Abstract: A sensor device for a terminal block arrangement including at least one sensor for measuring a physical quantity of the terminal block arrangement or of at least one terminal block of the terminal block arrangement.

Abstract: A system includes a first sensor system of a first modality and a second sensor system of a second modality. The system further includes a computing system that is configured to detect and identify objects represented in sensor signals output by the first and second sensor systems. The computing system employs a hierarchical arrangement of transformers to fuse features of first sensor data output by the first sensor system and second sensor data output by the second sensor system.

Abstract: The technologies described herein relate to computing a point cloud based upon data output by several radar sensors in a distributed radar system. The radar sensors generate tensors based upon echo signals detected by the radar sensors. Values are extracted from the tensors and a sequence of tokens is created, where the sequence of tokens includes the values extracted from the tensors. The sequence of tokens is provided as input to a transformer model, which outputs a point cloud based upon the sequence of tokens.

Abstract: A conductor terminal with a clamping spring, which with a busbar forms a clamping point for clamping an electrical conductor. At least one wall element is positioned behind a passage opening of the busbar in the conductor insertion direction and delimits an insertion space for the conductor and/or has a receiving area for a functional element coupled to the busbar and/or to the clamping spring and/or that is mechanically connectable to the conductor.

Abstract: Systems and methods for automatic removal for selected training data from a dataset are provided. Systems and methods are provided for identifying portions of radar data that contain information that is not present in camera and/or lidar data. The identified portions of the radar data can be processed by a neural network to provide the additional information. The identified parts of the radar data can then be processed while the remaining parts of the radar data remain unprocessed. In various examples, features can be extracted from identified regions of the radar data using a neural network and fused with camera and/or lidar features. In some examples, the fused features can be used for object detection.

Abstract: A method includes dividing an image into a plurality of patches; generating for the patches image tokens each including a halting score associated with the image token; generating a contextual features token representative of at least one contextual feature in connection with the image; processing the image tokens and the contextual features token using a vision transformer block; performing adaptive halting on the image tokens output from the transformer block, the adaptive halting comprising updating the halting scores associated with the image tokens and discarding ones of the image tokens having halting scores greater than or equal to a predetermined threshold score, wherein the non-discarded image tokens comprise remaining image tokens; and forwarding the remaining image tokens to a next processing block.

Abstract: The technologies described herein relate to a radar system that is configured to generate point clouds based upon radar tensors generated by the radar system. More specifically, the radar system is configured to identify bins in radar tensors that correspond to objects in an environment of the radar system, and to use energy values in other bins to construct a point cloud. A computing system detects objects in an environment of the radar system based upon the point clouds.

Abstract: The technologies described herein relate to a radar system that is configured to generate point clouds based upon radar tensors generated by the radar system. More specifically, the radar system is configured to generate heatmaps based upon radar tensors, wherein a neural network receives the radar tensors as input and constructs the heatmaps as output. Point clouds are generated based upon the heatmaps. A computing system detects objects in an environment of an autonomous vehicle (AV) based upon the point clouds, and the computing system further causes the AV to perform a driving maneuver based upon the detected objects.

Abstract: A conductor connection terminal with an insulation material housing having at least one conductor insertion opening for receiving an electrical conductor in a conductor insertion direction is described. At least one spring-loaded clamping connection for connecting an electrical conductor via elastic force is situated in the insulation material housing. The spring-loaded clamping connection has at least one busbar and a clamping spring with a clamping leg having a clamping edge for clamping the electrical conductor to a clamping point on a contact section of the busbar. The spring-loaded clamping connection, as a further component that is separate from the clamping spring and the busbar, has a connecting body via which the clamping spring is fixed to the busbar.

Abstract: A method of calibrating a radar sensor includes receiving radar returns from a plurality of objects based on a radar signal sent from the radar sensor, each of the radar returns having a magnitude, at least a subset of the objects are known static objects, identifying a location and orientation of the radar sensor when the signal was sent, identifying expected reflectance values for each of the plurality of known static objects, calculating a conversion function configured to adjust the magnitudes of each of the radar returns for the known static objects to an estimated reflectance value based on the expected reflectance values for each of the known static objects, and adjusting an output of the radar sensor based on the conversion function.

Abstract: A conductor terminal having an insulating material housing, a spring force clamping connection for connecting an electrical conductor in the insulating material housing and an actuating element. The spring force clamping connection has a busbar and a clamping spring. The clamping spring comprises a clamping leg having a clamping edge for clamping an electrical conductor to a contact section of the busbar, an attachment leg, which is in contact with the busbar and supports the clamping spring on the busbar, and a resilient bend, which connects the attachment leg with the clamping leg. In addition, the insulating material housing has a conductor insertion channel leading to the contact section and an actuating channel, which is movably mounted in the actuation channel and is set up to exert force on the clamping leg in order to displace the clamping leg against the force of the clamping spring into a hold-open position.

