720 research outputs found

    Unsupervised learning based fast beamforming design for downlink MIMO

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    In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input-multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the ’APoZ’-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm

    Existence results of positive solutions for Kirchhoff type equations via bifurcation methods

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    In this paper we address the following Kirchhoff type problem \begin{equation*} \left\{ \begin{array}{ll} -\Delta(g(|\nabla u|_2^2) u + u^r) = a u + b u^p& \mbox{in}~\Omega, u>0& \mbox{in}~\Omega, u= 0& \mbox{on}~\partial\Omega, \end{array} \right. \end{equation*} in a bounded and smooth domain Ω\Omega in IR{\rm I}\hskip -0.85mm{\rm R}. By using change of variables and bifurcation methods, we show, under suitable conditions on the parameters a,b,p,ra,b,p,r and the nonlinearity gg, the existence of positive solutions.Comment: 18 pages, 1 figur

    Quantifying the pathway and predicting spontaneous emulsification during material exchange in a two phase liquid system

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    Kinetic restriction of a thermodynamically favourable equilibrium is a common theme in materials processing. The interfacial instability in systems where rate of material exchange is far greater than the mass transfer through respective bulk phases is of specific interest when tracking the transient interfacial area, a parameter integral to short processing times for productivity streamlining in all manufacturing where interfacial reaction occurs. This is even more pertinent in high-temperature systems for energy and cost savings. Here the quantified physical pathway of interfacial area change due to material exchange in liquid metal-molten oxide systems is presented. In addition the predicted growth regime and emulsification behaviour in relation to interfacial tension as modelled using phase-field methodology is shown. The observed in-situ emulsification behaviour links quantitatively the geometry of perturbations as a validation method for the development of simulating the phenomena. Thus a method is presented to both predict and engineer the formation of micro emulsions to a desired specification

    Perceptual image quality using Dual generative adversarial network

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    Generative adversarial networks have received a remarkable success in many computer vision applications for their ability to learn from complex data distribution. In particular, they are capable to generate realistic images from latent space with a simple and intuitive structure. The main focus of existing models has been improving the performance; however, there is a little attention to make a robust model. In this paper, we investigate solutions to the super-resolution problems—in particular perceptual quality—by proposing a robust GAN. The proposed model unlike the standard GAN employs two generators and two discriminators in which, a discriminator determines that the samples are from real data or generated one, while another discriminator acts as classifier to return the wrong samples to its corresponding generators. Generators learn a mixture of many distributions from prior to the complex distribution. This new methodology is trained with the feature matching loss and allows us to return the wrong samples to the corresponding generators, in order to regenerate the real-look samples. Experimental results in various datasets show the superiority of the proposed model compared to the state of the art methods

    Illustration of boundary calculation for a single wavelength.

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    <p>Artificial data of two samples is demonstrated. A moving window is used to compute mean and standard deviation of intensity values inside the window. Upper and lower boundary values are calculated by . Corresponding fitting functions are calculated with the piecewise linear interpolation. The window size is set to 1.0 second and moves at a distance of half of its length, 0.5 seconds in this case.</p

    Fault detection result with the optimal sigma number 7.

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    <p>All 118 healthy testing samples and one typical faulty testing sample are presented. For the healthy samples, no alarm is triggered. All of them overlap with each other and have similar SR values which are close to SR value 100%. For the faulty sample, multiple faulty alarms are triggered from an early-stage in the process.</p

    Illustration of process shift between two healthy samples with similar etch rates at wavelength 253.29 nm.

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    <p>Etch rates of both sample A and B are the same, 67.55 arb. unit, which implies that a similar etching output quality is shared by them. Shapes of the two curves are similar to each other, but there are differences in time series. A clear time shift can be observed.</p

    Fault detection result with non-optimal sigma number of 4.

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    <p>All 118 healthy testing samples and one typical faulty testing sample are presented. For the healthy samples, multiple warnings and alarms are triggered.</p

    Association between miR-20b expression and clinicopathological features of esophageal cancer patients.

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    <p>Association between miR-20b expression and clinicopathological features of esophageal cancer patients.</p

    The PMF plot of process shift time in training samples.

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    <p>10.55% of samples have shift distance less than 0.1 second, 51.26% of shift distances are less than 0.5 second, 86.43% of shift distances are less than 1.0 second, 96.48% of shift distances are less than 1.5 seconds, and 99.50% of shift distances are less than 2.0 seconds.</p
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