arXiv subjects

Xu Zhu

Publications and source records attributed to Xu Zhu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Unraveling longitudinal field mediated versatile Stokes polarimetry

Stokes polarimetry has been considered as an alluring platform that enables a plethora of applications ranging from single-molecule orientation to deep-space sensing. Existing polarimetry avenues, however, rely primarily on the transversely polarized field reconstruction, thus suffering from several challenges such as multiple time sequenced detections, complex demodulation algorithms, and intricate engineering procedures. To circumvent these challenges, here we first demonstrate a longitudinally polarized field mediated recipe for the realization of efficacious and refined Stokes polarimetry in situ. This is achieved by unraveling the spin-to-orbit momentum conversion under non-paraxial focusing conditions enabling the direct mapping of the polarization ellipse. Leveraging this mechanism, we reveal an analytical solution of polarization ellipse via the local sampling of longitudinal field, in which the robustness can be fairly reinforced by relevant global retrieval based on convolutional neural network. The resultant Stokes polarimetry is shown to simultaneously exhibit in-situ signal acquisition (direct discernment), unparalleled demodulation time (up tomicrosecond level), superior detection efficiency (no need of troublesome design), and ultra-high retrieved accuracy (less than 1%), which is fundamentally inaccessible with traditional polarimetry methods. Our work holds great promise for empowering an allin-one versatile vector polarimeter, which opens up a host of applications relevant to polarization control.

physics.optics↗

arXiv2025

TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather

Adverse weather conditions such as snow, fog, and rain pose significant challenges to LiDAR-based perception models by introducing noise and corrupting point cloud measurements. To address this issue, we propose TripleMixer, a robust and efficient point cloud denoising network that integrates spatial, frequency, and channel-wise processing through three specialized mixer modules. TripleMixer effectively suppresses high-frequency noise while preserving essential geometric structures and can be seamlessly deployed as a plug-and-play module within existing LiDAR perception pipelines. To support the development and evaluation of denoising methods, we construct two large-scale simulated datasets, Weather-KITTI and Weather-NuScenes, covering diverse weather scenarios with dense point-wise semantic and noise annotations. Based on these datasets, we establish four benchmarks: Denoising, Semantic Segmentation (SS), Place Recognition (PR), and Object Detection (OD). These benchmarks enable systematic evaluation of denoising generalization, transferability, and downstream impact under both simulated and real-world adverse weather conditions. Extensive experiments demonstrate that TripleMixer achieves state-of-the-art denoising performance and yields substantial improvements across all downstream tasks without requiring retraining. Our results highlight the potential of denoising as a task-agnostic preprocessing strategy to enhance LiDAR robustness in real-world autonomous driving applications.

cs.CV↗

arXiv2025

Research on GEO SA-Bi SAR Imaging based on Joint Radar-Communications Waveform

Joint radar-communications (JRC) technology has attracted massive attention for decades, since it can effectively utilize allocated spectral resources by sharing frequency bands in increasingly crowded environments. In addition, the growing demand for hardware platform sharing which benefits both functionalities motivates more cooperation between radar and communication systems. In order to achieve the coexistence of sensing and communicating operations, joint systems should be designed to perform both tasks simultaneously. Developing a joint radar-communications waveform which is suitable for both functions is extremely crucial for this type of co-design, as it not only decreases spectral impact, but also benefits performances of both systems mutually. In this paper, a joint radar-communications waveform is utilized to perform GEO SA-Bi SAR imaging and wireless communication simultaneously. We also design a joint radar-communications receiver in this context to demonstrate feasibility of achieving both sensing and signaling with GEO SA-Bi SAR system.

cs.IT↗

arXiv2025

Cryogenic Performance Evaluation of Commercial SP4T Microelectromechanical Switch for Quantum Computing Applications

