arXiv subjects

Xianglei Liu

Publications and source records attributed to Xianglei Liu.

3 recordsLinked to original sources

arXiv2024

High-fidelity microsecond-scale cellular imaging using two-axis compressed streak imaging fluorescence microscopy

Compressed streak imaging (CSI) is a computational imaging strategy that can acquire video at over 150 trillion frames per second. Despite this achievement, CSI faces challenges in detecting subtle intensity fluctuations in slow-moving, continuously illuminated objects. This limitation, largely attributable to high streak compression and motion blur, has curtailed the broader adoption of CSI in cellular fluorescence microscopy. To address these issues and expand the utility of CSI, we developed a two-axis compressed streak imaging (TACSI) method that results in significant improvements to the reconstructed video fidelity. TACSI introduces a second scanning axis which shuttles a conjugate image of the object with respect to the coded aperture. The moving image decreases the streak compression ratio and produces a "flash and shutter" phenomenon that reduces coded aperture motion blur, overcoming the limitations of current CSI technologies. This approach is supported with an analytical model describing the TACSI compression ratio, along with simulated and empirical measurements. We demonstrate TACSI's ability to measure rapid variations in cell membrane potentials, previously unattainable with conventional CSI. This method has broad implications for high-speed photography, including visualization of action potentials, muscle contractions, and enzymatic reactions that occur on microsecond and faster timescales using fluorescence microscopy.

physics.optics↗

arXiv2021

Data-Driven Modeling of Geometry-Adaptive Steady Heat Transfer based on Convolutional Neural Networks: Heat Convection

In this paper, based on neural networks, we develop a data-driven model for extremely fast prediction of steady-state heat convection of a hot object with arbitrary complex geometry in a two-dimensional space. According to the governing equations, the steady-state heat convection is dominated by convection and thermal diffusion terms, thus the distribution of the physical fields would exhibit stronger correlations between adjacent points. Therefore, the proposed neural network model uses Convolutional Neural Network (CNN) layers as the encoder and Deconvolutional Neural Network (DCNN) layers as the decoder. Compared with fully connected (FC) network model, the CNN based model is good for capturing and reconstructing the spatial relationships of low-rank feature spaces, such as edge intersections, parallelism and symmetry. Furthermore, we applied the signed distance function (SDF) as the network input for representing the problem geometry, which contains more information compared to a binary image. For displaying the strong learning and generalization ability of the proposed network model, the training dataset only contains hot objects with simple geometries: triangles, quadrilaterals, pentagons, hexagons and dodecagons; while the validating cases use arbitrary and complex geometries. According to the study, the trained network model can accurately predict the velocity and temperature field of the problems with complex geometries which has never been seen by the network model during the model training; and the prediction speed is four orders faster than the CFD. The ability of the accurate and extremely faster prediction of the network model suggests the potentials of applying such kind of models to the applications of real-time control, optimization, and design in future.

physics.app-ph↗

arXiv2020

Data-Driven Modeling of Geometry-Adaptive Steady Heat Transfer based on Convolutional Neural Networks: Heat Conduction

Numerical simulation of steady-state heat conduction is common for thermal engineering. The simulation process usually involves mathematical formulation, numerical discretization and iteration of discretized ordinary or partial differential equations depending on complexity of problems. In current work, we develop a data-driven model for extremely fast prediction of steady-state heat conduction of a hot object with arbitrary geometry in a two-dimensional space. Mathematically, the steady-state heat conduction can be described by the Laplace's equation, where a heat (spatial) diffusion term dominates the governing equation. As the intensity of the heat diffusion only depends on the gradient of the temperature field, the temperature distribution of the steady-state heat conduction displays strong features of locality. Therefore, in current approach the data-driven model is developed using convolution neural networks (CNNs), which is good at capturing local features (sub-invariant) thus can be treated as numerical discretization in some sense. Furthermore, in our model, a signed distance function (SDF) is proposed to represent the geometry of the problem, which contains more information compared to a binary image. For the training datasets, the hot objects are consisting of five simple geometries: triangles, quadrilaterals, pentagons, hexagons and dodecagons. All the geometries are different in size, shape, orientation and location. After training, the data-driven network model is able to accurately predict steady-state heat conduction of hot objects with complex geometries which has never been seen by the network model; and the prediction speed is three to four orders faster than numerical simulation. According to the outstanding performance of the network model, it is hoped that this approach can serve as a valuable tool for applications of engineering optimization and design in future.

physics.app-ph↗