Abstract: A system and method implement biometric sensing with sensor fusion. A first sensor is coupled with a user and is capable of sensing a first characteristic of the user. A second sensor is coupled with the user and is capable of sensing a second characteristic of the user. A memory stores software with machine readable instructions that when executed by a processor implement an algorithm to correct for motion artifacts included within the second characteristic based upon activity of the user determined from the first characteristic.

Abstract: A system and a method determine an activity of a user. Sensor data is collected from a sensor within a pod worn by the user. The sensor data is matched to a signature definition corresponding to a known activity and the sensor. When the sensor data matches the signature definition, the activity of the user is determined. Sensed data, signature data, and/or matched signature data may be communicated to and from external devices. Signatures may be learned for known activities.

Abstract: Disclosed herein are representative embodiments of methods and apparatus for managing and allocating hardware resources during RTL synthesis. For example, in one exemplary method disclosed herein, an RTL description of a circuit to be implemented in a target architecture is received. The target architecture of this embodiment comprises a fixed number of hardware resources in a class of hardware resources. One or more operator instances are determined from the RTL description received, where at least some of the operator instances are implementable by the hardware resources in the class of hardware resources. In this embodiment, and prior to initially synthesizing the RTL description into a gate-level netlist, assignment information indicative of how the operator instances are to be implemented using the hardware resources in the class of hardware resources is automatically determined. A graphical user interface is also provided that allows a user to view and modify the assignment information.

Abstract: A system and method implement biometric sensing with sensor fusion. A first sensor is coupled with a user and is capable of sensing a first characteristic of the user. A second sensor is coupled with the user and is capable of sensing a second characteristic of the user. A memory stores software with machine readable instructions that when executed by a processor implement an algorithm to correct for motion artifacts included within the second characteristic based upon activity of the user determined from the first characteristic.

Abstract: Systems and methods provide location-based athlete pacing with biofeedback. A goal pace profile based upon historical activity records of one or more athletes is determined. The goal pace profile defines a goal of a user. A mobile computing device determines a current activity of the user and compares the current activity to the goal pace profile to generate real-time status and coaching cues during the current activity.

Abstract: Disclosed herein are representative embodiments of methods and apparatus for managing and allocating hardware resources during RTL synthesis. For example, in one exemplary method disclosed herein, an RTL description of a circuit to be implemented in a target architecture is received. The target architecture of this embodiment comprises a fixed number of hardware resources in a class of hardware resources. One or more operator instances are determined from the RTL description received, where at least some of the operator instances are implementable by the hardware resources in the class of hardware resources. In this embodiment, and prior to initially synthesizing the RTL description into a gate-level netlist, assignment information indicative of how the operator instances are to be implemented using the hardware resources in the class of hardware resources is automatically determined. A graphical user interface is also provided that allows a user to view and modify the assignment information.

Abstract: Disclosed herein are representative embodiments of methods and apparatus for managing and allocating hardware resources during RTL synthesis. For example, in one exemplary method disclosed herein, an RTL description of a circuit to be implemented in a target architecture is received. The target architecture of this embodiment comprises a fixed number of hardware resources in a class of hardware resources. One or more operator instances are determined from the RTL description received, where at least some of the operator instances are implementable by the hardware resources in the class of hardware resources. In this embodiment, and prior to initially synthesizing the RTL description into a gate-level netlist, assignment information indicative of how the operator instances are to be implemented using the hardware resources in the class of hardware resources is automatically determined. A graphical user interface is also provided that allows a user to view and modify the assignment information.

Abstract: Disclosed herein are representative embodiments of methods and apparatus for managing and allocating hardware resources during RTL synthesis. For example, in one exemplary method disclosed herein, an RTL description of a circuit to be implemented in a target architecture is received. The target architecture of this embodiment comprises a fixed number of hardware resources in a class of hardware resources. One or more operator instances are determined from the RTL description received, where at least some of the operator instances are implementable by the hardware resources in the class of hardware resources. In this embodiment, and prior to initially synthesizing the RTL description into a gate-level netlist, assignment information indicative of how the operator instances are to be implemented using the hardware resources in the class of hardware resources is automatically determined. A graphical user interface is also provided that allows a user to view and modify the assignment information.

Abstract: Disclosed herein are representative embodiments of methods and apparatus for managing and allocating hardware resources during RTL synthesis. For example, in one exemplary method disclosed herein, an RTL description of a circuit to be implemented in a target architecture is received. The target architecture of this embodiment comprises a fixed number of hardware resources in a class of hardware resources. One or more operator instances are determined from the RTL description received, where at least some of the operator instances are implementable by the hardware resources in the class of hardware resources. In this embodiment, and prior to initially synthesizing the RTL description into a gate-level netlist, assignment information indicative of how the operator instances are to be implemented using the hardware resources in the class of hardware resources is automatically determined. A graphical user interface is also provided that allows a user to view and modify the assignment information.

Abstract: Disclosed herein are representative embodiments of methods and apparatus for managing and allocating hardware resources during RTL synthesis. For example, in one exemplary method disclosed herein, an RTL description of a circuit to be implemented in a target architecture is received. The target architecture of this embodiment comprises a fixed number of hardware resources in a class of hardware resources. One or more operator instances are determined from the RTL description received, where at least some of the operator instances are implementable by the hardware resources in the class of hardware resources. In this embodiment, and prior to initially synthesizing the RTL description into a gate-level netlist, assignment information indicative of how the operator instances are to be implemented using the hardware resources in the class of hardware resources is automatically determined. A graphical user interface is also provided that allows a user to view and modify the assignment information.