edge_quantum_noise_filter.py — Causal Real-Time OPM Denoising with Gradient Estimation
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| Natura: | Recurso digital |
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2025
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| _version_ | 1866901695072567296 |
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| author | B, Britt |
| author_facet | B, Britt |
| contents | <pre><code>edge_quantum_noise_filter.py v1.0 — Causal Real-Time OPM Denoising with Gradient Estimation Features • Zero extra setup — single file (numpy + matplotlib + scipy) • Fully causal/online pipeline for hard real-time edge use • Spatial common-mode rejection across array • Stateful recursive IIR notch (lfilter, no lookahead) • Causal Savitzky-Golay via rolling buffer • New: Inter-sensor ∇B gradient computation (MHD mode localization proxy) • Synthetic multi-sensor data with realistic noise • Five-panel visualization + SNR improvement reporting Dependencies • Requires numpy>=1.21 • Requires matplotlib>=3.5 — only for --plot • Requires scipy>=1.8 Intended for fusion magnetics teams deploying OPM arrays for low-latency, high-fidelity magnetic feedback in next-gen tokamaks requiring zero-lookahead processing. Real usage: python edge_quantum_noise_filter.py python edge_quantum_noise_filter.py --duration 15 --sensors 12 --power-line 60 python edge_quantum_noise_filter.py --no-plot # headless mode Made by Britt (2025) — MIT License</code></pre> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18100507 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | edge_quantum_noise_filter.py — Causal Real-Time OPM Denoising with Gradient Estimation B, Britt optically pumped magnetometer OPM denoising real-time magnetic feedback fusion plasma control common-mode rejection power-line notch causal Savitzky-Golay edge signal processing low-latency filtering quantum sensor noise reduction tokamak diagnostics magnetic gradient estimation OPM real-time denoising causal filtering fusion magnetic diagnostics recursive IIR notch Savitzky-Golay online edge quantum sensing low-latency feedback tokamak control python cli tool single-file script <pre><code>edge_quantum_noise_filter.py v1.0 — Causal Real-Time OPM Denoising with Gradient Estimation Features • Zero extra setup — single file (numpy + matplotlib + scipy) • Fully causal/online pipeline for hard real-time edge use • Spatial common-mode rejection across array • Stateful recursive IIR notch (lfilter, no lookahead) • Causal Savitzky-Golay via rolling buffer • New: Inter-sensor ∇B gradient computation (MHD mode localization proxy) • Synthetic multi-sensor data with realistic noise • Five-panel visualization + SNR improvement reporting Dependencies • Requires numpy>=1.21 • Requires matplotlib>=3.5 — only for --plot • Requires scipy>=1.8 Intended for fusion magnetics teams deploying OPM arrays for low-latency, high-fidelity magnetic feedback in next-gen tokamaks requiring zero-lookahead processing. Real usage: python edge_quantum_noise_filter.py python edge_quantum_noise_filter.py --duration 15 --sensors 12 --power-line 60 python edge_quantum_noise_filter.py --no-plot # headless mode Made by Britt (2025) — MIT License</code></pre> |
| title | edge_quantum_noise_filter.py — Causal Real-Time OPM Denoising with Gradient Estimation |
| topic | optically pumped magnetometer OPM denoising real-time magnetic feedback fusion plasma control common-mode rejection power-line notch causal Savitzky-Golay edge signal processing low-latency filtering quantum sensor noise reduction tokamak diagnostics magnetic gradient estimation OPM real-time denoising causal filtering fusion magnetic diagnostics recursive IIR notch Savitzky-Golay online edge quantum sensing low-latency feedback tokamak control python cli tool single-file script |
| url | https://doi.org/10.5281/zenodo.18100507 |