Determining Amplitude Corrections for the Assessment of Surface Roughness Within A Lidar Footprint

The research presented in this thesis is under the context of the OSIRIS-REx mission, a NASA led asteroid sample return mission being launched in 2016 towards the asteroid 101955 Bennu. Aboard the spacecraft is the OSIRIS-REx Laser Altimeter (OLA), which is using the backscattered intensity for instrument calibration. By applying the novel solution of amplitude correction, it is possible to gain additional functionality out of this instrument. This thesis presents a simulation written by the author that accurately models laser altimeter performance. The simulation is used successfully to study OLA’s receiver to reduce error in the range measurements and to remove the effects of large-scale topographic features on the amplitude. The remaining amplitude variations will be interpreted as mineralogical or morphological variations that may impact the viability or the desirability of the site for sample collection

Regional Strategies for Atmospheric Protection Using Simulation Models

This paper summarizes the results of research on ecological-economic modeling carried out in the Natural Environment and Climate Monitoring Laboratory (GOSKOMGIDROMET) in the period 1979-1987, embodied as a multi-goal, automated system (MARS). The system is designed to assist in developing air quality management strategies for urban and mesoscale regions. Management decisions on controlling atmospheric pollution are made in practice at several administrative levels, i.e., that of a republic, an economic region, a territorial-production complex, a separate state and so on. Such a territorial scale corresponds to the concept of a mesoscale region. One can consider a city as an elementary territorial administrative unit. At the present time, management goals for air quality are not simple. In addition, it is impossible to formulate a model capable of estimating realistically the state of the near-earth layer of the atmosphere. In cities and mesoscale regions, some hundred or even thousand sources of pollutants are situated and the emissions contain various harmful components. To decrease the pollution of the near-earth layers of atmosphere, some concrete measures (usually from 5 to 15) can be taken at each of the sources. Thus the task of identifying and analyzing the effectiveness of various atmosphere protection strategies is important. The MARS program package solves this complicated task for stationary sources. A mesoscale region and a city are represented in MARS by a regular grid of 0.5 km to 10 km (usually 1 km for a city and 10 km for a region). MARS is able to analyze the effectiveness of various control measures. The application of MARS requires a relatively small data bank comprising two parts: (a) information on natural climatic features of the territory and parameters of emission sources; (b) information on technology to reduce emission sources. The first part of the data bank is well worked out and does not cause any difficulties. The second part of the data bank requires a design study of possible technological measures for reducing effluents at the sources. For this it is also necessary to generalize analogues for use in other cities/regions. Proposed models, algorithms, and program packages are used in the USSR as a basis for strategies of atmosphere protection in cities and regions

A Karst Feature Predictability Model within Barber County, Kansas

This research consisted of two topics: 1) geographic predictive models of karst features and 2), a petrographic study examining the lithology of the study area. The study area is a privately owned ranch in the Gypsum Hills of Barber County, Kansas and is known to have karst features. Two predictive models for karst features were utilized. Previously identified features, Light Detection and Ranging (LiDAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery aided in the creation of these predictive models. These predictability models also used the ESRI ArcMap software platform. The data for these models consists of slope, aspect, nearest neighbor elevation, Normalized Difference Vegetation Index (NDVI), land cover/land use, distance to geomorphic features, surface geology, and other attributes calculated in ArcMap. Other software platforms were also used in the creation of these models (Microcomputer Digital Elevation Models (MicroDEM), System for Automated Geoscientific Analyses (SAGA) GIS and Environment for Visualizing Images (ENVI) for imagery analysis). To test these models, features were identified using the sink-fill function in ArcMap on hillshade layers generated from LiDAR data. Field validation of these models successfully identified 52% of the validation points as having karst features, as well as 12 additional points in high probability areas that were visited. A total of 38 additional points (a 51% increase in the karst database) were added to the karst inventory for the property. Understanding the distribution and occurrence of karst features will help landowners mitigate risk such as collapse leading to structural damage and aquifer contamination. Although this model focused on Barber County, Kansas, the techniques and approaches used by these two models may be useful in creating future predictive models in other karst areas. The petrographic portion of this research identified two geologic sedimentary facies using petrographic thin sections from various karst features. The two facies were: 1) Algal Mat and 2) Peloidal. These facies are very close to one another spatially when plotted by sample location within the property. The relative elevation of these facies places the Algal Mat facies below the Peloidal facies. This suggests that there are multiple facies that control karst feature formation as opposed to only the basal carbonates suggested by previous studies

