This work highlights slab coupled optical sensors (SCOS) and their ability to measure high voltages. Although other high voltage measurement techniques exist, most of these techniques are electrical devices and are therefore more susceptible to stray ground currents and other electromagnetic interferences (EMI), which may cause signal distortion. Optical sensors are less susceptible to such interferences and these sensors, such as the Pockels cell, have been used in measuring high voltage. SCOS offer an alternative method of measuring high voltage optically. Consisting of an optical fiber and an electro-optic slab waveguide, SCOS have the advantage of being very small in size (0.2 mm x 0.3 mm cross-section), simpler composition, and potentially less coupling losses. Issues associated with high voltage measurements are addressed such as unwanted corona, arcing, and EMI. Solutions are also explored which include insolating materials, electrode geometries, Faraday cages, and using optical sensors such as SCOS. Although the SCOS has been traditionally used to measure electric field, the SCOS is able to measure high voltage through the use of an electrode structure. The SCOS' ability to measure high voltage is showcase through the construction and output measurements of several high voltage systems: an ignition coil-based circuit, a dual ignition coil circuit, a Marx generator, and a 200 kV generator used in a capacitor discharge configuration. These measurements show the SCOS' ability to measure at least 111 kV capacitor discharges with 6.6 ns rise times and other various high voltage waveforms.

<p>Metal wire deposition by means of robotized laser welding offers great saving potentials, i.e. reduced costs and reduced lead times, in many different applications, such as fabrication of complex components, repair or modification of high-value components, rapid prototyping and low volume production, especially if the process can be automated. Metal deposition is a layered manufacturing technique that builds metal structures by melting metal wire into beads which are deposited side by side and layer upon layer. This thesis presents a system for on-line monitoring and control of robotized laser metal wire deposition (RLMwD). The task is to ensure a stable deposition process with correct geometrical profile of the resulting geometry and sound metallurgical properties. Issues regarding sensor calibration, system identification and control design are discussed. The suggested controller maintains a constant bead height and width throughout the deposition process. It is evaluated through real experiments, however, limited to straight line deposition experiments. Solutions towards a more general controller, i.e. one that can handle different deposition paths, are suggested.</p><p>A method is also proposed on how an operator can use different sensor information for process understanding, process development and for manual on-line control. The strategies are evaluated through different deposition tasks and considered materials are tool steel and Ti-6Al-4V. The developed monitoring system enables an operator to control the process at a safe distance from the hazardous laser beam.</p><p>The results obtained in this work indicate promising steps towards full automation of the RLMwD process, i.e. without human intervention and for arbitrary deposition paths.</p> / RMS

Detection of chromium and vanadium is of interest for biomedical and environmental applications. The two metals have narrow limits between being essential and toxic for humans. Ultra-sensitive techniques have been studied to measure Cr and V at low concentrations found in human blood and environmental samples. Bismuth film and mercury-alloy electrodes have been developed as alternatives to traditional Hg-based electrodes for Cr and V detection. While catalytic adsorptive stripping voltammetry (CAdSV) has been used to detect Cr and V, little is known about the process. The mechanisms of CAdSV have been probed to provide a better understanding of its exceptional sensitivity and selectivity. Near-real time monitoring of plume gas constituents is desired as a diagnostic tool for combustion efficiency, ensuring safe testing conditions and observing releases of green house gasses. Ground testing rockets is a crucial preliminary step that ensures their performance during critical space missions. Optical sol-gel sensors for carbon dioxide have been developed for remote sensing applications. They are inexpensive and are compatible with the harsh environments encountered during rocket plume tests. The sensors are a viable approach to compliment current infrared (IR) measurements for real-time carbon dioxide detection. Additional work on kerosene and isopropyl alcohol sensing has been explored for incorporation into a multi-analyte sensing platform.

Electronic noses combine an array of cross-selective gas sensors with a pattern recognition engine to identify odors. Pattern recognition of multivariate gas sensor response is usually performed using existing statistical and chemometric techniques. An alternative solution involves developing novel algorithms inspired by information processing in the biological olfactory system. The objective of this dissertation is to develop a neuromorphic architecture for pattern recognition for a chemosensor array inspired by key signal processing mechanisms in the olfactory system. Our approach can be summarized as follows. First, a high-dimensional odor signal is generated from a chemical sensor array. Three approaches have been proposed to generate this combinatorial and high dimensional odor signal: temperature-modulation of a metal-oxide chemoresistor, a large population of optical microbead sensors, and infrared spectroscopy. The resulting high-dimensional odor signals are subject to dimensionality reduction using a self-organizing model of chemotopic convergence. This convergence transforms the initial combinatorial high-dimensional code into an organized spatial pattern (i.e., an odor image), which decouples odor identity from intensity. Two lateral inhibitory circuits subsequently process the highly overlapping odor images obtained after convergence. The first shunting lateral inhibition circuits perform gain control enabling identification of the odorant across a wide range of concentration. This shunting lateral inhibition is followed by an additive lateral inhibition circuit with center-surround connections. These circuits improve contrast between odor images leading to more sparse and orthogonal patterns than the one available at the input. The sharpened odor image is stored in a neurodynamic model of a cortex. Finally, anti-Hebbian/ Hebbian inhibitory feedback from the cortical circuits to the contrast enhancement circuits performs mixture segmentation and weaker odor/background suppression, respectively. We validate the models using experimental datasets and show our results are consistent with recent neurobiological findings.

