arXiv Machine Learning
Learned Compression of SAR Phase-History Data: A Rate-Honest Feasibility Study on GOTCHA
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arXiv Machine Learning
Aug 13
Hardware-Aware Deployment of Joint SAR Compression and Despeckling on FPGA
arXiv:2608. 11271v1 Announce Type: cross Abstract: Next-generation Synthetic Aperture Radar (SAR) missions will generate data far faster than they can downlink, making onboard data reduction essential for near-real-time Earth observation.
arXiv Computer Vision
Sep 1
Rad-R: A Raw-ADC Radar Dataset and Capture-Invariant SSM for Hardware-Fault Diagnosis
arXiv:2608.30896v1 Announce Type: new Abstract: Automotive mmWave radar can develop vibration, antenna misalignment, radome blockage, and receive-channel degradation that corrupt the signal before pe...
arXiv Machine Learning
Sep 1
Data Diversity, Not Frequency Invariance: A Controlled and Self-Audited Study of Compression-Robust Deepfake Detection
The study challenges the prevailing belief that frequency-based features and compression-invariant learning are essential for robust deepfake detection. Using a controlled, pre‑registered protocol, a simple EfficientNet‑B0 trained on diverse multi‑quality data outperformed the more complex CAFRL model across all compression levels, with a 3.66 AUC point advantage at CRF 40. After identifying and correcting four experimental defects, the authors found that frequency features added no marginal benefit, while data diversity—particularly real constant‑rate‑factor variants—proved to be the key factor for robustness against H.264 re‑encoding.
arXiv AI
4d ago
Quantization Error Is Spectrally Flat: A Single Random Probe Is a Calibrated, Data-Free Sensitivity Estimator, with Application to Budget-Targeted Mixed-Precision Quantization
arXiv:2609.33923v2 Announce Type: replace-cross Abstract: A single random Gaussian probe gives an unbiased estimate of the squared Frobenius norm of a layer's quantization error. The estimator is wel...
arXiv Machine Learning
Sep 22
On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurements
This study explores whether spatial structure can be learned directly from pre-beamforming per-antenna range-Doppler (RD) radar measurements, bypassing traditional beamforming steps. Using a 6‑TX × 8‑RX automotive radar with a chirp‑sequence FMCW transmit scheme, the authors train a dual‑chirp shared‑weight encoder on raw RD tensors and evaluate spatial recoverability via bird’s‑eye‑view occupancy maps. Experiments across different transmit configurations (A‑only, B‑only, A+B) and receive apertures demonstrate that meaningful spatial structure is indeed recoverable through learned spatial mixing, without hand‑crafted signal‑processing stages.
arXiv Machine Learning
Jul 7
Unsupervised Detection of Underground Tunnels in Ground-Penetrating Radar Using Depth-Restricted Reconstruction Scoring
arXiv:2607. 04882v1 Announce Type: cross Abstract: Clandestine tunneling beneath oil and gas pipelines enables fuel theft, smuggling, and sabotage, yet conventional monitoring detects damage only after a pipeline has been compromised.