1,721,338 research outputs found
Dataset for: Semantic segmentation of pollen grain images generated from scattering patterns via deep learning
No full textThis dataset supports the publication: Grant-Jacob, James A., Praeger, Matthew, Eason, Robert W. and Ben Mills (2021). Semantic segmentation of pollen grain images generated from scattering patterns via deep learning. IOP Journal of Physics Communications
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Dataset for Real-time particle pollution sensing using machine learning
No full textDataset supports:
Grant-Jacob, James A et al (2018). Real-time particle pollution sensing using machine learning. Optics Express</span
A 11.5 W Yb:YAG planar waveguide fabricated via pulsed laser deposition
No full textDataset for the figures in Grant-Jacob, James A, Beecher, Stephen J, Parsonage, Tina L, Hua, Ping, Mackenzie, Jacob I, Shepherd, David P and Eason, Robert W (2015) An 11.5 W Yb:YAG planar waveguide laser fabricated via pulsed laser deposition. Optical Materials Express
Data collection method: Optical spectrum analyzer (Ando AQ6317).</span
Noss, (Jacob) James, [No Service Number]
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/408102Surname: NOSS. Given Name(s) or Initials: (JACOB) JAMES. Military Service Number or Last Known Location: [No Registration Number]. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 52951.237263
Item: [2016.0049.40377] "Noss, (Jacob) James, [No Service Number]
Dataset: Fibre-optic based particle sensing via deep learning
No full textDataset to support the publication: Grant-Jacob, James et al."Fibre-optic based particle sensing via deep learning". Journal of Physics: Photonics. 2019.</span
Particle pollution detection using artificial intelligence
No full textTowards accurate, real-time, low-cost sensing
Pollen sensing using AI
No full textHay fever affects around 15% population, with different species affecting people in different ways. Therefore, we want to be able to monitor pollen in real-time. One way to achieve this is to use lensless sensing. We use neural networks to categorise pollen grains from their scattering patterns and use neural networks to transform the scattering patterns into images of the pollen grains. We also use neural networks to transform images of dehydrated pollen grains into images of hydrated pollen grains to aid in understanding their initial state and potentially their environment. We also explore pollen grain morphology using latent space to try to understand their evolution.<br/
Deep learning-based pollen imaging using a raspberry Pi and LED
No full textDeveloping an affordable, compact imaging sensor could significantly enhance global airborne pollen monitoring and alleviate hay fever symptoms. By using a white light LED to illuminate pollen grains and capturing their scattering patterns with a Raspberry Pi camera, we can transform these patterns into detailed images through deep learning techniques. Our method successfully generates images of pollen from plant species not included in the neural network's training data. This technique could also be applied to imaging fungal spores and airborne particulates that contribute to air pollution, offering valuable insights in environmental science, health science, and agriculture. Furthermore, it could help develop more efficient air quality monitoring systems and support research on the effects of airborne particles on human health and crop productivity. By providing detailed images and data on various airborne particulates, this approach can enhance our understanding of how these particles interact with the environment and affect respiratory health, allergies, and diseases. Additionally, it can contribute to agricultural research by examining the impact of pollen and other particulates on crop growth and yield, ultimately aiding in the development of strategies to improve air quality and agricultural productivity
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