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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 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.

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.