scholarly journals Photonic convolutional neural networks using integrated diffractive optics

2020 ◽  
Author(s):  
Jun Rong Ong ◽  
Thomas Yong Long Ang ◽  
Chin Chun Ooi ◽  
Soon Thor Lim ◽  
Ching Eng Png
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Integrated Circuits ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Speech Processing ◽  
Photonic Integrated Circuits ◽  
Diffractive Optics ◽  
Model Component ◽  
The Fourier Transform

<div>With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that have been highly successful in applications such as image classification and speech processing. We present an architecture to implement a photonic CNN using the Fourier transform property of integrated star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset. We also model component imperfections in photonic CNN and show that the performance degradation can be recovered in a programmable chip. Our proposed architecture provides a large reduction in physical footprint compared to current implementations as it utilizes the natural advantages of optics and hence offers a scalable pathway towards integrated photonic deep learning processors.</div>

scholarly journals Photonic convolutional neural networks using integrated diffractive optics

2020 ◽  
Author(s):  
Jun Rong Ong ◽  
Thomas Yong Long Ang ◽  
Chin Chun Ooi ◽  
Soon Thor Lim ◽  
Ching Eng Png
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Integrated Circuits ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Speech Processing ◽  
Photonic Integrated Circuits ◽  
Diffractive Optics ◽  
Model Component ◽  
The Fourier Transform

<div>With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that have been highly successful in applications such as image classification and speech processing. We present an architecture to implement a photonic CNN using the Fourier transform property of integrated star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset. We also model component imperfections in photonic CNN and show that the performance degradation can be recovered in a programmable chip. Our proposed architecture provides a large reduction in physical footprint compared to current implementations as it utilizes the natural advantages of optics and hence offers a scalable pathway towards integrated photonic deep learning processors.</div>

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals Image Classification: A Survey

2020 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Ankita Singh ◽  
◽  
Pawan Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Learning Ability ◽  
Final Decision

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.

scholarly journals Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Julie Chang ◽  
Vincent Sitzmann ◽  
Xiong Dun ◽  
Wolfgang Heidrich ◽  
Gordon Wetzstein
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Diffractive Optics

scholarly journals Dog Breed Prediction using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 318-322
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Graphical Representation ◽  
Outcome Analysis ◽  
Dog Breed ◽  
The One

Deep learning gives the strength on the way to train algorithms model that can handle the difficulties of info classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks (CNNs) gives single often used method for image classification and detection. In this exertion, we define a CNNbased approach for spotting dogs in per chance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm (CNN) gives good analysis accuracy for all the tested datasets

scholarly journals Deep Learning Based Remote Sensing using Convolutional Neural Networks

2019 ◽  
Vol 8 (5S3) ◽  
pp. 102-106
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Visual Quality ◽  
Recovery Phase ◽  
Diffractive Optics ◽  
Quality Image ◽  
Convergence Point ◽  
Remote Identification

We describe our achievements in collecting alternating convergence points with a thickness of 7 μm and focal lengths of 200 and 350 mm, combined with shadow correction, deconvolution and significant neural frame training for transmission close to photography. Visual quality image. Although images taken using diffractive optics have been shown in previous papers, important neural structures have been used in the recovery phase. We use the imagery component of our imaging structure to activate the rise of ultralight cameras with remote identification for Nano and pico satellites, as well as small drones and solar-guided aircraft for aeronautical remote identification systems. . We extend the customizability of the liquid center focus on non-circular surfaces, forcing movement at the liquid convergence point of the surface. We study their trends and whether we can use them in optical structures.

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

2017 ◽  
Vol 29 (9) ◽  
pp. 2352-2449 ◽  
Author(s):  
Waseem Rawat ◽  
Zenghui Wang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Rapid Progression ◽  
Deep Convolutional Neural Networks ◽  
Computing Power ◽  
Current Trends ◽  
Visual Tasks ◽  
Classification Tasks

Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.

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