Blood pressure is one of the most important indicators of human health. The symptoms of many cardiovascular diseases like stroke, atrial fibrillation, and acute myocardial infarction are usually indicated by the abnormal variation of blood pressure. Severe symptoms of diseases like coronary syndrome, rheumatic heart disease, arterial aneurysm, and endocarditis also usually appear along with the variation of blood pressure. Most of the current blood pressure measurements rely on the Korotkoff sounds method that focuses on one-time blood pressure measuring but cannot supervise blood pressure continuously, which cannot effectively detect diseases or alert patients. Previous researches indicating the relationship between photoplethysmogram (PPG) signal and blood pressure brought up the new research direction of blood pressure measurement method. Ideally, with the continuous supervision of the PPG signal, the blood pressure of the subject could be measured longitudinally, which matches the current requirements of blood pressure measurement better as an indicator of human health. However, the relationship between blood pressure and PPG signal is very comprehensive that is related to personal and environmental status, which leads to the research challenge for many previous works that tried to find the mapping from PPG signal to blood pressure without considering other factors. In this thesis, we propose two statistical methods modeling the comprehensive relationships among blood pressure, PPG signals, and other factors for blood pressure prediction. We also express the modeling and predicting process for the real data set and provide accurate prediction results that achieve the international blood pressure measurement standard. In the first part, we propose the Independent Variance Components Mixed- model (IVCM) that introduces the variance components to describe the relationship among observations. The relationship indicators are collected as information to divide observations into different groups. The latent impacts from the properties of groups are estimated and used for predicting the multiple responses. The Stochastic Approximation Minorization-maximization (SAM) algorithm is used for IVCM model parameter estimation. As the expansion of Minorization-maximization (MM) algorithm, the SAM algorithm could provide comparable-level estimations as MM algorithm but with faster computing speed and less computational cost. We also provide the subsampling prediction method for IVCM model prediction that could predict multiple responses variables with the conditional expectation of the model random effects. The prediction speed of the subsampling method is as fast as the SAM algorithm for parameter estimation with very small accuracy loss. Because the SAM algorithm and subsampling prediction method requires assigning tuning parameters, a great amount of simulation results are provided for the tuning parameter selection. In the second part, we propose the Groupwise Reweighted Mixed-model (GRM) to describe the variation of random effects as well as the potential components of mixture distributions. In the model, we combine the properties of mixed-model and mixture model for modeling the comprehensive relationship among observations as well as between the predictive variables and the response variables. We bring up the Groupwise Expectation Minorization-maximization (GEM) algorithm for the model parameter estimation. Developed from MM algorithm and Expectation Maximization (EM) algorithm, the algorithm estimates parameters fast and accurate with adopting the properties of the diagonal blocked matrix. The corresponding prediction method for GRM model is provided as well as the simulations for the number of components selection. In the third part, we apply the IVCM model and the GRM model in modeling real data and predicting blood pressure. We establish the database for modeling blood pressure with PPG signals and personal characteristics, extract PPG features from PPG signal waves, and analyze the comprehensive relationship between PPG signal and blood pressure with the IVCM model and the GRM model. The blood pressure prediction results from different models are provided and compared. The best prediction results not only achieve the international blood pressure measurement standard but also show great performance in high blood pressure prediction

Thermoelectric materials can provide solutions to power generation and refrigeration challenges. Layered chalchogenides are of particular interest, with bismuth telluride and lead telluride being the most common compounds. Bismuth telluride is often used for room temperature applications, while its solid solutions with antimony or selenium as well as lead tellurides show better thermoelectric properties at elevated temperatures. Regrettably, the efficiency of the known thermoelectric materials is still low. Evidently, bringing thermoelectric energy harvesting to commercial viability is a materials challenge: How can we obtain materials with figure of merit above 3? This question drives the research community since the successes of nanoengineering in the 1990s. Nowadays, high-pressure technology is a promising frontier for making further advances in thermoelectric material performance. The main goal of this thesis is to understand the electronic and thermoelectric properties of selected materials using density functional theory and semi-classical Boltzmann transport theory. Bulk and monolayer CuSbS2 and CuSbSe2 are studied to clarify the role of the interlayer coupling for the thermoelectric properties. The calculated band gaps of the bulk compounds turn out to be in agreement with experiments and significantly higher than those of the monolayers, which thus show lower Seebeck coefficients. Since also the electrical conductivity is lower, the monolayers are characterised by lower power factors. Therefore, the interlayer coupling, even though it is weak, is found to be essential for the thermoelectric response. We study Cu (Sb,Bi)(S,Se)2 under hydrostatic pressure up to 8 GPa, considering the van der Waals interaction, as these compounds have layered structures. We find an indirect band gap that decreases monotonically with increasing hydrostatic pressure. Only CuBiS2 shows an indirect-indirect band gap transition around 3 GPa, leading to conduction band convergence with a concomitant 20% increase in the Seebeck co-efficient. This enhancement results from a complex interplay between multivalley and multiband effects as well as changes of the band effective masses. The variation of the electronic band structure of AB2Te4 (A = Pb, Sn and B = Bi, Sb) under hydrostatic pressure up to 8 GPa is analyzed in detail and its consequences for the material properties are explained.

