Swept parameter oscilloscope
    1.
    发明授权

    公开(公告)号:US12085590B2

    公开(公告)日:2024-09-10

    申请号:US17862293

    申请日:2022-07-11

    Abstract: A test and measurement instrument has a user interface configured to allow a user to provide one or more user inputs, a display to display results to the user, a memory, one or more processors configured to execute code to cause the one or more processors to receive a waveform array containing waveforms resulting from sweeping one or more parameters from a set of parameters, recover a clock signal from the waveform array, generate a waveform image for each waveform, render the waveform images into video frames to produce an image array of the video frames, select at least some of the video frames to form a video sequence, and play the video sequence on a display. A method of animating waveform data includes receiving a waveform array containing waveforms resulting from sweeping one or more parameters from a set of parameters, recovering a clock signal from the waveforms, generating a waveform image from each of the waveforms, rendering the waveform images into video frames to produce an image array of the video frames, selecting at least some of the video frames to play as a video sequence, and playing the video sequence on a display.

    SWEPT PARAMETER OSCILLOSCOPE
    2.
    发明申请

    公开(公告)号:US20230019734A1

    公开(公告)日:2023-01-19

    申请号:US17862293

    申请日:2022-07-11

    Abstract: A test and measurement instrument has a user interface configured to allow a user to provide one or more user inputs, a display to display results to the user, a memory, one or more processors configured to execute code to cause the one or more processors to receive a waveform array containing waveforms resulting from sweeping one or more parameters from a set of parameters, recover a clock signal from the waveform array, generate a waveform image for each waveform, render the waveform images into video frames to produce an image array of the video frames, select at least some of the video frames to form a video sequence, and play the video sequence on a display. A method of animating waveform data includes receiving a waveform array containing waveforms resulting from sweeping one or more parameters from a set of parameters, recovering a clock signal from the waveforms, generating a waveform image from each of the waveforms, rendering the waveform images into video frames to produce an image array of the video frames, selecting at least some of the video frames to play as a video sequence, and playing the video sequence on a display.

    GENERAL DIGITAL SIGNAL PROCESSING WAVEFORM MACHINE LEARNING CONTROL APPLICATION

    公开(公告)号:US20220390515A1

    公开(公告)日:2022-12-08

    申请号:US17831181

    申请日:2022-06-02

    Abstract: A test and measurement system includes a machine learning system configured to communicate with a test automation system, a user interface configured to allow a user to provide one or more user inputs and to provide results to the user, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive one or more user inputs through the user interface, the one or more user inputs at least identifying a selected machine learning system configuration to be used to configure the machine learning system, receive a waveform created by operation of a device under test, apply the configured machine learning system to analyze the waveform, and provide an output of predicted metadata about the waveform.

    GENERATING TEST DATA USING PRINCIPAL COMPONENT ANALYSIS

    公开(公告)号:US20230409451A1

    公开(公告)日:2023-12-21

    申请号:US18211410

    申请日:2023-06-19

    CPC classification number: G06F11/2268

    Abstract: A system includes an input for accepting a dataset including at least two sets of data in a dataset domain and one or more processors configured to derive at least two principal components from the dataset using principal component analysis, the at least two principal components being orthogonal to one another, map the dataset to a principal component domain derived from the at least two principal components, generate additional data in the principal component domain, and remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset. Methods of operation and description of storage media, the operation of which performs the above operations, are also described.

    INSTRUMENT AND MEASUREMENT TRANSLATOR USING MACHINE LEARNING

    公开(公告)号:US20250004015A1

    公开(公告)日:2025-01-02

    申请号:US18746902

    申请日:2024-06-18

    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.

    OSCILLOSCOPE HAVING A PRINCIPAL COMPONENT ANALYZER

    公开(公告)号:US20230408551A1

    公开(公告)日:2023-12-21

    申请号:US18209110

    申请日:2023-06-13

    CPC classification number: G01R13/029 G01R13/0272

    Abstract: A system includes an input for accepting an input signal from a Device Under Test (DUT), a measurement unit for generating first measurement data and second measurement data from the input signal, and one or more processors configured to derive at least one principal component from the first and second measurement data using principal component analysis, and remap the first measurement data and the second measurement data to a principal component domain derived from the at least one principal component. Methods of operation and description of storage media, the operation of which performs the above operations, are also described.

    SYSTEMS AND METHODS FOR MACHINE LEARNING MODEL TRAINING AND DEPLOYMENT

    公开(公告)号:US20230222382A1

    公开(公告)日:2023-07-13

    申请号:US18081616

    申请日:2022-12-14

    CPC classification number: G06N20/00

    Abstract: A system to develop and test machine learning models has a waveform emulator machine learning system, a user interface to allow a user to input one or more design parameters for the waveform emulator machine learning system, one or more processors configured to execute code to cause the one or more processors to: send the one or more design parameters to the waveform emulator machine learning system; receive one or more data sets from the waveform emulator machine learning system, the one or more data sets based on the one or more design parameters; train a developed machine learning model using at least one of the one or more data sets, resulting in a trained machine learning model; validate the trained machine learning model using a previously unused one of the one or more data sets; adjust the trained machine learning model as needed; and repeat the training, validating, and adjusting until an optimal machine learning model is trained. A method of developing and testing machine learning models includes providing one or more design parameters to a waveform emulator machine learning system, receiving one or more data sets from the waveform emulator machine learning system, training a developed machine learning model using at least one of the one or more data sets, resulting in a trained machine learning model, validating the trained machine learning model using a previously unused one of the one or more data sets, adjusting the trained machine learning model as needed, and repeating the training, validating, and adjusting until an optimal machine learning model is trained.

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