Abstract: In an embodiment, a technique for identifying a target for a pointing device in a display. The technique may involve identifying a trajectory of movement of the pointing device and identifying the target for the pointing device based on at least the identified trajectory. The target may be, for example, a graphical user interface (GUI) element that may be displayed in the display or a location in the display.

Abstract: Systems and methods validate the operation of a component of an executable model without inadvertently altering the behavior of the component. The model may be partitioned into a design space and a verification space. The component may be placed in the design space, while an observer for validating the component may be placed in the verification space, and linked to the component. During execution of the model, input or output values for the component may be computed and buffered. Execution of the observer may follow execution of the component. The input or output values may be read out of the buffer, and utilized during execution of validation functionality defined for the observer. Model compilation operations that may inadvertently alter the behavior of the component, such as back propagation of attributes, are blocked between the observer and the component.

Abstract: A device may include one or more processors. The device may receive program code that identifies operations to be performed with regard to a data set to determine an output value. The device may identify a plurality of read operations corresponding to the operations. The plurality of read operations may be performed to obtain respective inputs of the operations. The device may generate a first data structure that identifies the operations and the plurality of read operations. The device may identify two or more operations, of the operations, that receive a particular input. The device may generate a second data structure that includes a combined operation. The combined operation may be generated based on the two or more operations, and may be associated with fewer read operations than the two or more operations. The device may determine the output value based on the second data structure.

Abstract: Filtering apparatus and methods associated with filtering large time series of data are described. A filtering process containing recursive dependencies can be organized as a series of computational tasks, at least some of which can be performed in parallel. Because of parallel execution of some tasks, an amount of time for filtering large time series of data with a filter exhibiting recursive data dependencies can be reduced significantly.

Abstract: A first request is received for one or more service interface specifications. The first request includes information about one or more model element interface specifications usable for identifying the service interface specifications. The model element interface specifications are part of or extracted from one or more executable model elements of an executable graphical model. The service interface specifications correspond to respective services that include features complying with the model element interface specifications and are associated with information necessary for implementing semantics of the model elements. The information about the model element interface specifications is analyzed, comprising evaluating semantics of the executable model elements within the executable graphical model. The service interface specifications are identified based on the analyzing. Information is provided representing the identified service interface specifications for display and selection.

Abstract: A device may obtain a model. The model, when executed, may simulate a behavior of a physical system. A user, such as, for example, an engineer, may specify a design interest for the model. The design interest may be associated with a particular behavior of a portion of the model. The device may analyze the model based on the design interest and may determine, based on analyzing the model, a group of model elements, included in the model, that are related to the design interest. The device may generate, based on the group of model elements, a model slice. When the model slice is executed, a behavior of the model slice may correspond to the particular behavior of the portion of the model. The device may analyze the particular behavior of the portion of the model based on executing the model slice.

Abstract: A system and method generates a contextual model for a source model. The system and method receives a designation of a component of interest in the source model. The system and method analyzes the source model and identifies those model elements within the source model that have an interaction behavior with the component of interest. The system and method includes the component of interest and the model elements having the interaction behavior with the component of interest in the contextual model. The system and method connects the model elements to the component of interest in the context model in a similar manner as in the source model. The context model may be run or evaluated.

Abstract: Apparatus and methods are described for adapting a programming environment to handle renamed programming constructs such as classes and packages. Non-canonical identifiers that reference renamed programming constructs can be used in code parts to be executed in the programming environment, and the code parts can execute, without disruption or user intervention, using related canonical identifiers. The solutions described herein provide backward and forward compatibility for code and data affected by renaming of programming constructs.

Abstract: Synthetic object detection data is generated for a modeled sensor, such as a camera. Scenario data specifying objects, such as vehicles, sensor intrinsics, such as focal length, principal point, and image size, and sensor extrinsics, such location and orientation in the scenario of the sensor, may be received. An object detector model may detect a given object in the scenario if it lies within the sensor's field of view, is large enough, and is not occluded. Two dimensional (2D) image plane position and velocity measurements may be generated. A measurement noise model may add noise to the measurements. Position, velocity, and measurement noise may be mapped into a three dimensional (3D) world coordinate system. An object detection list that includes time of detection, detected position and velocity, measurement accuracy, and an object classification for detected objects may be output.

Abstract: A method performed by at least one processing unit comprises receiving a selection of a control volume associated with a physical system; receiving selections of a time dependent potential variable for the transported property and a time dependent flow variable for the transported property; receiving at least one fluid flow equation; determining an advection process; determining a diffusion process; determining a time dependent scaling factor based on at least the advection process and the diffusion process; deriving a rescaled transport flow equation for the time dependent flow variable for the transported property by adding the advection process and the diffusion process, and by applying the time dependent scaling factor for normalization; solving a plurality of equations that include the fluid flow equation and the rescaled transport flow equation; deriving the time dependent flow variable for the transported property; and deriving the time dependent potential variable for the transported property.

Abstract: A device receives a selection of signals associated with a model, and analyzes the selected signals to identify signals, of the selected signals, that can be grouped into a semantically viable aggregation. The device receives an instruction to create a block for the identified signals. The device provides a block to group the identified signals into a semantically viable aggregation with a particular number of inputs, a particular number of outputs, a particular size, a particular position, and a particular orientation, in relation to the model, based on the identified signals, and provides the block in the model.

Abstract: Systems and methods may aggregate and organize implicit and explicit initialization, reset, and termination operations defined throughout the hierarchy of an executable. The systems and methods may analyze the model and identify implicit and explicit initialization, reset, and termination operations defined at various hierarchical levels. The systems and methods may aggregate the implicit and explicit initialization, reset, and termination operations into an initialize callable unit, a reset callable unit, and a termination callable unit. The systems and methods may apply optimizations to the callable units, and resolve conflicts. The systems and methods may define a single entry point for each of the initialize, reset, and termination callable units.

Abstract: Systems and methods provide, as part of an executable graphical model, a region for providing variants that includes one or more computational choices defining alternative execution implementations of the region. Conditions assigned to the one or more computational choices indicate which of the computational choices is active. The conditions specify logical expressions of variables that evaluate to True or False. For a given simulation of the executable graphical model, all of the logical expressions may evaluate to False, such that none of the computational choices are active. All of the computational choices of the executable graphical model may be removed for the given simulation.

Abstract: A device may receive input to perform a renaming operation in a graphical modeling environment. The device may identify a selected element, included in the graphical modeling environment, based on the input. The device may determine an element indicator associated with the selected element. The element indicator may be used to identify a relationship between the selected element and a related element included in the graphical modeling environment. The device may identify, based on the element indicator, the related element associated with the selected element. The device may modify the element indicator based on receiving the input to perform the renaming operation, and may modify a corresponding element indicator corresponding to the related element.

Abstract: Systems and methods provide, as part of an executable graphical model, a region for providing variants that includes one or more computational choices defining alternative execution implementations of the region. Conditions assigned to the one or more computational choices indicate which of the computational choices is active. The conditions specify logical expressions of variables that evaluate to True or False. Two or more of the logical subexpressions may be combined to form a combination logical expression, which may be assigned to two or more model elements included in at least one of the computational choices. The combination logical expression may be decomposed into its logical subexpressions, and at least one of the decomposed logical subexpressions may be traced to one or more of the model elements to which the at least one of the logical subexpressions is assigned.

Abstract: Filtering apparatus and methods associated with filtering large time series of data are described. A filtering process containing recursive dependencies can be organized as a series of computational tasks, at least some of which can be performed in parallel. Because of parallel execution of some tasks, an amount of time for filtering large time series of data with a filter exhibiting recursive data dependencies can be reduced significantly.

Abstract: A Discrete Event System model created or provided in a time domain modeling and simulation environment and/or an event domain modeling and simulation environment may be divided into multiple independent regions, e.g. “subgraphs”, to achieve interleaved execution of the components from different domains. The subgraphs are automatically identified by the modeling and simulation environment during the compilation. Each subgraph consists of one or more interconnected event-driven components. Each subgraph is associated with an event calendar that controls the execution of the associated subgraph. Such multiple event calendar design enables multi-domain simulation, where event-driven components modeled by an event domain modeling environment and time-driven components modeled by a time domain modeling environment are simulated in an integrated fashion.

Abstract: A method may include causing a model, including a set of core model elements and a set of diagnostic model elements, to be executed. The set of diagnostic model elements may be associated with a conditional trigger-point. The conditional trigger-point may be associated with a condition of the model for triggering the conditional trigger-point. The method may include determining that the condition of the model has been satisfied. The method may include causing the set of diagnostic model elements to be displayed via a user interface based on determining that the condition of the model has been satisfied. The set of diagnostic model elements may not have been displayed, during execution of the model, prior to determining that the condition of the model has been satisfied.

Abstract: Methods and systems associated with a programming environment are described that provide automated or semi-automated recovery during run-time or compile-time operation in response to attempts to access a program element that is not in a working folder or in a folder location for which a path has been specified for accessing the program element.

Abstract: Computer-implemented methods are provided for analyzing datasets. Consistent with disclosed embodiments, a computing system may be configured to select a cluster from clusters partitioning the dataset. The clusters may include a subset of the dataset, and may be associated with a current medoid of the cluster and a current cost of the cluster. The computing system may determine a new cost of the selected cluster and a new medoid of the selected cluster based on a matrix with rows corresponding to data in a subset of the cluster. The columns may correspond to data in the dataset or only to data in the cluster. The computer system may replace the current medoid of the selected cluster with the new medoid of the selected cluster based on the new cost of the selected cluster. The computer system may output the cluster information to determine a structure of the dataset.