Design and fabrication of a prototype aluminum nitride-based pressure sensor with finite element analysis and validation

Since 1985 when the first robot PUMA 560 was employed to place a needle during a brain CT biopsy, surgical robots have become ubiquitous in clinical surgeries. Despite its advantages and success in surgeries, the interactions between the robot and the surgeons remain deficient, especially for the pressure sensing which plays an important role. Inspired by our previous work on bacterial sensing, in the current work I have designed, fabricated, analyzed, and evaluated an innovative prototype pressure sensor based on Aluminum Nitride (AlN) Surface Acoustic Wave (SAW) and Shear Horizontal (SH)-SAW. This AlN-based device has unique superiority over other SAW devices, including relatively lower cost, higher sensitivity, intrinsically higher reliability, more compact size, and faster response. In this novel design a sandwich-like structure is adopted and the AlN thin film on the top is used as the insulated layer to make the device applicable in aqueous environment. The delta function analysis and structural mechanics analysis have been performed to validate the proposed design scheme qualitatively. So as to make a quantitative and comprehensive analysis, the numerical computational analysis using finite element method (FEM) has been carried out using the software package COMSOL Multiphysics®. The 2D plane-strain simulation and 3D simplified model simulation have been executed to analyze the device performance with or without insulator. A good agreement has been achieved between the simulation and the experimental measurements, which validates the design scheme and establishes the effectiveness of the device. This SAW/SH-SAW device has been fabricated in the WSU SSIM clean room. The crystalline AlN thin film is deposited on A-plane sapphire with 2 µm thickness using the PSMBE system. The aluminum interdigital transducer (IDT) is evaporated on the AlN thin film with predefined delay-line pattern using the BJD-1800 vacuum deposition system. Another layer of AlN thin film with 1 µm thickness is deposited on the top of the IDT area with some customized masks to make the device insulated. Furthermore, the differential frequency measurement system has been set up using electronic components to evaluate the system. Several signal processing algorithms are developed and compared to acquire system output. The thermal stability of the differential system is also studied and temperature compensation is developed to improve system robustness. The portable electrical circuit involving the frequency measurement system is finally designed and evaluated. Such a sensor could serve as a key component in artificial skin or be equipped on the end of a surgical robotic arm in the future

Identification and mechanical control of ferroelastic domain structure in rhombohedral CaMn7_7O12_{12}

We report on observation of ferroelastic domain structure in single crystals of multiferroic CaMn$_7$O$_{12}$ at room temperature. Two types of ferroelastic domain wall are found, consistent with the material's rhombohedral symmetry that is reduced from cubic symmetry at higher temperatures. Using Raman spectroscopy along with other measurements, we develop a systematic method to determine the microscopic domain orientation. Moreover, we find a switching behavior of the domains, which allows us to detwin the crystals conveniently at room temperature using a moderate uniaxial compression. Our result paves the way for further spectroscopic study and domain engineering in CaMn$_7$O$_{12}$.Comment: 7 pages, 4 figure

Soft vibrational mode associated with incommensurate orbital order in multiferroic CaMn7_7O12_{12}

We report inelastic light scattering measurements of lattice dynamics related to the incommensurate orbital order in $\mathrm{CaMn_7O_{12}}$. Below the ordering temperature $T_\mathrm{o} \approx 250 \,\mathrm{K}$, we observe extra phonon peaks as a result of Brillouin-zone folding, as well as a soft vibrational mode with a power-law $T$-dependent energy, $\Omega = \Omega_{0}(1 - T/T_{\mathrm{o}})^{1/2}$. This temperature dependence demonstrates the second-order nature of the transition at $T_\mathrm{o}$, and it indicates that the soft mode can be regarded as the amplitude excitation of the composite order parameter. Our result strongly suggests that the lattice degrees of freedom are actively involved in the orbital-ordering mechanism.Comment: 7 pages, 8 figure

SSL Framework for Causal Inconsistency between Structures and Representations

The cross-pollination of deep learning and causal discovery has catalyzed a burgeoning field of research seeking to elucidate causal relationships within non-statistical data forms like images, videos, and text. Such data, often being named `indefinite data', exhibit unique challenges-inconsistency between causal structure and representation, which are not common in conventional data forms. To tackle this issue, we theoretically develop intervention strategies suitable for indefinite data and derive causal consistency condition (CCC). Moreover, we design a self-supervised learning (SSL) framework that considers interventions as `views' and CCC as a `philosophy' with two implement examples on Supervised Specialized Models (SSMs) and Large Language Models (LLMs), respectively. To evaluate pure inconsistency manifestations, we have prepared the first high-quality causal dialogue dataset-Causalogue. Evaluations are also performed on three other downstream tasks. Extensive experimentation has substantiated the efficacy of our methodology, illuminating how CCC could potentially play an influential role in various fields

