Abstract: Embodiments described herein use depth images to extract user behavior, wherein each depth image specifies that a plurality of pixels correspond to a user. A depth-based center-of-mass position is determined for the plurality of pixels that correspond to the user. Additionally, a depth-based inertia tensor can also be determined for the plurality of pixels that correspond to the user. In certain embodiments, the plurality of pixels that correspond to the user are divided into quadrants and a depth-based quadrant center-of-mass position is determined for each of the quadrants. Additionally, a depth-based quadrant inertia tensor can be determined for each of the quadrants. Based on one or more of the depth-based center-of-mass position, the depth-based inertial tensor, the depth-based quadrant center-of-mass positions or the depth-based quadrant inertia tensors, an application is updated.

Abstract: Heartrate tracking is performed entirely optically without the subject being required to wear any monitoring equipment by processing a combination of signals representing frames of video of the sinusoidal motion of a subject's facial skin color changes captured by both IR and visible light (e.g., RGB—red/green/blue) cameras. The IR and RGB graphs that result from the processing are perfectly phase-shifted so that when the IR signal is going down in amplitude, the RGB signal is going up. Such phase-shifting enables the optical heartrate tracking to utilize diverse input feeds so that a tracked signal is accepted as the user's true heartrate when both IR and RGB signals are well correlated.

Abstract: Techniques described herein use signal analysis to detect and analyze repetitive user motion that is captured in a 3D image. The repetitive motion could be the user exercising. One embodiment includes analyzing image data that tracks a user performing a repetitive motion to determine data points for a parameter that is associated with the repetitive motion. The different data points are for different points in time. A parameter signal of the parameter versus time that tracks the repetitive motion is formed. The parameter signal is divided into brackets that delineate one repetition of the repetitive motion from other repetitions of the repetitive motion. A repetition in the parameter signal is analyzed using a signal processing technique. Curve fitting and/or autocorrelation may be used to analyze the repetition.

Abstract: Embodiments described herein can be used to detect holes in a subset of pixels of a depth image that has been specified as corresponding to a user, and to fill such detected holes. Additionally, embodiments described herein can be used to produce a low resolution version of a subset of pixels that has been specified as corresponding to a user, so that when an image including a representation of the user is displayed, the image respects the shape of the user, yet is not a mirror image of the user. Further, embodiments described herein can be used to identify pixels, of a subset of pixels specified as corresponding to the user, that likely correspond to a floor supporting the user. This enables the removal of the pixels, identified as likely corresponding to the floor, from the subset of pixels specified as corresponding to the user.

Abstract: Embodiments described herein use depth images to extract user behavior, wherein each depth image specifies that a plurality of pixels correspond to a user. In certain embodiments, information indicative of an angle and/or curvature of a user's body is extracted from a depth image. This can be accomplished by fitting a curve to a portion of a plurality of pixels (of the depth image) that correspond to the user, and determining the information indicative of the angle and/or curvature of the user's body based on the fitted curve. An application is then updated based on the information indicative of the angle and/or curvature of the user's body. In certain embodiments, one or more average extremity positions of a user, which can also be referred to as average positions of extremity blobs, are extracted from a depth image. An application is then updated based on the average positions of extremity blobs.

Abstract: Embodiments described herein use depth images to extract user behavior, wherein each depth image specifies that a plurality of pixels correspond to a user. A depth-based center-of-mass position is determined for the plurality of pixels that correspond to the user. Additionally, a depth-based inertia tensor can also be determined for the plurality of pixels that correspond to the user. In certain embodiments, the plurality of pixels that correspond to the user are divided into quadrants and a depth-based quadrant center-of-mass position is determined for each of the quadrants. Additionally, a depth-based quadrant inertia tensor can be determined for each of the quadrants. Based on one or more of the depth-based center-of-mass position, the depth-based inertial tensor, the depth-based quadrant center-of-mass positions or the depth-based quadrant inertia tensors, an application is updated.

Abstract: A method of treating diabetes by administering Carvedilol in patients and diabetes mellitus. This method of treatment will eliminate the need for insulin and other blood sugar controlling agents in hypertensive patients with Type II diabetes mellitus, and will significantly reduce the required dosage of insulin and eliminate the need for other blood controlling agents in patients with Type I diabetes mellitus. This method will also delay and/or prevent the progression of non-insulin dependent Type II diabetes mellitus to insulin-dependent Type II diabetes mellitus. Moreover, this method has been shown to preserve improve insulin receptor sensitivity such that patient's HbA1c level reaches and is maintained at or near 7% or less.

Abstract: A flow control system comprises a flow sensor, a valve controller, a signal processor, a control processor and an interface. The flow sensor generates a sensor signal characterizing a flow rate. The valve controller controls the flow rate as a function of a control output. The signal processor converts the sensor signal into a flow signal characterizing the flow rate as a function of time, and the control processor generates the control output as a function of a setpoint and the flow signals. The interface receives an input representative of the setpoint, transmits a flow output representative of the flow signals, and transmits a diagnostic output directly indicative of an operational condition of the flow control system.

Abstract: A method for evaluating measured electromagnetic (EM) data relating to a subsurface region, comprising the steps of: (a) specifying alternative models of the region in terms of input parameters with uncertainty; (b) receiving the measured EM data and an estimated error; and (c) carrying out a Bayesian inversion on the measured data using each of the alternative models to attribute a probability to each model on the basis of the measured data and estimated error. The method allows physical measurements with error to be translated into fundamental parameters which can be used to assess business risk and uncertainty for making decisions, and also provides for parameters and models which can typically be directly estimated by a geoscientist on a coarse spatial grid to be used as an input, and validated using the measured data with error.

Abstract: A method for evaluating measured electromagnetic (EM) data relating to a subsurface region, comprising the steps of: (a) specifying at least one model of the region in terms of fundamental parameters with uncertainty, using a fundamental inversion grid; (b) receiving the measured EM data and an estimated error; (c) translating the fundamental parameters of the model to meta parameters of the region and to a computational grid suitable for forward modelling and comparison to the measured EM data, using relationships with uncertainty; and (d) carrying out a Bayesian inversion using the measured data and estimated error to produce an output comprising fundamental parameters of the region on the fundamental inversion grid with uncertainty.

Abstract: A flow control system comprises a flow sensor, a valve controller, a signal processor, a control processor and an interface. The flow sensor generates a sensor signal characterizing a flow rate. The valve controller controls the flow rate as a function of a control output. The signal processor converts the sensor signal into a flow signal characterizing the flow rate as a function of time, and the control processor generates the control output as a function of a setpoint and the flow signals. The interface receives an input representative of the setpoint, transmits a flow output representative of the flow signals, and transmits a diagnostic output directly indicative of an operational condition of the flow control system.