arXiv (OAI Expanded)
open access
Random-mapped intensity optical neural network: all-optical two-layer computing for multimodal optical-field inference
Free-space optical neural networks offer distinct advantages for computational imaging and machine vision because they can compute directly on incident optical fields. However, conventional ONNs composed of cascaded linear optical components are bound to a general linear input-output relation with s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Random-mapped intensity optical neural network: all-optical two-layer computing for multimodal optical-field inference#924838
- Artificial Intelligence in a Photonic Temporal Processor#794251
- Artificial Intelligence in a Photonic Temporal Processor#790232
- Learning-Free Hierarchical Joint Estimation of AoA, Pointing Error, Receiver Jitter, and Turbulence in Multi-Aperture FSO Systems#1071379
- Machine-Learning-Accelerated Rational Design of Aggregation-Induced Emission Luminogens#471738
Record · ID 920604
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive