scholarly journals Variable-Order Fractional Diffusion Model-Based Medical Image Denoising

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
A. Abirami ◽  
P. Prakash ◽  
Y.-K. Ma
Keyword(s):  
Diffusion Model ◽  
Scattering Theory ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Fractional Diffusion ◽  
Vital Role ◽  
Variable Order ◽  
Order Model ◽  
Proposed Model ◽  
Fractional Differential

Fractional differential models are playing a vital role in many applications such as diffusion, probability potential theory, and scattering theory. In this study, the variable-order space and time fractional diffusion model is employed for denoising the medical images. The finite difference approach is implemented to find the numerical solution of the proposed model. Convergence and stability of the numerical method are presented. The experimental outcomes of the variable-order model are analyzed with those of the fractional and integer-order diffusion models. It was noticed that the peak signal-to-noise ratio (PSNR) value is increased considerably for the proposed model.

scholarly journals Medical Image Classification Based On Normalized Coding Network with Multiscale Perception

2019 ◽  
Vol 8 (11) ◽  
pp. 2694-2697
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Medical Image ◽  
Medical Images ◽  
Vital Role ◽  
Multi Scale ◽  
Proposed Model ◽  
Dataset Size ◽  
High Resolution Images ◽  
High Level

Medical imaging classification is playing a vital role in identifying and diagnoses the diseases, which is very helpful to doctor. Conventional ways classify supported the form, color, and/or texture, most of tiny problematic areas haven’t shown in medical images, which meant less efficient classification and that has poor ability to identify disease. Advanced deep learning algorithms provide an efficient way to construct a finished model that can compute final classification labels with the raw pixels of medical images. These conventional algorithms are not sufficient for high resolution images due to small dataset size, advanced deep learning models suffer from very high computational costs and limitations in the channels and multilayers in the channels. To overcome these limitations, we proposed a new algorithm Normalized Coding Network with Multi-scale Perceptron (NCNMP), which combines high-level features and traditional features. The Architecture of the proposed model includes three stages. Training, retrieve, fuse. We examined the proposed algorithm on medical image dataset NIH2626. We got an overall image classification accuracy of 91.35, which are greater than the present methods.

scholarly journals A Simultaneous Inversion Problem for the Variable-Order Time Fractional Differential Equation with Variable Coefficient

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shengnan Wang ◽  
Zhendong Wang ◽  
Gongsheng Li ◽  
Yingmei Wang
Keyword(s):  
Inverse Problem ◽  
Noisy Data ◽  
Fractional Diffusion ◽  
Basis Functions ◽  
Variable Order ◽  
Inversion Problem ◽  
Inversion Algorithm ◽  
Regularization Algorithm ◽  
Simultaneous Inversion ◽  
Fractional Differential

This paper deals with an inverse problem of simultaneously determining the space-dependent diffusion coefficient and the fractional order in the variable-order time fractional diffusion equation by the measurements at one interior point. Numerical solution to the forward problem is given by the finite difference scheme, and the homotopy regularization algorithm is applied to solve the inverse problem utilizing Legendre polynomials as the basis functions of the approximate space. The inversion solutions with noisy data which give good approximations to the exact solution demonstrate effectiveness of the inversion algorithm for the simultaneous inversion problem.

scholarly journals Signal Smoothing with Time-Space Fractional Order Model

2021 ◽  
Vol 21 (1) ◽  
pp. 25-32
Author(s):  
Yuanlu Li
Keyword(s):  
Diffusion Model ◽  
Fractional Order ◽  
Order Model ◽  
Comparison Result ◽  
Smoothing Methods ◽  
Fractional Order Model ◽  
Time Space ◽  
Proposed Model ◽  
Noisy Signals ◽  
And Performance

Abstract The time-space fractional-order model (TSFOM) is a generation of the classical diffusion model which is an excellent smoothing method. In this paper, the fractional-order derivative in the model is found to have good performance for peak-preserving. To check the validity and performance of the model, some noisy signals are smoothed by some commonly used smoothing methods and results are compared with those of the proposed model. The comparison result shows that the proposed method outperforms the classical nonlinear diffusion model and some commonly used smoothing methods.

NOISE AND TARGET SIGNALS SIMULATION IN PASSIVE SEISMIC LOCATION SYSTEMS

2018 ◽  
pp. 73-78
Author(s):  
Yu. V. Morozov ◽  
M. A. Rajfeld ◽  
A. A. Spektor
Keyword(s):  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Simulation Procedure ◽  
Proposed Model ◽  
Seismic Location ◽  
Passive Seismic ◽  
System Characteristics ◽  
Primary Pulse

The paper proposes the model of a person seismic signal with noise for the investigation of passive seismic location system characteristics. The known models based on Gabor and Berlage pulses have been analyzed. These models are not able wholly to consider statistical properties of seismic signals. The proposed model is based on almost cyclic character of seismic signals, Gauss character of fluctuations inside a pulse, random amplitude change from pulse to pulse and relatively small fluctuation of separate pulses positions. The simulation procedure consists of passing the white noise through a linear generating filter with characteristics formed by real steps of a person, and the primary pulse sequence modulation by Gauss functions. The model permits to control the signal-to-noise ratio after its reduction to unity and to vary pulse shifts with respect to person steps irregularity. It has been shown that the model of a person seismic signal with noise agrees with experimental data.

Soil Moisture based Automatic Irrigation System to Improve Water Productivity

2020 ◽  
Vol 7 (04) ◽  
Author(s):  
PRADEEP H K ◽  
JASMA BALASANGAMESHWARA ◽  
K RAJAN ◽  
PRABHUDEV JAGADEESH
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Content ◽  
Irrigation System ◽  
Water Productivity ◽  
Vital Role ◽  
Agricultural Water Management ◽  
Water Management System ◽  
Proposed Model ◽  
Crop Growth Stage ◽  
Experimental Findings

Irrigation automation plays a vital role in agricultural water management system. An efficient automatic irrigation system is crucial to improve crop water productivity. Soil moisture based irrigation is an economical and efficient approach for automation of irrigation system. An experiment was conducted for irrigation automation based on the soil moisture content and crop growth stage. The experimental findings exhibited that, automatic irrigation system based on the proposed model triggers the water supply accurately based on the real-time soil moisture values.

scholarly journals Implicit nonlinear fractional differential equations of variable order

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Amar Benkerrouche ◽  
Mohammed Said Souid ◽  
Kanokwan Sitthithakerngkiet ◽  
Ali Hakem
Keyword(s):  
Differential Equations ◽  
Boundary Value Problem ◽  
Fractional Differential Equations ◽  
Boundary Value ◽  
Variable Order ◽  
Stability Of Solutions ◽  
Nonlinear Fractional Differential Equations ◽  
Caputo Fractional Differential Equations ◽  
Fractional Differential ◽  
The Stability

AbstractIn this manuscript, we examine both the existence and the stability of solutions to the implicit boundary value problem of Caputo fractional differential equations of variable order. We construct an example to illustrate the validity of the observed results.

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