M.Ing. (Electrical and Electronic) / Please refer to full text to view abstract

Over the past decade, there has been an increasing interest in the real-time monitoring of ambulatory vital signs such as heart rate (HR) and arterial blood oxygen saturation (SpO2) using wearable medical sensors during field operations. These measurements can convey valuable information regarding the state of health and allow first responders and front-line medics to better monitor and prioritize medical intervention of military combatants, firefighters, miners and mountaineers in case of medical emergencies. However, the primary challenge encountered when using these sensors in a non-clinical environment has been the presence of persistent motion artifacts (MA) embedded in the acquired physiological signal. These artifacts are caused by the random displacement of the sensor from the skin and lead to erroneous output readings. Several signal processing techniques, such as time and frequency domain segmentation, signal reconstruction techniques and adaptive noise cancellation (ANC), have been previously developed in an offline environment to address MA in photoplethysmography (PPG) with varying degrees of success. However, the performance of these algorithms in a spasmodic noise environment usually associated with basic day to day ambulatory activities has still not been fully investigated. Therefore, the focus of this research has been to develop novel MA algorithms to combat the effects of these artifacts. The specific aim of this thesis was to design two novel motion artifact (MA) algorithms using a combination of higher order statistical tools namely Kurtosis (K) for classifying 10 s PPG data segments, as either ‘clean’ or ‘corrupt’ and then extracting the aforementioned vital parameters. To overcome the effects of MA, the first algorithm (termed ‘MNA’) processes these ‘corrupt’ PPG data segments by identifying abnormal amplitudes changes. The second algorithm (termed ‘MNAC’), filters these ‘corrupt’ data segments using a 16th order normalized least mean square (NLMS) ANC filter and then extracts HR and SpO2.

Over the past decade, there has been an increasing interest in the real-time monitoring of ambulatory vital signs such as heart rate (HR) and arterial blood oxygen saturation (SpO2) using wearable medical sensors during field operations. These measurements can convey valuable information regarding the state of health and allow first responders and front-line medics to better monitor and prioritize medical intervention of military combatants, firefighters, miners and mountaineers in case of medical emergencies. However, the primary challenge encountered when using these sensors in a non-clinical environment has been the presence of persistent motion artifacts (MA) embedded in the acquired physiological signal. These artifacts are caused by the random displacement of the sensor from the skin and lead to erroneous output readings. Several signal processing techniques, such as time and frequency domain segmentation, signal reconstruction techniques and adaptive noise cancellation (ANC), have been previously developed in an offline environment to address MA in photoplethysmography (PPG) with varying degrees of success. However, the performance of these algorithms in a spasmodic noise environment usually associated with basic day to day ambulatory activities has still not been fully investigated. Therefore, the focus of this research has been to develop novel MA algorithms to combat the effects of these artifacts. The specific aim of this thesis was to design two novel motion artifact (MA) algorithms using a combination of higher order statistical tools namely Kurtosis (K) for classifying 10 s PPG data segments, as either ‘clean’ or ‘corrupt’ and then extracting the aforementioned vital parameters. To overcome the effects of MA, the first algorithm (termed ‘MNA’) processes these ‘corrupt’ PPG data segments by identifying abnormal amplitudes changes. The second algorithm (termed ‘MNAC’), filters these ‘corrupt’ data segments using a 16th order normalized least mean square (NLMS) ANC filter and then extracts HR and SpO2.

The oxygen evolution reaction (OER) is kinetically slow and hence a significant efficiency loss in electricity-driven water electrolysis. Understanding the relationships between architecture, composition, and activity in high-performing catalyst systems are critical for the development of better catalysts. This dissertation discusses areas both fundamental and applied that seek to better understand how to accurately measure catalyst activity as well as ways to design higher performing catalysts. Chapter I introduces the work that has been done in the field to date. Chapter II compares various methods of determining the electrochemically active surface area of a film. It further discusses how pulsed and continuous electrodepostition techniques effect film morphology and behavior, and shows that using a simple electrodeposition can create high loading films with architectures that outperform those deposited onto inert substrates. The reversibility of the films, a measure of the films transport efficiency, is introduced and shown to correlate strongly with performance. Chapter III uses high energy x-ray scattering to probe the nanocrystalline domains of the largely amorphous NiFe oxyhydroxide catalysts, and shows that significant similarities in the local structure are not responsible for the change in performance for the films synthesized under different conditions. Bond lengths for oxidized and reduced catalysts are determined, and show no significant phase segregation occurs. Chapter IV seeks to optimize the deposition conditions introduced in Chapter II and to provide a physical representation of how tuning each of the parameters affects film morphology. The deposition current density is shown to be the most important factor affecting film performance at a given loading. Chapter V highlights the different design considerations for films being used in a photoelectrochemical cell, and how in situ techniques can provide information that may otherwise be unobtainable. Chapter VI serves as a summary and provides future directions. This dissertation contains previously published coauthored material.

