No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Convolutional Neural Networks have come a long way since AlexNet. Each year the limits of the state of the art are being pushed to new levels. EfficientNet pushed the performance metrics to a new high and EfficientNetV2 even more so. Even so, architectures for mobile applications can benefit from improved accuracy and reduced model footprint. The classic Inverted Residual block has been the foundation upon which most mobile networks seek to improve. EfficientNet architecture is built using the same Inverted Residual block. In this thesis we experiment with Harmonious Bottlenecks in place of the Inverted Residuals to observe a reduction in the number of parameters and improvement in accuracy. The designed network is then deployed on the NXP i.MX 8M Mini board for Image classification. / 2023-10-11

<p dir="ltr">The present work expands the state of the art in the design of soft actuators and flexible sensors manufactured through fused deposition modelling (FDM) and direct ink writing (DIW). The first design consisted of flexible sensors for rehabilitation. Three different designs were tested and compared according to their sensitivity and accuracy. The flexible sensor successfully responded to deformation by changing its resistance. The first design of soft actuator was the Closed Dual Pneumatic Bellow Actuator. The soft actuator was manufactured using FDM and included an inner chamber in which the input air flows through and produces the actuation. The actuator also included dielectric electroactive polymer (DEAP) that showed response to pressure between the actuator and the object to be grasped. The electrodes of the DEAP were manufactured using commercial conductive TPU. A second soft actuator was designed with a circular shape and embedded DEAP. The electrodes in the DEAP consisted of conductive carbon grease. Previous tests were performed to assess the functionality of a DEAP structure using conductive carbon grease. The DEAP showed an increase in capacitance as pressure was applied on one side of the structure parallel to the electrodes and computational simulations validated such response. Future work using the sensors and actuators presented includes the implementation of a closed-loop system to the soft actuators, using the readouts of the sensors to adjust the input pressure and apply precise pressure on objects. The flexible sensor for rehabilitation has the potential to be implemented in each of the fingers of the hand and use the data to characterize the movement of the hand under different configurations providing feedback to patients in task-oriented therapy.</p>

A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing those complex data in this dissertation. We have then provided several contributions to semiparametric and nonparametric methods for dealing with the following problems: the first is to propose a method for testing the significance of a functional association under the matched study; the second is to develop a method to simultaneously identify important variables and build a network in HDHC data; the third is to propose a multi-class dynamic model for recognizing a pattern in the time-trend analysis. For the first topic, we propose a semiparametric omnibus test for testing the significance of a functional association between the clustered binary outcomes and covariates with measurement error by taking into account the effect modification of matching covariates. We develop a flexible omnibus test for testing purposes without a specific alternative form of a hypothesis. The advantages of our omnibus test are demonstrated through simulation studies and 1-4 bidirectional matched data analyses from an epidemiology study. For the second topic, we propose a joint semiparametric kernel machine network approach to provide a connection between variable selection and network estimation. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among them. We develop our approach under a semiparametric kernel machine regression framework, which can allow for the possibility that each variable might be nonlinear and is likely to interact with each other in a complicated way. We demonstrate our approach using simulation studies and real application on genetic pathway analysis. Lastly, for the third project, we propose a Bayesian focal-area detection method for a multi-class dynamic model under a Bayesian hierarchical framework. Two-step Bayesian sequential procedures are developed to estimate patterns and detect focal intervals, which can be used for gas chromatography. We demonstrate the performance of our proposed method using a simulation study and real application on gas chromatography on Fast Odor Chromatographic Sniffer (FOX) system. / Doctor of Philosophy / A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing the following three types of data: (1) matched case-crossover data, (2) HCHD data, and (3) Time-series data. We contribute to the development of statistical methods to deal with such complex data. First, under the matched study, we discuss an idea about hypothesis testing to effectively determine the association between observed factors and risk of interested disease. Because, in practice, we do not know the specific form of the association, it might be challenging to set a specific alternative hypothesis. By reflecting the reality, we consider the possibility that some observations are measured with errors. By considering these measurement errors, we develop a testing procedure under the matched case-crossover framework. This testing procedure has the flexibility to make inferences on various hypothesis settings. Second, we consider the data where the number of variables is very large compared to the sample size, and the variables are correlated to each other. In this case, our goal is to identify important variables for outcome among a large amount of the variables and build their network. For example, identifying few genes among whole genomics associated with diabetes can be used to develop biomarkers. By our proposed approach in the second project, we can identify differentially expressed and important genes and their network structure with consideration for the outcome. Lastly, we consider the scenario of changing patterns of interest over time with application to gas chromatography. We propose an efficient detection method to effectively distinguish the patterns of multi-level subjects in time-trend analysis. We suggest that our proposed method can give precious information on efficient search for the distinguishable patterns so as to reduce the burden of examining all observations in the data.

