Abstract: The present disclosure provides a measurement application device comprising a signal processing module configured to at least one of generate measurement signals, and acquire measurement signals, a user interface configured to acquire gesture-based user input, and a processor coupled to the user interface, and executing a machine learning algorithm, wherein the machine learning algorithm is configured to analyze received gesture-based user input, and to output respective control information for controlling the signal processing module or configuration information for configuration of the signal processing module. Further, a respective computer implemented method is provided.

Abstract: A measurement instrument for testing a device under test is described. The device under test has at least two test points. The measurement instrument includes a first measurement channel, a second measurement channel, and a machine-learning circuit. The first measurement channel is configured to process a first input signal associated with one of the test points, thereby generating a first measurement signal. The second measurement channel is configured to process a second input signal associated with another one of the test points, thereby generating a second measurement signal. The machine-learning circuit is configured to determine at least one correlation quantity based on the first measurement signal and based on the second measurement signal, wherein the at least one correlation quantity is indicative of a correlation between the first measurement signal and the second measurement signal. Further, a measurement system and a signal processing method are described.

Abstract: A measurement instrument for testing a device under test is described. The device under test has at least two test points. The measurement instrument includes a first measurement channel, a second measurement channel, and a machine-learning circuit. The first measurement channel is configured to process a first input signal associated with one of the at least two test points, thereby generating a first measurement signal. The second measurement channel is configured to process a second input signal associated with another one of the at least two test points, thereby generating a second measurement signal. The machine-learning circuit is configured to determine at least one correlation quantity based on the first measurement signal and based on the second measurement signal, wherein the at least one correlation quantity is indicative of a correlation between the first measurement signal and the second measurement signal. Further, a measurement system and a signal processing method are described.

Abstract: The present disclosure provides an analog-to-digital converter system comprising a sampler configured to sample an input signal and provide at least two output signals with a predetermined output sample rate, and an analog-to-digital converter for each one of the output signals and configured to convert the respective output signal into a digital signal with a predetermined converter sample rate, wherein the converter sample rate is higher than the output sample rate. Further, the present disclosure provides a respective method.

Abstract: The present disclosure relates to a method of optimizing cabling of at least one device under test when performing tests on the at least one device under test. At least one device under test with a plurality of connectors is provided. The connectors are capable of supporting different frequencies and/or different signal directions. At least one testing device with a plurality of test ports is provided. The test ports are capable of supporting different frequencies and/or different signal directions. The at least one device under test and the testing device are to be connected with each other via a cabling. The cabling between the at least one device under test and the at least one testing device is optimized by a processing circuit. Further, a system is disclosed.

Abstract: A computer-implemented data processing method includes the steps of: capturing and/or receiving a data set, wherein the data set includes a plurality of data points; reducing a dimensionality of the data set, thereby obtaining an adapted data set having a reduced dimensionality, wherein the adapted data set includes adapted data points, wherein the adapted data points have a predetermined number of latent coordinates, respectively, and wherein the latent coordinates are associated with a latent space; partitioning the latent space into a plurality of latent subspaces; determining at least one representative parameter for the plurality of latent subspaces, respectively, wherein the at least one representative parameter includes additional information on the adapted data points located in the respective latent subspace; and generating joint visualization data based on the adapted data set and based on the representative parameters determined. Further, a measurement system is described.

Abstract: A computer-implemented method for detecting and localizing faults in an antenna array comprises performing fault detection on the antenna array to identify the presence of at least one faulty antenna element of the antenna array. The fault detection is performed using a machine learning technique. It is determined whether the antenna array is faulty or non-faulty, based on a result of the performed fault detection. The at least one faulty antenna element is localized if the antenna array is determined to be faulty, using a machine learning technique.

Abstract: A method of assessing a log-file is described. A log-file to be assessed is received. A data storage is accessed that includes several log-files. A group of log-files from the several log-files stored in the data storage is identified. The group of log-files includes log-files that are nearest to the log-file to be assessed. The identified group of log-files is returned to a user for further analysis. Further, a method of grouping several log-files as well as a system for processing at least one log-file are described.

Abstract: A phase-shifted sampling module for sampling a signal is described. The phase-shifted sampling module includes a primary sampler module, an ADC module, and an equalization module. The primary sampler module includes an analog signal input, a first signal path, and a second signal path. The equalization module includes a primary sampler equalizer sub-module. The primary sampler equalizer sub-module is configured to compensate low-frequency mismatches between the first signal path and the second signal path. Further, a method for determining filter coefficients of an equalization module of a phase-shifted sampling module is described.

Abstract: The present disclosure provides a main measurement device for simultaneously measuring signals with at least one secondary measurement device, the main measurement device comprising a reference signal output port configured to couple to the at least one secondary measurement device, a reference signal generator coupled to the reference signal output port and configured to generate a reference signal, a measurement port configured to receive a signal to be measured, a trigger output port configured to couple to a trigger input port of the at least one secondary measurement device and to output a trigger signal, and a controllably switchable internal signal path configured to selectively couple the reference signal generator with the measurement port. Further, the present invention discloses a respective secondary measurement device, a respective measurement system, and a respective method.

