Abstract: A method and user equipment (UE) for configuring a secondary cell in a dual connectivity (DC) mode by a UE. The method includes connecting to a primary cell and the secondary cell based on configurations received from a network, identifying at least one parameter related to performance of the UE for the secondary cell, comparing the at least one parameter with a threshold and releasing connection with the secondary cell based on a result of the comparison.

Abstract: The disclosure relates to a fifth generation (5G) or sixth generation (6G) communication system. A method performed by a user equipment (UE) in a wireless communication is provided. The method includes requesting a network on a first frequency for allocating an uplink (UL) grant, detecting at least one UL grant failure in which the UE lost scheduling request (SR) resources without receiving the UL grant from network, releasing the first frequency upon detecting the at least one UL grant failure by reporting the at least one UL grant failure to the network, receiving a radio resource control (RRC) reconfiguration including a second frequency from the network in response to reporting the failure and adding the second frequency to revive lost SR resources, the second frequency including the same first frequency, which has been released, or a different secondary primary cell (SPcell) associated with the first frequency.

Abstract: A vehicle control system as described herein can include one or more processors that can identify one or more geographic areas through which a vehicle group is scheduled to travel for an upcoming trip. This area or these areas may be identified as area(s) where there is an increased likelihood of a need for derating one or more engines of the vehicle group. The processor(s) can create or modify a trip plan that dictates one or more operational settings of the vehicle group for one or more of different locations, distances, or times of the upcoming trip. The processor(s) may create or modify the trip plan to avoid a decrease in total power output from the vehicle group within the geographic area(s).

Abstract: Systems and techniques that facilitate synthetic training data generation for improved machine learning generalizability are provided. In various embodiments, an element augmentation component can generate a set of preliminary annotated training images based on an annotated source image. In various aspects, a preliminary annotated training image can be formed by inserting at least one element of interest or at least one background element into the annotated source image. In various instances, a modality augmentation component can generate a set of intermediate annotated training images based on the set of preliminary annotated training images. In various cases, an intermediate annotated training image can be formed by varying at least one modality-based characteristic of a preliminary annotated training image. In various aspects, a geometry augmentation component can generate a set of deployable annotated training images based on the set of intermediate annotated training images.

Abstract: Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.

Abstract: An artificial intelligence (AI) lead marker detection system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to scan post-exposure X-ray images to detect and insert various lead markers, to digitize information provided by the type and location of the lead marker, and to employ the marker information in different X-ray system workflow automations. The marker information obtained by the AI lead marker detection system can also provide useful data for use in downstream clinical and quality applications apart from the X-ray system, such as either AI or non-AI analytical applications.

Abstract: Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.

Abstract: Techniques are provided for compressing deep neural networks using a structured filter pruning method that is extensible and effective. According to an embodiment, a computer-implemented method comprises determining, by a system operatively coupled to a processor, importance scores for filters of layers of a neural network model previously trained until convergence for an inferencing task on a training dataset. The method further comprises removing, by the system, a subset of the filters from one or more layers of the layers based on the importance scores associated with the subset failing to satisfy a threshold importance score value. The method further comprises converting, by the system, the neural network model into a compressed neural network model with the subset of the filters removed.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example time series event data processing apparatus includes memory storing instructions and one-dimensional time series healthcare-related data; and at least one processor. The example at least one processor is to: execute artificial intelligence model(s) trained on aggregated time series data to at least one of a) predict a future medical machine event, b) detect a medical machine event, or c) classify the medical machine event using the one-dimensional time series healthcare-related data; when the artificial intelligence model(s) are executed to predict the future medical machine event, output an alert related to the predicted future medical machine event to trigger a next action; and when the artificial intelligence model(s) are executed to detect and/or classify the medical machine event, label the medical machine event and output the labeled event to trigger the next action.

Abstract: Ambient conditions in which a marine vessel is to travel along a path are determined. Power settings for the marine vessel to travel along the path are determined based on the ambient conditions to direct the marine vessel to travel along a planned trajectory toward a designated location. Movement of the marine vessel is monitored while using the power settings and a discrepancy between movement of the marine vessel and the planned trajectory is identified. The power settings are automatically modified based on the discrepancy. Movement of the marine vessel then controlled according to the modified power settings.

Abstract: A route examination system determines a characteristic of a vehicle system traveling over a segment of a route under inspection, a characteristic of the route, and/or a characteristic of movement of the vehicle system over the segment of the route. The system also detects acoustic sounds generated by movement of the vehicle system over the segment of the route. The system selects a baseline signature representative of acoustic sounds generated by movement of the vehicle system or another vehicle system over a healthy segment of the route, and determines that the segment of the route under inspection is damaged based on a comparison between the baseline signature and the acoustic sounds generated by the movement of the vehicle system over the segment of the route under inspection.

