arXiv subjects

Arno Thielens

Publications and source records attributed to Arno Thielens.

2 recordsLinked to original sources

arXiv2026

Microwave-to-optical transduction using magnon-exciton coupling in a layered antiferromagnet

Coherent interfaces between microwave-frequency quantum systems and low-loss optical links are essential for quantum networks. However, existing microwave-optical transducers often trade conversion efficiency against added noise, bandwidth, and device integrability. Here, we demonstrate coherent microwave-to-optical transduction based on magnon-exciton coupling in the layered antiferromagnet CrSBr. Driving the antiferromagnetic resonance with microwave signals imprints coherent modulation on a reflected optical probe, generating optical sidebands that are resonantly enhanced near excitonic transitions. While prior magnon-based approaches to microwave-to-optical transduction have typically relied on intrinsically weak off-resonant magneto-optical effects (e.g., Faraday rotation), our scheme exploits strong light-matter interactions at exciton resonances. Even in a bulk crystal without cavity enhancement, we observe coherent conversion over an intrinsically broadband window of ~ 300 MHz. We further show that multiple exciton-polariton resonances inherit the magnon-coupled response, suggesting a route to broaden the usable optical detuning range and to mitigate optical dissipation. Our results establish magnon-coupled excitons in layered magnets as a scalable platform for broadband microwave-optical interfaces, with pathways to higher cooperativity via reduced magnetic volume and cavity integration.

cond-mat.mtrl-sci↗

arXiv2020

Adaptive Body Area Networks Using Kinematics and Biosignals

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.

cs.NI↗