Abstract: A robust range prediction system is proposed that estimates the range of an electric vehicle (EV) during operation, using a range prediction model trained on data relevant to the energy usage of the vehicle. The range prediction model may take a greater number of inputs than may be considered by an OEM range estimator provided with the EV. The predicted range generated by the range prediction system may then be compared with the estimated range of the OEM range estimator, and/or combined into an integrated display to provide the driver with a confirmation of the estimated range, which may decrease a range anxiety of the driver. Additionally, the proposed range prediction system may generate recommendations for how to maximize (e.g., maintain or increase) the predicted range by adjusting the operation of the vehicle or coordinating operation of the vehicle with other vehicles, traffic signals, etc.

Abstract: Embodiments are disclosed for an example telematics system for a vehicle. The example telematics system comprises a plurality of antennas capable of sending and receiving wireless signals and a telematics unit configured to select an antenna from the plurality of antennas based on network information and dynamic vehicle information and connect the selected antenna to the telematics unit to send and/or receive wireless signals via the selected antenna.

Abstract: Systems and methods are provided for a quality of service management (QoS) of a telematics control unit (TCU) of a vehicle. The quality of service management system includes a classification machine learning model adapted to receive TCU behavior parameters and environment parameters and output a usage scenario and QoS level according to the TCU behavior parameters and the environment parameters.

Abstract: The disclosure herein pertains to monitoring and quantifying vehicle drive behavior using an edge computing device. In one example, a method may include receiving vehicle data from a vehicle; receiving remote vehicle data from a remote vehicle; receiving road user data from a road user; receiving road infrastructure data from a road infrastructure device; calculating a vehicle behavior score of the vehicle using vehicle data and one or more of remote vehicle data, road user data, and road infrastructure data; and outputting and storing the vehicle behavior score.

Abstract: Methods and systems are provided for an advanced driver-assistance system (ADAS). In one example, the ADAS includes an onboard diagnostics (OBD) adaptor configured to obtain information from vehicle sensors and an in-vehicle device communicatively linked to the OBD adaptor and configured to monitor driving conditions. The in-vehicle device is further configured to provide alerts to an operator based on a first set of data from the OBD adaptor and a second set of data from sensors of the in-vehicle device.

Abstract: Embodiments allow rapid prediction of the impact of media data upon brand worth. One cloud service crawls service providers (e.g., TWITTER, FACEBOOK, blogging services) and provides sentiment analysis of internet feeds. Another cloud service may have pre-populated knowledge of an internal organization chart, in order to focus upon feeds relating to employees. Yet another machine learning (ML) service may predict an impact of the media data upon brand worth. Data models of the ML service can consider factors such as: a source of the information, a particular publisher sharing the news, a time since the news was published, and/or a specific individual associated with the news. An output identifier could be a severity index, the sentiment (e.g., positive or negative), financial impact trends, the time to react, and others. Following testing of the data model and the training data, embodiments may predict the impact of a future media communication.

Abstract: In-application file conversion using cloud services is described. In one or more embodiments, an application determines that a file includes features inserted by a subsequent version of the application. The application sends a request to a conversion service to convert the file to a format that is compatible the application. The application receives a converted file from the conversion service that is compatible with the application. The conversion service has multiple versions of application server software to convert files, a job queue to store requested conversion jobs, and a job manager that determines which version of application server software to use to convert the file and invokes an instance of the determined version of the application server software to convert the file.

Abstract: Various embodiments of systems, computer program products, and methods to provide centralized taxation evaluation are described herein. In an aspect, a request is received to determine taxation information of a user corresponding to a time interval. Baseline data corresponding to the taxation information is automatically obtained from sources associated with the time interval and carried forward taxation information. Further, the taxation information including liability owed by the user and/or refund owed to the user based on the obtained baseline data, carried forward taxation information and corresponding taxation rules is determined. The determined taxation information is provided to the user for taxation evaluation using a monitor widget displayed on a display device of a computing system.

Abstract: In-application file conversion using cloud services is described. In one or more embodiments, an application determines that a file includes features inserted by a subsequent version of the application. The application sends a request to a conversion service to convert the file to a format that is compatible the application. The application receives a converted file from the conversion service that is compatible with the application. The conversion service has multiple versions of application server software to convert files, a job queue to store requested conversion jobs, and a job manager that determines which version of application server software to use to convert the file and invokes an instance of the determined version of the application server software to convert the file.

Abstract: A computer implemented method and apparatus for automatically identifying a representative image for an image group. The method comprises dividing an image group into one or more clusters based on an average time gap of the image group, wherein the images in the image group are in sequential timestamp order wherein the average time gap is calculated using a time span calculated from the timestamp of a first image in the image group to the timestamp of a last image in the image group; recursively dividing a largest cluster in the one or more clusters to determine a resultant cluster, wherein the resultant cluster comprises no time gaps larger than the average time gap of the resultant cluster; and identifying a representative image from the resultant cluster as an image representative for the image group.

Abstract: A computer implemented method and apparatus for automatically identifying a representative image for an image group. The method comprises dividing an image group into one or more clusters based on an average time gap of the image group, wherein the images in the image group are in sequential timestamp order wherein the average time gap is calculated using a time span calculated from the timestamp of a first image in the image group to the timestamp of a last image in the image group; recursively dividing a largest cluster in the one or more clusters to determine a resultant cluster, wherein the resultant cluster comprises no time gaps larger than the average time gap of the resultant cluster; and identifying a representative image from the resultant cluster as an image representative for the image group.

Abstract: A cascade object classification structure for classifying one or more objects in an image is provided. The cascade object classification structure includes a plurality of nodes arranged in one or more layers. Each layer includes at least one parent node and each subsequent layer includes at least two child nodes. A parent node in a layer is operatively linked to two child nodes in a subsequent layer. Further, at least one child node in one of the subsequent layers is operatively linked to two or more parent nodes in a preceding layer. Each node includes classifiers for classifying the objects as a positive object and a negative object. The positive object and the negative object classified by the parent node in each layer are further classified by one or more operatively linked child nodes in the subsequent layer.

Abstract: Systems and methods for decoding of compressed video enable the storing of compressed video data in a memory shared by a group of symmetric multiple processors. The video includes a plurality of frames and each of the plurality of frames has one or more slices. Such one or more slices are assigned, by a main processor, of the group of symmetric multiple processors to the group of multiple processors. The one or more assigned slices are partially decoded by the one or more of the group of multiple processors and the partially decoded one or more slices are stored in the memory. Subsequently, each of the plurality of frames having at least one partially decoded slice is assigned to one or more of the group of multiple processors. In a successive progression, the group of multiple processors in combination fully decodes each of the plurality of frames.