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Navid Borhani

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Courses taught by this person (1)

MICRO-723: Deep Learning for Optical Imaging

This course will focus on the practical implementation of artificial neural networks (ANN) using the open-source TensorFlow machine learning library developed by Google for Python. ...

Related publications (32)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

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Courses taught by this person (1)

MICRO-723: Deep Learning for Optical Imaging

This course will focus on the practical implementation of artificial neural networks (ANN) using the open-source TensorFlow machine learning library developed by Google for Python. ...

Related publications (32)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

3D ALE Finite-Element Method for Two-Phase Flows With Phase Change

John Richard Thome, Navid Borhani, Gustavo Rabello Dos Anjos

We seek to study numerically two-phase flow phenomena with phase change through the finite-element method (FEM) and the arbitrary Lagrangian-Eulerian (ALE) framework. This method is based on the so-called one-fluid formulation; thus, only one set of equati ...

Taylor & Francis2014

Design and packaging of a highly integrated microreactor system for high-temperature on-board hydrogen production

John Richard Thome, Paul Muralt, Thomas Maeder, Navid Borhani, Bo Jiang

Highlights • A “plug-and-play” microreaction system for high-temperature reactions (>500 °C). • Efficient thermal decoupling for using standard electrical/fluidic connections. • Automatic and accurate temperature control for calorimetric measurements. • A ...

Elsevier2015

Imaging through multimode fibers using deep learning: The effects of intensity versus holographic recording of the speckle pattern

Demetri Psaltis, Christophe Moser, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin, Navid Borhani, Georgia Konstantinou

Information transmission through multimode fibers (MMFs) has been a topic of great interest for many years. Deep learning algorithms have been applied successfully to MMFs in particular to fiber endoscopy. In this work, we show how Deep Neural Networks (DN ...

2019