arXiv subjects

Chu Ma

Publications and source records attributed to Chu Ma.

6 recordsLinked to original sources

arXiv2026

Shape Ultrasound with Dynamic Microfluidic Lenses

Dynamic shaping of ultrasound into prescribed spatial patterns underlies a broad range of biomedical and engineering applications. However, existing modulation strategies face fundamental limitations: single element transducers paired with acoustic lenses lack reconfigurability, whereas phased arrays require large numbers of independently driven elements, leading to substantial hardware complexity, cost, and rigidity. Here we introduce a microfluidic ultrasound lens system that enables reconfigurable spatial modulation of ultrasonic fields using two orthogonal layers of soft microfluidic channels. Each channel is selectively filled with one of two liquids with distinct sound speeds via an FPGA controlled array of micropumps, generating programmable binary phase patterns. Integrating a 20-row-by-20-column microfluidic lens with a single element transducer, we demonstrate three-dimensional ultrasound focusing with approximately one second reconfiguration time and spatial resolution comparable to that of a 400-element transducer array. The system provides 400 addressable pixels through parallel control of 80 pumps, allowing hardware complexity to scale with the square root of the pixel count. Building on this platform, we demonstrate dynamic ultrasound heating, as well as remote particle manipulation. Furthermore, we demonstrate a cylindrical lens that manipulates ultrasound propagation in the azimuthal direction. Owing to its liquid based, soft architecture, the microfluidic lens offers design flexibility, scalable operation across ultrasound frequencies, low acoustic transmission loss, and stable performance under high acoustic power. Together, these results establish microfluidic phase modulation as a compact, scalable, and flexible approach for dynamic ultrasound field control.

physics.app-ph↗

arXiv2026

Fabric Phononic Crystals for Passive Vibration Control

Weaving patterns in fabrics, traditionally used for aesthetic purposes, present a largely untapped opportunity to create metamaterials that serve as passive layers for sensing, filtering, and signal processing. However, the hierarchical architecture of fabrics makes structural design and wave prediction challenging. Here, we establish fully woven fabrics as phononic crystals that passively filter and route elastic vibrations. Using double weaving, we integrate a soft cotton weave with stiff woven copper inclusions to form periodic fabric lattices with engineered dispersion. A multiscale modeling framework that combines homogenization of weave blocks with an effective-property macroscale model enables computationally efficient design of phononic crystals. Simulations and experiments confirm a pronounced phononic bandgap for out-of-plane vibrations in a finite fabric crystal, while an equivalent pure cotton weave shows no band suppression in the corresponding frequency range. Building on the same platform, we realize a fully woven higher-order topological insulator. Modal analysis and transmission measurements reveal in-gap edge states and localized corner states. These results show that phononic bandgaps and topological states can be directly encoded through weaving patterns and material contrast, enabling passive vibration management layers and multifunctional waveguiding fabrics for sensing, haptic interfaces, robotics, and noise mitigation.

physics.app-ph↗

arXiv2026

Exploiting RIS Optimization Limits for Multi-User Beamforming and Signal Suppression

This paper presents a unified framework for exploiting the boundaries of reconfigurable intelligent surfaces (RIS) joint optimization in multi-user wireless systems, where a single RIS accommodates diverse objectives.We first propose an adaptive gradient-scaling mechanism that accelerates the convergence of the underlying optimization algorithm while maintaining stable performance across varying channel and system parameters. The proposed mechanism enables the solver to reach a reasonably good solution rapidly without requiring manual tuning of step sizes or algorithmic hyperparameters when system inputs change. We then propose a low-complexity beamformer recovery method tailored for single-user scenarios, which circumvents the full matrix decomposition required by traditional approaches, thereby significantly reducing computational overhead. Building on these foundations, we develop an element allocation strategy that enables user-specific prioritization through assignment of RIS subsets. This is further extended by a modular add-drop mechanism that supports partial-panel optimization in general multi-user settings. The framework is evaluated across three representative scenarios: (i) signal amplification for all users, (ii) signal suppression for all users, and (iii) selective amplification and suppression. To characterize performance limits, we derive power trade-off boundaries using scalarized joint optimization, which closely align with Monte Carlo simulations. Our unified joint optimization method consistently yield solutions near these boundaries, confirming its near-optimality. Extensive simulations under realistic channel models demonstrate that the proposed approach outperforms conventional semidefinite relaxation techniques, offering a scalable and effective RIS control strategy for cooperative and competitive multi-user environments.

