A Multiarray Electromagnetic Instrument for Shallow Surface Real-Time Detection

Guo, Qun; Zhou, Sirui; Zhang, Xinhao; Sun, Chaoli; Li, Gang

doi:10.1109/tim.2021.3118097

Cited by 10 publications

(1 citation statement)

References 24 publications

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“…The over-long offset (>9 m) between the TX loop and RX coil makes it difficult to operate the all-terrain vehicle and obtain valid data consistently. In frequency-domain detection, heavy or large-volume mobile scanning devices also cause implementation challenges, which greatly reduce detection efficiency and increase the cost [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Detection System with Magnetic Dipole Source for Near-Surface Detection

Liao,

Xu,

Liu

et al. 2023

Sensors

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This paper proposes a nondestructive, separate transmitter-receiver (TX-RX) electromagnetic measurement system for near-surface detection. Different from the traditional dual-coil integrated design, the proposed transient electromagnetic (TEM) system performs shallow subsurface detection using independent TX coil and movable RX coils. This configuration requires a large primary field so that the far-away secondary field is able to generate reliably induced voltages. To achieve this goal, a bipolar current-pulsed power supply (BCPPS) with a late resonant charging strategy is designed to produce a sufficiently large magnetic moment for the exciting coil with low source interference. The magnetic dipole source (MDS) with a large proportion of weight is separated from the field observation device and does not need to be dragged or transported during the detection process. This setup lowers the weight of the scanning device to 3 kg and greatly improves the measurement efficiency. The results of the laboratory test verify the effectiveness of the separate MDS and RX module system. Field experimental detection further demonstrates that the proposed system can realize highly efficient and shallow surface detection within a 200 m range of the MDS device.

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Section: Introductionmentioning

confidence: 99%

Electromagnetic Detection System with Magnetic Dipole Source for Near-Surface Detection

Liao,

Xu,

Liu

et al. 2023

Sensors

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A retinex based non-local total generalized variation framework for OCT image restoration

Smitha

Febin

Jidesh

2022

Biomedical Signal Processing and Control

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Updates in deep learning research in ophthalmology

et al. 2021

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Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

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A Multiarray Electromagnetic Instrument for Shallow Surface Real-Time Detection

Cited by 10 publications

References 24 publications

Electromagnetic Detection System with Magnetic Dipole Source for Near-Surface Detection

Electromagnetic Detection System with Magnetic Dipole Source for Near-Surface Detection

A retinex based non-local total generalized variation framework for OCT image restoration

Updates in deep learning research in ophthalmology

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