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This project aims to achieve various goals related to the selection and optimisation of powertrain components in a hybrid powertrain system. To achieve these objectives, automatic generation, calibration, and comparison of algorithms have been implemented. This research offers the opportunity to optimise multiple targets, such as state of charge (SOC), components selection, CO2 emissions, drive modes, and driveability for McLaren's hybrid powertrain system. The robustness of the Equivalent Consumption Minimisation Strategy (ECMS) is generated automatically for different optimisation objectives, and robustness validation is performed. Additionally, automatic calibration of the torque split strategy is applied to optimise another PhD's work on powertrain components selection. All simulations are based on McLaren's powertrain model. Furthermore, the limitations of the McLaren powertrain model are discussed, which leads to the development of a new powertrain model. The research on powertrain modelling is explored, and the process of generating the new powertrain model is presented in this project. Calibration and validation of the model, along with the implementation of the control strategy, are also researched. The new powertrain model is named after me, YR-Sim. For the application of the Dynamic Programming (DP) algorithm, the new powertrain model has been modified and upgraded with more subsystems and features. All the inputs and outputs of these subsystems have been standardised. The calibration and validation of the model with different algorithms guarantees that the simulation results are comparable to McLaren's model with different algorithms and optimisation cases. Besides optimising CO2 emissions and SOC conditions, driveability has been introduced to research deeper into different algorithms. Driveability is still an open question, but in this project, some novel concepts have been introduced. CO2 emissions, SOC conditions, and driveability have been optimised and researched in different drive cycles, including NEDC, WLTC, FTP75, and even the Nürburgring race track. For different views of driveability, the benefits of the application of ECMS and DP have been deeply researched. Because this is a project that focuses on real industry applications, the research on the algorithms shows the pros and cons of these two algorithms in different dimensions. From the view of the industry, the research results bridge the gap between mathematical algorithm research and strategies application. A series of functions for automatic generation, calibration, and optimisation of control strategy, as well as the experiences in complex powertrain modelling, control, and validation, can also be used for future hybrid/electric vehicle platform development. The academic goals for algorithm research include the performance comparison between different algorithms, as well as the calibration and validation of the powertrain model. The industry goals include algorithm application, robustness testing, and multicomponent selection in different drive cycles. Both of these two series of requirements are satisfied by the end of this project. For the novelty part of this project, a new quantitative definition of driveability is introduced and discussed. Based on this new definition and research on the torque margin, the relationship between throttle pedal position and torque margin is studied. Driveability is numerically divided into prediction and linearity, and both dimensions are quantitatively researched and compared between different algorithms. Another novel research is the parameterisation method of the powertrain model, which significantly accelerates the simulation speed of the model and transfers the model to a steady-state model to ensure it generates reliable results on different control calibrations. Regarding the application and contribution of this project, it has helped the industry sponsor, McLaren, to solve the problem of high-performance hybrid powertrain system driveability. During the engagement of the high-torque electric motor, the torque margin can be controlled, and the relationship between driveability and drive modes is discovered. This will be applied as an important reference for future high-performance powertrain system development.

The promise of autonomous vehicles is a popular and exciting concept that has seen considerable investment over the last decade. However, most of this investment is in the automotive sector with far less investment in autonomous maritime. This thesis explores the application of computer vision and robotics to autonomous navigation and docking in a maritime domain. Sensor fusion is a core robotics concept allowing the outputs from multiple complementary sensors to be combined for robust localisation and perception. An essential first step of which is extrinsic sensor calibration, which determines the relative position and orientation of those sensors. However, once they are mounted to a vehicle it is usually difficult and unwieldy to carry out. As the first main contribution, this thesis proposes a method to automatically perform calibration of arbitrary sensor types during normal operation. In addition to enabling a non-specialist to perform extrinsic calibration, it can also provide automatic updates in the case of sensor failure. Furthermore, it allows the relative scale factor to be inferred, facilitating the inclusion of non-metric sensors into the fusion framework without an explicit metric scaling step. Autonomous vehicles can only navigate unseen environments if they are able to build a map from which they can perform localisation. This is a common robotics problem traditionally solved with depth sensors and/or 3D reconstruction. However, the operational range of many depth sensors is insufficient for maritime scenarios. The second contribution of this thesis proposes a bird's-eye-view mapping approach using only monocular cameras. By projecting and accumulating semantic information, a probabilistic map is constructed that represents navigable and non-navigable areas. The advantages of this approach versus depth sensors are demonstrated in the marine, robotic, and automotive domains. Any perception system with a vision-based backbone is vulnerable to adverse lighting conditions, whether that is low light, reflections or fog. These conditions are especially prevalent in the maritime domain. Other spectra, such as infra-red, are less affected but rarely used in automotive. The third contribution solves the stereo correspondence problem between visible, near-infrared, and thermal images. By learning a common representation between unaligned stereo pairs in a self-supervised manner, the proposed approach provides stereo cues between different arbitrary spectra. This also allows annotations, which are often easier to obtain for the visible spectrum, to be transferred to other sensors easing the collection of multispectral datasets. The final chapter brings the contributions in automated sensor calibration, and semantic ground plane mapping together with path planning to demonstrate autonomous functionality. The presented experiments show significant performance improvements over what is currently available in a real-world practical application. This thesis advances the field of robotics and autonomous vehicles by exploring approaches which cater to both the maritime and automotive domains. It also shows the viability of cost effective monocular cameras as a sensor.

Market makers quote bid and ask prices throughout the trading day for assets under their consideration. Consequently, they face an optimisation problem in which they seek to maximise profits based on their bid-ask spread, and to minimise price risk, which they bear from holding inventory. In this thesis, we will explore this problem and apply it to real trading data. We begin with a market making model framework à la Avellaneda-Stoikov, where the objective is to maximise the trader's utility function. We calibrate the model to real limit order book data which we back-test. Additionally, we consider the limit-order priority system, which is extremely important when trading on a limit order book, to identify a range that the model would perform within. Moreover, little literature has addressed this issue, and the problem of applying a model that is continuous in time and space to a real-world discrete market. For instance, we find that there is a significant improvement in the model when using, what we call, the implied parameters. These parameters implicitly take into account the trading algorithm mechanics and not just the data's characteristics. We extend the model by introducing stochastic volatility. Our model describes the intraday price and variance of the asset as a mean-reverting process. Through our choice of ansatz solution, we reduce the problem from 5 to 3 dimensions. Furthermore, we show that our numerical scheme is stable and converges to the viscosity solution. We compare our model to the original model and a simple strategy, which pegs quotes at a fixed distance from the best bid and ask prices. Our back-test results show that the market making models significantly outperform the simple strategy concerning the certainty-equivalent value and inventory control. However, our novel stochastic model framework is most beneficial for a risk-averse trader seeking stable returns. We approximate a limit order book using data that is more easily available to researchers than low-latency data. We found our approximation useful for estimating parameters on strategies that quote prices over larger time intervals, with profits similar to a model calibrated using real limit order book data. However, the approximated data for calibrating high-frequency strategies significantly underperformed.

In the present thesis, I examined exercise calibration using running and high-intensity functional movement exercise (HIFME). In doing so, I analysed and presented results from eight studies across Chapters 2, 3, 4, and 5. In Chapters 2 and 3, I explored the extent to which demographic factors (e.g. expertise, experience, age, and gender) are effective in informing us about running and HIFME calibration. Studies 1 and 2 demonstrated that such factors do exhibit associations with running calibration, though the relatively minor strength and inconsistency of these associations also indicate that we should not overestimate the factors' contributions. Study 3 found a positive role of having a HIFME background in HIFME calibration, but other demographic factors did not exhibit associations with it. Overall, results from Chapters 2 and 3 highlighted the importance of considering demographic factors when assessing athlete calibration. However, they also highlighted the importance of understanding their limitations when doing so. In Chapter 4, I examined whether we can use self-reports of exercise metacognition and cognitive calibration to predict running and HIFME calibration. There was no significant association between any of these measures and exercise calibration in Studies 4, 5, and 6, suggesting that metacognition self-reports and calibration from other modalities are not reliable predictors of exercise calibration. In Chapter 5, I tested whether a minimal metacognitive intervention in the form of prediction guidance would lead to improved exercise calibration when participants received strategic, as opposed to impulsive, instructions. Findings from Studies 7 and 8 illustrated that strategic predictions facilitated prediction precision compared to impulsive predictions, though their effects on bias appeared to be less consistent and more dependent upon instructions. In Chapter 6, I discussed the general implications of the present thesis, and proposed ways in which future research can further explore the field of exercise calibration.

Aim: To test the reliability of the Modified 5-Year-Olds’ Index using assessors with a range of experience, and to determine whether calibration improves reliability. Design: Prospective reliability study. Setting: Bristol Dental School, University of Bristol. Methods: Fifty study models of non-syndromic 5-year-olds with a repaired UCLP were selected from the CCUK archives. Fifteen participants with a range of clinical experience in orthodontics were divided into three groups of equal experience. Each group was given differing amounts of information: Group 1 information sheet; Group 2 information sheet and reference models; Group 3 calibration course, information sheet and reference models. Each participant scored the 50 models using the Modified 5-Year-Olds’ Index on two occasions at least four weeks apart. ICCs calculated from a two-way random effects model were used to calculate intra-rater reliability and inter-rater reliability comparing assessors’ scoring to the experts’ consensus scores (gold standard). Results: Group 2 (ICC 0.80 – 0.93) and Group 3 (ICC 0.80 – 0.91) demonstrated high levels of intra-rater agreement, with lower levels shown by Group 1 (0.68 – 0.93). Inter-rater agreement was high in Group 2 (ICC 0.87 – 0.93) and Group 3 (0.82 – 0.91), with Group 1 showing the lowest levels of agreement (ICC 0.69 – 0.94). The level of training of the assessors in Group 1 appears to influence reliability scores, with high intra- and inter-rater reliability scores of the consultant and post-CCST trainee at a similar level to those of the same grade in Groups 2 and 3. Conclusions: The Modified 5-Year-Olds’ Index is a reliable method of assessing outcomes when model scoring is carried out by consultants or post-CCST trainees. Calibration in use of the Index does not improve reliability.

