Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
Spatiotemporal diffractive deep neural networks
View through CrossRef
A spatiotemporal diffractive deep neural network (STD2NN) is proposed for spatiotemporal signal processing. The STD2NN is formed by gratings, which convert the signal from the frequency domain to the spatial domain, and multiple layers consisting of spatial lenses and space light modulators (SLMs), which conduct spatiotemporal phase modulation. An all-optical backpropagation (BP) algorithm for SLM phase tuning is proposed, with the gradient of the loss function computed by the inner product of the forward propagating optical field and the backward propagating conjugated error field. As a proof of concept, a spatiotemporal word “OPTICA” is generated by the STD2NN. Afterwards, a spatiotemporal optical vortex (STOV) beam multiplexer based on the STD2NN is demonstrated, which converts the spatially separated Gaussian beams into the STOV wave-packets with different topological charges. Both cases illustrate the capability of the proposed STD2NN to generate and process the spatiotemporal signals.
Title: Spatiotemporal diffractive deep neural networks
Description:
A spatiotemporal diffractive deep neural network (STD2NN) is proposed for spatiotemporal signal processing.
The STD2NN is formed by gratings, which convert the signal from the frequency domain to the spatial domain, and multiple layers consisting of spatial lenses and space light modulators (SLMs), which conduct spatiotemporal phase modulation.
An all-optical backpropagation (BP) algorithm for SLM phase tuning is proposed, with the gradient of the loss function computed by the inner product of the forward propagating optical field and the backward propagating conjugated error field.
As a proof of concept, a spatiotemporal word “OPTICA” is generated by the STD2NN.
Afterwards, a spatiotemporal optical vortex (STOV) beam multiplexer based on the STD2NN is demonstrated, which converts the spatially separated Gaussian beams into the STOV wave-packets with different topological charges.
Both cases illustrate the capability of the proposed STD2NN to generate and process the spatiotemporal signals.
Related Results
Fuzzy Chaotic Neural Networks
Fuzzy Chaotic Neural Networks
An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is still obscure. Both fuzzy logic ...
On the role of network dynamics for information processing in artificial and biological neural networks
On the role of network dynamics for information processing in artificial and biological neural networks
Understanding how interactions in complex systems give rise to various collective behaviours has been of interest for researchers across a wide range of fields. However, despite ma...
Hybrid Optical Diffractive Neural Networks (HODNNs) for Speckle Reconstruction and Physical Auto-encoding
Hybrid Optical Diffractive Neural Networks (HODNNs) for Speckle Reconstruction and Physical Auto-encoding
Abstract
All-optical Diffractive Deep Neural Network (D2NN) architecture can learn to implement various functions after the deep learning-based design of passive diffractiv...
Characterizing spatiotemporal population receptive fields in human visual cortex with fMRI
Characterizing spatiotemporal population receptive fields in human visual cortex with fMRI
AbstractThe use of fMRI and computational modeling has advanced understanding of spatial characteristics of population receptive fields (pRFs) in human visual cortex. However, we k...
Tilted-Mode All-Optical Diffractive Deep Neural Networks
Tilted-Mode All-Optical Diffractive Deep Neural Networks
Diffractive deep neural networks (D2NNs) typically adopt a densely cascaded arrangement of diffractive masks, leading to multiple reflections of diffracted light between adjacent m...
Deep convolutional neural network and IoT technology for healthcare
Deep convolutional neural network and IoT technology for healthcare
Background Deep Learning is an AI technology that trains computers to analyze data in an approach similar to the human brain. Deep learning algorithms can find complex patterns in ...
Neural stemness contributes to cell tumorigenicity
Neural stemness contributes to cell tumorigenicity
Abstract
Background: Previous studies demonstrated the dependence of cancer on nerve. Recently, a growing number of studies reveal that cancer cells share the property and ...
Data Science – deep learning of neural networks and their application in healthcare
Data Science – deep learning of neural networks and their application in healthcare
Introduction: Artificial intelligence, which is a set of algorithms, currently does an impressive amount of work related to its analysis and processing. The use of the computing po...