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.

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 includes a test and measurement instrument configured to receive waveform data from a device under test (DUT) on a manufacturing line, a machine learning system 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 optimal tuning parameter data sets from the DUT after the DUT is tuned on the manufacturing line, determine one or more parameter data sets from the optimal tuning parameter data, load the one or more parameter data sets into the DUT, collect waveform data from the DUT for the one or more parameter data sets as training data sets, train the machine learning system using the training data sets, and use the machine learning system after training to produce an output related to the DUT.

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 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 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 system has ovens configured to hold devices under test (DUTs), DUT switches, each connected to the DUTs in an oven, splitters, each splitter connected to a DUT switch, an instrument switch connected to one output of each splitter, the other output of each splitter connected to a test instrument, and one or more processors to control the instrument switch to select one of the DUT switches connected to an oven, control the selected DUT switch to connect each DUT in the oven to a channel of the test and measurement instrument, use machine learning to tune the DUT to a set of parameters until the DUT passes or fails, repeat the connecting, tuning, and testing of each DUT until all DUTs in an oven have been tested, and repeat the selection and control of the DUT switches until each DUT in each oven has been tuned and tested.

Abstract: In an automated defect detection and classification system, one or more computing devices access scan data acquired in an ultrasonic scan of an object. A first input feature map, including a two-dimensional (2D) scan image, is built from the scan data and input to a first deep neural network to generate a first output feature map. A second input feature map, including an image of a defect-free object, is input to a second deep neural network, having the same structure and weight values as first deep neural network, to produce a second output feature map. The scanned object is determined to contain a defect when a distance between first and second output feature maps is large. In an alternative approach, the 2D scan image and one or more images of the defect-free object are input to different channels of neural network trained using color images.

Abstract: In a method and apparatus for automated inspection, an image is acquired of an object under inspection and a difference image is generated showing the difference between the acquired image and a reference image of a defect-free object of the same type. Characteristics of the difference image, or detected isolated regions of the difference image, are passed to an automated defect classifier to classify defects in the object under inspection. The characteristics of the difference image may be pixels of the difference image or features determined therefrom. The features may be extracted using a neural network, for example. The automated defect classifier is trained using difference images and may be further trained, in operation, based on operator classifications and using simulated images of defects identified by an operator.

Abstract: A test and measurement machine learning model development system includes a user interface, one or more ports to allow the system to connect to one or more data sources, one or more memories, and one or more processors configured to execute code to cause the one or more processors to: display on the user interface one or more application user interfaces, the application user interfaces to allow a user to provide user inputs; use and application programming interface to configure the system based on the user inputs; receive data from the one or more data sources; apply one or more modules from a library of signal processing and feature extraction modules to the data to produce training data; apply one or more machine learning models to the training data; provide monitoring of the one or more machine learning models; and save the one or more machine learning models to at least one of the one or more memories.

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.