Surface profile and topography measurements of manufactured goods and components across all sectors of engineering are an integral component of a production process. Control measurements are taken on a regular basis to ensure items fulfil the required quality standards. Recent industry developments are driving towards more in-situ sensory measurement and monitoring systems. Depending on the dimensional layout and integral material properties of a sample, different metrological measurement techniques come into action for example Coherence Scanning Interferometry or focus variation microscopy. These techniques require high environmental stability and cannot be used for in-line inspection due to long mechanical scanning times. Recent developments in Hyperspectral Interferometry have laid the foundation for a potential single-shot absolute distance measurement technique suitable for in-line inspection. In this thesis a new version of Hyperspectral Interferometry is presented based on a pinhole array.

The design of electronics needs to account for the non-ideal characteristics of the device technologies used to realize practical circuits. This is particularly important in mixed analog-digital design since the best device technologies are very different for digital compared to analog circuits. One solution for this problem is to use a calibration correction approach to remove the errors introduced by devices, but this adds complexity and power dissipation, as well as reducing operation speed, and so must be optimised. This thesis addresses such an approach to improve the performance of certain types of analog-to-digital converter (ADC) used in advanced telecommunications, where speed, accuracy and power dissipation currently limit applications. The thesis specifically focuses on the design of compensation circuits for use in successive approximation register (SAR) ADCs. ADCs are crucial building blocks in communication systems, in general, and for mobile networks, in particular. The recently launched fifth generation of mobile networks (5G) has required new ADC circuit techniques to meet the higher speed and lower power dissipation requirements for 5G technology. The SAR has become one of the most favoured architectures for designing high-performance ADCs, but the successive nature of the circuit operation makes it difficult to reach ∼GS/s sampling rates at reasonable power consumption. Here, two calibration techniques for high-performance SAR ADCs are presented. The first uses an on-chip stochastic-based mismatch calibration technique that is able to accurately compute and compensate for the mismatch of a capacitive DAC in a SAR ADC. The stochastic nature of the proposed calibration method enables determination of the mismatch of the CAPDAC with a resolution much better than that of the DAC. This allows the unit capacitor to scale down to as low as 280aF for a 9-bit DAC. Since the CAP-DAC causes a large part of the overall dynamic power consumption and directly determines both the sizes of the driving and sampling switches and the size of the input capacitive load of the ADC and the kT/C noise power, a small CAP-DAC helps the power efficiency. To validate the proposed calibration idea, a 10-bit asynchronous SAR ADC was fabricated in 28-nm CMOS. Measurement results show that the proposed stochastic calibration improves the ADC’s SFDR and SNDR by 14.9 dB, 11.5 dB, respectively. After calibration, the fabricated SAR ADC achieves an ENOB of 9.14 bit at a sampling rate of 85 MS/s, resulting in a Walden FoM of 10.9 fJ/c-s. The second calibration technique is a timing-skew calibration for a time-interleaved (TI) SAR ADC that calibrates/computes the inter-channel timing and offset mismatch simultaneously. Simulation results show the effectiveness of this calibration method. When used together, the proposed mismatch calibration technique and the timing-skew calibration technique enables a TI SAR ADC to be designed that can achieve a sampling rate of ∼GS/s with 10-bit resolution and a power consumption as low as ∼10mW; specifications that satisfy the requirements of 5G technology.

Stochastic models are commonly used in quantitative finance to describe the dynamics of the derivatives market. As the market quotes are constantly changing, the models need to be calibrated to make real-time investment decisions. This can involve the sensitivity calculation to support the calibration process and investment portfolio management. For investment portfolios consisting of thousands of assets and options, the sensitivity calculation and calibration process are computationally expensive. This thesis presents a number of approaches to sensitivity calculation and model calibration utilizing high-performance computing architectures and Automatic Differentiation that improve performance and accuracy in financial modeling when compared to finite differences and pathwise methods. A parallel Monte-Carlo engine has been developed using the Adjoint methods for the first-order sensitivity calculation and model calibration This addresses the sensitivity and calibration problem for general stochastic differential models. The engine supports multi-/many-core CPU, GPU and distributed computing architectures. The work utilizes a graph representation and overloading operators to express general stochastic differential models. The sensitivities for the model calibration are calculated in parallel via a single simulation by the Adjoint method with the gradient computation cost being 1.8x that of function evaluation. The computational experiments consider both the Heston model and Heston with term-structure. These show that the engine improves performance by up to two orders of magnitude when compared to a sequential version. A hardware implementation has been developed for the Heston model calibration via the Adjoint on FPGA. The work also shows performance improvement of up to two orders of magnitude when compared to a sequential implementation. A parallel non-linear least squares optimization framework using Automatic Differentiation has been developed. This utilizes a graph representation and overloaded operator techniques to express the general objective and constraint functions. The framework supports multi-/many-core architectures such as GPUs and Intel Xeon/Xeon-Phi. The computational experiments consider the semi-closed form Heston model with the Gauss-Kronrod integration. These show performance improvement of 8.4x on GPU (OpenCL) and 7x on (CUDA) vs a sequential OMP (OpenMP) implementation. A Xeon-Phi implementation improves performance by 34x when compared to a single-thread implementation.

This thesis describes the optimisation of the calorimeter design for collider experiments at the future Compact Linear Collider (CLIC) and the International Linear Collider (ILC). The detector design of these experiments is built around high-granularity Particle Flow Calorimetry that, in contrast to traditional calorimetry, uses the energy measurements for charged particles from the tracking detectors. This can only be realised if calorimetric energy deposits from charged particles can be separated from those of neutral particles. This is made possible with fine granularity calorimeters and sophisticated pattern recognition software, which is provided by the PandoraPFA algorithm. This thesis presents results on Particle Flow calorimetry performance for a number of detector configurations. To obtain these results a new calibration procedure was developed and applied to the detector simulation and reconstruction to ensure optimal performance was achieved for each detector configuration considered. This thesis also describes the development of a software compensation technique that vastly improves the intrinsic energy resolution of a Particle Flow Calorimetry detector. This technique is implemented within the PandoraPFA framework and demonstrates the gains that can be made by fully exploiting the information provided by the fine granularity calorimeters envisaged at a future linear collider. A study of the sensitivity of the CLIC experiment to anomalous gauge couplings that {affect} vector boson scattering processes is presented. These anomalous couplings provide insight into possible beyond standard model physics. This study, which utilises the excellent jet energy resolution from Particle Flow Calorimetry, was performed at centre-of-mass energies of 1.4 TeV and 3 TeV with integrated luminosities of 1.5$\text{ab}^{-1}$ and 2$\text{ab}^{-1}$ respectively. The precision achievable at CLIC is shown to be approximately one to two orders of magnitude better than that currently offered by the LHC. In addition, a study into various technology options for the CLIC vertex detector is described.

The International Temperature Scale of 1990 (ITS-90) defines that the calibration of standard platinum resistance thermometers (SPRTs) is based upon the use of temperature fixed points. Since residual impurities (even below parts-per-million levels) present in the fixed point cells influence their realisation temperature (in the order of a few millikelvins), the fixed point material employed in the cell must be ≥ 99.9999 % pure. Impurities usually constitute the most substantial contribution to the uncertainty of primary SPRT calibrations. With a view to tackle this matter, the Consultative Committee for Thermometry (CCT) of the Bureau International of Weights and Measures (BIPM) has recommended the use of a specific correction methodology (the sum of individual estimates, SIE) but other methods have also emerged, each being advocated by a particular National Metrology Institute. The study reported in this thesis aims at investigating the application of seven available correction methodologies to the freezing point of aluminium (660.323 °C) to identify the most consistent methods together with any difficulties related to their implementation. In order to achieve this, a suite of five aluminium fixed point cells have been constructed according to a rigorous protocol, each cell using metal samples sourced from a different supplier. Glow discharge mass spectrometry (GDMS) assays have been obtained from three independent laboratories. Besides, for each cell constructed, a set of long duration freezing curves have been measured under nominally identical conditions. They provided the basis for the calculations of the correction methodologies investigated. The most consistent corrections were achieved with a hybrid method that combines the SIE and the overall maximum estimate (OME): the hybrid SIE/modified OME method. Furthermore, the correction methodology based on the fitting of a Scheil solidification model to the measured freezing curves was found to be highly consistent, provided certain constraints are applied.

In this thesis, we study the FX option pricing problem and put forward a 4-factor hybrid stochastic-local volatility model. The model, which describes the dynamics of an exchange rate, its volatility and the domestic and foreign short rates, allows for a perfect calibration to European options and has a good hedging performance. Due to the high-dimensionality of the problem, we propose a Monte Carlo simulation scheme that combines the full truncation Euler scheme for the stochastic volatility component and the stochastic short rates with the log-Euler scheme for the exchange rate. We analyze exponential integrability properties of Euler discretizations for the square-root process driving the stochastic volatility and the short rates, properties which play a key role in establishing the finiteness of moments and the strong convergence of numerical approximations for a large class of stochastic differential equations in finance, including the ones studied in this thesis. Hence, we prove the strong convergence of the exchange rate approximations and the convergence of Monte Carlo estimators for a number of vanilla and exotic options. Then, we calibrate the model to market data and discuss its fitness for pricing FX options. Next, due to the relatively slow convergence of the Monte Carlo method in the number of simulations, we examine a variance reduction technique obtained by mixing Monte Carlo and finite difference methods via conditioning. We consider a purely stochastic version of the model and price vanilla and exotic options by simulating the paths of the volatility and the short rates, and then evaluating the "inner" Black-Scholes-type expectation by means of a partial differential equation. We prove the convergence of numerical approximations and carry out a theoretical variance reduction analysis. Finally, we illustrate the efficiency of the method through a detailed quantitative assessment.

The millimeter-wave (mmWave) scanner is a system built a few years ago by the phased array antenna research and development group (PAARD) in the Advanced Radar Research Center (ARRC) with the purpose of measuring, characterizing, and calibrating mmWave antennas without significantly changing the setup. The most important reason for developing a multitasking system is to utilize a vector network analyzer (VNA), in many applications. This thesis proposes upgrades to this system, starting with the software, which restricts the system with poor motion flow, and addition of new features to the system that allow faster and reliable measurements. A relevant feature that was added is the calibration and measurement of a W-band active phased array antenna in a package that was manufactured for commercial purposes. Due to the nature of the active element in a phased array antenna, calibration of the system is required for precise and correct performance, which is critical for radio frequency systems. Over-the-air (OTA) calibration is a powerful technique used in phased array antennas in real-world environments. This thesis proposes a novel measurement system to calibrate and characterize the antenna. The system is a multi-axis hybrid system with nine degrees of freedom, used to perform the calibration process and also to scan the antenna radiation pattern in both near-field (NF) and far-field (FF) regions. Experimental results of the calibration method show high precision in the element radiation values, and excellent agreement between the antenna array radiation pattern measured in the near-field and far-field is achieved.

