acquisition and control unit. A user interface program was developed to control the laser system and to collect the sensors data through a field laptop. Laboratory experiments were conducted to evaluate the performance of the laser sensor on different type of targets. The results indicated that the laser measurement on a white paper had the least variability than that on other targets. The laser distance measurement was calibrated using the data acquired on the white paper. Static accuracy tests of the gyroscope sensor on a platform that allowed two-axis rotations showed that angle measurement errors observed in combined pitch/roll rotations were larger than those in single rotations. Within ±30° of single rotations, the measurement errors for pitch and roll angles were within 0.8° and 0.4°, respectively. A model to study the effect of attitude measurement error on elevation measurement was also developed. DEM models were created by interpolating the raw laser data using a two-dimensional, three-nearest neighbor, distance-weighted algorithm. The DEM models can be used to identify shapes of different objects. The accuracy of the laser system in elevation measurement was evaluated by comparing the DEM data generated by the laser system for an unknown surface with that generated by a more accurate laser system for the same surface. Within four replications, the highest correlation coefficient between the measured and reference DEMs was 0.9371. The correlation coefficients among the four replications were greater than 0.948. After a median threshold filter and a median filter were applied to the raw laser data before and after the interpolation, respectively, the correlation coefficient between the measured and reference DEMs was improved to 0.954. Correlation coefficients of greater than 0.988 were achieved among the four replications. Grayscale images, which were created from the intensity data provided by the laser scanner, showed the potential to identify crop residues on soil surfaces. Results of an ambient light test indicated that neither sunlight nor fluorescent light affected the elevation measurement of the laser system. A rail vibration test showed that the linear rail slightly titled towards the laser scanner, which caused small variations in the pitch angle. A preliminary test on a bare soil surface was conducted to evaluate the capability of the laser system in measuring the DEM of geo-referenced surfaces. A cross-validation algorithm was developed to remove outliers. The results indicated that the system was capable of providing geo-referenced DEM data.

We present a statistical model of recreational trails, and a method to infer trail routes from geophysical data, namely aerial imagery and terrain elevation. We learn a set of textures (textons) that characterize the imagery, and use the textons to segment each image into super-pixels. We also model each texton's probability of generating trail pixels, and the direction of such trails. From terrain elevation, we model the magnitude and direction of terrain gradient on-trail and off-trail. These models lead to a likelihood function for image and elevation. Consistent with Bayesian reasoning, we combine the likelihood with a prior model of trail length and smoothness, yielding a posterior distribution for trails, given an image. We search for good values of this posterior using both a novel stochastic variation of Dijkstra's algorithm, and an MCMC-inspired sampler. Our experiments, on trail images and groundtruth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding methods.