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 method for obtaining a feature extraction model, a method for human fall detection and a terminal device are provided. The method for human fall detection includes: inputting a human body image into a feature extraction model for feature extraction to obtain a target image feature; in response to a distance between the target image feature and a pre-stored mean value of standing category image features being greater than or equal to a preset distance threshold, determining that the human body image is a human falling image; and in response to the distance being less than the preset distance threshold, determining that the human body image is a human standing image. The feature extraction model is obtained based on constraint training to aggregate standing category image features and separate falling category image features from the standing category image features.

Abstract: A test and measurement instrument includes an input configured to receive an input signal from a device under test (DUT), an output display, and one or more processors configured to execute code that causes the one or more processors to measure a noise component of the input signal, compensate the measured noise component based on the measurement population and a relative amount of noise generated by the test and measurement instrument and a total noise measurement, and produce the compensated measured noise component as a noise measurement on the output display. Methods are also described.

Abstract: A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform.

Abstract: A test and measurement system includes a test and measurement device, a connection to allow the test and measurement device to connect to an optical transceiver, and one or more processors, configured to execute code that causes the one or more processors to: set operating parameters for the optical transceiver to reference operating parameters; acquire a waveform from the optical transceiver; repeatedly execute the code to cause the one or more processors to set operating parameters and acquire a waveform, for each of a predetermined number of sets of reference operating parameters; build one or more tensors from the acquired waveforms; send the one or more tensors to a machine learning system to obtain a set of predicted operating parameters; set the operating parameters for the optical transceiver to the predicted operating parameters; and test the optical transceiver using the predicted operating parameters.

Abstract: A test and measurement instrument has an input configured to receive a signal from a device under test, a memory, a user interface to allow the user to input settings for the test and measurement instrument, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to: acquire a waveform representing the signal received from the device under test; generate one or more tensor arrays based on the waveform; apply machine learning to the one or more tensor arrays to produce equalizer tap values; and apply equalization to the waveform using the equalizer tap values to produce an equalized waveform; and perform a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

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.

Abstract: A test and measurement instrument has an input port to allow the instrument to receive one or more waveforms from a device under test (DUT), one or more low pass filters to remove a portion of the noise from the one or more waveforms, and one or more processors to: select a waveform pattern from the waveforms, measure noise in the one or more waveforms and generate a noise representation of the noise removed, create one or more images using the waveform pattern and the one or more filtered waveforms, add the noise representation to the one or more images to produce at least one combined image, input the at least one combined image to one or more deep learning networks, and receive one or more predicted values for the DUT.

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.

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 includes one or more ports to connect to one or more devices under test (DUT) having tuning screws, and to a robot, one or more processors to configured to: send commands to the robot to position the tuning screws on the one or more DUTs to one or more sets of positions, each set of positions being a parameter set for the tuning screws, acquire a set of operating parameters for each parameter set from the one or more DUTs, generate a parameter set image for each set, create a combined image of the parameter set images, provide the combined image to a machine learning system to obtain a predicted set of values, adjust the predicted set of values to produce a set of predicted positions, send commands to the robot to position the tuning screws to positions in the set of predicted positions, obtain a set of tuned operating parameters from the one or more DUTs, and validate operation of the one or more DUTs.

Abstract: A test and measurement instrument has one or more input ports to connect the instrument to a device under test (DUT), one or more processors configured to execute code to cause the one or more processors to: receive an equalized waveform and an un-equalized waveform through the input port from the DUT, without any knowledge of a digital pattern that corresponds to the waveforms and without extracting the digital pattern from the waveforms, align the un-equalized waveform and the equalized waveform in time to produce an aligned un-equalized waveform and an aligned equalized waveform, and use the aligned equalized waveform and the aligned un-equalized waveform to determine equalizer tap values.

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: The present disclosure relates to the field of resource utilization-oriented treatment technologies for wastewater, and more particularly, to a resource utilization-oriented treatment method for a spent electroless nickel plating bath. The method includes oxidation de-complexation, synchronous precipitation of nickel and phosphorus, secondary precipitation of nickel, and resource utilization of sodium salt. In the present disclosure, in a reaction process, no sludge is generated to avoid secondary pollution to the environment. Further, the present disclosure has the advantages of short flow and less chemical use, greatly reducing treatment costs. In this way, this method is a low-cost and clean resource utilization-oriented treatment method capable of achieving resource utilization-oriented recovery of nickel, phosphorus, sodium, sulfate radical, or chlorine in the spent electroless nickel plating bath.

Abstract: A method of equalizing a communication link includes setting a number of coefficients to a required number, determining a number of pulse responses for a waveform, setting all values in a set of values to zero, repeating, until all values have been assigned, determining a current lowest parameter, using a position of the current lowest parameter as an index, determining a minimum value between a first term multiplied by a main pulse response minus a summation of each parameter multiplied by each value, divided by the current lowest parameter, and a corresponding pulse response, and assigning the minimum value to the value having a position equal to the position of the current lowest parameter, and determining a value of each coefficient in a set of coefficients by multiplying each value with the sign of a corresponding pulse response; defining an equalizer having a number of taps having a value based on the corresponding coefficient; and applying the equalizer to a waveform.

Abstract: A test and measurement device has a port to receive a signal from a device under test (DUT), one or more analog-to-digital converters (ADC) to digitize the signal to create one or more waveforms, a display, and one or more processors configured to execute code that causes the one or more processors to: generate a histogram from the waveform, the histogram having one or more dimensions; and calculate one or more entropy values for each of the one or more dimensions. A method includes receiving a signal from a device under test (DUT) at a test and measurement device, digitizing the signal using one or more analog-to-digital converters (ADC) to produce a waveform, generating a histogram from the waveform, the histogram having one or more dimensions, and calculating one or more entropy values for each of the one or more dimensions,.

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.

Abstract: A method determines scattering parameters, S-parameters, for a device under test for a first frequency range. The method includes receiving S-parameters for the device under test for a second frequency range, the second frequency range greater than the first frequency range. Generally, the S-parameters for the device under test for the second frequency range can be determined using known methods. The method further includes measuring an actual response of the device under test, determining a desired signal of the device under test, and determining the S-parameters for the device under test for the first frequency range based the S-parameters for the second frequency range, actual response of the device under test and the desired signal of the device under test.

Abstract: A test and measurement instrument has an input configured to receive a signal from a device under test, a memory, a user interface to allow the user to input settings for the test and measurement instrument, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to: acquire a waveform representing the signal received from the device under test; generate one or more tensor arrays based on the waveform; apply machine learning to the one or more tensor arrays to produce equalizer tap values; and apply equalization to the waveform using the equalizer tap values to produce an equalized waveform; and perform a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

Abstract: A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform.