Dong Xiao

Dong Xiao, Zuoqiang Shi, Siyu Li et al.

Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in the implicit space and propose a novel approach to orient point cloud normals by incorporating isovalue constraints to the Poisson equation. In implicit surface reconstruction, the reconstructed shape is represented as an isosurface of an implicit function defined in the ambient space. Therefore, when such a surface is reconstructed from a set of sample points, the implicit function values at the points should be close to the isovalue corresponding to the surface. Based on this observation and the Poisson equation, we propose an optimization formulation that combines isovalue constraints with local consistency requirements for normals. We optimize normals and implicit functions simultaneously and solve for a globally consistent orientation. Thanks to the sparsity of the linear system, our method can work on an average laptop with reasonable computational time. Experiments show that our method can achieve high performance in non-uniform and noisy data and manage varying sampling densities, artifacts, multiple connected components, and nested surfaces. The source code is available at \url{https://github.com/Submanifold/IsoConstraints}.

Dong Xiao, Zahra Sharif-Khodaei, M. H. Aliabadi

The Semi-Analytical Finite Element (SAFE) method is widely used for computing guided wave dispersion curves in waveguides of arbitrary cross-section. Accurate mode tracking across consecutive wavenumber steps remains challenging, particularly in mode veering regions where eigenvalues become nearly degenerate and eigenvectors vary rapidly. This work establishes a rigorous theoretical framework for mode tracking in single-parameter Hermitian eigenvalue problems arising from SAFE formulations. We derive an explicit expression for the eigenvector derivative, revealing its inverse dependence on the eigenvalue gap, and prove that for any wavenumber and mode there exists a sufficiently small step ensuring unambiguous identification via the Modal Assurance Criterion. For symmetry-protected crossings, the Wigner-von Neumann non-crossing rule guarantees bounded eigenvector derivatives and reliable tracking even with coarse sampling. For continuous symmetries leading to degenerate subspaces, we introduce a rotation-invariant subspace MAC that treats each degenerate pair as a single entity. Based on these insights, we propose an adaptive wavenumber sampling algorithm that automatically refines the discretization where the MAC separation falls below a tolerance, using a novel error indicator to quantify tracking confidence. Validation on symmetric and unsymmetric laminates, an L-shaped bar, and a steel pipe demonstrates robust tracking in veering regions with substantially fewer points than uniform sampling or continuation-based approaches, while comparisons with open-source codes SAFEDC and Dispersion Calculator confirm accuracy and efficiency. The framework provides both theoretical guarantees and practical tools for reliable dispersion curve computation.

Zhenya Zang, Dong Xiao, Quan Wang et al.

We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM) using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. Results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

Dong Xiao, Zahra Sharif Khodaei, M. H. Aliabadi

This paper presents the first systematic application of a material homotopy continuation framework for efficient, automated computation of dispersion curves in viscoelastic waveguides of arbitrary cross-section. A material homotopy continuously maps the original lossy problem to an auxiliary lossless one via an attenuation parameter s in [0,1], addressing the core challenges of the non-Hermitian eigenvalue problem. Grounded in analytic perturbation theory, the method guarantees branch identity continuity--a one-to-one correspondence between solutions at s=0 and s=1--provided the real-parameter path does not cross any exceptional points. Under a Type I exceptional point topology, physical mode labels established at the elastic stage remain valid at the viscoelastic stage without post-processing, yielding the characteristic real-part veering with imaginary-part crossing. The decoupling strategy performs reliable mode tracking in the Hermitian regime via adaptive wavenumber refinement, then propagates a sparse set of key solutions to the target viscoelastic state through predictor-corrector homotopy continuation. Numerical examples across symmetric and unsymmetric laminates validate the framework's robustness and efficiency, with the majority of cases verified at a loss factor of approximately 0.003 and a single symmetric laminate providing additional support at 0.02. For a challenging unsymmetric laminate at a loss factor of 0.05, the method still produces numerically accurate solutions; two complementary diagnostic signatures--an extremely sharp imaginary-part crossing and a discernible discrepancy between spectral group velocity and energy flux velocity--warn of potential label mismatch and guide further analysis.