Engine Management Systems (EMS) is getting more complicated each year with new functions being introduced due to tighter emission regulations of both air quality and CO2. This directly a ects the calibration process of a powertrain because the number of vehicle parameters has increased about 10 times in the last 10 years. Self-calibrating feature such as proposed in this thesis has the potential to increase the e ciency of calibrating a complex EMS. The feature is intended to adapt itself to the engine behaviour and performance by continuously updating its calibration maps as the engine is being operated. This process will reduce the needs for new calibration data and additional ne-tuning when an EMS is being carried over to a di erent vehicle. The self-calibrating feature automatically adjusts the air path calibration maps of an engine. It adjusts the air path setpoint maps in real-time for steady state operating conditions.

Measurement uncertainty is an indication of the quality of a given measurement and ultimately translates into the confidence with which a decision can be made. In the context of PV, measurement uncertainties propagate into energy yield uncertainty, which in turn culminates into financial risk associated with an investment. This risk increases the cost of a PV installation. The aim of this thesis is to contribute towards the reduction of PV related measurement uncertainties. This is done in two ways. One is via developing and utilising more comprehensive methodologies for uncertainty propagation of complex measurands. The other is via more detailed estimates of the uncertainty contributors. In particular, the areas addressed in this thesis are the uncertainty estimation of the temperature coefficient measurements of modules; the uncertainty estimation of energy rating and module performance ratio measurements; and the uncertainties due to spectral effects on measurements performed with a flash solar simulator. The reported deviation in measurements of the temperature coefficients of P_MAX of modules is in the order of ±10% to ±15%. This is larger than the difference in the temperature coefficients of modules of the same type. The first step to improving the deviation between measurements is to estimate the uncertainty in a robust way. It was identified that there was no accepted approach of doing this. These measurements are strongly correlated, which complicates the uncertainty estimates. For the sake of simplicity, previously correlations have been avoided and conservative estimates used instead. In this work, uncertainties in both temperature and power and their correlations are estimated and propagated into the overall temperature coefficient uncertainty. Furthermore, temperature coefficients were calculated via weighing the measurements with their associated uncertainties. This was done for five different measurement setups that represent the majority of setups used worldwide. The approach was validated with measurement intercomparison of two modules measured on all systems. The approach reduced the overall uncertainty by half compared to the previous conservative estimates. It was demonstrated that uncertainties as low as 3% are achievable. The improved uncertainty estimates enabled the identification of a systematic effect due to a class B spectrum. This work culminated in the lowest reported measurement deviation of ±3.2% for module P_MAXtemperature coefficient measurements that was within the stated measurement uncertainties. The clear benefit of accounting for correlations was extended to measurements at different irradiance conditions and into the calculation of module performance ratio and energy rating. This was done via defining all the correlations between measurements and then propagating them with Monte Carlo simulations. The simulations are done with samples of a multivariate normal distribution with a variance-covariance matrix that corresponds to the estimated measurement correlations. It is demonstrated that both the energy rating and module performance ratio uncertainties strongly depend on the correlation estimates and that they cannot be conservatively overestimated. The module performance ratio uncertainty can be significantly lower than the measurement uncertainty at STC. This is because of the additional knowledge encoded into the selection of the underlying model used for calculating the energy rating. Therefore, the significance of the choice of model in the upcoming standard has been highlighted. It was confirmed that both bilinear interpolation and the proposed climatic datasets could be used for energy rating, but there are some areas that may need further investigation. An alternative way of improving uncertainty estimates and in turn reducing the associated uncertainty is via a more detailed characterisation of the uncertainty sources. A key uncertainty source is due to spectral effects in flash solar simulators. To better quantify this source, a complementary device was built to monitor the spectrum. The device is based on a matrix of photodiodes with commercially available interference filters situated on top and custom designed data acquisition electronics. This device is used in conjunction with the spectroradiometer to estimate the effects of flash-variation on the spectrum, the spectral temporal stability of the flash and spectral uniformity of the simulator and the attenuation masks used for altering the irradiance levels. It was demonstrated that the spectrum changes significantly during the flash and between flashes. While this effect is partially corrected for via the monitoring cell, it introduces additional uncertainty for non c-Si modules. This uncertainty is minimised by changes in the operational procedures. The spectral non-uniformity of the attenuation masks was shown to be significant, i.e. as large as 4%, in the NIR, prompting further investigation of the additional uncertainty for non c-Si modules. In this work, the methodology of estimating and propagating correlations in PV related measurements and the benefits of doing so are demonstrated. It is also highlighted that the uncertainty due to spectral effects goes beyond the uncertainty of spectroradiometer measurements. Finally, it is shown how they can be estimated with a complementary spectral monitor.

Most existing houses in the UK have a single thermostat, a timer and conventional thermostatic radiator valves to control the low pressure, hot water space heating system. A number of companies are now offering a solution for room-by-room temperature and time control in such older houses. These systems comprise of motorised radiator valves with inbuilt thermostats and time control. There is currently no evidence of any rigorous scientific study to support the energy saving claims of these zonal control systems. This thesis quantifies the potential savings of zonal control for a typical UK home. There were three components to the research. Firstly, full-scale experiments were undertaken in a matched pair of instrumented, three bedroom, un-furbished, 1930s, test houses that included equipment to replicate the impacts of an occupant family. Secondly, a dynamic thermal model of the same houses, with the same occupancy pattern, that was calibrated against the measured results. Thirdly, the experimental and model results were assessed to explore how the energy savings might vary in different UK climates or in houses with different levels of insulation. The results of the experiments indicated that over an 8-week winter period, the house with zonal control used 12% less gas for space heating compared with a conventionally controlled system. This was despite the zonal control system resulting in a 2 percentage point lower boiler efficiency. A calibrated dynamic thermal model was able to predict the energy use, indoor air temperatures and energy savings to a reasonable level of accuracy. Wider scale evaluation showed that the annual gas savings for similar houses in different regions of the UK would be between 10 and 14% but the energy savings in better insulated homes would be lower.

Ultra-broadband Monolithic Microwave Integrated Circuit (MMIC) amplifiers find applications in multi-gigabit communication systems for 5G and millimeter wave measurement instrumentation systems. The aim of the research was to achieve maximum bandwidth of operation of the amplifier from the foundry process used and high reverse isolation ( < -25.0 dB) across the whole bandwidth. To achieve this, several design variations of DC - 110 GHzMMIC Cascode TravellingWave Amplifier (TWA) on 100 nm AlGaAs/GaAs pHEMT process were done for application in next generation instrumentation and high data transfer rate (100 Gb/s) optical modulator systems. The foundry service and device models used for the design are of the WINPP10-10 process from WIN Semiconductor Corp., Taiwan, a commercial and highly stable process. The cut-off frequency ft and maximum frequency of oscillation fmax for this process are 135 GHz and 185 GHz respectively. Thus, the design was aimed at pushing the ultimate limits of operation for this process. The design specifications were targeted to have S21 = 9.0 to 10.0 ± 1.0 dB, S11 & S22 ≤ -10.0 dB and S12 ≤ -25.0 dB in the whole frequency range. In order to achieve the targeted RF performance, it is imperative to have accurate transistor models over the frequency range of operation, transistor configuration mode and operating bias points. Using smaller periphery transistors results in lower extrinsic & intrinsic input and output capacitances that lead to achieving very wide band performance. Thus, device sizes as small as 2x10 μm were used for the design. A cascode topology, which is a series connection of a common-source and common-gate field effect transistor (FET), was used to achieve large bandwidth of operation, high reverse isolation and high input and output impedance. Using very small periphery devices at cascode bias points posed limitation in the design in terms of accuracy of transistor models under these conditions, specifically at high frequencies i.e., above 50 GHz. One of the major systemrequirements for the application of MMIC ultra-broadband amplifiers in instrumentation is to achieve and maintain high reverse isolation (≤ -25.0 dB) over the whole frequency range of operation which cannot be achieved alone by the cascode topology and new design techniques have to be devised. These twomajor challenges, namely high frequency small periphery FET model modification & development and design technique to achieve high reverse isolation in ultra-broadband frequency range have been addressed in this research.

Fringe projection is a commonly used optical technique for measuring the shapes of objects with dimensions of up to about 1 m across. There are however many instances in the aerospace and automotive industries where it would be desirable to extend the benefits of the technique (e.g., high temporal and spatial sampling rates, non-contacting measurements) to much larger measurement volumes. This thesis describes a process that has been developed to allow the creation of a large global measurement volume from two or more independent shape measurement systems. A new 3-D large volume calibration artefact, together with a hexapod positioning stage, have been designed and manufactured to allow calibration of volumes of up to 3 x 1 x 1 m3. The artefact was built from carbon fibre composite tubes, chrome steel spheres, and mild steel end caps with rare earth rod magnets. The major advantage over other commonly used artefacts is the dimensionally stable relationship between features spanning multiple individual measurement volumes, thereby allowing calibration of several scanners within a global coordinate system, even when they have non-overlapping fields of view. The calibration artefact is modular, providing the scalability needed to address still larger measurement volumes and volumes of different geometries. Both it and the translation stage are easy to transport and to assemble on site. The artefact also provides traceabitity for calibration through independent measurements on a mechanical CMM. The dimensions of the assembled artefact have been found to be consistent with those of the individual tube lengths, demonstrating that gravitational distortion corrections are not needed for the artefact size considered here. Deformations due to thermal and hygral effects have also been experimentally quantified. The thesis describes the complete calibration procedure: large volume calibration artefact design, manufacture and testing; initial estimation of the sensor geometry parameters; processing of the calibration data from manually selected regions-of-interest (ROI) of the artefact features; artefact pose estimation; automated control point selection, and finally bundle adjustment. An accuracy of one part in 17 000 of the global measurement volume diagonal was achieved and verified.

