Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive a video image stream of a scene. The device may be further configured to perform person detection on the received video image stream to identify a plurality of persons; perform gesture detection on the received video image stream to identify a gesture; select a person from the plurality of persons to associate with the identified gesture; and use the identified gesture to enable the selected person to control an object or area displayed on a video screen associated with the scene.

Abstract: A device may store first data structures that include first data identifying computing resources provided at an edge of a network for a computing environment. The device may store second data structures that include second data identifying aggregations of portions of the first data, and may store a third data structure that includes third data identifying an aggregation of the second data. The device may receive, from a first client device, client data identifying an address and resources associated with the first client device, and may update a first data structure based on the client data. The device may update a second data structure based on updating the first data structure, and may update the third data structure based on updating the second data structure. The device may provide a notification indicating that the first client device has been added as a computing resource for the computing environment.

Abstract: An integrated electric booster braking system with a pedal force compensation function, comprising a booster motor, a first gear, a second gear, a lead screw, a brake master cylinder, a pedal push rod, a master cylinder push rod, a fluid storage tank, a hydraulic control unit and an electric control unit, wherein first gear and second gear are assembled in a housing, first gear is engaged with the second gear, the booster motor is connected with the first gear and drives the first gear to rotate, the first gear drives the second gear to rotate during rotation, the second gear is in threaded connection with the lead screw, the second gear drives the lead screw to move during rotation, the lead screw is of a hollow structure, and the rear end of the lead screw is abutted with the front end of a first piston in the brake master cylinder.

Abstract: A kit for detecting urothelial carcinoma is provided. The kit includes at least one primer or probe for detecting one or more miRNAs, and the one or more miRNAs are selected from the group consisting of miR-19b-1, miR-636, miR-150, miR-155, miR-183, miR-378, miR-210, miR-487, and combinations thereof. A method for detecting urothelial carcinoma is also provided. The method includes detecting the expression level(s) of one or more miRNAs in a sample of a subject, and the one or more miRNAs are selected from the group consisting of miR-19b-1, miR-636, miR-150, miR-155, miR-183, miR-378, miR-210, miR-487, and combinations thereof.

Abstract: A bullet comment processing method and apparatus, a server, and a storage medium are provided. When a bullet comment needs to be released, at least one candidate track matching a to-be-released bullet comment format is identified; then, a display duration of an existing bullet comment content in each candidate track is determined as a first duration corresponding to the each candidate track, and a display duration of a to-be-released bullet comment content when being placed in the each candidate track is determined as a second duration corresponding to the each candidate track; the second duration and the first duration of the each candidate track are compared, and a target track is identified to display the to-be-released bullet comment, where the second duration of the target track is greater than the first duration; and the to-be-released bullet comment content is displayed at the target track.

Abstract: A machine-learning method for training a decision-making model includes: obtaining a rationale vector group for a rationale included in a labeled natural language text file; assembling an effective vector group for the labeled natural language text file by connecting the rationale vector groups for the rationales using a specific order; and executing a supervised classification algorithm to train the decision-making model using the effective vector group and a target decision for the natural language text file. The decision-making model is trained to be configured to label an unlabeled natural language text file using one of a plurality of potential decisions that serves as a target decision.

Abstract: The present disclosure provides a high power amplifier (HPA) linearization method, applied to a ground hub which includes a predistorter and a PD controller. The ground hub is arranged in a satellite communication system together with a transmitter and a satellite transponder, and the satellite transponder includes an HPA. The HPA linearization method includes determining an initial correction signal based on a physical model with a plurality of PD parameters to compensate AM-AM and AM-PM characteristics of the HPA; receiving a signal from the satellite transponder; determining a reward function for an action taken by the PD controller; examining an action-value function for actions taken in a preset past period; taking an action to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.

Abstract: An example described herein includes a device to receive, from a user device, a request message to split processing, of an application, between the user device and the server device; determine a processing capability of the server device; and determine whether the server device is capable of executing a process of the application based on the processing capability of the server device. When the server device is determined to be capable of executing the process of the application, the device may: send an acceptance message to the user device, wherein the acceptance message identifies a first set of processes of the application and includes instructions to permit the user device to execute the first set of processes; and execute a second set of processes of the application, wherein the user device executes the first set of processes of the application substantially simultaneously as the server device executes the second set of processes of the application.

Abstract: A device obtains previously created data content. The device unmasks and extracts one or more chain of custody blocks stored in association with the data content. The one or more chain of custody blocks includes chain of custody data identifying who, when, where, and, with what hardware and/or software, created or edited the data content. The device analyzes the one or more chain of custody blocks and validates an origination of the data content based on the analysis of the one or more chain of custody blocks.

Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive a video image stream of a scene. The device may be further configured to perform person detection on the received video image stream to identify a plurality of persons; perform gesture detection on the received video image stream to identify a gesture; select a person from the plurality of persons to associate with the identified gesture; and use the identified gesture to enable the selected person to control an object or area displayed on a video screen associated with the scene.

Abstract: A method may include receiving, at a wireless station, device capability information associated with a number of user equipment (UE) devices and determining, by the wireless station, uplink and downlink configurations for a number of carriers associated with the wireless station. The method may also include configuring uplink and downlink time slots for each of the carriers based on the determined uplink and downlink configurations and modifying the uplink and downlink configurations for at least some of the carriers over time based on data usage or congestion associated with the at least some of the plurality of carriers.

