Ryosuke Hara | Searcharxiv

arXiv2026

Exploiting Skyrmions in Free-Space Optical Communication

In this paper, we propose a novel free-space optical (FSO) communication system utilizing optical skyrmions. We introduce a scheme referred to as skyrmion number modulation (SkM), which employs index modulation by encoding information onto the skyrmion number, a topological invariant preserved during free-space propagation. This topological nature offers the potential for inherent robustness against atmospheric turbulence-induced wavefront distortions, which limit the performance of conventional FSO systems. More specifically, we demonstrate that the fluctuation of the received skyrmion number is mitigated by a proposed intensity-based masking technique. Finally, our performance analysis based on a discrete memoryless channel framework confirms that the proposed system exhibits near-ideal robustness under weak turbulence and supports high-order modulation in moderate regimes.

eess.SP↗

arXiv2024

Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Camera

Artificial intelligence enables smarter control in building automation by its learning capability of users' preferences on facility control. Reinforcement learning (RL) was one of the approaches to this, but it has many challenges in real-world implementations. We propose a new architecture for logic-free building automation (LFBA) that leverages deep learning (DL) to control room facilities without predefined logic. Our approach differs from RL in that it uses wall switches as supervised signals and a ceiling camera to monitor the environment, allowing the DL model to learn users' preferred controls directly from the scenes and switch states. This LFBA system is tested by our testbed with various conditions and user activities. The results demonstrate the efficacy, achieving 93%-98% control accuracy with VGG, outperforming other DL models such as Vision Transformer and ResNet. This indicates that LFBA can achieve smarter and more user-friendly control by learning from the observable scenes and user interactions.

cs.CV↗