Abstract: An artificial intelligence (“AI”) rendering engine is provided herein. The AI rendering engine renders frames for an entity such as an application. This engine supplements or substitutes for at least some of the “normal” functions of a game engine. More specifically, the AI rendering engine includes an “analytical” game engine as well as an AI-based component that performs some of the processing that a traditional game engine would perform.

Abstract: Methods and systems for optimizing cloud gaming performance using a neural network-based rendering pipeline are provided. The rendering pipeline includes multiple processing stages that generate intermediate data based on received scene data. At least one processing stage utilizes a trained neural network to perform transformations such as denoising, encoding, decoding, and upscaling on the intermediate data. In certain embodiments, local and/or predicted user input is provided as input to the trained neural networks to adjust the rendering process. The frame is rendered for display based on the intermediate data generated by the neural network.

Abstract: An apparatus and method for efficiently performing efficient video processing that provides visual fidelity with changes in lighting and animation details. In various implementations, a computing system includes multiple processing circuits executing a variety of types of machine learning (ML) data models according to a particular architecture to implement a generative artificial intelligence (Gen AI) model. The Gen AI model receives input image data and generates an output image while reducing the amount of real-time data to transfer from a host processing circuit to other processing circuits. The Gen AI model performs rendering operations on the input low level of detail objects at a low resolution in panoramic mode. The multiple processing circuits execute a first subset of video processing tasks at a rate of every frame, whereas other processing circuits execute a second subset of video processing tasks at a rate less than each video frame.

Abstract: The present disclosure relates to apparatus (100) for estimating depth of scene, comprising plurality of sensors (101) including Time of Flight (ToF) sensors and stereo sensors pairs, memory (103) and one or more processors (104). At least one pair of stereo sensors is placed perpendicular to other pairs of stereo sensors. One or more processors (104) are configured to determine disparity for plurality of sensors (101) based on comparison between one or more features of each image with other images received from plurality of pairs of stereo sensors, and evaluation of each image based on one or more pre-defined cost function parameters; determine disparity based on depth information received from ToF sensors; determine confidence value for each of plurality of sensors (101), based on disparity, to generate plurality of confidence values and estimate depth of scene based on disparity associated with a sensor selected based on plurality of confidence values.

Abstract: The present disclosure relates to apparatus (100) for estimating depth of scene, comprising plurality of sensors (101) including Time of Flight (ToF) sensors and stereo sensors pairs, memory (103) and one or more processors (104). At least one pair of stereo sensors is placed perpendicular to other pairs of stereo sensors. One or more processors (104) are configured to determine disparity for plurality of sensors (101) based on comparison between one or more features of each image with other images received from plurality of pairs of stereo sensors, and evaluation of each image based on one or more pre-defined cost function parameters; determine disparity based on depth information received from ToF sensors; determine confidence value for each of plurality of sensors (101), based on disparity, to generate plurality of confidence values and estimate depth of scene based on disparity associated with a sensor selected based on plurality of confidence values.

Abstract: A method, computer program, and computer system of a base station is provided for determining a location of a mobile device by receiving data from a mobile device associated with a location of the mobile device and generating a world model database corresponding to a physical location of the base station. The received data is constrained based on the generated world model database, and an updated location of the mobile device is determined based on the constrained data.

Abstract: A method, computer program, and computer system of a base station is provided for determining a location of a mobile device by receiving data from a mobile device associated with a location of the mobile device and generating a world model database corresponding to a physical location of the base station. The received data is constrained based on the generated world model database, and an updated location of the mobile device is determined based on the constrained data.

Abstract: A front camera module includes a camera and a substrate, and the camera is mounted on one surface (camera mounting surface) of the substrate. A rear camera module includes a camera and a substrate, and the camera is mounted on one surface (camera mounting surface) of the substrate. An IC mounting surface of the substrate of the front camera module and an IC mounting surface of the substrate of the rear camera module are opposed to each other in a front-and-rear direction. The camera of the rear camera module is oriented in a direction opposite to the camera of the front camera module. Moreover, a camera assembly includes a cooling fan configured to send air to a region between the two substrates. With this structure, temperature of the camera modules is prevented from exceeding an allowable operation temperature that has been defined in advance.

Abstract: A front camera module includes a camera and a substrate, and the camera is mounted on one surface (camera mounting surface) of the substrate. A rear camera module includes a camera and a substrate, and the camera is mounted on one surface (camera mounting surface) of the substrate. An IC mounting surface of the substrate of the front camera module and an IC mounting surface of the substrate of the rear camera module are opposed to each other in a front-and-rear direction. The camera of the rear camera module is oriented in a direction opposite to the camera of the front camera module. Moreover, a camera assembly includes a cooling fan configured to send air to a region between the two substrates. With this structure, temperature of the camera modules is prevented from exceeding an allowable operation temperature that has been defined in advance.

Abstract: Aspects of the present disclosure are directed to cell reselection procedures that can mitigate adjacent channel interference (ACI) related problems in a wireless network. A mobile station (MS) determines a signal level quality for each of a plurality of cells including a serving cell and a plurality of neighbor cells. The MS further determines ACI in each of the plurality of cells in a same public land mobile network (PLMN). In addition, the MS performs a cell reselection procedure based on a respective signal level quality and ACI of the plurality of cells in the same PLMN.

Abstract: A method for performing cell reselection by a wireless communication device is described. The method includes monitoring one or more neighbor cells of a same radio access technology as a serving cell while camped on the serving cell. The method also includes determining that reselection criteria is met for a target cell. The method further includes starting a reselection timer for the target cell. The method additionally includes determining whether to perform reselection to the target cell based on a target cell signal to noise ratio (SNR) and a serving cell SNR.