Abstract: A method for training a machine learning model. The method includes: determining a plurality of training sequences of training-input data elements, wherein for each training sequence each training-input data element contains sensor data for a time point from a time period assigned to the training sequence in which a prespecified event takes place at least once at one or more respective event time points; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains sensor data and one of the one or more respective event time points; and training the machine learning model depending on the determined temporal distances.

Abstract: Representative implementations of devices and techniques provide local and/or remote control for one or more connected loads or remote appliances. A load control device includes controls for switching and varying the power to associated loads and also includes communication components for remote operations. A user interface device includes a configurable and user customizable interface to communicate control instructions to the load control device. The load control device and the user interface device may be installed as a single unit, mechanically and electrically coupled together, or they may be installed remote from each other. Accordingly, the load control device and the user interface device include wired and wireless communication capabilities.

Abstract: A method for generating additional training data for training a machine learning algorithm is disclosed. The method includes (i) providing training data for training the machine learning algorithm, wherein the training data includes labeled sensor data from at least one sensor, (ii) transforming the training data for training the machine learning algorithm in a graph structure, wherein nodes in the graph structure represent objects represented in the corresponding sensor data, and wherein a starting node of the graph structure represents the position of the at least one sensor with respect to the objects represented in the corresponding sensor data, and (iii) generating additional training data for training the machine learning model by modifying the graph structure.

Abstract: A method for training a deep-learning-based machine learning algorithm. The method includes: providing training data for training the deep-learning-based machine learning algorithm, wherein the training data comprise sensor data; training, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data; and subsequently optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function.

Abstract: The described techniques facilitate the secure transmission of sensor measurement data to an ECU by implementing an authentication procedure. The authentication procedure includes an integrated circuit (IC) generating authentication tags by encrypting portions of sensor measurement data. These authentication tags are then transmitted together with the sensor measurement data as authenticated sensor measurement data. The ECU may then use the authentication tags to authenticate the sensor measurement data based upon a comparison of the portions of the sensor measurement data sensor measurement data to the authentication tag that is expected to be generated for those portions of sensor measurement data.

Abstract: A method for ascertaining an explanation map of an image. All those pixels of the image are highlighted which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is being selected in such a way that it selects a smallest possible subset of the pixels of the image as relevant. The explanation map leads to the same classification result as the image when the explanation map is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.

Abstract: Some described techniques address issues of latency and lengthy processing times associated with conventional redundant sensor measurement systems that rely upon digital transmission protocols by implementing a diverse analog sensor interface architecture. Additional described techniques provide a redundant signaling solution to achieve signaling diversity using a combination of analog and digital sensor interface architectures. The described architectures may advantageously use a number of sensor measurement paths that may be independent of one another or share any suitable number of common components to provide varying levels of redundancy. When redundant analog sensor interface architectures are implemented, the analog interfaces may also provide signal diversity with respect to the use of different types of analog transmission protocols, which may include different signaling interfaces (e.g. differential versus single-ended), different transmission interfaces (e.g.

Abstract: A mat seal for an electrical connector includes a plurality of through-holes extending in a through-hole direction through the mat seal, a grid of a first material having a plurality of grid meshes, the grid is arranged with the through-holes extending through the grid meshes, a first material layer of a second material disposed on a first side of the grid, and a second material layer of a third material disposed on a second side of the grid opposite the first side. The first material of the grid has a greater compression modulus than the second material of the first material layer and the third material of the second material layer.

Abstract: The described techniques address the issues of latency and lengthy processing times associated with conventional redundant sensor measurement systems that rely upon digital transmission protocols by implementing a diverse analog sensor interface architecture. The described architecture may advantageously use a number of sensor measurement paths that may be independent of one another or share any suitable number of common components to provide varying levels of redundancy. The analog interfaces may provide signal diversity with respect to the use of different types of analog transmission protocols, which may include different signaling interfaces (e.g. differential versus single-ended), different transmission interfaces (e.g. voltage versus current interfaces), and/or the use of different signalization schemes.

