Master of Science, University of Akron, 2007, Computer Science

When dealing with randomly located or clustered data, interpolation error will vary as the distance to the nearest sample or cluster of samples. The current predominant methods for interpolating non-uniform data are not guaranteed to handle this variability of error well. The non-uniformity of the error surface can easily lead to gross misinterpretations of the interpolated values by the end user. In order to address this limitation of the existing algorithms, this paper examines a method based on the physical structure of an infinitesimally small sphere at the point of interpolation. Using this structure we are able to interpolate based on the ‘illumination' of nearby sample points. Our analysis shows that Microsphere Projection is a viable interpolation technique, and in some cases surpasses the abilities of existing techniques. In one dimension, Microsphere Projection proves to be as accurate as piecewise cubic spline interpolation. In two dimensions, the accuracy of Microsphere Projection seems to outperform thin-plate spline interpolation; and in three dimensions its performance is at least on par with existing techniques. In hyper dimensions it is expected that Microsphere Projection will be even more useful due to its stable extrapolation properties.

Committee: Yingcai Xiao (Advisor) Subjects: Computer Science

Master of Science in Civil Engineering, University of Toledo, 2012, Civil Engineering

According to the United States Environmental Protection Agency, radon is the number one cause of lung cancer among non-smokers, and it is responsible for about 21,000 lung cancer deaths every year in the United States. In the State of Ohio, 14% of lung cancer deaths are caused due to radon. It is essential to have the radon concentration data for every location (i.e., zip codes) so that necessary preventive measures can be taken up. Measuring the radon concentration across the entire State of Ohio will be very expensive and time consuming. This research focuses on the application of six geographical information system (GIS) based interpolation techniques to estimate the radon concentration in the unmeasured zip codes in the State of Ohio. The radon concentration in homes has been obtained by The University of Toledo researchers from various commercial testing services, university researchers, and county health departments. The data are divided into two sets. The first set uses 80% of the data for training different interpolation schemes, and the second data set includes 20% of the data to evaluate the interpolation techniques. Statistical performance measures such as coefficient of correlation (r), Spearman correlation coefficient (¿¿), slope of the regression line (m), ratio of the intercept of the regression line to the average observed concentrations (b/Co), fractional variance (FV), fraction of prediction within a factor of two of the observations (FA2), model comparison measure (MCM2), geometric mean bias (MG), geometric mean variance (VG), normalized mean square error (NMSE), fractional bias (FB), revised index of agreement (IOAr), accuracy for paired peak (Ap), maximum ratio (Rmax), scatter plots, quantile – quantile (QQ) plots and bootstrap 95% confidence interval estimates based on extreme-end concentrations (i.e., peak-end/low-end), and the mid-range concentrations of indoor air quality (IAQ) models are performed on the predicted data points to evaluate th (open full item for complete abstract)

Committee: Ashok Kumar PhD (Committee Chair); Brian W. Randolph PhD (Committee Member); Matthew Franchetti PhD (Committee Member) Subjects: Environmental Engineering

Doctor of Philosophy, The Ohio State University, 2015, Electrical and Computer Engineering

In vivo imaging provides a venue for studying and understanding the biological mechanism of a living system noninvasively. High resolution scanning for MR imaging is practically limited by the length of the scan for in vivo applications. In vivo small animal MRI suffers from subject motion which can degrade image quality with blurring and artifacts. In many small animal imaging studies, multiple imaging views are already obtained as part of the normal workflow but the information taken from one view is not generally combined with that from another view. The main objective of this dissertation is to study the use of multiple imaging views for improving image quality in small animal MR imaging studies. The goal of the study is to evaluate post-processing techniques that could make use of multiple low resolution image acquisitions for increasing resolution in through-plane 3D images and to reduce motion artifacts in in-plane 2D images. Both qualitative and quantitative comparisons are carried out to evaluate the performance of the algorithms and they are demonstrated in in vivo settings.

