Abstract: Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Abstract: Systems, methods and apparatus for processing video can include a processor. The processor can be configured to perform object detection to detect visual indications of potential objects of interest in a video scene, to receive a selection of an object of interest from the potential objects of interest, and to provide enhanced video content within the video scene for the object of interest indicated by the selection.

Abstract: A system and method are disclosed for provisioning IP features in a system-on-chip. A plurality of identical chips are fabricated, each of which is capable of have a number of features enabled or disabled. As a default, all features are disabled. A production process is later carried out, in which the chip is installed in a greater device. During this process, the manufacturer requests a license the IP owner for enablement of various features. Using secure communications, a license is granted identifying the features to be enabled, and a volume of units permitted to be manufactured. The license information is encrypted using a key already known to the chip, and sent to the manufacturer. The chip receives the license information during provisioning, extracts relevant provisioning information using the key, and a secure processing system provisions the relevant features. Log information is generated to allow the IP owner to verify license compliance.

Abstract: Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Abstract: A system for controlling power distribution within a vehicular communication network, including a power source equipment comprising a first port in communication with a network node module of a device, and a Power over Ethernet (POE) management module. The POE management module is configured to enable POE to the device via the first port, monitor a current draw of the device, determine whether the current draw of the device exceeds a threshold, and disable POE to the device, responsive to determining that the current draw exceeds the threshold.

Abstract: The present application relates to an internally embedded copper plate-type soft magnetic powder core inductor, a preparation method therefor, and a use thereof. The internally embedded copper plate-type soft magnetic powder core inductor comprises a copper plate, a surface of the copper plate is covered with a soft magnetic material, and an interfacing area of the soft magnetic material and the copper plate includes an insulating resin material.

Abstract: A computer-implemented method for failure diagnosis using fault tree can include: receiving a fault tree comprising a node representing a top event, a plurality of nodes representing respective basic events, and one or more logic gates connecting the plurality of nodes representing the respective basic events to the node representing the top event; obtaining reliability parameters corresponding to the basic events; calculating fault tree importance measures corresponding to the basic events; calculating failure impact factors of the top event corresponding to the basic events, wherein the failure impact factors of the top event are products of the corresponding reliability parameters and the corresponding fault tree importance measures; ranking the basic events based on the failure impact factors of the top event; and identifying a most significant contributor to the top event, wherein the most significant contributor is a basic event having the highest failure cause probability of the top event.

Abstract: This disclosure relates to assays for detecting activating antibodies in a sample that are capable of antibody-dependent cell-mediated cytotoxicity. In certain embodiments, this disclosure relates to target cells comprising an antigen on the exterior of the cells and a luciferase inside the cells. In certain embodiments, the antigen is an Ebola-virus glycoprotein. In certain embodiments, this disclosure relates to detecting changes in chemiluminescence of the luciferase as an indication of activating antibodies in a sample capable of cell lysis when mixed with effector cells.

Abstract: Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Abstract: In some aspects, the disclosure is directed to methods and systems for providing coordinative security among network devices across multi-level networks. Shared cryptographic secrets among the network devices are used as the basis for mutual security authentication and peering among these devices. The cryptographic secrets can be embedded in the SoC devices for these devices or dynamically generated based on unique identification information and attributes of these SoC devices. The messages for authentication and peering can be communicated directly among the network devices or indirectly via a cloud security portal entity that acts as a messaging proxy. The mutual authentication and peering process can be carried out coordinately among the network devices and a cloud security portal in a one-to-one mesh relationship, or in a transitive layering relationship, where each network entity authenticates and peers with its direct subordinates in a multi-level network.

Abstract: A system for sensing in an industrial environment includes a processing node configured to receive a first sensor data from a first sensor and a second sensor data from a second sensor, both of which are disposed in the industrial environment. The processing node is further configured to process randomly sampled data from at least one of the first sensor data and the second sensor data to generate processed sensor data that includes the randomly sampled data. The processing node further configured to identify data of interest from the processed sensor data based on comparison of the processed sensor data with a sampling dictionary of predetermined information. The processing node further configured to filter and assemble the identified data of interest to create a set of compressed data, and transmit the set of compressed data to a predetermined destination.

Abstract: A security-monitoring system includes a data-collection module, a security-monitoring module and an attack-detection module. The data-collection module can collect data associated with a data stream. The security-monitoring module monitors a number of activities within a device. The attack-detection module can detect one or more attacks on the device based on the monitored plurality of activities by using one or more trusted processors. The collected data includes one or more security parameters associated with the data stream. The attack-detection module identifies the attacks based on the security parameters and data from the one or more trusted processors.