Superconducting quantum computers have emerged as a leading platform for next-generation computing, offering exceptional scalability and unprecedented computational speeds. However, scaling these systems to millions of qubits for practical applications poses substantial challenges, particularly due to interconnect bottlenecks. To address this challenge, extensive research has focused on developing cryogenic multiplexers that enable minimal wiring between room-temperature electronics and quantum processors. This paper investigates the viability of commercial microelectromechanical system (MEMS) switches for cryogenic multiplexers in large-scale quantum computing systems. DC and RF characteristics of the MEMS switches are evaluated at cryogenic temperatures (< 10 K) through finite element simulations and experimental measurements. Our results demonstrate that MEMS switches exhibit improved on-resistance, lower operating voltage, and superior RF performance at cryogenic temperatures, with reliable operation over 100 million cycles. Furthermore, stable single-pole four-throw (SP4T) switching and logical operations, including NAND and NOR gates, are demonstrated at cryogenic temperatures, validating their potential for quantum computing. These results underscore the promise of MEMS switches in realizing large-scale quantum computing systems.

quant-ph↗

arXiv2025

Basis Expansion Extrapolation based Long-Term Channel Prediction for Massive MIMO OTFS Systems

Massive multi-input multi-output (MIMO) combined with orthogonal time frequency space (OTFS) modulation has emerged as a promising technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) massive MIMO-OTFS systems. Specifically, first, an iterative basis expansion model (BEM) based UL channel estimation scheme is proposed to accurately estimate UL channels with the aid of carefully designed OTFS frame pattern. Then a set of Slepian sequences are used to model the estimated UL channels, and the dynamic Slepian coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels by iteratively extrapolating the Slepian coefficients. Simulation results verify that the proposed UL channel estimation and DL channel prediction schemes outperform the existing schemes in terms of normalized mean square error of channel estimation/prediction and DL spectral efficiency, with less pilot overhead.

eess.SP↗

arXiv2025

An Iterative Task-Driven Framework for Resilient LiDAR Place Recognition in Adverse Weather

LiDAR place recognition (LPR) plays a vital role in autonomous navigation. However, existing LPR methods struggle to maintain robustness under adverse weather conditions such as rain, snow, and fog, where weather-induced noise and point cloud degradation impair LiDAR reliability and perception accuracy. To tackle these challenges, we propose an Iterative Task-Driven Framework (ITDNet), which integrates a LiDAR Data Restoration (LDR) module and a LiDAR Place Recognition (LPR) module through an iterative learning strategy. These modules are jointly trained end-to-end, with alternating optimization to enhance performance. The core rationale of ITDNet is to leverage the LDR module to recover the corrupted point clouds while preserving structural consistency with clean data, thereby improving LPR accuracy in adverse weather. Simultaneously, the LPR task provides feature pseudo-labels to guide the LDR module's training, aligning it more effectively with the LPR task. To achieve this, we first design a task-driven LPR loss and a reconstruction loss to jointly supervise the optimization of the LDR module. Furthermore, for the LDR module, we propose a Dual-Domain Mixer (DDM) block for frequency-spatial feature fusion and a Semantic-Aware Generator (SAG) block for semantic-guided restoration. In addition, for the LPR module, we introduce a Multi-Frequency Transformer (MFT) block and a Wavelet Pyramid NetVLAD (WPN) block to aggregate multi-scale, robust global descriptors. Finally, extensive experiments on Weather-KITTI, Boreas, and our proposed Weather-Apollo datasets demonstrate that, ITDNet outperforms existing LPR methods, achieving state-of-the-art performance in adverse weather.

cs.RO↗

arXiv2025

MEMS Switch Enabled Spatiotemporally Modulated Isolators

This work reports the simulation, design, and implementation of a compact MEMS switch based spatiotemporally modulated (STM) bandpass filtering isolator to improve self-interference cancellation (SIC) in underwater acoustic communication networks. Conventional ferrite circulators are unavailable in ultrasonic frequency ranges limiting SIC to techniques such as spatial cancellation and adaptive digital cancellation. This study details a sub-megahertz electronic non-magnetic filtering isolator. High power-handling, compact, and reliable MEMS switches enable the periodically time varying filter circuit to be non-reciprocal. A printed circuit board (PCB) implementation shows strong agreement with spectral admittance matrix simulations with a maximum measured isolation of 15.99 dB. In conjunction with digital SIC methods, this isolator can enable in-band full duplex underwater communication, environmental sensing, and imaging.

physics.app-ph↗

arXiv2025

Multi-Reference and Adaptive Nonlinear Transform Source-Channel Coding for Wireless Image Semantic Transmission