O biodiesel vem se consolidando como combustível alternativo devido às suas vantagens e importância econômica. Conforme seu percentual no diesel comercial cresce, o monitoramento da qualidade se torna cada vez mais importante para uma comercialização segura. No entanto, a estabilidade oxidativa do biodiesel é inferior à do diesel fóssil, assim antioxidantes sintéticos, tais como BHA, BHT, PG e TBHQ, são adicionados para prevenir a degradação do mesmo, evitando danos ao sistema de combustão automotivo. Os métodos para avaliar a estabilidade oxidativa e a quantidade de antioxidantes são em geral demorados, requerem preparação amostral ou equipamentos de alto custo. Com o objetivo de contornar tais problemas, neste trabalho foi aplicada uma metodologia para análise direta de antioxidantes em amostras de biodiesel por meio de voltametria de pulso diferencial. Conjuntamente, foi estudada a viabilidade da associação dessa metodologia com técnicas quimiométricas para a determinação simultânea desses antioxidantes em misturas de antioxidantes em biodiesel. Para permitir uma análise direta, sem extração e pré-concentração dos analitos, o biodiesel foi diluído em meio etanólico. Medidas de voltametria de pulso diferencial para cada antioxidante individualmente mostraram relação linear entre as concentrações dos antioxidantes e a corrente de oxidação. Os limites de detecção individuais obtidos foram de 20,5 mg L-1 para BHA, 32,4 mg L-1 para BHT, 35,5 mg L-1 para PG e 26,5 mg L-1 para TBHQ. A modelagem quimiométrica foi aplicada por meio das ferramentas Mínimos Quadrados Clássico (CLS), Mínimos Quadrados Parciais (PLS), Redes Neuronais Artificiais (ANN), Componentes Principais-Redes Neuronais Artificiais (PC-ANN) e Árvore de Decisão-Redes Neuronais Artificiais (DT-ANN). O modelo construído por PLS se mostrou melhor quando comparado à modelagem por CLS. O modelo construído por ANN’s sem seleção de dados de entrada apresentou erros semelhantes ao PLS. Quando a redução da quantidade de dados de entrada foi aplicada em conjunto com ANN’s através de Análise por Componentes Principais (PCA) e DT, a aplicação de PCA levou a aumento de 10,2% no erro de predição, enquanto que na seleção por DT os erros de predição foram reduzidos em 8,5%. A determinação simultânea dos quatro compostos pelo modelo DT-ANN apresentou precisão satisfatória, com recuperação de 98% para BHA, 97% para BHT, 103% para PG e 100% para TBHQ, o que indica que a técnica analítica e a modelagem quimiométrica são viáveis e promissoras para aplicação no controle de qualidade do biodiesel, bem como em análises de monitoramento nas plantas industriais. / Biodiesel is becoming established as an alternative fuel because its advantages and economic importance. As the levels of biodiesel in commercial diesel grows, quality monitoring becomes increasingly important for safe marketing. However, the oxidative stability of biodiesel is smaller than that of fossil diesel and synthetic antioxidants such as BHA, BHT, PG and TBHQ are added to it in order to prevent its degradation, avoiding damage to the automotive combustion system. Methods for evaluating oxidative stability and the amount of antioxidants are usually time-consuming, require sample preparation or expensive equipment. In order to overcome such problems, in this work a methodology was applied for direct analysis of antioxidants in biodiesel samples by means of differential pulse voltammetry. The viability of associating this methodology with chemometric techniques was studied for the simultaneous determination of these antioxidants in biodiesel. To allow a direct analysis, without extraction and preconcentration of analytes, biodiesel was diluted in ethanolic medium. Differential pulse voltammetric measurements for each antioxidant individually showed a linear relationship between antioxidant concentrations and oxidation current. The individual detection limits were 20,5 mg L-1 for BHA, 32,4 mg L-1 for BHT, 35,5 mg L-1 for PG and 26,5 mg L-1 for TBHQ. The chemometric modeling was applied using the Classical Least Squares (CLS), Partial Least Squares (PLS), Artificial Neural Networks (ANN), Principal Component-Artificial Neural Networks (PC-ANN) and Decision Tree-Artificial Neural Networks (DT-ANN) techniques. The model constructed by PLS was better than that obtained with CLS. The model constructed by ANN’s without input selection presented similar deviations in comparison to PLS. When amount of input data reduction was applied together with ANNs through Principal Component Analysis (PCA) and DT, the PCA application led to a 10.2% increase in prediction error, whereas in the selection by DT prediction errors were reduced by 8,5%. The simultaneous determination of the four compounds by the DT-ANN model presented satisfactory accuracy with 98% recovery for BHA, 97% for BHT, 103% for PG and 100% for TBHQ, indicating that the analytical technique and the chemometric modeling are feasible and promising for application in biodiesel quality control, as well as in monitoring analyzes in the industrial plants.

