arXiv subjects

Chaoran Wang

Publications and source records attributed to Chaoran Wang.

17 recordsLinked to original sources

arXiv2026

Dual-polarization control of broadband nonreciprocal thermal radiation by combining local and nonlocal metasurfaces

Nonreciprocal thermal radiation offers a route to decouple spectral directional absorptivity and emissivity, thereby enabling new paradigms in thermal-photonic systems. However, in magneto-optical platforms, the intrinsic gyroelectric response generally confines observable nonreciprocity to transverse-magnetic (TM) polarization, while the transverse-electric (TE) response is absent. In this work, we experimentally demonstrate, for the first time, a local thermal metasurface strategy to activate TE-polarized nonreciprocity by creating artificial gyromagnetic response in a gyroelectric semiconductor platform. We further extend this mechanism to broadband dual-polarization operation employing a nonlocal thermal metasurface, which combines a resonator supercell with gradient-doped epsilon-near-zero magneto-optical multilayers. Pronounced absorptivity contrast is maintained over 22-27 {\mu}m for TE polarization and 19-27 {\mu}m for TM polarization. This platform provides a mechanism-based route to achieve broadband and dual-polarization nonreciprocal thermal absorption, opening new opportunities for advancing radiative energy-conversion devices.

physics.optics↗

arXiv2026

Quantitative Dynamic Phase Mapping via Single-Arm Field-Correlation Ghost Imaging

We demonstrate a single-arm optical platform for phase-retrieval-free, quantitative dynamic phase mapping of continuous transparent media via field-correlation ghost imaging. By modeling the medium as a dynamic pure-phase object, we spatially encode and compress its two-dimensional (2D) complex transmittance into a single bucket detector. Balanced heterodyne detection downconverts the optical frequencies for direct digitization. Crucially, by mapping spatial information into the temporal domain, this single-pixel architecture exploits high-speed digitization to continuously resolve 2D phase dynamics, effectively bypassing the frame-rate bottlenecks of traditional array sensors. Coupled with intermediate-frequency spectral analysis, this establishes a direct linear mapping from the recorded signal to the physical phase. The complex amplitude is thus deterministically extracted via field-correlation, enabling the spatial reconstruction of 2D acoustic pressure distributions using a pseudo-inverse algorithm. Experimental validations in an acoustic levitator confirm that the optically extracted acoustic wavelengths strictly match theoretical dispersion models, exhibiting a robust linear correlation between the retrieved phase shift and local sound pressure levels. This deterministic methodology provides a real-time-capable metrological tool for characterizing rapidly evolving phenomena, including transient aeroacoustic flows, shockwaves, and microfluidic biological dynamics.

physics.optics↗

arXiv2026

Decoupling Spatio-Temporal Dynamics: Microvibration Imaging Using Coherent Detection Ghost Imaging Lidar

Imaging the full-field microvibration of extended targets remains a formidable challenge for conventional remote sensing. Traditional array-based sensors are often severely constrained by data throughput and sensitivity limits when scaling to high spatial resolutions, while point-scanning interferometric systems lack the instantaneous full-field capability required to capture transient, spatially coupled vibration modes. To overcome these limitations, we propose a Coherent Detection-Ghost Imaging (CD-GI) framework that synergizes the spatial multiplexing capability of single-pixel imaging with the high-dimensional sensitivity of coherent detection. We establish a comprehensive mathematical model that describes the coupling mechanism of the target's spatial distribution and temporal micro-dynamics within a 1D bucket detector signal. To resolve the resulting inverse problem, we develop a frequency-channel self-calibration scheme. This approach effectively decouples the micro-Doppler signatures from spatial speckle patterns without requiring prior knowledge of the vibration frequency. Experimental results demonstrate that our system successfully reconstructs the spatially resolved microvibration patterns of adjacent targets with a frequency difference as small as 1 Hz, achieving sub-wavelength vibration sensitivity. This work bridges the gap between computational imaging and coherent metrology, offering a robust solution for non-invasive, high-precision structural health monitoring.

