arXiv:2606.01421v1 Announce Type: new Abstract: We show that an array of scatterers which has been designed to have latent ("hidden") symmetries can be used as a sensor. We use the capacitance matrix as a canonical model for three-dimensional hybridisation and study how the introduction of an "intruder'' scatterer breaks the latent symmetries. By analysing the degree to which each symmetry is broken, we identify the radius of the intruder and localize its position. This can be achieved using a dictionary-based approach, however Bayesian inference or an artificial neural network (multi-layer pe
The paper, published in 2026, details a novel sensing method leveraging AI, indicating ongoing advancements in AI-driven physical detection and measurement technologies.
This research introduces a new paradigm for high-precision, non-intrusive sensing using AI and latent symmetries, which could significantly enhance capabilities in various fields from materials science to security.
The ability to accurately localize and identify targets at a refined level using AI analysis of symmetry breaking offers superior resolution and methodology compared to traditional sensing.
- · AI/ML developers
- · Sensor manufacturers
- · Robotics industry
- · Defence & security contractors
- · Legacy sensing technologies
- · Industries relying on less precise detection
Improved sensing capabilities will enable more precise control and understanding in automated systems and critical infrastructure.
The integration of such detailed sensing into AI agents could significantly enhance their ability to interact with and understand physical environments.
Advanced pervasive sensing based on these principles could lead to new forms of environmental monitoring and even 'smart dust' applications, raising privacy and ethical concerns.
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