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Prediction of mechanical performance of Ti6Al4V cast alloy based on microCT-based load simulation

Journal of alloys and compounds, 2017-11, Vol.724, p.267-274

2017

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Predicting failure in additively manufactured parts using X-ray computed tomography and simulation

Procedia engineering, 2018, Vol.213, p.69-78

2018

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Characterization of the electrical behavior of a discontinuous hybrid yarn textile made of recycled carbon and PA6 fibers during Joule heating

Journal of thermoplastic composite materials, 2020-10, Vol.33 (10), p.1317-1335

2020

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Wafer-scale integration of analog neural networks

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008, Vol.10, p.431-438

2008

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A convolutional neural network tolerant of synaptic faults for low-power analog hardware

Lecture notes in computer science, 2006, p.122-132

2006

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Realizing biological spiking network models in a configurable wafer-scale hardware system

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008, Vol.10, p.969-976

2008

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Training convolutional networks of threshold neurons suited for low-power hardware implementation

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006, p.21-28

2006

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A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware

Artificial Neural Networks in Pattern Recognition, p.122-132

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Training fast mixed-signal neural networks for data classification

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004, Vol.4, p.2647-2652 vol.4

2004

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Prediction of mechanical performance of Ti6Al4V cast alloy based on microCT-based load simulation

Predicting failure in additively manufactured parts using X-ray computed tomography and simulation

Characterization of the electrical behavior of a discontinuous hybrid yarn textile made of recycled carbon and PA6 fibers during Joule heating

Wafer-scale integration of analog neural networks

A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems

A convolutional neural network tolerant of synaptic faults for low-power analog hardware

Realizing biological spiking network models in a configurable wafer-scale hardware system

Training convolutional networks of threshold neurons suited for low-power hardware implementation

A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware

Training fast mixed-signal neural networks for data classification

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