Abstract: A method can be performed by a first node for determining a parameter of physical (PHY) layer circuitry of a second node. The method can include implementing a cascaded hierarchy of techniques to determine, based on an electrical signal from a second node, a parameter of the PHY layer circuitry of the second node, and causing an antenna of the first node to transmit an electromagnetic wave consistent with the determined parameter.

Abstract: In one example, a head mounted display system includes at least one memory; and at least one processor to execute instructions to: detect a first position and a first view direction of a head of a user based on sensor data generated by at least one of an accelerometer, at least one camera, or a gyroscope at a first point in time; determine a latency associated with a time to cause an image to be presented on the display; determine a predicted position and a predicted view direction of the head of the user at a second point in time based on the latency; render, prior to the second point in time, the image for presentation on the display based on the predicted position and the predicted view direction of the head of the user; and cause the display to present the rendered image.

Abstract: In one example, a head mounted display system includes at least one memory; and at least one processor to execute instructions to: detect a first position and a first view direction of a head of a user based on sensor data generated by at least one of an accelerometer, at least one camera, or a gyroscope at a first point in time; determine a latency associated with a time to cause an image to be presented on the display; determine a predicted position and a predicted view direction of the head of the user at a second point in time based on the latency; render, prior to the second point in time, the image for presentation on the display based on the predicted position and the predicted view direction of the head of the user; and cause the display to present the rendered image.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: Systems and methods of reducing power consumption associated with paging or cDRX mode are described. A wake-up receiver (WUR) wakes up from an idle mode or cDRX state. Whether a wake-up signal (WUS) has been received by the WUR is determined. The WUS is a low-complexity signal that is less complicated than a PDCCH or PDSCH and is repeated multiple times at resource elements as indicated in a configuration from an eNB. If received, a baseband transceiver wakes up for reception of a PDCCH for the UE in a PO when the UE is in the idle mode or a PDSCH for the UE when the UE is in the cDRX state.

Abstract: In one example, a head mounted display system includes at least one memory; and at least one processor to execute instructions to: detect a first position and a first view direction of a head of a user based on sensor data generated by at least one of an accelerometer, at least one camera, or a gyroscope at a first point in time; determine a latency associated with a time to cause an image to be presented on the display; determine a predicted position and a predicted view direction of the head of the user at a second point in time based on the latency; render, prior to the second point in time, the image for presentation on the display based on the predicted position and the predicted view direction of the head of the user; and cause the display to present the rendered image.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Abstract: Systems and methods of using machine-learning to improve communications across different networks are described. A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model. A neural network is trained to avoid interference using rewards associated with reduced interference or retransmission levels in each network or improved throughput at the CIRN node. A machine-learning scheduling algorithm determines a relay mode of the CIRN node for source and destination node transmissions. The scheduling algorithm is based on the probability of successful transmission between the source and destination nodes multiplied by a collaboration score for successful transmission and the probability of unsuccessful transmission of the particular packet multiplied by a collaboration score for unsuccessful transmission.

Abstract: In one example, a head mounted display system includes at least one memory; and at least one processor to execute instructions to: detect a first position and a first view direction of a head of a user based on sensor data generated by at least one of an accelerometer, at least one camera, or a gyroscope at a first point in time; determine a latency associated with a time to cause an image to be presented on the display; determine a predicted position and a predicted view direction of the head of the user at a second point in time based on the latency; render, prior to the second point in time, the image for presentation on the display based on the predicted position and the predicted view direction of the head of the user; and cause the display to present the rendered image.

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Abstract: In one example, a head mounted display system includes at least one memory; and at least one processor to execute instructions to: detect a first position and a first view direction of a head of a user based on sensor data generated by at least one of an accelerometer, at least one camera, or a gyroscope at a first point in time; determine a latency associated with a time to cause an image to be presented on the display; determine a predicted position and a predicted view direction of the head of the user at a second point in time based on the latency; render, prior to the second point in time, the image for presentation on the display based on the predicted position and the predicted view direction of the head of the user; and cause the display to present the rendered image.

Abstract: Various systems and methods for providing variable bitrate compression for split deep neural network (DNN) computing are described herein. A system may be configured to manage a split DNN, the split DNN configured to operate on a compute system and a second system over a communication network. The system may access a performance metric; determine, based on the performance metric, a split point of the split DNN, the split point defining a head portion of the split DNN and a tail portion of the split DNN; determine, based on the performance metric, a bottleneck layer configuration for a bottleneck layer at the split point, the bottleneck layer including a bottleneck encoder and a bottleneck decoder; execute the head portion of the DNN and the bottleneck encoder on the compute system; and recurrently access an updated performance metric and determine a revised split point or a revised bottleneck layer configuration based on the updated performance metric.

Abstract: A computing node to implement a management entity in a CP-based network. The node including processing circuitry configured to encode an inquiry message requesting information on CPS capabilities. Response messages are received from a set of sensing nodes of a plurality of sensing nodes in response to the inquiry message. The response messages include the information on the CPS capabilities of the set of sensing nodes. A notification message indicating selecting of a sensing node as a sensing coordinator is encoded for transmission. Sensed data received in a broadcast message from the sensing coordinator is decoded. The sensed data including data associated with one or more non-V2X capable sensing nodes.

Abstract: Systems and methods of reducing power consumption associated with paging or cDRX mode are described. A wake-up receiver (WUR) wakes up from an idle mode or cDRX state. Whether a wake-up signal (WUS) has been received by the WUR is determined. The WUS is a low-complexity signal that is less complicated than a PDCCH or PDSCH and is repeated multiple times at resource elements as indicated in a configuration from an eNB. If received, a baseband transceiver wakes up for reception of a PDCCH for the UE in a PO when the UE is in the idle mode or a PDSCH for the UE when the UE is in the cDRX state.

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

Abstract: In one example, a head mounted display system includes at least one memory; and at least one processor to execute instructions to: detect a first position and a first view direction of a head of a user based on sensor data generated by at least one of an accelerometer, at least one camera, or a gyroscope at a first point in time; determine a latency associated with a time to cause an image to be presented on the display; determine a predicted position and a predicted view direction of the head of the user at a second point in time based on the latency; render, prior to the second point in time, the image for presentation on the display based on the predicted position and the predicted view direction of the head of the user; and cause the display to present the rendered image.