A Systematic Literature Review: The Modalities, Pedagogies, Benefits, and Implications of Storytelling Approaches in Early Childhood Education Classroom

Abstract The purpose of this systematic literature review was to investigate and synthesize several aspects of storytelling in the reviewed scholarly research, providing a holistic summary and potential insights for early childhood educators. The study asked: (1) What are the various forms, modes and media, and involved pedagogies that storytelling in early childhood education can take? (2) What are the reported benefits of storytelling in early childhood education? (3) Based on the literature, what understandings and pedagogical implications are enriched for early childhood educators to utilize storytelling in their pedagogies? Using a theoretical framework based in multimodal literacy and sociocultural theory, data for the study were derived from 33 screened articles that had been published in the last 10 years. The findings showcase that educators use diverse storytelling approaches with multimodal ensembles in early childhood education, and storytelling was found to provide children a variety of different opportunities to make meaning of the world and express it. By being immersed in storytelling, children were documented in the literature as benefiting from considerable immediate and long-term effects. This study offers understandings of a diversity of forms of storytelling and instructional implications for engaging children through multimodal participation. Additionally, this study may provide baseline knowledge for teacher education to improve storytelling strategies and corresponding multimodal scaffolding feedback, which may provide insights into supporting young children’s storytelling experiences

Machine Learning Approaches to Predict PM2.5 Using Satellite Images

According to World Health Organization, air pollution is considered to be one of the greatest environmental health threats. PM2.5, fine particles with a diameter that is generally 2.5 micrometers and smaller, is inhalable into the lungs and can induce adverse health effects. In order to mitigate the effects of PM2.5 on health outcomes, it is crucial to make accurate predictions of ambient concentrations. In the past, most studies developed traditional linear models from meteorological data or Environmental Protection Agency (EPA) ambient air quality monitors. However, the non-linear relationship between PM2.5 and other factors impacts the effectiveness of the models. Some other barriers are the sparseness of air quality monitors and the limited data sources, which also hinder researchers’ ability to get accurate predictions. Recently, advanced technologies in satellite remote sensing have been widely used to estimate PM2.5 concentrations. The implementation of machine learning approaches has improved computational efficiency and accuracy. In this study, we used daily satellite imagery from January 2017 to October 2021 in 25 U.S. locations, and implemented an eXtreme Gradient Boosting (XGBoost) algorithm, a deep Convolutional Neural Network (CNN), and a CNN-XGBoost pipeline to make predictions based on the extracted features from each satellite image and atmospheric information along with meteorological conditions. To evaluate the performance of each model, we used daily EPA Federal Reference Method PM2.5 measurements as the validation data, and calculated the corresponding root mean squared error (RMSE) and the coefficient of determinant (R^2). After combining each daily observation from 25 locations, the XGBoost approach demonstrated the highest performance with an RMSE of 3.98 microgram per cubic meter and an R^2 of 0.65. The CNN-XGBoost pipeline, tending to overestimate PM2.5 concentrations, had an RMSE of 5.87 microgram per cubic meter and an R^2 of 0.37. In conclusion, our study showed that XGBoost achieved reasonable PM2.5 prediction performance, indicating that the application of satellite remote sensing data and machine learning approaches has significant potential use in PM2.5 concentrations prediction. The data and R code used in this thesis is available on GitHub (https://github.com/sindydu0904/Satellite_pmPredict)

Online Camera-to-ground Calibration for Autonomous Driving

Online camera-to-ground calibration is to generate a non-rigid body transformation between the camera and the road surface in a real-time manner. Existing solutions utilize static calibration, suffering from environmental variations such as tire pressure changes, vehicle loading volume variations, and road surface diversity. Other online solutions exploit the usage of road elements or photometric consistency between overlapping views across images, which require continuous detection of specific targets on the road or assistance with multiple cameras to facilitate calibration. In our work, we propose an online monocular camera-to-ground calibration solution that does not utilize any specific targets while driving. We perform a coarse-to-fine approach for ground feature extraction through wheel odometry and estimate the camera-to-ground calibration parameters through a sliding-window-based factor graph optimization. Considering the non-rigid transformation of camera-to-ground while driving, we provide metrics to quantify calibration performance and stopping criteria to report/broadcast our satisfying calibration results. Extensive experiments using real-world data demonstrate that our algorithm is effective and outperforms state-of-the-art techniques