Takuya Sasatani

Takuya Sasatani, Alanson P. Sample, Yoshihiro Kawahara

Magnetoquasistatic wireless power transfer can deliver substantial power to mobile devices over near-field links. Room-scale implementations, such as quasistatic cavity resonators, extend this capability over large enclosed volumes, but their efficiency drops sharply for centimeter-scale or misoriented receivers because the magnetic field is spatially broad and weakly coupled to small coils. Here, we introduce hierarchical resonators that act as selectively activated relays within a room-scale quasistatic cavity resonator, capturing the ambient magnetic field and re-emitting it to concentrate flux at a target receiver. This architecture reconfigures the wireless power environment on demand and enables localized energy delivery to miniature devices. Experimentally, the hierarchical link improves power transfer efficiency by more than two orders of magnitude relative to direct room-scale transfer and delivers up to 500 mW of DC power to a 15 mm receiver. We further demonstrate selective multi-relay operation and field reorientation for furniture-embedded charging scenarios. These results establish a scalable route to reconfigurable wireless power delivery for miniature and batteryless devices in room-scale environments.

Takuya Sasatani, Yoshihiro Kawahara

Traditional wireless power transfer (WPT) systems are largely limited to 1-D charging pads or 2-D charging surfaces and therefore do not support a truly ubiquitous device-powering experience. Although room-scale WPT based on multimode quasistatic cavity resonance (QSCR) has demonstrated full-volume coverage by leveraging multiple resonant modes, existing high-coverage implementations require obstructive internal conductive structures, such as a central pole. This letter presents a new structure, termed the patched-wall QSCR, that eliminates such internal obstructions while preserving full-volume coverage. By using conductive wall segments interconnected by capacitors, the proposed structure supports two complementary resonant modes that cover both the peripheral and central regions without obstructions within the charging volume. Electromagnetic simulations show that, by selectively exciting these two resonant modes, the proposed structure achieves a minimum power-transfer efficiency of 48.1% across the evaluated 54 m^3 charging volume while preserving an unobstructed interior space.

Damyon Kim, Yuichi Honjo, Tatsuya Iizuka et al.

Air-dispersed sensor networks deployed from aerial robotic systems (e.g., UAVs) provide a low-cost approach to wide-area environmental monitoring. However, existing methods often rely on active actuators for mid-air shape or trajectory control, increasing both power consumption and system cost. Here, we introduce a passive elastic-folding hinge mechanism that transforms sensors from a flat, stackable form into a three-dimensional structure upon release. Hinges are fabricated by laminating commercial sheet materials with rigid printed circuit boards (PCBs) and programming fold angles through a single oven-heating step, enabling scalable production without specialized equipment. Our geometric model links laminate geometry, hinge mechanics, and resulting fold angle, providing a predictive design methodology for target configurations. Laboratory tests confirmed fold angles between 10 degrees and 100 degrees, with a standard deviation of 4 degrees and high repeatability. Field trials further demonstrated reliable data collection and LoRa transmission during dispersion, while the Horizontal Wind Model (HWM)-based trajectory simulations indicated strong potential for wide-area sensing exceeding 10 km.

Yuichi Honjo, Cedric Caremel, Ken Takaki et al.

Wireless power transfer (WPT) with coupled resonators offers a promising solution for the seamless powering of electronic devices. Interactive design approaches that visualize the magnetic field and power transfer efficiency based on system geometry adjustments can facilitate the understanding and exploration of the behavior of these systems for dynamic applications. However, typical electromagnetic field simulation methods, such as the Method of Moments (MoM), require significant computational resources, limiting the rate at which computation can be performed for acceptable interactivity. Furthermore, the system's sensitivity to positional and geometrical changes necessitates a large number of simulations, and structures such as ferromagnetic shields further complicate these simulations. Here, we introduce a machine learning approach using Gaussian Process Regression (GPR), demonstrating for the first time the rapid estimation of the entire magnetic field and power transfer efficiency for near-field coupled systems. To achieve quick and accurate estimation, we develop 3D adaptive grid systems and an active learning strategy to effectively capture the nonlinear interactions between complex system geometries and magnetic fields. By training a regression model, our approach achieves magnetic field computation with sub-second latency and with an average error of less than 6% when validated against independent electromagnetic simulation results.