The advances in data-collecting technologies provides great opportunities to access large sample-size data sets with high dimensionality. Variable selection is an important procedure to extract useful knowledge from such complex data. While in many real-data applications, appropriate selection of variables should facilitate the model interpretation and computation efficiency. It is thus important to incorporate domain knowledge of underlying data generation mechanism to select key variables for improving the model performance. However, general variable selection techniques, such as the best subset selection and the Lasso, often do not take the underlying data generation mechanism into considerations. This thesis proposal aims to develop statistical modeling methodologies with a focus on the structured variable selection towards better model interpretation and computation efficiency. Specifically, this thesis proposal consists of three parts: an additive heredity model with coefficients incorporating the multi-level data, a regularized dynamic generalized linear model with piecewise constant functional coefficients, and a structured variable selection method within the best subset selection framework. In Chapter 2, an additive heredity model is proposed for analyzing mixture-of-mixtures (MoM) experiments. The MoM experiment is different from the classical mixture experiment in that the mixture component in MoM experiments, known as the major component, is made up of sub-components, known as the minor components. The proposed model considers an additive structure to inherently connect the major components with the minor components. To enable a meaningful interpretation for the estimated model, we apply the hierarchical and heredity principles by using the nonnegative garrote technique for model selection. The performance of the additive heredity model was compared to several conventional methods in both unconstrained and constrained MoM experiments. The additive heredity model was then successfully applied in a real problem of optimizing the Pringlestextsuperscript{textregistered} potato crisp studied previously in the literature. In Chapter 3, we consider the dynamic effects of variables in the generalized linear model such as logistic regression. This work is motivated from the engineering problem with varying effects of process variables to product quality caused by equipment degradation. To address such challenge, we propose a penalized dynamic regression model which is flexible to estimate the dynamic coefficient structure. The proposed method considers modeling the functional coefficient parameter as piecewise constant functions. Specifically, under the penalized regression framework, the fused lasso penalty is adopted for detecting the changes in the dynamic coefficients. The group lasso penalty is applied to enable a sparse selection of variables. Moreover, an efficient parameter estimation algorithm is also developed based on alternating direction method of multipliers. The performance of the dynamic coefficient model is evaluated in numerical studies and three real-data examples. In Chapter 4, we develop a structured variable selection method within the best subset selection framework. In the literature, many techniques within the LASSO framework have been developed to address structured variable selection issues. However, less attention has been spent on structured best subset selection problems. In this work, we propose a sparse Ridge regression method to address structured variable selection issues. The key idea of the proposed method is to re-construct the regression matrix in the angle of experimental designs. We employ the estimation-maximization algorithm to formulate the best subset selection problem as an iterative linear integer optimization (LIO) problem. the mixed integer optimization algorithm as the selection step. We demonstrate the power of the proposed method in various structured variable selection problems. Moverover, the proposed method can be extended to the ridge penalized best subset selection problems. The performance of the proposed method is evaluated in numerical studies. / Doctor of Philosophy / The advances in data-collecting technologies provides great opportunities to access large sample-size data sets with high dimensionality. Variable selection is an important procedure to extract useful knowledge from such complex data. While in many real-data applications, appropriate selection of variables should facilitate the model interpretation and computation efficiency. It is thus important to incorporate domain knowledge of underlying data generation mechanism to select key variables for improving the model performance. However, general variable selection techniques often do not take the underlying data generation mechanism into considerations. This thesis proposal aims to develop statistical modeling methodologies with a focus on the structured variable selection towards better model interpretation and computation efficiency. The proposed approaches have been applied to real-world problems to demonstrate their model performance.

