Abstract: A signal and power analysis instrument includes one or more high-bandwidth input channels configured as one or more real-equivalent time (RET) input channels and/or one or more radio frequency (RF) channels, one or more input channels configured as one or more low-bandwidth real-time (RT) input channels, one or more analog-to-digital converters (ADCs) having pipes, a first set of pipes connected to the one or more high-bandwidth input channels to produce high-bandwidth data, and a second set of the pipes connected to the one or more low-bandwidth RT input channels to produce low-bandwidth RT data, a system clock connected to high-bandwidth input channels and the low-bandwidth RT input channels, a memory connected to the system clock, the first set of pipes, and the second set pipes, and one or more processors to store low-bandwidth RT data and high-bandwidth data in the memory, and align the high-bandwidth data and the low-bandwidth data.

Abstract: A real-time oscilloscope includes one or more ports to connect to a device under test (DUT) to establish a communication channel between the oscilloscope and the DUT, one or more analog-to-digital converters (ADCs) to sample a signal received from the DUT to convert the signal to a waveform, one or more processors to receive and sample a run-time signal including actual symbols from the DUT to create a run-time waveform, resample the run-time waveform to locate samples at a center of each unit interval, use a communication channel characterization, the samples at the center of each unit interval, a symbol constellation for the run-time waveform, and a configuration of a traceback length as inputs to a Most Likely Sequence Estimation (MLSE) process to estimate symbols in the run-time waveform, and compare the estimated symbols to the actual symbols to obtain a symbol error ratio.

Abstract: A test and measurement system includes a test and measurement instrument, a computing device connected to the test and measurement instrument, and one or more processors configured to execute code that causes the one or more processors to collect measurement data from a full parameter sweep of a first device under test, the full parameter sweep comprising a total number of measurement points for every parameter combination of a number of parameters, identify core parameter sweeps from the full parameter sweep, the core parameter sweeps comprising a total number of measurement points for fewer parameter sweeps than the full parameter sweep, use the core parameter sweeps to calculate auxiliary parameters sweeps. use the core parameter sweeps and the auxiliary parameter sweeps to determine a reduced parameter sweep, and use the reduced parameter sweep for testing subsequent devices under test of a same type as the first device under test.

Abstract: A test and measurement instrument connects to one or more devices under test (DUT) having tuning screws, and to a robot, and has one or more processors to: command the robot to position the tuning screws on the 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 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, command the robot to position the tuning screws to positions in the set of predicted positions, obtain a set of tuned operating parameters from the DUTs, and validate operation of the DUTs.

Abstract: A test and measurement instrument includes one or more channels to receive a signal under test, each channel comprising an input port, a filter, and a sampler, at least one analog-to-digital converter (ADC), the at least one ADC having two pipes connected to the sampler of one of the one or more channels, the at least one ADC to produce digital samples of the signal at a sample rate, and one or more processors configured to execute code that causes the one more processors to acquire a spectrum of the digital samples for each pipe in the at least one ADC, and use the spectrums of the digital samples for each pipe in the at least one ADC to reconstruct the spectrum of the signal under test. A method of operating a test and measurement instrument, and a method a method of calibrating a test and measurement instrument is included.

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.

Abstract: A test and measurement instrument includes a port to allow the test and measurement instrument to connect to a device under test (DUT) to receive signals from the DUT, one or more analog-to-digital converters (ADCs) to receive a signal from the DUT and convert the signal to one or more digital waveforms, a user interface to allow a user to enter a query, and one or more processors configured to execute code that causes the one or more processors to: build one or more images of the one or more digital waveforms from the one or more ADCs, send the one or more images to a domain-adapted multimodal large language model (MLLM), receive parameters from the domain-adapted MLLM, provide the user with parameters for the DUT in response to the query, and apply the parameters to the DUT.

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 instrument has an input to receive a signal under test having a repeating pattern. one or more analog-to-digital converters (ADC) to sample the signal under test at a sample rate over many repeating patterns to digitize the signal, one or more processors configured to execute code to cause the one or more processors to: recover a clock from the sampled signal under test, use the clock to generate an original pattern waveform, interpolate and resample from the original pattern waveform to generate an evenly time-spaced pattern waveform, apply an equalizer to the evenly time-spaced pattern waveform to produce an equalized pattern waveform, interpolate and resample from the equalized pattern waveform to produce a new waveform having equalized samples at sample times of the sampled signal under test, recover an updated clock from the new waveform, and use the updated clock to produce an updated waveform.

