National Instruments Corporation

Subramanian Ramamoorthy

Lothar Wenzel

Robert E Dye

Michael L Santori

John C Limroth

Ram Kudukoli

Gregory O Morrow

Bin Wang

Dinesh R Nair

Darren R Schmidt

Matthew S Fisher

Yong Rao

Satish V Kumar

James C Nagle

James M Lewis

Michael D Cerna

Kyle P Gupton

Bruce Smyth

Benjamin R Weidman

Naxiong Zhang

Jeanne S Falcon

System and method for approximating a system. A multi-parameter representation of a family of systems is stored. An embedding of the family into an abstract geometrical continuous space with a metric and defined by the parameters is determined. Coordinates of the space specify values for the parameters of systems of the family. The space includes a grid of points representing respective discrete approximations of the systems. A first point corresponding to a desired instance of a system is determined. The first point's coordinates specify values for the parameters of the instance. The space is sampled using a mapping of a well-distributed point set from a Euclidean space of the parameters to the abstract space. A nearest discrete point to the first point is determined which specifies values for parameters for an optimal discrete approximation of the desired instance, which are useable to implement the discrete approximation of the desired instance.

Approximating a system using an abstract geometrical space

System and method for programmatically generating a second graphical program associated with a second programming development environment based on a first graphical program associated with a first programming development environment. The second graphical program may be generated programmatically, without relying on user input, or may prompt for user input to determine various options to use in generating the second graphical program. The second graphical program may implement the functionality of, or a portion of the functionality of, the first graphical program. The method preferably generates the second graphical program such that the second programming development environment is operable to treat the second graphical program identically to a graphical program interactively developed by a user using the second programming development environment. Thus, once the second graphical program has been generated, the user may use the second programming development environment to edit the second graphical program, execute the second graphical program, etc.

Automatically generating a second graphical program based on a first graphical program

Converting a first graphical program into an intermediate abstract representation for new graphical program generation

A geometric pattern matching method to locate instances of a template image in a target image using a plurality of models with different feature combinations. A learning phase involves learning geometric information about the template image and creating a model for each of a plurality of different respective parent features. Each model comprises a relationship tree from the respective parent feature to one or more other child features, and hence each model may have different feature combinations. In a matching phase, the method may examine a plurality of different models to determine matches in the target image being analyzed. The matching phase may select different models dynamically based on prior matching results.

Geometric pattern matching using dynamic feature combinations

System and method for programmatically generating a second graphical program based on a first graphical program

System and method for user configuration of an autotuning algorithm for a controller in a motion control system. A desired trajectory in one or more dimensions for the motion control system is received. Values of one or more gains for a controller are initialized. A response trajectory of the controller and motion control system in response to the desired trajectory is received, and an error determined between the response trajectory and the desired trajectory. A gain space is then experimentally searched to determine final values of the one or more gains for the controller that minimize the error (e.g., Euclidean norm) between the response trajectory and the desired trajectory, e.g., via simulated annealing, or other stochastic, quasi-random, and/or deterministic approaches. After experimentally searching, the controller is operable to control the motion control system substantially in accordance with the desired trajectory using the determined values of the one or more gains.

Automatic tuning of motion controllers using search techniques

Growing community of inventors

Austin, TX, United States of America

Subramanian Ramamoorthy