Digital images captured under a low-light environment often struggle to clearly assign the intensity due to uncertainty and insufficient illumination. To address such issues, fuzzy set theory plays a crucial role. Bipolar fuzzy set, an important extension of the conventional fuzzy set, provides an advanced framework to deal with uncertainty by focusing on positive and negative membership grades. However, handling negative membership grades and developing an image enhancement model that accesses bipolar fuzzy information pose significant challenges. To address this issue, the present study designs a bipolar fuzzy set based low-light image enhancement model by leveraging the one-to-one correspondence between bipolar fuzzy set and two-polar fuzzy set. Additionally, an image fusion approach is employed to combine the images of positive and negative membership grades. Finally, the experimental study revealed that the proposed model is superior to several state-of-the-art techniques in terms of both enhancement quality and computational efficiency.

Fingerprint biometrics plays a crucial role in biometric identification, especially in applications such as criminal investigations. Although recent progress in recognition methodology has significantly enhanced automated fingerprint recognition, these systems still rely heavily on the quality of the input fingerprints. In criminal investigations, fingerprints are often of low quality due to their incidental deposition from natural oils and sweat, rather than being deliberately captured under controlled conditions. This degradation can significantly impact usability and identification accuracy, underscoring the need for effective Fingerprint Quality Assessment (FQA) methods. In this paper, we establish the Crime Scene Fingerprints quality assessment Dataset (CSFD-10k), the largest dataset of its kind, containing 11,500 fingerprint images from real criminal investigations. Of these, 10,000 samples are assigned Mean Opinion Scores (MOSs) for correlation testing, while the remaining 1,500 are labeled based on matching performance for generalizability testing. All labels are provided by frontline criminal police officers. Using this dataset, we propose a deep neural network-based Dual-Branch FQA (DB-FQA) framework that integrates image-level and edge-level features. The DB-FQA enhances ridge details by transforming raw grayscale fingerprints into edge maps using the Logical/Linear operator. A dual-branch network processes both the raw fingerprint and the edge map, and the Multi-scale Adaptive Cross feature Fusion (MACF) module fuses these features, guided by the edge map to highlight quality-related regions of interest. Extensive experiments demonstrate the robustness and superiority of our proposed method, offering substantial support for forensic fingerprint biometrics. The code and dataset are available at https://github.com/wzhsysu/FIQA.

Federated learning (FL) has gained prominence in electroencephalogram (EEG)-based emotion recognition because of its ability to enable secure collaborative training without centralized data. However, traditional FL faces challenges due to model and data heterogeneity in smart healthcare settings. For example, medical institutions have varying computational resources, which creates a need for personalized local models. Moreover, EEG data from medical institutions typically face data heterogeneity issues stemming from limitations in participant availability, ethical constraints, and cultural differences among subjects, which can slow model convergence and degrade model performance. To address these challenges, we propose FedKDC, a novel FL framework that incorporates clustered knowledge distillation (CKD). This method introduces a consensus-based distributed learning mechanism to facilitate the clustering process. It then enhances the convergence speed through intraclass distillation and reduces the negative impact of heterogeneity through interclass distillation. Additionally, we introduce a DriftGuard mechanism to mitigate client drift, along with an entropy reducer to decrease the entropy of aggregated knowledge. The framework is validated on the SEED, SEED-IV, SEED-FRA, and SEED-GER datasets, demonstrating its effectiveness in scenarios where both the data and the models are heterogeneous. Experimental results show that FedKDC outperforms other FL frameworks in emotion recognition, achieving a maximum average accuracy of 85.2%, and in convergence efficiency, with faster and more stable convergence.

Evidence-based deep learning represents a burgeoning paradigm for uncertainty estimation, offering reliable predictions with negligible extra computational overheads. Existing methods usually adopt Kullback-Leibler divergence to estimate the uncertainty of network predictions, ignoring domain gaps among various modalities. To tackle this issue, this paper introduces a novel algorithm based on Hölder Divergence (HD) to enhance the reliability of multi-view learning by addressing inherent uncertainty challenges from incomplete or noisy data. Generally, our method extracts the representations of multiple modalities through parallel network branches, and then employs HD to estimate the prediction uncertainties. Through the Dempster-Shafer theory, integration of uncertainty from different modalities, thereby generating a comprehensive result that considers all available representations. Mathematically, HD proves to better measure the “distance” between real data distribution and predictive distribution of the model and improve the performances of multi-class recognition tasks. Specifically, our method surpasses the existing state-of-the-art counterparts on all evaluating benchmarks. We further conduct extensive experiments on different backbones to verify our superior robustness. It is demonstrated that our method successfully pushes the corresponding performance boundaries. Finally, we perform experiments on more challenging scenarios,i.e., learning with incomplete or noisy data, revealing that our method exhibits a high tolerance to such corrupted data.

Phonocardiogram classification methods using deep neural networks have been widely applied to the early detection of cardiovascular diseases recently.Despite their excellent recognition rate, the sizeable computational complexity limits their further development.Nowadays, knowledge distillation (KD) is an established paradigm for model compression.While current research on multi-teacher KD has shown potential to impart more comprehensive knowledge to the student than single-teacher KD, this approach is not suitable for all scenarios.This paper proposes a novel KD strategy to realise an adaptive multi-teacher instruction mechanism.We design a teacher selection strategy called voting network to tell the contribution of different teachers on each distillation points, so that the student can choose the useful information and renounce the redundant one.An evaluation demonstrates that our method reaches excellent accuracy (92.8 %) while maintaining a low computational complexity (0.7 M).

How to determine the velocity of an S-wave from measured seismic data is an important topic in seismology. A modern technique for obtaining the S-wave velocity is to solve an inverse problem so that the simulated dispersion curve (the relation between the frequencies and phase velocities) coincides with the actual experimental results. In this work, by using the seismic impedance tensor, we propose a new function describing Rayleigh wave dispersion in the layered medium model of the Earth, which offers an efficient way to compute the dispersion curve. With this newly established forward model, based on mixture density networks (MDNs), we develop a physics-informed neural network, named MDN based on a physics informed forward model (FW-MDN), to estimate the S-wave velocity from dispersion curves. The FW-MDN method deals with the nonuniqueness issue encountered in the inversion of dispersion curves for the crust and upper mantle models, and attains satisfactory performance on an artificial dataset with various noise structures. Numerical simulations are performed to show that the FW-MDN offers easy calculation, efficient computation, and high precision for model characterization.

Motivated by important applications in image processing, we study a class of second-order geometric quasilinear hyperbolic partial differential equations (PDEs). This is inspired by the recent development of second-order damping systems associated to gradient flows for energy decaying. In numerical computations, it turns out that the second-order methods are superior to their first-order counter-parts. We concentrate on (i) a damped second-order total variation flow for, e.g., image denoising and (ii) a damped second-order mean curvature flow for level sets of scalar functions. The latter is connected to a nonconvex variational model capable of correcting displacement errors in image data (e.g., dejittering). For the former equation, we prove the existence and uniqueness of the solution and its long time behavior and provide an analytical solution given some simple initial datum. For the latter, we draw a connection between the equation and some second-order geometric PDEs evolving the hypersurfaces and show the existence and uniqueness of the solution for a regularized version of the equation. Finally, some numerical comparisons of the solution behavior for the new equations with first-order flows are presented.