Computational Materials for Photonic Stochastic Sensor Frontend Validation

Supplementary materials support a modeling study for a universal stochastic edge frontend designed for heterogeneous sensors and event-level inference in photonic systems. The package contains five computational stages with Jupyter notebooks, Python scripts, and generated results. It was authored by Oleg Angelsky and published in March 2026.

Use Cases

• Validate heterogeneous sensor-to-stochastic mapping performance using the core frontend models in S1.
• Compare equal, expert, reliability-aware, and data-driven weighting strategies for sensor fusion as detailed in S2.
• Analyze static synchronization and decorrelation with autonomous, shared, and grouped stochastic pulse sequences from S3.
• Study time-varying synchronization effects, including fixed lags and random jitter, using the slot-wise event inference models in S3b.
• Test model robustness and scaling under noise, dropout, missing slots, weight perturbation, and increasing channel count as performed in S4.

Strengths

• Five structured computational stages provide a complete replication pipeline for the published study.
• Includes both interactive Jupyter notebooks and executable Python scripts for each analysis stage.
• Results folders contain generated data, figures, and JSON summaries for each validation step.

Limitations

• The 14.6 MB size indicates a small-scale, simulation-based dataset without large raw sensor data.
• No explicit row or column counts are provided for the underlying generated data tables.
• The dataset's utility is confined to the specific photonic stochastic system model described in the paper.

Provenance

Source

figshare, as supplementary material for the associated research paper.

Collection Method

Computationally generated materials from a modeling study.

Time Range

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Freshness

Last updated March 2026.

Geography

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