Abstract: Proactive management of devices provided at a site includes, responsive to an indication that a part of a device at a site needs to be replaced at a specified replacement time, determining additional parts for replacement at the specified replacement time.

Abstract: Systems, methods, and other embodiments associated with graphical user interface (GUI) component classification are described. One example method includes generating a first vector. The first vector may be generated based on image data describing a GUI component. The example method may also include assigning a GUI component classifier to the GUI component. Assigning the GUI component classifier may comprise comparing the first vector to members of a vector set. Members of the vector set may describe GUI elements. The example method may also include providing the GUI component classifier.

Abstract: A signal from a system, such as a reactive system, that reflects health indicators of the system may be selected. A signal analyzer may extract the health indicators from the signal and conduct a diagnostics of the health of the system based on the health indicators.

Abstract: Systems, methods, and other embodiments associated with graphical user interface (GUI) component identification are described. One example method includes identifying a set of GUI components. The identifying may be based on a screen image of a GUI. The screen image may comprise data describing a visual representation of the GUI. The example method may also include providing data identifying the set of GUI components.

Abstract: A computer can display a graphical user interface (GUI) that includes a control. The GUI can receive text instructions in a natural language that describe a location of the control on the GUI. The text instructions can instruct an application to perform a user interface (UI) event on the control.

Abstract: A signal from a system, such as a reactive system, that reflects health indicators of the system may be selected. A signal analyzer may extract the health indicators from the signal and conduct a diagnostics of the health of the system based on the health indicators.

Abstract: Method embodiments of the present invention provide for semi-automated or fully automated assessment of the quality of device-rendered text, including graphical symbols, characters, and other printable or displayable patterns. Method embodiments of the present invention employ one or more metrics that involve the comparison of input characters, including text characters, symbols, and character-like graphics, with corresponding digital output characters obtained by capturing images of characters rendered by a character-rendering device. The one or more metrics include individual computed relative entropy APQi metrics, reflective of differences in character-to-character distance distributions between one input character c, and the set of input characters c*, a cumulative computed relative entropy APQ, stroke irregularity, contrast measure, sharpness, and background uniformity.

Abstract: Systems, methods, and other embodiments associated with graphical user interface input element identification are described. One example system identifies a graphical user interface (GUI) element through which an input to an application was received based on comparing images generated from data provided by the application. Data that describes the GUI element may then be provided.

Abstract: A method is provided for decoupling the control of dot gain and optical density in electrophotographic based printing by varying the exposure level for each dot in an image.

Abstract: A computer displays a graphical user interface (GUI) that includes a control and receives text instructions in a natural language that describe a location of the control on the GUI. The text instructions instruct an application to perform a user interface (UI) event on the control.

Abstract: Systems, methods, and other embodiments associated with graphical user interface input element identification are described. One example system identifies a graphical user interface (GUI) element through which an input to an application was received based on comparing images generated from data provided by the application. Data that describes the GUI element may then be provided.

Abstract: Systems, methods, and other embodiments associated with graphical user interface (GUI) hierarchy generation are described. One example method includes generating a graph based on an image of a GUI. Nodes in the graph may represent GUI components depicted in the image. Edges in the graph may represent relationships between the GUI components. The example method may include parsing the graph according to a formal graph grammar to produce a GUI hierarchy. The GUI hierarchy may comprise descriptions of groups of the GUI components.

Abstract: Systems, methods, and other embodiments associated with graphical user interface (GUI) component identification are described. One example method includes identifying a set of GUI components. The identifying may be based on a screen image of a GUI. The screen image may comprise data describing a visual representation of the GUI. The example method may also include providing data identifying the set of GUI components.

Abstract: Systems, methods, and other embodiments associated with graphical user interface (GUI) component classification are described. One example method includes generating a first vector. The first vector may be generated based on image data describing a GUI component. The example method may also include assigning a GUI component classifier to the GUI component. Assigning the GUI component classifier may comprise comparing the first vector to members of a vector set. Members of the vector set may describe GUI elements. The example method may also include providing the GUI component classifier.

Abstract: Method embodiments of the present invention provide for semi-automated or fully automated assessment of the quality of device-rendered text, including graphical symbols, characters, and other printable or displayable patterns. Method embodiments of the present invention employ one or more metrics that involve the comparison of input characters, including text characters, symbols, and character-like graphics, with corresponding digital output characters obtained by capturing images of characters rendered by a character-rendering device. The one or more metrics include individual computed relative entropy APQi metrics, reflective of differences in character-to-character distance distributions between one input character c, and the set of input characters c*, a cumulative computed relative entropy APQ, stroke irregularity, contrast measure, sharpness, and background uniformity.

Abstract: A method is provided for decoupling the control of dot gain and optical density in electrophotographic based printing by varying the exposure level for each dot in an image.

Abstract: A method comprises receiving a focusing image and shifting the focusing image to obtain a shifted focusing image. In the method, a focus metric of the focusing image is calculated from the focusing image and the shifted focusing image, where the focus metric is configured for use as a factor in making an automatic focus adjustment determination. An automatic focusing apparatus includes a controller configured to implement the method.

Abstract: A method comprises receiving a focusing image and shifting the focusing image to obtain a shifted focusing image. In the method, a focus metric of the focusing image is calculated from the focusing image and the shifted focusing image, where the focus metric is configured for use as a factor in making an automatic focus adjustment determination. An automatic focusing apparatus includes a controller configured to implement the method.

Abstract: A kill index classification method for prioritizing relational aspects of topological defect intersections, particularly in association with an intermediate analytical testing stage of a multi-stage semiconductor fabrication process. The method relates to an analysis of the geometrical relationship between non-predetermined portion(s), generally referred to as defects, and the surrounding predetermined topology of a conductive semiconductor pattern, to determine the effect of defects on the functionality and reliability of a wafer, and particularly an examined die thereon. Further, in accordance with this geometrical information, a preferred classification of the effects of defects into a numerical value, the “kill index”, is achieved. Preferably, this kill index is strongly linked, correlated and related to the damage caused by the defect to the functionality and/or reliability of the underlying integrated circuit.

Abstract: A kill index classification method for prioritizing relational aspects of topological defect intersections, particularly in association with an intermediate analytical testing stage of a multi-stage semiconductor fabrication process. The method relates to an analysis of the geometrical relationship between non-predetermined portion(s), generally referred to as defects, and the surrounding predetermined topology of a conductive semiconductor pattern, to determine the effect of defects on the functionality and reliability of a wafer, and particularly an examined die thereon. Further, in accordance with this geometrical information, a preferred classification of the effects of defects into a numerical value, the “kill index”, is achieved. Preferably, this kill index is strongly linked, correlated and related to the damage caused by the defect to the functionality and/or reliability of the underlying integrated circuit.