Suppressing Electron Exposure Artifacts: An Electron Scanning Paradigm with Bayesian Machine Learning.

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Suppressing Electron Exposure Artifacts: An Electron Scanning Paradigm with Bayesian Machine Learning.

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http://www.wikidata.org/entity/Q36085313

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2016 nî lūn-bûn

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2016年の論文

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2016年論文

@yue

2016年論文

@zh-hant

2016年論文

@zh-hk

2016年論文

@zh-mo

2016年論文

@zh-tw

2016年论文

@wuu

2016年论文

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2016年论文

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Suppressing Electron Exposure ...... ith Bayesian Machine Learning.

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Suppressing Electron Exposure ...... ith Bayesian Machine Learning.

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label

Suppressing Electron Exposure ...... ith Bayesian Machine Learning.

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Suppressing Electron Exposure ...... ith Bayesian Machine Learning.

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Suppressing Electron Exposure ...... ith Bayesian Machine Learning.

@ast

Suppressing Electron Exposure ...... ith Bayesian Machine Learning.

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P1476

Suppressing Electron Exposure ...... ith Bayesian Machine Learning.

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P2093

Benjamin D Myers

http://www.w3.org/2001/XMLSchema#string

Eric Roth

http://www.w3.org/2001/XMLSchema#string

Karl Hujsak

http://www.w3.org/2001/XMLSchema#string

Vinayak P Dravid

http://www.w3.org/2001/XMLSchema#string

Yue Li

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P2860

Using molecular dynamics to quantify the electrical double layer and examine the potential for its direct observation in the in-situ TEM

Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images

Electron microscopy of whole cells in liquid with nanometer resolution

Radiation damage in the TEM and SEM.

Element-selective imaging of atomic columns in a crystal using STEM and EELS.

The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images.

Engineering controllable anisotropy in electrospun biodegradable nanofibrous scaffolds for musculoskeletal tissue engineering.

Electron microscopy of specimens in liquid.

Experimental procedures to mitigate electron beam induced artifacts during in situ fluid imaging of nanomaterials.

Radiation damage in the high resolution electron microscopy of biological materials: a review.

P304

778-788

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P31

scholarly article

P356

10.1017/S1431927616011417

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P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

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2016-07-26T00:00:00Z

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P698

27456711

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P921

machine learning