physics.optics↗

arXiv2026

Multi-mode Coherent Detection Ghost Imaging Lidar and Vibration-Mode Imaging

Coherent detection ghost imaging lidar (CD-GI lidar) integrates ghost imaging with coherent detection, thereby achieving enhanced anti-interference and phase-resolved imaging capability. Here, we propose a bucket-detector-based multi-mode coherent detection scheme for CD-GI lidar, where the reflected multi-mode light fields are coherently mixed with a single-mode local oscillator (LO) at the bucket detector photosensitive plane. The bucket-detector-based multi-mode CD-GI lidar system breaks the constraints of Siegman antenna theorem by utilizing field correlation to decouple the reflected multi-mode light fields and reconstructs the spatial distribution of targets' vibration modes. Theoretical analysis of the bucket-detector-based multi-mode CD-GI lidar system is presented in this work, and its feasibility is verified through a series of experiments.

physics.optics↗

arXiv2026

Light Propagation through Space-Time Non-Markovian Random Media

Here, we introduce a stochastic partial differential equation (SPDE) formulation driven by temporally correlated noise to describe light propagation beyond the standard Markov approximation. By representing the squared refractive index fluctuations as a random field with explicit long-range temporal correlations, we demonstrate that the propagation dynamics map exactly onto the hyperbolic Anderson model. This rigorous mapping enables the derivation of new quantitative scaling relations that connect the environment's non-Markovian memory effects to the statistical properties of the emergent light field. We experimentally validate these analytical predictions in an outdoor atmospheric environment, confirming the memory-dependent statistical signatures of the propagated light. Our results establish a precise physical foundation for understanding memory-driven wave phenomena, providing crucial insights for free-space optical communication, remote sensing, and coherent imaging.

physics.optics↗

arXiv2025

Bridging Probabilistic Inference and Behavior Trees: An Interactive Framework for Adaptive Multi-Robot Cooperation

This paper proposes an Interactive Inference Behavior Tree (IIBT) framework that integrates behavior trees (BTs) with active inference under the free energy principle for distributed multi-robot decision-making. The proposed IIBT node extends conventional BTs with probabilistic reasoning, enabling online joint planning and execution across multiple robots. It remains fully compatible with standard BT architectures, allowing seamless integration into existing multi-robot control systems. Within this framework, multi-robot cooperation is formulated as a free-energy minimization process, where each robot dynamically updates its preference matrix based on perceptual inputs and peer intentions, thereby achieving adaptive coordination in partially observable and dynamic environments. The proposed approach is validated through both simulation and real-world experiments, including a multi-robot maze navigation and a collaborative manipulation task, compared against traditional BTs(https://youtu.be/KX_oT3IDTf4). Experimental results demonstrate that the IIBT framework reduces BT node complexity by over 70%, while maintaining robust, interpretable, and adaptive cooperative behavior under environmental uncertainty.

cs.RO↗

arXiv2025

LLM-HBT: Dynamic Behavior Tree Construction for Adaptive Coordination in Heterogeneous Robots

We introduce a novel framework for automatic behavior tree (BT) construction in heterogeneous multi-robot systems, designed to address the challenges of adaptability and robustness in dynamic environments. Traditional robots are limited by fixed functional attributes and cannot efficiently reconfigure their strategies in response to task failures or environmental changes. To overcome this limitation, we leverage large language models (LLMs) to generate and extend BTs dynamically, combining the reasoning and generalization power of LLMs with the modularity and recovery capability of BTs. The proposed framework consists of four interconnected modules task initialization, task assignment, BT update, and failure node detection which operate in a closed loop. Robots tick their BTs during execution, and upon encountering a failure node, they can either extend the tree locally or invoke a centralized virtual coordinator (Alex) to reassign subtasks and synchronize BTs across peers. This design enables long-term cooperative execution in heterogeneous teams. We validate the framework on 60 tasks across three simulated scenarios and in a real-world cafe environment with a robotic arm and a wheeled-legged robot. Results show that our method consistently outperforms baseline approaches in task success rate, robustness, and scalability, demonstrating its effectiveness for multi-robot collaboration in complex scenarios.

