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A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

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A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification. / Aldabashi, Nawaf; Morton Williams, Samuel; Eltokhy, Amira et al.
Yn: IEEE Transactions on Microwave Theory and Techniques, Cyfrol 71, Rhif 9, 09.2023, t. 4098-4108.

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

HarvardHarvard

Aldabashi, N, Morton Williams, S, Eltokhy, A, Palmer, E, Cross, P & Palego, C 2023, 'A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification', IEEE Transactions on Microwave Theory and Techniques, cyfrol. 71, rhif 9, tt. 4098-4108. https://doi.org/10.1109/TMTT.2023.3248785

APA

Aldabashi, N., Morton Williams, S., Eltokhy, A., Palmer, E., Cross, P., & Palego, C. (2023). A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification. IEEE Transactions on Microwave Theory and Techniques, 71(9), 4098-4108. https://doi.org/10.1109/TMTT.2023.3248785

CBE

Aldabashi N, Morton Williams S, Eltokhy A, Palmer E, Cross P, Palego C. 2023. A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification. IEEE Transactions on Microwave Theory and Techniques. 71(9):4098-4108. https://doi.org/10.1109/TMTT.2023.3248785

MLA

Aldabashi, Nawaf et al. "A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification". IEEE Transactions on Microwave Theory and Techniques. 2023, 71(9). 4098-4108. https://doi.org/10.1109/TMTT.2023.3248785

VancouverVancouver

Aldabashi N, Morton Williams S, Eltokhy A, Palmer E, Cross P, Palego C. A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification. IEEE Transactions on Microwave Theory and Techniques. 2023 Medi;71(9):4098-4108. Epub 2023 Maw 6. doi: 10.1109/TMTT.2023.3248785

Author

Aldabashi, Nawaf ; Morton Williams, Samuel ; Eltokhy, Amira et al. / A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification. Yn: IEEE Transactions on Microwave Theory and Techniques. 2023 ; Cyfrol 71, Rhif 9. tt. 4098-4108.

RIS

TY - JOUR

T1 - A Machine Learning Integrated 5.8-GHz Continuous-Wave Radar for Honeybee Monitoring and Behavior Classification

AU - Aldabashi, Nawaf

AU - Morton Williams, Samuel

AU - Eltokhy, Amira

AU - Palmer, Edward

AU - Cross, Paul

AU - Palego, Cristiano

PY - 2023/9

Y1 - 2023/9

N2 - A 5.8-GHz continuous-wave (CW) radar was devel- oped and integrated on a compact printed circuit board (PCB) for near-hive monitoring of honeybees. It supported noninvasive detection of free-flying honeybees at 2 m and micro-Doppler extraction of bee wingbeat signatures at a closer range using both a double-balanced mixer and an in-phase/quadrature (IQ) mixer. An original technique combining full-wave simulations and Doppler-radar monitoring of pendulum motion was used for: radar calibration through spherical targets of different material and size; precise extraction of bee radar cross section (RCS) at 5.8 GHz; and estimation of detection range enhancement through partial silver coating of targets. Finally, the CW radar was integrated with machine learning (ML) to allow automated classification of incoming, outgoing, and hovering honeybees. Different ML approaches were tested, where the highest accuracy of 93.37% was found in ternary classification via support vector machine acting on line spectral frequencies.

AB - A 5.8-GHz continuous-wave (CW) radar was devel- oped and integrated on a compact printed circuit board (PCB) for near-hive monitoring of honeybees. It supported noninvasive detection of free-flying honeybees at 2 m and micro-Doppler extraction of bee wingbeat signatures at a closer range using both a double-balanced mixer and an in-phase/quadrature (IQ) mixer. An original technique combining full-wave simulations and Doppler-radar monitoring of pendulum motion was used for: radar calibration through spherical targets of different material and size; precise extraction of bee radar cross section (RCS) at 5.8 GHz; and estimation of detection range enhancement through partial silver coating of targets. Finally, the CW radar was integrated with machine learning (ML) to allow automated classification of incoming, outgoing, and hovering honeybees. Different ML approaches were tested, where the highest accuracy of 93.37% was found in ternary classification via support vector machine acting on line spectral frequencies.

KW - Continuous-wave (CW) radar, Doppler radar, machine learning (ML), micro-Doppler.

U2 - 10.1109/TMTT.2023.3248785

DO - 10.1109/TMTT.2023.3248785

M3 - Article

VL - 71

SP - 4098

EP - 4108

JO - IEEE Transactions on Microwave Theory and Techniques

JF - IEEE Transactions on Microwave Theory and Techniques

SN - 0018-9480

IS - 9

ER -