Abstract: Provided are an optical lens, a camera module and a front camera. From an object side to an Imaging plane, the optical lens sequentially includes: a stop; a first lens having a positive focal power and a concave object side surface; a second lens having a negative focal power and a concave image side surface; a third lens having a positive focal power, a convex object side surface and a concave image side surface; a fourth lens having a positive focal power, a concave object side surface and a convex image side surface; a fifth lens having a negative focal power. An entrance pupil diameter EPD of the optical lens is smaller than 1.58 mm, and the maximum distance between the projection of an entrance pupil and the projection of the object side surface of the first lens on the optical axis is greater than 0.17 mm.

Abstract: A holographic 3D display system based on virtual array splicing of a spatial light modulator includes a laser configured to generate a coherent light beam, first, second and third beam splitters, first and second reflectors, a shutter array, a spatial filter array, a solid lens, first and second light beam deflection elements and a spatial light modulator. The first and second beam splitters and the first reflector are configured to split the light beam generated by the laser into three parallel light beams to irradiate the shutter array. The shutter array is configured to control the three parallel light beams to sequentially pass therethrough according to a set time sequence. The three parallel light beams passing through the shutter array are expanded and collimated by the spatial filter array and the solid lens to form three parallel light beams with the same size and uniform intensity.

Abstract: An interference cancellation method, a medium, and a device, capable of cancelling interference signals from measurement signals received on the basis of a plurality of channels to obtain effective signals. The method comprises: collecting, from a first-type channel, measurement signals in which effective signals and a first interference signal are mixed, and collecting a second interference signal from a second-type channel; estimating the first interference signal in the measurement signals according to a coupling relationship between first calibration data and second calibration data and on the basis of the second interference signal; removing the first interference signal from the measurement signals to obtain a target effective signal; wherein the first calibration data and the second calibration data are interference signals respectively collected from the first-type channel and the second-type channel when an electronic device is in a preset state.

Abstract: Image reconstruction methods for multi-slice and multi-contrast magnetic resonance imaging with complementary sampling schemes are provided, comprising: data acquisition using complementary sampling schemes between slices or/and contrasts) in spiral imaging or Cartesian acquisition; joint calibrationless reconstruction of multi-slice and multi-contrast data via block-wise Hankel tensor completion.

Abstract: The present invention relates to a fetal cell capture module, a microfluidic chip for fetal cell capture, and methods for using the same. The fetal cell capture module comprises a cell capture carrier and recognition molecule(s) for specific capture the cell(s). The recognition molecule is attached to the surface of the carrier via an organic conjugate L comprising disulfide bonds. The surface of the chip is modified with recognition molecules that specifically capture fetal cells via organic conjugates comprising disulfide bonds. The recognition molecule, after capturing the cell, achieves the release of the cell by chemically cleaving the disulfide bonds in the organic coupling conjugate. The present invention enables the capture of fetal cell(s) from whole blood without pre-treatment with a high capture rate, low cell loss, simple and accurate cell release operation, and the efficient and noninvasive release of fetal cells and whole genome analysis.

Abstract: Disclosed are deep learning based methods for magnetic resonance imaging (MRI) image reconstruction from partial Fourier-space (i.e., k-space) data, involving: obtaining high-quality complex MRI image data or fully-sampled k-space data as training data; training reconstruction models to predict high-quality complex MRI image data or complete k-space data from incomplete or partial k-space data; and applying trained models to reconstruct high-quality complex MRI image data or complete k-space data from partial k-space data.