cs.RO↗

arXiv2025

ATRos: Learning Energy-Efficient Agile Locomotion for Wheeled-legged Robots

Hybrid locomotion of wheeled-legged robots has recently attracted increasing attention due to their advantages of combining the agility of legged locomotion and the efficiency of wheeled motion. But along with expanded performance, the whole-body control of wheeled-legged robots remains challenging for hybrid locomotion. In this paper, we present ATRos, a reinforcement learning (RL)-based hybrid locomotion framework to achieve hybrid walking-driving motions on the wheeled-legged robot. Without giving predefined gait patterns, our planner aims to intelligently coordinate simultaneous wheel and leg movements, thereby achieving improved terrain adaptability and improved energy efficiency. Based on RL techniques, our approach constructs a prediction policy network that could estimate external environmental states from proprioceptive sensory information, and the outputs are then fed into an actor critic network to produce optimal joint commands. The feasibility of the proposed framework is validated through both simulations and real-world experiments across diverse terrains, including flat ground, stairs, and grassy surfaces. The hybrid locomotion framework shows robust performance over various unseen terrains, highlighting its generalization capability.

cs.RO↗

arXiv2025

HANDO: Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation

Seamless loco-manipulation in unstructured environments requires robots to leverage autonomous exploration alongside whole-body control for physical interaction. In this work, we introduce HANDO (Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation), a two-layer framework designed for legged robots equipped with manipulators to perform human-centered mobile manipulation tasks. The first layer utilizes a goal-conditioned autonomous exploration policy to guide the robot to semantically specified targets, such as a black office chair in a dynamic environment. The second layer employs a unified whole-body loco-manipulation policy to coordinate the arm and legs for precise interaction tasks-for example, handing a drink to a person seated on the chair. We have conducted an initial deployment of the navigation module, and will continue to pursue finer-grained deployment of whole-body loco-manipulation.

cs.RO↗

arXiv2025

$\textit{Eppur Si Muove}$: Self-Sustained Streaming Motions in Multi-Phase MHD

Radiative cooling can drive dynamics in multi-phase gas. A dramatic example is hydrodynamic `shattering', the violent, pressure-driven fragmentation of a cooling cloud which falls drastically out of pressure balance with its surroundings. We run MHD simulations to understand how shattering is influenced by magnetic fields. In MHD, clouds do not `shatter' chaotically. Instead, after initial fragmentation, both hot and cold phases coherently `stream' in long-lived, field-aligned, self-sustaining gas flows, at high speed ($\sim 100 \, {\rm km \, s^{-1}}$). MHD thermal instability also produces such flows. They are due to the anisotropic nature of MHD pressure support, which only operates perpendicular to B-fields. Thus, even when $P_{\rm B} + P_{\rm gas} \approx$const, pressure balance only holds perpendicular to B-fields. Field-aligned gas pressure variations are unopposed, and results in gas velocities $v \sim (2 \Delta P/\rho)^{1/2}$ from Bernoulli's principle. Strikingly, gas in adjacent flux tubes $\textit{counter-stream}$ in opposite directions. We show this arises from a cooling-induced, MHD version of the thin shell instability. Magnetic tension is important both in enabling corrugational instability and modifying its non-linear evolution. Even in high $\beta$ hot gas, streaming can arise, since magnetic pressure support grows as gas cools and compresses. Thermal conduction increases the sizes and velocities of streaming cloudlets, but does not qualitatively modify dynamics. These results are relevant to the counter-streaming gas flows observed in solar coronal rain, as well as multi-phase gas cooling and condensation in the ISM, CGM and ICM.

astro-ph.GA↗

arXiv2025

Beyond the Human-AI Binaries: Advanced Writers' Self-Directed Use of Generative AI in Academic Writing

This study explores the self-directed use of Generative AI (GAI) in academic writing among advanced L2 English writers, challenging assumptions that GAI undermines meaningful learning and holds less value for experienced learners. Through case studies, we investigate how three (post)doctoral writers engage with GAI to address specific L2 writing challenges. The findings revealed a spectrum of approaches to GAI, ranging from prescriptive to dialogic uses, with participants positioning AI as a tool versus an interactive participant in their meaning-making process, reflecting different views of AI as a mechanical system, social construct, or distributed agency. We highlight the ways AI disrupts traditional notions of authorship, text, and learning, showing how a poststructuralist lens allows us to transcend human-AI, writing-technology, and learning-bypassing binaries in our existing discourses on AI. This shifting view allows us to deconstruct and reconstruct AI's multifaceted possibilities in L2 writers' literacy practices. We also call for more nuanced ethical considerations to avoid stigmatizing L2 writers' use of GAI and to foster writerly virtues that reposition our relationship with AI technology.

