Abstract: Certain aspects of the present disclosure are generally directed to apparatus and techniques for event state detection. One example method generally includes receiving a plurality of sensor signals at a computing device, determining, at the computing device, probabilities of sub-event states based on the plurality of sensor signals using an artificial neural network for each of a plurality of time intervals, and detecting, at the computing device, the event state based on the probabilities of the sub-event states via a state sequence model.

Abstract: Embodiments include methods executed by a processor of a robotic device for selecting a frontier goal for autonomous map building within a space, including determining trajectory costs for the robotic device traversing from an occupied area to each of a plurality of available frontier goals, wherein the plurality of available frontier goals include positions within the space from which the robotic device is configured to collect information to autonomously build a map of the space, determining co-visibility costs for each of the plurality of available frontier goals, determining a frontier cost for each of the plurality of available frontier goals based on a difference between the trajectory cost and the co-visibility cost of the respective available frontier goal; and selecting one of the plurality of available frontier goals having a lowest determined frontier cost.

Abstract: Certain aspects of the present disclosure are generally directed to apparatus and techniques for event state detection. One example method generally includes receiving a plurality of sensor signals at a computing device, determining, at the computing device, probabilities of sub-event states based on the plurality of sensor signals using an artificial neural network for each of a plurality of time intervals, and detecting, at the computing device, the event state based on the probabilities of the sub-event states via a state sequence model.

Abstract: Embodiments include devices and methods for processing an image captured by an image sensor of a robotic vehicle. A processor of the robotic vehicle may calculate an image output orientation matrix for an image that is output by the image sensor. The processor may calculate image sensor orientation matrix the image sensor. The processor may calculate a body orientation matrix of the robotic vehicle. The processor may transform the image captured by the image sensor based on the image output orientation matrix, the image sensor orientation matrix, and the body orientation matrix.

Abstract: Embodiments include devices and methods for processing an image captured by an image sensor of an unmanned autonomous vehicle (UAV). A processor of the UAV may determine a body coordinate matrix of the UAV. The processor may determine an estimated rotation of the image sensor of the UAV. The processor may determine an estimated rotation of the UAV. The processor may transform an image captured by the image sensor based on the body coordinate matrix, the estimated rotation of the image sensor, and the estimated rotation of the UAV.

Abstract: Embodiments include devices and methods for adaptive image processing in an unmanned autonomous vehicle (UAV). In various embodiments, an image sensor may capture an image, while a processor of the UAV obtains attitude information from one or more attitude sensors. Such information may include the relative attitude of the UAV and changes in attitude. The processor of the UAV may determine a UAV motion mode based, at least in part, on the obtained attitude information. The UAV motion mode may result in the modification of yaw correction parameters. The processor of the UAV may further execute yaw filtering on the image based, at least in part, on the determined motion mode.

Abstract: Embodiments include devices and methods for processing an image captured by an image sensor of a robotic vehicle. A processor of the robotic vehicle may calculate an image output orientation matrix for an image that is output by the image sensor. The processor may calculate image sensor orientation matrix the image sensor. The processor may calculate a body orientation matrix of the robotic vehicle. The processor may transform the image captured by the image sensor based on the image output orientation matrix, the image sensor orientation matrix, and the body orientation matrix.

Abstract: Embodiments include devices and methods for processing an image captured by an image sensor of an unmanned autonomous vehicle (UAV). A processor of the UAV may determine a body coordinate matrix of the UAV. The processor may determine an estimated rotation of the image sensor of the UAV. The processor may determine an estimated rotation of the UAV. The processor may transform an image captured by the image sensor based on the body coordinate matrix, the estimated rotation of the image sensor, and the estimated rotation of the UAV.

Abstract: Embodiments include devices and methods for adaptive image processing in an unmanned autonomous vehicle (UAV). In various embodiments, an image sensor may capture an image, while a processor of the UAV obtains attitude information from one or more attitude sensors. Such information may include the relative attitude of the UAV and changes in attitude. The processor of the UAV may determine a UAV motion mode based, at least in part, on the obtained attitude information. The UAV motion mode may result in the modification of yaw correction parameters. The processor of the UAV may further execute yaw filtering on the image based, at least in part, on the determined motion mode.

Abstract: Embodiments include devices and methods for adaptive image processing in an umnanned autonomous vehicle (UAV). In various embodiments, an image sensor may capture an image. Images may be obtained during motion or hover modes of the UAV. The UAV may determine whether stabilizing a line of the image causes a breach of an image crop margin. That is, the UAV may estimate or begin to adjust image distortion and crop the image, and may evaluate during or after the estimation/adjustment whether an image crop margin is breached by the result. The UAV may reduce stabilizing of the line of the image in response to determining that stabilizing the line of the image causes a breach of the image crop margin.

Abstract: Methods, computer-readable medium, and apparatus are described for adjusting uplink communication at a user equipment (UE) during wireless communication. The described aspects include determining uplink communication characteristic information corresponding to an uplink data rate based on one or more measurement values associated with transmissions by a modem on an uplink communication channel. Further, the described aspects include determining an adjusted uplink data rate based at least in part on the uplink communication characteristic information. Additionally, the described aspects include adjusting data transmission rate of an application layer entity based at least in part on the adjusted uplink data rate.

Abstract: An apparatus, system, and method for examining authenticity of printed material and distinguishing between original printed material and counterfeit printed material is disclosed. In one embodiment, the method is performed by a mobile device. The mobile device recognizes a reference point on a page of the printed material, computes a color balance ratio for that reference point, and compares the computed color balance ratio to an expected value for original printed material. Based on the comparison between the computed color balance ratio and the expected value, a determination is made as to the authenticity of the printed material. If the printed material is authentic, the mobile device may provide supplementary, complementary and/or additional information and content, for example, information related to the page or chapter of the printed material. If the printed material is not authentic, the mobile device may inhibit the presentation of additional material.