Abstract: A data communication network includes a data communication node, an imaging device, and an information handling system. The data communication node establishes a data connection with a user equipment device. The imaging device provides image information for a coverage area associated with the data communication node. The information handling system is coupled to the data communication node and to the imaging device. The information handling system receives the image information, synthesizes a 3D map of the coverage area based upon the image information, receives first coverage information from the first data communication node, correlates the first coverage information with the 3D map to generate a coverage map of the coverage area, and determines a first location of the first user equipment device within the coverage map based on the image information.

Abstract: The present application provides a processing method and device of a hematoma aspiration decision-making system for intracerebral hemorrhage. A two-layer deep neural network is constructed for processing of a hematoma aspiration protocol, and a perception-decision-making-control method based on a time series is thus realized, which overcomes unpredictability of results of hematoma aspiration processes existing in the prior art, provides direct and convenient information transmission for surgeons to make decisions, can effectively improve the accuracy of aspiration treatment protocols for intracerebral hematoma, and provides digital, intelligent, and powerful support for clinical hematoma aspiration treatments.

Abstract: Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations that consider a resource utilization associated with at least one adjustment to a resolution of at least a portion of at least one rendering workload to assist a client in selecting from one or more of a plurality of candidate execution plans.

Abstract: Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations to assist a client in selecting from one or more of a plurality of candidate execution plans. The candidate execution plans are generated based on execution criteria for the one or more rendering workloads. At least one value of the execution criteria is generated by at least one language model based on an input from the client.

Abstract: Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations that consider a resource utilization associated with at least one adjustment to a resolution of one or more identified regions of at least one rendering workload to assist a client in selecting from one or more of a plurality of candidate execution plans.

Abstract: Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations that consider a resource utilization associated with embedding at least one digital watermark in at least a portion of at least one rendering workload to assist a client in selecting from one or more of a plurality of candidate execution plans.

Abstract: Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes performing a compliance evaluation operation corresponding to at least one compliance attribute associated with execution of the one or more rendering workloads to assist a client in selecting from one or more of a plurality of candidate execution plans.

Abstract: Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes performing a security evaluation operation corresponding to at least one security attribute associated with execution of the one or more rendering workloads to assist a client in selecting from one or more of a plurality of candidate execution plans.

Abstract: Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing energy consumption estimations, using at least one carbon footprint computation algorithm, to assist a client in selecting from one or more of a plurality of candidate execution plans.

Abstract: Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations, using at least one machine learning algorithm, to assist a client in selecting from one or more of a plurality of candidate execution plans.

Abstract: A model and query system for local inferencing and/or training. A model and query server (MQS) includes a model manager and a cache manager. The model manager is configured to manage deployment of models to clients in the local network, execute queries at the server, control models cached, and manage workload execution. The cache manager is configured to manage a cache of models. The model and query system is configured to orchestrate or manage query execution, which includes inferencing operations, at a local level.

Abstract: A playback device including a housing, an audio player, a sound insulation cover, a target detection apparatus, and a driving mechanism. A sound hole is disposed on the housing, the sound insulation cover is disposed in the housing, the audio player is disposed in the sound insulation cover, a sound outlet is disposed on the sound insulation cover, the target detection apparatus is configured to detect a target direction, the driving mechanism is connected to the sound insulation cover, and in a case that the sound outlet does not face the target direction, the driving mechanism is controlled to start, and the driving mechanism drives the sound insulation cover to rotate, so that the sound outlet faces the target direction. This application discloses a control method for a playback device, a control apparatus for a playback device, and a computer-readable storage medium.

Abstract: The technology described herein is directed towards a transcoder that can be used to couple non-terrestrial network satellites to user equipment, including by decoding and reencoding data packets at the packet level. A metasurface (reconfigurable intelligent surface, or RIS) redirects signals from the satellite to a satellite radio frequency (RF) interface of the transcoder, with the transcoder also coupled by a user equipment (UE) RF interface to user equipment, such as a computing device or cellphone. The transcoder converts, at the packet level, satellite-originating signals to UE-compliant signals, and converts UE-originating signal to satellite-compliant signals. The transcoder performs various additional conversion-related functions to facilitate such satellite service, including via frequency conversion, doppler manipulation, a repeater, frequency equalization/negative-slope compensation and RIS-related conversion in both the receive mode and transmit mode of the RIS.

