Abstract: This application provides an image processing method and an electronic device. The image processing method includes: acquiring a plurality of frames of original images; adding a reference coordinate image layer to a second field-of-view image; obtaining an image layer set according to a first field-of-view image, the second field-of-view image, and the reference coordinate image layer; processing, by using a deep learning network model, the image layer set to obtain a first enhanced image; and obtaining a second enhanced image according to the first enhanced image. In the method, since the added reference coordinate image layer reflects a mapping relationship between a field of view corresponding to the first field-of-view image and a field of view corresponding to the second field-of-view image, through the addition of the reference coordinate image layer, priori information can be added, so that different adjustments can be made subsequently according to different field-of-view relationships.

Abstract: The present disclosure relates to systems and methods for image generation. The methods may include obtaining projection data generated by a scanner; generating, based on a first weighting function, a first image by back-projecting the projection data, the first image having a first region corresponding to a first part of the object; generating, based on a second weighting function, a second image by back-projecting the projection data, the second image having a second region corresponding to the first part of the object, the second region of the second image presenting a better CT number uniformity than the first region of the first image; and generating a third image based on the first image and the second image.

Abstract: The present disclosure relates to systems and methods for image generation. The methods may include obtaining projection data generated by a scanner; generating, based on a first weighting function, a first image by back-projecting the projection data, the first image having a first region corresponding to a first part of the object; generating, based on a second weighting function, a second image by back-projecting the projection data, the second image having a second region corresponding to the first part of the object, the second region of the second image presenting a better CT number uniformity than the first region of the first image; and generating a third image based on the first image and the second image.

Abstract: The present disclosure describes techniques to forecast financial data using deep learning. These techniques are operative to transform time series data in a financial context into a machine learning model configured to predict future financial data. The machine learning model may implement a deep learning structure to account for a sequence-sequence prediction where a movement/distribution of the time series data is non-linear. The machine learning model may incorporate features related to one or more external factors affecting the future financial data. Other embodiments are described and claimed.

Abstract: A method and a system for rail transit communication. The method includes: selecting a second target FSO transceiver from the FSO transceivers that are currently located within a signal coverage of a target FSO base station, in response to detecting that a first target free-space-optics (FSO) transceiver moves out of the signal coverage of the target FSO base station when data communication between the target FSO base station and the target user terminal is performed via the first target FSO transceiver, where the target FSO base station is one of the FSO base stations located on the rail; and maintaining the data communication between the target FSO base station and the target user terminal, by using the second target FSO transceiver. Handover among the base stations is reduced for user equipment, data communication rate is increased, and communication experience of passengers can be improved.

Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

Abstract: Techniques to use deep learning to forecast future orders for a financial service provider to execute by a future date. These techniques leverage a variety of features to predict an appropriate number of orders to execute, for example, on a next trading day. Some features correspond to market indices including their reconstitution schedule while others may correspond to historical orders by the financial service provider. By segregating the features and filtering individual features, these techniques are able to eliminate some noise and focus on a particular feature for insight into the future orders. Other embodiments are described and claimed.

Abstract: The present disclosure describes techniques to forecast financial data using deep learning. These techniques are operative to transform time series data in a financial context into a machine learning model configured to predict future financial data. The machine learning model may implement a deep learning structure to account for a sequence-sequence prediction where a movement/distribution of the time series data is non-linear. The machine learning model may incorporate features related to one or more external factors affecting the future financial data. Other embodiments are described and claimed.

Abstract: The present disclosure relates to systems and methods for image generation. The methods may include obtaining projection data generated by a scanner; generating, based on a first weighting function, a first image by back-projecting the projection data, the first image having a first region corresponding to a first part of the object; generating, based on a second weighting function, a second image by back-projecting the projection data, the second image having a second region corresponding to the first part of the object, the second region of the second image presenting a better CT number uniformity than the first region of the first image; and generating a third image based on the first image and the second image.

Abstract: The present disclosure relates to systems and methods for image generation. The methods may include obtaining projection data generated by a scanner; generating, based on a first weighting function, a first image by back-projecting the projection data, the first image having a first region corresponding to a first part of the object; generating, based on a second weighting function, a second image by back-projecting the projection data, the second image having a second region corresponding to the first part of the object, the second region of the second image presenting a better CT number uniformity than the first region of the first image; and generating a third image based on the first image and the second image.

Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

Abstract: The present disclosure relates to systems and methods for image generation. The methods may include obtaining projection data generated by a scanner; generating, based on a first weighting function, a first image by back-projecting the projection data, the first image having a first region corresponding to a first part of the object; generating, based on a second weighting function, a second image by back-projecting the projection data, the second image having a second region corresponding to the first part of the object, the second region of the second image presenting a better CT number uniformity than the first region of the first image; and generating a third image based on the first image and the second image.

Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

Abstract: A method and a system for rail transit communication. The method includes: selecting a second target FSO transceiver from the FSO transceivers that are currently located within a signal coverage of a target FSO base station, in response to detecting that a first target free-space-optics (FSO) transceiver moves out of the signal coverage of the target FSO base station when data communication between the target FSO base station and the target user terminal is performed via the first target FSO transceiver, where the target FSO base station is one of the FSO base stations located on the rail; and maintaining the data communication between the target FSO base station and the target user terminal, by using the second target FSO transceiver. Handover among the base stations is reduced for user equipment, data communication rate is increased, and communication experience of passengers can be improved.

Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.