We propose a multi-reference and adaptive nonlinear transform source-channel coding (MA-NTSCC) system for wireless image semantic transmission to improve rate-distortion (RD) performance by introducing multi-dimensional contexts into the entropy model of the state-of-the-art (SOTA) NTSCC system. Improvements in RD performance of the proposed MA-NTSCC system are particularly significant in high-resolution image transmission under low bandwidth constraints. The proposed multi-reference entropy model leverages correlations within the latent representation in both spatial and channel dimensions. In the spatial dimension, the latent representation is divided into anchors and non-anchors in a checkerboard pattern, where anchors serve as reference to estimate the mutual information between anchors and non-anchors. In the channel dimension, the latent representation is partitioned into multiple groups, and features in previous groups are analyzed to estimate the mutual information between features in previous and current groups. Taking mutual information into account, the entropy model provides an accurate estimation on the entropy, which enables efficient bandwidth allocation and enhances RD performance. Additionally, the proposed lightweight adaptation modules enable the proposed MA-NTSCC model to achieve transmission quality comparable to separately trained models across various channel conditions and bandwidth requirements. In contrast, traditional NTSCC models provide signal-to-noise ratio (SNR)-distortion performance degrading with channel quality deviating from the fixed training SNR, and consume inflexible bandwidth to transmit an image. Comprehensive experiments are conducted to verify the peak signal-to-noise ratio (PSNR) performance and adaptability of the proposed MA-NTSCC model superior to SOTA methods over both additive white Gaussian noise channel and Rayleigh fading channel.

eess.SP↗

arXiv2025

CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System

With open-source projects growing in size and complexity, manual compilation becomes tedious and error-prone, highlighting the need for automation to improve efficiency and accuracy. However, the complexity of compilation instruction search and error resolution makes automatic compilation challenging. Inspired by the success of LLM-based agents in various fields, we propose CompileAgent, the first LLM-based agent framework dedicated to repo-level compilation. CompileAgent integrates five tools and a flow-based agent strategy, enabling interaction with software artifacts for compilation instruction search and error resolution. To measure the effectiveness of our method, we design a public repo-level benchmark CompileAgentBench, and we also design two baselines for comparison by combining two compilation-friendly schemes. The performance on this benchmark shows that our method significantly improves the compilation success rate, ranging from 10% to 71%. Meanwhile, we evaluate the performance of CompileAgent under different agent strategies and verify the effectiveness of the flow-based strategy. Additionally, we emphasize the scalability of CompileAgent, further expanding its application prospects.

cs.SE↗

arXiv2025

Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and post-capillary PH is critical for guiding treatment decisions. This study develops and validates a deep learning-based diagnostic model for PH, designed to classify patients as non-PH, pre-capillary PH, or post-capillary PH. This retrospective study analyzed data from 204 patients (112 with pre-capillary PH, 32 with post-capillary PH, and 60 non-PH controls) at the First Affiliated Hospital of Nanjing Medical University. Diagnoses were confirmed through right heart catheterization. We selected 6 samples from each category for the test set (18 samples, 10%), with the remaining 186 samples used for the training set. This process was repeated 35 times for testing. This paper proposes a deep learning model that combines Graph convolutional networks (GCN), Convolutional neural networks (CNN), and Transformers. The model was developed to process multimodal data, including short-axis (SAX) sequences, four-chamber (4CH) sequences, and clinical parameters. Our model achieved a performance of Area under the receiver operating characteristic curve (AUC) = 0.81 +- 0.06(standard deviation) and Accuracy (ACC) = 0.73 +- 0.06 on the test set. The discriminative abilities were as follows: non-PH subjects (AUC = 0.74 +- 0.11), pre-capillary PH (AUC = 0.86 +- 0.06), and post-capillary PH (AUC = 0.83 +- 0.10). It has the potential to support clinical decision-making by effectively integrating multimodal data to assist physicians in making accurate and timely diagnoses.

eess.IV↗

arXiv2025

Carleman estimate for full-discrete approximations of the complex Ginzburg-Landau equation with dynamic boundary conditions and applications to controllability

