Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Towards an Autonomous Algal Turf Scrubber: Development of an Ecologically-Engineered Technoecosystem(2010) Blersch, David Michael; Kangas, Patrick C.; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The development of an autonomous and internally-controlled technoecological hybrid is explored. The technoecosystem is based on an algal turf scrubber (ATS) system that combines engineered feedback control programming with internal feedback patterns within the ecosystem. An ATS is an engineered, high-turbulent aquatic system to cultivate benthic filamentous algae for the removal of pollutants from an overlying water stream. This research focuses on designing a feedback control system to control the primary production of algae in an ATS through monitoring of the algal turf metabolism and manipulation of the turbulence regime experienced by the algae. The primary production of algae in an ATS, and thus the potential of the waste treatment process, is known to be directly related to the level of turbulence in the flowing water stream resulting from the amplitude and frequency of the wave surge. Experiments are performed to understand the influence of turbulence on the biomass production rate of algae in an ATS. These results show that biomass production is correlated with wave surge amplitude at a constant frequency. Further, the influence of turbulence on the net ecosystem metabolism of an algal turf in an ATS was investigated. Results showed that both net primary production and respiration, measured through the diurnal change of inorganic carbon, follow a subsidy-stress relationship with increasing wave surge frequency, although some of this trend may be explained by the transfer of metabolic gases across the air-water interface. A feedback control algorithm, developed to monitor the net primary production and manipulate a controlling parameter, was found to converge quickly on the state of maximum primary production when the variance of the input data was low, but the convergence rate was slow at only moderate levels of input variance. The elements were assembled into a physical system in which the feedback control algorithm manipulated the turbulence of the flow in an ATS system in response to measured shifts in ecosystem metabolism. Results from this testing show that the system can converge on the maximum algal productivity at the lowest level of turbulence--the most efficient state from an engineering perspective--but in practice the system was often confounded by measurement noise. Investigation into the species composition of the dominant algae showed shifting relative abundance for those units under automated control, suggesting that certain species are more suited for utilizing the technological feedback pathways for manipulating the energy signature of their environment.Item Advanced imaging and data mining technologies for medical and food safety applications(2009) Jiang, Lu; Tao, Yang; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As one of the most fast-developing research areas, biological imaging and image analysis receive more and more attentions, and have been already widely applied in many scientific fields including medical diagnosis and food safety inspection. To further investigate such a very interesting area, this research is mainly focused on advanced imaging and pattern recognition technologies in both medical and food safety applications, which include 1) noise reduction of ultra-low-dose multi-slice helical CT imaging for early lung cancer screening, and 2) automated discrimination between walnut shell and meat under hyperspectral florescence imaging. In the medical imaging and diagnosis area, because X-ray computed tomography (CT) has been applied to screen large populations for early lung cancer detection during the last decade, more and more attentions have been paid to studying low-dose, even ultra-low-dose X-ray CTs. However, reducing CT radiation exposure inevitably increases the noise level in the sinogram, thereby degrading the quality of reconstructed CT images. Thus, how to reduce the noise levels in the low-dose CT images becomes a meaningful topic. In this research, a nonparametric smoothing method with block based thin plate smoothing splines and the roughness penalty was introduced to restore the ultra-low-dose helical CT raw data, which was acquired under 120 kVp / 10 mAs protocol. The objective thorax image quality evaluation was first conducted to assess the image quality and noise level of proposed method. A web-based subjective evaluation system was also built for the total of 23 radiologists to compare proposed approach with traditional sinogram restoration method. Both objective and subjective evaluation studies showed the effectiveness of proposed thin-plate based nonparametric regression method in sinogram restoration of multi-slice helical ultra-low-dose CT. In food quality inspection area, automated discrimination between walnut shell and meat has become an imperative task in the walnut postharvest processing industry in the U.S. This research developed two hyperspectral fluorescence imaging based approaches, which were capable of differentiating walnut small shell