Abstract: Automation station and method for restoring license keys in a modular automation station which includes a main unit having a backplane bus connection and a plurality of modules having another backplane bus connection, wherein the plurality of modules or the main unit having a respective backplane bus connection communicate with one another via a backplane bus, where the plurality of modules are operated via a license key to provide software-based functionality tied to a respective license key after a module is replaced with a replacement module, and the license key is restored via automated reading of three data blocks, even when only two of the three data blocks are readable and the license key for the software-based functionality of the replaced module is assigned to the replacement module and thus the software-based functionality is enabled in the replacement module.

Abstract: A test kit that is configured for bioanalysis, such as an immunoassay, is provided. The test kit includes at least one measuring sensor for quantitative detection of a substance, and at least one reference sensor that is already supplied with the substance in a defined manner. In a method for analyzing a test kit, the at least one measuring sensor is read, and a measurement value for a concentration, a substance quantity, or a mass is obtained. A read value of the at least one measuring sensor is scaled using read values of the at least one reference sensor, or a measured value that corresponds to the read value is obtained using a compensation curve that puts the read values of the at least one reference sensor into relationship with the defined supply of the substance to the at least one reference sensor.

Abstract: The invention specifies an apparatus for evaluating sensor measured values (1.1), having: —a sensor (1), wherein a model function that is suitable for a least squares regression and definable by a parameter vector is provided for evaluating the sensor measured values (1.1) of the sensor (1), wherein at least one parameter of the parameter vector forms a sensor output signal (3), and —a computing and evaluation unit (2) that has a neural network (2.1), which estimates the parameter vector on the basis of actually ascertained sensor measured values (1.1), and a least squares regression module (2.2), wherein the neural network (2.1) is trained with parameter vectors and the associated sensor measured values, and that is set up: ?—to use the trained neural network (2.1) to ascertain at least one parameter estimate vector for sensor measured values (1.1) measured using the sensor (1) as an input variable for the least squares regression module (2.

Abstract: A method for the computer-assisted configuration of a data-driven model on the basis of training data is provided. The method is characterised in that the series of measurements are subjected to a suitable preprocessing process comprising a binning step, wherein measurement characteristics which existed during the measurement of the measurement values in question are taken into consideration. A suitable data-driven model such as a neural network is then learned on the basis of the pre-processed series of measurements. This learned data-driven model makes it possible to accurately forecast target vectors in accordance with associated series of measurements. The method can, for example, be used to analyse optical spectra. More particularly, it is possible to predict using the learned model whether the tissue sample for which an optical spectrum was detected represents diseased tissue.

Abstract: A test kit that is configured for bioanalysis, such as an immunoassay, is provided. The test kit includes at least one measuring sensor for quantitative detection of a substance, and at least one reference sensor that is already supplied with the substance in a defined manner. In a method for analyzing a test kit, the at least one measuring sensor is read, and a measurement value for a concentration, a substance quantity, or a mass is obtained. A read value of the at least one measuring sensor is scaled using read values of the at least one reference sensor, or a measured value that corresponds to the read value is obtained using a compensation curve that puts the read values of the at least one reference sensor into relationship with the defined supply of the substance to the at least one reference sensor.

Abstract: Various embodiments include a method for processing a data set comprising: obtaining a measurement dataset; applying a preprocessing algorithm to the measurement dataset to obtain a preprocessed measurement dataset; applying a classification algorithm to the preprocessed measurement dataset to classify a feature represented by the measurement dataset; determining a quality of the classification of the feature; and adjusting the preprocessing algorithm based on the determined quality. Adjusting the preprocessing algorithm comprises applying a selection algorithm. The selection algorithm describes the change in a value of a parameter of the preprocessing algorithm depending on the determined quality. The preprocessing algorithm comprises at least one of the following operations applied to the measurement dataset: binning; differentiation; integration; and forming tuples having a predetermined relationship from the measurement dataset.

Abstract: The invention relates to a test kit which is designed for bioanalysis, in particular for an immunoassay. The test kit comprises at least one measuring sensor (M) for the quantitative detection of a substance and at least one reference sensor (R1, R2, R3) which is already supplied with the substance in a defined manner. In the method for analyzing a test kit, the measuring sensor (M) is read and a measurement value for a concentration, a substance quantity, or a mass is obtained, wherein the read value of the at least one measuring sensor (M) is scaled using the read values of the at least one reference sensor (R1, R2, R3), or a measured value which corresponds to the read value is obtained by means of a compensation curve which puts the read values of the reference sensors (R1, R2, R3) into relationship with the defined supply of the substance to the reference sensors (R1, R2, R3).

Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

Abstract: Various embodiments include a method for processing a data set comprising: obtaining a measurement dataset; applying a preprocessing algorithm to the measurement dataset to obtain a preprocessed measurement dataset; applying a classification algorithm to the preprocessed measurement dataset to classify a feature represented by the measurement dataset; determining a quality of the classification of the feature; and adjusting the preprocessing algorithm based on the determined quality. Adjusting the preprocessing algorithm comprises applying a selection algorithm. The selection algorithm describes the change in a value of a parameter of the preprocessing algorithm depending on the determined quality. The preprocessing algorithm comprises at least one of the following operations applied to the measurement dataset: binning; differentiation; integration; and forming tuples having a predetermined relationship from the measurement dataset.

Abstract: A method for the computer-assisted configuration of a data-driven model on the basis of training data is provided. The method is characterised in that the series of measurements are subjected to a suitable preprocessing process comprising a binning step, wherein measurement characteristics which existed during the measurement of the measurement values in question are taken into consideration. A suitable data-driven model such as a neural network is then learned on the basis of the pre-processed series of measurements. This learned data-driven model makes it possible to accurately forecast target vectors in accordance with associated series of measurements. The method can, for example, be used to analyse optical spectra. More particularly, it is possible to predict using the learned model whether the tissue sample for which an optical spectrum was detected represents diseased tissue.

Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

Abstract: The invention relates to a method for determining a tissue type of a tissue of an animal or human individual, in which method: electromagnetic radiation (26) emitted by a tissue sample (24) of the tissue is sensed (10) by means of a radiation sensor (22), the radiation sensor (22) providing a sensor signal (28) in accordance with the sensed electromagnetic radiation, and the sensor signal (28) is evaluated (12) by means of an evaluation unit (30) in order to determine and output the tissue type. The problem addressed by the invention is that of enabling improved determination of the tissue type. According to the invention, the evaluation unit (30) is a self-learning evaluation unit (30) that is initially trained (14) by means of training data sets (32) on the basis of at least one model, which is based on a method for machine learning, the training of the evaluation unit being conducted by means of such training data sets (32) each comprising a training sensor signal with an associated training tissue type.

Abstract: The invention relates to a test kit which is designed for bioanalysis, in particular for an immunoassay. The test kit comprises at least one measuring sensor (M) for the quantitative detection of a substance and at least one reference sensor (R1, R2, R3) which is already supplied with the substance in a defined manner. In the method for analyzing a test kit, the measuring sensor (M) is read and a measurement value for a concentration, a substance quantity, or a mass is obtained, wherein the read value of the at least one measuring sensor (M) is scaled using the read values of the at least one reference sensor (R1, R2, R3), or a measured value which corresponds to the read value is obtained by means of a compensation curve which puts the read values of the reference sensors (R1, R2, R3) into relationship with the defined supply of the substance to the reference sensors (R1, R2, R3).

Abstract: The present disclosure relates to measuring light emission. The teachings thereof may be embodied in emission-measuring devices. For example, a device may include: a sample region; an illumination unit for irradiating the sample region and a sample positioned therein; and a radiation detector. The illumination unit may include: a radiation source; a first dispersive element arranged downstream, decomposing the radiation into spectral components; a first micromirror field arranged downstream; and a second dispersive element arranged downstream of the first micromirror field. The second dispersive element may unify spectral components selected by the first micromirrror field into a common excitation beam.

Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

Abstract: An optical sensor is arranged in an indentation of a dust line, the indentation being equipped with at least one gas inlet nozzle for removing the dust from the optical sensor. Dust is transported through the dust line. An optical property of the dust is measured using at least one optical sensor arranged in an indentation of the dust line, and the dust is then removed from the optical sensor by blowing in air using the at least one gas inlet nozzle arranged in the indentation.

Abstract: An optical sensor is arranged in an indentation of a dust line, the indentation being equipped with at least one gas inlet nozzle for removing the dust from the optical sensor. Dust is transported through the dust line. An optical property of the dust is measured using at least one optical sensor arranged in an indentation of the dust line, and the dust is then removed from the optical sensor by blowing in air using the at least one gas inlet nozzle arranged in the indentation.