Abstract: A system includes a vehicle system that is operating in accordance with the operational settings of a trip plan. The operational settings dictate how the vehicle system is to travel at different locations along the route. A processor of the system may identify differences between the operational settings of the trip plan and the operational settings at which the vehicle actually travels. The processor may further identify whether the differences are caused by a grade error.

Abstract: A system includes one or more processors configured to predict, using a predictive deration model, that a first vehicle system scheduled to travel along a route will experience a deration event when traveling through one or more designated geographic areas along the route. The predictive deration model is generated based on historical data of deration events experienced by plural vehicle systems. The historical data includes at least geographic locations of the deration events and times of the year in which the deration events occurred. The one or more processors are further configured to generate control signals to control movement of the first vehicle system along the route based on the prediction such that the first vehicle system does not derate when traveling through the one or more designated geographic areas along the route.

Abstract: A method and user equipment (UE) for configuring a secondary cell in a dual connectivity (DC) mode by a UE. The method includes connecting to a primary cell and the secondary cell based on configurations received from a network, identifying at least one parameter related to performance of the UE for the secondary cell, comparing the at least one parameter with a threshold and releasing connection with the secondary cell based on a result of the comparison.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example apparatus includes a data processor to process one-dimensional data captured over time with respect to patient(s). The example apparatus includes a visualization processor to transform the processed data into graphical representations and to cluster the graphical representations including the first graphical representation into at least first and second blocks arranged with respect to an indicator of a criterion to provide a visual comparison of the first block and the second block with respect to the criterion. The example apparatus includes an interaction processor to facilitate interaction, via the graphical user interface, with the first and second blocks of graphical representations to extract a data set for processing from at least a subset of the first and second blocks.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example time series event data processing apparatus includes memory storing instructions and one-dimensional time series healthcare-related data; and at least one processor. The example at least one processor is to: execute artificial intelligence model(s) trained on aggregated time series data to at least one of a) predict a future medical machine event, b) detect a medical machine event, or c) classify the medical machine event using the one-dimensional time series healthcare-related data; when the artificial intelligence model(s) are executed to predict the future medical machine event, output an alert related to the predicted future medical machine event to trigger a next action; and when the artificial intelligence model(s) are executed to detect and/or classify the medical machine event, label the medical machine event and output the labeled event to trigger the next action.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data generation are disclosed. An example synthetic time series data generation apparatus is to generate a synthetic data set including multi-channel time-series data and associated annotation using a first artificial intelligence network model. The example apparatus is to analyze the synthetic data set with respect to a real data set using a second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a first classification, the example apparatus is to adjust the first artificial intelligence network model using feedback from the second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a second classification, the example apparatus is to output the synthetic data set.

Abstract: A system includes one or more processors configured to obtain environmental data geographically and temporally corresponding to scheduled travel of a vehicle system. The one or more processors are further configured to determine a power output capability range for the vehicle system traveling during a trip based on the environmental data that is obtained. The one or more processors are also configured to communicate instructions to at least one of a propulsion system of the vehicle system or a vehicle controller of the vehicle system for controlling movement of the vehicle system during the trip such that the vehicle system produces a power output within the power output capability range as the vehicle system travels. The environmental data includes historical values of one or more of temperature, pressure, or air constituency in geographic areas through which the vehicle system will travel during the trip.

Abstract: A vehicle control system determines a predicted location of wheel slip for an upcoming trip of a vehicle system by comparing a vehicle characteristic, route characteristic, and/or weather characteristic associated with the upcoming trip with a vehicle characteristic, route characteristic, and/or weather characteristic associated with a previous detection of wheel slip. Movement of the vehicle system is controlled during the upcoming trip by reducing tractive effort generated by a leading vehicle of the vehicle system relative to a trailing vehicle of the vehicle system during movement over the predicted location, reducing tractive effort generated by a leading axle in a vehicle of the vehicle system relative to a trailing axle of the vehicle during movement over the predicted location, and/or directing an adhesion modifying device to automatically dispense an adhesion modifying substance onto the predicted location.

Abstract: Ambient conditions in which a marine vessel is to travel along a path are determined. Power settings for the marine vessel to travel along the path are determined based on the ambient conditions to direct the marine vessel to travel along a planned trajectory toward a designated location. Movement of the marine vessel is monitored while using the power settings and a discrepancy between movement of the marine vessel and the planned trajectory is identified. The power settings are automatically modified based on the discrepancy. Movement of the marine vessel then controlled according to the modified power settings.