fragments from meat. Firstly, a principal component analysis (PCA) and Gaussian mixture model (PCA-GMM)-based Bayesian classification method was introduced. PCA was used to extract features, and then the optimal number of components in PCA was selected by a cross-validation technique. The PCA-GMM-based Bayesian classifier was further applied to differentiate the walnut shell and meat according to the class-conditional probability and the prior estimated by the Gaussian mixture model. The experimental results showed the effectiveness of this PCA-GMM approach, and an overall 98.2% recognition rate was achieved. Secondly, Gaussian-kernel based Support Vector Machine (SVM) was presented for the walnut shell and meat discrimination in the hyperspectral florescence imagery. SVM was applied to seek an optimal low to high dimensional mapping such that the nonlinear separable input data in the original input data space became separable on the mapped high dimensional space, and hence fulfilled the classification between walnut shell and meat. An overall recognition rate of 98.7% was achieved by this method. Although the hyperspectral fluorescence imaging is capable of differentiating between walnut shell and meat, one persistent problem is how to deal with huge amount of data acquired by the hyperspectral imaging system, and hence improve the efficiency of application system. To solve this problem, an Independent Component Analysis with k-Nearest Neighbor Classifier (ICA-kNN) approach was presented in this research to reduce the data redundancy while not sacrifice the classification performance too much. An overall 90.6% detection rate was achieved given 10 optimal wavelengths, which constituted only 13% of the total acquired hyperspectral image data. In order to further evaluate the proposed method, the classification results of the ICA-kNN approach were also compared to the kNN classifier method alone. The experimental results showed that the ICA-kNN method with fewer wavelengths had the same performance as the kNN classifier alone using information from all 79 wavelengths. This demonstrated the effectiveness of the proposed ICA-kNN method for the hyperspectral band selection in the walnut shell and meat classification.Item Modeling Approaches for Treatment Wetlands(2009) Carleton, James N.; Montas, Hubert J; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Although treatment wetlands can reduce pollutant loads, reliably predicting their performance remains a challenge because removal processes are often complex, spatially heterogeneous, and incompletely understood. Although initially popular for characterizing wetland performance, plug flow reactor models are problematic because their parameters exhibit correlation with hydraulic loading. One-dimensional advective-dispersive-reactive (ADE) models are also inadequate because longitudinal dispersion in wetlands is often non-Fickian as a result of steep velocity gradients. Models that make use of residence time distributions have shown promise in improving performance characterization, particularly when interdependencies of stream-tube scale velocities and reaction rate coefficients are considered (the "DND" approach). However this approach is limited to steady-state conditions, and to an assumption that transverse mixing is nil. This dissertation investigates three aspects of wetland modeling and is organized in a journal paper format. The first paper describes development of a DND model which accommodates non-steady-state conditions. The model processes flow and inlet concentration time series, and calculates as output effluent concentration time series. A version of the code allows optimization of model parameters by minimization of summed squared deviations between predicted and measured effluent concentrations. In example comparisons, model results compare favorably with measured data. The second paper develops an analytical solution to a two-dimensional advective-dispersive-reactive equation, in which all flux terms are expressed as power functions of the transverse dimension. For uniform inlet concentration this idealized heterogeneity model is similar to a DND model, but with the inclusion of transverse diffusion. An example is used to illustrate the beneficial impact that transverse mixing has on reactor performance. The third paper describes development of a model based upon a stochastic interpretation of the ADE. The solution technique that is employed results in a bicontinuum model that for steady-state conditions becomes a weighted sum of two exponential decline functions. For low and intermediate degrees of mixing, model results nicely match those of the corresponding idealized heterogeneity model, and for high mixing they match results of the corresponding one-dimensional ADE. Comparisons against data suggest the bicontinuum model may represent wetland performance better than the DND model in some but not all cases.Item Hyperspectral Reflectance as an Indicator of Foliar Nutrient Levels in Hybrid Poplar Clone OP-367 Grown on Biosolid Amended Soil(2009) Griffeth, Tommy; Felton, Gary; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Trees of the genus Populus are fast growing trees that require considerable amounts