This thesis concerns methods to enhance current and explore future radiometric imaging systems for security screening. Its main focus is on the test and calibration procedures for the BorderWatch system – an established 33 GHz passive millimetre-wave imager developed by QinetiQ PLC as an outdoor security scanning portal for soft-sided heavy goods vehicles at ports of entry. The effects of the limited size of the sky background reflector of the operational system are addressed and modifications to mitigate these effects are proposed. Systematic diurnal and seasonal variations in the receiver output powers are characterised and strongly linked with variations in the physical temperature of the RF components. The proposed BorderWatch temperature calibration subsystem requires a reliable cold sky temperature reference point; the aim being to reduce the level of fixed pattern noise in present-day imagery and allow for post-processing methods requiring absolute temperature values. Cost considerations rule out independent millimetre-wave radiometers at each site so a proposed alternative is to use infra-red measurements as a proxy. A literature investigation is made into the millimetre-wave and infrared atmospheric opacities for a variety of meteorological conditions. The design, calibration and operation of a 35 GHz switching radiometer is described together with a comparison of the millimetre-wave radiometric brightness temperature measurements against the data from a low cost commercial infra-red sensor and from a nearby meteorological station. The results show an excellent correlation between the zenith sky temperatures in the infrared and millimetre-wave bands but only in clear sky conditions – as anticipated the presence of clouds affects the infra-red brightness distribution much more strongly than that of the millimetre-wave. Future security imagers may well incorporate interferometric arrays. An extensive simulation programme has been carried out to explore appropriate numbers of antennas and whether radio astronomy configurations and post-processing techniques can offer cost-effective routes to high image fidelity. The results of a quantitative analysis are promising and array configurations and techniques appropriate for potential future security imagers are suggested.

We study a sequential binary prediction setting where the forecaster is evaluated in terms of the calibration distance, which is defined as the L1 distance between the predicted values and the set of predictions that are perfectly calibrated in hindsight. This is analogous to a calibration measure recently proposed by Błasiok, Gopalan, Hu and Nakkiran (STOC 2023) for the offline setting. The calibration distance is a natural and intuitive measure of deviation from perfect calibration, and satisfies a Lipschitz continuity property which does not hold for many popular calibration measures, such as the L1 calibration error and its variants. We prove that there is a forecasting algorithm that achieves an O(√T) calibration distance in expectation on an adversarially chosen sequence of T binary outcomes. At the core of this upper bound is a structural result showing that the calibration distance is accurately approximated by the lower calibration distance, which is a continuous relaxation of the former. We then show that an O(√T) lower calibration distance can be achieved via a simple minimax argument and a reduction to online learning on a Lipschitz class. On the lower bound side, an Ω(T^(1/3)) calibration distance is shown to be unavoidable, even when the adversary outputs a sequence of independent random bits, and has an additional ability to early stop (i.e., to stop producing random bits and output the same bit in the remaining steps). Interestingly, without this early stopping, the forecaster can achieve a much smaller calibration distance of polylog(T).

The aim of this research is to make a newly constructed Stewart-Gough Platform-based test frame Tiger 66.1 operational by developing control software and estimating the error in its pose accuracy. The accuracy of the platform is affected by one source or multiple sources. The typical error sources are kinematic and structural, some of them originate from manufacturing imperfections, assembly deviations, elastic deformations, thermal deformations, and joint clearances which change the expected kinematic behavior of the manipulator. Also, some non-mechanical errors like transmission error, sensor accuracy, algorithm error, and truncation error in calculation contribute significantly in some cases. Using pose deviations as a foundation, this research further aims to develop a calibration method that enhances pose accuracy, leveraging the pose deviations observed during the initial measurements. This research presents a novel calibration method for a Stewart-Gough platform using photogrammetry, a digital image-based technique for 3D measurement and modeling. As part of this research, a new forward kinematic algorithm has been developed to implement an accurate non-contact calibration method that can improve the accuracy and precision of a general Stewart-Gough platform. The Stewart-Gough platform is one of the most popular Parallel Kinematic Machines (PKM). This mechanism, also known as a parallel manipulator or parallel robot, consists of a fixed base and a movable platform connected by six actuators or struts. The Stewart-Gough platform has remained an interesting machine for research due to its flexibility, structural rigidity, high accuracy, and reliability in motion control. The calibration of a Stewart-Gough platform is one of the essential steps in ensuring the accuracy and stability of the moving platform center. This dissertation investigates a forward kinematic calibration method for improving the accuracy of the Stewart-Gough platform-based test platform “Tiger 66.1” which is intended for use in the characterization of additively manufactured parts, although the method is not restricted to this platform only and can be extended to any similar platforms designed for use in various applications. For calibrating Tiger 66.1, a new forward kinematic algorithm has been developed in this research. The forward kinematics for parallel manipulator generates multiple solutions and they include both feasible and unfeasible solutions. The developed algorithm is a new way of finding unique feasible solution for a platform pose. The proposed algorithm utilizes the high power of modern computing systems and finds a unique solution for a pose through iterations. The advantage of this new algorithm is that the solution obtained from the iterations does not need to be verified manually to check the feasibility in real life and can be directly used as input for any further calculations without stopping the computation process. The proposed calibration methods used photogrammetry which minimizes the need for manual handling of the platform during the calibration process. Photogrammetry uses images of targets taken by one or more cameras to reconstruct 3D positions and orientations. In this research a high-resolution digital camera has been used to take multiple images of the moving platform center from three different angles for each pose and analyze those images through the commercial software package “Photomodeler”, to measure the pose of the platform in three-dimensional space. This method eliminates the need for any additional measurement instrument to be used directly or indirectly interacting with the Stewart-Gough platform. The proposed forward kinematic technique is validated through both simulations and experiments on the physical Stewart-Gough platform-based test frame Tiger 66.1. The vision-based approach is showcased to improve absolute positioning accuracy by up to 25% compared to an uncalibrated platform. The method requires minimal hardware modification and renders it highly suitable for precision application. It provides a practical approach to the calibration of parallel manipulators intended for high-precision tasks. The dissertation concludes by summarizing the contributions of this research, which includes the development of a novel photogrammetry-based calibration method that involves minimal hardware modification and full extrinsic calibration from vision data. The demonstration shows the substantial potential of the proposed method in augmenting the positioning performance of the Stewart-Gough platform tailored for precision applications. Furthermore, the work identifies the areas for further work, such as implementing an online calibration method. Overall, this research demonstrates the capabilities of photogrammetry for parallel manipulator calibration while minimizing hardware modifications, thereby presenting a pragmatic approach to the calibration of such systems in the pursuit of high-precision applications.

Neutrinos hold the promise of untangling many unresolved questions in particle physics. Their unique properties and behaviors offer a distinctive window into understanding the fundamentals of the universe, potentially providing answers to some of the most deep puzzles in modern physics. CEνNS, or Coherent Elastic Neutrino-Nucleus Scattering, is a process where a neutrino interacts with an atomic nucleus and scatters away, leaving the nucleus to recoil. CEνNS is an important area of study for understanding neutrino properties as well as their role in the universe. The COHERENT collaboration was the first to measure CEνNS, using neutrinos from the Spallation Neutron Source (SNS). The direct measurement of the SNS neutrino flux is vital for the precision of CEνNS measurements. This work introduces the latest addition to the COHERENT's armory –a D2O detector specifically designed to measure the SNS neutrino flux. In the present dissertation, the emphasis is made on the steps taken to operationalize COHERENT's D2O detector. This work unfolds the intensive simulation work directed to determine the detector's optimal design, ensuring it stands strong to the demands of neutrino physics experiments. Establishing the detector's calibration is essential to its operational phase. A dedicated calibration system, described in detail in this work, has been developed, utilizing encapsulated LED flashers controlled by a microcontroller unit to ensure the systematic and reliable calibration of the detector. A significant portion of the document is devoted to the calibration analysis, where we use Michel electrons to obtain an energy scale for the detector, thereby ensuring the reliability and accuracy of the future neutrino flux measurements.

Radar cross section (RCS) of electrically large targets can be challenging and expensive to measure. The use of scale models to predict the RCS of such large targets saves time and reduces facility requirements. This study investigates Ka-band (27 to 29 GHz) RCS prediction from scale model measurements at 500 to 750 GHz. Firstly, the coherent quasi-monostatic turntable RCS measurement system is demonstrated. Secondly, three aluminum 18:1 scale dihedrals with surface roughness up to 218 icroinches are measured to investigate how the roughness affects the Ka-band prediction. The measurements are compared to a parametric scattering model for the specular response, and indicate that the models’ surface roughness have negligent effect on the RCS prediction.

