Abstract: A method of testing an impedance-sensitive system with a switching device, wherein the method comprises: switching the disconnecting device into the on-state configured to permit transmission of energy via the coil; implementing a first measurement with the impedance-sensitive system; switching the disconnecting device into the off-state configured to permit damping of the external positioning signal that couples into the coil so as to reduce the undesirable oscillations of the coil; implementing a second measurement with the impedance-sensitive system; performing a comparison of the first measurement and the second measurement; performing a verification of the comparison with a target specification; and displaying a correct function and/or a malfunction depending on the verification.

Abstract: A method of testing an impedance-sensitive system with a switching device, wherein the method comprises: switching the disconnecting device into the on-state configured to permit transmission of energy via the coil; implementing a first measurement with the impedance-sensitive system; switching the disconnecting device into the off-state configured to permit damping of the external positioning signal that couples into the coil so as to reduce the undesirable oscillations of the coil; implementing a second measurement with the impedance-sensitive system; performing a comparison of the first measurement and the second measurement; performing a verification of the comparison with a target specification; and displaying a correct function and/or a malfunction depending on the verification.

Abstract: A system associated with predicting segmentation quality of segmented objects implemented in the analysis of copious image data is disclosed. The system receives a collection of image data related to a particular type of data. The image data is segmented into segmented data portions based on an object associated with the collection of image data. Regions of interest associated with the segmented data portions are determined. The quality of segmentation of the segmented data portions is determined for respective classification of the regions of interest. A classification label is assigned to the regions of interest. Regions of interest are partitioned into sub-regions. Features associated with the sub-regions of the segmented data portions are determined. A training dataset is generated based on the determined features associated with the sub-regions in order to train a classification model based on a predetermined threshold value.