Metal wire deposition by means of robotized laser welding offers great saving potentials, i.e. reduced costs and reduced lead times, in many different applications, such as fabrication of complex components, repair or modification of high-value components, rapid prototyping and low volume production, especially if the process can be automated. Metal deposition is a layered manufacturing technique that builds metal structures by melting metal wire into beads which are deposited side by side and layer upon layer. This thesis presents a system for on-line monitoring and control of robotized laser metal wire deposition (RLMwD). The task is to ensure a stable deposition process with correct geometrical profile of the resulting geometry and sound metallurgical properties. Issues regarding sensor calibration, system identification and control design are discussed. The suggested controller maintains a constant bead height and width throughout the deposition process. It is evaluated through real experiments, however, limited to straight line deposition experiments. Solutions towards a more general controller, i.e. one that can handle different deposition paths, are suggested. A method is also proposed on how an operator can use different sensor information for process understanding, process development and for manual on-line control. The strategies are evaluated through different deposition tasks and considered materials are tool steel and Ti-6Al-4V. The developed monitoring system enables an operator to control the process at a safe distance from the hazardous laser beam. The results obtained in this work indicate promising steps towards full automation of the RLMwD process, i.e. without human intervention and for arbitrary deposition paths. / RMS

Electronic noses combine an array of cross-selective gas sensors with a pattern recognition engine to identify odors. Pattern recognition of multivariate gas sensor response is usually performed using existing statistical and chemometric techniques. An alternative solution involves developing novel algorithms inspired by information processing in the biological olfactory system. The objective of this dissertation is to develop a neuromorphic architecture for pattern recognition for a chemosensor array inspired by key signal processing mechanisms in the olfactory system. Our approach can be summarized as follows. First, a high-dimensional odor signal is generated from a chemical sensor array. Three approaches have been proposed to generate this combinatorial and high dimensional odor signal: temperature-modulation of a metal-oxide chemoresistor, a large population of optical microbead sensors, and infrared spectroscopy. The resulting high-dimensional odor signals are subject to dimensionality reduction using a self-organizing model of chemotopic convergence. This convergence transforms the initial combinatorial high-dimensional code into an organized spatial pattern (i.e., an odor image), which decouples odor identity from intensity. Two lateral inhibitory circuits subsequently process the highly overlapping odor images obtained after convergence. The first shunting lateral inhibition circuits perform gain control enabling identification of the odorant across a wide range of concentration. This shunting lateral inhibition is followed by an additive lateral inhibition circuit with center-surround connections. These circuits improve contrast between odor images leading to more sparse and orthogonal patterns than the one available at the input. The sharpened odor image is stored in a neurodynamic model of a cortex. Finally, anti-Hebbian/ Hebbian inhibitory feedback from the cortical circuits to the contrast enhancement circuits performs mixture segmentation and weaker odor/background suppression, respectively. We validate the models using experimental datasets and show our results are consistent with recent neurobiological findings.

Water quality in Tampa Bay was examined using concurrent in situ and satellite remote sensing observations. Chlorophyll and suspended sediment concentrations showed large short-term variability, primarily driven by tide and wind forcing. Superimposed on these high frequency variations were recurrent phytoplankton blooms stimulated by decreases in turbidity 1-2 days after wind-induced bottom sediment resuspension events; the blooms were particularly strong if neap tides occurred after the wind events. The in situ data show that observations once per month are inadequate to sample short-term variability and that therefore the current monthly water quality surveys may have uncertainties of -50 to 200% if they are used to represent the monthly mean concentrations of chlorophyll or suspended sediment. Such uncertainties make it difficult to identify trends and interannual variability based on the in situ monitoring program. Colored dissolved organic matter (CDOM) generally showed good relationship with salinity and primarily delivered by riverine inputs but showed conservative and non-conservative mixing behaviors for the dry and wet seasons, respectively. CDOM in Old Tampa Bay (OTB), however, showed properties that were different from those in other Bay segments, and the non-conservative CDOM mixing behavior may be simply due to a three-end-member mixing scenario in which Hillsborough Bay and Middle Tampa Bay also receive water from Old Tampa Bay. A turbidity algorithm was successfully developed for application of MODIS/Aqua 250 m imagery. The MODIS turbidity images showed distinct spatial and temporal patterns related to river runoff in the upper bay and wind-induced sediment resuspension events in the middle and lower portions of the Bay. Similarly, light attenuation from SeaWiFS estimated using a new semi-analytical algorithm confirmed that water clarity was related to river runoff and to wind-induced sediment resuspension events. Wind is shown repeatedly to be another important factor controlling water quality in the Bay. The study shows that remote sensing products have the potential to be an important tool to help resource managers assess conditions in a large estuary like Tampa Bay synoptically, frequently and repeatedly.