Water quality in lakes and reservoirs has been significantly degraded due to anthropogenic activities including point and non-point source pollution. Agricultural practices, particularly excessive fertilizer application, have been consistently identified as a major contributor to water contamination in the lakes and reservoirs. The escalation of nutrient loading into water bodies has raised serious concerns regarding eutrophication in lakes, as well as the potability of drinking water and other consumptive use of water. In order to address these problems, Best Management Practices (BMPs) have been implemented globally to reduce nutrient loadings and improve water quality in lakes and reservoirs.This study aims to assess the effectiveness of BMPs in reducing nutrient levels in the Atwood and Tappan Lakes of the Tuscarawas basin in Ohio by monitoring the sites for water quality sampling and using the Soil and Water Assessment Tool (SWAT) for watershed modeling. Stream flow data from five USGS gauge stations were gathered for multi-site calibration and validation of the model, whereas water quality data from five representative stations within the watersheds were monitored to calibrate the model for nutrients. The performance of the model for streamflow calibration at various USGS gauging stations was satisfactory with Nash-Sutcliffe Efficiency (NSE) values ranging from 0.54 to 0.79 during calibration, and 0.50 to 0.89 during validation. However, due to limited availability of water quality data, the calibration of nutrient was not as good as hydrological calibration. Subsequently, a scenario analysis was carried out using the calibrated SWAT model to assess the effectiveness of different management practices in reducing nutrient levels from the sub-watersheds. The selection of management practices, such as filter strips, grass waterways, fertilizer reduction, crop rotation, and cover crops, were considered for analysis based on active consultation with local stakeholders involved in nutrient reduction initiatives in each watershed. The analysis encompassed 12 subwatersheds of Atwood Lake and 10 sub-watersheds of Tappan Lake, evaluating the performance of these BMPs in both watersheds.The analysis revealed a remarkable outcome, demonstrating that a synergistic implementation of cover crops (rye), grass waterways, and a 10% reduction in fertilizer usage caused the most substantial reduction in nutrient flow by 88%. On the contrary, only 10% reduction in fertilizer application without the incorporation of other BMPs resulted in a small reduction (9%) of nutrient levels. These results underscore the significance of implementing a comprehensive and integrated approach to effectively combat nutrient pollution while maintaining agricultural productions. The overall analysis suggested that notable reductions in total nitrogen and total phosphorus could be achieved, ranging from 8% to 53% for nitrogen, and from 7% to 88% for phosphorus, depending upon the specific combination of Best Management Practices (BMPs) implemented in the watersheds. The study emphasizes the efficacy of employing grass waterways with cover crop and fertilizer reduction to mitigate nutrient losses in both nitrogen and phosphorus.Since the water quality model was not well calibrated, the comparison of calibrated and uncalibrated was accomplished to report the discrepancy in modeling outcome with less calibrated models. When comparing the efficacies of BMPs with the calibrated and uncalibrated water quality models, the calibrated model demonstrated a slightly higher reduction in nutrient load but not a significant difference in nutrient load reduction compared to the uncalibrated model. Furthermore, when the rankings of BMPs in terms of nutrient load reduction were compared, both the calibrated and uncalibrated models demonstrated a similar pattern and retained their relative effectiveness with a consistent order in terms of rankings despite the absolute changes in overall nutrient reduction levels between the two models. The same order of rankings from both calibrated and uncalibrated models affords credibility to the findings and imply that the relative efficacy of different BMPs in decreasing nutrient loads could be independent of model calibration. Also, the study explored on the significant impact of cattle grazing on nutrient loads within the watersheds. It was observed that allowing cattle to graze freely on pastureland resulted in a substantial increase in total nitrogen and total phosphorus. However, by reducing the grazing rate to 50% and 25%, the model predicted less increment in nutrient levels suggesting the sensitivity of cattle grazing in nutrient loadings from pastureland.The research findings also suggested that the effectiveness of implemented BMPs could rely on the specific characteristics of each site, including the land cover, local land use pattern, and climatic conditions. This study emphasizes the selection and implementation of BMPs are site-specific and the BMP efficacy reported in this study are not simply transferable to other regions rather rely based on the unique land use and land cover characteristics of each area. By adopting the suggested BMPs in this study, significant improvement can be made in curbing nutrient runoff and its detrimental impacts on water quality in the study area. Also, this study underscored the challenges of monitoring and calibrating the SWAT model for nutrients at a small sub-watershed scale. Nonetheless, the findings provide valuable insights for stakeholders involved in the restoration of the Atwood and Tappan Lakes watersheds.

A method for software-defined radio array calibration is presented. The method implementsa matched filter approach to calculate the phase shift between channels. Thetemporal stability of the system and calibration coefficients are shown through the standarddeviation over the course of four weeks. The standard deviation of the phase correction wasshown to be less than 2 deg. for most channels in the array and within 8 deg. for the most extremecase. The standard deviation in amplitude scaling was calculated to be less than 0.06 forall channels in the array. The performance of the calibration is evaluated by the antennagain and the difference from the ideal beam shape for the peak side lobe level and first nulldepth. For one example data collection, the gain was 61 dB for the array with a maximumdifference of 0.2246 dB for the peak side lobe level and 0.3998 dB for the first null depth.

X-Ray Fluorescence (XRF) spectroscopy is a good analytical method for determination of both trace and major elements in different samples, it has the important advantages of allowing direct analysis of soils and sediments and is also non-destructive.The major aim of this study was to develop an XRF method for the determination of total metal concentrations in soils and sediments. Limit of detection and limit of quantitation were used as the figures of merit to evaluate the method. Standard Reference materials: NIST-2586, NIST-2587, NIST-1645 and NIST-1645 were used for calibration. Different calibration methods were explored. background corrected peak height to Background corrected Compton peak height ratio had the highest correlation coefficient (0.99) for Fe, Cu, Mn, Zn, Al, Cr, Ca and Pb.The method was applied to measure 120 sediment samples that were collected from the yellow creek watershed Poland, Ohio. The results of the XRF method were compared with those obtained by ICP-MS. Al, Mn, Zn, Fe and Cu had good agreement. A sequential extraction approach was developed for characterizing the availability of metals in the water from the sediments. Ca and Mn were found to be easily extracted under weakly acidic conditions.

Classical receiver architectures demodulate a high frequency bandpass signal to baseband before sampling the in-phase and quadrature components. With the advent of high-speed analog-to-digital converters (ADCs) and wide bandwidth sample-and-hold (S/H) circuits, it has become practicable to sample a bandpass signal directly without any demodulation operation and then process it with robust DSP technology. Direct sampling methods do present their own challenges. When a single channel is used to digitize the signal, not all frequencies above the Nyquist rate are allowed and only signals in certain frequency bands can be sampled at this minimum rate. As first shown by Kohlenberg, the restriction on spectral location can be removed with a two-channel time-interleaved ADC (TIADC) where two ADCs separated by a timing skew independently sample the signal. In this dissertation, we propose a general and flexible technique for sampling the complex envelope of a bandpass signal using a nonuniformly interleaved $M$-channel TIADC. The bandpass signal is sampled directly by the sub-ADCs, and the overall TIADC sampling frequency is at or slightly above the Nyquist rate. Reconstruction of the complex envelope entails inverting a matrix of filters resulting in some TIADC timing skews being forbidden. The proposed sampling scheme requires the implementation of $M$ digital FIR filters and can be used to digitize bandpass signals with any carrier frequency in software defined radio applications. Reconstruction analysis is provided for the case of two, three, and four channels. Multi-tone and MSK signals are used in simulations to validate the proposed method and assess its performance. Quadrature sampling, a special case of the two-channel TIADC that assumes certain parameter relations, is investigated as an approximation technique. It is well known that gain and timing skew mismatches can severely degrade TIADC performance. To mitigate the effect of these mismatches on complex envelope reconstruction, we present a novel blind calibration method which assumes that there exists a frequency band where the complex envelope signal has no power, due for example to oversampling. Mismatches give rise to errors in this band, which are extracted and used to estimate adaptively the gain and timing skew mismatches. Simulations with multi-tone, MSK, and bandlimited white noise signals demonstrate calibration can significantly improve reconstruction performance measured in the mean-square error (MSE) sense. Suggestions for further research are provided.

Buildings play a major role in energy expenditure, representing 40% of Europe’s total energy consumption. It is estimated that heating, ventilation, and air conditioning systems consume between 50–60% of the total energy spent inside the building, thus corresponding to 20% of global worldwide energy consumption. Hence, there is a need to improve the accuracy of building thermal simulation and energy models that are essential in regulatory compliance calculations. In the present study, the authors empirically validate an optimizationbased calibration methodology based on its application to a fully operational commercial building located in Pamplona, Navarre. The methodology used a white-box two-stage model in EnergyPlus, which combines a load profile object and a district cooling component to distribute the cooling load inside the building’s thermal zones. The study optimized the parameters and performance curves of different cooling system components using a second-generation non-sorting genetic algorithm in jEPlus software and 985 h of ten-minute time-step data. Finally, a multi-level benchmark is executed, which evaluates the electric energy consumption of the building’s heat pumps and the interior temperature of the different thermal zones for summer 2020 conditions. The assessment of the thermal and energy performance of the simulation model was conducted according to the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Guideline 14-2002, and the Chartered Institution of Building Services Engineers, Operation Performance Technical Memoranda 63.

The overarching objective of this research was to enhance our comprehension of the three-dimensional precision of meteorological measurements obtained using small unmanned aircraft systems (UAS). Two complimentary experiments were conducted to achieve this objective. The first experiment entailed the development and implementation of a system to determine the global navigation satellite system (GNSS) position accuracy on a UAS platform. This system was utilized to assess the static and dynamic accuracy of L1 and L1/L2 GNSS receivers in real-time kinematic (RTK) and non-RTK fix modes. Adjusted two-sample t-tests revealed significant differences in horizontal and vertical error between RTK and non-RTK receivers across the various deployment strategies. The findings indicate that RTK receivers produce more precise measurements with significantly fewer errors compared to non-RTK receivers. However, the practical significance of these differences warrants separate consideration. The study emphasizes the importance of using RTK receivers when conducting meteorological measurements, especially vertical profiles, and suggests the need for further research to differentiate the impact of treatments from the influence of external factors on receiver accuracy. The second experiment involved the development of a set of miniature pressure, temperature, and relative humidity (PTH) probes for UAS integration. An automated calibration/validation routine was devised to calibrate the PTH probes using an environmental chamber. The results of the Tukey-Kramer procedures revealed that fewer calibrated sensors were statistically different from each other compared to uncalibrated sensors, reducing intersensory bias and demonstrating the value of calibrating different sensor models to a common reference. The linear regression offsets showed that calibration was necessary to meet the desired accuracy specification. In most UAS-based applications, the differences between the calibrated PTH probes were expected to be negligible, irrespective of their significance. The outcomes of this research will enhance our ability to quantify minor variations in ambient conditions during coordinated multi-UAS flights. The low-cost GNSS receivers evaluated demonstrated centimeter-level accuracy under RTK mode, which eliminates the need to use barometric pressure sensors to correct for short-term drift in elevation from non-RTK GNSS measurements. The PTH probes demonstrated that research-grade meteorological measurements can be made using embedded sensors through careful design and calibration.

The ability to precisely localize a robot within the environment is a core capability for mobile robotics. Knowing a robot’s precise pose permits various tasks, such as navigation in an environment, interaction between multiple agents, or mobile manipulation of an object. Various applications such as, for example, autonomous driving, delivery robots, augmented reality (AR), mobile manipulation, and robot inspection greatly benefit from an accurate and robust underlying Simultaneous Localization And Mapping (SLAM) system. Motivated by these widespread applications, the goal of this thesis is to improve the existing limits of SLAM. We focus on integrating deep learning into various components of SLAM, as we aim to integrate the advances in perceptual understanding deep learning has provided. We wish to draw inspiration from how humans perceive the environment to include more semantically meaningful landmarks in the mapping process. We believe deep learning will not only improve existing solutions but provide novel solutions to yet unsolved problems. Different robots and environments have different optimal sensor configurations and perception requirements regarding SLAM. For example, micro aerial vehicles have limited payload and a different perspective than indoor wheeled robotic platforms. Accommodating these differences in software requires a flexible framework that can support multiple different modalities and types of sensors. However, many existing frameworks are very rigid and require significant changes when prototyping new products or testing novel research ideas. Therefore, in the first part of the thesis, we will focus on a new multi-modal, multi-robot, and modular SLAM framework. We show through experiments that state-of-the-art accuracy is possible, and we showcase the integration of various experimental modules. In the second part of the thesis, we will focus on using deep learning to improve SLAM, first, by working on a new type of landmark and descriptor. We further explore the inclusion of semantics into the localization process of our proposed framework. Finally, we use learning to address maintaining accurate long-term calibration in SLAM, which is essential to the operation of all downstream tasks. We focus on the modular and meaningful integration of learning, as we believe this leads to robust robotic systems that remain understandable and easier to design.

