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Passive magnetic shielding in static gradient fields
(American Institute of Physics, 2014)The effect of passive magnetic shielding on dc magnetic field gradients imposed by both external and internal sources is studied for two idealized shield models: concentric spherical and infinitely-long cylindrical shells ... -
The metallic sphere in a uniform ac magnetic field: A simple and precise experiment for exploring eddy currents and non-destructive testing
(American Association of Physics Teachers, 2018-06)We describe a very simple experiment that utilizes standard laboratory equipment to measure the electromagnetic response of a metallic sphere exposed to a uniform ac magnetic field. Measurements were made for a variety of ... -
A fast MOSFET rf switch for low-field NMR and MRI
(Elsevier, 2019-11-02)TRansmit Array Spatial Encoding (TRASE) MRI uses trains of rf pulses alternately produced by distinct transmit coils. Commonly used coil switching involving PIN diodes is too slow for low- and ultra-lowfield MRI and would ... -
An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
(PLOS, 2020-12-17)A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models ... -
Magnetic diffusion, inductive shielding, and the Laplace transform
(American Institute of Physics (AIP) for the American Association of Physics Teachers, 2021-04-21)In the quasistatic limit, a time-varying magnetic field inside a conductor is governed by the diffusion equation. Despite the occurrence of this scenario in many popular physics demonstrations, the concept of magnetic ... -
Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification
(2022-05-11)The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop ... -
Inside out: transforming images of lab-grown plants for machine learning applications in agriculture
(Frontiers, 2023-07-06)Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical ... -
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
(Elsevier B.V., 2023-09-14)Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It has the ability to capture detailed information about the ... -
Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks
(Canadian Science Publishing, 2024-04-10)Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small-grain cereals worldwide. Developing FHB-resistant cultivars is critical but requires field and greenhouse disease assessment, ...
