Conformal Active Beamforming Metasurfaces
Abstract
A traditional sensor is designed to be sensitive and respond only to a single target input, necessitating the use of multiple sensors to sense multiple target inputs. From a traditional sensor viewpoint, a sensor sensitive to various stimuli simultaneously is considered useless. However, by changing this conception, we propose the development of a machine-learned multi-modal sensor, where a single sensor can extract multiple signals corresponding to various stimuli without the need for multiple sensors. This proposal encompasses an integrated bottom-up research approach covering materials, fabrications, devices, and algorithms for development to real-world applications. By elucidating the inherent characteristics of the sensor and their correlation with responses to multiple stimuli, we aim for the single multi-modal sensor to achieve a data-rich signal output. This approach addresses the issue of increased systemic complexity usually associated with enhanced functionality in conventional sensor systems. Additionally, it includes an analysis of sensor utility from a machine learning perspective, presenting key criteria for future sensor development. This work will fundamentally alter the concept of traditional sensor research.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 05, 2025
- Source ID
- FA23862414090
Entities
People
- Kamran Ghorbani
Organizations
- Air Force Office of Scientific Research
- RMIT University
- United States Air Force