Information recovery from rank-order encoded images

The time to detection of a visual stimulus by the primate eye is recorded at 100 – 150ms. This near instantaneous recognition is in spite of the considerable processing required by the several stages of the visual pathway to recognise and react to a visual scene. How this is achieved is still a matter of speculation. Rank-order codes have been proposed as a means of encoding by the primate eye in the rapid transmission of the initial burst of information from the sensory neurons to the brain. We study the efficiency of rank-order codes in encoding perceptually-important information in an image. VanRullen and Thorpe built a model of the ganglion cell layers of the retina to simulate and study the viability of rank-order as a means of encoding by retinal neurons. We validate their model and quantify the information retrieved from rank-order encoded images in terms of the visually-important information recovered. Towards this goal, we apply the ‘perceptual information preservation algorithm’, proposed by Petrovic and Xydeas after slight modification. We observe a low information recovery due to losses suffered during the rank-order encoding and decoding processes. We propose to minimise these losses to recover maximum information in minimum time from rank-order encoded images. We first maximise information recovery by using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder decoding. We then apply the biological principle of lateral inhibition to minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap Correction algorithm. To test the perfomance of rank-order codes in a biologically realistic model, we design and simulate a model of the foveal-pit ganglion cells of the retina keeping close to biological parameters. We use this as a rank-order encoder and analyse its performance relative to VanRullen and Thorpe’s retinal model

Suspension Near-Field Electrospinning: a Nanofabrication Method of Polymer Nanoarray Architectures for Tissue Engineering

Chapter 1. This chapter is divided into six sections. The first will discuss the issue of nerve tissue loss, and the strategies of therapy (1.1). The second describes the role of nanofabrication in tissue engineering (1.2). The third section details the theoretical background of electrospinning in terms of solution and process parameters (1.3). The fourth section introduces near-field electrospinning (NFES), recent advances in this field and the principles of NFES techniques (1.4). The fifth section details objectives for a tissue engineered construct for neural cell therapy, and presents possible viable solutions (1.5). The sixth summarizes the aims and structure of this thesis (1.6)..

LIQUID CHROMATOGRAPHIC/MASS SPECTROMETRIC INVESTIGATIONS OF BIO-OIL AND ADVANCES IN LASER-INDUCED ACOUSTIC DESORPTION FUNDAMENTALS AND INSTRUMENTATION

Dow, Alexander Russell. Ph. D., Purdue University, August 2015. Liqui

Doctor of Philosophy

dissertationMagnetic Resonance guided High Intensity Focused Ultrasound (MRgHIFU) treatments are a promising modality for cancer treatments in which a focused beam of ultrasound energy is used to kill tumor tissue. However, obstacles still exist to its widespread clinical implementation, including long treatment times. This research demonstrates reductions in treatment times through intelligent selection of the usercontrollable parameters, including: the focal zone treatment path, focal zone size, focal zone spacing, and whether to treat one or several focal zone locations at any given time. Several treatments using various combinations of these parameters were simulated using a finite difference method to solve the Pennes bio-heat transfer equation for an ultrasonically heated tissue region with a wide range of acoustic, thermal, geometric, and tumor properties. The total treatment time was iteratively optimized using either a heuristic method or routines included in the Matlab software package, with constraints imposed for patient safety and treatment efficacy. The results demonstrate that large reductions in treatment time are possible through the intelligent selection of user-controllable treatment parameters. For the treatment path, treatment times are reduced by as much as an order of magnitude if the focal zones are arranged into stacks along the axial direction and a middle-front-back ordering is followed. For situations where normal tissue heating constraints are less stringent, these focal zones should have high levels of adjacency to further decrease treatment times; however, adjacency should be reduced in some cases where normal tissue constraints are more stringent. Also, the use of smaller, more concentrated focal zones produces shorter treatment times than larger, more diluted focal zones, a result verified in an agar phantom model. Further, focal zones should be packed using only a small amount of overlap in the axial direction and with a small gap in the transverse direction. These studies suggest that all treatment time reductions occur due to selection of parameters that advantageously use mechanisms of decreasing the focal zone size to concentrate the power density, increasing thermal superposition in the tumor, decreasing thermal superposition in the normal tissue, and advantageously using nonlinear rates of thermal dose deposition with increasing temperature

