Abstract: Disclosed is a swirl-type liquid raw material in-line static mixer. In an apparatus, a pipe body extends along its central axis; a first raw material inlet is arranged at a top end of the pipe body and is collinear with the central axis; a second raw material inlet is arranged on a side wall of the pipe body and is perpendicular to the central axis; a first swirl pipe is arranged in the pipe body and connects with the first raw material inlet; a second swirl pipe is arranged in the pipe body and connects with the first swirl pipe and the second raw material inlet; a third swirl pipe is arranged in the pipe body and connects with the second swirl pipe; an outlet is arranged at a bottom end of the pipe body and connects with the third swirl pipe.

Abstract: The disclosure relates to system and method for image quality detection. The method may include: obtaining an image acquired by a camera; obtaining an image quality detection model, the image quality detection model being provided by training a machine learning model using a plurality of training samples; determining a detection result of the image using the image quality detection model; and in response to a determination that the detection result includes a quality anomaly of the image, generating a strategy in response to the quality anomaly.

Abstract: Provided are an apparatus and a method of inputting note information to an image of a shot object in order to display an image of a shot object in which the note information has been reflected.

Abstract: Provided are an apparatus and a method of inputting note information to an image of a shot object in order to display an image of a shot object in which the note information has been reflected.

Abstract: An image reconstruction method includes receiving volume data comprising a plurality of sampling points, determining a first conditioning of the sampling points suppressing low amplitudes and conserving maximum amplitudes, determining a second conditioning of the sampling points wherein an influence of a sampling point depends on its distance to a grid point in a sampling grid, determining a kernel comprising a plurality of weighting functions for the first conditioning and the second conditioning to determine an energy spread of each of the plurality of sampling points without determining a shape or size of the kernel, and outputting a reconstructed volume according to the energy spread of each of the plurality of sampling points.

Abstract: An image reconstruction method includes receiving volume data comprising a plurality of sampling points, determining a first conditioning of the sampling points suppressing low amplitudes and conserving maximum amplitudes, determining a second conditioning of the sampling points wherein an influence of a sampling point depends on its distance to a grid point in a sampling grid, determining a kernel comprising a plurality of weighting functions for the first conditioning and the second conditioning to determine an energy spread of each of the plurality of sampling points without determining a shape or size of the kernel, and outputting a reconstructed volume according to the energy spread of each of the plurality of sampling points.