This thesis focuses on the environmental impact of car-following (CF) driving behavior in mixed autonomy traffic. Given the shortage of real-world mixed autonomy trajectory data, this area of research is not yet well-understood and is often explored using classical simulation models with assumed and unvalidated parameters with little or no consideration of autonomous eco-driving behavior. The thesis introduces a new calibration framework applicable to various CF models. The framework uses the concept of ‘adaptability’ which improves the simulation of AVs’ driving behavior by enabling real-time changes of the CF model parameters based on prevailing traffic conditions. The thesis also provides an environmental assessment of mixed autonomy traffic considering different vehicle arrangements in a platoon using real data and simulation outcomes. The thesis provides several novel findings regarding the impact of the driving behavior of AVs on-road emissions. For instance, the analysis of the trade-off between traffic stability and mobility in a mixed autonomy environment reveals that an automated eco-driving strategy that minimizes the reciprocal of the platoon average velocity produces fewer emissions compared to strategies that aim to minimize traffic instability measured by the standard deviation of velocity or average acceleration. This is because the latter can considerably decrease the platoon average velocity to increase stability. In addition, vehicle arrangements, specifically whether the AV is leading or following, affect the AV’s driving behavior and thus, the emissions, especially in congested conditions. Furthermore, the correlation between mobility and emissions shows that traffic emissions are highly influenced by acceleration at a high-velocity level and by time headway at a low-velocity level. Overall, the thesis explores the environmental implications of AV driving behavior considering different aspects such as traffic network, market penetration rate, and vehicle arrangements. Therefore, the findings of this thesis can be insightful at this early stage of AV deployment to ensure that the future mixed autonomy environment does not only improve traffic and safety but also contribute to a more environmentally friendly transport system.

Aluminum-26 (Al-26) is a radioactive isotope produced in massive star processes. High-resolution spectroscopy of its 1.809 MeV gamma-ray (γ-ray) decay signature constrains the dynamics of its emission as it is ejected from its progenitor sites and incorporated into the interstellar medium of the Milky Way Galaxy. Imaging reveals dominant emission in the Inner Galaxy and emission in localized regions of massive star activity. Taken together, spectroscopy and imaging of Al-26 shed light on the chemical evolution of the Galaxy over millions of years. The Compton Spectrometer and Imager (COSI) is a compact Compton telescope designed to measure astrophysical γ-rays of energy 0.2–5 MeV. Its high-purity germanium detectors track incident photons as they Compton scatter throughout the detector volume. In 2016, COSI flew on a NASA ultra-long duration balloon for 46 days. This dissertation details the first analysis of Al-26 in the flight data and reports a measurement of 3.7σ significance above background and an Inner Galaxy flux of (8.6 ± 2.5) × 10−4 ph cm−2 s−1. All scientific achievements from the flight are predicated on calibrations performed before launch. These calibration procedures were repeated in advance of an intended 2020 balloon flight. Analyzing calibration data validates instrument performance and informs studies of detector effects, including charge sharing and charge trapping, that complicate the measurement process.The next generation of COSI as a NASA Small Explorer satellite is slated for launch in 2027. It is anticipated to strengthen the spectroscopic measurement of Al-26 in the balloon flight and yield the most detailed images of Al-26 to date. Extensive testing of the imaging algorithm in COSI’s new analysis toolkit is presented as a first step towards producing these images. The desired culmination of these efforts is an enhanced understanding of Galactic Al-26 by way of thorough calibrations, novel insight into undesirable detector effects, and advanced analyses of high-resolution data from the COSI balloon and satellite instruments.

High-speed, high-resolution pipelined ADCs require each pipelined stage's reference circuit to have very low output impedance for accurate reference voltages, often within a small fraction of the amplification phase. However, this results in significant power dissipation in the reference circuit. This thesis presents two digital background calibration techniques that calibrate for reference errors due to incomplete reference setting, reducing power dissipation. Additionally, the reference calibration technique extends to calibrate for second- and third-order reference errors, further reducing required power. The conventional op amp is replaced with the energy-efficient and high output swing ring amplifier, but published ring amplifiers require external biases to adjust the dead-zone manually, limiting commercial applications. This thesis proposes a method to automatically adjust the dead-zone voltage, overcoming this limitation.

Calibration is a fundamental step in using sensors. It helps to maintain precise measurements and repeatability of the measurements. The joint calibration between multiple sensors of different types is complicated because the results suffer from different error sources. In this dissertation, first, we designed a mobile robot mapping system based on a low-cost 2D Light Detection And Ranging sensor (LiDAR) to test its capability. We extended this project to do Visual SLAM (Simultaneous Localization And Mapping) with a monocular camera, performed several state-of-the-art algorithms on our robot, and then tested the performance and power consumption to verify the capabilities of different embedded systems for Visual SLAM applications on a mobile robot. Third, we investigate the calibration of a 2D LiDAR and camera. We develop a lightweight simulation tool in Matlab for more convenient simulation data generation than Robot Operating System (ROS). Since the ground truth of calibration is unavailable, we modify the camera by removing the IR filter to make the LiDAR scan visible. The current calibration method using a planar calibration target performs well in a simulation environment but usually fails in practice because of poor LiDAR performance and pose selection. We propose a new pose selection method and exploit the poor performance of LiDAR to contribute to the calibration process. This experimental result shows that the improved calibration framework reduces calibration error and improves the robustness of calibration results.

In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene, canonically known as reflectance conversion, is an essential component of many diagnostic analysis tasks. This process is crucial to the production of accurate information for a variety of applications, notably precision agriculture. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently researched method, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection. Unlike ELM, AARR does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions, which can substantially reduce the level of effort required for collection setup and subsequent data analysis. Illumination changes during a collection greatly impact the recorded scene radiance, which can confuse subsequent analysis results. For this research, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from multispectral and hyperspectral radiance data at varying aircraft altitudes and is cross compared to the ELM approach. The observed error introduced by AARR is often acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Furthermore, this work introduces several avenues to improve the AARR method based on additional experimentation.

In recent years, spinal cord injury (SCI) researchers have been criticized by SCI patient advocates for prioritizing research without immediate translation potential. Stakeholders have emphasized that researchers should be pursuing simple objectives that directly improve the lives of individuals with SCI rather than long-term, theoretical academic projects (Lemmon, 2019). These frustrations are amplified by the lack of accessible assistive technology addressing disabilities associated with SCI, amyotrophic lateral sclerosis (ALS), and similar conditions. One assistive technology ripe for translation is the intracortical brain-computer interface (BCI). These systems interpret neural activity from implanted recording sensors and allow the user to control a diverse set of effector devices to restore lost function. Patient stakeholders believe this technology augments their abilities enough to justify its deployment in a home setting (Solzbacher et al., 2018). However, with few rare exceptions (Weiss et al., 2020; Simeral et al., 2021), these systems have been restricted to controlled laboratory experimentation under close technician supervision. This dissertation is focused on identifying and addressing barriers to intracortical BCI deployment in a home setting.Chapter 1 introduces intracortical brain-computer interfaces (BCIs) and outlines the major challenges this technology must overcome before it is deployed as an assistive device in unsupervised settings. This introduction leads directly into Chapter 2 which is focused on the many issues that interfere with BCI signal stability and require neural decoder recalibration. Decoder recalibration has historically been performed manually by the user with technician oversight and is a significant obstacle to quick and convenient BCI operation. To address this challenge, Chapter 2 first introduces new classifications for organizing signal disruptors. Rather than organizing disruptions according to their root cause, they are classified by their impact on signal quality and amenability to interventions. This organizational framework is designed to help researchers develop algorithmic countermeasures against signal disruptors and sustain BCI performance. This chapter concludes with a discussion of relevant algorithms to each category of disruptions.Chapter 3 is an analysis of intracortical Utah Microelectrode Array recordings spanning five years with a focus on disruptions that affect signal quality and decoding performance. In addition to modeling long-term signal metric trends, several signal disruptions are examined in detail. We investigate the causes and effects of these disruptions and discuss how they could be prevented, mitigated, or remedied. This analysis spans one of the longest intracortical implant duration in humans and thus is an important reference for anticipated long-term signal disruptions.Chapter 4 explores repurposing neural decoders across operational contexts instead of de novo training for each context. In this chapter, the intracortical BCI participant uses a motor imagery decoder to operate a virtual vehicle. This context switching emulates real-world applications where a user may alternate between controlling a motor prosthesis and driving their wheelchair with the BCI. We analyzed driving ability on several custom courses and correlated motor decoder performance with driving performance. Finally, to demonstrate transferability to real-world tasks, we mapped online decoder predictions to control an RC car offline in the laboratory.Chapter 5 introduces a novel method for calibrating high-density functional electrical (FES) stimulation end-effectors for a BCI. Upper-limb FES augments motor function by stimulating intact peripheral nerves and muscles to generate coordinated functional hand movements. Our research group previously developed a high-density upper-limb FES garment to deliver targeted stimulation. With up to 160 stimulating channels and many stimulating waveform parameters, the number of possible stimulation patterns is astronomic. This chapter introduces virtual patch electrodes that designate geometric areas for stimulation rather than individual electrodes. Virtual patch electrodes dramatically reduce the stimulation parameter space, and they can be manipulated during stimulation with a graphical user interface to accelerate FES calibration.Finally, Chapter 6 connects these studies and links them to promoting independent and sustainable intracortical BCI operation. This chapter concludes by reflecting on future research that could accelerate BCI deployment and immediately impact the daily lives of individuals with severe neurological disabilities.

