Abstract: The present disclosure provides a method (500) comprising receiving (510) images (e.g., 125A to 125G) of an object (110), the images (e.g., 125A to 125G) comprising first and second images. The method (500) then determines (530) feature points (810, 820) of the object (110) using the first images and determines (530, 540, 550) a three-dimensional reconstruction of a scene having the object (110). The method (500) then proceeds with aligning (560) the three-dimensional reconstruction with a three-dimensional mesh model of the object (110). The alignment can then be used to map (570) pixel values of pixels of the second images onto the three-dimensional mesh model. The directional radiosity of each mesh element of the three-dimensional mesh model can then be determined (580) and the hemispherical radiosity of the object (110) is determined (590) based on the determined directional radiosity.

Abstract: The present disclosure provides a method (500) comprising receiving (510) images (e.g., 125A to 125G) of an object (110), the images (e.g., 125A to 125G) comprising first and second images. The method (500) then determines (530) feature points (810, 820) of the object (110) using the first images and determines (530, 540, 550) a three-dimensional reconstruction of a scene having the object (110). The method (500) then proceeds with aligning (560) the three-dimensional reconstruction with a three-dimensional mesh model of the object (110). The alignment can then be used to map (570) pixel values of pixels of the second images onto the three-dimensional mesh model. The directional radiosity of each mesh element of the three-dimensional mesh model can then be determined (580) and the hemispherical radiosity of the object (110) is determined (590) based on the determined directional radiosity.

Abstract: In one embodiment, a method includes receiving, from one or more sensors, biomechanical data of a first user performing a plurality of actions of a first action-type and outcome data of a plurality of outcomes corresponding to the respective plurality of actions. A plurality of sets of action-parameter values may be determined based on the received biomechanical data, where each of the sets of action-parameter values corresponds to a respective action of the plurality of actions. An athletic-performance model of the first user may be generated based on the plurality of sets of action-parameter values. The athletic-performance model may be a statistical model including probabilities computed with respect to the sets of action-parameter values and the outcome data.

Abstract: In one embodiment, a method includes accessing an athletic-performance model of a first user, where the model is based on a plurality of sets of action-parameter values of action parameters of the first user. The action-parameter values may be determined based on biomechanical data of the first user performing a plurality of actions of a first action-type and outcome data of each action. A current skill level of the first user may be determined based on a measure of variances associated with action parameters of the first action-type. Target ranges of action-parameter values may be calculated for action parameters based on the athletic-performance model. Each target range may be based on a measure of probability with respect to the particular action parameters and the outcome data. A report of athletic-performance feedback may be generated and may include current skill level information and the target ranges of action-parameter values.

Abstract: In one embodiment, a method includes accessing an athletic-performance model of a first user, where the model is based on a plurality of sets of action-parameter values of action parameters of the first user. The action-parameter values may be determined based on biomechanical data of the first user performing a plurality of actions of a first action-type and outcome data of each action. A current skill level of the first user may be determined based on a measure of variances associated with action parameters of the first action-type. Target ranges of action-parameter values may be calculated for action parameters based on the athletic-performance model. Each target range may be based on a measure of probability with respect to the particular action parameters and the outcome data. A report of athletic-performance feedback may be generated and may include current skill level information and the target ranges of action-parameter values.

Abstract: In one embodiment, a method includes receiving, from one or more cameras, video data associated with a first user performing a plurality of actions of a first action-type. The cameras may be positioned to observe one or more biomechanical acts of the first user and an outcome for each action. The video data may be optically processed to determine a plurality of sets of action-parameter values corresponding to the respective plurality of actions. Each action-parameter value may be based on biomechanical acts for the respective action from the video data. Outcome data corresponding to the respective plurality of actions may be determined based on the video data. An athletic-performance model of the first user may be generated based on the sets of action-parameter values. The athletic-performance model may be a statistical model including probabilities computed with respect to the sets of action-parameter values and the outcome data.

Abstract: In one embodiment, a method includes receiving, from one or more sensors, biomechanical data of a first user performing a plurality of actions of a first action-type and outcome data of a plurality of outcomes corresponding to the respective plurality of actions. A plurality of sets of action-parameter values may be determined based on the received biomechanical data, where each of the sets of action-parameter values corresponds to a respective action of the plurality of actions. An athletic-performance model of the first user may be generated based on the plurality of sets of action-parameter values. The athletic-performance model may be a statistical model including probabilities computed with respect to the sets of action-parameter values and the outcome data.

Abstract: In a exhaust smoke sensor a light emitter generates a light beam which passes through a region containing the exhaust gas to a first light sensor. To reduce interference the light emitter is driven by a near square wave signal produced by passing a square wave through a low pass filter.

Abstract: An exhaust gas particle sensor has an inlet tube and a sensing region having different cross sections and linked by a transition region including a number of baffles arranged so that the time taken for gas to travel from the inlet tube to the sensing region is substantially independent of the position of the gas across the inlet tube. The sensor operates by measuring the opacity of the exhaust gas using a light beam which passes in and out of the sensing region through windows, the exhaust gas being prevented from impinging on each window by a double air curtain.

Abstract: Communication networks, specifically network topologies and apparatus therefor suitable for data communication between computers, tend to be of three main types--those with a star topology (the older sort), those with a bus topology, and those with a ring topology. The ring topology networks have many advantages, but their simpler forms are highly vulnerable to link or repeater failure.