Specific objectives of this study were to develop, prototype, and test a corn population sensor. Both intrusive mechanical and non-intrusive capacitive techniques have been used to develop the stalk population sensors in previous research. However, neither could generate consistent performance. The mechanical method required high maintenance and resulted in significant underestimations of stalk counts. The performance of capacitive systems was limited by inadequate sensing distance, especially at low stalk moisture levels. In this research, the sensitivity of the capacitive sensor was optimized for corn stalks. This system utilized a single-sided capacitive sensor, Wien bridge oscillator, phase-locked loop, and an operational amplifier to transform stalk presence to a change in electrical potential signal. The capacitive sensor patterns were simulated using the finite element method, which provided useful conceptual information. A number of different detection element patterns were modeled and tested. The patterns examined included single-sided two-plate, interdigital, polarized interdigital, semi-interdigital, and solid ground electrode. The key parameters affecting pattern sensitivity were investigated. The most promising pattern, the solid ground electrode, was selected for further evaluation and development. The solid ground electrode detection element was incorporated into circuitry including Wien-Bridge oscillator, a phase-locked loop used as a high-speed frequency-tovoltage converter, and an operational amplifier to provide impedance matching and maximize data acquisition resolution. The operational configuration, optimum operating parameters, and associated component sizes were determined using both modeling and laboratory testing. With an acceptable signal-sided pattern and signal-to-noise ratio, this sensing system was investigated in a realistic production environment. A preliminary field test was used to evaluate the sensor system (including a protective housing and mounting system) and data acquisition system to identify problems before conducting the final field test. Stalk moisture content and harvest speed were used as treatment blocks in the final test. The influences of environmental and mechanical noise and the noise-like influence of corn leaves and weeds were also investigated. The final field test accurately simulated realistic harvesting conditions and real-time data was collected for stalk identification analysis. Post-acquisition processing, feature extraction, and principal component analysis of the extracted features were performed on the raw field data. Three sensor signal features were selected to identify stalks. A backpropagation artificial neural network technique was used to develop the pattern classification model. Numerous neural network structures were evaluated and two-layer structure with four neurons in the first layer and one neuron in the second layer was selected based on maximum prediction precision and accuracy and minimum structure complexity. This structure was then evaluated to determine the prediction accuracy at various resolution levels. Results showed that the model can predict stalk population at 99.5% accuracy when the spatial resolution is 0.025 ha. The sensor can predict stalk population with a 95% accuracy when the resolution is a 9-meter row segment (approximately 10 seconds).

Nitrogen fertilization is important in attaining high yielding, quality tobacco. However, practices that use excessive N can be uneconomical, threaten the environment and produce leaves that are high in nitrates. Leaves high in nitrates have been positively correlated with leaves that are high in tobacco specific nitrosamines (TSNA), which are considered potent carcinogens. Competition from cheaper, foreign leaf, increasing costs of fertilizers and new market structures which show purchasers seeking low TSNA leaf demand that producers become more efficient in their N use. The objective of this study is an examination of burley (TN 90) and dark (KY 171) tobacco cultural practices with the hypothesis that optimizing growing conditions will enhance N efficiency. This experiment took place during 2005 and 2006 in the traditional tobacco growing regions of Springfield (Dickson silt loam) and Greeneville, TN (Lindside silt loam). Experimental isolated growing condition variables. Irrigation treatments isolate the importance of soil moisture. Fertigation, while using irrigation practices, isolates the effects of synchronizing crop N demand with N supply. Plasticulture, using fertigation protocol, isolates the importance of soil temperature. Season long measurements of soilwater tension, soil temperature and leaf nitrates were used to evaluate the ability of each practice to keep plants in optimal N uptake and utilization growing conditions. Results showed that the most dramatic and consistent treatment effects were found in the TSNA analysis. Even during a season characterized by precipitation being sufficient in volume and timing to meet plant water demands, irrigation was successfully able to decrease TSNA concentration by about 30%. During drier growing seasons, TSNA was reduced by 50% or more. Measurements of leaf nitrates taken with a Horiba monitor were able to consistently detect treatment and N rate differences. The last sample taken around eight weeks after transplanting correlated strongly with TSNA content (0.81). This tool could prove effective in characterizing optimal N management. Cultural practices that offer control over soil water tension, nitrate content in leaves and soil temperatures can be effective in increasing the ability of the plant to uptake and utilize N towards achieving high yielding, high GRI quality and low TSNA leaf.