Committee: Bradley Clymer PhD (Advisor); Kimerly Powell PhD (Advisor); Can Koksal PhD (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2014, College of Engineering

The most stable isotope of radon is Radon-222, which is a decay product of radium-226 and an indirect decay product of uranium-238, a natural radioactive element. According to the United States Environmental Protection Agency (USEPA), radon is the primary cause of lung cancer among non-smokers. The USEPA classifies Ohio as a zone 1 state because the average radon screening level is more than 4 picocuries per liter. To perform preventive measures, knowing radon concentration levels in all the zip codes of a geographic area is necessary. However, it is impractical to collect the information from all the zip codes due to its inapproachability. Several interpolation techniques have been implemented by researchers to predict the radon concentrations in places where radon data is not available. Hence, to improve the prediction accuracy of radon concentrations, a new technique called Quantile Regression Forests (QRF) is proposed in this thesis. The conventional techniques like Kriging, Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI), and Radial Basis Function (RBF) estimate output using complex mathematics. Artificial Neural Networks (ANN) have been introduced to overcome this problem. Although ANNs show better prediction accuracy in comparison to more conventional techniques, many issues arise, including local minimization and over fitting. To overcome the inadequacies of existing methods, statistical learning techniques such as Support Vector Regression (SVR) and Random Forest Regression (RFR) were implemented. In this thesis, Quantile Regression Forest (QRF) is introduced and compared with SVR, RFR, and other interpolation techniques using available operational performance measures. The study shows that QRF has least validation error compared with other interpolation techniques.

Committee: Vijay Devabhaktuni (Committee Chair); Ashok Kumar (Committee Member); Mansoor Alam (Committee Member) Subjects: Applied Mathematics; Electrical Engineering; Mathematics

Master of Science, The Ohio State University, 2009, Mechanical Engineering

Closed-loop control of combustion is of great importance for conventional diesel engines in order to reduce the deterioration in engine performance and emissions caused by different sources of variability. Diesel engine emissions and performance are affected by variability in the combustion root-cause variables like air mass, residual mass, injection parameters etc. If combustion can be referenced based on the root-cause combustion variables, the engine performance and emissions can be improved. Such a referencing, however, leads to increased calibration effort for the combustion controller due to increase in the number of scheduling variables for generating such references as well as due to the increase in the number of references. Conventional methods for generating the set-points for the closed-loop combustion controller are not suitable as they become impractical as the number of scheduling variables increases. In this work, inverse-distance based interpolation methods have been developed to generate the set-points for the closed-loop combustion controller. The inverse-distance interpolation method provides the advantage of reduced calibration effort and can be extended to multiple-dimensional interpolation without significant increase in computational effort. In this work, a closed-loop combustion control architecture has been developed for a heavy-duty diesel engine. The inverse-distance based calibration method has been demonstrated for a simplified version of the closed-loop combustion control architecture. The method involves an optimization approach that generates a scattered set of engine operating points where the engine should be calibrated. The inverse-distance interpolation method can interpolate with the scattered calibration data set to generate the set-points for the closed-loop combustion controller. The proposed method showed a great potential to improve the calibration effort when compared to conventional calibration methods that can be applied (open full item for complete abstract)

Committee: Rizzoni Giorgio PhD (Advisor); Shawn Midlam-Mohler PhD (Committee Member); Yann Guezennec PhD (Committee Member); Steve Yurkovich PhD (Committee Member) Subjects: Mechanical Engineering

Master of Science in Computer Science, Miami University, 2025, Computer Science and Software Engineering

View synthesis for 360° panoramic images is critical for immersive experiences in virtual reality (VR), augmented reality (AR), and interactive media. However, existing methods struggle with handling spherical projections and generating accurate, parallax-consistent views. This thesis proposes a novel approach for view synthesis using Multiplane Images (MPIs) constructed from 360° video frames. To address the complexities of spherical imagery, each frame is converted into six-face cube maps, and MPIs are generated for each face. Depth maps are estimated using the DepthAnything v2 model, providing metric depth in meters. The depth range is divided into intervals to create MPI layers and a cubic alpha transition is applied to smooth blending between layers. The method supports novel view synthesis and view interpolation to generate intermediate perspectives, which are evaluated using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The proposed method is compared against a neural network-based MPI method developed by Google Research to benchmark its effectiveness. The results demonstrate that the depth-based approach achieves comparable or superior performance, offering an interpretable, efficient alternative for view synthesis. This work contributes to computer vision, VR, and AR, enabling more realistic and immersive experiences in virtual environments.