In this thesis, novel multi-section laser diodes based on quantum-dot material are designed and investigated which exhibit a number of advantages such as low threshold current density; temperature-insensitivity and suppress carrier diffusion due to discrete nature of density of state of quantum-dots. The spectral versatility in the range of 1.1 µm – 1.3 µm wavelengths is demonstrated through novel mode-locking regimes such as dual-wavelength mode-locking, wavelength bistability and broad tunability. Moreover, broad pulse repetition rate tuning using an external cavity configuration is presented. A high peak power of 17.7 W was generated from the quantum-dot laser as a result of the tapered geometry of the gain section of the laser has led to successful application of such device for two-photon imaging. Dual-wavelength mode-locking is demonstrated via ground (?=1180 nm) and excited (?=1263 nm) spectral bands with optical pulses from both states simultaneously in the 5-layer quantum-dot two-section diode laser. The widest spectral separation of 83 nm between the modes was achieved in a dual-wavelength mode-locked non-vibronic laser. Power and wavelength bistability are achieved in a mode-locked multi-section laser which active region incorporates non-identical QD layers grown by molecular beam epitaxy. As a result the wavelength can be electronically controlled between 1245 nm and 1290 nm by applying different voltages to the saturable absorber. Mode-locked or continuous-wave regimes are observed for both wavelengths over a 260 mA – 330 mA current ranges with average power up to 28 mW and 31 mW, respectively. In mode-locked regime, a repetition rate of 10 GHz of optical pulses as short as 4 ps is observed. Noticeable hysteresis of average power for different bias conditions is also demonstrated. The wavelength and power bistability in QD lasers are potentially suitable for flip-flop memory application. In addition, a unique mode-locked regime at expense of the reverse bias with 50 nm wavelength tuning range from 1245 nm to 1290 nm is also presented. Broad repetition rate tunability is shown from quantum-dot external cavity mode-locked 1.27 µm laser. The repetition rate from record low of 191 MHz to 1 GHz from fundamental mode-locking was achieved. Harmonic mode-locking allows further to increase tuning up to 6.8 GHz (34th-order harmonic) from 200 MHz fundamental mode-locking. High peak power of 1.5 W can be generated directly from two-section 4 mm long laser with bent waveguide at angle of 7° at 1.14 GHz repetition rate without the use of any pulse compression and optical amplifier. Stable mode-locking with an average power up to 60 mW, corresponding to 25 pJ pulse energy is also obtained at a repetition frequency of 2.4 GHz. The minimum time-bandwidth product of 1.01 is obtained with the pulse duration of 8.4 ps. Novel tapered quantum-dot lasers with a gain-guided geometry operating in a passively mode-locked regime have been investigated, using structures that incorporated either 5 or 10 quantum dot layers. The peak power of 3.6 W is achieved with pulse duration of 3.2 ps. Furthermore, the record peak power of 17.7 W and transform limited pulses of 672 fs were achieved with optimized structure. The generation of picosecond pulses with high average power of up to 209 mW was demonstrated, corresponding to 14.2 pJ pulse energy. The improved optical parameters of the tapered laser enable to achieve nonlinear images of fluorescent beads. Thus it is for the first time that QD based compact monolithic device enables to image biological samples using two-photon microscopy imaging technique.

