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Hardware Implementation of Neural Ne...

Gupta, Tushar.

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Hardware Implementation of Neural Network for Image Classification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hardware Implementation of Neural Network for Image Classification./
Author:	Gupta, Tushar.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	56 p.
Notes:	Source: Masters Abstracts International, Volume: 82-04.
Contained By:	Masters Abstracts International82-04.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28090965
ISBN:	9798672197760

Hardware Implementation of Neural Network for Image Classification.
Gupta, Tushar.

Hardware Implementation of Neural Network for Image Classification. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 56 p.

Source: Masters Abstracts International, Volume: 82-04.

Thesis (M.S.)--State University of New York at Buffalo, 2020.

This item must not be sold to any third party vendors.

Image Classification has been the topic of research for long time due to its wide range of applications which includes vehicle license plate recognition, text recognition, medical diagnosis and the list goes on. Traditional computer vision approaches can become inefficient as the number of classes to classify increase. It also has a plethora of feature parameter which becomes an overhead. Machine learning algorithms can be utilized to utilized to discover the underlying patterns in classes of images.This work aims at developing hardware for image classification. Neural Network has been modeled to classify the USPS dataset consisting of handwritten digit (0-9) images with 16x16 grayscale pixels as input features. First the neural network is trained using 8800 of these images and then tested with 2200 images. After achieving the promising results on MATLAB circuit was designed using 65 nm MOS switched capacitors. The circuit consists of input layer, hidden layer and the output layer. After simulating the circuit on cadence virtuoso we achieved 88% accuracy.

ISBN: 9798672197760Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Handwritten digits classification

Hardware Implementation of Neural Network for Image Classification.
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245 1 0 $a Hardware Implementation of Neural Network for Image Classification.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 56 p.
500 $a Source: Masters Abstracts International, Volume: 82-04.
500 $a Advisor: Sanyal, Arindam.
502 $a Thesis (M.S.)--State University of New York at Buffalo, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Image Classification has been the topic of research for long time due to its wide range of applications which includes vehicle license plate recognition, text recognition, medical diagnosis and the list goes on. Traditional computer vision approaches can become inefficient as the number of classes to classify increase. It also has a plethora of feature parameter which becomes an overhead. Machine learning algorithms can be utilized to utilized to discover the underlying patterns in classes of images.This work aims at developing hardware for image classification. Neural Network has been modeled to classify the USPS dataset consisting of handwritten digit (0-9) images with 16x16 grayscale pixels as input features. First the neural network is trained using 8800 of these images and then tested with 2200 images. After achieving the promising results on MATLAB circuit was designed using 65 nm MOS switched capacitors. The circuit consists of input layer, hidden layer and the output layer. After simulating the circuit on cadence virtuoso we achieved 88% accuracy.
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650 4 $a Computer science. $3 523869
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653 $a Image classification
653 $a Machine learning
653 $a Neural network
653 $a Switched capacitor
653 $a USPS
653 $a Vehicle license plate recognition
653 $a Text recognition
653 $a Medical diagnosis
653 $a Computer vision
653 $a Hardware development
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710 2 $a State University of New York at Buffalo. $b Electrical Engineering. $3 1019366
773 0 $t Masters Abstracts International $g 82-04.
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