In this paper, we investigate Carleman estimate and controllability result for the fully-discrete approximations of a one-dimensional Ginzburg-Landau equation with dynamic boundary conditions. We first establish a new discrete Carleman estimate for the corresponding adjoint system. Based on this Carleman estimate, we obtain a relaxed observability inequality for the adjoint system, and then a controllability result for the fully-discrete Ginzburg-Landau equation with dynamic boundary conditions.

math.AP↗

arXiv2025

Analysis of Information Loss on Composition Measurement in Stiff Chemically Reacting Systems

Gas sampling methods have been crucial for the advancement of combustion science, enabling analysis of reaction kinetics and pollutant formation. However, the measured composition can deviate from the true one because of the potential residual reactions in the sampling probes. This study formulates the initial composition estimation in stiff chemically reacting systems as a Bayesian inference problem, solved using the No-U-Turn Sampler (NUTS). Information loss arises from the restriction of system dynamics by low dimensional attracting manifold, where constrained evolution causes initial perturbations to decay or vanish in fast eigen-directions in composition space. This study systematically investigates the initial value inference in combustion systems and successfully validates the methodological framework in the Robertson toy system and hydrogen autoignition. Furthermore, a gas sample collected from a one-dimensional hydrogen diffusion flame is analyzed to investigate the effect of frozen temperature on information loss. The research highlights the importance of species covariance information from observations in improving estimation accuracy and identifies how the rank reduction in the sensitivity matrix leads to inference failures. Critical failure times for species inference in the Robertson and hydrogen autoignition systems are analyzed, providing insights into the limits of inference reliability and its physical significance.

stat.AP↗

arXiv2025

Cost-Effective Single-Antenna RSSI Positioning Through Dynamic Radiation Pattern Analysis

This paper presents a novel indoor positioning approach that leverages antenna radiation pattern characteristics through Received Signal Strength Indication (RSSI) measurements in a single-antenna system. By rotating the antenna or reconfiguring its radiation pattern, we derive a maximum likelihood estimation (MLE) algorithm that achieves near-optimal positioning accuracy approaching the Cramer-Rao lower bound (CRLB). Through theoretical analysis, we establish three fundamental theorems characterizing the estimation accuracy bounds and demonstrating how performance improves with increased signal-to-noise ratio, antenna rotation count, and radiation pattern variations. Additionally, we propose a two-position measurement strategy that eliminates dependence on receiving antenna patterns. Simulation results validate that our approach provides an effective solution for indoor robot tracking applications where both accuracy and system simplicity are essential considerations.

eess.SP↗

arXiv2025

OptMetaOpenFOAM: Large Language Model Driven Chain of Thought for Sensitivity Analysis and Parameter Optimization based on CFD

Merging natural language interfaces with computational fluid dynamics (CFD) workflows presents transformative opportunities for both industry and research. In this study, we introduce OptMetaOpenFOAM - a novel framework that bridges MetaOpenFOAM with external analysis and optimization tool libraries through a large language model (LLM)-driven chain-of-thought (COT) methodology. By automating complex CFD tasks via natural language inputs, the framework empowers non-expert users to perform sensitivity analyses and parameter optimizations with markedly improved efficiency. The test dataset comprises 11 distinct CFD analysis or optimization tasks, including a baseline simulation task derived from an OpenFOAM tutorial covering fluid dynamics, combustion, and heat transfer. Results confirm that OptMetaOpenFOAM can accurately interpret user requirements expressed in natural language and effectively invoke external tool libraries alongside MetaOpenFOAM to complete the tasks. Furthermore, validation on a non-OpenFOAM tutorial case - namely, a hydrogen combustion chamber - demonstrates that a mere 200-character natural language input can trigger a sequence of simulation, postprocessing, analysis, and optimization tasks spanning over 2,000 lines of code. These findings underscore the transformative potential of LLM-driven COT methodologies in linking external tool for advanced analysis and optimization, positioning OptMetaOpenFOAM as an effective tool that streamlines CFD simulations and enhances their convenience and efficiency for both industrial and research applications. Code is available at https://github.com/Terry-cyx/MetaOpenFOAM.

