A system maps and stores data in digital three-dimensional oscilloscope, wherein an ADC module has four ADC submodules. Four acquired waveform data are sent to an extraction module, and buffered in a FIFO module. When a trigger signal arrives, FIFO module outputs four extracted waveform data to a mapping address calculation module for calculating a mapping address and a RAM serial number for each point data, and the waveform data comparison and control module performs the reading and writing control of the 4×N dual port RAMs. When mapping number reaches a frame number, the RAM array module outputs its waveform probability values to the upper computer module to convert each value into RBG values, and the display module displays the waveforms of input signals of four channels on a screen according the RBG values.

A test and measurement device has an input port configured to receive a signal from a device under test, the signal having a symbol rate, one or more analog-to-digital converters to convert the signal to waveform samples at a sampling rate, and one or more processors configured to execute code that, when aliasing is present in the waveform samples, causes the one or more processors to: up-sample the waveform samples to produce up-sampled samples; use the up-sampled samples to produce a real-time waveform; perform clock recovery on the real-time waveform to produce a recovered clock; and resample the waveform samples using the recovered clock to produce a non-aliased waveform. A method of acquiring a waveform in a test and measurement device includes receiving a signal from a device under test, the signal having a symbol rate, converting the signal to waveform samples at a sampling rate of the test and measurement device, when aliasing is present in the waveform samples, up-sampling the waveform samples to produce up-sampled samples, using the up-sampled samples to produce a real-time waveform, performing clock recovery on the real-time waveform to produce a recovered clock, and resampling the waveform samples using the recovered clock to produce a non-aliased waveform.

The invention relates to an electrical, preferably portable and battery-powered, measurement device. The device comprises: an electrical measurement unit supplying electrical measurement signals, a processing unit processing said electrical measurement signals, a memory, and a display controlled by the processing unit and displaying the processed electrical signals, wherein the processor is arranged to execute an operating system stored in the memory, and wherein the in the memory furthermore a local web browser application is stored which can be executed by the processor in order to control the display.

A method, a system, and a computer program for executing high resolution spectrum monitoring. A sensor receives an input signal having a varying frequency content over time. One or more samples of the received input signal are sampled. The samples of the received input signal include one or more swept signal samples generated by sweeping, using a center frequency of the sensor, the received input signal across an entire frequency spectrum associated with the received input signal. Sampling of the samples of the received signal is performed while performing the sweeping. The signal samples are processed.

A method and system are provided for reducing noise in a time domain waveform of a signal under test (SUT). The method includes performing cross-correlation of multiple first complex signals and multiple second complex signals, respectively, from the SUT to provide multiple cross-correlated signals, respectively, where the cross-correlated signals have amplitude components and no phase components from the SUT, and where the first and second complex signals include uncorrelated noise, respectively. The method further includes determining an average of the cross-correlated signals to provide an average cross-correlated signal with reduced uncorrelated noise; obtaining a representative phase component from one of the first complex signals or the second complex signals; and combining the representative phase component with the average cross-correlated signal to provide a representative complex signal corresponding to the SUT with reduced uncorrelated noise.

A configuration device in a test and measurement system including an event generator and a Device Under Test (DUT) to receive one or more events generated by the event generator includes an output display structured to graphically illustrate a first event timeline that includes source event markers for a first test channel for a second test channel, in which the first event timeline and the second event timeline appear on the output display as separate timelines vertically separated from one another. The position of the event delay indicator or a position of the event width indicator may be movable by a user, and moving the position of the event delay indicator or moving the position of the event width indicator causes the event generator to change one or more event generation parameters of the first event based on such movement. Methods are also disclosed.

An inertial measurement system includes three acceleration sensors detecting along three orthogonal axes, a display having several display areas, and a processor programed to perform a process. The processor calculates frequencies and spectral intensities of vibrations based on detected time series accelerations by three acceleration sensors and obtains relationship values between the calculated frequencies and the calculated spectral intensities. The processor causes the display to successively display an N-th value, intermediate values, and an (N+1)-th value of the relationship values at one display area in time series.

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.

A test and measurement instrument including a display and one or more processors configured to display on the display a waveform viewing area with a vertical dimension and an adjustable horizontal dimension, the test and measurement instrument configured to display one or more waveforms in the waveform viewing area, a global settings readout bar located vertically adjacent to the waveform viewing area, the global settings readout bar including a first selectable information badge, wherein when the first selectable information badge is selected, displaying a first menu originating from the first selectable information badge to modify a setting of the test and measurement instrument related to the first selectable information badge. The first selectable information badge including a warning indicator when an error or safety condition occurs.

A method for analyzing noise spectrum of an electronic device includes storing a waveform data including a plurality of data points, the waveform data is obtained by measuring a target signal from the electronic device, removing data points corresponding to a background noise fluctuation based on a smooth curve of the waveform data, data points considered candidates for peaks are extracted from the waveform data, classifying the extracted data points based on a distance between adjacent data points in order to discriminate a cluster of distant data points from data points closely positioned to dominant peaks, determining the dominant peaks based on the cluster of distant data points such that the data points closely positioned to the dominant peaks are ignored, each dominant peak corresponds to the characteristic of the electronic device, and outputting the dominant peaks as an analysis result for the electronic device.