Committee: John Femiani (Advisor); Xianglong Feng (Committee Member); Eric Bachmann (Committee Member) Subjects: Computer Science

Master of Arts, The Ohio State University, 1940, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2007, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1966, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Philosophy, Case Western Reserve University, 2024, Biomedical Engineering

Transradial amputation and chronic hemiparetic stroke both significantly reduce an individual's ability and quality of life by severely limiting end effector (hand) function. The mechanism behind these functional losses differs greatly between the two populations, but many of the clinical goals remain the same. In individuals with transradial amputations, no changes have occurred at the level of the central nervous system. Rather, the losses involve the musculature and joints of the residual limb. To return function to these individuals requires a prosthetic device, with myoelectric devices being the most promising for the recovery of greater function. The bottleneck of motor control research in this area, however, is deriving user intent from the remaining neural control signals. Electromyography, or recording muscle activation induced by neuronal firing, provides an amplified neural control signal. However, such signals can be unreliable when measured from the skin, and the approach is further limited by the lack of remaining musculature of the residual limb. To remedy this shortcoming, we examined the use of chronically implanted intramuscular electrodes to improve prosthetic device controller stability, as well as introduced a novel regression method utilizing Synergy Theory to provide greater degrees of control from the limited available electromyographic signals. For individuals with moderate to severe chronic stroke, the most impactful and debilitating changes occur at the level of the central nervous system, with the peripheral end effector musculature remaining relatively intact in most individuals. However, the changes in descending drive from the central nervous system are so dramatic and impactful that extensor weakness and loss of independent joint control can outright prevent opening of the paretic hand. The expression of the flexion synergy during shoulder abduction loading renders approaches, such as functional electrical stimulation, function (open full item for complete abstract)

Committee: Robert Kirsch (Advisor); Dustin Tyler (Committee Member); Jun Yao (Committee Member); John Chae (Committee Member); Dominique Durand (Committee Chair) Subjects: Biomedical Engineering

Master of Music (MM), Bowling Green State University, 2021, Music Composition

herbstlied or "autumn song" is a composition for two percussionists, two pianos, and fixed media. This work explores how bipolar disorder and subtle, seasonal light fluctuations can affect one's emotional state and sense of stability (or lack thereof). Reconciling ever-fluctuating emotional states with an exploration of timbre and colors, herbstlied formally consists of three modal areas, further subdivided into various textural spaces. The distinctions between these modal areas are obfuscated by assigning each player a partition of the composite and slowly interpolating between the modal areas at different rates. The sound of the ensemble is further augmented through the use of a multitude of implements, including glass bottles, EBows, protractors, and various sticks, mallets, and brushes. Emotional fragility is represented in herbstlied through a limited dynamic range of pppp - p and a slow, nebulous tempo. This nearly constant state of dynamic frailty is punctuated and foiled by two aggressive ff outbursts at the end of the work. By filtering out the central bandwidth of the dynamic range, the ensemble is forced to operate between two dynamic poles, gesturally and literally representing a bipolar relationship. I sincerely hope that herbstlied can be my small contribution toward greater mental health awareness and acceptance in the contemporary music and academic communities. Moreover, it is a sincere expression of gratitude to my wife, Autumn, who acts as my emotional ground in the times that I am most intensely affected by the bipolar phenomena explored in this work.

Committee: Mikel Kuehn PhD (Advisor); Laurello Michael AD (Committee Member); Lillios Elainie DMA (Committee Member) Subjects: Music

Master of Science, The Ohio State University, 2020, Environmental Science

Understanding of within-field spatial and temporal variability of crop yield and the potential drivers for such variability is critical for site-specific crop management (a.k.a precision agriculture) from both economic and environmental perspectives. The objectives of this study are to 1) improve crop yield estimates at a field scale by integrating remote sensing data to assess spatial and temporal within-field yield variability, and 2) evaluate how the design of management zones varies using crop yield data of various spatial resolutions. To meet these objectives, yield monitor data of three fields (~32.5 hectares) that are in corn-soybean and corn-wheat rotations over the period 2016-2019 in the Molly Caren Agriculture Center at London, Ohio were used. Crop grain yield data collected from yield monitor was integrated with topographic variables derived from digital elevation model (DEM) (0.76 m) and vegetation indices derived from high- and medium-resolution remotely sensed imagery (0.3 m to 3 m) using linear regression (LR) and random forest (RF) algorithms to create high-and medium-resolution crop yield maps at a field scale. Yield monitor data were cleaned using Yield Editor software. Topographic variables, such as slope, elevation and wetness index, were calculated using DEM data. Remotely sensed imagery were preprocessed and analyzed, and various vegetation indices (e.g., normalized difference vegetation index (NDVI), Green NDVI (GNDVI), Excess Greenness (ExG)) were calculated. Using high-and medium-resolution yield maps, temporal and spatial standard deviations (SD) of crop yields were calculated. Based on SD and average crop yield, areas within a field were classified into four zones (z), with z1 and z2 having consistently higher and lower yield than average yield, respectively; z3 with variable but below average yield, and z4 with variable but above average yield. DEM derived topographic variables were used to assess their impact on yield variability within (open full item for complete abstract)

