arXiv subjects

Fubei Liu

Publications and source records attributed to Fubei Liu.

3 recordsLinked to original sources

arXiv2026

Topological Metamaterial for Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) is crucial in global healthcare, but the traditional receive coils, as a core component of MRI, SNR enhancement is limited due to the optimization of channel number and magnetic field strength faces high cost and complexity challenges. Here, we demonstrate the use of a topological material to enhance MRI signal reception. Designed with a stack of weak couplings, this material forms quasi-two-dimensional dual topological boundary states. High properties are achieved through low-loss signal transmission via these topological states, as well as only enhanced local magnetic fields and increased number of channels. Initial tests demonstrate superior performance and accessibility compared to commercial coils, suggesting significant potential. This concept introduces a transformative paradigm for all MRI coil designs.

physics.app-ph↗

arXiv2024

Diffractive Magic Cube Network with Super-high Capacity Enabled by Mechanical Reconfiguration

Free-space wavefront manipulation devices have emerged as powerful platforms for advanced optical information systems. In response to the challenges posed by the exponential growth of optical information, optical multiplexing and dynamic reconfigurable devices are being actively explored to the enhance system capacity. Among them, coarse-grained mechanically reconfigurable mechanism offers a cost-effective and low-complexity approach for capacity enhancement. However, the channel numbers achieved in current studies are insufficient for practical applications because of inadequate mechanical transformations and suboptimal optimization models. In this article, a diffractive magic cube network (DMCN) is proposed to advance the multiplexing capacity of mechanically reconfigurable system. We utilized the diffractive deep neural network (D2NN) model to jointly optimize the subset of channels generated by the combination of three mechanical operations, permutation, translation, and rotation. The 144-channel holograms, 108-channel single/double focus, 60-channel single/multi-mode OAM beam generation were experimentally demonstrated using diffractive optical elements (DOEs). An equivalent connectivity law was formulated to improve model scalability. Our strategy not only provides a novel paradigm to improve system capacity to super-high level with low crosstalk, but also paves the way for new advancements in optical storage, computing, communication, and photolithography.

physics.optics↗

arXiv2024

All-optical autoencoder machine learning framework using linear diffractive processors

Diffractive deep neural network (D2NN), known for its high speed and strong parallelism, has been widely applied across various fields, including pattern recognition, image processing, and image transmission. However, existing network architectures primarily focus on data representation within the original domain, with limited exploration of the latent space, thereby restricting the information mining capabilities and multifunctional integration of D2NNs. Here, we propose an all-optical autoencoder (OAE) framework that linearly encodes the input wavefield into a prior shape distribution in the diffractive latent space (DLS) and decodes the encoded pattern back to the original wavefield. By leveraging the bidirectional multiplexing property of D2NN, the OAE models function as encoders in one direction of wave propagation and as decoders in the opposite direction. We further apply the models to three key areas: image denoising, noise-resistant reconfigurable image classification, and image generation. Proof-of-concept experiments have been conducted to validate numerical simulations. Our OAE framework fully exploits the potential of latent representations, enabling a single set of diffractive processors to simultaneously achieve image reconstruction, representation, and generation. This work not only offers fresh insights into the design of optical generative models but also paves the way for developing multifunctional, highly integrated, and general optical intelligent systems.

physics.app-ph↗