Abstract: The embodiments of the present application relates to a camera, comprising a first housing, a stitching lens mechanism, and a driving assembly. The stitching lens mechanism is mounted in the first housing, and the stitching lens mechanism comprises at least two first assemblies, the first lens assembly comprises a first lens, a first included angle is formed between at least two first lenses. A driving assembly is connected to the first lens assembly through a rotating assembly. At least two first lenses are distributed in a first plane, the first housing is rotatably arranged in a second plane, and the second plane is perpendicular to the first plane. Each of the first lens assemblies takes pictures in different orientations for the same scene, so that the imaging field of view is larger. So that the first housing can rotate under the drive of the driving assembly, that is, the stitching lens mechanism can rotate.

Abstract: A technology of 3D printing integration of hard materials and cells, a preparation of bone-repair functional module with osteogenic microenvironment, bone organoid method and the application of quick repair of bone defects are provided. A preparation method of biological microenvironmental factors as independent osteogenic factors is further provided. The present integrated 3D printing technology realizes 3D printing of cells and hard materials synchronously by adjusting the temperature, so as to build a real sense of biomimetic bone tissue, which can be customized according to the specific defects and clinical needs of patients.

Abstract: The embodiments of the present application relates to a camera, comprising a first housing, a stitching lens mechanism, and a driving assembly. The stitching lens mechanism is mounted in the first housing, and the stitching lens mechanism comprises at least two first assemblies, the first lens assembly comprises a first lens, a first included angle is formed between at least two first lenses. A driving assembly is connected to the first lens assembly through a rotating assembly. At least two first lenses are distributed in a first plane, the first housing is rotatably arranged in a second plane, and the second plane is perpendicular to the first plane. Each of the first lens assemblies takes pictures in different orientations for the same scene, so that the imaging field of view is larger. So that the first housing can rotate under the drive of the driving assembly, that is, the stitching lens mechanism can rotate.

Abstract: Provided are an acceleration method, an apparatus and a system on chip. The acceleration method includes: the accelerator receives N-th parameter information of an N-th layer from a controller, wherein M layer of the deep neural network correspond to M application specific integrated circuit in the accelerator, wherein M and N are positive integer, M?2, 1?N?M, executes computation of the N-th layer according to the N-th parameter information, and transmits N-th computation result information of the N-th layer indicates that the computation of the N-th layer is completed, to the controller, wherein the computation result information comprises computation result of the N-th layer. Then a complete flexibility for ASIC implementations of hardware accelerator can be achieved, and any kind of DNN based algorithms can be supported, which improves the universality of the accelerator.

Abstract: This invention relates to a zinc-rich epoxy anti-corrosion coating comprising a component A capable of being cured into a film, and a component B containing a curing agent, wherein the component A comprises, by mass percentage, 0.1-5 wt % graphene, 0-35 wt % zinc powder, 30-70 wt % filler, 10-20 wt % epoxy resin having 54 wt % solid content, and 10-20 wt % solvent, wherein the sum of the mass percentage contents of the graphene, zinc powder and filler is 60-80 wt % and the sum of the mass percentage contents of all components of component A is 100 wt %. According to the present invention, the addition of graphene to an anti-corrosion coating decreases the thickness of the coating film, reduces the amount of zinc powder while increases the anti-corrosion effect. It reduces zinc oxide vapour generated during welding and is environmentally friendly.

Abstract: A system and process for video characterization that facilitates video classification and retrieval, as well as motion detection, applications. This involves characterizing a video sequence with a gray scale image having pixel levels that reflect the intensity of motion associated with a corresponding region in the sequence of video frames. The intensity of motion is defined using any of three characterizing processes. Namely, a perceived motion energy spectrum (PMES) characterizing process that represents object-based motion intensity over the sequence of frames, a spatio-temporal entropy (STE) characterizing process that represents the intensity of motion based on color variation at each pixel location, a motion vector angle entropy (MVAE) characterizing process which represents the intensity of motion based on the variation of motion vector angles.

