Search Results for author: David Hughes

Found 5 papers, 1 papers with code

MUSCAT: The Mexico-UK Sub-Millimetre Camera for AsTronomy

no code implementations23 Jul 2018 Thomas L. R. Brien, Peter A. R. Ade, Peter S. Barry, Edgar Castillo-Domìnguez, Daniel Ferrusca, Thomas Gascard, Victor Gómez, Peter C. Hargrave, Amber L. Hornsby, David Hughes, Enzo Pascale, Josie D. A. Parrianen, Abel Perez, Sam Rowe, Carole Tucker, Salvador Ventura González, Simon M. Doyle

The Mexico-UK Sub-millimetre Camera for AsTronomy (MUSCAT) is a large-format, millimetre-wave camera consisting of 1, 500 background-limited lumped-element kinetic inductance detectors (LEKIDs) scheduled for deployment on the Large Millimeter Telescope (Volc\'an Sierra Negra, Mexico) in 2018.

Instrumentation and Methods for Astrophysics

Mexico-UK Sub-millimeter Camera for AsTronomy

no code implementations27 Jun 2018 Edgar Castillo-Dominguez, Peter Ade, Peter Barry, Thom Brien, Simon Doyle, Daniel Ferrusca, Victor Gomez-Rivera, Peter Hargrave, Amber Hornsby, David Hughes, Phillip Mauskopf, Paul Moseley, Enzo Pascale, Abel Perez-Fajardo, Giampaolo Pisano, Samuel Rowe, Carole Tucker, Miguel Velazquez

MUSCAT is a large format mm-wave camera scheduled for installation on the Large Millimeter Telescope Alfonso Serrano (LMT) in 2018.

Instrumentation and Methods for Astrophysics

Assessing a mobile-based deep learning model for plant disease surveillance

no code implementations4 May 2018 Amanda Ramcharan, Peter McCloskey, Kelsee Baranowski, Neema Mbilinyi, Latifa Mrisho, Mathias Ndalahwa, James Legg, David Hughes

If the potential of smartphone CNNs are to be realized our data suggest it is crucial to consider tuning precision and recall performance in order to achieve the desired performance in real world settings.

object-detection Object Detection

Using Transfer Learning for Image-Based Cassava Disease Detection

no code implementations19 Jun 2017 Amanda Ramcharan, Kelsee Baranowski, Peter McCloskey, Babuali Ahmed, James Legg, David Hughes

Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.

Transfer Learning

Using Deep Learning for Image-Based Plant Disease Detection

5 code implementations11 Apr 2016 Sharada Prasanna Mohanty, David Hughes, Marcel Salathe

When testing the model on a set of images collected from trusted online sources - i. e. taken under conditions different from the images used for training - the model still achieves an accuracy of 31. 4%.

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