Abstract: A stream management platform may receive a request to provide, via a network, a content stream to an area, wherein the request indicates a requested latency associated with providing the content stream to the area. The stream management platform may determine an entry node of the network that is to receive the content stream from a content source. The stream management platform may identify a plurality of transmission nodes associated with the area. The stream management platform may determine respective latencies associated with multicasting the content stream from the entry node to the plurality of transmission nodes. The stream management platform may determine whether the respective latencies satisfy the requested latency. The stream management platform may perform, based on whether the respective latencies satisfy the requested latency, an action associated with the content stream according to the respective latencies.

Abstract: An analog-to-digital conversion circuit includes a first comparator, a second comparator and a counter circuit. The first comparator compares an analog signal with a ramp signal. The second comparator compares the analog signal with the ramp signal plus a predetermined offset. When a signal level of the ramp signal is less than a signal level of the analog signal, the counter circuit counts a number of clock cycles of a first clock signal to generate a first portion of a digital signal. When the signal level of the ramp signal plus the predetermined offset is greater than the signal level of the analog signal, the counter circuit counts a number of clock cycles of a second clock signal to generate a second portion of the digital signal. A frequency of the first clock signal is less than a frequency of the second clock signal.

Abstract: A network device receives one or more first key performance indicators (KPIs) associated with a cell site providing wireless service within a geographic area and determines at least one first capacity usage parameter associated with the cell site providing the wireless service. The network device determines placement of an additional antenna array within the geographic area to provide a certain coverage and capacity for the geographic area based on the one or more first KPIs and the determined at least one first capacity usage parameter.

Abstract: An illustrative orchestration system is configured to provide, to a user equipment (UE) device communicatively coupled to a federation of compute clusters, cluster selection data for a subset of compute clusters from the federation of compute clusters. The cluster selection data is configured to facilitate the UE device in characterizing the compute clusters of the subset with respect to a service optimization policy. The orchestration system is further configured to receive, from the UE device, an orchestration request indicative of a compute cluster selected by the UE device based on the characterizing of the compute clusters of the subset. The orchestration system is configured to then orchestrate, in response to the orchestration request, fulfillment of a service request to be performed by the selected compute cluster for the UE device. Corresponding methods, systems, and devices are also disclosed.

Abstract: A network device in a Multi-Access Edge Computing (MEC) cluster is configured to: receive, from a wireless station, information associated with a User Equipment (UE) device that is wirelessly attached to a network through the wireless station and estimate a first time interval to complete a task for the UE device based on the information. If the first time interval is less than a threshold time interval, the processor is to: signal, through the wireless station, to the UE device to be in a connected state; execute the task; and send a result of the execution, through the wireless station, to the UE device.

Abstract: Systems and methods described herein provide a multi-access edge computing (MEC) prediction service. A network device in a MEC network receives a predicted MEC route for an end device executing an application instance. The predicted MEC route identifies a projected time when the end device will connect to the network device for services and a service requirement for the application instance. The network device reserves resources to service the application instance at the projected time, based on the predicted MEC route. The network device provides a computing service for the application instance using the reserved resources when the end device initiates a data session with the network device.

Abstract: An output compensation circuit of an image sensor includes a first current mirror circuit, a first current generator circuit and a second current generator circuit. The first current mirror circuit, coupled to a select transistor of a pixel circuit of the image sensor, is configured to, in response to a first current, generate a second current flowing through the select transistor. The select transistor is selectively turned on according to a power supply voltage. When the select transistor is turned on, the pixel circuit outputs the second current through the select transistor. The first current generator circuit outputs a compensation current, serving as a first portion of the first current, to the first current mirror circuit in response to a variation in the power supply voltage. The second current generator circuit outputs a reference current, serving as a second portion of the first current, to the first current mirror circuit.

Abstract: An exemplary user equipment (“UE”) device provides a service request to an orchestration system associated with a federation of compute clusters available to fulfill the service request. The UE device also identifies a service optimization policy associated with a user preference for balancing performance priorities during fulfillment of the service request. In response to the service request, the UE device receives cluster selection data from the orchestration system. Based on the cluster selection data, the UE device characterizes compute clusters, within a subset of compute clusters represented in the cluster selection data, with respect to the performance priorities. Based on the service optimization policy and the characterization of compute clusters in the subset, the UE device selects a compute cluster from the subset to fulfill the service request. The UE device then provides an orchestration request indicative of the selected compute cluster to the orchestration system.

Abstract: An exemplary user equipment (“UE”) device provides a service request to an orchestration system associated with a federation of compute clusters available to fulfill the service request. The UE device also identifies a service optimization policy associated with a user preference for balancing performance priorities during fulfillment of the service request. In response to the service request, the UE device receives cluster selection data from the orchestration system. Based on the cluster selection data, the UE device characterizes compute clusters, within a subset of compute clusters represented in the cluster selection data, with respect to the performance priorities. Based on the service optimization policy and the characterization of compute clusters in the subset, the UE device selects a compute cluster from the subset to fulfill the service request. The UE device then provides an orchestration request indicative of the selected compute cluster to the orchestration system.