The purpose of this quantitative study was to determine to what extent the 4 M-PULSE scale scores (interpersonal difficulties, unprofessional conduct, discharge of weapon, and inappropriate use of weapon) account for variance in the MMPI-2RF (aggression and antisocial) subscales scores. Archival data was provided by a private business that conducts these screenings for multiple law enforcement organizations (local and state). Law enforcement candidates (N = 127) were evaluated by a private business during the prehiring psychological screening process using the M-PULSE and MMPI-2RF to assess their risk factors for employment as law enforcement officers. Using Social Learning theory as the basis, the scores from the 2 measures were provided for regression analysis to determine what effect the M-PULSE factors had on the MMPI-2RF factors. This research did not find any significant effect on either MMPI-2RF factor by the 4 M-PULSE factors. This study adds to the growing body of knowledge of law enforcement psychological screening processes and how different measures provide critical information on personality, aggression, and risk factors that should be considered for individuals seeking employment in a law enforcement position. This study has implications for positive social change by increasing understanding of how current psychological screening processes determine suitability of candidates and help to ensure that individuals who would put the public and law enforcement organizations at higher risk should be screened out prior to completion of any law enforcement training.

The use of composite materials in aerospace, electronics, and wind industries has become increasingly common, and these composite components are required to carry mechanical, electrical, and thermal loads simultaneously. A unique property of carbon fiber composites is that when an electric current is applied to the specimen, the mechanical strength of the specimen increases. Previous studies have shown that the higher the electric current, the greater the increase in impact strength. However, as current passes through the composite, heat is generated through Joule heating. This Joule heating can cause degradation of the composite and thus a loss in strength. In order to minimize the negative effects of heating, it is desired to apply a very high current for a very short duration of time. This thesis investigated the material responses of carbon fiber composite plates subjected to electrical current pulse loads of up to 1700 Amps. For 32 ply unidirectional IM7/977-3 specimens, the peak impact load and absorbed energy increased slightly with the addition of a current pulse at the time of an impact event. In 16 ply cross-ply IM7/977-2 specimens, the addition of the current pulse caused detrimental effects due to electrical arcing at the interface between the composite and electrodes. Further refinement of the experimental setup should minimize the risk of electrical arcing and should better elucidate the effects of a current pulse on the impact strength of the specimens.

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With the rise in prevalence of cardiovascular (CV) disease, risk stratification is becoming increasingly important. Accurate characterization of the CV system is required, for which central aortic blood pressure (BP) parameters form an integral part. However, invasive measurement of central aortic BP parameters (aP) is difficult. Therefore, non-invasive methods to estimate aP from the radial pressure pulse (rPulse) have been proposed. To analyze accuracy of estimated aP (aPhat) and applicability in risk stratification and diagnosis, this study presents: (1) a novel representation of the rPulse with minimal loss of information, (2) a framework for strict definition and statistical analysis of aPhat, and (3) a dynamic analysis of effects of mean BP (MP) and heart rate (HR) in the rPulse shape. Methods: (1) 2671 rPulse s measured by applanation tonometry were represented using the first eight principal components (PC) scores after standard PC transformation. rPulse shapes were compared in three subpopulations. (2) The concept of &quotestimation option&quot (EO) for aP estimation was presented. A framework for strict definition of aPhat and the comparison of EOs was proposed, and 7 different EOs compared. (3) A sequence of rPulse s was analyzed during soft exhalation maneuver (SEM) %, a mild Valsalva type maneuver, in eight healthy subjects. Radial BP and respiration pressure were continuously measured. The effects of MP and HR in the rPulse parameters were analyzed by standard linear regression for each subject. Results: (1) PC representation of the rPulse improves accuracy of the estimation of aPhat compared with the simple use of rPulse parameters. Subpopulations have distinctive rPulse shapes. (2) No single EO was better for the estimation of all aPhat. Inclusion of MP improves estimation accuracy. Despite further improvement when rPulse is included, the general transfer function EO is a biased estimator. (3) The dynamic analysis of the rPulse provides information of the effects of MP and HR in the rPulse not available in static analysis. The effects were specific for each individual and different from the results obtained from a general population. Conclusions: For accurate CV risk stratification, future studies should include a dynamic measurement of calibrated radial pressure pulse during SEM maneuver. Risk analysis and diagnosis should be based on representations of the rPulse with minimum loss of information. aPhat should be used for better understanding of the underlying physiological principles.