Deep Learning Enabled Design of Complex Transmission Matrices for Universal Optical Components

In an optical measurement system using an interferometer, a phase extracting technique from interferogram is the key issue. When the object is varying in time, the Fourier-transform method is commonly used since this method can extract a phase image from a single interferogram. However, there is a limitation, that an interferogram including closed-fringes cannot be applied. The closed-fringes appear when intervals of the background fringes are long. In some experimental setups, which need to change the alignments of optical components such as a 3-D optical tomographic system, the interval of the fringes cannot be controlled. To extract the phase from the interferogram including the closed-fringes we propose the use of deep learning. A large amount of the pairs of the interferograms and phase-shift images are prepared, and the trained network, the input for which is an interferogram and the output a corresponding phase-shift image, is obtained using supervised learning. From comparisons of the extracted phase, we can demonstrate that the accuracy of the trained network is superior to that of the Fourier-transform method. Furthermore, the trained network can be applicable to the interferogram including the closed-fringes, which is impossible with the Fourier transform method.

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Deep learning the design of optical components

Emerging Topics in Artificial Intelligence 2020 ◽

10.1117/12.2568664 ◽

2020 ◽

Author(s):

Joeri Lenaerts ◽

Hannah Pinson ◽

Vincent Ginis

Keyword(s):

Deep Learning ◽

Optical Components

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Preparation of Thin Cadmium Telluride Samples for Electron Microscope Studies

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100052754 ◽

1974 ◽

Vol 32 ◽

pp. 548-549

Author(s):

T. J. Magee ◽

J. Peng ◽

J. Bean

Keyword(s):

Electron Microscope ◽

Sulfuric Acid ◽

Solid State ◽

Sample Preparation ◽

Cadmium Telluride ◽

Potassium Dichromate ◽

Optical Components ◽

The Past ◽

Solid State Devices

Cadmium telluride has become increasingly important in a number of technological applications, particularly in the area of laser-optical components and solid state devices, Microstructural characterizations of the material have in the past been somewhat limited because of the lack of suitable sample preparation and thinning techniques. Utilizing a modified jet thinning apparatus and a potassium dichromate-sulfuric acid thinning solution, a procedure has now been developed for obtaining thin contamination-free samples for TEM examination.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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