Dong Xiao, Jian Wang

Wideband spectrum sensing motivates sub-Nyquist sampling architectures that exploit spectral sparsity, yet in blind scenarios where subband locations are unknown, existing schemes require sampling rates at least twice the theoretical minimum. To this end, we propose a dual-frequency aliasing wideband converter (DAWC), which partitions the multiband spectrum into non-uniform frequency intervals and selectively samples only a subset of them, requiring no prior knowledge of subband locations. We demonstrate that under mild conditions on the signal and the system, DAWC achieves perfect subband localization and waveform reconstruction at the theoretical minimum rate. Moreover, we introduce an innovative side-information-aided subspace pursuit (MSSP) algorithm exploiting the common support structure inherent in the signal column submatrices for exact recovery of the spectrum support set. Based on the restricted isometry property (RIP), we provide stable recovery guarantees for MSSP in the presence of noise. Numerical simulations show that the proposed scheme achieves superior spectrum recovery accuracy compared to state-of-the-art methods.

Dong Xiao, Guangyao Chen, Peixi Peng et al. · pku

Anomaly detection is essential for the safety and reliability of autonomous driving systems. Current methods often focus on detection accuracy but neglect response time, which is critical in time-sensitive driving scenarios. In this paper, we introduce real-time anomaly detection for autonomous driving, prioritizing both minimal response time and high accuracy. We propose a novel multimodal asynchronous hybrid network that combines event streams from event cameras with image data from RGB cameras. Our network utilizes the high temporal resolution of event cameras through an asynchronous Graph Neural Network and integrates it with spatial features extracted by a CNN from RGB images. This combination effectively captures both the temporal dynamics and spatial details of the driving environment, enabling swift and precise anomaly detection. Extensive experiments on benchmark datasets show that our approach outperforms existing methods in both accuracy and response time, achieving millisecond-level real-time performance.

Dong Xiao, Zahra Sharif-Khodaei, M. H. Aliabadi

Physics-guided approaches offer a promising path toward accurate and generalisable impact identification in composite structures, especially when experimental data are sparse. This paper presents a hybrid framework for impact localisation and force estimation in composite plates, combining a data-driven implementation of First-Order Shear Deformation Theory (FSDT) with machine learning and uncertainty quantification. The structural configuration and material properties are inferred from dispersion relations, while boundary conditions are identified via modal characteristics to construct a low-fidelity but physically consistent FSDT model. This model enables physics-informed data augmentation for extrapolative localisation using supervised learning. Simultaneously, an adaptive regularisation scheme derived from the same model improves the robustness of impact force reconstruction. The framework also accounts for uncertainty by propagating localisation uncertainty through the force estimation process, producing probabilistic outputs. Validation on composite plate experiments confirms the framework's accuracy, robustness, and efficiency in reducing dependence on large training datasets. The proposed method offers a scalable and transferable solution for impact monitoring and structural health management in composite aerostructures.

Yueji Ma, Yanzun Meng, Dong Xiao et al.

Unoriented surface reconstruction is an important task in computer graphics and has extensive applications. Based on the compact support of wavelet and orthogonality properties, classic wavelet surface reconstruction achieves good and fast reconstruction. However, this method can only handle oriented points. Despite some improved attempts for unoriented points, such as iWSR, these methods perform poorly on sparse point clouds. To address these shortcomings, we propose a wavelet-based method to represent the mollified indicator function and complete both the orientation and surface reconstruction tasks. We use the modifying kernel function to smoothen out discontinuities on the surface, aligning with the continuity of the wavelet basis function. During the calculation of coefficient, we fully utilize the properties of the convolutional kernel function to shift the modifying computation onto wavelet basis to accelerate. In addition, we propose a novel method for constructing the divergence-free function field and using them to construct the additional homogeneous constraints to improve the effectiveness and stability. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both orientation and reconstruction for sparse models. We align the matrix construction with the compact support property of wavelet basis functions to further accelerate our method, resulting in efficient performance on CPU. Our source codes will be released on GitHub.

Dong Xiao, Siyou Lin, Zuoqiang Shi et al.

Surface reconstruction is a fundamental problem in 3D graphics. In this paper, we propose a learning-based approach for implicit surface reconstruction from raw point clouds without normals. Our method is inspired by Gauss Lemma in potential energy theory, which gives an explicit integral formula for the indicator functions. We design a novel deep neural network to perform surface integral and learn the modified indicator functions from un-oriented and noisy point clouds. We concatenate features with different scales for accurate point-wise contributions to the integral. Moreover, we propose a novel Surface Element Feature Extractor to learn local shape properties. Experiments show that our method generates smooth surfaces with high normal consistency from point clouds with different noise scales and achieves state-of-the-art reconstruction performance compared with current data-driven and non-data-driven approaches.