Query: (microscopy OR image OR images) AND  (2d material AND biofilm) AND (machine learning OR Data Mining)

Candidate papers:

abstract: Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame based camera, converted into a digital format and processed afterwards using a machine learning algorithm such as an artificial neural network (ANN) 1 The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption. Various visual data preprocessing techniques have thus been developed 2 7 to increase the efficiency of the subsequent signal processing in an ANN. Here we demonstrate that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images without latency. Our device is based on a reconfigurable two dimensional (2D) semiconductor 8 9 photodiode 10 12 array, and the synaptic weights of the network are stored in a continuously tunable photoresponsivity matrix. We demonstrate both supervised and unsupervised learning and train the sensor to classify and encode images that are optically projected onto the chip with a throughput of 20 million bins per second. A two dimensional semiconductor photodiode array senses and processes optical images simultaneously without latency, and is trained to classify and encode images with high throughput, acting as an artificial neural network.
author_names: Lukas Mennel

Joanna Karolina Symonowicz

Stefan Wachter

Dmitry K Polyushkin

Aday J Molina-Mendoza

T Mueller
title: Ultrafast machine vision with 2D material neural network image sensors
venue: Nature
year: 2020

abstract: Abstract A combination of Fresnel law and machine learning method is proposed to identify the layer counts of 2D materials. Three indexes, which are optical contrast, red green blue, total color difference, are presented to illustrate and simulate the visibility of 2D materials on Si/SiO2 substrate, and the machine learning algorithms, which are k mean clustering and k nearest neighbors, are employed to obtain thickness database of 2D material and test the optical images of 2D materials via red green blue index. The results show that this method can provide fast, accurate and large area property of 2D material. With the combination of artificial intelligence and nanoscience, this machine learning assisted method eases the workload and promotes fundamental research of 2D materials.
author_names: Yuhao Li

Yangyang Kong

Jinlin Peng

Chuan-bin Yu

Zhi Li

Penghui Li

Yunya Liu

Cun-fa Gao

Rong Wu
title: Rapid identification of two dimensional materials via machine learning assisted optic microscopy
venue: Journal of Materiomics
year: 2019

abstract: For advanced materials characterization, a novel and extremely effective approach of pattern recognition in optical microscopic images of steels is demonstrated. It is based on fast Random Forest statistical algorithm of machine learning for reliable and automated segmentation of typical steel microstructures. Their percentage and location areas excellently agreed between machine learning and manual examination results. The accurate microstructure pattern recognition/segmentation technique in combination with other suitable mathematical methods of image processing and analysis can help to handle the large volumes of image data in a short time for quality control and for the quest of new steels with desirable properties.
author_names: Dmitry S Bulgarevich

Susumu Tsukamoto

T Kasuya

Masahiko Demura

Makoto Watanabe
title: Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
venue: Scientific Reports
year: 2018

abstract: Abstract We present a research study aimed at testing of applicability of machine learn ing techniques for permeability prediction. We prepare a training set containing. 3D scans of Berea sandstone subsamples imaged with X ray microtomography and corresponding permeability values simulated with Pore Network approach. We also use Minkowski functionals and Deep Learning based descriptors of 3D images and 2D slices as input features for predictive model training and pre diction. We compare predictive power of various descriptors and methods. The; latter include Gradient Boosting, Deep Neural Networks (DNN) and Convo lutional Neural Networks (CNN) Introduced Deep Learning based descriptors; outperform previously used alternatives. 3D CNN outperforms the competitors in terms of the percent error and prediction time. The results demonstrate the applicability of machine learning for image based permeability prediction and open a new area of Digital Rock research.
author_names: Oleg Sudakov

Evgeny Burnaev

Dmitry Koroteev
title: Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks
venue: Comput. Geosci.
year: 2019

abstract: Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense. Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis. Therefore, a solid understanding of machine learning techniques have become essential for remote sensing researchers and practitioners. This paper reviews and compares recent machine learning based hyperspectral image analysis methods published in literature. We organize the methods by the image analysis task and by the type of machine learning algorithm, and present a two way mapping between the image analysis tasks and the types of machine learning algorithms that can be applied to them. The paper is comprehensive in coverage of both hyperspectral image analysis tasks and machine learning algorithms. The image analysis tasks considered are land cover classification, target detection, unmixing, and physical parameter estimation. The machine learning algorithms covered are Gaussian models, linear regression, logistic regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian mixture models, ensemble learning, directed graphical models, undirected graphical models, clustering, Gaussian processes, Dirichlet processes, and deep learning. We also discuss the open challenges in the field of hyperspectral image analysis and explore possible future directions.
author_names: Utsav B Gewali

Sildomar Takahashi Monteiro

Eli S Saber
title: Machine learning based hyperspectral image analysis: A survey
venue: ArXiv
year: 2018

abstract: Two dimensional (2D) materials and their heterostructures, with wafer scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine learning strategy in the optical identification of 2D nanostructures. The machine learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer scale device applications of 2D nanostructures.
author_names: Xiaoyang Lin