Abstract: In a system for 360 degree video capture and playback, 360 degree video may be captured, stitched, encoded, decoded, rendered, and played-back. In one or more implementations, a stitching device may be configured to stitch the 360 degree video using an intermediate coordinate system between an input picture coordinate system and a capture coordinate system. In one or more implementations, the stitching device may be configured to stitch the 360 degree video into at least two different projection formats using a projection format decision, and an encoding device may be configured to encode the stitched 360 degree video with signaling that indicates the at least two different projection formats. In one or more implementations, the stitching device may be configured to stitch the 360 degree video with multiple viewing angles, and a rendering device may be configured to render the decoded bitstream using one or more suggested viewing angles.

Abstract: A network may include multiple allocations. The allocations may include: a first allocation encompassing central infrastructure, such as central office servers, data centers, or other core infrastructure; an second allocation encompassing gateway elements or other central consumer premises network infrastructure; and a third allocation encompassing nodes, such as client devices, terminals, or other nodes. A virtualization management engine may coordinate resources from the various allocations to support virtual functions distributed over multiple allocations of the network. The virtualization management engine may determine the distribution across the allocations for the virtual functions. The virtualization management engine may be implemented as a virtual function and be distributed across the allocations of the network.

Abstract: An apparatus and method for providing ML processing for one or more ML applications operating on one or more Internet of Things (IoT) devices includes receiving a ML request from an IoT device. The ML request can be generated by a ML application operating on the IoT device and include input data collected by the first ML application. A ML model to perform ML processing of the input data included in the ML request is identified and provided to an ML core for ML processing along with the input data included in the first ML request. The ML core produces ML processing output data based on ML processing by the ML core of input data included in the ML request using the ML model. The ML processing output data can be transmitted to the IoT device.

Abstract: A system for controlling power distribution within a vehicular communication network, including a power source equipment comprising a first port in communication with a network node module of a device, and a Power over Ethernet (POE) management module. The POE management module is configured to enable POE to the device via the first port, monitor a current draw of the device, determine whether the current draw of the device exceeds a threshold, and disable POE to the device, responsive to determining that the current draw exceeds the threshold.

Abstract: A vehicular communication device and method for communicating with a remote server. In various embodiments, the vehicular communication device receives a plurality of device packets from a vehicle device(s) via a vehicular communication network, including device packets having a first priority and device packets having a second priority. Processing circuitry of the vehicular communication device determines the respective priorities of the received device packets based upon the contents of the packets, and queues at least some of the received device packets (and/or informational packets derived therefrom) in accordance with the respective priorities. The queued packets are output to a remote server for processing. The vehicular communication device receives responsive content from the remote server, which may include safety content, a servicing notice and/or targeted commercial content.

Abstract: A network may include multiple allocations. The allocations may include: a first allocation encompassing central infrastructure, such as central office servers, data centers, or other core infrastructure; an second allocation encompassing gateway elements or other central consumer premises network infrastructure; and a third allocation encompassing nodes, such as client devices, terminals, or other nodes. A virtualization management engine may coordinate resources from the various allocations to support virtual functions distributed over multiple allocations of the network. The virtualization management engine may determine the distribution across the allocations for the virtual functions. The virtualization management engine may be implemented as a virtual function and be distributed across the allocations of the network.

Abstract: A system for determining the servicing needs of a vehicle. In various embodiments, the system includes a remote server and a vehicle control module of the vehicle. The vehicle control module includes a first communication interface to enable communications with at least one vehicle device via a network fabric of the vehicle. The vehicle control module is configured to receive status data, from the vehicle device, relating to a performance status or operational status of the vehicle. The vehicle control module further includes a second communication interface that enables wireless communications with the remote server. The wireless communications include sending status data to the remote server. The remote server is configured to receive and interpret the status data to determine if the vehicle requires service, and send a response to the vehicle. When service is required, the response may cause the vehicle to provide a service indication.

Abstract: A system and method for performing vehicle safety processing within a vehicle includes determining whether a driver is present in a vehicle, receiving driver sensed data, processing the driver sensed data to recognize a safety issue. The method continues by determining if the driver sensed data also indicates an externally sensed safety issue. For the driver sensed data without the externally sensed safety issue, the method includes generating one or more control signals to activate a driver awareness measure. For the driver sensed data with an externally sensed safety issue, the method includes activating auto safety precaution measures, determining whether the auto safety measures were successful in avoiding an accident, and, if not, recording the driver sensed data in a one-time programmable (OTP) memory device.