A new method for rapid chemical analysis of lignocellulosic biomass was developed using Fourier transform near-infrared (FT-NIR) spectroscopic techniques. The new method is less time-consuming and expensive than traditional wet chemistry. A mathematical model correlated FT-NIR spectra with concentrations determined by wet chemistry. Chemical compositions of corn stover and switchgrass were evaluated in terms of glucose, xylose, galactose, arabinose, mannose, lignin, and ash. Model development evaluated multivariate regressions, spectral transform algorithms, and spectral pretreatments and selected partial least squares regression, log(1/R), and extended multiplicative signal correction, respectively. Chemical composition results indicated greater variability in corn stover than switchgrass, especially among botanic parts. Also, glucose percentage was higher in internodes (>40%) than nodes or leaves (~30- 40%). Leaves had the highest percentage of lignin (~23-25%) and ash (~4-9%). Husk had the highest total sugar percentage (~77%). Individual FT-NIR predictive models were developed with good accuracy for corn stover and switchgrass. Root mean square errors for prediction (RMSEPs) from crossvalidation for glucose, xylose, galactose, arabinose, mannose, lignin and ash were 0.633, 0.620, 0.235, 0.374, 0.203, 0.458 and 0.266 (%w/w), respectively for switchgrass, and 1.407, 1.346, 0.201, 0.341, 0.321, 1.087 and 0.700 (%w/w), respectively for corn stover. A unique general model for corn stover and switchgrass was developed and validated for general biomass using a combination of independent samples of corn stover, switchgrass and wheat straw. RMSEPs of this general model using cross-validation were 1.153, 1.208, 0.425, 0.578, 0.282, 1.347 and 0.530 %w/w for glucose, xylose, galactose, arabinose, mannose, lignin and ash, respectively. RMSEPs for independent validation were less than those obtained by cross-validation. Prediction of major constituents satisfied standardized quality control criteria established by the American Association of Cereal Chemists. Also, FT-NIR analysis predicted higher heating value (HHV) with a RMSEP of 53.231 J/g and correlation of 0.971. An application of the developed method is the rapid analysis of the chemical composition of biomass feedstocks to enable improved targeting of plant botanic components to conversion processes including, but not limited to, fermentation and gasification.

Decentralized wastewater treatment systems are often designed at flows of either 284 L/person/d (75 gal/person/d) or 568 L/bedroom/d (150 gal/bedroom/d). Water use data suggest that designing systems at these flow rates can lead to overly conservative designs. A study quantifying the risk of failure (exceeding a system design flow) was needed to create a design basis for future systems. The objectives of the study were to quantify the risk of failure of decentralized system design flows depending on the number of residences served by a system and to develop new guidelines for design flows of cluster systems based on quantifiable research. Data sets were from Consolidated Utility District of Rutherford County, Tennessee and contain water use information from July 2005 through July 2006 for seven subdivisions (636 residences) served by cluster systems. Water use was adjusted to wastewater production in each data set using a factor of 80 percent, and from each data set, probability distributions of average monthly flows and monthly peaking factors were made to model the variance due to residences and months, respectively. Monte Carlo simulations were conducted to simulate monthly flow distributions for differing numbers of residences, which were evaluated for risk of exceeding differing design flows. For subdivisions with thirty or more three-bedroom residences, the results show that a design flow of 25552 L/month/residence (225 gal/d/residence) limits the yearly risk of exceeding a month’s design flow to less than one percent. The results of this study can be used to design future cluster systems in similar regions.