Abstract: A test and measurement system has a test and measurement instrument that includes a connection to a device under test (DUT); one or more analog-to-digital converters (ADCs) to receive and convert a signal from the DUT to one or more digital waveforms; and one or more processors to: receive the one or more digital waveforms corresponding to one set of tuning parameters applied to the DUT; build one or more image tensors of the one or more digital waveforms; use an artificial intelligence embedding model that generates one or more text strings from metadata and embeds the metadata and the one or more image tensors into a vector; access a vector database; receive a set of indexes having a number of indexes corresponding to a number of matches; use the set of indexes to find one or more sets of optimal tuning parameters; and validate operation of the DUT.

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 to receive a signal from a device under test (DUT), the signal having a symbol rate, one or more analog-to-digital converters (ADC) to convert the signal to waveform samples at a sampling rate, and one or more processors, when aliasing is present: up-sample a portion of the signal having aliased 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 aliased samples to produce a non-aliased waveform.

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: An oscilloscope having a Nyquist frequency lower than an analog bandwidth includes an input configured to receive a signal under test; an analog-to-digital converter (ADC) to receive the signal under test, sample the signal under test at a sample rate, and produce digital samples of the signal under test; one or more processors configured to execute code that causes the one or more processors to: determine a set of candidate unit intervals by generating corresponding candidate histograms using the candidate unit intervals; determine a best unit interval from the candidate unit intervals based upon entropy measures of each candidate histogram; and reconstruct a representation of the signal under test using the digital samples and the best unit interval.

Abstract: An oscilloscope includes one or more ports to connect to a device under test (DUT) and receive a signal, one or more analog-to-digital converter (ADC) to produce a waveform of digital samples of the signal, and one or more processors to: acquire and determine a measure of a noise waveform, acquire a waveform of a repeating pattern from the ADCs and determine its frequency spectrum, identify a spectral impulse portion of the frequency spectrum, determine a measure of a flat portion of the frequency spectrum, use the measure of the flat portion and the measure of the noise waveform to produce a noise compensation ratio, scale the flat portion with the noise compensation ratio and combine it with the spectral impulse portion of the frequency spectrum to produce a noise compensated frequency spectrum, convert the noise compensated frequency spectrum to a time domain waveform to measure performance of the DUT.

Abstract: A system for generating images on a test and measurement device includes a first input for accepting a waveform input signal carrying sequential digital information and an image generator structured to generate a visual image using a segment of the waveform input only when two or more sequential codes of digital information match sequential codes carried in the sequential digital information of the segment of the waveform input. A user-defined state-machine comparator may be used to determine which segments of the waveform input signal are used in the image generation.

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 includes one or more ports to allow the test and measurement instrument to receive a signal from a device under test (DUT), a user interface to allow the user to send inputs to the test and measurement instrument and receive results, and one or more processors configured to acquire the signal from the DUT, make measurements on the signal to create a decimated measurement set, convert the decimated measurement set into a tensor, send the tensor to a machine learning network, and receive a pass/fail value from the machine learning network. A method includes acquiring a signal from a device under test (DUT), making measurements on the signal to create a decimated measurement set, convert the decimated measurement set into a tensor, sending the tensor to a machine learning network, and receiving a pass/fail value from the machine learning network.

Abstract: A wideband signal generator has one or more digital-to-analog converters (DAC), each of the one or more DACs having one or more pipes and a sample rate, a multiplexer to receive analog outputs from at least two pipes from the one or more DACs and multiplex the analog outputs and zero into an output stream, a bandpass filter to receive the output stream and filter out frequency components in the output stream that are outside a target frequency band and produce a radio frequency (RF) output signal in the in the target frequency band, and one or more processors configured to execute code that causes the one or more processors to generate digital samples and transfer the digital samples to the one or more DACs, the digital samples generated to produce analog outputs that cause the RF output signal to match the target RF frequency band.

Abstract: A margin tester includes one or more ports to allow the margin tester to connect to a device under test (DUT), a memory, the memory containing a margin tester signature, a transmitter, a receiver to receive signals from the DUT, one or more processors configured to execute code that causes the one or more processors to: receive multiple signals from the receiver through the one or more ports, generate a performance indicator from the multiple signals, send the performance indicator and the margin tester signature to one or more machine learning networks, and receiving a result from the one or more machine learning networks containing a performance measurement prediction for the DUT.