Rohde and Schwarz Gmbh & Co. Kg

Andrew Schaefer

Thomas Kuhwald

Markus Freidhof

Bernhard Nitsch

Andreas Ziegler

Detlef Schlager

Adam Tankielun

Martin Peschke

Benoit Derat

Andreas Maier

Baris Guezelarslan

Wolfgang Herbordt

Michael Grimm

Sebastian Petzsch

Cornelius Kaiser

Jens Naumann

Kai Uwe Schmidt

Benjamin Fischbach

Victor Arroyo

Iqbal Bawa

Ernst Flemming

Marwa Sai

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.

Method and device for detecting and localizing faults in an antenna array, and test system

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.

Computer-implemented data processing method, and measurement system

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.

Measurement system for identifying aggressor signals

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.

Analog-to-digital converter system and method

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.

Phase-shifted sampling module and method for determining filter coefficients

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.

Main measurement device, secondary measurement device, measurement system and method

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.

Method for training a neural network, method for automatically characterizing a measurement signal, measurement apparatus and method for analyzing

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.

Circuitry distortion corrector, measurement device, correction data generator, and correction method

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.

Method and apparatus for detecting signal features

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.

Method for operating an oscilloscope as well as oscilloscope

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.

Signal analyzer and method of analyzing a signal

A signal analysis method for analyzing a pulse modulated input signal is described. The signal analysis method includes: receiving the pulse modulated input signal, the input signal including a symbol sequence; recovering a clock signal from the input signal, the clock signal being associated with the input signal; sampling the input signal based on the clock signal, thereby obtaining a set of input signal samples, each of the input signal samples having a certain level being constant over time; determining at least two different levels of input signal samples being associated with different symbols of the symbol sequence; and determining at least one decision threshold based on the at least two different levels determined previously, the decision threshold being associated with a symbol transition of the symbol sequence. Further, a signal analysis apparatus is described.

Signal analysis method and signal analysis module

An electrical measurement device for measuring at least one electrical signal is described, the electrical measurement device comprising an electrical measurement unit for measuring the electrical signal and for deriving at least one measurement parameter from the measured electrical signal, an acoustic processing unit for processing a signal obtained from the electrical measurement unit, and a loudspeaker for outputting an acoustic signal representative of the signal obtained from the electrical measurement unit. The acoustic processing unit is configured to generate the acoustic signal to be outputted by the loudspeaker. The acoustic processing unit is further configured to modulate a predetermined acoustic signal by the at least one measurement parameter.

Electrical measurement device

An approach for determining link transmission quality identifiers is provided. For this purpose, a two-step approach is applied. In a first step, quality indicators are obtained from a black-box device and related signal are obtained and recorded. Based on the recorded data, a model can be established or a neural network can be trained. The generated model or the trained network may be used for determining further quality indicators at any arbitrary point in time.

Method and system for analyzing a determination of a link transmission quality indicator and method and apparatus for determining a link transmission quality indicator

A method for and apparatus processing a measurement signal of a dynamic physical quantity is provided which includes acquiring a measurement signal representing the dynamic physical quantity over time; comparing the acquired measurement signal with a predefined signal characteristic of the respective physical quantity or with a signal characteristic of another physical quantity being dependent from the respective physical quantity to provide a comparison result; and adjusting a waveform representing the acquired measurement signal based upon the comparison result. A digital oscilloscope is also provided.

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Oberhaching, Germany

Andrew Schaefer