Abstract: An unmanned aerial vehicle comprises a central body; at least one rotor motor configured to drive at least one propeller to rotate, rotation of the at least one propeller generating thrust and causing the unmanned aerial vehicle to fly; and an integrated micro hybrid generator system configured to provide power to the at least one rotor motor. The integrated micro hybrid generator system includes an engine configured to generate mechanical energy, and a generator motor directly coupled to the engine and configured to generate AC power using the mechanical energy generated by the engine.

Abstract: Systems and methods for registering arbitrary visual features for use as fiducial elements are disclosed. An example method includes aligning a geometric reference object and a visual feature and capturing an image of the reference object and feature. The method also includes identifying, in the image of the object and the visual feature, a set of at least four non-colinear feature points in the visual feature. The method also includes deriving, from the image, a coordinate system using the geometric object. The method also comprises providing a set of measures to each of the points in the set of at least four non-colinear feature points using the coordinate system. The measures can then be saved in a memory to represent the registered visual feature and serve as the basis for using the registered visual feature as a fiducial element.

Abstract: Example implementations described herein are directed to systems and methods for managing internet protocol (IP) address assignment to servers on rack(s) based on their physical locations within the rack(s). Through the example implementations, the physical location of a server within a data center rack can be determined based on the IP address. Example implementations can involve issuing a ping local to determine a plurality of servers; retrieving power on time, current system time, and operating system (OS) uptime for each of the plurality of servers; determining a power on order for the plurality of servers; determining physical locations of the plurality of servers within one or more racks based on installation instructions and power on order; and assigning IP addresses to the plurality of servers based on the physical locations.

Abstract: Example implementations described herein are directed to systems and methods for managing internet protocol (IP) address assignment to servers on rack(s) based on their physical locations within the rack(s). Through the example implementations, the physical location of a server within a data center rack can be determined based on the IP address. Example implementations can involve issuing a ping local to determine a plurality of servers; retrieving power on time, current system time, and operating system (OS) uptime for each of the plurality of servers; determining a power on order for the plurality of servers; determining physical locations of the plurality of servers within one or more racks based on installation instructions and power on order; and assigning IP addresses to the plurality of servers based on the physical locations.

Abstract: Example implementations described herein are directed to systems and methods for managing internet protocol (IP) address assignment to servers on rack(s) based on their physical locations within the rack(s). Through the example implementations, the physical location of a server within a data center rack can be determined based on the IP address. Example implementations can involve issuing a ping local to determine a plurality of servers; retrieving power on time, current system time, and operating system (OS) uptime for each of the plurality of servers; determining a power on order for the plurality of servers; determining physical locations of the plurality of servers within one or more racks based on installation instructions and power on order; and assigning IP addresses to the plurality of servers based on the physical locations.

Abstract: Systems and methods for registering arbitrary visual features for use as fiducial elements are disclosed. An example method includes aligning a geometric reference object and a visual feature and capturing an image of the reference object and feature. The method also includes identifying, in the image of the object and the visual feature, a set of at least four non-colinear feature points in the visual feature. The method also includes deriving, from the image, a coordinate system using the geometric object. The method also comprises providing a set of measures to each of the points in the set of at least four non-colinear feature points using the coordinate system. The measures can then be saved in a memory to represent the registered visual feature and serve as the basis for using the registered visual feature as a fiducial element.

Abstract: Methods and systems regarding importance sampling for the modification of a training procedure used to train a segmentation network are disclosed herein. A disclosed method includes segmenting an image using a trainable directed graph to generate a segmentation, displaying the segmentation, receiving a first selection directed to the segmentation, and modifying a training procedure for the trainable directed graph using the first selection. In a more specific method, the training procedure alters a set of trainable values associated with the trainable directed graph based on a delta between the segmentation and a ground truth segmentation, the first selection is spatially indicative with respect to the segmentation, and the delta is calculated based on the first selection.

Abstract: A trained network for point tracking includes an input layer configured to receive an encoding of an image. The image is of a locale or object on which the network has been trained. The network also includes a set of internal weights which encode information associated with the locale or object, and a tracked point therein or thereon. The network also includes an output layer configured to provide an output based on the image as received at the input layer and the set of internal weights. The output layer includes a point tracking node that tracks the tracked point in the image. The point tracking node can track the point by generating coordinates for the tracked point in an input image of the locale or object. Methods of specifying and training the network using a three-dimensional model of the locale or object are also disclosed.

Abstract: Systems and methods for registering arbitrary visual features for use as fiducial elements are disclosed. An example method includes aligning a geometric reference object and a visual feature and capturing an image of the reference object and feature. The method also includes identifying, in the image of the object and the visual feature, a set of at least four non-colinear feature points in the visual feature. The method also includes deriving, from the image, a coordinate system using the geometric object. The method also comprises providing a set of measures to each of the points in the set of at least four non-colinear feature points using the coordinate system. The measures can then be saved in a memory to represent the registered visual feature and serve as the basis for using the registered visual feature as a fiducial element.