cs.CY↗

arXiv2024

Purposeful remixing with generative AI: Constructing designer voice in multimodal composing

Voice, the discursive construction of the writer's identity, has been extensively studied and theorized in composition studies. In multimodal writing, students are able to mobilize both linguistic and non linguistic resources to express their real or imagined identities. But at the same time, when students are limited to choose from available online resources, their voices might be compromised due to the incompatibility between their authorial intentions and the existing materials. This study, therefore, investigates whether the use of generative AI tools could help student authors construct a more consistent voice in multimodal writing. In this study, we have designed a photo essay assignment where students recount a story in the form of photo essays and prompt AI image generating tools to create photos for their storytelling. Drawing on interview data, written reflection, written annotation, and multimodal products from seven focal participants, we have identified two remixing practices, through which students attempted to establish a coherent and unique voice in writing. The study sheds light on the intentional and discursive nature of multimodal writing with AI as afforded by the technological flexibility, while also highlighting the practical and ethical challenges that could be attributed to students insufficient prompt and multimodal literacy and the innate limitations of AI systems. This study provides important implications for incorporating AI tools in designing multimodal writing tasks.

cs.CY↗

arXiv2023

Bayesian Recursive Information Optical Imaging: A Ghost Imaging Scheme Based on Bayesian Filtering

Computational imaging~(CI) has been attracting a lot of interest in recent years for its superiority over traditional imaging in various applications. In CI systems, information is generally acquired in an encoded form and subsequently decoded via processing algorithms, which is quite in line with the information transmission mode of modern communication, and leads to emerging studies from the viewpoint of information optical imaging. Currently, one of the most important issues to be theoretically studied for CI is to quantitatively evaluate the fundamental ability of information acquisition, which is essential for both objective performance assessment and efficient design of imaging system. In this paper, by incorporating the Bayesian filtering paradigm, we propose a framework for CI that enables quantitative evaluation and design of the imaging system, and demonstate it based on ghost imaging. In specific, this framework can provide a quantitative evaluation on the acquired information through Fisher information and Cram\'er-Rao Lower Bound (CRLB), and the intrinsic performance of the imaging system can be accessed in real-time. With simulation and experiments, the framework is validated and compared with existing linear unbiased algorithms. In particular, the image retrieval can reach the CRLB. Furthermore, information-driven adaptive design for optimizing the information acquisition procedure is also achieved. By quantitative describing and efficient designing, the proposed framework is expected to promote the practical applications of CI techniques.

physics.optics↗

arXiv2022

Micro-Vibration Modes Reconstruction Based on Micro-Doppler Coincidence Imaging

Micro-vibration, a ubiquitous nature phenomenon, can be seen as a characteristic feature on the objects, these vibrations always have tiny amplitudes which are much less than the wavelengths of the sensing systems, thus these motions information can only be reflected in the phase item of echo. Normally the conventional radar system can detect these micro vibrations through the time frequency analyzing, but these vibration characteristics can only be reflected by time-frequency spectrum, the spatial distribution of these micro vibrations can not be reconstructed precisely. Ghost imaging (GI), a novel imaging method also known as Coincidence Imaging that originated in the quantum and optical fields, can reconstruct unknown images using computational methods. To reconstruct the spatial distribution of micro vibrations, this paper proposes a new method based on a coincidence imaging system. A detailed model of target micro-vibration is created first, taking into account two categories: discrete and continuous targets. We use the first-order field correlation feature to obtain objective different micro vibration distribution based on the complex target models and time-frequency analysis in this work.