Adaptable Spatial Agent-Based Facility Location for Healthcare Coverage

Lack of access to healthcare is responsible for the world’s poverty, mortality and morbidity. Public healthcare facilities (HCFs) are expected to be located such that they can be reached within reasonable distances of the patients’ locations, while at the same time providing complete service coverage. However, complete service coverage is generally hampered by resource availability. Therefore, the Maximal Covering Location Problem (MCLP), seeks to locate HCFs such that as much population as possible is covered within a desired service distance. A consideration to the population not covered introduces a distance constraint that is greater than the desired service distance, beyond which no population should be. Existing approaches to the MCLP exogenously set the number of HCFs and the distance parameters, with further assumption of equal access to HCFs, infinite or equal capacity of HCFs and data availability. These models tackle the real-world system as static and do not address its intrinsic complexity that is characterised by unstable and diverse geographic, demographic and socio-economic factors that influence the spatial distribution of population and HCFs, resource management, the number of HCFs and proximity to HCFs. Static analysis incurs more expenditure in the analytical and decision-making process for every additional complexity and heterogeneity. This thesis is focused on addressing these limitations and simplifying the computationally intensive problems. A novel adaptable and flexible simulation-based meta-heuristic approach is employed to determine suitable locations for public HCFs by integrating Geographic Information Systems (GIS) with Agent-Based Models (ABM). Intelligent, adaptable and autonomous spatial and non-spatial agents are utilized to interact with each other and the geographic environment, while taking independent decisions governed by spatial rules, such as •containment, •adjacency, •proximity and •connectivity. Three concepts are introduced: assess the coverage of existing HCFs using travel-time along the road network and determine the different average values of the service distance; endogenously determine the number and suitable locations of HCFs by integrating capacity and locational suitability constraints for maximizing coverage within the prevailing service distance; endogenously determine the distance constraint as the maximum distance between the population not covered within the desired service distance and its closest facility. The models’ validations on existing algorithms produce comparable and better results. With confirmed transferability, the thesis is applied to Lagos State, Nigeria in a disaggregated analysis that reflects spatial heterogeneity, to provide improved service coverage for healthcare. The assessment of the existing health service coverage and spatial distribution reveals disparate accessibility and insufficiency of the HCFs whose locations do not factor in the spatial distribution of the population. Through the application of the simulation-based approach, a cost-effective complete health service coverage is achieved with new HCFs. The spatial pattern and autocorrelation analysis reveal the influence of population distribution and geographic phenomenon on HCF location. The relationship of selected HCFs with other spatial features indicates agents’ compliant with spatial association. This approach proves to be a better alternative in resource constrained systems. The adaptability and flexibility meet the global health coverage agenda, the desires of the decision maker and the population, in the support for public health service coverage. In addition, a general theory of the system for a better-informed decision and analytical knowledge is obtained

Process planning for robotic wire ARC additive manufacturing

Robotic Wire Arc Additive Manufacturing (WAAM) refers to a class of additive manufacturing processes that builds parts from 3D CAD models by joining materials layerupon- layer, as opposed to conventional subtractive manufacturing technologies. Over the past half century, a significant amount of work has been done to develop the capability to produce parts from weld deposits through the additive approach. However, a fully automated CAD-topart additive manufacturing (AM) system that incorporates an arc welding process has yet to be developed. The missing link is an automated process planning methodology that can generate robotic welding paths directly from CAD models based on various process models. The development of such a highly integrated process planning method for WAAM is the focus of this thesis

Development of scanning ion conductance microscopy for biomedical applications

Imperial Users onl