A new method for rapid chemical analysis of lignocellulosic biomass was developed using Fourier transform near-infrared (FT-NIR) spectroscopic techniques. The new method is less time-consuming and expensive than traditional wet chemistry. A mathematical model correlated FT-NIR spectra with concentrations determined by wet chemistry. Chemical compositions of corn stover and switchgrass were evaluated in terms of glucose, xylose, galactose, arabinose, mannose, lignin, and ash. Model development evaluated multivariate regressions, spectral transform algorithms, and spectral pretreatments and selected partial least squares regression, log(1/R), and extended multiplicative signal correction, respectively. Chemical composition results indicated greater variability in corn stover than switchgrass, especially among botanic parts. Also, glucose percentage was higher in internodes (>40%) than nodes or leaves (~30- 40%). Leaves had the highest percentage of lignin (~23-25%) and ash (~4-9%). Husk had the highest total sugar percentage (~77%). Individual FT-NIR predictive models were developed with good accuracy for corn stover and switchgrass. Root mean square errors for prediction (RMSEPs) from crossvalidation for glucose, xylose, galactose, arabinose, mannose, lignin and ash were 0.633, 0.620, 0.235, 0.374, 0.203, 0.458 and 0.266 (%w/w), respectively for switchgrass, and 1.407, 1.346, 0.201, 0.341, 0.321, 1.087 and 0.700 (%w/w), respectively for corn stover. A unique general model for corn stover and switchgrass was developed and validated for general biomass using a combination of independent samples of corn stover, switchgrass and wheat straw. RMSEPs of this general model using cross-validation were 1.153, 1.208, 0.425, 0.578, 0.282, 1.347 and 0.530 %w/w for glucose, xylose, galactose, arabinose, mannose, lignin and ash, respectively. RMSEPs for independent validation were less than those obtained by cross-validation. Prediction of major constituents satisfied standardized quality control criteria established by the American Association of Cereal Chemists. Also, FT-NIR analysis predicted higher heating value (HHV) with a RMSEP of 53.231 J/g and correlation of 0.971. An application of the developed method is the rapid analysis of the chemical composition of biomass feedstocks to enable improved targeting of plant botanic components to conversion processes including, but not limited to, fermentation and gasification.

Decentralized wastewater treatment systems are often designed at flows of either 284 L/person/d (75 gal/person/d) or 568 L/bedroom/d (150 gal/bedroom/d). Water use data suggest that designing systems at these flow rates can lead to overly conservative designs. A study quantifying the risk of failure (exceeding a system design flow) was needed to create a design basis for future systems. The objectives of the study were to quantify the risk of failure of decentralized system design flows depending on the number of residences served by a system and to develop new guidelines for design flows of cluster systems based on quantifiable research. Data sets were from Consolidated Utility District of Rutherford County, Tennessee and contain water use information from July 2005 through July 2006 for seven subdivisions (636 residences) served by cluster systems. Water use was adjusted to wastewater production in each data set using a factor of 80 percent, and from each data set, probability distributions of average monthly flows and monthly peaking factors were made to model the variance due to residences and months, respectively. Monte Carlo simulations were conducted to simulate monthly flow distributions for differing numbers of residences, which were evaluated for risk of exceeding differing design flows. For subdivisions with thirty or more three-bedroom residences, the results show that a design flow of 25552 L/month/residence (225 gal/d/residence) limits the yearly risk of exceeding a month’s design flow to less than one percent. The results of this study can be used to design future cluster systems in similar regions.

Infant apnea is a widespread condition in which infants fail to effectively breathe, and can lead to death. Clinical solutions exist for continuous monitoring of respirations in a hospital setting and requiring constant skin contact. This thesis investigates the construction of a proof of concept device that performs in-home monitoring without skin contact and with commonly available off-the-shelf components. The device constructed used a directional microphone to detect breathing sounds, an omnidirectional microphone to detect ambient noise as a baseline to help isolate the breathing sounds, and LabVIEW software deployed on an inexpensive laptop computer to quantify incidents of apparent lapses in breathing meeting the clinical definition of apnea. Testing results indicate that these components are effective in capturing these events in pre-term infants as well as adults, which provides promising evidence that a low-cost system could be manufactured for home detection to assist in infant monitoring.

