This work presents the design, analysis and experimental validation of a novel chip-based 3D-printed UHF RFID sensor designed for leaf moisture detection for smart farming applications. The presented sensor is designe...
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The research investigates the impact of birefringence loss in quartz-based mid-infrared photonic waveguides over various wavelength ranges. The analysis of three specified wavelength ranges from 1 to 12 µm a...
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This work presents a new class of micromachined electrostatic actuators capable of producing output force and displacement unprecedented for MEMS electrostatic *** actuators feature submicron high aspect ratio transdu...
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This work presents a new class of micromachined electrostatic actuators capable of producing output force and displacement unprecedented for MEMS electrostatic *** actuators feature submicron high aspect ratio transduction gaps lined up in two-dimensional *** an arrangement of microscale actuator cells allows the addition of force and displacements of a large number of cells(up to 7600 in one demonstrated array),leading to displacements ranging in the hundreds of microns and several gram forces of axial *** 50μm thick actuators with horizontal dimensions in the 1-4 millimeter range,an out-of-plane displacement of up to 678μm at 46 V,a bending moment of up to 2.0μNm,i.e.,0.08 N(~8 gram-force)of axial force over a 50μm by 2 mm cross-sectional area of the actuator(800 kPa of electrostatically generated stress),and an energy density(mechanical work output per stroke per volume)up to 1.42 mJ/cm3 was demonstrated for the actuators.
Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection...
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Typically, optimal power flow (OPF) analysis in a power grid is a nonlinear and non-convex problem. While analyzing OPF in a centralized manner in modern large power systems, nonlinearity generates a computational bur...
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This article presents an O(N log N) method for numerical solution of Maxwell's equations for dielectric scatterers using a 3D boundary integral equation (BIE) method. The underlying BIE method used is based on a h...
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This paper presents a novel Substrate Integrated Waveguide (SIW) Cavity-Backed Slot Antenna (CBSA) to enable the next generation of communication and sensing infrastructure. The antenna achieves a bandwidth of 40%, wi...
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We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature maskin...
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We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly s
Research into Medicare fraud detection that utilizes machine learning methodologies is of great national interest due to the significant fiscal ramifications of this type of fraud. Our big data analysis pertains to th...
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