Abstract: Methods and systems for performing automatic speed-based audio control. One method includes receiving, with an electronic control unit included in a vehicle, a speed of the vehicle and receiving, with the electronic control unit, an audio signal. The method also includes accessing, with the electronic control unit, a plurality of equalization curves based on the speed of the vehicle, each of the plurality of equalization curves associated with the speed of the vehicle and each of the plurality of equalization curves defining a gain adjustment for one of a plurality of frequencies, and, for each curve of the plurality of equalization curves, applying the gain adjustment defined by the curve to one of the plurality of frequencies of the audio signal.

Abstract: Methods and systems for performing automatic speed-based audio control. One method includes receiving, with an electronic control unit included in a vehicle, a speed of the vehicle and receiving, with the electronic control unit, an audio signal. The method also includes accessing, with the electronic control unit, a plurality of equalization curves based on the speed of the vehicle, each of the plurality of equalization curves associated with the speed of the vehicle and each of the plurality of equalization curves defining a gain adjustment for one of a plurality of frequencies, and, for each curve of the plurality of equalization curves, applying the gain adjustment defined by the curve to one of the plurality of frequencies of the audio signal.

Abstract: Methods and systems for performing automatic speed-based audio control. One method includes receiving, with an electronic control unit included in a vehicle, a speed of the vehicle and receiving, with the electronic control unit, an audio signal. The method also includes accessing, with the electronic control unit, a plurality of equalization curves based on the speed of the vehicle, each of the plurality of equalization curves associated with the speed of the vehicle and each of the plurality of equalization curves defining a gain adjustment for one of a plurality of frequencies, and, for each curve of the plurality of equalization curves, applying the gain adjustment defined by the curve to one of the plurality of frequencies of the audio signal.

Abstract: Scene flow represents the three-dimensional (3D) structure and movement of objects in a video sequence in three dimensions from frame-to-frame and is used to track objects and estimate speeds for autonomous driving applications. Scene flow is recovered by a neural network system from a video sequence captured from at least two viewpoints (e.g., cameras), such as a left-eye and right-eye of a viewer. An encoder portion of the system extracts features from frames of the video sequence. The features are input to a first decoder to predict optical flow and a second decoder to predict disparity. The optical flow represents pixel movement in (x,y) and the disparity represents pixel movement in z (depth). When combined, the optical flow and disparity represent the scene flow.

Abstract: Scene flow represents the three-dimensional (3D) structure and movement of objects in a video sequence in three dimensions from frame-to-frame and is used to track objects and estimate speeds for autonomous driving applications. Scene flow is recovered by a neural network system from a video sequence captured from at least two viewpoints (e.g., cameras), such as a left-eye and right-eye of a viewer. An encoder portion of the system extracts features from frames of the video sequence. The features are input to a first decoder to predict optical flow and a second decoder to predict disparity. The optical flow represents pixel movement in (x,y) and the disparity represents pixel movement in z (depth). When combined, the optical flow and disparity represent the scene flow.

Abstract: Disclosed herein is a computer-implemented method of monitoring a situation by generating an overall similarity score between a received data stream and a case in case-based reasoning (CBR) the method comprising: receiving a data stream comprising information on a monitored situation; generating a plurality of parallel data streams, wherein each of the generated plurality of data streams is dependent on the received data stream; generating, for each of the generated data streams, a similarity score for a feature of a case, wherein each similarity score is generated in dependence on a comparison between information in the generated data stream and stored information on the feature of a case, and each of the similarity scores is generated in dependence on a comparison with stored information on a different feature of the same case; and generating an overall similarity score between the received data stream and the case in dependence on the generated similarity scores.

Abstract: Disclosed herein is a computer-implemented method of monitoring a situation by determining a set of one or more cases in case-based reasoning (CBR) the method comprising: receiving a data stream comprising information on a monitored situation; generating a plurality of parallel data streams from the received data stream; generating, for each of the parallel data streams, an overall similarity score between the parallel data stream and one of a plurality of cases, wherein each overall similarity score is generated from a comparison between one of the plurality of data streams and a different case; and determining a set of one or more cases in dependence on the generated overall similarity scores.

Abstract: A case-based reasoning, CBR, platform for monitoring a situation includes: a CBR engine; a unified data cache; a case library; an agent application programming interface, API; a data source API; an application API; and a persistence API. The data source API provides an interface between live and/or static data sources, external from the CBR platform, and the unified data cache. The persistence API provides an interface between a persistence database, external from the CBR platform, and the unified data cache and the case library. The application API provides an interface between the systems of data analysts, platform administrators and/or operators, external from the CBR platform, and the unified data cache and the case library. The agent API receives information from data interpretation agents external from the CBR platform and sends the received information to the CBR engine to provide the CBR engine with information for generating the CBR results.