Abstract: A method for real-time volumetric 3-D reconstruction of an object uses at least one depth sensor camera. In a preparation step, voxels are collected in a reconstructed scene depending on a new depth map frame, and the collected voxels are cached in order to perform an update of the reconstructed scene. In an integration step, the collected and cached voxels of the preparation step are updated with a newly captured depth map frame. However, the preparation step, and the integration step are separated from each other so that both steps can be carried out in parallel, and at the same time.

Abstract: A trained network for point tracking includes an input layer configured to receive an encoding of an image. The image is of a locale or object on which the network has been trained. The network also includes a set of internal weights which encode information associated with the locale or object, and a tracked point therein or thereon. The network also includes an output layer configured to provide an output based on the image as received at the input layer and the set of internal weights. The output layer includes a point tracking node that tracks the tracked point in the image. The point tracking node can track the point by generating coordinates for the tracked point in an input image of the locale or object. Methods of specifying and training the network using a three-dimensional model of the locale or object are also disclosed.

Abstract: Systems and methods for point marking using virtual fiducial elements are disclosed. An example method includes placing a set of fiducial elements in a locale or on an object and capturing a set of calibration images using an imager. The set of fiducial elements is fully represented in the set of calibration images. The method also includes generating a three-dimensional geometric model of the set of fiducial elements using the set of calibration images. The method also includes capturing a run time image of the locale or object. The run time image does not include a selected fiducial element, from the set of fiducial elements, which was removed from a location in the locale or on the object prior to capturing the run time image. The method concludes with identifying the location relative to the run time image using the run time image and the three-dimensional geometric model.

Abstract: Trained networks configured to detect fiducial elements in encodings of images and associated methods are disclosed. One method includes instantiating a trained network with a set of internal weights which encode information regarding a class of fiducial elements, applying an encoding of an image to the trained network where the image includes a fiducial element from the class of fiducial elements, generating an output of the trained network based on the set of internal weights of the network and the encoding of the image, and providing a position for at least one fiducial element in the image based on the output. Methods of training such networks are also disclosed.

Abstract: Systems and methods for registering arbitrary visual features for use as fiducial elements are disclosed. An example method includes aligning a geometric reference object and a visual feature and capturing an image of the reference object and feature. The method also includes identifying, in the image of the object and the visual feature, a set of at least four non-colinear feature points in the visual feature. The method also includes deriving, from the image, a coordinate system using the geometric object. The method also comprises providing a set of measures to each of the points in the set of at least four non-colinear feature points using the coordinate system. The measures can then be saved in a memory to represent the registered visual feature and serve as the basis for using the registered visual feature as a fiducial element.

Abstract: Methods and systems regarding importance sampling for the modification of a training procedure used to train a segmentation network are disclosed herein. A disclosed method includes segmenting an image using a trainable directed graph to generate a segmentation, displaying the segmentation, receiving a first selection directed to the segmentation, and modifying a training procedure for the trainable directed graph using the first selection. In a more specific method, the training procedure alters a set of trainable values associated with the trainable directed graph based on a delta between the segmentation and a ground truth segmentation, the first selection is spatially indicative with respect to the segmentation, and the delta is calculated based on the first selection.

Abstract: A method for extracting information about a body's skeleton from a 3D body mesh includes measuring the closest distance from every vertex of the 3-D body mesh to the centroid of the 3-D body mesh and using these measurements to compute a geodesic map of these distances. Key anchors are then extracted from the geodesic map. The geodesic map and the key anchors are used to segment the 3-D body mesh into body parts. A circumference of each body part is computed, and a centroid of the circumference is determined. For each body part, a body mean curve along the centroid is determined. Skeleton joints between the body parts and connections between these joints are determined. A 3-D body scanner employing this skeleton estimation method is also disclosed.

Abstract: The present invention relates to novel compounds which active HIV expression in latently infected cells. More particularly, the invention relates to pharmaceutical compositions comprising the novel compounds and their use in activating HIV expression in latently infected cells. Further still, the invention relates to pharmaceutical compositions comprising the novel compounds in combination with anti-HIV therapy compounds and their use in treating HIV infection in both animals and humans. The invention further provides means for preparing the compounds.

Abstract: A method for extracting information about a body's skeleton from a 3D body mesh includes measuring the closest distance from every vertex of the 3-D body mesh to the centroid of the 3-D body mesh and using these measurements to compute a geodesic map of these distances. Key anchors are then extracted from the geodesic map. The geodesic map and the key anchors are used to segment the 3-D body mesh into body parts. A circumference of each body part is computed, and a centroid of the circumference is determined. For each body part, a body mean curve along the centroid is determined. Skeleton joints between the body parts and connections between these joints are determined. A 3-D body scanner employing this skeleton estimation method is also disclosed.

Abstract: A method for creating a 3D-Model of an object exposed in front of a number of stationary sensors mounted on a mast or carrier. The sensors include depth sensors, and the method includes turning the object around while scanning the object with the sensors. The method includes segmenting of the body and fitting a virtual skeleton in the segmented body with anatomically correct degrees of freedom. The method includes following the required motions of the body as it is turning around and performing non-rigid modelling of the shape of the body for one or more defined or normalized poses around the virtual skeleton. Furthermore, the invention relates to a 3D-Body-Scanner.