of water and nutrients to meet physiological growth demands. The determination of correlations between hybrid poplar leaf spectral reflectance in the 325-1100 nm range, laboratory foliar analysis of leaf macronutrient and micronutrient concentrations, and leaf water potential datasets were analyzed using Full Cross-Validation and Test Set Models via the partial least squares (PLS) method of regression analysis. Based on an evaluation of the slope of the Predicted vs. Measured regression line, the root mean squared error (RMSE), and r-squared, the majority of the models constructed did not adequately model foliar concentrations from spectral data. However, the models for H, N, P, K, Cu and Al had values (slope of the Predicted vs. Measured regression line greater than 0.50 and r-squared values greater than 0.50 in at least one type of model) that warrant future study.Item Novel Statistical Pattern Recognition and 3D Machine Vision Technologies for Automated Food Quality Inspection(2008-12-02) Zhu, Bin; Tao, Yang; Fischell Department of Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Machine vision technologies have received a lot of attention for automated food quality inspection. This dissertation describes three techniques developed to improve the quality inspection of apple and poultry products. First, a Gabor feature-based kernel principal component analysis (PCA) method was introduced by combining Gabor wavelet representation of apple images and the kernel PCA method for apple quality inspection using near-infrared (NIR) imaging. Gabor wavelet decomposition was employed to extract appropriate Gabor features of whole apple NIR images. Then, the kernel PCA method with polynomial kernels was applied in the Gabor feature space to handle nonlinear separable features. The experimental results showed the effectiveness of the Gabor-based kernel PCA method. Using the proposed Gabor kernel PCA eliminated the need for local feature segmentation and also resolved the nonlinear separable problem in the Gabor feature space. An overall 90.5% detection rate was achieved. Second, a novel 3D-based apple near-infrared (NIR) data analysis strategy was utilized so that the apple stem-end/calyx could be identified, and hence differentiated from defects and normal tissue according to their different 3D shapes. Two automated 3D data processing approaches were developed in this research: 1) A 3D quadratic facet model fitting, which employed a small concave 3D patch to fit the 3D apple surface and the best fit could be found around stem-end/calyx area; and 2) A 3D shape enhanced transform (SET), which enhanced the apple stem-end/calyx area and made it easily detectable because of the 3D surface gradient difference between the stem-end/calyx and the apple surface. An overall 92.6% accuracy was achieved. Third, high resolution on-line laser 3D imaging was investigated for improving the 3D profile recovery for thickness compensation purposes. Parallel processing and memory management were also considered to improve the processing speed of the detection system. Multiple-lane coverage was fulfilled such that a wider conveyor could be used and overall throughput would be increased. To further improve the detection performance of the dual X-ray and laser imaging system, a dynamic thresholding approach was introduced to suppress the errors and noise involved by the imaging system. Unlike the traditional single threshold method, dynamic thresholding monitored the responses of the region of interest under a set of thresholds to determine the true physical contaminants, making it more tolerant to the noise than the single threshold method. An overall 98.6% detection rate was achieved.Item Predicting Water Table Fluctuations Using Artificial Neural Network(2008-11-17) Wu, Chung-Yu; Shirmohammadi, Adel; Fischell Department of Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Correctly forecasting groundwater level fluctuations can assist water resource managers and engineers in efficient allocation of the regional water needs. Modeling such systems based on satellite remotely sensed data may be a viable option to predict water table fluctuations. Two types of water table prediction models based on Artificial Neural Network (ANN) technology were developed to simulate the water table fluctuations at two well sites in Maryland. One was based on the relationship between the variations of brightness temperature and water table depth. The other one was based on the relationship between the changes of soil moisture and water table depth. Water table depths recorded at these two wells, brightness temperature retrieved from the Advanced Microwave Scanning Radiometer, and soil moisture data produced by the Land Data Assimilation System were used to train and validate the models. Three models were constructed and they all performed well in predicting water table fluctuations. The root mean square errors of the water table depth forecasts for 12 months were between 0.043m and 0.047m for these three models. The results of sensitivity test showed that the models were more sensitive to the uncertainty in water table depth than to that in brightness temperature or in soil moisture content. This suggests that for