eess.SP↗

arXiv2022

Turbulent Heating in a Stratified Medium

There is considerable evidence for widespread subsonic turbulence in galaxy clusters, most notably from {\it Hitomi}. Turbulence is often invoked to offset radiative losses in cluster cores, both by direct dissipation and by enabling turbulent heat diffusion. However, in a stratified medium, buoyancy forces oppose radial motions, making turbulence anisotropic. This can be quantified via the Froude number ${\rm Fr}$, which decreases inward in clusters as stratification increases. We exploit analogies with MHD turbulence to show that wave-turbulence interactions increase cascade times and reduces dissipation rates $\epsilon \propto {\rm Fr}$. Equivalently, for a given energy injection/dissipation rate $\epsilon$, turbulent velocities $u$ must be higher compared to Kolmogorov scalings. High resolution hydrodynamic simulations show excellent agreement with the $\epsilon \propto {\rm Fr}$ scaling, which sets in for ${\rm Fr} < 0.1$. We also compare previously predicted scalings for the turbulent diffusion coefficient $D \propto {\rm Fr}^2$ and find excellent agreement, for ${\rm Fr} < 1$. However, we find a different normalization, corresponding to stronger diffusive suppression by more than an order of magnitude. Our results imply that turbulent diffusion is more heavily suppressed by stratification, over a much wider radial range, than turbulent dissipation. Thus, the latter potentially dominates. Furthermore, this shift implies significantly higher turbulent velocities required to offset cooling, compared to previous models. These results are potentially relevant to turbulent metal diffusion (which is likewise suppressed), and to planetary atmospheres.

astro-ph.GA↗

arXiv2019

Chaotic cold accretion in giant elliptical galaxies heated by AGN cosmic rays

Black hole feedback plays a central role in shaping the circumgalactic medium (CGM) of elliptical galaxies. We systematically study the impact of plasma physics on the evolution of ellipticals by performing three-dimensional non-ideal magneto-hydrodynamic simulations of the interactions of active galactic nucleus (AGN) jets with the CGM including magnetic fields, and cosmic rays (CRs) and their transport processes. We find that the physics of feedback operating on large galactic scales depends very sensitively on plasma physics operating on small scales. Specifically, we demonstrate that: (i) in the purely hydrodynamical case, the AGN jets initially maintain the atmospheres in global thermal balance. However, local thermal instability generically leads to the formation of massive cold disks in the vicinity of the central black hole in disagreement with observations; (ii) including weak magnetic fields prevents the formation of the disks because local B-field amplification in the precipitating cold gas leads to strong magnetic breaking, which quickly extracts angular momentum from the accreting clouds. The magnetic fields transform the cold clouds into narrow filaments that do not fall ballistically; (iii) when plasma composition in the AGN jets is dominated by CRs, and CR transport is neglected, the atmospheres exhibit cooling catastrophes due to inefficient heat transfer from the AGN to CGM despite Coulomb/hadronic CR losses being present; (iv) including CR streaming and heating restores agreement with the observations, i.e., cooling catastrophes are prevented and massive cold central disks do not form. The AGN power is reduced as its energy is utilized efficiently.

astro-ph.GA↗

arXiv2018

AGN Feedback and Multi-phase Gas in Giant Elliptical Galaxies

Recent observations have found extended multi-phase gas in a significant fraction of massive elliptical galaxies. We perform high-resolution three-dimensional hydrodynamical simulations of two idealized elliptical galaxies -- one representing a typical galaxy characterized by initial conditions conducive to the development of thermal instability and the other one less likely to develop thermal instability -- in order to study the development of thermal instability and the formation of multi-phase structures. We analyze the interplay between radiative cooling, momentum-driven AGN feedback, star formation, and stellar feedback from both young and old stars. We find that in one class of elliptical galaxies, the entropy of the hot halo gas rises sharply as a function of radius, and the hot halo is thermally stable and run-away cooling can only happen in the very center of the galaxy. In other class of ellipticals, the hot halo gas has a cooling to free-fall time ratio close to 10, and the non-linear perturbation driven by AGN feedback can cause the hot gas to frequently precipitate into extended multi-phase filaments. Both multi- and single-phase elliptical galaxies experience cooling-driven AGN feedback cycles. Interestingly, AGN feedback maintains the multi- or single-phase nature of the halo but does not turn multi-phase galaxies into single-phase ones or vice versa. Some of the extended cold gas in the multi-phase galaxy also forms young stars. The level of star formation and its spatial distribution are in excellent agreement with {\it Hubble} observations of nearby elliptical galaxies.

astro-ph.GA↗