Microfabricated 18 μm YIG waveguides with MSSW dispersion engineering deliver tunable group delays of 3.3-42.8 ns from 6-19.6 GHz at 2.5-10.1 dB insertion loss and 24-39 dB isolation, outperforming fixed-frequency acoustic lines.
8-25 GHz Broadband Experimental Quality Factor Extraction of 30% ScAlN with Acoustic Delay Lines
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A thick top-layer acoustic platform cuts temperature rise by 70% and raises power density threshold over 10x compared to thin-film SAW at the same wavelength.
A photoacoustic beacon in the needle bevel allows real-time 3D ultrasonic tracking with sub-2 mm accuracy in water and tissue phantoms, cutting biopsy failure rates by 35% in a clinician usability study.
NL-MambaXCT combines masked image modeling pretraining with nested learning in a hybrid RegNet-Mamba encoder to reach 96.91% accuracy on Nomex honeycomb XCT defect classification using limited labels.
Cross-comparison of five k-Wave skull models on 19 skull regions at three frequencies finds mean peak-pressure errors of 20-31% and no consistently superior model.
citing papers explorer
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Dispersion Engineered Frequency Tunable Delay Platform based on Magnetostatic Surface Waves
Microfabricated 18 μm YIG waveguides with MSSW dispersion engineering deliver tunable group delays of 3.3-42.8 ns from 6-19.6 GHz at 2.5-10.1 dB insertion loss and 24-39 dB isolation, outperforming fixed-frequency acoustic lines.
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Suppressing Acoustomigration and Temperature Rise for High-power Robust Acoustics
A thick top-layer acoustic platform cuts temperature rise by 70% and raises power density threshold over 10x compared to thin-film SAW at the same wavelength.
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Real-time 3D Ultrasonic Needle Tracking with a Photoacoustic Beacon
A photoacoustic beacon in the needle bevel allows real-time 3D ultrasonic tracking with sub-2 mm accuracy in water and tissue phantoms, cutting biopsy failure rates by 35% in a clinician usability study.
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NL-MambaXCT: Self-Supervised Nested-Learning Mamba for Nomex Honeycomb X-ray CT Defect Classification
NL-MambaXCT combines masked image modeling pretraining with nested learning in a hybrid RegNet-Mamba encoder to reach 96.91% accuracy on Nomex honeycomb XCT defect classification using limited labels.