situations where high resolution remotely sensed data is not available, an ANN water table prediction model still can be built if the trend of the time series of the data, such as brightness temperature or soil moisture, over the study site correlates well with the trend of the time series of the ground measurement at the study site. An extension of the study to a regional scale was also performed at 12 available well sites in Piedmont Plateau, Maryland. Hydrologic soil types, LDAS soil moistures, and water table depths at these locations were used in the ANN modeling. The root mean square error of one month long water table depth forecast was 0.142m. However, the accuracy of the monthly forecast decreases with the increase of time. A further study to improve the accuracy of long-term water table fluctuation forecast is recommended.Item Comparison of Infiltration Equations and their Field Validation with Rainfall Simulation(2006-12-13) Turner, Ellen Rebecca; Felton, Gary K; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Infiltration is a complex process with many factors contributing to the rate. Different approximate equations for infiltration differ in the parameters they require and predict different infiltration rate curves. Five equations including those of Kostiakov, Horton, Holtan, Philip and Green-Ampt were compared to determine which one most accurately predicted measured infiltration rates from rainfall simulation events at two different locations. Parameters were developed from measured infiltration data and laboratory analyses of soil samples. The Green-Ampt, Holtan and Philip equations with respective root mean squared errors of 0.15, 0.17, and 0.19 cmh-1, provided the first, second and third best estimates of infiltration rates, for observed infiltration data at the University of Maryland's Research and Education Center in Upper Marlboro, Maryland. An atypical infiltration curve was observed for the Poplar Hill site on the Eastern Shore of Maryland for which infiltration rate was constant and equal to rainfall rate.Item INTEGRATED ECONOMIC DECISION SUPPORT SYSTEM MODEL FOR DETERMINING IRRIGATION APPLICATION AND PROJECTED AGRICULTURAL WATER DEMAND ON A WATERSHED SCALE(2006-11-27) Hanna, Kalim; Shirmohammadi, Adel; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study involves the development of an irrigation economic model used to determine the estimated net benefit of various irrigation systems when used in temperate zones. The model processes SWAT (Soil and Water Assessment Tool) output data together with user supplied economic data as a basis for identifying agricultural fields likely to result in the greatest economic return for irrigation installations, based on irrigation installation costs, water costs, and the expected revenue from increased yields due to applied water. The model is capable of not only identifying those agricultural fields within the area of interest likely to result in the greatest net benefit, but is able to prescribe the most profitable irrigation system from an array of possible systems, based on user supplied economic and performance data. The model can also be used to determine the optimal average monthly irrigation volume to be applied to a given field, by balancing the expected revenue due to the estimated yield increase as a result of irrigation application verses the cost of water. The model is applied in this study to a range of water cost levels and crop types from which general conclusions about the use of irrigation in temperate zones are made. The primary product of this study is an irrigation economic tool capable of determining the profitability of irrigation installations verses non-irrigated systems for a wide range of hydrological and environmental conditions. The project included the collection and compilation of required data on land-use, topography, and soil properties, into a GIS project, used as a data input basis for the SWAT model. For demonstration purposes the model is applied to the Pocomoke River basin located in the Coastal Plain of Maryland's Eastern Shore. Input data for the model is taken from multiple SWAT simulations for various crops, modeled with a statistically generated artificial weather pattern typical of the region. Further analysis is conducted on the environmental impact of irrigation, using SWAT model simulations over a range of irrigation application levels. General conclusions are drawn on the effects of irrigation on water quality parameters and the nutrient/sediment transport processes involved.Item MULTI-SCALE INVERSE MODELING IN BIOLOGICAL MASS TRANSPORT PROCESSES(2006-11-24) SADEGH ZADEH, KOUROUSH; MONTAS, HUBERT J; Fischell Department of Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A state-of-the-art inverse modeling strategy was developed, analyzed, and applied in two different biological mass transport processes. The strategy was developed in the framework of the nonlinear optimization problem in which model parameters were estimated by minimizing an appropriate objective function which represents the discrepancy between the observed and predicted responses of the biological systems. The forward problems were solved numerically using the mass conservative Galerkin