Zhizhong Si

Wenzhi Fu

Jianlei Yang

Side Guo

Yuan Cao

Jin Zhang

Xinhe Wang

Peng Liu

Kaili Jiang

Weisheng Zhao
title: Intelligent identification of two dimensional nanostructures by machine learning optical microscopy
venue: Nano Research
year: 2018

abstract: For centuries, the scientific discovery process has been based on systematic human observation and analysis of natural phenomena1. Today, however, automated instrumentation and large scale data acquisition are generating datasets of such large volume and complexity as to defy conventional scientific methodology. Radically different scientific approaches are needed, and machine learning (ML) shows great promise for research fields such as materials science2 5. Given the success of ML in the analysis of synthetic data representing electronic quantum matter (EQM)6 16, the next challenge is to apply this approach to experimental data for example, to the arrays of complex electronic structure images17 obtained from atomic scale visualization of EQM. Here we report the development and training of a suite of artificial neural networks (ANNs) designed to recognize different types of order hidden in such EQM image arrays. These ANNs are used to analyse an archive of experimentally derived EQM image arrays from carrier doped copper oxide Mott insulators. In these noisy and complex data, the ANNs discover the existence of a lattice commensurate, four unit cell periodic, translational symmetry breaking EQM state. Further, the ANNs determine that this state is unidirectional, revealing a coincident nematic EQM state. Strong coupling theories of electronic liquid crystals18,19 are consistent with these observations.A machine learning approach is used to train artificial neural networks to analyse experimental scanning tunnelling microscopy image arrays of quantum materials.
author_names: Yuhong Zhang

Andrej Mesaros

Kazuhiro Fujita

S D Edkins

M H Hamidian

K Ch'ng

Hiroshi Eisaki

S Uchida

J C Seamus Davis

Ehsan Khatami

Eun-Ah Kim
title: Machine learning in electronic quantum matter imaging experiments
venue: Nature
year: 2019

abstract: With the widespread uptake of two dimensional (2D) and three dimensional (3D) single molecule localization microscopy (SMLM) a large set of different data analysis packages have been developed to generate super resolution images. In a large community effort, we designed a competition to extensively characterize and rank the performance of 2D and 3D SMLM software packages. We generated realistic simulated datasets for popular imaging modalities 2D, astigmatic 3D, biplane 3D and double helix 3D and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D SMLM software and provides a holistic view of how the latest 2D and 3D SMLM packages perform in realistic conditions. This resource allows researchers to identify optimal analytical software for their experiments, allows 3D SMLM software developers to benchmark new software against the current state of the art, and provides insight into the current limits of the field.This study reports results from the second community wide single molecule localization microscopy software challenge, which tested over 30 software packages on realistic simulated data for multiple popular 3D image acquisition modes, as well as 2D localization microscopy.
author_names: Daniel Sage

Thanh-an Michel Pham

Hazen P Babcock

Tomas Lukes

Thomas Pengo

Jerry Chao

Ramraj Velmurugan

Alex Herbert

Anurag Agrawal

Silvia Colabrese

Ann Wheeler

Anna Archetti

Bernd Rieger

Raimund J Ober

Guy M Hagen

Jean-Baptiste Sibarita

Jonas Ries

Ricardo Henriques

Michael A Unser

Seamus J Holden
title: Super resolution fight club: Assessment of 2D 3D single molecule localization microscopy software
venue: Nature Methods
year: 2019

abstract: This article presents the results of research on a new method of spatial analysis of walls and buildings moisture. Due to the fact that destructive methods are not suitable for historical buildings of great architectural significance, a non destructive method based on electrical tomography has been adopted. A hybrid tomograph with special sensors was developed for the measurements. This device enables the acquisition of data, which are then reconstructed by appropriately developed methods enabling spatial analysis of wet buildings. Special electrodes that ensure good contact with the surface of porous building materials such as bricks and cement were introduced. During the research, a group of algorithms enabling supervised machine learning was analyzed. They have been used in the process of converting input electrical values into conductance depicted by the output image pixels. The conductance values of individual pixels of the output vector made it possible to obtain images of the interior of building walls as both flat intersections (2D) and spatial (3D) images. The presented group of algorithms has a high application value. The main advantages of the new methods are: high accuracy of imaging, low costs, high processing speed, ease of application to walls of various thickness and irregular surface. By comparing the results of tomographic reconstructions, the most efficient algorithms were identified.
author_names: Tomasz Rymarczyk

Grzegorz Klosowski

Edward Kozlowski
title: A Non Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings
venue: Sensors
year: 2018

abstract: Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE Net) to capture more high level information and preserve spatial information for 2D medical image segmentation. CE Net mainly contains three major components: a feature encoder module, a context extractor, and a feature decoder module. We use the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi kernel pooling block. We applied the proposed CE Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U Net method and other state of the art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation, and retinal optical coherence tomography layer segmentation.
author_names: Zaiwang Gu

Jun Cheng

H Fu

Kang Zhou

Huaying Hao

Yitian Zhao

Tianyang Zhang

Shenghua Gao

Jiang Liu
title: CE Net: Context Encoder Network for 2D Medical Image Segmentation
venue: IEEE Transactions on Medical Imaging
year: 2019