Corn nitrogen (N) applications are still done on a field basis in Kentucky, according to previous crop, soil tillage management and soil drainage. Soil tests, as well as plant analysis for N, are not very useful in making N fertilizer rate recommendations for corn. Recommended rates assume that only 1/3 to 2/3 of applied N is recovered, variability largely due to the strong affect of weather on the release of soil N and fertilizer N fate. Many attempts have been made to apply N in a more precise and efficient way. Two experiments were conducted at Spindeltop, the University of Kentucky’s experimental farm near Lexington, over two years (2010, 2011), using a commercially available active optical sensor (GreanSeekerTM) to compute the normalized difference vegetative index (NDVI), and with this tool/index assess the possibility of early (V4-V6) N deficiency detection, grain yield prediction by NDVI with and without side-dressed N, and determination of the confounding effect of soil background on NDVI measurements. Results indicated that the imposed treatments affected grain yield, leaf N, grain N and grain N removal. Early N deficiency detection was possible with NDVI. The NDVI value tended to saturate in grain yield prediction models. The NDVI was affected by tillage management (residue/soil color background differences), which should be taken into account when using NDVI to predict grain yield. Side-dress N affected NDVI readings taken one week after side-dressing, reducing soil N variability and plant N nutrition. There is room for improvement in the use of this tool in corn N management.

Este trabalho apresenta a síntese de novos compostos fotoativos contendo os grupos uréia e tiouréia e suas potenciais aplicações como sensores de ânions. Os compostos sintetizados foram caracterizados pelas técnicas de FTIR, RMN de 1H e 13C, onde foi possível confirmar obtenção dos compostos. Os compostos obtidos apresentam absorção na região do ultravioleta com valores de extinção molar de acordo com as transições -*. Com objetivo de testar os novos compostos sintetizados como sensor de ânions foi realizado um estudo fotofísico na presença de diferentes ânions, sendo todos como sais de tetrabutilamônio. Esses testes também foram realizados utilizando o método de detecção visual e a espectroscopia de RMN 1H indicando que os compostos 32 e 33 apresentaram resposta colorimétrica após a adição de fluoreto. / This work presents the synthesis of novel photoactive compounds containing the urea and thiourea groups and its potential application as sensors for anions in solution. The synthesized compounds were characterized by FTIR, 1H and 13C NMR techniques. The compounds show absorption in the ultraviolet region with values of molar extinction accordingly to -* electronic transitions. In order to test the new compounds as optical sensors for anions, photophysical studies, as well as the method of visual detection and 1H NMR titration were performed in the presence of different anions as tetrabutylammonium salts. Fluoride could be successfully detected by UVVis and 1H NMR titration using compounds 32 and 33.

[pt] O comportamento animal serviu de inspiração para o controle baseado por comportamento aplicado a robôs móveis autônomos. Este se baseia em diretivas simples, ou reações, separadas por camadas com respectivas prioridades. Quando agrupadas, estas diretivas conseguem executar as mais diversas e complexas funções no ambiente, tornando o controle em si uma tarefa segmentada, na qual se divide o objetivo principal em pequenos módulos chamados de comportamentos primários. Estes atuam independentes e, ao trabalharem de forma paralela resultam em comportamentos complexos capazes de realizar tarefas mais complexas, uma evolução do controle reativo. A lógica desta técnica torna mais simples a programação e organização das tarefas porque possui uma estrutura modular que permite a adição de novos sensores (novos comportamentos), sem grandes mudanças no código existente. Esta dissertação desenvolve e implementa a programação baseada em comportamento em robôs móveis autônomos com sensores óticos e ultrassônicos em um ambiente de simulação open source muito comum chamado Player/Stage e validada experimentalmente em 2 robôs autônomos. A arquitetura utilizada no processo de desenvolvimento das camadas comportamentais foi a arquitetura de esquemas motores em conjunto com a técnica de campos potenciais, originalmente idealizada por Ronald C. Arkin em 1998. Os resultados simulados e experimentais foram confrontados com métodos de programação clássica e comprovam todas as vantagens do controle baseado em comportamento. / [en] The animal behavior served as the inspiration for the behavior based control applied to autonomous mobile robots. This is based on simple directives, or reactions, separated by layers with their priorities. When combined, these directives can run the most diverse and complex functions in the environment, making the control itself a segmented task, which divides the main objective into small modules called primary behaviors. Those act independently and, when working in parallel, result in complex behaviors that are capable of executing more complex tasks, an evolution of reactive control. The logic from this technique makes it easy to program and organize robot controllers because it has a modular structure that allows the addition of new sensors (new behaviors) without major changes in the existing code. This thesis develops and implements a behavior based programming on autonomous mobile robots with optical and ultrasonic sensors in very common open source simulation software, called Player/Stage, and validates experimentally in 2 autonomous robots. The architecture used in the development of behavioral layers was the motor schema with potential fields, originally created by Ronald C. Arkin in 1998. The simulated and experimental results were confronted with classical methods of programming and proved all the benefits of behavior based control.