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新论文 · v1arXiv:2611.07358

A Synchronized Tri-Modality Wireless System for Multi-Point Non-Contact Cardiac Monitoring

Wei Ren, Kaili Zhao, Huansheng Yi, Miaoyang Hu, Jianqi Wang, Canhua Xu, Fugui Qi

原文摘要

Cardiac physiological monitoring is fundamental to evaluating cardiovascular health and diagnosing related diseases. Non-contact radar sensing can capture chest-wall vibrations with spatial specificity, enabling localized physiological monitoring and cardiac assessment. Progress, however, has been limited by the absence of a reliable, research-grade hardware platform for rigorous validation of radar-based methods. Prior systems typically pair radar, namely Doppler cardiography (DCG), with only electrocardiography (ECG) or other coarse references, providing no direct mechanical ground truth and thus constraining fine-grained evaluation of radar outputs. We present a tri-modality wireless system that integrates ECG, seismocardiography (SCG), and DCG under a unified acquisition and control framework for time-aligned recording and analysis. The tri-modality system combines miniaturized wearable ECG/SCG modules with a high-resolution mmWave radar module, supporting spatially resolved sensing and subsequent feature extraction. To our knowledge, this is the first integrated ECG--SCG--DCG hardware system for comprehensive cardiac monitoring; it enables multi-point thoracic acquisition, achieves precise cross-modal synchronization, and maintains specifications suitable for wearable operation. Experiments on a linear-motion platform show close agreement between DCG- and SCG-derived displacements, confirming accurate displacement measurement. Human-subject studies further demonstrate cross-modal consistency and robust extraction of feature points, establishing ECG as the electrical anchor and SCG as the mechanical ground truth for DCG. Overall, the system provides a rigorous, spatially resolved validation platform that advances non-contact cardiac sensing and supports next-generation radar-based monitoring.

cs.AR · eess.SP · eess.SY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.15786

DexCanvas: Bridging Human Demonstrations and Robot Learning for Dexterous Manipulation

Xinyue Xu, Jieqiang Sun, Jing, Dai, Siyuan Chen, Lanjie Ma, Ke Sun, Bin Zhao 等 12 位作者

原文摘要

A dexterous hand succeeds or fails through contact, yet every scalable record of human manipulation (video, vision-based hand tracking, motion capture) observes how the hand moves and misses how it pushes. We present DexCanvas, a hybrid real-synthetic dataset of 7,000 hours of dexterous hand-object interaction with per-frame contact points and contact forces, grown from 70 hours of optical motion capture that spans 21 manipulation types, from power and precision grasps to in-hand manipulation. The forces come from a real-to-sim pipeline: one reinforcement learning (RL) policy, conditioned on object geometry and manipulation type, drives an actuated MANO hand in physics simulation to reproduce each captured object trajectory, and the simulator measures the contacts that produce the reproduced motion. The policy reproduces 87.5% of the demonstrations and still succeeds on 80.9% of rollouts from perturbed initial object poses; these perturbed rollouts expand the 70 captured hours a hundredfold. On six YCB objects absent from the capture set it succeeds 72.5% of the time. The contact channels make better policies: a Diffusion Policy that observes DexCanvas contact features succeeds 82.2% of the time, against 67.5% without them, over 18,522 trials. Contact-driven retargeting carries the same demonstrations to two dexterous robot hands, where the RL stage again produces stable rollouts. A first release of 111,857 force-annotated trajectories is publicly available on Hugging Face.

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04274

Reasoning about Shared-Qubit Concurrency in Quantum Computing

Mingsheng Ying

原文摘要

In this paper, we develop a semantic model for the parallel execution of quantum circuits, providing a formal foundation for studying shared-qubit concurrency in quantum computing. As in classical shared-memory concurrency, parallel quantum circuits may interfere with one another through operations on shared qubits. However, quantum entanglement and the noncommutativity of quantum operations introduce additional challenges for reasoning about their correctness. To address these challenges, we generalise the classical Owicki-Gries method to quantum computing at the level of quantum circuits. Our approach enables compositional reasoning about parallel quantum circuits by separating the verification of individual components from the analysis of interference between them. We formulate proof rules for establishing the correctness of parallel quantum circuits and prove their soundness with respect to the proposed semantic model.

quant-ph · cs.DC · cs.LO · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.24093

Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning

Zihao Yang, Chengyuan Liu, Yu Zhou, Runze Lv, Tianyu Cui, Sheng Yi, Haohua Zhu, Irvine Lu 等 9 位作者

原文摘要

Learning dexterous manipulation from demonstrations is bottlenecked by data: the contact forces that determine whether a grasp succeeds are absent from every scalable source of human demonstrations. This paper builds on two observations. First, what survives the change from a human hand to a robot hand is the contact structure of a demonstration - which finger regions touch which object locations, and in what order - rather than its joint motion. Second, physical consistency need not be engineered per task: a single residual reinforcement learning (RL) policy, trained once across diverse demonstrations, can repair kinematic recordings into physically consistent, contact-annotated trajectories, and the same residual formulation restores dynamic feasibility after retargeting. These observations yield a three-stage pipeline that converts human motion-capture recordings into dexterous robot policies with no real-robot training data: physics refinement with a simulated MANO hand recovers contacts and forces, contact-anchored retargeting transfers the demonstrated contact structure through an objective independent of hand morphology, and residual policy learning adapts the result to robot actuation. The pipeline reconstructs 25,454 single-hand trajectories (success 7.3% -> 59.3%) and 25 dual-hand tasks (16.0% -> 62.4%) with one shared policy per setting, transfers one human dataset to four morphologically distinct robot hands (+62.4 pp), and executes four contact-rich bimanual tasks on physical hardware with zero real-robot training data.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07763

SateBeam: Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites

Xiucheng Wang, Nan Cheng, Zhisheng Yin, Conghao Zhou, Ruijin Sun

原文摘要

Dense low-Earth-orbit (LEO) networks increasingly reuse spectrum across moving satellites and overlapping beams, making a spatial radio map essential for interference diagnosis and coordination. Constructing such a map from sparse ground power measurements is difficult because the active satellite subset and its cardinality are unknown, candidate footprints can be highly coherent, and beam pointing, width, effective power, Doppler, and background must be inferred jointly. Known ephemerides do more than enumerate visible candidates. Propagated positions determine candidate-specific ranges, viewing geometry, and spatial footprints, while radial velocities induce distinct time-frequency signatures. Their joint spatial--Doppler structure rejects candidates whose geometry or time--frequency signature conflicts with the observations before continuous beam inference. SateBeam therefore screens all candidates through Doppler-resolved nonnegative fitting. GPU-accelerated grid correlations initialize continuous off-grid refinement; a Gamma-likelihood Bayesian information criterion (BIC) selects model cardinality, and backward deletion and exchange repair greedy support errors. Across paired real-ephemeris scenarios, SateBeam recovers the exact support in every case and reduces independent-query RMSE from $0.655$ to $0.050$ dB relative to orthogonal matching pursuit, with consistent advantages under model mismatch and larger candidate sets. The code is available at https://github.com/UNIC-Lab/SateBeam.

cs.IT · eess.SY

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arXiv ↗PDF ↗