Bubble Size and Frequency in Corrugated-Wall Bubbling Fluidized Beds—Image Analysis and Neural Network Correlations

2012 ◽  
Vol 51 (37) ◽  
pp. 12107-12116 ◽  
Author(s):  
A. N. Khan Wardag ◽  
F. Larachi ◽  
B.P.A. Grandjean
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Bubble Size ◽  
Fluidized Beds ◽  
Corrugated Wall

Label-Free Estimation of Therapeutic Efficacy on 3D Cancer Spheres Using Convolutional Neural Network Image Analysis

2019 ◽  
Vol 91 (21) ◽  
pp. 14093-14100 ◽  
Author(s):  
Zhixiong Zhang ◽  
Lili Chen ◽  
Yimin Wang ◽  
Tiantian Zhang ◽  
Yu-Chih Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Therapeutic Efficacy ◽  
Label Free

scholarly journals Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Cailing Hao
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Image Analysis ◽  
Frequency Band ◽  
High Frequency ◽  
Principal Component ◽  
Speech Signals ◽  
College English ◽  
Neural Network Algorithm ◽  
Component Image

With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency band. However, due to the limitations of current voice equipment and channel conditions, the existing voice band expansion technology often ignores the high-frequency and low-frequency correlation of the audio, resulting in excessive smoothing of the recovered high-frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA-NN (principal components analysis-neural network) based on principal component image analysis is proposed. Based on the nonlinear characteristics of the audio image signal, the model reduces the dimension of high-dimensional data and realizes the effective recovery of the high-frequency detailed spectrum of audio signal in phase space. The results show that the PCA-NN, i.e., neural network based on principal component analysis, is superior to other audio expansion algorithms in subjective and objective evaluation; in log spectrum distortion evaluation, PCA-NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286 dB, 0.51 dB, and 0.15 dB, respectively. The above results show that in the image frequency band expansion of college English listening, the neural network algorithm based on principal component analysis (PCA-NN) can obtain better high-frequency reconstruction accuracy and effectively improve the audio quality.

scholarly journals A Survey on Techniques used in Medical Imaging Processing

2021 ◽  
Vol 2089 (1) ◽  
pp. 012013
Author(s):  
Priyadarshini Chatterjee ◽  
Dutta Sushama Rani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Medical Image Analysis ◽  
Artificial Neural ◽  
Automated Screening ◽  
To Come

Abstract Automated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.

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