arXiv subjects

Huawei Jiang

Publications and source records attributed to Huawei Jiang.

3 recordsLinked to original sources

arXiv2026

ECG-Mamba-V2: Architectural Refinements to a Bidirectional State Space Model for Multi-Label 12-Lead ECG Classification

State space models offer linear-time sequence modeling and are a promising backbone for multi-label 12-lead ECG classification, but the design choices that drive their accuracy remain unclear. This letter presents ECG-Mamba-V2, a set of empirical refinements to a bidirectional Vision Mamba encoder: the class token is appended at the end of the token sequence instead of the midpoint, the forward and backward scan outputs are summed without the conventional 1/2 scaling, and dropout is applied at a uniform rate across blocks. On PhysioNet/CinC Challenge 2021, ECG-Mamba-V2 reaches 0.6494 macro AUPRC and 0.9716 macro AUROC, against 0.6100 and 0.9646 for its predecessor, while using 34\% fewer parameters and delivering 38\% higher throughput; it wins all 15 paired runs.

cs.CV↗

arXiv2026

HexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities

The accurate automated diagnosis of cardiac abnormalities from 12-lead electrocardiograms (ECGs) is critical for managing cardiovascular disease. However, detecting concurrent conditions remains a challenge for traditional deep learning models, which often have limited ability to model the long-range dependencies inherent in ECG signals. This manuscript proposes HexagonalWarriorMamba (HWMamba), a framework built on the Mamba architecture that processes 12-lead ECGs as single-channel 2D images rather than conventional 1D time series. By integrating a hierarchical architecture with a 2D Selective Scan mechanism, HWMamba is designed to model global context and complex spatial relationships within the data. The model is evaluated on the PhysioNet/Computing in Cardiology Challenge 2021 dataset, which includes 26 diagnostic labels and comprises recordings collected from seven institutions across four countries and three continents. Results demonstrate that HWMamba outperforms current state-of-the-art (SOTA) methods across five key threshold-dependent metrics, including Challenge Score and Subset Accuracy. These improvements provide a balance between strong discriminative capability and effective threshold selection derived from the training data, while maintaining near-SOTA performance in Macro AUROC. This Hexagonal Warrior performance, reflecting consistent performance across multiple evaluation dimensions, positions HWMamba as a robust and versatile approach for multi-label ECG classification.

cs.CV↗

arXiv2026

Phase conjugated master oscillator fiber power amplifier

High-power narrow-linewidth fiber lasers are fundamentally limited by stimulated Brillouin scattering (SBS), which constrains further power scaling while maintaining spectral linewidth. Traditional mitigation techniques, such as active phase modulation, often introduce trade-offs among complexity, cost, and spectral brightness. In this study, we propose and experimentally demonstrate a novel all-optical approach for spectral linewidth manipulation and SBS suppression using optical phase conjugation (OPC). By leveraging nonlinear spectral broadening followed by phase conjugation, this method enables sophisticated linewidth narrowing in fiber amplifier, resulting in narrow linewidth output and enhanced SBS threshold. Using a low-cost fiber oscillator as the seed source, we achieve a spectral compression ratio exceeding 3 times. This method not only eliminates the need for complex electro-optic modulation systems but also provides a pathway toward simpler, high-brightness fiber laser systems. Our findings underscore the viability of OPC as a transformative tool for nonlinearity management and power scaling in high-performance fiber laser architectures.

physics.optics↗