The paper proposes a method for learning nonlinear waveform fusion across scales to improve integrated sensing and communication (ISAC). By adaptively weighting temporal features and updating spectral components, the authors generate a waveform that maintains fixed total energy while combining image features with a deterministic frequency‑modulated reference. Experiments on 512 test images show that the learned waveforms reduce normalized localization error by 29.0 % and 27.0 % and increase mean peak SNR by 1.40 dB and 0.82 dB compared to baseline approaches.
arXiv:2511.18028v2 Announce Type: replace Abstract: Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches main...
The paper introduces a physics‑guided, data‑driven framework for reconstructing dense ultrasound RF data from sparse acquisitions. It trains an end‑to‑end interpolation network with a hybrid RF‑ and beamforming‑domain loss, stabilized by exponential moving average, and employs random‑skip masking to generalize across varying sparsity patterns and channel configurations. On a held‑out test set, the method achieves a mean SSIM of about 0.95 across decimation factors from ×2 to ×13, consistently improving RF reconstruction and post‑beamforming image quality.
arXiv:2608. 15972v1 Announce Type: cross Abstract: Synchronized camera and wireless measurements observe the same scene through different physical channels.
arXiv:2607. 24110v1 Announce Type: cross Abstract: Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception.
arXiv:2606. 01446v1 Announce Type: cross Abstract: Radio frequency spectrum awareness requires the ability to detect, localize, and characterize emitters in dense and contested wireless environments.
arXiv:2608. 15647v1 Announce Type: cross Abstract: Semantic segmentation of very-high-resolution (VHR) remote sensing imagery increasingly benefits from strong pretrained hierarchical encoders, yet exploiting their multi-stage representations remains difficult.
arXiv:2608. 03319v1 Announce Type: cross Abstract: Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function (SF) that performs inference, creating a need for compact, task-oriented feedback on the SE-SF interface.
The paper introduces a self‑localizing MIMO beam‑mapping framework that builds a hierarchical wireless memory using sparse channel state information (CSI) without explicit location labels. It employs beam‑domain RSS as compact inputs, a dual‑scale extractor for angular and temporal dependencies, and a hybrid temporal encoder to infer physical anchors that index a structured radio map. The radio‑map embedding enables continuous updates and full‑CSI reconstruction, yielding over 30% better anchor recovery and more than 20% channel‑capacity gains in NLOS beam tracking compared to Kalman‑filter methods.
arXiv:2608. 08439v1 Announce Type: cross Abstract: Heterogeneous RF sensing differs substantially in feature structure, spatial layout, and temporal scale, making existing models difficult to reuse across devices, environments, and RF modalities.
arXiv:2609.39926v1 Announce Type: new Abstract: Achieving cross-sensor generalization and arbitrary-scale reconstruction with a single model remains challenging in hyperspectral super-resolution (HSR...
IR275K is a curated benchmark for infrared multi‑frame super‑resolution, comprising 594 video sequences and 275,196 frames with train/validation/test splits and a reproducible x4 evaluation protocol. It evaluates seven learned baselines, the CGMamba reference model, and bicubic interpolation under both bicubic degradation and synthetic mixed sensor noise, revealing that EDVR performs best under bicubic degradation while MSDTGP excels under mixed sensor noise. Ablations with CGMamba demonstrate that using real neighboring frames improves PSNR by about 1 dB compared to center‑frame repetition, and that center‑frame conditioning also benefits reconstruction across conditions.
SymNetPro is a localization framework that extends SymNet by adding a line‑of‑sight aware attention bias and a transmitter‑drop augmentation technique. These components help the model learn obstruction‑aware spatial relations and handle varying numbers of transmitters. Experiments on ray‑traced urban environments demonstrate lower OSPA errors compared to baseline methods, especially under sparse sampling, noise, and higher transmitter counts.