arXiv Machine Learning

When Magnitude Suffices: Reconstructing Time-Domain-Gated Microwave Signals Without Phase on a Thoracic Phantom

The study demonstrates that time‑domain‑gated microwave signals can be reconstructed from magnitude‑only measurements using a spectrum analyzer, eliminating the need for phase data typically obtained with a vector network analyzer. By selecting appropriate gate centers, applying per‑bin reference corrections, and training a fully connected neural network on paired sweeps, the authors achieve reconstruction errors comparable to VNA‑based methods for both reflection ($S_{11}$) and transmission ($S_{21}$) measurements on a thoracic phantom with multiple dielectric states. The approach shows that when magnitude sufficiently encodes phase information—particularly in reflection—scalar instruments can provide gated responses without phase measurement, and it outperforms linear mapping on an open breast phantom dataset.

arXiv AI
Oct 5

Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing

The paper presents HEAR, a dual‑task Transformer that learns to assess heartbeat observability from mmWave radar phase spectra and simultaneously estimates heart rate. Using a controllable FMCW simulator, the authors generate labeled data where observability is defined by the agreement between the dominant heartbeat‑band peak and the known heart rate. Trained only on simulated data, HEAR transfers zero‑shot to real‑world datasets at 60 and 120 GHz, enabling selective heart‑rate estimation that dramatically reduces error and achieves sub‑50 ms latency on edge hardware.

By Yuxuan Hu, Shilin Shan, Jianfei Yang, Feng Xu
arXiv AI
Jun 12

Acquisition state behaves as a structured, measurable variable governing lung-nodule AI: kernel-driven measurement instability and noise-driven detection fragility, invisible to DICOM metadata

arXiv:2606. 12824v1 Announce Type: cross Abstract: AI governance for medical imaging is formalizing: the 2026 ACR-SIIM Practice Parameter recommends local acceptance testing and ongoing drift monitoring, and the ACR Assess-AI registry monitors AI outputs using DICOM metadata for context.

By Daniel Soliman
arXiv Machine Learning
Oct 9

SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing

The paper introduces SR‑TTA, a spatial‑redundancy test‑time adaptation method for respiration sensing using a 64‑antenna cell‑free massive MIMO base station. It replaces hand‑crafted fusion with a learned complex‑weight beamformer that spatially nulls in‑band motion interference, and further closes the performance gap to an oracle through label‑free adaptation. Experiments show the respiration‑rate error drops from 5.8 to 0.8 breaths per minute under simulated interference, and real‑world recordings see failure rates fall from 47% to 7% with a single cross‑subject calibration.

By Jingyuan Liu, Zheng Chang, Haoqiu Xiong, Zhuangzhuang Cui, Sofie Pollin
arXiv Machine Learning
Jun 8

On the conditional equivalence of phase retrieval algorithms

arXiv:2606. 07257v1 Announce Type: cross Abstract: Phase retrieval - recovering a complex-valued field from intensity measurements - is typically solved using variants of the Gerchberg-Saxton (GS) algorithm, understood as alternating projections between measurement planes.

By Jakob Schroeder, Andreas D\"opp
arXiv Machine Learning
Sep 25

Physics-Guided Multi-Objective Deep Learning for Ultrasound RF Data Interpolation in Resource-Constrained Imaging

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.

By Luoyuan Zhang, Yiyang You, Ananya Tandri, Yinan Feng, Hyunwoo Song, Jeeun Kang, Youzuo Lin
arXiv Machine Learning
Jul 31

RIPPLE: Generating Multi-Channel Phase, Not Recovering It

arXiv:2607. 27775v1 Announce Type: new Abstract: Generative models synthesize magnitude spectra with high fidelity, while phase is delegated to a recovery module---Griffin--Lim, a vocoder, or a latent decoder---applied independently to each channel.

By Jaehyuk Lee, Yeajin Lee, Dayeon Shin, Donghun Lee
arXiv Machine Learning
Jul 27

IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data

arXiv:2607. 22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.

By Masashi Sode, Gianmarco Pinton