Abstract:
A method for determining jitter and noise of an input signal. The method includes acquiring one or more uncorrelated waveform records by an acquisition unit of a test and measurement instrument, determining a correlated waveform from the acquired waveform(s), dividing the correlated waveform into unit intervals, dividing an uncorrelated waveform into unit intervals, measuring a timing displacement (t1) between the correlated waveform and the uncorrelated waveform for each unit interval to form an apparent-jitter array ([t1]), measuring a voltage displacement (V1) between the correlated waveform and the uncorrelated waveform for reach unit interval to form an apparent-noise array ([V1]), calculating a horizontal shift (ts) between the correlated waveform and the uncorrelated waveform for each unit interval to form a compensated edge time array ([ts]), and calculating a vertical shift (Vs) between the correlated waveform and the uncorrelated waveform for each unit interval to form a compensated amplitude voltage array ([Vs]).
Abstract:
A serial data link measurement and simulation system for use on a test and measurement instrument presents on a display device a main menu having elements representing a measurement circuit, a simulation circuit and a transmitter. The main menu includes processing flow lines pointing from the measurement circuit to the transmitter and from the transmitter to the simulation circuit. The main menu includes a source input to the measurement circuit and one or more test points from which waveforms may be obtained. The simulation circuit includes a receiver. The measurement and simulation circuits are defined by a user, and the transmitter is common to both circuits so all aspects of the serial data link system are tied together.
Abstract:
A test and measurement system includes a first test and measurement instrument having an input to allow the test and measurement instrument to receive signals from one or more devices under test (DUT), and one or more digitizers to convert the signals from the one or more DUTs to digital waveforms, a machine learning network, and one or more processors to: perform one or more measurements of the digital waveforms, send the one or more measurements of the digital waveforms to the machine learning network as an input, use the machine learning network to translate the one or more measurements to measurements made by a reference instrument to produce one or more translated measurements, the reference instrument being more accurate than the first test and measurement instrument, and determine whether the DUT meets a performance requirement based upon the one or more translated measurements.
Abstract:
A test and measurement instrument includes one or more ports to allow the test and measurement instrument to receive data from a device under test (DUT), a connection to a machine learning network, a display configured to display a user interface, one or more controls to allow the test and measurement instrument to receive inputs from a user, and one or more processors configured to execute code that causes the one or more processors to: render a menu on the display that displays different types of tensors, receive, from the one or more controls, a user selection that identifies a selected type of tensor, and build the selected type of tensor from the data from the DUT and send the selected type of tensor to the machine learning network. A method of providing a user interface is also disclosed.
Abstract:
A method of characterizing a communication channel includes receiving a first signal from a set of transmitters reflected along a reflected channel from each element of a reconfigurable intelligent surface (RIS) set at a nominal angle, receiving a second signal reflected in the reflected channel from each element of the RIS set at an adjusted angle, using the first and second signals to determine a transfer function for a combined channel comprised of a reflected channel and a direct channel, and using the transfer function as an input to a machine learning network to determine optimized settings for the elements of the RIS. A communications system includes a set of transmitters, a reconfigurable intelligent surface (RIS), one or more receivers positioned to receive signals reflected by the RIS from the set of transmitters, and a machine learning system configured to produce optimized angles for elements of the RIS.
Abstract:
A test and measurement instrument includes a port to connect to a device under test (DUT) to receive waveform data, a connection to a machine learning network, and one or more processors configured to: receive one or more inputs about a three-dimensional (3D) tensor image; scale the waveform data to fit within the 3D tensor image; build the 3D tensor image; send the 3D tensor image to the machine learning network; and receive a predictive result from the machine learning network. A method includes receiving waveform data from one or more device under test (DUT), receiving one or more inputs about a three-dimensional (3D) tensor image, scaling the waveform data to fit within the 3D tensor image, building the 3D tensor image, sending the 3D tensor image to a pre-trained machine learning network, and receiving a predictive result from the machine learning network.
Abstract:
A test and measurement instrument has one or more ports configured to receive a signal one or more devices under test (DUT), and one or more processors configured to execute code that causes the one or more processors to: acquire a waveform from the signal, derive a pattern waveform from the waveform, perform linear response extraction on the pattern waveform, present one or more data representations including a data representation of the extracted linear response to a machine learning system, and receive a prediction for a measurement from the machine learning system. A method of performing a measurement on a waveform includes acquiring the waveform at a test and measurement device, deriving a pattern waveform from the waveform, performing linear response extraction on the pattern waveform, presenting one or more data representations including a data representation of the extracted linear response to a machine learning system, and receiving a prediction of the measurement from the machine learning system.
Abstract:
A test and measurement instrument, such as an oscilloscope, having a Nyquist frequency lower than an analog bandwidth, the test and measurement instrument having an input configured to receive a signal under test having a repeating pattern, a single analog-to-digital converter configured to receive the signal under test and sample the signal under test over a plurality of repeating patterns at a sample rate, and one or more processors configured to determine a frequency of the signal under test and reconstruct the signal under test based on the determined frequency of the signal, the pattern length of the signal under test, and/or the sample rate without a trigger.
Abstract:
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.
Abstract:
A test and measurement device includes one or more ports configured to connect to a device under test (DUT), a time domain reflectometry (TDR) source configured receive a source control signal and to produce an incident signal to be applied to the DUT, one or more analog-to-digital converters (ADC) configured to receive a sample clock and sample the incident signal from the TDR source and a time domain reflection (TDR) signal or a time domain transmission (TDT) signal from the DUT to produce an incident waveform and a TDR/TDT waveform, one or more processors configured to execute code to cause the one or more processors to: control a clock synthesizer to produce the sample clock and the source control signal, and use a period of the TDR source, a period of the sample clock, and the number of samples to determine time locations for samples in the incident waveform and the TDR/TDT waveform, and a display configured to display the incident waveform and the TDR/TDT waveform. A method of sampling a waveform using a real-equivalent-time oscilloscope having a time domain reflectometry source, comprising: controlling a clock synthesizer to produce a sample clock and a source control signal; using a time domain reflectometry (TDR) source to receive the source control signal and to produce an incident signal to be applied to a device under test (DUT); receiving the sample clock at one or more analog-to-digital converters (ADC) and sampling the incident signal from the TDR source and a TDR/TDT signal from the DUT to produce an incident waveform and a TDR/TDT waveform; determining time locations for samples in the incident waveform and the TDR/TDT waveform, using a period of the TDR source, a period of the sample clock, and a number of samples; and displaying the incident waveform and the TDR/TDT waveform.