Abstract: Particular embodiments may receive a request to perform a task to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may identify a sequence of a service instances based on the task. Each of the service instances may belong to one of the multiple decomposed service types. The sequence of service instances may be scheduled for deployment to accomplish the task by the core network. The core network may deploy the sequence of the service instances to one or more server machines of the core network.

Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g.

Abstract: Particular embodiments may receive a request to perform a task to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may identify a sequence of a service instances based on the task. Each of the service instances may belong to one of the multiple decomposed service types. The sequence of service instances may be scheduled for deployment to accomplish the task by the core network. The core network may deploy the sequence of the service instances to one or more server machines of the core network.

Abstract: Particular embodiments may communicate to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may receive a user task associating with service instances. Each of the service instances may belong to one of the multiple decomposed service types and be configured by a service chaining orchestration entity. The service instances may be deployed to one or more of server machines of the core network with respect to the configurations of the service instances, by a service chaining orchestration entity. The capacity of the core network may be scaled up or down by network dimensioning.

Abstract: Particular embodiments may communicate to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may receive a user task associating with service instances. Each of the service instances may belong to one of the multiple decomposed service types and be configured by a service chaining orchestration entity. The service instances may be deployed to one or more of server machines of the core network with respect to the configurations of the service instances, by a service chaining orchestration entity. The capacity of the core network may be scaled up or down by network dimensioning.

Abstract: A method and apparatus is provided to obtain projection data representing an intensity of X-ray radiation detected at a plurality of detector elements after traversing an object, the projection data being corrected for a baseline offset, correct the projection data by performing a positivity mapping to generate corrected projection data, perform a logarithm operation on the corrected projection data to generate post-log projection data, correct for a bias of the post-log projection data, using the projection data, to generate bias-corrected projection data, and reconstruct an image of the object from the bias-corrected projection data.

Abstract: A method and apparatus is provided to obtain projection data representing an intensity of X-ray radiation detected at a plurality of detector elements after traversing an object, the projection data being corrected for a baseline offset, correct the projection data by performing a positivity mapping to generate corrected projection data, perform a logarithm operation on the corrected projection data to generate post-log projection data, correct for a bias of the post-log projection data, using the projection data, to generate bias-corrected projection data, and reconstruct an image of the object from the bias-corrected projection data.

Abstract: An apparatus for performing a non-local means (NLM) filter is described. The pixel of the NLM-filtered image are weighted averages of pixels from a noisy image, where the weights are a measure of the similarity between patches of the noisy image. The similarity weights can be calculated using a Kullback-Leibler or a Euclidean distance measure. The similarity weights can be based on filtered patches of the noisy image. The similarity weights can be based on a similarity measure between patches of an anatomical image corresponding to the noisy image. The similarity weights can be calculated using a time series of noisy images to increase the statistical sample size of the patches. The similarity weights can be calculated using a weighted sum of channel similarity weights calculated between patches of noisy image that have been band-pass filtered. The NLM-filtered image can also be blended with a non-NLM-filtered image.

Abstract: An apparatus for performing a non-local means (NLM) filter is described. The pixel of the NLM-filtered image are weighted averages of pixels from a noisy image, where the weights are a measure of the similarity between patches of the noisy image. The similarity weights can be calculated using a Kullback-Leibler or a Euclidean distance measure. The similarity weights can be based on filtered patches of the noisy image. The similarity weights can be based on a similarity measure between patches of an anatomical image corresponding to the noisy image. The similarity weights can be calculated using a time series of noisy images to increase the statistical sample size of the patches. The similarity weights can be calculated using a weighted sum of channel similarity weights calculated between patches of noisy image that have been band-pass filtered. The NLM-filtered image can also be blended with a non-NLM-filtered image.

Abstract: The present invention provides a floor through-passage molding apparatus adapted for a floor construction, which comprises a forming sleeve, a first covering plate assembly, a second covering plate assembly and a plurality of retaining assemblies. Said forming sleeve has a sleeve wall defining a hollow channel, a first opening and a second opening. Said first and second covering plate assemblies are respectively disposed on the first and second openings of said forming sleeve. Said plurality of retaining assemblies passes through the hollow channel of said forming sleeve, and are respectively retained with the first and second covering plate assemblies by a plurality of corresponding retaining holes formed on said first and second covering plate assemblies, wherein positions of the plurality of corresponding retaining holes distributed on said first covering plate assembly or said second covering plate assembly jointly define at least one plane.

Abstract: The present invention provides a floor through-passage molding apparatus adapted for a floor construction, which comprises a forming sleeve, a first covering plate assembly, a second covering plate assembly and a plurality of retaining assemblies. Said forming sleeve has a sleeve wall defining a hollow channel, a first opening and a second opening. Said first and second covering plate assemblies are respectively disposed on the first and second openings of said forming sleeve. Said plurality of retaining assemblies passes through the hollow channel of said forming sleeve, and are respectively retained with the first and second covering plate assemblies by a plurality of corresponding retaining holes formed on said first and second covering plate assemblies, wherein positions of the plurality of corresponding retaining holes distributed on said first covering plate assembly or said second covering plate assembly jointly define at least one plane.

Abstract: The present invention provides a floor through-passage molding apparatus adapted for a floor construction having steel bars, includes: a forming sleeve and a constricting module. Said forming sleeve has a sleeve wall forming an outer circumference surface. Said constricting module has an elongated curved body disposed on said sleeve wall, and a first connection terminal and a second connection terminal are respectively located on two opposite ends of said elongated curved body. By connection of said first connection terminal with the second connection, said elongated curved body can radially support outer the circumference surface of the sleeve wall to reinforce the entire structure of the forming sleeve and thereby prevent the floor through-passage molding apparatus from deforming or slanting, and simultaneously reinforce a firm connectivity between the floor through-passage molding apparatus and the concrete floor.