Results 221 to 230 of about 1,037 (242)
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SN Computer Science, 2021
Approximating a function with a learning neural network (LNN) has been considered for a long time by many authors and is known as “the universal approximation property”. The smaller the precision, the more neurons in the hidden layer one should take to reach the required precision. An other challenge for learning neural networks is understanding how to
Khadidja Benmansour, Jerome Pousin
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Approximating a function with a learning neural network (LNN) has been considered for a long time by many authors and is known as “the universal approximation property”. The smaller the precision, the more neurons in the hidden layer one should take to reach the required precision. An other challenge for learning neural networks is understanding how to
Khadidja Benmansour, Jerome Pousin
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Blind Watermarking Algorithm for Color Images Based on Nearest Neighbor Interpolation
Applied Mechanics and Materials, 2013In this paper, we present a blind digital watermarking method for color image based on nearest neighbor interpolation. The algorithm has the following characteristics: (1) it generate adaptive watermark based on the size of carrier image,so as to improve the robustness of binary watermark; (2) the watermark is embedded in the DCT coefficient of ...
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ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
Several well known imaging techniques operate by recording samples of the Fourier transform of the object function and then reconstructing the object function by means of the 2D inverse FFT. A central problem arises in interpolating from the inherent polar raster to a rectangular raster, so the inverse FFT can be properly applied.
W. Kenneth Jenkins +2 more
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Several well known imaging techniques operate by recording samples of the Fourier transform of the object function and then reconstructing the object function by means of the 2D inverse FFT. A central problem arises in interpolating from the inherent polar raster to a rectangular raster, so the inverse FFT can be properly applied.
W. Kenneth Jenkins +2 more
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2018
Spatial interpolations are commonly used in geometric modeling for life science applications. In large-scale spatial interpolations, it is always needed to find a local set of data points for each interpolated point using the k Nearest Neighbor (kNN) search procedure.
Naijie Fan +4 more
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Spatial interpolations are commonly used in geometric modeling for life science applications. In large-scale spatial interpolations, it is always needed to find a local set of data points for each interpolated point using the k Nearest Neighbor (kNN) search procedure.
Naijie Fan +4 more
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Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, 2019
In order to improve the accuracy of fault diagnosis of power transformer in the presence of missing values, we propose a missing value imputation method by iterative k nearest neighbor (KNN), and ensemble learning is used to diagnose the power transformer.
Yunfei Liu +5 more
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In order to improve the accuracy of fault diagnosis of power transformer in the presence of missing values, we propose a missing value imputation method by iterative k nearest neighbor (KNN), and ensemble learning is used to diagnose the power transformer.
Yunfei Liu +5 more
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The KD-Tree-based nearest-neighbor search algorithm in GRID interpolation
2012 International Conference on Image Analysis and Signal Processing, 2012The nearest neighbor search algorithm constitutes one of the major elements that influence the efficiency of GRID interpolation. Hence, this paper introduces the two-dimensional index structure of KD-Tree, puts forward an improved J-nearest neighbor search strategy based on “priority queue” and “neighbor lag”, designs respectively two kinds of J ...
Liu Qiang +5 more
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Holography, Diffractive Optics, and Applications VIII, 2018
Digital holographic diffraction tomography combines digital holography with optical diffraction tomography. According to the Fourier diffraction theory, the spectrum information is unevenly distributed on a Ewald sphere, and most of these data cannot exactly locate on the 3D matrix points.
Yunxin Wang, Dayong Wang, Lu Rong
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Digital holographic diffraction tomography combines digital holography with optical diffraction tomography. According to the Fourier diffraction theory, the spectrum information is unevenly distributed on a Ewald sphere, and most of these data cannot exactly locate on the 3D matrix points.
Yunxin Wang, Dayong Wang, Lu Rong
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IRBM, 2017
Abstract In the diffusion MRI domain, the HARDI methods were proposed to better characterize the complex biological tissues such as the white matter. In fact, they allow to overcome the problem of crossing fibers detection in the case of the Diffusion Tensor Imaging (DTI).
I. Ben Alaya +4 more
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Abstract In the diffusion MRI domain, the HARDI methods were proposed to better characterize the complex biological tissues such as the white matter. In fact, they allow to overcome the problem of crossing fibers detection in the case of the Diffusion Tensor Imaging (DTI).
I. Ben Alaya +4 more
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SSRN Electronic Journal, 2017
We propose two modifications to the method of endogenous grid points that greatly decreases the computational time for life cycle models with many exogenous state variables. First, we use simulated stochastic grids on the exogenous state variables. Second, when we interpolate to find the continuation value of the model, we split the interpolation step ...
Jakob Almerud, Anders Eskil sterling
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We propose two modifications to the method of endogenous grid points that greatly decreases the computational time for life cycle models with many exogenous state variables. First, we use simulated stochastic grids on the exogenous state variables. Second, when we interpolate to find the continuation value of the model, we split the interpolation step ...
Jakob Almerud, Anders Eskil sterling
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Proceedings of 1995 American Control Conference - ACC'95, 2005
Tools from computer science, namely data structures and computational geometry, may be useful to control engineers. In this paper we investigate KD tree data structures with Delaunay triangulation as an alternative approach to supervised learning neural networks.
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Tools from computer science, namely data structures and computational geometry, may be useful to control engineers. In this paper we investigate KD tree data structures with Delaunay triangulation as an alternative approach to supervised learning neural networks.
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