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10 results for “optical neural networks”
Image sensing with multilayer, nonlinear optical neural networks
<p>This data repository contains the information necessary to reproduce the main results of the paper “Image sensing with multiplayer, nonlinear optical neural networks”.</p> <p>This repository contains the data and the code for generating the figures in the manuscript "Image sensing with multilayer, nonlinear optical neural networks", including figures in the main text and in supplementary materials. The repository also contains the code for controling the experiment setup and running the experiments conducted in the paper:</p> <ul> <li>Folder 'Data_Collection_Example' and 'Data_Extraction_Example' contain example scripts for instrument control and data collection using the multilayer optical-neural-network sensor.</li> <li>Other folders are organized according to the figure panels in the main text, each containing the data and the code required to reproduce the plots in a main figure panel and its associated supplementary figures. In each of these folders, there is a README.txt file that summarizes the role of each file in the folder. </li> </ul>
Global Positioning System Based on Optical Flow and Convolutional Neural Network
<p>Two datasets used in the paper Global Positioning System Based on Optical Flow and Convolutional Neural Network, to evaluate the proposed CNN model to the task of position estimation. The datasets cover 2 different modes of motion of a drone, and were captured using Google API.</p>
Dataset of "Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network"
<p>Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files.</p>
Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks
<p>During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53±0.39%, and the average testing relative error in distance estimation reached 4.42±0.56% (36.09±4.92 μm).</p> <p>The dataset is split into two parts:<br> (1) <strong>Classification</strong>. The zip file <em>veress_classification_raw_images.zip</em> contains 40K images from 8 swine samples where there are 1K images per layer (skin, fat, muscle, abdominal space, and small intestine)<br> (2) <strong>Regression</strong>. The zip file <em>veress_regression_raw_images.zip</em><strong> </strong>contains 8K images of the abdominal space from the same 8 swine samples, and the ground truth distance labels for each sample are found in the Excel files <em>S[1-8]_distance_measurement_20210803.xlsx.</em></p>
Artificial Neural Network Symbol Demapper for Coherent Optical Fiber Systems
<p>M-files and datasets that implement an artificial neural network (ANN) demapper targeted to the compensation of fiber nonlinearities in coherent optical transmission systems. </p> <p>The dataset contains simulation data of a 11-channel WDM fiber link with numerical propagation implemented by the split-step Fourier method over standard single-mode fiber with 100 km per span and inline optical amplification with 5 dB noise figure. The launched optical power is varied in the range of 0 to 5 dBm and the distance is swept up to 30 fiber spans. The transmitted signal is a root-raised cosine single-carrier 16QAM at 64 Gbaud. </p>
Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""
<p>Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em></p> <p>1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a></p> <p>2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared)</p> <p>3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> </a>on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.</p> <p> </p>
Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"
<p>This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper.</p> <p>The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks.</p> <p><strong>Where to Start</strong></p> <p>The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'. </p> <p>The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file.</p> <p>For more information, please refer to 'README.txt'.</p>
Single-Shot Optical Neural Network
<p>Data from classification of MNIST [1], Fashion-MNIST [2] and QuickDraw [3] images reported in "Single-Shot Optical Neural Network" by L. Bernstein et al. The networks of 784 -> N (-> N) -> 10 activations performed inference on the test sets through consecutive matrix products implemented on the optical hardware, with ReLU applied electronically between each layer (see main text for more details). Folders for each tested network contain the following text files:</p> <ul> <li>Inputs: 2D matrices of size B x 784 containing training (B = 50,000 or 100,000), validation (B = 10,000) and test (B = 10,000) sets. Each row is an input vector that can be reshaped into an input image of size 28 x 28.</li> <li>True labels: B-length vectors containing the true label of each input in the training, validation and test sets.</li> <li>Neural network weights: 2D matrices of size K x N used in inference experiments to classify the test sets. Weights were pre-trained on the training set using a digital electronic computer as described in Materials and Methods. Weight values were normalized such that all values fall between -255 and 255. In the optical neural network, the weighting SLM displays the absolute values of the weights (rounded to the nearest integer), and the negative weight signs are applied in post-processing. The 32-bit weight values were used for inference performed on the digital electronic computer for the ground truth comparison.</li> <li>Outputs (normalized): 2D output matrices of size 10,000 x 10 from the networks processed on a digital electronic computer (ground truth) and the optical neural network. Each row is an output vector where the position of the maximum value indicates the predicted label of the input in the same row of the test set.</li> <li>Predicted labels: 10,000-element vectors that represent the labels predicted by the networks processed on a digital electronic computer (ground truth) and the optical neural network. These predicted labels were used to generate the confusion matrices and calculate the classification accuracies (versus the true labels).</li> </ul> <p>The classes for the Fashion-MNIST dataset are the following:</p> <ul> <li>0: T-shirt</li> <li>1: Trouser</li> <li>2: Pullover</li> <li>3: Dress</li> <li>4: Coat</li> <li>5: Sandal</li> <li>6: Shirt</li> <li>7: Sneaker</li> <li>8: Bag</li> <li>9: Ankle boot</li> </ul> <p>And the randomly selected classes for QuickDraw are:</p> <ul> <li>0: Hourglass</li> <li>1: Saw</li> <li>2: Golf club</li> <li>3: See saw</li> <li>4: Spoon</li> <li>5: Horse</li> <li>6: Onion</li> <li>7: Light bulb</li> <li>8: Harp</li> <li>9: Flip flops</li> </ul> <p>[1] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. <em>Proceedings of the IEEE</em> <strong>86</strong>, 2278–2324 (1998).</p> <p>[2] H. Xiao, K. Rasul, R. Vollgraf, Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. Preprint at https://arxiv.org/abs/1708.07747 (2017).</p> <p>[3] J. Jongejan, H. Rowley, T. Kawashima, J. Kim, N. Fox-Gieg, The Quick, Draw! AI experiment, https://quickdraw.withgoogle.com/ (2016).</p>
Maximally Error-Tolerant MZI Mesh-based Optical Neural Networks
<p>Codes.zip contains code that trains and tests maximally error-tolerant Mach-Zehnder mesh optical neural networks on MNIST, FashionMNIST, and KMNIST.</p> <p>models.zip contains all the trained model parameters.</p>
dataset where a Convolutional Neural Network controls a part of the TEM optics and OAM sorter
<p>Here are uploaded the data collected when we succesfully connected a Convolutional Neural Network to the Holo-TEM in Julich. In particular the CNN controls a part of the TEM optics (beam shift x and y, C3 intensity) and the OAM sorter (the Sorter1 and Sorter 2 bias, in particular it is able to change the S1 bias to perfectly match the phase profile generated from the second sorting element).</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.