A measurement apparatus and a method for controlling a measurement apparatus is provided. The measurement apparatus comprises an ultrasonic sensor for receiving a further user input. The further user input may be used to identify a specific user. Accordingly, the measurement apparatus can be automatically configured based on data related to the identified user. Since the ultrasonic sensor may be operated even if the ultrasonic sensor is covered by a protecting element, the robustness of the measurement apparatus can be further improved.

A measurement apparatus and a method for controlling a measurement apparatus is provided. The measurement apparatus comprises an ultrasonic sensor for receiving a further user input. The further user input may be used to identify a specific user. Accordingly, the measurement apparatus can be automatically configured based on data related to the identified user. Since the ultrasonic sensor may be operated even if the ultrasonic sensor is covered by a protecting element, the robustness of the measurement apparatus can be further improved.

A measurement input circuit for a measurement device for measuring an electric signal in a device under test comprises a signal input that receives the electronic signal from the device under test and provides the received electronic signal at a signal node, a direct signal coupling path that is coupled between the signal node an electrical ground and comprises a first impedance value, an alternating signal coupling path that is coupled between the signal node and the electrical ground, and comprises a second impedance value that is lower than the first impedance value, and a signal output that is coupled to the signal node and outputs the received electronic signal.

A measurement input circuit for a measurement device for measuring an electric signal in a device under test comprises a signal input that receives the electronic signal from the device under test and provides the received electronic signal at a signal node, a direct signal coupling path that is coupled between the signal node an electrical ground and comprises a first impedance value, an alternating signal coupling path that is coupled between the signal node and the electrical ground, and comprises a second impedance value that is lower than the first impedance value, and a signal output that is coupled to the signal node and outputs the received electronic signal.

The invention relates to a digital low frequency correction circuit and a corresponding method for correction of low frequency disturbances within a digital signal, in particular to a reduction of distortions caused by analog circuitry, such as analog signal amplifiers. The low frequency correction circuit comprises a main signal path adapted to delay a digital input signal received by a signal input terminal and at least one correction signal path including a digital correction filter adapted to filter the received digital input signal. Furthermore, an adder of the circuit is adapted to add the digital signal delayed by the main signal path and the digital signal corrected by said correction signal path to generate a digital output signal output by a signal output terminal of the circuit.

The invention relates to a digital low frequency correction circuit and a corresponding method for correction of low frequency disturbances within a digital signal, in particular to a reduction of distortions caused by analog circuitry, such as analog signal amplifiers. The low frequency correction circuit comprises a main signal path adapted to delay a digital input signal received by a signal input terminal and at least one correction signal path including a digital correction filter adapted to filter the received digital input signal. Furthermore, an adder of the circuit is adapted to add the digital signal delayed by the main signal path and the digital signal corrected by said correction signal path to generate a digital output signal output by a signal output terminal of the circuit.

The present invention relates to an improved recording of context information for a measurement. For this purpose, it is suggested to receive context information from a user and to generate a dataset comprising the received context information. The context information may comprise one or more voice annotation. By using voice annotations for specifying context information of a measurement, a very simple and easy format for acquiring the context information from a user is achieved.

The present invention relates to an improved recording of context information for a measurement. For this purpose, it is suggested to receive context information from a user and to generate a dataset comprising the received context information. The context information may comprise one or more voice annotation. By using voice annotations for specifying context information of a measurement, a very simple and easy format for acquiring the context information from a user is achieved.

An oscilloscope including an input port for receiving training data including waveforms and corresponding known classifications and a processor for training a plurality of classifiers on the training data. Training includes iteratively applying each classifier to each waveform of the training data to obtain corresponding predicted waveform classifications and comparing the predicted waveform classifications with the known classifications. Classifiers are corrected when predicted waveform classifications does not match the known classifications. Models for each classification are constructed with suggested measurements or actions. Subsequently, live waveform data is captured by the oscilloscope and the classifiers are applied to the live data. When a confidence value for a single classification exceeds a threshold, the waveform data is classified, and suggested measurements or actions are implemented in the oscilloscope based on the classification.

An oscilloscope including an input port for receiving training data including waveforms and corresponding known classifications and a processor for training a plurality of classifiers on the training data. Training includes iteratively applying each classifier to each waveform of the training data to obtain corresponding predicted waveform classifications and comparing the predicted waveform classifications with the known classifications. Classifiers are corrected when predicted waveform classifications does not match the known classifications. Models for each classification are constructed with suggested measurements or actions. Subsequently, live waveform data is captured by the oscilloscope and the classifiers are applied to the live data. When a confidence value for a single classification exceeds a threshold, the waveform data is classified, and suggested measurements or actions are implemented in the oscilloscope based on the classification.