cs.AI↗

arXiv2025

RSSI Positioning with Fluid Antenna Systems

We introduce a novel received signal strength intensity (RSSI)-based positioning method using fluid antenna systems (FAS), leveraging their inherent channel correlation properties to improve location accuracy. By enabling a single antenna to sample multiple spatial positions, FAS exhibits high correlation between its ports. We integrate this high inter-port correlation with a logarithmic path loss model to mitigate the impact of fast fading on RSSI signals, and derive a simplified multipoint positioning model based on the established relationship between channel correlation and RSSI signal correlation. A maximum likelihood estimator (MLE) is then developed, for which we provide a closed-form solution. Results demonstrate that our approach outperforms both traditional least squares (LS) methods and single-antenna systems, achieving accuracy comparable to conventional multi-antenna positioning. Furthermore, we analyze the impact of different antenna structures on positioning performance, offering practical guidance for FAS antenna design.

eess.SP↗

arXiv2025

MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for Automating CFD Simulation and Post-Processing

Computational Fluid Dynamics (CFD) is widely used in aerospace, energy, and biology to model fluid flow, heat transfer, and chemical reactions. While Large Language Models (LLMs) have transformed various domains, their application in CFD remains limited, particularly for complex tasks like post-processing. To bridge this gap, we introduce MetaOpenFOAM 2.0, which leverages Chain of Thought (COT) decomposition and iterative verification to enhance accessibility for non-expert users through natural language inputs. Tested on a new benchmark covering simulation (fluid flow, heat transfer, combustion) and post-processing (extraction, visualization), MetaOpenFOAM 2.0 achieved an Executability score of 6.3/7 and a pass rate of 86.9%, significantly outperforming MetaOpenFOAM 1.0 (2.1/7, 0%). Additionally, it proved cost-efficient, averaging $0.15 per case. An ablation study confirmed that COT-driven decomposition and iterative refinement substantially improved task performance. Furthermore, scaling laws showed that increasing COT steps enhanced accuracy while raising token usage, aligning with LLM post-training scaling trends. These results highlight the transformative potential of LLMs in automating CFD workflows for industrial and research applications. Code is available at https://github.com/Terry-cyx/MetaOpenFOAM

cs.AI↗

arXiv2024

Inference-Aware State Reconstruction for Industrial Metaverse under Synchronous/Asynchronous Short-Packet Transmission

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average MSE of reconstruction and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, and they have a negative correlation in some conditions under inference with asynchronous transmission scheme. Also, it is proved that the average MSE of reconstruction with inference can be significantly lower than that without inference, even under weak mean squared spatial correlation (MSSC). In addition, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average MSE of reconstruction. Simulation results show that the two schemes significantly outperform the no inference case, with an average MSE reduction of more than 50%.

eess.SY↗

arXiv2024

MetaOpenFOAM: an LLM-based multi-agent framework for CFD

Remarkable progress has been made in automated problem solving through societies of agents based on large language models (LLMs). Computational fluid dynamics (CFD), as a complex problem, presents unique challenges in automated simulations that require sophisticated solutions. MetaOpenFOAM, as a novel multi-agent collaborations framework, aims to complete CFD simulation tasks with only natural language as input. These simulation tasks include mesh pre-processing, simulation and so on. MetaOpenFOAM harnesses the power of MetaGPT's assembly line paradigm, which assigns diverse roles to various agents, efficiently breaking down complex CFD tasks into manageable subtasks. Langchain further complements MetaOpenFOAM by integrating Retrieval-Augmented Generation (RAG) technology, which enhances the framework's ability by integrating a searchable database of OpenFOAM tutorials for LLMs. Tests on a benchmark for natural language-based CFD solver, consisting of eight CFD simulation tasks, have shown that MetaOpenFOAM achieved a high pass rate per test (85%), with each test case costing only $0.22 on average. The eight CFD simulation tasks encompass a range of multidimensional flow problems, covering compressible and incompressible flows with different physical processes. This demonstrates the capability to automate CFD simulations using only natural language input, iteratively correcting errors to achieve the desired simulations. An ablation study was conducted to verify the necessity of each component in the multi-agent system and the RAG technology. A sensitivity study on the randomness of LLM showed that LLM with low randomness can obtain more stable and accurate results. Additionally, MetaOpenFOAM owns the ability to identify and modify key parameters in user requirements, and excels in correcting bugs when failure match occur,which demonstrates the generalization of MetaOpenFOAM.

cs.AI↗