Variation diminishing convolution kernels associated with fractional Hankel-type transform

2021 ◽  
Author(s):  
Akhilesh Prasad ◽  
S. K. Verma ◽  
U. K. Mandal
Keyword(s):  
Variation Diminishing ◽  
Convolution Kernels

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

scholarly journals When the Whole Is Not Greater Than the Combination of Its Parts: A “Decompositional” Look at Compositional Distributional Semantics

2015 ◽  
Vol 41 (1) ◽  
pp. 165-173 ◽  
Author(s):  
Fabio Massimo Zanzotto ◽  
Lorenzo Ferrone ◽  
Marco Baroni
Keyword(s):  
Distributional Semantics ◽  
Important Class ◽  
Strong Link ◽  
Composition Methods ◽  
Convolution Kernels ◽  
Composition Operations ◽  
Compositional Distributional Semantics

Distributional semantics has been extended to phrases and sentences by means of composition operations. We look at how these operations affect similarity measurements, showing that similarity equations of an important class of composition methods can be decomposed into operations performed on the subparts of the input phrases. This establishes a strong link between these models and convolution kernels.

Higher-Order Discretization Method for Computations of Separated Flows

1999 ◽  
Author(s):  
B. Song ◽  
R. S. Amano
Keyword(s):  
Velocity Field ◽  
Weighted Average ◽  
Higher Order ◽  
Separated Flows ◽  
Sudden Expansion ◽  
Order Accuracy ◽  
Variation Diminishing ◽  
Average Coefficient ◽  
Convective Fluxes ◽  
Boundedness Of Solution

Abstract This paper presents a new higher-order bounded scheme, WACEB, for approximating the convective fluxes in the transport equations. The weighted-average formulation is used for interpolating the variables at cell faces and the weighted-average coefficient is determined from normalized variable formulation and total variation diminishing (TVD) constrains to ensure the boundedness of solution. The new scheme is tested by solving three problems: 1) a pure convection of a box-shaped step profile in an oblique velocity field; 2) a sudden expansion of an oblique velocity field in a cavity, and; 3) a laminar flow over a fence. The results obtained by the present WACEB were compared with the UPWIND and the QUICK schemes and showed that this scheme has at least the second-order accuracy while ensuring boundedness of solutions. Moreover, it was demonstrated that this scheme produces results that better agree with the experimental data in comparison with other schemes.

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