Abstract: This invention relates to a zinc-rich epoxy anti-corrosion coating comprising a component A capable of being cured into a film, and a component B containing a curing agent, wherein the component A comprises, by mass percentage, 0.1-5 wt % graphene, 0-35 wt % zinc powder, 30-70 wt % filler, 10-20 wt % epoxy resin having 54 wt % solid content, and 10-20 wt % solvent, wherein the sum of the mass percentage contents of the graphene, zinc powder and filler is 60-80 wt % and the sum of the mass percentage contents of all components of component A is 100 wt %. According to the present invention, the addition of graphene to an anti-corrosion coating decreases the thickness of the coating film, reduces the amount of zinc powder while increases the anti-corrosion effect. It reduces zinc oxide vapour generated during welding and is environmentally friendly.

Abstract: A system and process for video characterization that facilitates video classification and retrieval, as well as motion detection, applications. This involves characterizing a video sequence with a gray scale image having pixel levels that reflect the intensity of motion associated with a corresponding region in the sequence of video frames. The intensity of motion is defined using any of three characterizing processes. Namely, a perceived motion energy spectrum (PMES) characterizing process that represents object-based motion intensity over the sequence of frames, a spatio-temporal entropy (STE) characterizing process that represents the intensity of motion based on color variation at each pixel location, a motion vector angle entropy (MVAE) characterizing process which represents the intensity of motion based on the variation of motion vector angles.

Abstract: A system and process for video characterization that facilitates video classification and retrieval, as well as motion detection, applications. This involves characterizing a video sequence with a gray scale image having pixel levels that reflect the intensity of motion associated with a corresponding region in the sequence of video frames. The intensity of motion is defined using any of three characterizing processes. Namely, a perceived motion energy spectrum (PMES) characterizing process that represents object-based motion intensity over the sequence of frames, a spatio-temporal entropy (STE) characterizing process that represents the intensity of motion based on color variation at each pixel location, a motion vector angle entropy (MVAE) characterizing process which represents the intensity of motion based on the variation of motion vector angles.

Abstract: A system and process for video characterization that facilitates video classification and retrieval, as well as motion detection, applications. This involves characterizing a video sequence with a gray scale image having pixel levels that reflect the intensity of motion associated with a corresponding region in the sequence of video frames. The intensity of motion is defined using any of three characterizing processes. Namely, a perceived motion energy spectrum (PMES) characterizing process that represents object-based motion intensity over the sequence of frames, a spatio-temporal entropy (STE) characterizing process that represents the intensity of motion based on color variation at each pixel location, a motion vector angle entropy (MVAE) characterizing process which represents the intensity of motion based on the variation of motion vector angles.

Abstract: A system and process for video characterization that facilitates video classification and retrieval, as well as motion detection, applications. This involves characterizing a video sequence with a gray scale image having pixel levels that reflect the intensity of motion associated with a corresponding region in the sequence of video frames. The intensity of motion is defined using any of three characterizing processes. Namely, a perceived motion energy spectrum (PMES) characterizing process that represents object-based motion intensity over the sequence of frames, a spatio-temporal entropy (STE) characterizing process that represents the intensity of motion based on color variation at each pixel location, a motion vector angle entropy (MVAE) characterizing process which represents the intensity of motion based on the variation of motion vector angles.

Abstract: A system and process for video characterization that facilitates video classification and retrieval, as well as motion detection, applications. This involves characterizing a video sequence with a gray scale image having pixel levels that reflect the intensity of motion associated with a corresponding region in the sequence of video frames. The intensity of motion is defined using any of three characterizing processes. Namely, a perceived motion energy spectrum (PMES) characterizing process that represents object-based motion intensity over the sequence of frames, a spatio-temporal entropy (STE) characterizing process that represents the intensity of motion based on color variation at each pixel location, a motion vector angle entropy (MVAE) characterizing process which represents the intensity of motion based on the variation of motion vector angles.