Committee: Sami Khanal Dr. (Committee Chair) Subjects: Environmental Science

Master of Science, The Ohio State University, 2020, Electrical and Computer Engineering

Baud rate clock and data recovery circuits are critical to high speed serial links since these require only one sample per data period thereby requiring low speed samplers and comparators. This work models and discusses the backend of one particular Baud rate CDR – Mueller Muller, and analyses some of the building blocks of the CDR – Phase Detector, Phase Interpolator and the Quadrature Phase Generator. Firstly, a PAM-4 Quadrature Phase Detector operating at 80Gb/s is discussed. The challenges associated with designing a Mueller-Muller PD for an asymmetric channel are discussed and one way to resolve this issue is proposed. Then the underlying digital blocks that make up the Phase detector are expanded upon. Secondly, a 64-step digitally controlled Phase Interpolator running at 16GHz clock rate is analyzed and its design challenges with regards to achieving linearity and ensuring duty cycle fidelity are explored. Finally, a Quadrature Phase Generator with digital delay control is analyzed. It is modeled at 16GHz clock rate and the range/resolution problem and its impact on clock jitter is explored.

Committee: Tawfiq Musah (Advisor); Ayman Fayed (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2019, Horticulture and Crop Science

Soybean agronomic optimum seeding rate (AOSR) varies from less than 200,000 seeds ha-1 to over 400,000 seeds ha-1 based on yield potential and environmental factors, and planting at or near the AOSR helps farmers maximize yield. Understanding where AOSR is likely to be high or low is useful for soybean farmers utilizing variable rate seeding. An AOSR representing an area smaller than a whole field is referred to as local ideal seeding rate (LISR). The objective of this on-farm study was to identify soil and terrain attributes that were most predictive of differences in LISR. Randomized, replicated seeding rate strip trials were established at 4 fields in 2017 and 3 fields in 2018. Yield data taken from yield monitors were used to estimate LISR 33 to 68 times per field. Soil physical and chemical properties were measured across the field using 0.2 hectare grid samples. In order to estimate soil fertility at the same scale as LISR, geographically weighted regression and random forest interpolation methods were compared. Geographically weighted regression (GWR) had lower root mean square error and better identified low-phosphorous areas of the field, so GWR was used to interpolate all soil properties. Terrain attributes calculated from 0.76 m digital elevation models were also summarized to this scale. Random forest analysis was performed to identify which soil and terrain attributes were most important for predicting LISR within each site-year. Terrain attributes were generally more important than soil properties at all site-years. Univariate linear models were used to relate the most important soil and terrain attributes to LISR. Valley depth was an important variable for model stability in multiple sites and had a strong univariate relationship with LISR across 7 site-years. Moving from the lowest valley to the highest ridge was associated with an LISR increase of 76,000 seeds ha-1. Aspect and relative slope position also had large univariate impacts on LISR. While (open full item for complete abstract)

Committee: Laura Lindsey (Advisor); John Fulton (Committee Member); Elizabeth Hawkins (Committee Member); Pierce Paul (Committee Member); Sakthi Subburayalu (Committee Member) Subjects: Agronomy; Soil Sciences

MS, University of Cincinnati, 2017, Engineering and Applied Science: Environmental Engineering

Daily PM2.5 (fine particulate matter with an aerodynamic diameter of less or equal to 2.5 micrometers) speciation measurements are only available in Cincinnati, OH for every third day, which limits their use in epidemiologic studies. A temporal interpolation model was applied in order to interpolate the PM2.5 speciated data based on total PM2.5 mass (Redman et al., 2013). In the current work, two types of interpolation techniques were applied: interpolation of the speciated PM2.5, and interpolation of source apportionment (SA) results. Overall, five datasets were generated to be independently used as the input to the health models: A. Original speciated data, B. Original source apportionment results, C. Interpolated speciated data, D. Source apportionment on the interpolated speciated data, E. Interpolation of the original source apportionment results. SA was performed using the EPA model PMF on the original speciated data from 2008 to 2015. Contributions were quantified for seven factors that are proxies for sources: secondary sulfate, secondary nitrate, mobile, road dust, calcium dust (a dust rich in calcium), biomass burning, SOC. With the interpolated data set, PMF was unable to converge using total potassium (used in the original SA) as an input species. Therefore, total potassium was substituted with potassium ion. PMF on the interpolated data shifted mass from the biomass burning factor to mobile and SOC. PMF was then re-run for a slightly modified original data set –with potassium ion instead of total potassium - in order to compare the models with the same input species. The biomass burning factor contributions using the modified data set had higher correlation (R²=0.99) with potassium ion compared to interpolated data but apportioned very low mass (less than 3% of PM2.5). PMF on the interpolated data apportioned mass to a biomass burning factor which had a relatively high correlation with potassium ion (R²=0.78) and accounted for approximat (open full item for complete abstract)

