Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluat...
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Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detectin...
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ISBN:
(数字)9798350387117
ISBN:
(纸本)9798350387124
Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n
3
) and space complexity of O(n
2
), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin
In this paper, a nodal discontinuous Galerkin time-domain (NDGTD) algorithm with parallel scheme is proposed to solve transient Maxwell's equations. With the aim to analyze the electromagnetic (EM) features of ele...
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Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professiona...
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Convolutional neural networks(CNNs) have demonstrated remarkable capability and scalability in a variety of vision-related tasks. Due to privacy and latency constraints, in some scenarios, the CNNs are deployed on-sit...
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ISBN:
(数字)9798350383638
ISBN:
(纸本)9798350383645
Convolutional neural networks(CNNs) have demonstrated remarkable capability and scalability in a variety of vision-related tasks. Due to privacy and latency constraints, in some scenarios, the CNNs are deployed on-site where power supply, computation power, and memory capacity are limited. These constraints hinder the traditional training or modification of CNN models, which typically involves network-scale backpropagation of the gradients. In this work, we proposed a framework enabling the derivation of lightweight models from the original model at the edge only utilizing hardware-friendly operations. In the proposed framework, all models are binary quantized and the gradients are obtained by layer-wise decision boundary matching. Hence, the whole flow can be executed with bit-wise and fixed-point arithmetic operations without network-scale gradient backpropagations. The derived model serves as a viable alternative to the original, in scenarios where the accuracy requirements are less stringent, delivering enhanced efficiencies in power and memory consumption. We validate the framework on digit recognition tasks, obtaining a better accuracy than naively deploying the same lightweight model. Furthermore, an FPGA demonstration of our framework achieved a throughput of 2.2 TOPS/s, underscoring its practical applicability.
Bi-isotropic and bi-anisotropic materials are expected to revolutionize wireless communications through the extended use of metasurfaces. This paper proposes a time-domain integral equation solver to simulate non-disp...
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ISBN:
(数字)9788831299107
ISBN:
(纸本)9798350366327
Bi-isotropic and bi-anisotropic materials are expected to revolutionize wireless communications through the extended use of metasurfaces. This paper proposes a time-domain integral equation solver to simulate non-dispersive biisotropic materials under the thin-sheet approximation. Thin-sheet approximation is used to convert the time-domain volume integral equation for non-dispersive bi-isotropic media into a surface integral equation. This surface equation is discretized in space using Rao-Wilton-Glisson (RWG) basis functions for the tangential components of the electric and magnetic fluxes and pulse basis functions for their normal components (relative to the surface of the thin sheet). In time, shifted Lagrange polynomials are used for discretization. Applying Galerkin testing in space and point matching in time yields a matrix system. This matrix system is solved for the unknown basis expansion coefficients at each time step via time-marching. Numerical results demonstrate the accuracy of the proposed method. Future work includes extending this solver to dispersive bi-anisotropic media and use it in the simulation of metasurface represented by generalized sheet transition conditions (GSTCs).
Photonic metasurfaces are efficiently and accurately simulated using a surface integral equation (SIE) solver. This solver models the metasurface as an infinitesimally thin sheet on which generalized sheet transition ...
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ISBN:
(数字)9798331527587
ISBN:
(纸本)9798331527594
Photonic metasurfaces are efficiently and accurately simulated using a surface integral equation (SIE) solver. This solver models the metasurface as an infinitesimally thin sheet on which generalized sheet transition conditions (GSTCs) are enforced. GSTCs connect the electromagnetic fields on both sides of the metasurface using equivalent susceptibility tensors and avoid modelling of geometrically intricate metasurface unit cells by the SIE solver. Susceptibility tensors are obtained from the transmission and the reflection coefficients associated with a unit cell. A numerical example, involving a metasurface that supports high-quality factor resonances and excited by a Gaussian beam, is presented to demonstrate the accuracy of the proposed approach.
Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict softwa...
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Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
In recent years, the integration of Multi-Input Multi-Output (MIMO) technology with In-Band Full-Duplex (IBFD) systems has emerged as a promising approach for multi-targets Integrated Sensing and Communication (ISAC),...
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We study a multi-agent decision problem in population games, where agents select from multiple available strategies and continually revise their selections based on the payoffs associated with these strategies. Unlike...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
We study a multi-agent decision problem in population games, where agents select from multiple available strategies and continually revise their selections based on the payoffs associated with these strategies. Unlike conventional population game formulations, we consider a scenario where agents must estimate the payoffs through local measurements and communication with their neighbors. By employing task allocation games - dynamic extensions of conventional population games - we examine how errors in payoff estimation by individual agents affect the convergence of the strategy revision process. Our main contribution is an analysis of how estimation errors impact the convergence of the agents’ strategy profile to equilibrium. Based on the analytical results, we propose a design for a time-varying strategy revision rate to guarantee convergence. Simulation studies illustrate how the proposed method for updating the revision rate facilitates convergence to equilibrium.
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