HIBL Operator
Cross-source consensus on HIBL Operator from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Risks & contraindications
Highlighted claims
- HIBL is introduced as a realization operator that maps learned phase distributions into physically recordable interference patterns. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- The cosine fringe term carries learned phase information as an amplitude-modulated interference pattern. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- HIBL forms a holographic intensity by interfering an object beam carrying the learned phase with a known reference beam. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- HIBL is not treated as a lossless embedding because dc terms and fringe envelopes reduce reconstruction fidelity. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- HIBL makes physical embodiment part of the computational model rather than assuming learned phase profiles are automatically manufacturable. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification