Qualcomm Incorporated

Omnitracs, LLC

Muralidhar Reddy Akula

Ashwin Deepak Swaminathan

Abhijeet Bisain

Amir Said

Shankar Sadasivam

Aditya Narain Srivastava

Steven S Thomson

Vitor Rocha De Carvalho

Tadeusz Jarosinski

Abhijit Khobare

Auke Joris Wiggers

Rajeev Jain

Liang Zhang

Ties Jehan Van Rozendaal

Hoang Cong Minh Le

Suhas H Sheshadri

Ryan Michael Carey

Jinwon Lee

Guillaume Konrad Sautiere

Bhuwan Dhingra

Piero Zappi

Krishna Buska

Tushar Singhal

Santiago Mazuelas

Ryan Moore

Vineet Thanedar

Frank Steven Mayer

Anjuman Raha

Suhas Hariharapura Sheshadri

An example computing device may include memory and one or more processors. The one or more processors may be configured to parallel entropy decode encoded video data from a received bitstream to generate entropy decoded data. The one or more processors may be configured to predict a motion vector based on the entropy decoded data. The one or more processors may be configured to decode a motion vector residual from the entropy decoded data. The one or more processors may be configured to add the motion vector residual and motion vector. The one or more processors may be configured to warp previous reconstructed video data with an overlapped block-based warp function using the motion vector to generate predicted current video data. The one or more processors may be configured to sum the predicted current video data with a residual block to generate current reconstructed video data.

Efficient warping-based neural video codec

A method of auto-calibrating light sensor data of a mobile device includes, obtaining, by the mobile device, one or more reference parameters representative of light sensor data collected by a reference device. The method also includes collecting, by the mobile device, light sensor data from a light sensor included in the mobile device, itself. One or more sample parameters of the light sensor data obtained from the light sensor included in the mobile device are then calculated. A calibration model is then determined for auto-calibrating the light sensor data of the light sensor included in the mobile device based on the one or more reference parameters and the one or more sample parameters.

Auto-calibrating light sensor data of a mobile device

A context aware system, for use in a mobile device, includes a context change detector (CCD) coupled to a context classifier (CCL). The CCD is configured to receive sensor data and to detect a change in a current context state of the mobile device based on the received sensor data. The CCL is configured to transition from a low power consumption mode to a normal power consumption mode in response to the CCD detecting the change in the current context state. The CCL is further configured to determine a next context state of the mobile device while in the normal power consumption mode.

Context aware system with multiple power consumption modes

PourOver is the process of replicating databases across clusters, in order to achieve real and/or near real-time database consistency between clusters (e.g., continually provide the ability to construct a consistent database on the alternate cluster that is up to date within a few minutes worth of transactions).

Pourover journaling

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San Diego, CA, United States of America

Muralidhar Reddy Akula