Metacognition refers to the critical awareness of or ability to monitor, regulate, control, and sequence of one's thoughts and performance. There is limited research that examines the relationship between metacognition and (a) academic performance and (b) general cognition among undergraduates. Moreover, there is an even greater paucity of literature that focuses more specifically on undergraduate biology students’ neural activity in relation to their metacognition. This study aimed to examine the relationship between undergraduate life sciences students' metacognitive calibration, i.e., their capacity to self-evaluate their own performance, and their behavioral performance and brain activity during a biological error reasoning task. Thirty-four undergraduate students (Mage= 19.47, 85% female) from a Midwestern university completed a model reasoning task during functional magnetic resonance imaging (fMRI). Two distinct groups of students with individual differences— Calibrated and Non-Calibrated—emerged based on the match of their task accuracy to their self-reported confidence in their accuracy. General patterns indicated that participants tended to overestimate their performance. Findings indicated that task accuracy was associated with stronger activation in the left middle frontal gyrus when evaluating correct models. Additionally, students in the Calibrated group showed higher levels of activity in the anterior cingulate gyrus on trials they were confident on in the model evaluation task than students in the Non-Calibrated group. These results suggest that students in the Calibrated group are better at recognizing the need for effortful and strategic reasoning during trials that demand error detection and that they, therefore, deploy PFC during these trials. These findings also highlight metacognition, and specifically students' self-monitoring, as a core target for STEM educators to promote effective reasoning, as well as a need to nurture and foster metacognition and self-awareness in the classroom. Advisor: Caron A.C. Clark

In the fight against climate change, energy modeling is a key tool used to analyze the performance of proposed energy conservation measures for buildings. Studies on the integration of photovoltaic energy in buildings must use calibrated building energy models, as only with them is the demand curve real, and the savings obtained at the self-consumption level, energy storage in the building, or feed into the grid are accurate. The adjustment process of a calibrated model depends on aspects inherent to the building properties (envelope parameters, internal loads, use schedules) as well as external to them (weather, ground properties, etc.). Naturally, the uncertainty of each is essential to obtaining good results. As for the meteorological data, it is preferable to use data from a weather station located in the building or its surroundings, although this is not always possible due to the cost of the initial investment and its maintenance. As a result, weather stations with public access to their data, such as those located at airports or specific locations in cities, are largely used to perform calibrations of building energy models, making it challenging to converge the simulated model with measured data. This research sheds light on how this obstacle can be overcome by using weather data provided by a third-party company, bridging the gap between reality and energy models. For this purpose, calibrations of the two buildings proposed in Annex 58 were performed with different weather configurations, using the mean absolute error (MAE) uncertainty index and Spearman‘s rank correlation coefficient (rho) as comparative measures. An optimal and cost-effective solution was found as an alternative to an on-site weather station, based on the use of a single outdoor temperature sensor in combination with third-party weather data, achieving a robust and reliable building energy model.

In Study #1, we develop a novel, data-driven method to estimate the kinematic parameters of multi-joint linkages such as the human hand. The method can identify up to two consecutive revolute joint axis orientations between connected rigid bodies. We introduce kinematic constraints into a Generative Topographic Mapping formulation in order to estimate the joint axis parameters. The method was evaluated using simulated motion and via motion capture and a physical 2-DOF mechanism modeled after the metacarpophalangeal joint of the human finger. Our method compares well against state-of-the-art kinematic parameter estimation techniques with regards to reliability and computational efficiency.In Study #2, we introduce a sensor-embedded soft skin capable of multimodal sensing (contact force and two axes of shear force) in pressurized underwater environments up to 1000kPa. We embed liquid-metal strain gauges within a durable elastomeric skin that is molded around a solid finger core. We demonstrate that the sensor skin is capable of measuring forces up to 220N underwater and while subjected to a range of hydrostatic pressures. We determine that the performance of the sensor skin is unaffected by the submerged, pressurized environment.In Study #3, we propose a set of design considerations for tactile sensor skins using embedded, microfluidic single-axis strain gauges for the purpose of estimating 3D forces and 1D torque about the skin's surface normal. By displacing shear force taxels such that their principal axes are offset from the point of contact, we are able to more accurately measure torque. We use an experimental testbed to apply force-torque loads to the sensor skin. We develop CNN-based models to evaluate the combined force-torque estimation performance of numerous taxel configurations and provide a detailed discussion of how performance relates to design choices.In summary, we developed methods that improve sensing in harsh environments such as granular media and underwater. Kinematic and kinetic considerations during hand-object interaction were carefully integrated into the development of novel sensing hardware, data-driven estimation methods, and task-specific sensor design criteria. We advanced the state-of-the-art in tactile sensing using tactile sensor skins and improved the accuracy of hand pose estimation using low-cost motion tracking tools.

Today, Building Energy Models (BEM) have become essential in regulatory compliance calculations, the correct assessment of it’s Air Conditioning (AC) systems is critical for the reduction of the performance gap between BEMs and reality and increase the accuracy of evaluating buildings energy performance and it’s systems efficiency. Given that multi-split Variable Refrigerant Flow (VRF) systems have grown in the market in recent years becoming a particular trending solution to achieve building indoor comfort; the present paper focus on technical issues when modelling such VRF systems inside EnergyPlus, a white- box simulation environment, especially regarding the effects weather conditions have on the behaviour of VRF systems and it’s correlation with the AC system performance curves. The study performs an empir- ical validation of an optimization-based calibration methodology assessing multiple levels: average inte- rior temperature of the different building spaces and electric energy consumption from VRF outdoor unit. It is performed using fifteen minute time-step seasonal data obtained from a fully operational building located in a typical Mediterranean climate (Greece), adjusting the parameter and curve values of the VRF system using a genetic NGSA-II algorithm (Jeplus software) for both summer and winter conditions. The generated BEM captures the building’s hourly performance for summer conditions using 1717 hours to fit into international standards. Complying with the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guidelines 14-2002 for hourly energy consump- tion, reaching an NMBE 6–10% ,Cv(RMSE) 630% and R2 P75% while keeping indoor temperatures on every room with a RMSE 61 C. The resulting BEM proved stable during the 2077 hours of it’s summer evaluation period, fitting into the new unseen weather and building operation conditions of 2020 which can be considered a step forward in the area of calibrating white box models. While for winter conditions the study demonstrates the value of the calibration methodology while presenting the importance of weather influence on VRF systems. Using a total of 802 hours the applied technology greatly improves the results from the baseline model, reaching a partially calibrated BEM model for winter. Which rein- forces the fact that regardless of how good a baseline model is, building operating conditions and weather may will always generate a design/performance gap and therefore the calibration of a BEM is unavoidable.

Abstract: Water is considered a cross-cutting resource for all the ecosystem services (ES) types, namely: for the provision of drinking water and food; regulation through flood control; guarantee of a suitable habitat for fauna and flora; and inspiration for many cultures and their intangible heritage around the planet. The evaluation of water ecosystem services has drawn much attention both in the political arena and the scientific community. Still, there is not a consensus in the approach of evaluating them. Recent notions suggest that the study of ES cannot be assessed from a single discourse but through comprehensive frameworks. Therefore, the main objective of this thesis was to evaluate the water provision as an ecosystem service of the Conservation Area of Guanacaste (ACG, for its acronym in Spanish) by adapting an Ecosystem Services (ES)-based approach with four core elements: (1) effects on human well-being, (2) bio-physical underpinning of service delivery, (3) transdisciplinarity, and (4) assessment of services for decision-making. I have deepened in core element two by assessing all the physically-based processes involved in the hydrologic cycle, which generates the quantity of water available for the study region. As a result, here I present the first calibrated and validated Soil and Water Assessment Tool (SWAT) for the entire ACG, including the land use and cover change (LUCC) impact on water availability. I tested seven different climatic and geospatial information resources to establish the best input data to build the SWAT model. The model showed excellent performance for both the calibration period (r2 = 0.81, NSE = 0.75, and br2 = 0.80) and validation period (r2 = 0.85, NSE = 0.82, and br2 = 0.85). I have found that the ACG has a high potential for the provision of water as an ecosystem service. I also identified the spatial and temporal patterns and changes in the water availability in this region between 1980 - 2019. I deepened in the vegetation-water dynamic understanding by evaluating the land use cover change (LUCC), finding that the recovery of forests, and decrease in grasslands and agricultural territories have a positive effect on the water availability also associated with exceptional events in Costa Rica’s history that put in place the conditions for the recovery of natural resources. I also demonstrated the advantages of using spatially explicit and physically-based models, such as SWAT, to quantify water provision as an ecosystem service. Finally, this work complements the baseline for future applications of ES-based approaches in this region.

Experimental modal analysis (EMA), which is an integral part of vibration analysis, deals with finding the dynamic characteristics of a system, namely the natural frequencies, damping, and the corresponding mode shapes. One of the essential requirements to ensure the validity of results obtained in experimental modal analysis (EMA) is the consistency or regularity of the input-output measurements. The non-conformity of the measurement set to the constraints can result in deviation from the structural system's actual characteristics. A consistent data set meets the constraints of methods in modal parameter estimation (MPE) and uniformly portrays identical information.Many validation procedures in the form of principal component analysis (PCA), synthesis correlation coefficient, modal assurance criterion (MAC) exist as a post MPE check to verify the estimated model's quality and hence of the measured data. However, since these methods are employed post-MPE, there is a need for pre-MPE methods to check the validity of measurements beforehand and save any additional effort. This thesis work attempts to develop some of the data sanity checks over the collected experimental data before the modal estimation procedure is performed to ensure that the measurements are consistent with respect to each other and comply with experimental modal analysis assumptions. Building on the concept of reciprocity and driving point, MATLAB based tools are developed which identifies the driving points and performs consistency evaluation. A system equivalent reduction-expansion process (SEREP) based method is then explored, which checks each measurement's consistency to the entire data set. The calibration consistency is evaluated by calculating the frequency response assurance criterion (FRAC) and frequency response scale factor (FRSF) values between the cross-frequency response function measurements. The performance of these developed methods is then tested using analytical and experimental data sets. The results show that these techniques can precisely spot and identify various imperfect measurements and warn the user about the potential errors in the measurement set.

