Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data
2026-04-22T08:51:42Z•3dc6b296f75e2527236796bb75a74ced034f627d8475c3efadd182cd90c05693
CRTISACMIMORISSentinel-2TomoSARUAV-communicationsalgorithmic-complexity-attackarxivcovariance-completiondeep-unfoldingearth-observationhybrid-beamformingneuromorphic-computingonboard-processingreconfigurable-intelligent-surfaceremote-sensingsignal-processingsparse-FFTthermal-anomaly-detection
What happened
Multiple new arXiv submissions (22 Apr 2026) on signal processing, remote sensing, and communications research. Key topics include: a Physics-Aware Neuromorphic Network (PANN) for real-time onboard thermal-anomaly detection on Sentinel-2 raw (L0) and L1C data with low latency and small memory footprint; a structured covariance completion approach (RR2D) enabling hybrid sample-matrix-inversion (HSMI) for reduced-hardware hybrid beamforming; tensor-decomposition methods for multi-target estimation with beyond-diagonal RIS in bistatic MIMO sensing; an analysis of CRT-based sparse FFT algorithms,:
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- 3dc6b296f75e2527236796bb75a74ced034f627d8475c3efadd182cd90c05693
- Enrichment time
- 2026-04-22T08:51:42Z
- AI-assisted enrichment
- Yes
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