Abstract: Disclosed herein is a computer-implemented method of creating a new case in a case-based reasoning (CBR) system for monitoring a situation, the method comprising: determining a set of one or more cases from a plurality of cases in dependence on a received data stream comprising information on a monitored situation, wherein each case comprises information describing a problem and information describing a solution to the problem and the process of determining the set of one or more cases is performed without comparing the description of the problems of any of the plurality of cases with a previously generated current case comprising information describing the monitored situation; generating information describing a solution in dependence on information obtained from the determined set of one or more cases and/or in dependence on information received from a user interface; generating a current case comprising information describing the monitored situation in dependence on the received data stream; and generating

Abstract: Disclosed herein is a computer-implemented method in case-based reasoning, CBR, for creating components of a comparison agent for monitoring a situation, the method comprising: obtaining one or more parameter values and comparison information for each of a plurality of features of a case, wherein the comparison information of each feature defines a configuration of a computation unit; and creating, for each of the plurality of features, a computation unit in dependence on the obtained one or more parameter values and the comparison information of the feature, such that the created computation unit is configured to generate an output in dependence on the obtained one or more parameter values and the comparison information of the feature.

Abstract: Disclosed herein is a computer-implemented method of monitoring a situation by determining a set of one or more cases in case-based reasoning (CBR) the method comprising: receiving a data stream comprising information on a monitored situation; generating a plurality of parallel data streams from the received data stream; generating, for each of the parallel data streams, an overall similarity score between the parallel data stream and one of a plurality of cases, wherein each overall similarity score is generated from a comparison between one of the plurality of data streams and a different case; and determining a set of one or more cases in dependence on the generated overall similarity scores.

Abstract: Disclosed herein is a computer-implemented method of monitoring a situation by generating an overall similarity score between a received data stream and a case in case-based reasoning (CBR) the method comprising: receiving a data stream comprising information on a monitored situation; generating a plurality of parallel data streams, wherein each of the generated plurality of data streams is dependent on the received data stream; generating, for each of the generated data streams, a similarity score for a feature of a case, wherein each similarity score is generated in dependence on a comparison between information in the generated data stream and stored information on the feature of a case, and each of the similarity scores is generated in dependence on a comparison with stored information on a different feature of the same case; and generating an overall similarity score between the received data stream and the case in dependence on the generated similarity scores.

Abstract: The present invention provides a computer-implemented software tool that is adapted to listen continuously to data streams from a drilling operation and to process the data to generate a situation description for a current drilling situation in a form useful for automated continuous matching with a set of past cases stored in a knowledge database. The invention implements a case-based reasoning (CBR) approach to match the current drilling situation as defined by the situation description with one or more stored past cases having a degree of similarity above a predetermined threshold level. Matching cases are displayed to the drilling engineer as symbols on a case “radar”, allowing the drilling engineer to retrieve and view the details of a past case and take appropriate action based on drilling advice provided within the past case.

Abstract: The present invention provides a computer-implemented software tool that is adapted to listen continuously to data streams from a drilling operation and to process the data to generate a situation description for a current drilling situation in a form useful for automated continuous matching with a set of past cases stored in a knowledge database. The invention implements a case-based reasoning (CBR) approach to match the current drilling situation as defined by the situation description with one or more stored past cases having a degree of similarity above a predetermined threshold level. Matching cases are displayed to the drilling engineer as symbols on a case “radar”, allowing the drilling engineer to retrieve and view the details of a past case and take appropriate action based on drilling advice provided within the past case.

Abstract: The present invention provides a computer-implemented software tool that is adapted to listen continuously to data streams from a drilling operation and to process the data to generate a situation description for a current drilling situation in a form useful for automated continuous matching with a set of past cases stored in a knowledge database. The invention implements a case-based reasoning (CBR) approach to match the current drilling situation as defined by the situation description with one or more stored past cases having a degree of similarity above a predetermined threshold level. Matching cases are displayed to the drilling engineer as symbols on a case “radar”, allowing the drilling engineer to retrieve and view the details of a past case and take appropriate action based on drilling advice provided within the past case.

Abstract: The present invention provides a computer-implemented software tool that is adapted to listen continuously to data streams from a drilling operation and to process the data to generate a situation description for a current drilling situation in a form useful for automated continuous matching with a set of past cases stored in a knowledge database. The invention implements a case-based reasoning (CBR) approach to match the current drilling situation as defined by the situation description with one or more stored past cases having a degree of similarity above a predetermined threshold level. Matching cases are displayed to the drilling engineer as symbols on a case “radar”, allowing the drilling engineer to retrieve and view the details of a past case and take appropriate action based on drilling advice provided within the past case.

Abstract: The present invention provides a computer-implemented software tool that is adapted to listen continuously to data streams from a drilling operation and to process the data to generate a situation description for a current drilling situation in a form useful for automated continuous matching with a set of past cases stored in a knowledge database. The invention implements a case-based reasoning (CBR) approach to match the current drilling situation as defined by the situation description with one or more stored past cases having a degree of similarity above a predetermined threshold level. Matching cases are displayed to the drilling engineer as symbols on a case “radar”, allowing the drilling engineer to retrieve and view the details of a past case and take appropriate action based on drilling advice provided within the past case.

Abstract: A hearing test device and method are disclosed which involve the placement of a testing probe in the ear canal of a test subject. The device analyzes responses to stimuli applied to the ear canal to determine whether the testing probe has been properly placed in the ear canal. The device may determine, for example, whether the probe is stable, whether it is sealed in the ear canal, whether the resulting volume of the ear canal is acceptable, and/or whether the stimuli delivery system is blocked in any way. If the device determines that the testing probe has been properly placed in the ear canal, the device automatically starts a hearing test without requiring any operator input.