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<p> The spontaneous oscillations of a fluidized bed with a variable volume below the bed support are investigated.</p> <p> The pressure vs. time dependence of the fixed bed is determined for glassbeads of 3 size ranges and for hematite.</p> <p> For each case several different bed masses were employed. For the oscillating bed the influence on the frequency of the oscillations of the materials fluidized, the size of the particles, the chamber volume and the superficial gas velocity are studied.</p> <p> Models of five different authors have been reviewed and a model to describe the relationship between the frequency and various parameters such as chamber volume, bed mass, gas velocity and the pressure drop characteristic (Δp vs. μ) is derived. The latter together with three other models have been evaluated by means of the experimental data.</p> / Thesis / Master of Engineering (MEngr)

A MEMS piezoresistive pressure sensor provides a low-cost and accurate means of detecting and quantifying small-scale disturbances in underwater environments. A highly sensitive MEMS pressure sensor has been developed that can be packaged in two different ways – one in a cylindrical housing, and the other in a flexible, yet robust, strip configuration – enabling more freedom for the user to choose an option that fits their needs. The sensing element of each consists of four piezoresistive elements in a Wheatstone Bridge configuration arranged on a deformable buried-oxide layer, which is then bonded to a Silicon base layer with a hollow cavity carved using reactive-ion etching. Previous work has shown the survivability of these sensors in an underwater environment and also measurements of low frequency pressure changes due to flow and varying turbulence intensities. The present work is focused on evaluating these pressure sensors and testing the limits of the sensing element in the low, medium, and high frequency regime (<100Hz to >1kHz) to gain further insight into the performance. Five experimental tests were developed and conducted to guide this research objective. The sensor responses under different flow conditions were measured and analyzed with selected filtering and resampling techniques to eliminate background noises. First, the sensors were calibrated to ensure their linearity and to determine their pressure sensitivities. Then, using bench-top testing rigs and a water tunnel, the sensor performance was evaluated in submerged environments when subjected to multiple small-scale flow disturbances across the tested frequency regime. It was found that the present sensors are capable of providing more accurate measurements across a tested frequency regime of 0 to 20,000Hz when compared to other off-the-shelf products. Testing in submerged environment showed that the sensors are capable of detecting small-scale pressure fluctuations as a result of eddies which are evident in a Von Karman vortex street and a turbulent flow. Despite the presence of EMI noise within a water tunnel, the sensors demonstrated a decay of pressure fluctuations that is consistent with previous research in the field. Overall, the present work increases understanding of the sensors' performances across a broad range of frequencies and provides insight into potential uses and future work. / Master of Science / Pressure sensors are an important, if not the most important, measurement device available today. Pressure sensors play an integral role in the everyday lives for everyone around the world; from applications in medicine, aerospace, autonomy and computation, these sensors provide real-time feedback and help gain a deeper understanding of a system. However, with the technological advances in the Modern Age, there has been a growing need for smaller, cheaper, and faster sensors. As a result, engineers continued to improve sensor performance in the past century with new technologies. A micro-electromechanical system (MEMS) pressure sensor offers a low-cost and energy efficient method to quantify pressure fluctuations within a system. This work focuses on evaluating the performance of three MEMS pressure sensors for use in a submerged environment to detect small-scale pressure fluctuations across a broad range of frequencies. Five different tests were conducted to investigate this research objective. The first three were performed in a controlled underwater environment from which direct conclusions could be made. The last two were performed in an uncontrolled underwater environment from which comparisons to literature and known phenomena were used to draw conclusions. A key result showed that the sensor measurements aligned with prior research in the field. Multiple data reduction techniques were also used during post-processing to ensure accurate data was being collected. The studies showed that the developed MEMS pressure sensors provided the same capabilities as other commercially available pressure measurement devices, all the while displaying a higher sensitivity and broader frequency range. Furthermore, the survivability and robustness of the sensor was proven when subjected to large- and small-scale flow disturbances in a water tunnel.

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