arXiv subjects

Lijun Yan

Publications and source records attributed to Lijun Yan.

5 recordsLinked to original sources

arXiv2026

Weak Magnetic Sensing via Floquet Driving in an Active Cavity Magnon Coupled System

While significant advancements have been made in weak magnetic field detection, conventional high-sensitivity techniques are often limited by requirements for cryogenic operation or bulky setups. In this work, we develop a sensitive alternating magnetic field sensor based on a coupled system of an active microwave cavity and yttrium iron garnet (YIG), with the components implemented on printed circuit boards (PCBs). By introducing electrically tunable gain to compensate for cavity losses, we substantially improve both the quality factor and the signal intensity. Under the coupled system, Floquet modulation is induced by the alternating magnetic field, allowing for weak field detection by driving a specific hybrid mode and measuring the resulting Floquet sidebands. This miniaturized device operates at room temperature, achieving a detection limit of 121 pT/\sqrt{Hz}.

cond-mat.mes-hall↗

arXiv2025

Ultrasensitive Magnetometer based on Cusp Points of the Photon-Magnon Synchronization Mode

Ultrasensitive magnetometers based on spin resonances have led to remarkable achievements. However, the gyromagnetic ratios of these spin resonances that determine the responsivity of magnetometers to weak magnetic fields are inherently constrained by the Land$\acute{e}$ g-factor of particles, such as the electron, with a constant gyromagnetic ratio of $\gamma_e=2\pi\times28$ GHz/T. Here, we demonstrate an ultrasensitive magnetometer based on the cusp point (CP) of photon-magnon synchronization modes (PMSMs). The PMSM's gyromagnetic ratio at the CP is enhanced to $37\gamma_e$ and further amplified to $236\gamma_e$ by utilizing the sixth-order oscillating mode of the PMSM. Moreover, the emission linewidth of the PMSM can be reduced to 0.06 Hz, resulting in excellent sensitivity to weak magnetic fields. These outstanding properties position our magnetometer to potentially achieve superior sensitivity to conventional magnetometers. Our work introduces a cost-effective prototype for the next generation of magnetometry, and may advance scientific research and technologies that rely on ultrasensitive magnetic field detection.

cond-mat.mes-hall↗

arXiv2025

Clinical Inspired MRI Lesion Segmentation

Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting pathological tissues in various diseases. Different MRI sequences have different contrast mechanisms and sensitivities for different types of lesions, which pose challenges to accurate and consistent lesion segmentation. In clinical practice, radiologists commonly use the sub-sequence feature, i.e. the difference between post contrast-enhanced T1-weighted (post) and pre-contrast-enhanced (pre) sequences, to locate lesions. Inspired by this, we propose a residual fusion method to learn subsequence representation for MRI lesion segmentation. Specifically, we iteratively and adaptively fuse features from pre- and post-contrast sequences at multiple resolutions, using dynamic weights to achieve optimal fusion and address diverse lesion enhancement patterns. Our method achieves state-of-the-art performances on BraTS2023 dataset for brain tumor segmentation and our in-house breast MRI dataset for breast lesion segmentation. Our method is clinically inspired and has the potential to facilitate lesion segmentation in various applications.

cs.CV↗

arXiv2024

Enhanced MRI Representation via Cross-series Masking

Magnetic resonance imaging (MRI) is indispensable for diagnosing and planning treatment in various medical conditions due to its ability to produce multi-series images that reveal different tissue characteristics. However, integrating these diverse series to form a coherent analysis presents significant challenges, such as differing spatial resolutions and contrast patterns meanwhile requiring extensive annotated data, which is scarce in clinical practice. Due to these issues, we introduce a novel Cross-Series Masking (CSM) Strategy for effectively learning MRI representation in a self-supervised manner. Specifically, CSM commences by randomly sampling a subset of regions and series, which are then strategically masked. In the training process, the cross-series representation is learned by utilizing the unmasked data to reconstruct the masked portions. This process not only integrates information across different series but also facilitates the ability to model both intra-series and inter-series correlations and complementarities. With the learned representation, the downstream tasks like segmentation and classification are also enhanced. Taking brain tissue segmentation, breast tumor benign/malignant classification, and prostate cancer diagnosis as examples, our method achieves state-of-the-art performance on both public and in-house datasets.

eess.IV↗

arXiv2019

Bilinear discriminant feature line analysis for image feature extraction

A novel bilinear discriminant feature line analysis (BDFLA) is proposed for image feature extraction. The nearest feature line (NFL) is a powerful classifier. Some NFL-based subspace algorithms were introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For image classification tasks, the image samples should be transformed to vectors first. This process induces a high computational complexity and may also lead to loss of the geometric feature of samples. The proposed BDFLA is a matrix-based algorithm. It aims to minimise the within-class scatter and maximise the between-class scatter based on a two-dimensional (2D) NFL. Experimental results on two-image databases confirm the effectiveness.

cs.CV↗