Abstract: A protocol designer for a test and measurement instrument, comprising an input to receive a signal, a memory configured to store the signal, an author configured to generate protocol definitions based on a user input, a debugger configured to output textual and visual decode results based on the protocol definitions and the signal, and a deployer configured to output a compiled protocol definition file to the test and measurement instrument.

Abstract: A test and measurement system that allows a user to use an activation code coupled with a user-owned communication device to provide security credentials to cloud or web-based services with security enhancements or preferences. The test and measurement system includes a test and measurement instrument that can initiate the connection and a remote server which provide an activation code for a user to enter on the communication device to connect the test and measurement instrument to the cloud or web-based services.

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 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 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 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 system includes a primary instrument having an input for receiving a test signal for measurement or analysis from a Device Under Test (DUT) and generating a test waveform from the test signal, and a duplicator for sending a copy of the test waveform to one or more secondary instruments. The one or more secondary instruments are each structured to access the copy of the test signal for analysis, and each of the one or more secondary instruments includes a receiver structured to receive a command related to measurement or analysis of the copy of the test waveform, one or more processes for executing the received command, and an output for sending results of the executed command to be displayed on a user interface that is separate from any user interface of the one or more secondary instruments.

Abstract: A test and measurement system that allows a user to use an activation code coupled with a user-owned communication device to provide security credentials to cloud or web-based services with security enhancements or preferences. The test and measurement system includes a test and measurement instrument that can initiate the connection and a remote server which provide an activation code for a user to enter on the communication device to connect the test and measurement instrument to the cloud or web-based services.

Abstract: A test and measurement instrument includes a processor configured to execute instructions that cause the processor to: receive a bus auto-detect signal; receive signals from a bus connected to the test and measurement instrument; and apply machine learning to the signals from the bus to output a predicted a bus type; at least one memory to store the instructions and data used in the machine learning, and a display to display information for a user including the predicted bus type. A method of automatically detecting a bus type includes receiving a bus auto-detect signal when a bus is connected to a test and measurement instrument, receiving signals from the bus at a processor, and using the processor to apply machine learning to the signals from the bus to predict a bus identity.

Abstract: A protocol designer for a test and measurement instrument, comprising an input to receive a signal, a memory configured to store the signal, an author configured to generate protocol definitions based on a user input, a debugger configured to output textual and visual decode results based on the protocol definitions and the signal, and a deployer configured to output a complied protocol definition file to the test and measurement instrument.

Abstract: Disclosed herein are systems and methods for converting physical input signals into bitstreams using syntax trees regardless of the physical input signal's protocol. Using declarative language definitions within a protocol declaration, a test and measurement system can compile a syntax tree that automatically translates the input data into a proper bitstream output. The declarative language definitions within the protocol declaration allow custom or standard protocol rules to be written for multiple or arbitrary input protocols without writing unsafe functions, having to access memory, or debugging more complex language codes.

Abstract: Disclosed herein are systems and methods for converting physical input signals into bitstreams using syntax trees regardless of the physical input signal's protocol. Using declarative language definitions within a protocol declaration, a test and measurement system can compile a syntax tree that automatically translates the input data into a proper bitstream output. The declarative language definitions within the protocol declaration allow custom or standard protocol rules to be written for multiple or arbitrary input protocols without writing unsafe functions, having to access memory, or debugging more complex language codes.

Abstract: A closed loop compensation system including a deformable mirror including an array of spaced actuators. An array of spaced sensors is mapped in optical space to reside between pairs of actuators. A lens system receives a wavefront from the deformable mirror and focuses sub-apertures of the wavefront onto individual ones of the spaced sensors. A sequencer addresses each actuator and associated sensor in the arrays. A compute unit is configured to respond to the sequencer to set a first actuator to an adjusted stroke position and then adjust the stroke of subsequent actuators to locate, to a pre-established position, the focused sub-aperture on a sensor in the pathway between each particular subsequent actuator and a neighboring previously adjusted portion of the mirror to compensate for sub-aperture tilt while maintaining relative phase between sub-apertures.

Abstract: An integrated wavefront correction module including a mounting device a deformable mirror carried by the mounting device for correcting for high spatial and temporal frequency error in an incident wavefront and a tip-tilt correction mechanism including a base and a plurality of tip-tilt actuators interconnected between the base and one of the flexible and fixed portions and a plurality of support posts interconnected between the base and the other of the flexible and fixed portions or alternatively the tip-tilt correction mechanism may include at least one actuator platform, a base, a first set of tip-tilt actuators for adjustably supporting the at least one actuator platform from the base and a second set of tip-tilt actuators for adjustably supporting the mounting device from the at least one actuator platform for adjusting the mounting device and the deformable mirror together to compensate for tip-tilt errors in the incident wavefront with the tip-tilt actuators being operated typically at mid-range in or