Committee: Sivaraman Balachandran Ph.D. (Committee Chair); Dominic Boccelli Ph.D. (Committee Member); Patrick Ryan Ph.D. (Committee Member) Subjects: Environmental Engineering

PHD, Kent State University, 2017, College of Arts and Sciences / Department of Geography

The Great Lakes-riverine region of eastern North America has been inhabited by human populations for at least 11,500 years. Following the initial colonization by highly mobile early Paleoindian populations (11,500-10,500 RYBP), it has generally been accepted that regional groups exploited successively smaller home ranges and were less mobile as a function of increasing population density and increased packing during the subsequent Archaic period (9,500-4,000 RYBP). This line of thinking can be traced back to Joseph Caldwell (1958) and his seminal concept of a “Primary Forest Efficiency.” This dissertation seeks to systematically and quantitatively evaluate Caldwell's premise by mapping the spatial interaction and distribution of diagnostic and distinctively styled prehistoric hunting implements at five intervals across a 7,500-year span of time. Established methodologies of analysis from the field of archaeology are combined with the introduction of spatial interpolation and a geographic perspective to create a more refined visualization of the transition in mobility behavior throughout time for the region. While there is a trend towards the localization of prehistoric populations, the actual reduction in mobility range size throughout time is more complex than previously assumed.

Committee: Mandy Munro-Stasiuk PhD (Advisor); Mark Seeman PhD (Advisor); James Tyner PhD (Committee Member); Eric Shook PhD (Committee Member); Richard Meindl PhD (Committee Member); Alison Smith PhD (Committee Member) Subjects: Archaeology; Geography

Master of Science (MS), Ohio University, 2017, Geography (Arts and Sciences)

Across Antarctica, most meteorological observations did not begin until the International Geophysical Year of 1957-58, making it difficult to understand Antarctic climate variability during the early 20th century. To overcome this hurdle, this thesis creates, evaluates, and analyzes several seasonal spatial pressure reconstructions that extend back to 1905 across the Antarctic continent. A kriging interpolation method is used to generate the seasonal spatial pressure reconstruction using 19 Antarctic stations as predictors. Multiple evaluation techniques were used to assess the reliability of the spatial pressure reconstructions when compared to ERA-Interim, which is deemed the most reliable gridded pressure dataset after 1979. From all these evaluation metrics, it is concluded that the most reliable spatial pressure reconstructions are for the summer and winter seasons, but all seasons have enough skill to be useful in interpreting pressure variability throughout the 20th century. Using the newly generated spatial reconstructions, it is clearly seen that the negative pressure trend in the late 20th century across the entire continent in DJF is unique when compared to the 100+ year record. Given this uniqueness and contemporary modeling studies, it is likely that stratospheric ozone depletion plays a leading role in the recent negative Antarctic pressure trends in summer. In contrast, the early 20th century in DJF and the entire 20th century for the other seasons are characterized by interannual variability, with strong decadal-scale variability especially prevalent in winter. This highlights the importance of natural variability in causing the majority of ongoing Antarctic circulation pattern changes.

Committee: Ryan Fogt (Advisor); Gaurav Sinha (Committee Member); Jana Houser (Committee Member) Subjects: Atmosphere; Atmospheric Sciences; Climate Change; Geography; Meteorology

Doctor of Philosophy, The Ohio State University, 1982, Graduate School

Committee: Not Provided (Other) Subjects: Mathematics

Doctor of Philosophy, The Ohio State University, 1977, Graduate School

Committee: Not Provided (Other) Subjects: Mathematics

Doctor of Philosophy, The Ohio State University, 1972, Graduate School

Committee: Not Provided (Other) Subjects: Mathematics