Current assessments for evaluating the progression of Parkinson’s Disease are largely qualitative and based on small sets of data obtained from occasional doctor-patient interactions. There is a clinical need to improve the techniques used for mitigating common Parkinson’s Disease symptoms. Available data sets for researching the disease are minimal, hindering advancement toward understanding the underlying causes and effectiveness of treatment and therapies. Mobile devices present an opportunity to continuously monitor Parkinson’s Disease patients and collect important information regarding the severity of symptoms. The evolution of digital technology has opened doors for clinical research to extend beyond the clinic by incorporating complex sensors in commonly used devices. Leveraging these sensors to quantify characteristic Parkinson’s Disease symptoms may drastically improve patient care and the reliability of symptom assessment. The goal of this project is to design and develop a system for measuring and analyzing the cardinal symptoms of Parkinson’s using mobile devices. An application for the iPhone and Apple Watch is developed, utilizing the sensors on the devices to collect data during the performance of motor tasks. Assessments for tremor, bradykinesia, and postural instability are implemented to mimic UPDRS evaluations normally performed by a neurologist. The application connects to a cloud-based server to transfer the collected data for remote access and analysis. Example MatLab analysis demonstrates potential approaches for extracting meaningful data to be used for monitoring the progression of Parkinson’s Disease and the effectiveness of treatment and therapies. High-level verification testing is performed to show general efficacy of the assessment tasks. The system design successfully lays the groundwork for a mobile device-based assessment tool to objectively measure Parkinson’s Disease symptoms

This thesis successfully integrates laminate microfluidic devices with an analytic Surface Plasmon Resonance (SPR) instrument. Integration was accomplished at low-cost using materials such as polydimethylsiloxane (PDMS), Poly(methyl methacrylate) (PMMA), Tygon tubing, and a 3-way stopcock. The main components of this thesis are the design and fabrication of the low-cost, in-house fluidics that can integrate with upstream microfluidics and the validation of the in-house fluidics using the Biosensing Instruments BI-2000 SPR instrument. The low-cost fluidics was designed and fabricated “in-house” using a novel investment casting technique that required the use of laser cutting technology to make a master cast, and candle wax to make the fluidic flow gasket. Integration of upstream microfluidic devices is the next step towards fully integrated point-of-care (POC) diagnostics. Development of low-cost POC diagnostics will enable physicians to diagnosis patients outside of clinical settings, granting treatment access to a much wider population. Surface Plasmon Resonance is used for its detection abilities combined with its ability to perform real-time sample analysis. Validation of the in-house fluidics was accomplished by conducting (2) experiments: (1) to compare the angular shift elicited by ethanol solutions between in-house fluidics, factory fluidics, and the literature, and (2) to compare the angular shift between in-house fluidics and factory fluidics caused by the cleaving of fibroblasts from the SPR sensor chip. Successful comparisons made in both experiments proved successful development of low-cost fluidics that could integrate upstream microfluidic devices.

In this work, a novel microfluidic cell culture platform capable of automated electrical impedance measurements and immunofluorescence and brightfield microscopy was developed for further in-vitro cellular research intended to optimize cell culture conditions. The microfluidic system design, fabrication, automation, and design verification testing are described. Electrical and optical measurements of the 16 parallel cell culture chambers were automated via a custom LabView interface. A proposed design change will enable gas diffusion, removing the need for an environmental enclosure and allow long-term cell culture experiments. This "lab on a chip" system miniaturizes and automates experiments improving testing throughput and accuracy while creating a highly controllable microenvironment for cell culture. Such a system can be applied to drug development, bioassays, diagnostics, and animal testing alternatives. This work is part of a collaborative effort to define protocols for the electrical and optical characterization of cell culture within a novel microfluidic device with the intent of optimizing microenvironment conditions.