Abstract: Some embodiments of the invention provide a novel method for training a multi-layer node network to reliably determine depth based on a plurality of input sources (e.g., cameras, microphones, etc.) that may be arranged with deviations from an ideal alignment or placement. Some embodiments train the multi-layer network using a set of inputs generated with random misalignments incorporated into the training set. In some embodiments, the training set includes (i) a synthetically generated training set based on a three-dimensional ground truth model as it would be sensed by a sensor array from different positions and with different deviations from ideal alignment and placement, and/or (ii) a training set generated by a set of actual sensor arrays augmented with an additional sensor (e.g., additional camera or time of flight measurement device such as lidar) to collect ground truth data.

Abstract: Some embodiments of the invention provide a novel multi-layer node network to determine a set of misalignment values for a set of cameras that may be arranged with deviations from an ideal alignment or placement based on images captured by the set of cameras. A set of misalignment values for a set of cameras, in some embodiments, takes the form of translation vectors indicating the offsets between the centers of projection of the cameras relative to some useful coordinate system, and quaternions indicating the orientations of the cameras' optical axes and reference vectors associated with the cameras. Some embodiments train the multi-layer network using a set of inputs generated with random misalignments incorporated into the training set.

Abstract: Some embodiments of the invention provide a novel multi-layer node network to reliably determine depth based on a plurality of input sources (e.g., cameras, microphones, etc.) that may be arranged with deviations from an ideal alignment or placement. Determined depths are used, in some embodiments, to process data captured by the plurality of input sources. Other embodiments use the calculated depth to determine whether warnings must be provided or other actions taken. Some embodiments train the multi-layer network using a set of inputs generated with random misalignments incorporated into the training set.

Abstract: Some embodiments of the invention provide a novel method for training a multi-layer node network to reliably determine depth based on a plurality of input sources (e.g., cameras, microphones, etc.) that may be arranged with deviations from an ideal alignment or placement. Some embodiments train the multi-layer network using a set of inputs generated with random misalignments incorporated into the training set. In some embodiments, the training set includes (i) a synthetically generated training set based on a three-dimensional ground truth model as it would be sensed by a sensor array from different positions and with different deviations from ideal alignment and placement, and/or (ii) a training set generated by a set of actual sensor arrays augmented with an additional sensor (e.g., additional camera or time of flight measurement device such as lidar) to collect ground truth data.

Abstract: A computer-implemented system and method for generating a hardware implementation of graphical code. The method may operate to configure an instrument to perform measurement functions, wherein the instrument includes a programmable hardware element. The method comprises first creating a graphical program, wherein the graphical program may implement a measurement function. A portion of the graphical program may optionally be compiled into machine code for execution by a CPU, and another portion of the graphical program may be converted into a hardware implementation on a programmable hardware element. The programmable hardware element is configured utilizing a hardware description to produce a configured hardware element. The configured hardware element thus implements a hardware implementation of the second portion of the graphical program.

Abstract: A computer-implemented system and method for generating a hardware implementation of graphical code. The method may operate to configure an instrument to perform measurement functions, wherein the instrument includes a programmable hardware element. The method comprises first creating a graphical program, wherein the graphical program may implement a measurement function. A portion of the graphical program may optionally be compiled into machine code for execution by a CPU, and another portion of the graphical program may be converted into a hardware implementation on a programmable hardware element. The programmable hardware element is configured utilizing a hardware description to produce a configured hardware element. The configured hardware element thus implements a hardware implementation of the second portion of the graphical program.

Abstract: A computer-implemented system and method for generating a hardware implementation of graphical code. The method may operate to configure an instrument to perform measurement functions, wherein the instrument includes a programmable hardware element. The method comprises first creating a graphical program, wherein the graphical program may implement a measurement function. A portion of the graphical program may optionally be compiled into machine code for execution by a CPU, and another portion of the graphical program may be converted into a hardware implementation on a programmable hardware element. The programmable hardware element is configured utilizing a hardware description to produce a configured hardware element. The configured hardware element thus implements a hardware implementation of the second portion of the graphical program.