Abstract: The present invention relates to a method for training a signal characterization neural network. The method comprises the steps of: providing a measurement signal having at least one distortion; assigning at least one predefined signal integrity identifier to a corresponding distortion within the measurement signal; generating at least one input training vector based on the provided measurement signal and the corresponding assigned signal integrity identifier; and applying the generated input training vector on input terminals of the signal characterization neural network for training the signal characterization neural network. The present invention also relates to a method for automatically characterizing a measurement signal. The present invention further relates to a measurement apparatus and a corresponding method for analyzing a waveform signal.

Abstract: The present disclosure provides an analog-to-digital converter system comprising a sampler configured to sample an input signal and provide at least two output signals with a predetermined output sample rate, and an analog-to-digital converter for each one of the output signals and configured to convert the respective output signal into a digital signal with a predetermined converter sample rate, wherein the converter sample rate is higher than the output sample rate. Further, the present disclosure provides a respective method.

Abstract: The present disclosure provides a circuitry distortion corrector for correcting distortions of electrical signals. The circuitry distortion corrector comprises a first correction filter that filters the received signals, and a second correction filter that is coupled to the first correction filter and filters the signals that are filtered by the first correction filter. The first correction filter operates based on first filter coefficients that are based on first value tuples, each first value tuple comprising a first frequency and a respective first circuitry characterizing value, and wherein the first frequencies are equally spaced apart, and the second correction filter operates with second filter coefficients that are based on second value tuples, each second value tuple comprising a second frequency and a respective second circuitry characterizing value, wherein the second frequencies are logarithmically spaced apart.

Abstract: A measurement instrument for acquiring an input signal is described. The measurement instrument includes a first acquisition path with a first acquisition circuit having a first sampling rate. The measurement instrument includes at least one second acquisition path with a second acquisition circuit, having a second sampling rate. The measurement instrument is configured to acquire the input signal with an overall sampling rate being higher than the first sampling rate and the second sampling rate. The first acquisition path and the at least one second acquisition path each have a decimation filter and a decimator connected in series to the decimation filter, thereby equalizing a low frequency band in the input signal when processing the input signal. Further, method of acquiring an input signal is described.

Abstract: A measurement apparatus comprising an acquisition memory adapted to store data sections of at least one acquired measurement signal; a processor adapted to calculate a measurement parameter vector, v, for each data section of the acquired measurement signal; and a trained autoencoder neural network adapted to process the measurement parameter vectors, v, applied as input data to the trained autoencoder neural network to provide at a middle layer of said autoencoder neural network an encoded vector, h, with characteristic signal features of the acquired measurement signal.

Abstract: A method for operating an oscilloscope is described, wherein a waveform axis scale input value is detected. Further, a record length input value is detected. An oscilloscope operating point is determined relative to at least one predetermined operating mode limit. In addition, a method for operating an oscilloscope as well as an oscilloscope are described.

Abstract: A signal analyzer for analyzing a signal includes a frontend with at least two interleaved digitizers configured to digitize an input signal, thereby generating a digitized input signal. The signal analyzer also includes a first interleave alignment filter established by a hardware interleave alignment filter that is configured to hardware-compensate non-ideal effects of the frontend in the digitized input signal in real-time, thereby generating a hardware-compensated, digitized input signal. Further, the signal analyzer includes an acquisition memory configured to store the hardware-compensated, digitized input signal, thereby acquiring an acquired signal. Moreover, the signal analyzer includes a second interleave alignment filter configured to fine-compensate further non-ideal effects of the frontend in a post-processing of the acquired signal.

Abstract: A phase-shifted sampling module for sampling a signal is described. The phase-shifted sampling module includes a primary sampler module, an ADC module, and an equalization module. The primary sampler module includes an analog signal input, a first signal path, and a second signal path. The equalization module includes a primary sampler equalizer sub-module. The primary sampler equalizer sub-module is configured to compensate low-frequency mismatches between the first signal path and the second signal path. Further, a method for determining filter coefficients of an equalization module of a phase-shifted sampling module is described.

Abstract: A measurement instrument for testing a device under test is described. The device under test has at least two test points. The measurement instrument includes a first measurement channel, a second measurement channel, and a machine-learning circuit. The first measurement channel is configured to process a first input signal associated with one of the at least two test points, thereby generating a first measurement signal. The second measurement channel is configured to process a second input signal associated with another one of the at least two test points, thereby generating a second measurement signal. The machine-learning circuit is configured to determine at least one correlation quantity based on the first measurement signal and based on the second measurement signal, wherein the at least one correlation quantity is indicative of a correlation between the first measurement signal and the second measurement signal. Further, a measurement system and a signal processing method are described.

Abstract: The present disclosure provides a main measurement device for simultaneously measuring signals with at least one secondary measurement device, the main measurement device comprising a reference signal output port configured to couple to the at least one secondary measurement device, a reference signal generator coupled to the reference signal output port and configured to generate a reference signal, a measurement port configured to receive a signal to be measured, a trigger output port configured to couple to a trigger input port of the at least one secondary measurement device and to output a trigger signal, and a controllably switchable internal signal path configured to selectively couple the reference signal generator with the measurement port. Further, the present invention discloses a respective secondary measurement device, a respective measurement system, and a respective method.