Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least one attribute in the form of a detection quality is determined for each raw detection. At least one further attribute for each raw detection is determined. A temporally or spatially resolved distance measure is determined for at least one attribute of the raw detections. Raw detections of a defined distance measure are combined to form a group of raw detections. The object is recognized from a group with at least one raw detection with the smallest distance measure of the at least one attribute in comparison with another raw detection, or from a group with at least one raw detection which were combined by combining at least two raw detections with the smallest distance measure of the at least one attribute to form said one raw detection.

Abstract: A method for training a convolutional neural network. For each of a multiplicity of training input images, the method includes processing of the training input image by the convolutional network; processing of a scaled version of the training input image by the convolutional network; determining a pair of convolutional layers of the convolutional network so that a convolutional layer of the pair generates a first feature map for the training input image which has the same size as a second feature map which is generated by the other convolutional layer of the pair for the scaled version of the training input image; and calculating a loss between the first feature map and the second feature map; and training the convolutional neural network to reduce an overall loss which includes the calculated losses.

Abstract: A computer-implemented method for training a machine learning system for transferring images of a source domain into a target domain. The method includes: ascertaining source patches based on source images of a source domain and target patches based on target images of a target domain, the source patches and the target patches each being assigned pixel-by-pixel pieces of meta-information; ascertaining tuples, each including one source patch and at least one target patch which characterizes a neighbor of the source patch nearest to k according to a similarity measure, k being a hyperparameter of the method and the similarity measure characterizing a similarity between a source patch and a target patch based on the pixel-by-pixel meta-information of the source patch and of the target patch; training the machine learning system based on the source patches of the tuples and on the target patches of the tuples.

Abstract: Computer-implemented method for training a machine learning system. The method includes: providing a source image from a source domain and a target image of a target domain; determining a first generated image based on the source image using a first generator, and determining a first reconstruction based on the first generated image using a second generator; determining a second generated image based on the target image using the second generator, and determining a second reconstruction based on the second generated image using the first generator; determining a first loss value, the first loss value characterizing a first difference between the source image and the first reconstruction, and determining a second loss value, the second loss value characterizing a second difference between the target image and the second reconstruction; and training the machine learning system based on the first loss value and/or the second loss value.

Abstract: A method for determining at least one anchor for an anchor-based lane line recognition and/or roadway marking recognition in a digital image representation on the basis of sensor data that are obtained from at least one surroundings sensor of a system. The method includes at least the following steps: a) receiving a digital image representation, b) setting at least one row or one column of possible anchors in at least one area of the digital image representation, the row or column of possible anchors being situated at a distance from at least the upper and lower or left and right edge of the area of the digital image representation.

Abstract: Method for obtaining at least one significant feature in a series of components of the same type on the basis of data sets by non-destructive testing. The method includes examining a classified random sample of components which have a known production sequence, by a non-destructive testing. A three-dimensional data set for each component is obtained, and components of the sample are divided by good and rejected parts. Defect-free component regions from all of the components of the random sample are extracted. At least one feature which is characteristic of the type of component and production process which, over a predetermined time of component production, exhibits considerable characteristic differences between the good and rejected parts is determined. The determination can be accomplished using neural networks, machine learning approaches, or statistics from the field of data analytics. The at least one feature and its characteristic is defined as a trained classifier.

Abstract: A device and method for processing data, in particular unnormalized, multidimensional data, of a neural network, in particular a deep neural network, especially for detecting objects in an input image. The data includes at least one first classification value for a multitude of positions in the input image in each case, a classification value quantifying a presence of a class. The method includes the following steps: evaluating the data as a function of a threshold value, a first classification value for a respective position in the input image that lies either below or above the threshold value being discarded, and a first classification value for a respective position in the input image that lies either above or below the threshold value not being discarded.

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least two objects are determined. At least one relational attribute is determined for the at least two objects. The at least one relational attribute defines a relationship between the at least two objects. An object is recognized taking account of the at least one relational attribute.

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least one attribute in the form of a detection quality is determined for each raw detection. At least one further attribute for each raw detection is determined. A temporally or spatially resolved distance measure is determined for at least one attribute of the raw detections. Raw detections of a defined distance measure are combined to form a group of raw detections. The object is recognized from a group with at least one raw detection with the smallest distance measure of the at least one attribute in comparison with another raw detection, or from a group with at least one raw detection which were combined by combining at least two raw detections with the smallest distance measure of the at least one attribute to form said one raw detection.

Abstract: An operating device for a shift by wire assembly in an automobile includes a selection apparatus configured to select an operation mode (R, N, D) of the automobile, wherein the selectable operation modes comprise a reverse operation mode (R), a neutral operation mode (N) and a drive operation mode (D), wherein the selection apparatus is further configured to select an autonomous drive operation mode (AD).

Abstract: Method for obtaining information from short-fibre-reinforced plastic components sequentially produced by an X-ray computed tomography. A learning phase includes: generating CT data sets for a random sample of plastic components from a production process; extracting at least one defect-free region of the plastic components; determining characteristic feature(s) in the extracted regions, relevance of individual features, and regions which are characteristic of the plastic component type and production process thereof, over a predetermined period of the plastic components productions, which exhibit considerable characteristic differences between good parts and reject parts; and defining the feature(s) with its characteristic as trained classifier.

