Conversion of Artificial Neural Network to Spiking Neural Network for Hardware Implementation

2019 ◽  
Author(s):  
Yi-Lun Chen ◽  
Chih-Cheng Lu ◽  
Kai-Cheung Juang ◽  
Kea-Tiong Tang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hardware Implementation ◽  
Spiking Neural Network ◽  
Artificial Neural

scholarly journals Design of an OCR System and its Hardware Implementation

2021 ◽  
Vol 9 (12) ◽  
pp. 159-174
Author(s):  
Gulfeshan Parween
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Character Recognition ◽  
Hardware Implementation ◽  
Gray Level ◽  
Document Images ◽  
English Alphabet ◽  
Barcode Recognition ◽  
Printed Text ◽  
Artificial Neural

Abstract: In this paper, we present a scheme to develop to complete OCR system for printed text English Alphabet of Uppercase of different font and of different sizes so that we can use this system in Banking, Corporate, Legal industry and so on. OCR system consists of different modules like preprocessing, segmentation, feature extraction and recognition. In preprocessing step it is expected to include image gray level conversion, binary conversion etc. After finding out the feature of the segmented characters artificial neural network and can be used for Character Recognition purpose. Efforts have been made to improve the performance of character recognition using artificial neural network techniques. The proposed OCR system is capable of accepting printed document images from a file and implemented using MATLAB R2014a version. Key words: OCR, Printed text, Barcode recognition

scholarly journals Transceiver error reduction by design prototype system based on neural network analysis method

2020 ◽  
Vol 18 (3) ◽  
pp. 1244
Author(s):  
Muthna Jasim Fadhil ◽  
Maitham Ali Naji ◽  
Ghalib Ahmed Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Code Word ◽  
Prototype System ◽  
Spiking Neural Network ◽  
Analysis Method ◽  
Neural Network Structure ◽  
Artificial Neural ◽  
Source Dimension ◽  
Proper Condition

<p><span>Code words traditional can be decoding when applied in artificial neural network. Nevertheless, explored rarely for encoding of artificial neural network so that it proposed encoder for artificial neural network forward with major structure built by Self Organizing Feature Map (SOFM). According to number of bits codeword and bits source mentioned the dimension of forward neural network at first then sets weight of distribution proposal choosing after that algorithm appropriate using for sets weight initializing and finally sets code word uniqueness check so that matching with existing. The spiking neural network (SNN) using as decoder of neural network for processing of decoding where depending on numbers of bits codeword and bits source dimension the spiking neural network structure built at first then generated sets codeword by network neural forward using for train spiking neural network after that when whole error reached minimum the process training stop and at last sets code word decode accepted. In tests simulation appear that feasible decoding and encoding neural network while performance better for structure network neural forward a proper condition is achieved with γ node output degree. The methods of mathematical traditional can not using for decoding generated Sets codeword by encoder network of neural so it is prospect good for communication security. </span></p>

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