Abstract:
Systems and methods may use radar channels for virtual reality streaming or output. A method may include sending virtual reality content to a head-mounted device over a radar channel, detecting a signal on the radar channel, propagating channel switch feedback to a virtual reality subsystem using an interface between the virtual reality subsystem and a wireless component. The method may include modifying the virtual reality content based on the channel switch feedback, such as by using the virtual reality subsystem.
Abstract:
A semiconductor package apparatus may include technology to aggregate region of interest information for omni-directional video content from two or more sources, select video information from the omni-directional video content based on the aggregated region of interest information, and generate one or more two-dimensional videos based on the selected video information. Other embodiments are disclosed and claimed.
Abstract:
Source devices are provided that increase quality of displayed images by dynamically integrating higher fidelity update frames into a base stream encoded using an encoding technique (e.g., chroma-subsampling and/or another lossless encoding technique). Use of base image frames enables backward compatibility with existing technology and serves as a baseline for bandwidth scaling. The fidelity update frames may include raw image data, lossy, or losslessly compressed image data, and/or additional subsampled image data. The image data included in the fidelity update frames may apply to the entire base image frame or a portion thereof. The fidelity update frames may include incremental data or complete, high fidelity image data for a portion of an entire image. The source devices may store and implement fidelity management policies that control operation of the devices to balance resource consumption against fidelity to meet the needs of specific operational environments.
Abstract:
A computing device and method to adjust operation of a smart device to customize an environment surrounding the computing device. The adjustment is made via an Application Program Interface (API) having a language known to the smart device, and via a transport abstraction module to internally route data in a transfer stack and facilitate delivery of the data to the smart device.
Abstract:
A method of mapping user movements captured by a capture device external to a computing device, to inputs events on the computing device, may comprise executing an application on the computing device, using at least one processor of the computing device. The computing device may transmit video data of the application to a receiver device. The computing device may receive gesture data associated with the application, the gesture data based on movements of a user captured from a capture device communicatively coupled to the receiver device. The gesture data may be mapped to an input event on the computing device and data simulating the input event may be provided to a sensor on the computing device.
Abstract:
In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data, wherein the sensor data is captured by a plurality of sensors within an educational environment. The processor is to: access the sensor data captured by the plurality of sensors: identify a student within the educational environment based on the sensor data: detect a plurality of events associated with the student based on the sensor data, wherein each event is indicative of an attention level of the student within the educational environment: generate a report based on the plurality of events associated with the student; and send the report to a third party associated with the student.
Abstract:
One embodiment provides for a graphics processor comprising a block of graphics compute units, a graphics processor pipeline coupled to the block of graphics compute units, and a programmable neural network unit including one or more neural network hardware blocks. The programmable neural network unit is coupled with the block of graphics compute units and the graphics processor pipeline. The one or more neural network hardware blocks include hardware to perform neural network operations and activation operations for a layer of a neural network. The programmable neural network unit can configure settings of one or more hardware blocks within the graphics processor pipeline based on a machine learning model trained to optimize performance of a set of workloads.
Abstract:
Embodiments are directed to neural network processing for multi-object three-dimensional (3D) modeling. An embodiment of a computer-readable storage medium includes executable computer program instructions for obtaining data from multiple cameras, the data including multiple images, and generating a 3D model for 3D imaging based at least in part on the data from the cameras, wherein generating the 3D model includes one or more of performing processing with a first neural network to determine temporal direction based at least in part on motion of one or more objects identified in an image of the multiple images or performing processing with a second neural network to determine semantic content information for an image of the multiple images.
Abstract:
Technologies for end of frame marking and detection in streaming digital media content include a source computing device communicatively coupled to a destination computing device. The source computing device is configured to encode a frame of digital media content and insert an end of frame marker into a transport stream header of a network packet that includes an encoded payload corresponding to a chunk of data of the frame of digital media content. The destination computing device is configured to de-packetize received network packets and parse the transport stream headers of the received network packets to determine whether the network packet corresponds to an end of frame of the frame of digital media content. The destination computing device is further configured to transmit the encoded payloads of the received network packets to a decoder in response to a determination that the end of frame network packet has been received. Other embodiments are described and claimed.
Abstract:
Technologies for providing hints for adjusting digital media properties include a destination computing device wirelessly coupled to multiple source computing devices. The destination computing device is configured to receive digital media streams from each of a multiple number of source computing devices, process each of the received digital media streams, and output one or more of the processed digital media streams based on one or more output settings and/or or more digital media properties of the digital media. The destination computing device is further configured to determine one or more performance metrics based on an analysis of the output digital media streams, determine one or more hints for one or more of the digital media streams based on the analysis, and transmit each of the hints to a corresponding one of the source computing devices. Other embodiments are described and claimed herein.