Abstract: A connection assembly includes a first housing part, a second housing part pluggable with the first housing part, and a joint connector held between the first housing part and the second housing part. The first housing part has a plurality of contact element receptacles and a plurality of latching elements. The latching elements latch a plurality of contact elements in the contact element receptacles. The second housing part has a locking section locking one of the latching elements in a latching position. The joint connector connects at least two of the contact elements.

Abstract: A method for ascertaining an explanation map of an image, in which all those pixels of the image are changed which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is selected in such a way that a smallest possible subset of the pixels of the image are changed, and the explanation map preferably does not lead to the same classification result as the image when it is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.

Abstract: The present disclosure relates to a sensor chip, including a semiconductor substrate, a first sensor circuit monolithically integrated into the semiconductor substrate, at least one second sensor circuit monolithically integrated into the semiconductor substrate, wherein the first and second integrated sensor circuits are embodied identically.

Abstract: Sensor devices and corresponding methods are provided. When a signal path output (COMP OUT) indicates no threshold crossings, diagnosis is performed with a timing based on a clock. In case threshold crossings are indicated, diagnosis is performed with a timing based on threshold crossings.

Abstract: The described techniques facilitate the secure transmission of sensor measurement data to an ECU by implementing an authentication procedure. The authentication procedure includes an integrated circuit (IC) generating authentication tags by encrypting portions of sensor measurement data. These authentication tags are then transmitted together with the sensor measurement data as authenticated sensor measurement data. The ECU may then use the authentication tags to authenticate the sensor measurement data based upon a comparison of the portions of the sensor measurement data sensor measurement data to the authentication tag that is expected to be generated for those portions of sensor measurement data.

Abstract: A device comprises a magnet and an angle sensor, wherein the angle sensor is configured to detect a rotation angle of the magnet. The device also contains a rotation counter, wherein the rotation counter is configured to record a number of rotations of the magnet. The angle sensor and the rotation counter are implemented in physically separate components.

Abstract: A rotational angle sensor device and a corresponding method are provided. Two signals which have a phase offset with respect to one another are generated based on a modulated magnetic field using a sensor arrangement. Three signals which have a phase offset with respect to one another are generated from the signals.

Abstract: The invention relates to a method for performing a movement correction when measuring a human eye, in which the eye is scanned by a measurement beam in order to obtain a set of position values of an eye structure. Scanning extends over a measurement time interval, wherein the position values are each provided with a timestamp within the measurement time interval. A surface shape and a displacement function are determined, with the displacement function representing a movement pattern during the measurement time interval, and the surface shape and the displacement function being determined in such a way that the position values are approximated by the surface shape when the surface shape is moved according to the displacement function. Moreover, the invention relates to an associated measurement system.

Abstract: The described techniques address the issues of latency and lengthy processing times associated with conventional redundant sensor measurement systems that rely upon digital transmission protocols by implementing a diverse analog sensor interface architecture. The described architecture may advantageously use a number of sensor measurement paths that may be independent of one another or share any suitable number of common components to provide varying levels of redundancy. The analog interfaces may provide signal diversity with respect to the use of different types of analog transmission protocols, which may include different signaling interfaces (e.g. differential versus single-ended), different transmission interfaces (e.g. voltage versus current interfaces), and/or the use of different signalization schemes.

Abstract: The invention relates to a method for performing a movement correction when measuring a human eye, in which the eye is scanned by a measurement beam in order to obtain a set of position values of an eye structure. Scanning extends over a measurement time interval, wherein the position values are each provided with a timestamp within the measurement time interval. A surface shape and a displacement function are determined, with the displacement function representing a movement pattern during the measurement time interval, and the surface shape and the displacement function being determined in such a way that the position values are approximated by the surface shape when the surface shape is moved according to the displacement function. Moreover, the invention relates to an associated measurement system.

Abstract: A method for ascertaining an explanation map of an image, in which all those pixels of the image are changed which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is selected in such a way that a smallest possible subset of the pixels of the image are changed, and the explanation map preferably does not lead to the same classification result as the image when it is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.