Open-shell non-alternant polycyclic hydrocarbons (PHs) are attracting increasing attention due to their promising applications in organic spintronics and quantum computing. Herein we report the synthesis of three cyclohepta[def]fluorene-based diradicaloids (1–3), by fusion of benzo rings on its periphery for the thermodynamic stabilization, as evidenced by multiple characterization techniques. Remarkably, all of them display a very narrow optical energy gap (Egopt=0.52–0.69 eV) and persistent stability under ambient conditions (t1/2=11.7–33.3 h). More importantly, this new type of diradicaloids possess a low-lying triplet state with an extremely small singlet–triplet energy gap, as low as 0.002 kcal mol−1, with a clear dependence on the molecular size. This family of compounds thus offers a new route to create non-alternant open-shell PHs with high-spin ground states, and opens up novel possibilities and insights into understanding the structure–property relationships.

Yes / Three dimensional printing (3DP) was used to engineer novel oral drug delivery devices, with specialised design configurations loaded with multiple actives, with applications in personalised medicine. A filament extruder was used to obtain drug-loaded - paracetamol (acetaminophen) or caffeine - filaments of polyvinyl alcohol with characteristics suitable for use in fused-deposition modelling 3D printing. A multi-nozzle 3D printer enabled fabrication of capsule-shaped solid devices, containing paracetamol and caffeine, with different internal structures. The design configurations included a multilayer device, with each layer containing drug, whose identity was different from the drug in the adjacent layers; and a two-compartment device comprising a caplet embedded within a larger caplet (DuoCaplet), with each compartment containing a different drug. Raman spectroscopy was used to collect 2-dimensional hyper spectral arrays across the entire surface of the devices. Processing of the arrays using direct classical least squares component matching to produce false colour representations of distribution of the drugs showed clearly the areas that contain paracetamol and caffeine, and that there is a definitive separation between the drug layers. Drug release tests in biorelevant media showed unique drug release profiles dependent on the macrostructure of the devices. In the case of the multilayer devices, release of both drugs was simultaneous and independent of drug solubility. With the DuoCaplet design it was possible to engineer either rapid drug release or delayed release by selecting the site of incorporation of the drug in the device, and the lag-time for release from the internal compartment was dependent on the characteristics of the external layer. The study confirms the potential of 3D printing to fabricate multiple-drug containing devices with specialized design configurations and unique drug release characteristics, which would not otherwise be possible using conventional manufacturing methods. / The full-text of this article will be released for public view at the end of the publisher embargo on 10 Oct 2016.

The identification and quantification of atoms, molecules, or ions concentrations in gaseous samples are in great demand for medical, environmental, industrial, law enforcement and national security applications. These applications require in situ, high-resolution, non-destructive, sensitive, miniature, inexpensive, rapid detection, remotely accessed, real time and continuously operating chemical sensing devices. The aim of this work is to design a miniature optical sensing device that is capable of detecting and measuring chemical species, compatible with being integrated into a large variety of monitoring systems, and durable enough to be used under extreme conditions. The miniature optical sensor has been realized by employing technologies from the optical communication industry and spectroscopic methods and techniques. Fused silica capillary tubing along with standard communication optical fibers have been utilized to make miniature gas sensor based on near-infrared spectroscopy for acetylene gas detection. In this work, the basic principles of infrared spectroscopy are reviewed. Also, the principle of operation, fabrication, testing, and analysis of the proposed sensor are discussed in details. / Master of Science

Three-dimensional (3D) printing has become broadly available and can be utilized to customize clamping mechanisms in biomechanical experiments. This report will describe our experience using 3D printed clamps to mount soft tissues from different anatomical regions. The feasibility and potential limitations of the technology will be discussed. Tissues were sourced in a fresh condition, including human skin, ligaments and tendons. Standardized clamps and fixtures were 3D printed and used to mount specimens. In quasi-static tensile tests combined with digital image correlation and fatigue trials we characterized the applicability of the clamping technique. Scanning electron microscopy was utilized to evaluate the specimens to assess the integrity of the extracellular matrix following the mechanical tests. 3D printed clamps showed no signs of clamping-related failure during the quasi-static tests, and intact extracellular matrix was found in the clamping area, at the transition clamping area and the central area from where the strain data was obtained. In the fatigue tests, material slippage was low, allowing for cyclic tests beyond 105 cycles. Comparison to other clamping techniques yields that 3D printed clamps ease and expedite specimen handling, are highly adaptable to specimen geometries and ideal for high-standardization and high-throughput experiments in soft tissue biomechanics.

No description available.