Abstract: Technologies described herein relate to a distributed aperture radar (DAR) system that includes multiple radar sensors. A graph neural network (GNN) is employed to cause radar data output by the multiple radar sensors to correspond to a same coordinate system.

Abstract: Autonomous vehicles (AVs) utilize perception and understanding of objects on the road to predict behaviors of the objects, and to plan a trajectory for the vehicle. In some scenarios, an AV may benefit from having information about an object that is not within a perceivable area of the sensors of the AV. Another AV in the area that has the object within a perceivable area of the sensors of the other AV may share information with the AV. Rich information about the object determined by the other AV can be compressed using an encoder and transferred efficiently to the AV for inclusion in a temporal map that combines locally determined object information and transferred object information determined by other AV(s). The temporal map may be used in one or more parts of the software stack of the AV.

Abstract: Disclosed are embodiments for generating long-range objects dataset using proximal time-synced data for object detection models. In some aspects, a method includes receiving a set of sensor data for a first autonomous vehicle (AV) corresponding to first scene data that is part of an intersecting field of view (FoV) with second scene data of a second AV; inputting the set of sensor data to a simulated trained object detection model of the first AV to generate a first set of object detections for the first scene data; comparing the first set with a second set of object detections generated for the second scene data; supplementing the first set of object detections with information from the second set of object detections to generate a supplemental set of object detections; and generating, using the supplemental set of object detections, a training data set for an object detection model of the first AV.

Abstract: Technologies for detecting, identifying, and tracking objects in a scene based upon images generated by a camera are described. A computing system obtains a perspective image from the camera and generates a feature image based upon the perspective image. The feature image has X×Y pixels, and each pixel in the X×Y pixels has a feature vector that includes values assigned thereto. The feature image is provided as input to a machine learning model, where the machine learning model generates a tensor based upon the feature image, where the tensor includes several bins that include values that are representative of likelihoods that an object exists in the scene at distances from the camera that correspond to the bins. The machine learning model is trained using a Gumbel-softmax algorithm. The computing system generates the image in the overhead view based upon the tensor output by the machine learning model.

Abstract: A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.

Abstract: A method and apparatus for implementing a method are disclosed. The method includes providing point cloud data to a machine learning algorithm, the point cloud data detected in the vicinity of an autonomous vehicle. The method further includes differentiating, via the machine learning algorithm, in the point cloud, data directly representing a location of a first object and data indirectly representing a location of a second object. The method includes transforming the data indirectly representing the location of the second object into data directly representing the location of the second object and generating corrected point cloud data based on the data directly representing the location of the first object and the data directly representing the location of the second object. The method includes outputting the corrected point cloud data to the autonomous vehicle.

Abstract: Metal matrix composites for use in oilfield applications are disclosed herein. The metal matrix composites comprise a metallic matrix comprising nickel in an amount of from 10 percent to 80 percent by weight based on total weight of the metallic matrix; chromium in an amount of from 10 percent to 45 percent by weight based on total weight of the metallic matrix; and molybdenum in an amount of from 10 percent to 45 percent by weight based on total weight of the metallic matrix. The metal matrix composite further comprises a particulate phase comprising tungsten carbide particles. The metal matrix composite exhibits desirable physical properties, including low relative magnetic permeability, high hardness, and high abrasion resistance. Articles comprising the metal matrix composites disclosed herein and methods of making the articles are also disclosed.

Abstract: An apparatus comprises a vacuum chamber configured to produce a substantially atmospheric vacuum environment for plasma processing; a radio frequency (RF) power supply located outside of the vacuum chamber; an RF matching network operatively coupled to the RF power supply; and a plurality of electrodes mounted within the vacuum chamber, the plurality of electrodes configured to receive RF power signals from the RF power supply through the RF matching network. The RF power signals are simultaneously delivered to the plurality of electrodes during a sputtering operation. The plurality of electrodes and a set of electrical components are operative to manage the inductive and capacitive reactance for coupling the RF power signals to provide more desirable plasma coupling during the sputtering operation.

Abstract: Technologies described herein relate to learning parameter values of a preprocessing algorithm that is executed in a radar system. The preprocessing algorithm is configured to receive raw radar data as input and is further configured to generate three-dimensional point clouds as output, where the preprocessing algorithm generates the three-dimensional point clouds based upon the raw radar data and the parameter values that are assigned to the preprocessing algorithm. To learn the parameter values, the preprocessing algorithm is converted to auto-differentiated form and is added as an input network layer to a deep neural network (DNN) that is configured to identify objects represented in three-dimensional point clouds. The parameter values are learned jointly with weight matrices of the DNN.