The assessment of model fit in latent trait modelling, better known as item response theory (IRT), is an integral part of model testing if one is to make valid inferences about the estimated parameters and their properties based on the selected IRT model. Though important, the assessment of model fit has been less utilized in IRT research than it should. For example, there have been less research investigating fit for polytomous dominance models such the Graded Response Model (GRM), and to a lesser extent ideal point models such as the Generalized Graded Unfolding Models (GGUM), both in its dichotomous and polytomous forms. For such reasons, examining fit for the GGUM is paramount and should be investigated thoroughly. The current study tests for different fit indices when calibrating the GGUM model to generated data from different IRT models. The tested outcomes consist of type I error and power rates across 100 replications for selected number of items and sample sizes with respect to different model fit indices utilized in previous IRT literature. Results from the simulation study show that relative fit indices performed well in identifying the correct dichotomous data model (i.e., GGUM) when the delta ranges are extended beyond the specified distribution ranges for the dominance models. Also, polytomous GGUM data were identified as the best fitting model in almost all the cases, irrespective of the number of items and sample size. On the other hand, the majority of absolute fit indices did not perform well in identifying fit/misfit. Advisor: Ralph De Ayala

The environmental impact attributed to vehicular emissions at interchanges and other roadway designs should be considered as significant as their traffic safety and operational performances. In today's world, we all are aware of the importance of climate change and global warming. As freight and passenger travel demands increase, so do congestion and emissions from the transportation sector, especially on-road vehicles, which have drawn significant attention in recent years. With an estimated 29% contribution to the total U.S. greenhouse gas (GHG) emissions by economic sectors, transportation is the highest contributor to GHGs in the nation. PTV Vissim emission calculator provides an opportunity for us to perform a comparative emissions analysis. This thesis involves a case study of an existing service interchange, a conventional diamond interchange (CDI) at Austin Blvd on I-75 located about 12 miles south of downtown Dayton Ohio, with other two alternative designs, a diverging diamond interchange (DDI) and a single-point urban interchange (SPUI), in terms of fuel consumptions, emissions, and traffic operations for similar traffic conditions, and roadway characteristics through microsimulation. In this research study, we focused on carbon dioxide (CO2) and two other critical pollutant gases emitted from vehicles' exhaust pipes, carbon monoxide (C.O.) and nitrogen oxides (NOx), including fuel consumption. The signal optimization for each interchange was conducted utilizing PTV Vistro and traffic simulation and emissions analysis using PTV Vissim. The results indicate that the existing CDI design results in much higher emission rates than the other two alternative designs, the SPUI and the DDI, for each traffic level condition considered. A reduction of 85% on average in emissions rates and fuel consumption for both alternative designs compared to the existing CDI was observed. Although the SPUI's and DDI's performances were very close, some significant difference was observed at high traffic volume and high left-turning traffic volumes. The emissions at the DDI design were lower compared to the SPUI design.

The expected received power is a key design parameter when designing a radar system. The radar’s receiver must be sensitive enough to detect the object of interest without being saturated by the highest received power. The object of interest’s visibility to the radar is determined by its radar cross section (RCS), which is a function of the object’s size, shape, material, and orientation as well as the polarization and frequency of the incident wave. Even for simple objects like spheres, calculating the RCS is cumbersome, and there are only closed-form solutions for a few geometries. Of the existing analytical solutions, almost all assume that the incident wavelength is much larger than the object. Simulations can be used to find RCS, but the computational requirements quickly increase with the object’s size and complexity. The best method to determine the RCS of large, complex objects or any object over a wide bandwidth is measurement. There are many challenges that must be overcome to obtain accurate RCS measurements, especially when the RCS is measured over a wide bandwidth. Traditionally, these measurements are collected with two co-located antennas connected to a vector network analyzer. The signal received from the OUT is very small due to free-space path loss and cable loss, while the mutual coupling between the antennas and reflections from other clutter can be significant. At the University of Oklahoma’s Advanced Radar Research Center, a single measurement configuration is designed and tested to measure the RCS of a 6-inch sphere with less than 5% average error from 2-18 GHz. This accuracy is achieved through strategic hardware selection, careful calibration, and clutter suppression algorithms in post-processing. Stepped Frequency Continuous Wave (SFCW) radar principles are applied to design the clutter suppression techniques, including vector background subtraction and time-gating. After verifying the validity of the measurement results with spheres, the techniques are applied to the measurement of distributed targets. Multiple spheres and an OU Skywalker unmanned aerial vehicle (UAV) are measured across angle with vertical and horizontal wave polarizations. The measurement frequency capabilities are then expanded by using single antenna reflection coefficient results to extract the RCS. This single-antenna configuration introduces new obstacles that are overcome to achieve an average error of less than 12.5% from 300-1000 MHz. With a few hardware alterations, the measurement bandwidth could be expanded even further to characterize the RCS values of interest more extensively.

The double beta decay process offers a practical way to learn more about neutrinos. If this decay is observed to occur without the emission of these particles (neutrinoless double beta decay), it would imply that they have a Majorana nature, allowing for the measurement of their effective mass. The SuperNEMO experiment is focused on the search for neutrinoless double beta decay (0νββ) using a unique approach which combines tracking and calorimetry techniques. The SuperNEMO demonstrator will operate for 2.5 years, and the maintenance of its energy resolution throughout this time is a key parameter in its search for the 0νββ process. Two different systems have been developed for this purpose: The Radioactive Source Calibration System (RSCS) and the Light Injection and Monitoring System (LIMS). The RSCS will perform absolute calibrations of the detector at least twice a month through the automatic insertion and retrieval of ²⁰⁷Bi sources, while the LIMS will run daily surveys of the demonstrator using pulsed LED light to monitor the stability of its energy response to within 1%.

The vast majority of drivers utilize arterial roadways to travel to their destinations. The time it takes to travel on an arterial roadway can vary considerably from day to day. The factors that affect this day to day variability include weather, changes in traffic demand, crashes, incidents, work zones, and special events. As a result, there is a growing interest in the transportation field to better understand, measure, estimate, forecast, and portray this variability to both drivers and system operators. In the past, delay on arterial roadways has typically been reported using the Level of Service (LOS) concept where delay is ranked from best (e.g. LOS A) to worst (LOS F). The issue with using LOS to report delay is that the LOS is based on average travel times, which is a measure of central tendency. In reality, it is also important to utilize measures of dispersion such as travel time variance. Consequently, travel time reliability (TTR), which attempts to utilized measures of both central tendency and dispersion, has recently gained importance. The Highway Capacity Manual is the standard manual used in the United States for estimating and forecasting arterial roadway capacity, LOS, travel time, and delay in the US. The sixth version of the HCM, known as the HCM-6, was released in 2016. For the first time, this new version included a methodology for estimating TRR on urban arterial roadways. Interestingly, the HCM-6 TTR methodology was neither calibrated nor validated using empirical data. Recently, this methodology was compared to empirical data for one urban arterial roadway in Lincoln, Nebraska. It was found that while the HCM-6 methodology matched the average travel time, it underestimated travel time variance by approximately 65 percent (1). The research in this thesis examined five arterial roadways in the state of Nebraska to see if the HCM-6 underestimation of variability exists on other arterial roadways. The empirical travel time data for five urban arterial roadways were statistically compared to the estimated travel time distributions derived using the HCM-6 methodology. It was found that the HCM-6 methodology underestimated, on average, the mean travel time and standard travel time by 11.5% and 51.5%, respectively. Lastly, a calibration approach was applied to these five urban arterials. It was found that the HCM-6 TTR methodology, when calibrated, resulted in statistically similar results as compared to the empirical data for all five corridors. Advisor: Laurence Rilett

The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient (R 2 ). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.

With ongoing research in intelligent transport systems and connected and automated vehicles, enabled by advancements in artificial intelligence, the large-scaleadvanced simulation has become an important part of product/software developmentfor the automotive industry. Nowadays, traffic simulations are used to mimic real-worldenvironment scenarios for connected vehicle technologies. The focus of this thesis liesin the development of microscopic traffic simulation calibration and enhance trafficsignal control systemsThis thesis makes the following major contributions. First, a calibration frameworkis proposed which harnesses the exiting data set of OSU campus shuttles (CABS) todetermine the traffic state and create a microscopic traffic simulation. The trafficsimulation is implemented for a section of the OSU campus(“Woody Hayes Drive")which can be extended to the entire OSU campus.The second contribution is an investigation of an intelligent traffic signal controlsystem. The signal control operation is formulated as a decision-making process whereeach controller or control component is modeled as an intelligent agent. The agentsmake decisions based on traffic conditions and their past knowledge of the environment.A state estimation method and an adaptive control scheme by reinforcement learning(RL) are introduced to implement such an intelligent system. Simulation experimentsiihave been performed to verify the improvements of intelligent traffic control systemsand compare them with the existing control policy.The third contribution summarises the initial integration work for the co-simulationframework completed by dSpace ASM and SUMO to create a complete real-timesimulation of urban environments for ADAS testing. The demo scenario is the OSUcampus with traffic demand generated using the calibrated model from the first partof the thesis.

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the deadliest infectious diseases in the world and remains a significant global health burden. Central to the immune response against Mtb are T cells, a type of adaptive immune cell that can kill infected cells, secrete cytokines to activate other immune cells, and orchestrate the broader immune response. Over the past few decades, experimental studies have significantly furthered our understanding of T-cell biology and function during Mtb infection. However, these findings have yet to translate to a clinically effective TB vaccine. As a complementary approach to experimental studies, systems biology and computational modeling can provide context to T-cell function by describing T-cell interactions with other immune cells across multiple scales. In this thesis we utilize a systems biology approach to characterize T-cell behavior, function, and movement across multiple physiological and temporal scales during Mtb infection. In addition, we develop a whole-host model of the immune response to Mtb. Following infection with Mtb, the immune response leads to the development of multiple lung granulomas – organized structures composed of immune cells that surround bacteria. Using a previously developed agent-based model of granuloma formation and function, we explore the role of T cells within the granuloma and predict that T-cell exhaustion, a type of T-cell dysfunction, is prevented from occurring by the physical structure of the granuloma. Next, we develop a novel whole lung model that tracks the formation of multiple granulomas. Using this model, we predict that a special type of T-cell, called a multi-functional CD8+ T cell, is key in preventing dissemination events - when bacteria escape one granuloma and seed the formation of a new one elsewhere in the lung. We also present a model of T-cell priming, proliferation, and differentiation within the lymph nodes and blood following TB vaccination and illustrate that non-human primates and humans respond similarly when receiving TB vaccination. We mathematically link the whole lung model and lymph node and blood model to create a whole-host model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. Using this model, we predict that resident memory T cells are important mediators of protection against reinfection with Mtb and additionally predict the lifespan of these crucial cells in humans. Finally, we develop a protocol for calibrating mathematical and computational models to experimental datasets. Overall, this dissertation builds on our knowledge of the various roles T cells play in responding to Mtb infection, presents a set of computational models for evaluating the T-cell response to either infection or vaccination, and identifies mechanisms that control different outcomes across multiple scales following Mtb infection, reinfection, or vaccination.