Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: Devices, systems and methods for non-invasive modulation of the baroreflex system of a patient. In embodiments, the present disclosure may be used to measure and monitor baroreflex function for diagnostic purposes in patients acutely or chronically to inform and guide medical treatment, assess disease severity, or assess morbidity/mortality risk. In embodiments, the present disclosure may be used to provide non-invasive baroreflex activation therapy acutely or chronically to treat a variety of disease conditions through rebalancing of the sympathetic and parasympathetic limbs of the autonomic nervous system and their connections to higher brain centers.

Abstract: One or more embodiments of the present disclosure relate to aligning sensor data. In some embodiments, the aligning may be used for performing localization. In these or other embodiments, the aligning may be used for map creation.

Abstract: One or more embodiments of the present disclosure relate to generation of map data. In these or other embodiments, the generation of the map data may include determining whether objects indicated by the sensor data are static objects or dynamic objects. Additionally or alternatively, sensor data may be removed or included in the map data based on determinations as to whether it corresponds to static objects or dynamic objects.

Abstract: Embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be used to generate map data. Additionally or alternatively, the RADAR point clouds may be used for performing localization.

Abstract: One or more embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be communicated to a distributed map system that is configured to generate map data based on the RADAR point clouds. In some embodiments of the present disclosure, certain compression operations may be performed on the RADAR point clouds to reduce the amount of data that is communicated from the ego-machines to the map system.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams – or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data – corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data – and ultimately a fused high definition (HD) map – that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: Described is an in-scan phantom for use during an imaging procedure. The phantom can include at least one measuring insert and/or at least one measured insert. The measuring insert may have radiation detecting capabilities while the measured insert may include a radioactive material. Also described is an imaging modality system that includes an imaging modality and an in-scan phantom as well as methods of using the in-scan phantom for imaging a patient or performing a scout scan.

Abstract: Methods for generating a set of ordered point clouds using a mobile scanning device are presented, the method including: causing the mobile scanning device to perform a walkthrough scan of an interior building space; storing data scanned during the walkthrough; creating a 3-dimensional (3D) mesh from the scanned data; and creating the set of ordered point clouds aligned to the 3D mesh, where the creating the set of ordered point clouds aligned to the 3D mesh includes, aligning a number of scanned points with the 3D mesh, performing in parallel the steps of, coloring each of a number of scanned points, calculating a depth of each of the number of scanned points, and calculating a normal of each of the number of scanned points.

Abstract: Embodiments of a system and method of classifying remote users according to link quality, and scheduling wireless transmission of information to the users based upon the classifications are generally disclosed.

Abstract: The present invention includes a method of wirelessly transmitting data between a base station and a plurality of users. The method includes determining a transmission link quality between a user and the base station. A class type is assigned to the user based upon the transmission link quality. A channelization mode is set for transmission with the user based upon the class type. The channelization mode can be used to determine a quantity of frequency spectrum allocated for transmission between the user and the base station. Further, the quantity of frequency spectrum allocated can be for the duration of a particular transmission time slot. The allocated frequency spectrum can include contiguous frequency slots or non-contiguous frequency slots. The frequency slots can include multi-carrier or single carrier signals. The invention can also include communicating the class type of the user to a media access controller (MAC) scheduler.

Abstract: The present invention includes a method and system for wirelessly transmitting data between a plurality of subscriber units and a base transceiver station. The method comprises at least one subscriber unit transmitting a service flow request to the base transceiver station, determining if the service flow request was received by the base transceiver station, utilizing a back-off algorithm to re-transmit the service flow request if the service flow request was not received by the base transceiver station and transmitting data blocks to the base transceiver station based on the service flow request.

Abstract: A technique for rapidly and efficiently adjusting characteristics of communication in a time-and-frequency varying channel involves the adaptive selection of a transmission mode (i.e., various kinds of modulation, coding, and antenna combining schemes) and a channelization mode (i.e., the manner in which the spectrum is used during transmission). The method includes determining signal quality estimates [32] from received signals, deriving a set of transmission mode parameters [38] from the estimates, and sending parameters [38] as feedback [40] from a receiver to a transmitter. In one embodiment, each of the transmission mode parameters [38] is a transmission mode selected for use with one of the system channelization modes. In another embodiment, transmission mode parameters [38] comprise one parameter selected for use with any system channelization mode.