Abstract: An electrical substation that transforms AC voltage to DC voltage is configurable between a current-source mode for charging batteries of a stationary work machine and a voltage-source mode for powering motors of a moving work machine. The substation receives medium-voltage AC power from a grid and includes a pair of DC power converters each formed from three-phase transformers and a single stage of silicon-controlled rectifier units. Adjustable connectors enable combining the power converters in parallel or keeping them separate to operate in the current-source mode for charging the machine batteries with up to 4800ADC at 6 MW or for providing up to 1500VDC in powering voltage. The connectors further enable combining the power converters in series to operate in the voltage-source mode for powering the machine motors with up to 3000VDC at 6 MW.

Abstract: The technology described herein is directed towards a metasurface (reconfigurable intelligent surface) that can be used to redirect signals to and from non-terrestrial network satellites. The metasurface can be passive, and provide signal array gain with respect to indoor user equipment sending and receiving signals to and from the network satellites. The metasurface can be portable for use in various scenarios. Further, the metasurface can be configured to operate in a transmission mode, in which incoming signal is passed through the metasurface, or can be reconfigured to operate in a reflection mode, in which incoming signal is reflected by the metasurface. Different phase shifts of the metasurface's unit cells result in array gain through constructive interference, by refraction in the transmission mode, or reflection in the reflection mode. The presence or absence of a removable back plane determines the transmission or reflection operating mode of the metasurface.

Abstract: A method for storing data and a method for processing data, for a large model scenario, and corresponding apparatus are provided, relating to the field of artificial intelligence such as data processing, data storage, cloud platforms, cloud services, deep learning, large models, and large language models. An implementation of the method includes: in response to receiving to-be-stored data to be stored into a target data lake, detecting a data type of the to-be-stored data; in response to the data type of the to-be-stored data being an unstructured data type, generating a feature vector of the to-be-stored data; and storing the feature vector, as a substitute of the to-be-stored data, into the target data lake.

Abstract: The technology described herein is directed towards a transcoder that can be deployed and used to couple non-terrestrial network satellites to user equipment, including by decoding and reencoding data packets at the packet level. A metasurface (reconfigurable intelligent surface, or RIS) redirects signals from the satellite to a satellite radio frequency (RF) interface of the transcoder, with the transcoder also coupled by a user equipment (UE) RF interface to user equipment, such as a computing device or cellphone. The transcoder converts satellite-originating signals to UE-compliant signals, and converts UE-originating signal to satellite-compliant signals. The transcoder performs various additional conversion-related functions to facilitate such satellite direct-to-device service, including via frequency conversion, doppler manipulation, a repeater, frequency equalization/negative-slope compensation and RIS-related conversion in both the receive mode and transmit mode of the RIS.

Abstract: The technology described herein is directed towards a transcoder that can be deployed on a drone and used to couple non-terrestrial network satellites to user equipment. A metasurface (reconfigurable intelligent surface, or RIS, e.g., also mounted on the drone or integrated with the transcoder) redirects signals from the satellite to a satellite radio frequency (RF) interface of the transcoder, with the transcoder also coupled by a user equipment (UE) RF interface to user equipment. The drone-mounted transcoder converts satellite-originating signals to UE-compliant signals, and converts UE-originating signal to satellite-compliant signals. The transcoder performs various conversion-related functions to facilitate such satellite direct-to-device service, including via packet conversion, frequency conversion, doppler manipulation, a repeater, frequency equalization/negative-slope compensation and RIS-related conversion in both the receive mode and transmit mode of the RIS.

Abstract: A data communication network includes data communication nodes, imaging devices, and an information handling system. The nodes establish data connections with user equipment devices. The imaging devices provide image information for a coverage area of the network. The information handling system synthesizes a 3D map of the coverage area based upon the image information, determines that a first region of the 3D map is an inactive region of an emergency response incident and determines that a second region of the 3D map is an active region of the emergency response incident based on the image information, and directs a group of user equipment devices to the inactive region.

Abstract: A structure inspection system includes an image conversion device located in a far edge of the structure inspection system that is proximate to a physical structure, and a near edge processor located in a near edge of the structure inspection system that is remote from the physical structure. The image conversion device receives an image of the physical structure, and renders a real time 3D model of the physical structure based on the first images. The near edge processor is configured to provide a baseline 3D model of the physical structure to the image conversion device. The image conversion device further compares the first real time 3D model with the baseline 3D model, and provides an indication when the first real time 3D model deviates from the baseline 3D model.