Abstract: A system to classify signals includes an input to receive incoming waveform data; a memory, and one or more processors configured to execute code to cause the one or more processors to: generate a ramp sweep signal from the incoming waveform data, locate a data burst in the incoming waveform data using a burst detector, receive a signal from the burst detector to cause the memory to store cyclic loop image data in the form of the incoming waveform data as y-axis data and the ramp sweep signal as x-axis data, and employ a machine learning system to receive the cyclic loop image data and classify the data burst. A method of classifying signals includes generating a ramp sweep signal from incoming waveform data, locating a data burst in the incoming waveform data, storing cyclic loop image data for the data burst in the form of the incoming waveform data as Y-axis data and the ramp sweep signal as X-axis data, and using a machine learning system to receive the cyclic loop image and classify the data burst.

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 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.

Abstract: A test and measurement instrument has a port to receive a signal from a device under test (DUT), 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 using one of either hardware or software clock recovery, perform linear fit pulse response (LFPR) extractions on the pattern waveform to extract more than one LFPR, determine a reference pulse response from the more than one LFPRs, compare at least one of the LFPRs to the reference pulse response to determine a difference, and tune the DUT to reduce the difference. The test and measurement instrument may also use the multiple LFPRs as an input to a machine learning network to perform measurement predictions for the DUT.

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 test and measurement system includes a machine learning system, a test and measurement device including a port configured to connect the test and measurement device to a device 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 device under test (DUT), transform the waveform into a composite waveform image, and send the composite waveform image to the machine learning system to obtain a bit error ratio (BER) value for the DUT. A method of determining a bit error ratio for a device under test (DUT), includes acquiring one or more waveforms from the DUT, transforming the one or more waveforms into a composite waveform image, and sending the composite waveform image to a machine learning system to obtain a bit error ratio (BER) value for the DUT.

Abstract: A test system includes a test and measurement instrument, ovens to hold devices under test (DUT), each oven having an oven switch selectably connected to the DUTs, channel switches selectably connected to the oven switches and to one channel of the instrument, one or more processors to: select an oven and its oven switch, connect that oven switch to a subset of DUTs in that oven, connect the channel switches to that oven switch to receive signals from the subset of DUTs, send the signals to channels of the instrument to acquire waveforms from the subset of DUTs in parallel, and repeat connecting of the channel switches and that oven switch until the instrument has acquired waveforms from each DUT in that oven, use machine learning to tune each DUT, test whether each DUT in that oven is optimally tuned, and repeat until all DUTs have been tuned and tested.

Abstract: A test and measurement instrument includes an input to receive a non-return-to-zero (NRZ) waveform signal from a device under test, a ramp generator to use the NRZ waveform signal to generate a ramp sweep signal, a gate to gate the ramp sweep signal and the NRZ waveform signal to produce gated X-axis and Y-axis data, and a display to display the gated X-axis and Y-axis data as a cyclic loop image. A method of generating a cyclic loop image includes receiving an input waveform, using the input waveform to generate a ramp sweep signal, gating the ramp sweep signal and the input waveform to produce gated X-axis and Y-axis data, and displaying the gated X-axis and Y-axis data as a cyclic loop image.

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.

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 system includes a test and measurement instrument, including a port to receive a signal from a device under test (DUT), and one or more processors, configured to execute code that causes the one or more processors to: adjust a set of operating parameters for the DUT to a first set of reference parameters; acquire, using the test and measurement instrument, a waveform from the DUT; repeatedly execute the code to cause the one or more processors to adjust the set of operating parameters and acquire a waveform, for each of a predetermined number of sets of reference 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 optimal operating parameters; adjust the set of operating parameters for the DUT to the predicted optimal operating parameters; and determine whether the DUT passes a predetermined performance measurement when adjusted to the set of predicted optimal operating parameters.

Abstract: A method of training a machine learning system to determine operating parameters for optical transceivers includes connecting the transceiver to a test and measurement device, tuning the transceiver with a set of parameters, capturing a waveform from the transceiver, sending the waveform and the set of parameters to a machine learning system, and repeating the tuning, capturing, and sending until a sufficient number of samples are gathered.

Abstract: A machine learning management system includes a repository having one or more partitions, the one or more partitions being separate from others of the partitions, a communications interface, and one or more processors configured to execute code to: receive a selected model and associated training data for the selected model through the communications interface from a customer; store the selected model and the associated training data in a partition dedicated to the customer; and manage the one or more partitions to ensure that the customer can only access the customer's partition. A method includes receiving a selected model and associated training data for the selected model from a customer, storing the selected model and the associated training data in a partition dedicated to the customer in a repository, and managing the one or more partitions to ensure that the customer can only access the partition dedicated to the customer.

Abstract: A test system includes a repository of component models containing characteristic parameters for each component model, one or more processors to receive a list of selected component models through a user interface to be tested as a combination, access the characteristic parameters for each selected component model, build a tensor image using the characteristic parameters, send the tensor image to one or more trained neural networks to predict interoperability of the combination, and receive a prediction about the combination. A method includes receiving a list of selected component models through a user interface to be tested as a combination, accessing characteristic parameters for the selected component models, building a tensor image for each combination of the selected component models, sending the tensor image to one or more trained neural networks to predict interoperability of the combination, and receiving a prediction about the combination.

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 manufacturing system has a machine learning (ML) system having one or more neural networks and a configuration file associated with a trained neural network (NN), a structured data store having interfaces to the ML system a test automation application, a training store, a reference parameter store, a communications store, a trained model store, and one or more processors to control the data store to receive and store training data, allow the ML system to access the training data to train the one or more NNs, receive and store reference parameters and to access the reference parameters, receive and store prediction requests for optimal tuning parameters and associated data within the communication store, to provide requests to the ML system, allow the ML system to store trained NNs in the trained models store, and to recall a selected trained NN and provide the prediction to the test automation application.

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 device has a connection to allow the test and measurement device to connect to an optical transceiver, one or more processors, configured to execute code that causes the one or more processors to: initially set operating parameters for the optical transceiver to average parameters, acquire a waveform from the optical transceiver, measure the acquired waveform and determine if operation of the transceiver passes or fails, send the waveform and the operating parameters to a machine learning system to obtain estimated parameters if the transceiver fails, adjust the operating parameters based upon the estimated parameters, and repeat the acquiring, measuring, sending, and adjusting as needed until the transceiver passes.