Abstract: A method includes defining a two-dimensional geographic region by two-dimensional geographic coordinates to define the bounds of the region, converting each of the two-dimensional coordinates to three dimensional coordinates by way of a lookup stored in a computer readable medium, generating a three-dimensional grid of points, each spaced in an arrangement to encompass coverage of a predetermined ground area, and applying heuristics for a shortest path planning, relative to the three-dimensional grid of points.

Abstract: A system, method and computer program product for composing persistent object instances that link resources across multiple, disparate systems. An example method includes associating resources with namespace-URLs and object instances. One of the namespace-URLs is designated as primary namespace-URL. A web-based object API is provided through which the object instance can be accessed. The web-based object API uses the primary namespace-URL as an identifier of the object instance.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, storing heterogeneous data captured by the one or more drones and creating spatio-temporal indices for identifying spatial or temporal coverage gaps in the data necessary to answer the request, controlling the one or more drones to fly over the spatial location to obtain a plurality of data types from the identified spatial or temporal coverage gaps and extracting and analyzing data to answer the request.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, storing heterogeneous data captured by the one or more drones and creating spatio-temporal indices for identifying spatial or temporal coverage gaps in the data necessary to answer the request, controlling the one or more drones to fly over the spatial location to obtain a plurality of data types from the identified spatial or temporal coverage gaps and extracting and analyzing data to answer the request.

Abstract: A method of disseminating a message is disclosed. The message is encoding in a barcode. A selected region of the barcode is damaged so as to hinder decoding of the message at a scanning device. The damaged region of the barcode may be occluded from the scanning device in order to enable the scanning device to read the barcode and the message therein.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A method for accommodating object occlusion in a point-of-view (POV) display device includes projecting, by a point-of-view (POV) display device, an image viewable by a user of the POV display device, receiving an image stream from at least one image acquisition device, recognizing, by a computing device associated with the POV display device and the image acquisition device, one or more objects in the image stream that are visible or predicted to be visible within a field of view of the user that are occluded by an occluding portion of the projected image, determining a significance of the one or more objects relative to the occluding portion of the projected image, and rendering transparent the occluding portion of the projected image based on the significance of the one or more objects wherein the one or more objects are revealed to the user.

Abstract: Systems for proximity-based access control include a proximity module configured to determine whether a distance from a first mobile device to each of one or more safe mobile devices falls below a threshold distance; a policy engine comprising a processor configured to determine whether a number of safe mobile devices within the threshold distance exceeds a safe gathering threshold; and a security module configured to activate a safe gathering policy in accordance with the safe gathering threshold that decreases a security level in the first mobile device.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: Methods and systems for proximity-based access control include determining whether a distance from a first mobile device to each of one or more safe mobile devices falls below a threshold distance; determining whether a number of safe mobile devices within the threshold distance exceeds a safe gathering threshold with a processor; and activating a safe gathering policy in accordance with the safe gathering threshold that decreases a security level in the first mobile device.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A method for controlling a drone includes receiving a natural language request for information about a spatial location, parsing the natural language request into data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: Aspects of the invention relate to a UAV (drone) data marketplace where requests for UAV services are matched with registered UAVs. Drone operators register in the marketplace drones in their fleet with their capabilities and requesters of drone services make their request in the marketplace. The requests can be a set of data to be collected (e.g., optical images, NIR data, temperatures, etc.) and/or actions to be performed (e.g., deploying spare machinery parts to a tractor in the field), a location from which that data is collected and/or location to which a delivery is to be made, a pipeline of analytics to be performed on the data (e.g., optical recognition, NDVI computation, fertilizer application recommendation), a timeframe in which to collect the data (e.g., “by next week”, “by the end of today”), and a market value for how much the Requester is willing to pay for that data or operation.

Abstract: Verifying a set of safety equipment criteria may include receiving a first unit of data at a first computing device. The first unit of data may be received via a wireless signal between the first computing device and a set of sensing devices. A set of safety equipment that includes the set of sensing devices may be identified as the first unit of data. The set of sensing devices may be configured to transmit the first unit of data. The wireless signal between the first computing device and the set of sensing devices may be monitored and the signal strength for the wireless signal may be determined to be below a threshold value. A second computing device may be notified in response to the determining that the signal strength for the wireless signal is below the threshold value.

Abstract: Verifying a location of a tag is provided. A request for content associated with the tag is received from a client device that scanned the tag. It is determined whether location data was received from the client device. In response to determining that the location data was received from the client device, it is determined whether a current location of the tag is a predefined location for the tag based on the location data received from the client device. In responsive to determining that the current location of the tag is the predefined location for the tag based on the location data received from the client device, the content associated with the tag is sent to the client device.

Abstract: Verifying a location of a tag is provided. A request for content associated with the tag is received from a client device that scanned the tag. It is determined whether location data was received from the client device. In response to determining that the location data was received from the client device, it is determined whether a current location of the tag is a predefined location for the tag based on the location data received from the client device. In responsive to determining that the current location of the tag is the predefined location for the tag based on the location data received from the client device, the content associated with the tag is sent to the client device.

Abstract: A method of disseminating a message is disclosed. The message is encoding in a barcode. A selected region of the barcode is damaged so as to hinder decoding of the message at a scanning device. The damaged region of the barcode may be occluded from the scanning device in order to enable the scanning device to read the barcode and the message therein.

Abstract: A method for controlling a drone includes receiving a natural language request for information about a spatial location, parsing the natural language request into data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A method includes defining a two-dimensional geographic region by two-dimensional geographic coordinates to define the bounds of the region, converting each of the two-dimensional coordinates to three dimensional coordinates by way of a lookup stored in a computer readable medium, generating a three-dimensional grid of points, each spaced in an arrangement to encompass coverage of a predetermined ground area, and applying heuristics for a shortest path planning, relative to the three-dimensional grid of points.