scholarly journals Foreground removal from WMAP 7 yr polarization maps using an MLP neural network

2012 ◽  
Vol 340 (1) ◽  
pp. 161-173 ◽  
Author(s):  
H. U. Nørgaard-Nielsen
Keyword(s):  
Neural Network ◽  
Mlp Neural Network ◽  
Foreground Removal

scholarly journals Foreground removal from CMB temperature maps using an MLP neural network

2008 ◽  
Vol 318 (3-4) ◽  
pp. 195-206 ◽  
Author(s):  
H. U. Nørgaard-Nielsen ◽  
H. E. Jørgensen
Keyword(s):  
Neural Network ◽  
Mlp Neural Network ◽  
Temperature Maps ◽  
Foreground Removal ◽  
Cmb Temperature

Hybrid version of MLP neural network for transformer fault diagnosis system

2008 ◽  
Author(s):  
Wan Mohd Fahmi Wan Mamat ◽  
Nor Ashidi Mat Isa ◽  
Kamal Zuhairi Zamli ◽  
Wan Mohd Fairuz Wan Mamat
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diagnosis System ◽  
Mlp Neural Network ◽  
Fault Diagnosis System ◽  
Transformer Fault

scholarly journals Neural Network-based System for Automatic Passport Stamp Classification

2020 ◽  
Vol 49 (4) ◽  
pp. 583-607
Author(s):  
Wala Zaaboub ◽  
Lotfi Tlig ◽  
Mounir Sayadi ◽  
Basel Solaiman
Keyword(s):  
Neural Network ◽  
Automatic System ◽  
Texture Feature ◽  
Illegal Immigrants ◽  
International Tourism ◽  
Multi Layer Perceptron ◽  
Texture Feature Extraction ◽  
Mlp Neural Network ◽  
Artificial Network ◽  
Systematic Control

The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

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