Abstract: The present invention refers to a loudspeaker, in especially to a bass reflex tube for a loudspeaker. Commonly, said loudspeakers include at least one woofer driver, by which an electric audio signal is transduced via a voice coil and a diaphragm into soundwaves of medium to low frequency. A bass reflex tube for a loudspeaker should be provided, which enables said loudspeaker to produce an acoustic signal according to an electrical signal, wherein the acoustic signal shows optimized an improved quality characteristics, in especially with regards to quality and accuracy of the conversion of electrical signal into the acoustic signal. The problem mentioned above is solved by a bass reflex tube for a loudspeaker, wherein said bass reflex tube is at least partially made of a ceramic material. Further, the bass reflex tube comprises a tubular portion, which is extending in an axial direction of a longitudinal axis of the bass reflex tube.

Abstract: Method for obtaining at least one significant feature in a series of components of the same type on the basis of data sets by non-destructive testing. The method includes examining a classified random sample of components which have a known production sequence, by a non-destructive testing. A three-dimensional data set for each component is obtained, and components of the sample are divided by good and rejected parts. Defect-free component regions from all of the components of the random sample are extracted. At least one feature which is characteristic of the type of component and production process which, over a predetermined time of component production, exhibits considerable characteristic differences between the good and rejected parts is determined. The determination can be accomplished using neural networks, machine learning approaches, or statistics from the field of data analytics. The at least one feature and its characteristic is defined as a trained classifier.

Abstract: Method for obtaining information from short-fibre-reinforced plastic components sequentially produced by an X-ray computed tomography. A learning phase includes: generating CT data sets for a random sample of plastic components from a production process; extracting at least one defect-free region of the plastic components; determining characteristic feature(s) in the extracted regions, relevance of individual features, and regions which are characteristic of the plastic component type and production process thereof, over a predetermined period of the plastic components productions, which exhibit considerable characteristic differences between good parts and reject parts; and defining the feature(s) with its characteristic as trained classifier.

Abstract: The present invention refers to a loudspeaker, in especially to a bass reflex tube for a loudspeaker. Commonly, said loudspeakers include at least one woofer driver, by which an electric audio signal is transduced via a voice coil and a diaphragm into soundwaves of medium to low frequency. A bass reflex tube for a loudspeaker should be provided, which enables said loudspeaker to produce an acoustic signal according to an electrical signal, wherein the acoustic signal shows optimized an improved quality characteristics, in especially with regards to quality and accuracy of the conversion of electrical signal into the acoustic signal. The problem mentioned above is solved by a bass reflex tube for a loudspeaker, wherein said bass reflex tube is at least partially made of a ceramic material. Further, the bass reflex tube comprises a tubular portion, which is extending in an axial direction of a longitudinal axis of the bass reflex tube.

Abstract: In a laser line projection apparatus, six spaced-apart unpolarized laser-beams are plane-polarized with low loss by a combination of a thin-film polarizer, a reflector, and two polarization rotators. Two beams are polarized in each of three polarization-orientations. Two of the polarization-orientations are orthogonally aligned with each other in P and S orientations. The other polarization-orientation is non-orthogonally aligned in an intermediate orientation. The beams are intensity-homogenized and projected into a line of radiation. Any point on the line of radiation is formed from rays with an angular distribution of polarization-orientation from S to P through the intermediate orientation and back to S through the intermediate orientation.

Abstract: In a laser line projection apparatus, six spaced-apart unpolarized laser-beams are plane-polarized with low loss by a combination of a thin-film polarizer, a reflector, and two polarization rotators. Two beams are polarized in each of three polarization-orientations. Two of the polarization-orientations are orthogonally aligned with each other in P and S orientations. The other polarization-orientation is non-orthogonally aligned in an intermediate orientation. The beams are intensity-homogenized and projected into a line of radiation. Any point on the line of radiation is formed from rays with an angular distribution of polarization-orientation from S to P through the intermediate orientation and back to S through the intermediate orientation.

Abstract: A plant having a first device for the preparation of ethyne by partial oxidation of at least one hydrocarbon, generating a first ethyne-containing product gas stream, having a second device for the electrothermal preparation of ethyne, generating a second ethyne-containing product gas stream, and having a separating device for separating ethyne from a gas stream, to which both the first and the second product gas streams are fed, can make efficient use of excess electrical energy by operating the device for the electrothermal preparation of ethyne with excess electrical energy.

Abstract: In one embodiment, a method includes, by a base station, communicating a challenge to a transponder through a first communication link; and establishing a second communication link with the transponder. The second communication link is a capacitive link. The method also includes receiving a first response to the challenge through the first communication link with the transponder; sampling the second communication link to detect a signal corresponding to a second response to the challenge from the transponder; receiving the second response through the second communication link; and authorizing the transponder based on the first and second responses.

Abstract: In certain embodiments, a touch sensor includes a plurality of first electrodes, and a plurality of second electrodes. The plurality of second electrodes are separated from the plurality of first electrodes by an insulator and are operable, when operated by a controller, to capacitively couple with one or more first electrodes across the insulator. The touch sensor also includes a touch panel, a plurality of proximity electrodes, and a plurality of shield electrodes. Each shield electrode is associated with a respective proximity electrode of the plurality of proximity electrodes such that each shield electrode substantially surrounds its respective proximity electrode from a perspective orthogonal to a surface of the touch panel.