eess.SP↗

arXiv2026

Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction

Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite Element Method (FEM) provide high accuracy but struggle with large-scale or high-frequency problems due to prohibitive computational costs. Pure data-driven approaches excel in speed but often lack sufficient labeled data for complex scenarios. Physics-informed neural networks (PINNs) integrate physical principles into machine learning models, offering a promising solution by bridging these gaps. However, standard PINNs embed physical principles only in loss functions, leading to slow convergence, optimization instability, and spectral bias, limiting their ability for large-scale wave field reconstruction. This work introduces architecture physics embedded (PE)-PINN, which integrates additional physical guidance directly into the neural network architecture beyond Helmholtz equations and boundary conditions in loss functions. Specifically, a new envelope transformation layer is designed to mitigate spectral bias with kernels parameterized by source properties, material interfaces, and wave physics. Experiments demonstrate that PE-PINN achieves more than 10 times speedup in convergence compared to standard PINNs and several orders of magnitude reduction in memory usage compared to FEM. This breakthrough enables high-fidelity modeling for large-scale 2D/3D electromagnetic wave reconstruction involving reflections, refractions, and diffractions in room-scale domains, readily applicable to wireless communications, sensing, room acoustics, and other fields requiring large-scale wave field analysis.

cs.LG↗

arXiv2023

Passive Mechanical Vibration Processor for Wireless Vibration Sensing

Real-time, low-cost, and wireless mechanical vibration monitoring is necessary for industrial applications to track the operation status of equipment, environmental applications to proactively predict natural disasters, as well as day-to-day applications such as vital sign monitoring. Despite this urgent need, existing solutions, such as laser vibrometers, commercial Wi-Fi devices, and cameras, lack wide practical deployment due to their limited sensitivity and functionality. In this work, we propose and verify that a fully passive, resonance-based vibration processing device attached to the vibrating surface can improve the sensitivity of wireless vibration measurement methods by more than 10 times at designated frequencies. Additionally, the device realizes an analog real-time vibration filtering/labeling effect, and the device also provides a platform for surface editing, which adds more functionalities to the current non-contact sensing systems. Finally, the working frequency of the device is widely adjustable over orders of magnitudes, broadening its applicability to different applications.

physics.app-ph↗

arXiv2023

Ultra-broadband suppression of sound scattering via illusion metamaterials

The scattering of waves is a ubiquitous phenomenon in physics, yet there are numerous scenarios, such as the pursuit of invisibility, where suppressing it is of utmost importance. In comparison to prior methods which are restricted by limited bandwidths, here we present a technique to suppress sound scattering across an ultra-broad spectrum by utilizing illusion metamaterials. This illusion metamaterial, consisting of subwavelength tunnels with precisely crafted internal structures, has the ability to guide acoustic waves around the obstacles and recreate the incoming wavefront on the exit surface. Consequently, two ultra-broadband illusionary effects are produced: disappearing space and time shift. Simultaneously, all signs of sound scattering are removed across an exceptionally wide spectrum, ranging from the quasistatic limit to an upper limit of the spectrum, as confirmed by full-wave simulations and acoustic experiments. Our approach represents a major step forward in the development of broadband functional metamaterials and holds the potential to revolutionize various fields, including acoustic camouflage and reverberation control.

physics.app-ph↗