Abstract: A Fifth Generation (5G) access node exchanges user data with User Equipment (UE) and exchanges the user data with a 5G User Plane Function (UPF). A 5G Core (5GC) identifies a handover to a Long Term Evolution (LTE) access node. In response, the 5GC selects an LTE Gateway Identifier (GW ID) for an LTE gateway that is physically co-located with the 5G UPF. The 5GC transfers handover signaling indicating the LTE GW ID and the LTE access node ID to a Mobility Management Entity (MME). The MME transfers signaling to the LTE access node and the LTE gateway. The LTE access node wirelessly exchanges user data with the UE and exchanges the user data with the LTE GW in response to the signaling. The LTE gateway exchanges the user data with the LTE access node.

Abstract: A method and apparatus for processing information of a terminal, is provided. The method includes parsing at least one message for at least one keyword and at least one phone number related to the at least one keyword; mapping the at least one keyword and the at least one phone number related to the at least one keyword and storing the at least one keyword and the at least one phone number related to the at least one keyword that have been mapped in a database; in response to a predetermined keyword being received as a search input, searching the database for the at least one phone number that has been mapped with the predetermined keyword; and displaying the at least one phone number related to the predetermined keyword that is found, on a display.

Abstract: A wireless communication network serves a User Equipment (UE) over an optimal wireless network slice for a user application. The wireless communication network receives a download message when the UE downloads the user application that indicates a UE Identifier (ID) for the UE and an application ID for the user application. The wireless communication network selects a slice ID for the optimal network slice based on the application ID for the user application. The wireless communication network transfers a slice message to the UE that indicates the application ID and the slice ID. The wireless communication network exchanges application data for the user application with the UE. The wireless communication network processes the application data with the optimal wireless network slice.

Abstract: A data communication network serves User Equipment (UE) over an optimal wireless network slice for a user application. A Network Exposure Function (NEF) receives a notice message indicating a UE Identifier (ID) for the UE and an application ID for the user application when the UE downloads the user application. In response, the NEF transfers a download message indicating the UE ID and the application ID to a network control-plane. The network control-plane receives the download message from the NEF, and in response, selects a slice ID for the optimal network slice based on the application ID for the user application. The network control-plane transfers a slice message indicating the application ID and the slice ID to the UE. In response to the slice message, the UE uses the optimal network slice when executing the user application.

Abstract: A wireless communication network delivers a video-conferencing service from a video-conferencing system to wireless User Equipment (UEs) over a wireless network core and a Radio Access Network (RAN). The wireless network core monitors performance of video-conferencing functions in the video-conferencing system and prioritizes the video-conferencing functions based on their performance. The wireless network core transfers a video-conferencing function list to the RAN that prioritizes the video-conferencing functions by their performance. The RAN wirelessly transfers the video-conferencing function list to the wireless UEs. The RAN wirelessly exchanges video-conference signaling between the wireless UEs and the wireless network core. The wireless network core exchanges the video-conference signaling between the RAN and the video-conferencing functions.

Abstract: A data communication network serves User Equipment (UE) over an optimal wireless network slice for a user application. A Network Exposure Function (NEF) receives a notice message indicating a UE Identifier (ID) for the UE and an application ID for the user application when the UE downloads the user application. In response, the NEF transfers a download message indicating the UE ID and the application ID to a network control-plane. The network control-plane receives the download message from the NEF, and in response, selects a slice ID for the optimal network slice based on the application ID for the user application. The network control-plane transfers a slice message indicating the application ID and the slice ID to the UE. In response to the slice message, the UE uses the optimal network slice when executing the user application.

Abstract: A wireless communication network is configured to wirelessly deliver a media content service to wireless User Equipment (UEs). The wireless communication network comprises network circuitry. The network circuitry executes wireless network functions and media content functions. The media content functions distribute network addresses to the wireless UEs which use the network addresses to transfer media-content for the media-content service. The wireless network functions monitor performance of the media content functions. The wireless network functions prioritize the media content functions based on their performance. The wireless network functions transfer the prioritized list of the media content functions to the wireless UEs. The wireless UEs communicate with the media content functions on a priority basis responsive to the prioritized list.

Abstract: A predictive queue control and allocation system includes a queue and a queue control server communicatively coupled to the queue. The queue includes a first and second allocation of queue locations. The queue stores a plurality of resources. The queue control server includes an interface and a queue control engine implemented by a processor. The interface monitors the plurality of resources before the plurality of resources are stored in the queue. The queue control engine predicts that one or more conditions indicate that a queue overflow will occur in the first allocation of queue locations. The queue control engine prioritizes the plurality of resources being received by the queue. The queue control engine may apply a machine learning technique to the plurality of resources. The queue control engine transfers the plurality of resources prioritized by the machine learning technique.

Abstract: A data communication network serves User Equipment (UE) over an optimal wireless network slice for a user application. A Network Exposure Function (NEF) receives a notice message indicating a UE Identifier (ID) for the UE and an application ID for the user application when the UE downloads the user application. In response, the NEF transfers a download message indicating the UE ID and the application ID to a network control-plane. The network control-plane receives the download message from the NEF, and in response, selects a slice ID for the optimal network slice based on the application ID for the user application. The network control-plane transfers a slice message indicating the application ID and the slice ID to the UE. In response to the slice message, the UE uses the optimal network slice when executing the user application.

Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: A synthetically generated noise image is generated from at least one high signal-to-noise ratio target image and at least two zero-mean Gaussian noise images, scaled according to a noise scale. The images are combined in a non-linear manner to produce the synthetically generated noise image which can be used as a training image in a machine learning-based system that “denoises” images. The process can be repeated for a number of different noise scales to produce a set of training images. In one embodiment, the synthetically generated noise image IN is generated according to: IN=?{square root over (I12+I22)} where I is the original target image, I1=I+pG1 and I2=pG2, and where G1 and G2 are zero-mean Gaussian noise images, and p is the noise scale.

Abstract: A synthetically generated noise image is generated from at least one high signal-to-noise ratio target image and at least two zero-mean Gaussian noise images, scaled according to a noise scale. The images are combined in a non-linear manner to produce the synthetically generated noise image which can be used as a training image in a machine learning-based system that “denoises” images. The process can be repeated for a number of different noise scales to produce a set of training images. In one embodiment, the synthetically generated noise image IN is generated according to: IN=?{square root over (I12+I22)} where I is the original target image, I1=I+pG1 and I2=pG2, and where G1 and G2 are zero-mean Gaussian noise images, and p is the noise scale.

Abstract: Network circuitry authorizes User Equipment (UEs) for wireless services from wireless networks. The network circuitry stores lists of network identifiers that are associated with UE types. The network circuitry receives an authorization request that indicates a network identifier and a UE type. The UE type comprises model, operating system, user application, and/or radio frequency. The network circuitry retrieves a networks list for the UE type and compares the network identifier from the authorization request to the network identifiers on the network list. The authorization circuitry authorizes the UE responsive to a match between the network identifier from the authorization request and a network identifier on the network list.

Abstract: A deep learning (DL) network is proposed to mitigate artifacts in simultaneous multi-slice (SMS) magnetic resonance imaging (MRI) data. For example, unaliased images generated from SMS aliased images can exhibit leakage artifacts due to inaccuracies in the receive-coil sensitives used during sensitivity encoding (SENSE) processing. To mitigate leakage artifacts, the DL network learns to correct the receive-coil sensitives before SENSE processing, and/or learns to detect and subtract the artifacts from the unaliased images after SENSE processing. The DL network can also be trained to denoise the unaliased images.

Abstract: A magnetic resonance imaging system includes an array radiofrequency coil and processing circuitry operatively linked to the array radiofrequency coil and configured to receive output signals from the array radiofrequency coil commensurate with a simultaneous multi-slice magnetic imaging characterized by simultaneous multi-slice parameters, estimate distorted regions of the image volume using either data obtained via a pre-scan or a pre-computed model, minimize overlap of the distorted regions with image voxels representing tissue to obtain optimized values of the simultaneous multi-slice parameters, configuring and executing the simultaneous multi-slice imaging sequence based on the optimized values of the simultaneous multi-slice parameters, and reconstruct simultaneous multi-slice images with minimized artifacts.

Abstract: Magnetic resonance imaging (MRI) systems and methods determine slice leakage and/or residual aliasing in the image domain in accelerated MRI imaging. Implementations process one slice of MRI image domain data by input to a sensitivity encoding (SENSE) un-aliasing matrix built from predetermined RF signal reception sensitivity maps, thereby producing as matrix output SENSE-decoded MRI image domain data for one pass through image slice and at least one extra slice, and determine inter-slice leakage and/or in-plane residual aliasing based on content of the at least one extra output slice from the SENSE-decoded MRI image domain data.

Abstract: A method of machine learning that teaches a computer to determine likelihood that a medical journal article is classified as high value for an intended system. In some embodiments, the method includes procuring, in a medical articles database on the computer, a training set including medical articles recommended by subject matter experts associated with medical journals that have published the medical articles. The method can also include identifying, by a feature extraction controller of the computer, first features in the medical articles via a remote annotation service and a remote article information service. The method can also include identifying, by intended system services electronically available to the intended system, second features in the medical articles. The method can also include reducing, by the feature extraction controller, the first and second features to form a set of relevant features.

Abstract: An adaptive traffic management system includes a plurality of traffic lights positioned at a plurality of roadway intersections. Associated with each intersection is one or more sensors to detect traffic flow. A control system is connected to the traffic lights and the sensors and based upon data received and predetermined weights for various situations generates a signal timing plan that operates the traffic lights. The control system also may be connected to multiple sources, wearables, and/or in-vehicle sensors that are also used to generate the signal timing plan and operate the traffic lights.

Abstract: An apparatus and method of detecting a characteristic in an image is performed by obtaining, from an image capturing apparatus, raw signal data formed from a plurality of data samples and including a signal of interest captured by the image capturing apparatus and classifying, using a neural network, samples other than the signal of interest using a classifier having been determined using a first parameter based on information about the sample and a second parameter based on information identifying a position of the sample within the raw image data.

Abstract: A Fifth Generation Core (5GC) Long Term Evolution (LTE) network authenticates a wireless User Equipment (UE) for LTE data service using a 5GC Subscriber Concealed Identifier (SUCI). A Mobility Management Entity (MME) receives the 5GC SUCI and transfers the 5GC SUCI to a 5GC authentication system. The 5GC authentication system determines a 5GC Subscriber Permanent Identifier (SUPI) and authentication vector based on the 5GC SUCI. The 5GC authentication system transfers the 5GC SUPI and the 5GC authentication vector to the MME. The MME exchanges 5GC authentication data with the UE. The MME authenticates the UE based on the 5GC authentication vector and the 5GC authentication data. In response to the 5GC authentication, the MME initiates the LTE data service for the UE based on the 5GC SUPI.