based linear finite element and finite difference methods. Before incorporating in the framework of the inverse code, the numerical simulators were validated with either analytical or reference solutions. In the inverse code, the Osborne- Moré extended version of the Levenberg- Marquardt algorithm was used to determine the search direction. The Jacobian matrix was constructed using partial derivatives of the state variables with respect to model parameters by one and two-sided finite difference approximations. A mixed termination criterion was used to end the optimization. The strategy was applied to parameter identification problem in Fluorescence Recovery after Photobleaching (FRAP) protocol to estimate the optimized values of the mass transport and binding rate parameters for GFP-tagged glucocorticoid receptor. Results indicate that the protocol provides enough information to uniquely estimate one parameter. It also provides enough information to uniquely estimate the individual values of the binding rate coefficients given the value of the molecular diffusion coefficient is known. However, the protocol provides insufficient information for unique simultaneous estimation of three parameters (diffusion coefficient and binding rate parameters) owing to the high intercorrelation between the molecular diffusion coefficient and pseudo-association rate parameter. Attempts to estimate macromolecule mass transport and binding rate parameters simultaneously from FRAP data result in misleading conclusions regarding concentrations of free macromolecule and bound complex inside the cell, average binding time per vacant site, average time for diffusion of macromolecules from one site to the next, and slow or rapid mobility of biomolecules in cells. To obtain unique values for molecular diffusion coefficient and binding rate parameters of biomolecule, two FRAP experiments should be conducted on the same class of macromolecule and cell. One experiment should be used to measure the molecular diffusion coefficient independently of binding in an effective diffusion regime and the other should be conducted in a reaction dominant or reaction-diffusion regime to quantify the binding rate parameters. The inverse modeling strategy was also successfully used to identify hydraulic parameters for both single and multi-objective optimization problems in homogeneous and heterogeneous variably saturated soils. Incorporating both soil water content information and soil water pressure head data in the framework of the multi-objective parameter optimization, produced excellent result for both soil water content and pressure head profiles.Item IMPROVEMENT IN ESTIMATING POLLUTION TRANSPORT BY DEVELOPING STREAMFLOW COMPONENTS ASSESSMENT IN THE GIS ENVIRONMENT(2006-11-10) Nejadhashemi, Amirpouyan; Shirmohammadi, Adel; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Water by nature is a suitable domain for the transport of contaminants through watersheds. Evaluating the relative amounts of stored or moving water via the different components of the hydrological cycle is required for precise and strict management and planning of water resources. One of the most challenging parts of this process is the separation and quantification of baseflow from the total streamflow hydrograph. The aim of this study was to separate the storm runoff hydrograph into its components, thus being able to infer about sources and hydrological pathways of the storm runoff. The specific objectives of this study were to identify the most accurate and user-friendly streamflow partitioning method, to evaluate the accuracy of each of these methods using separately measured surface and subsurface flow data, and finally to improve available techniques or develop a more precise approach for separation of hydrograph components. In the early stage of this study, forty different streamflow partitioning methods were reviewed and classified into three-component, analytical, empirical, graphical, geochemical and automated methods and five methods were identified as being the most relevant and least input intensive. The performance of these methods were tested against independently measured surface and subsurface flow data obtained on a field scale watershed Boughton's method produced the most consistent and accurate results. However, its accuracy depends upon the proper estimation of the end of surface runoff, and the fraction factor (α). It was demonstrated that incorporating physical and hydrologic characteristics of a watershed can significantly improve the accuracy of hydrograph separation techniques when used jointly with enhanced recession limb analysis, calibration approach, and time-discretization method. Finally, simulation of the model for different scenarios (e.g., soils, land use, etc.) was performed within the geographical information systems for a large scale watershed (Little River Watershed in Georgia). Results showed that the weighted discharge method is better than the weighted average curve number method and the modified Boughton's method because it divides a watershed into small filed scale pixels and treats each pixel separately, thus mimicking the field scale station Z conditions where the method was successfully applied.