Photonic neuromorphic accelerator for convolutional neural
The proposed scheme acts as an analogue convolutional engine, enabling information preprocessing in the optical domain, dimensionality reduction, and extraction of spatio-temporal
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Optical Convolution Processing Based on an Amplified Fiber-Optic
Convolution processing is a key function in convolutional neural networks (CNNs). To increase the computational speed of a CNN, optical convolution processing (
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Michelson Interferometric Methods for Full Optical
In this study, the proposed Reconfigurable Complex Convolution Module (RCCM) is capable of independently modulating both phase and
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Enhancing Ship Classification in Optical Satellite Imagery: Integrating
ABSTRACT In this study, we present an advanced convolutional neural network (CNN) architecture for ship classification based on optical satellite imagery, which significantly enhances performance
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Multiresolution cascaded attention U-Net for localization and
However, its optic cup (OC) segmentation performance was less favorable. Woo et al. 28 introduced the Convolutional Block Attention Module (CBAM) for CNNs.
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Experimental optical computing of complex vector convolution with
Here, we present a highly efficient optical computing protocol for complex vector convolution with the superposition of high-dimensional OAM eigenmodes. We used two cascaded spatial light modulators
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Analog optical computer for AI inference and combinatorial
An analog optical computer that combines analog electronics, three-dimensional optics, and an iterative architecture accelerates artificial intelligence inference and combinatorial
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Optical-Vortex-Convolution-Enhanced Neural Network
This work presents a hybrid optical-electronic convolutional neural network for infrared image recognition, featuring an optical multi-kernel vortex filter based on a nonlinear crystal and
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_ORS Classification
Abstract This study presents an advanced Convolutional Neural Network (CNN) architecture for ship classification from optical satellite imagery, significantly enhancing performance through the
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A Simple Optical Convolution Strategy Based on Versatile Adjustable
Here, a novel optical convolution computing strategy is proposed that leverages a continuously adjustable photoluminescent device (CA-PLD) as the optical convolution kernel,
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The Most Comprehensive Guide Of Optical Modules
Explore the ultimate guide to optical modules. Learn types, functions, performance metrics & how to choose the right module for your fiber network.
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A fiber channel modeling method based on complex neural networks
Channel modeling plays a pivotal role in the field of communications, particularly in the optical communication networks of backbone communication systems. Recent studies on optical
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Optical convolution operations with optical neural networks for
This study introduces optical neural networks (ONNs) designed to accelerate optical convolution operations using a red, green, and blue (RGB) pixel ar
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Microcomb-enabled parallel self
Here, we propose the microcomb-enabled parallel optical convolution streaming processor (OCSP) with time, space, and wavelength three-dimensional multiplexing, operating at data rates of
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Opto-Electronic Convolutional Neural Network Design Via
We introduce a two-stage strategy for designing opto-electronic convolutional neural networks (CNNs): first, train a standard electronic CNN, then realize the optical front
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Experimental optical computing of complex vector convolution with
Orbital angular momentum (OAM), emerging as an inherently high-dimensional property of photons, has boosted information capacity in optical communications. However, the potential of OAM in optical
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A dense optical flow map-guided convolutional neural network for multi
To overcome the challenge of large-scale object motion in multi-exposure fusion, this paper proposes an optical flow-aligned and attention-guided deep neural network called DOFM
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Convolutional Optical Neural Networks Herald a New Era for AI Imaging
Convolutional Neural Networks (CNNs), with its exceptional image recognition capabilities, have performed outstandingly in the field of AI and notably within platforms like
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Michelson Interferometric Methods for Full Optical Complex Convolution
We introduce the Reconfigurable Complex Convolution Module (RCCM), an approach for optical computing based on established theoretical principles. We compare the RCCM with existing
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Hybrid optical convolutional neural network with convolution kernels
Here, we demonstrate a hybrid convolutional neural network based on an optimized optical convolution processor—the system uses kernels trained in the spatial domain and
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Wave Optics Software for Analyzing Micro
Simulate and optimize optical devices by combining the COMSOL Multiphysics® software and the add-on Wave Optics Module. Learn more here.
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Multimodal deep learning using on-chip diffractive optics with
Here, authors propose and demonstrate a trainable diffractive optical neural network chip based on on-chip diffractive optics with tunable elements to address these constraints.
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Introduction to Convolution Neural Network
Convolutional Neural Networks (CNNs), are neural network architectures inspired by the human visual system, designed to process image
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Enhanced segmentation of optic disc and cup using attention
Delineating the boundaries of the optic disc and cup regions is a critical pre-requisite for glaucoma screening because it allows for precise measurement of key parameters, such as cup-to
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Optical Convolutional Neural Networks: Methodology
As a leading branch of deep learning, the convolutional neural network (CNN) is inspired by the natural visual perceptron mechanism of living
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Optical convolutional spectrometer
A centimetre-scale convolutional spectrometer offers high performance, low cost and ~500-nm bandwidth. The system classifies diverse solid samples and quantifies concentrations of
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Fully Convolutional Networks for Monocular Retinal Depth Estimation
Our results outperform the existing techniques for depth estimation on the INSPIRE dataset. To extend the use of depth map for optic disc and cup segmentation, we propose a novel
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Monolithically Integrated Optical Convolutional Processors on Thin
Here, we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate (TFLN) that harness inherent parallelism in photonics to enable large-scale programmable
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