Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Abstract: A method, system and non-transitory computer-readable medium for automated reconstruction of an application. The method, the system, and the non-transitory computer-readable medium cause a processor: receive a workflow that comprises a sequence of microservice operations that correspond to the application; transfer, from a current host platform that is associated with the application, to a backup memory, a copy of an active database that is associated with the application; deactivate the active database from the current host platform; rebuild an operating system of the application; remove a pre-build configuration of the application; install a recovery mode of the application on the current host platform; deploy a post-build configuration of the application; perform a quality assurance process that comprises validating the post-build configuration of the application; and transfer from the backup memory to the current host platform, the copy of the active database.

Abstract: The present disclosure provides a method comprising: generating a map comprising an index of a plurality of scan tables; imaging a first target region using one or more scan tables selected from the plurality of scan tables; computing a parameter of interest based on one or more images obtained from the imaging of the first target region; and imaging the first target region using a subset of the one or more scan tables, which subset may be selected based on the computed parameter of interest.

Abstract: An apparatus, a method, and computer-implemented media. The apparatus is to receive, simultaneously, electrical signals based on respective reflected frequencies of a reflected ultrasonic waveform reflected from a target object as a result of a transmitted ultrasonic waveform; compound information from the electrical signals to generate compounded electrical signals; and cause generation of an output image on a display based on the compounded electrical signals.

Abstract: A method and a target monitoring system (TMS) to generate descriptive parameters of one or more target objects in a region are provided. The TMS dynamically receives first data comprising image data and/or audio data of the target objects in the region and second data comprising circumstantial information related to the first data from one or more sensors positioned in one or more spatial directions in the region over a network. The TMS filters the dynamically received first data and identifies the target objects in the region using image data extracted from the filtered data. The TMS generates descriptive parameters associated with each identified target object in the region using the filtered first data and the circumstantial information.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about at least one of respective protocols and respective interfaces associated with respective ones of the plurality of components. The data is analyzed to determine at least one of the respective protocols and the respective interfaces associated with the respective ones of the plurality of components. In the method, operations of one or more components of the plurality of components are tested based at least in part on the determination of the at least one of the respective protocols and the respective interfaces. The method further includes outputting respective statuses of the one or more components, wherein the respective statuses are derived at least in part from the testing.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Abstract: A method comprises receiving a request for validation of software comprising one or more applications, analyzing the request and generating one or more validation steps based at least in part on the analysis. In the method, a time to complete the one or more validation steps is predicted. The predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications.

Abstract: An ultrasound system (100) includes an ultrasound transducer, a processing circuit (210, 300), and a display. The ultrasound transducer is configured to detect ultrasound information regarding a patient and output the ultrasound information as an ultrasound data sample. The processing circuit (210, 300) is configured to segment the ultrasound data sample into a binary image including at least one first region and at least one second region, obtain a first location of a first vascular feature of the binary image based on a boundary between the at least one first region and the at least one second region, and modify the binary image based on the first location of the first vascular feature. The first vascular feature is associated with an intima media thickness. The display is configured to display the modified image.

Abstract: A method comprises managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities. The managing step comprises tracking a status of one or more of the multiple tasks, and predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks.

Abstract: A method and a target monitoring system (TMS) to generate descriptive parameters of one or more target objects in a region are provided. The TMS dynamically receives first data comprising image data and/or audio data of the target objects in the region and second data comprising circumstantial information related to the first data from one or more sensors positioned in one or more spatial directions in the region over a network. The TMS filters the dynamically received first data and identifies the target objects in the region using image data extracted from the filtered data. The TMS generates descriptive parameters associated with each identified target object in the region using the filtered first data and the circumstantial information.

Abstract: An ultrasound processing system includes an ultrasound interface and processing electronics. The ultrasound interface receives imaging information. The processing electronics are coupled to the ultrasound interface and are configured to utilize the ultrasound imaging information to use time-varying frame selection to estimate strain so that the processing electronics perform the time-varying frame selection by comparing a single frame to a plurality of previous frames and selecting a single comparison set for each frame for strain estimation.

Abstract: A method and a target monitoring system (TMS) to generate descriptive parameters of one or more target objects in a region are provided. The TMS dynamically receives first data comprising image data and/or audio data of the target objects in the region and second data comprising circumstantial information related to the first data from one or more sensors positioned in one or more spatial directions in the region over a network. The TMS filters the dynamically received first data and identifies the target objects in the region using image data extracted from the filtered data. The TMS generates descriptive parameters associated with each identified target object in the region using the filtered first data and the circumstantial information.

Abstract: An ultrasound processing system includes an ultrasound interface and processing electronics. The ultrasound interface receives imaging information. The processing electronics are coupled to the ultrasound interface and are configured to perform processing across a plurality of ultrasound channels by combining channel data for adaptively reducing the common mode noise prior to beamforming for a transmit event. The combination of the channel data may be computing an arithmetic mean, which is then multiplied to a weighting coefficient. This value may then be removed from the individual channel data. The modified channel data is then transmitted to a beamformer, which processes the channel data for directional signal transmission and reception.

Abstract: A computing system includes a memory configured to store instructions, and one or more processors configured to execute the instructions to receive data relating to an image or a user, determine a feature from the data, identify a user preference from a user profile, obtain a model, and segment the image based on the feature, the user preference, and the model. The model is generated by determining a historical feature from historical data as an input, determining a desired output, obtaining a preliminary model based on the input and the desired output, determining an actual output of the preliminary model, determining error criteria between the actual output and the desired output, and generating the model by updating the preliminary model based on the error criteria.

Abstract: A steering adjustment for a needle visualization ultrasound system includes a needle, an ultrasound interface and one or more processing electronics. The ultrasound interface receives ultrasound imaging information from a first set of ultrasound firings. The processing electronics are coupled to the ultrasound interface. The processing electronics utilize the information from the first set of firings and identify the angle of the needle and the optimized steering frame angle. The processing electronics are further configured to cause a second set of firings. The second set of firings are configured for the identified needle angle and steering frame angle. The processing electronics are further configured to utilize the ultrasound information from the second set of firings to adaptively and dynamically enhance the visualization of the needle.

Abstract: A method comprises managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities. The managing step comprises tracking a status of one or more of the multiple tasks, and predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks.

Abstract: Methods, apparatus, and processor-readable storage media for transaction visibility framework implemented using artificial intelligence are provided herein.

Abstract: A method and a target monitoring system (TMS) to generate descriptive parameters of one or more target objects in a region are provided. The TMS dynamically receives first data comprising image data and/or audio data of the target objects in the region and second data comprising circumstantial information related to the first data from one or more sensors positioned in one or more spatial directions in the region over a network. The TMS filters the dynamically received first data and identifies the target objects in the region using image data extracted from the filtered data. The TMS generates descriptive parameters associated with each identified target object in the region using the filtered first data and the circumstantial information.