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PubMed Journals Articles About "Optical Neural Network Demo" RSS

06:12 EDT 21st March 2019 | BioPortfolio

Optical Neural Network Demo PubMed articles on BioPortfolio. Our PubMed references draw on over 21 million records from the medical literature. Here you can see the latest Optical Neural Network Demo articles that have been published worldwide.

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We have published hundreds of Optical Neural Network Demo news stories on BioPortfolio along with dozens of Optical Neural Network Demo Clinical Trials and PubMed Articles about Optical Neural Network Demo for you to read. In addition to the medical data, news and clinical trials, BioPortfolio also has a large collection of Optical Neural Network Demo Companies in our database. You can also find out about relevant Optical Neural Network Demo Drugs and Medications on this site too.

Showing "Optical neural network demo" PubMed Articles 1–25 of 10,000+

Color image identification and reconstruction using artificial neural networks on multimode fiber images: towards an all-optical design.

The rapid growth of applications that rely on artificial neural network (ANN) concepts gives rise to a staggering increase in the demand for hardware implementations of neural networks. New types of hardware that can support the requirements of high-speed associative computing while maintaining low power consumption are sought, and optical artificial neural networks fit the task well. Inherently, optical artificial neural networks can be faster, support larger bandwidth, and produce less heat than their ele...


Model-driven convolution neural network for inverse lithography.

Optical lithography is a fundamental process to fabricate integrated circuits, which are the basic fabric of the information age. Due to image distortions inherent in optical lithography, inverse lithography techniques (ILT) are extensively used by the semiconductor industry to improve lithography image resolution and fidelity in semiconductor fabrication. As the density of integrated circuits increases, computational complexity has become a central challenge in ILT methods. This paper develops a new and po...

Implementing artificial neural networks through bionic construction.

It is evident through biology research that, biological neural network could be implemented through two means: by congenital heredity, or by posteriority learning. However, traditionally, artificial neural network, especially the Deep learning Neural Networks (DNNs) are implemented only through exhaustive training and learning. Fixed structure is built, and then parameters are trained through huge amount of data. In this way, there are a lot of redundancies in the implemented artificial neural network. This...


Intelligent adaptive coherent optical receiver based on convolutional neural network and clustering algorithm.

In a cognitive, heterogeneous, optical network, it would be important to identify physical layer information, especially the modulation formats of transmitted signals. The modulation format information is also indispensable for carrier-phase-recovery in a coherent optical receiver. Because constellation diagrams of modulation signals are susceptible to various noises, we utilize a convolutional neural network to process the amplitude data after the modulation-format-agnostic clock recovery. Furthermore, for...

A compact network learning model for distribution regression.

Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to encode function inputs compactly as each node encodes just a real value. We propose a novel idea to address this shortcoming: to encode an entire function in a single network node. To that end, we design a compact network representation that encodes and propagates functions in single nodes for the distribution regression task. Ou...

Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.

Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases according to the structures of macular lesions, whose morphologies, size, and numbers are important criteria. In this paper, we propose a novel lesion-aware convolutional neural network (LACNN) method for retinal OCT image classification, in which retinal lesions withi...

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.

We propose an approach of 3D convolutional neural network to segment the prostate in MR images.

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation.

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this work, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-patient information. An innovative feature of the proposed method is that we embed the neural network in the ite...

A library-based LAMMPS implementation of high-dimensional neural network potentials.

Neural networks and other machine learning approaches have been successfully used to accurately represent atomic interaction potentials derived from computationally demanding electronic structure calculations. Due to their low computational cost, such representations open the possibility for large scale reactive molecular dynamics simulations of processes with bonding situations that cannot be described accurately with traditional empirical force fields. Here, we present a library of functions developed for...

Regression convolutional neural network for improved simultaneous EMG control.

Deep learning models can learn representations of data that extract useful information in order to perform prediction without feature engineering. In this paper, an electromyography (EMG) control scheme with a regression convolutional neural network (CNN) is proposed as a substitute of conventional regression models that use purposefully designed features.

Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results.

Lung ultrasound comets are "comet-tail" artifacts appearing in lung ultrasound images. They are particularly useful in detecting several lung pathologies and may indicate the amount of extravascular lung water. However, the comets are not always well defined and large variations in the counting results exist between observers. This study uses a convolutional neural network to quantify these lung ultrasound comets on a 4864-image clinical lung ultrasound dataset labeled by the authors. The neural network cou...

The importance of recurrent top-down synaptic connections for the anticipation of dynamic emotions.

Different studies have shown the efficiency of a feed-forward neural network in categorizing basic emotional facial expressions. However, recent findings in psychology and cognitive neuroscience suggest that visual recognition is not a pure bottom-up process but likely involves top-down recurrent connectivity. In the present computational study, we compared the performances of a pure bottom-up neural network (a standard multi-layer perceptron, MLP) with a neural network involving recurrent top-down connecti...

A survey of neural network-based cancer prediction models from microarray data.

Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Analyzing the studies reveals that neural network methods have been either ...

Molecular imaging with neural training of identification algorithm (neural network localization identification).

Superresolution localization microscopy strongly relies on robust identification algorithms for accurate reconstruction of the biological systems it is used to measure. The fields of machine learning and computer vision have provided promising solutions for automated object identification, but usually rely on well represented training sets to learn object features. However, using a static training set can result in the learned identification algorithm making mistakes on data that is not well represented by ...

Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.

As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images can help reveal disease-related alterations below the surface of the retina, such as the presence of edema, or accumulation of fluid which can distort vision, and are an indication of disruptions in the vasculature of the retina. In this paper, a new framework is proposed for multiclass fluid segmentation and detection in the retinal OCT images. Based on the ...

Resting-state connectivity within and across neural circuits in anorexia nervosa.

Obsessional thoughts and ritualized eating behaviors are characteristic of Anorexia Nervosa (AN), leading to the common suggestion that the illness shares neurobiology with obsessive-compulsive disorder (OCD). Resting-state functional connectivity MRI (rs-fcMRI) is a measure of functional neural architecture. This longitudinal study examined functional connectivity in AN within the limbic cortico-striato-thalamo-cortical (CSTC) loop, as well as in the salience network, the default mode network, and the exec...

Clone-Based Encoded Neural Networks to Design Efficient Associative Memories.

In this paper, we introduce a neural network (NN) model named clone-based neural network (CbNN) to design associative memories. Neurons in CbNN can be cloned statically or dynamically which allows to increase the number of data that can be stored and retrieved. Thanks to their plasticity, CbNN can handle correlated information more robustly than existing models and thus provides better memory capacity. We experiment this model in encoded neural networks also known as Gripon-Berrou NNs. Numerical simulations...

A deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound.

We designed a deep convolutional neural network (CNN) to diagnose thyroid malignancy on ultrasound (US) and compared the diagnostic performance of CNN with that of experienced radiologists.

Recurrent Neural Network for Kinematic Control of Redundant Manipulators With Periodic Input Disturbance and Physical Constraints.

Input disturbances and physical constraints are important issues in the kinematic control of redundant manipulators. In this paper, we propose a novel recurrent neural network to simultaneously address the periodic input disturbance, joint angle constraint, and joint velocity constraint, and optimize a general quadratic performance index. The proposed recurrent neural network applies to both regulation and tracking tasks. Theoretical analysis shows that, with the proposed neural network, the end-effector tr...

Bipartite synchronization in coupled delayed neural networks under pinning control.

This paper considers the bipartite leader-following synchronization in a signed network composed by an array of coupled delayed neural networks by utilizing the pinning control strategy and M-matrix theory, where the communication links between neighboring nodes of the network can be either positive or negative. Under the assumption that the node-delay is bounded and differentiable, a sufficient condition in terms of a low-dimensional linear matrix inequality is derived for reaching bipartite leader-followi...

Dual Neural Network Method for Solving Multiple Definite Integrals.

This study, which examines a calculation method on the basis of a dual neural network for solving multiple definite integrals, addresses the problems of inefficiency, inaccuracy, and difficulty in finding solutions. First, the method offers a dual neural network method to construct a primitive function of the integral problem; it can approximate the primitive function of any given integrand with any precision. On this basis, a neural network calculation method that can solve multiple definite integrals whos...

A neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints.

This paper presents a neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints. The proposed neural network endows with a time-varying auxiliary function, which can guarantee that the state of the neural network enters the feasible region in finite time and remains there thereafter. Moreover, the state with any initial point is shown to be convergent to the critical point set when the objective function is generally nonconvex. Especially, when the objec...

Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification.

Instagram, with millions of posts per day, can be used to inform public health surveillance targets and policies. However, current research relying on image-based data often relies on hand coding of images, which is time-consuming and costly, ultimately limiting the scope of the study. Current best practices in automated image classification (eg, support vector machine (SVM), backpropagation neural network, and artificial neural network) are limited in their capacity to accurately distinguish between object...

A Projection Neural Network for Identifying Copy Number Variants.

The identification of copy number variations (CNVs) helps the diagnosis of many diseases. One major hurdle in the path of CNVs discovery is that the boundaries of normal and aberrant regions cannot be distinguished from the raw data since various types of noise contaminate them. To tackle this challenge, the total variation regularization is mostly used in the optimization problems to approximate the noise-free data from corrupted observations. The minimization using such regularization is challenging to de...

Multi-Memory Convolutional Neural Network for Video Super-Resolution.

Video super-resolution (SR) is focused on reconstructing high-resolution (HR) frames from consecutive lowresolution (LR) frames. Most previous video SR methods based on convolutional neural network (CNN) use a direct connection and single-memory module within the network, and they thus fail to make full use of spatio-temporal complementary information from LR observed frames. To fully exploit spatio-temporal correlations between adjacent LR frames and reveal more realistic details, this paper proposes a mul...


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