Abstract: An unmanned vehicle (UV) system provides network data connectivity for terminals. UV nodes in the system may transmit and receive terminal data in a network of UV nodes and other nodes according to a routing protocol. The nodes may include fixed nodes or UV nodes with a wired or wireless network connection to a destination terminal. A UV node may receive data from a source terminal for transmission to the destination terminal, and when the UV node is within range of a node in the network connected the destination terminal, the UV node transmits the received data to the node within range for forwarding to the destination terminal via the network.

Abstract: According to examples, inventory, growth, and risk prediction using image processing may include receiving a plurality of images captured by a vehicle during movement of the vehicle along a vehicle path. The images may include a plurality of objects. The images may be pre-processed for feature extraction. A plurality of features of the objects may be extracted from the pre-processed images by using a combination of computer vision techniques. A parameter related to the objects may be determined from the extracted features. A spatial density model may be generated, based on the determined parameter and the extracted features, to provide a visual indication of density of distribution of the objects related to a portion of the images, and/or to provide an alert corresponding to the objects related to the portion of the images.

Abstract: An unmanned vehicle (UV) system provides network data connectivity for terminals. UV nodes in the system may transmit and receive terminal data in a network of UV nodes and other nodes according to a routing protocol. The nodes may include fixed nodes or UV nodes with a wired or wireless network connection to a destination terminal. A UV node may receive data from a source terminal for transmission to the destination terminal, and when the UV node is within range of a node in the network connected the destination terminal, the UV node transmits the received data to the node within range for forwarding to the destination terminal via the network.

Abstract: According to examples, inventory, growth, and risk prediction using image processing may include receiving a plurality of images captured by a vehicle during movement of the vehicle along a vehicle path. The images may include a plurality of objects. The images may be pre-processed for feature extraction. A plurality of features of the objects may be extracted from the pre-processed images by using a combination of computer vision techniques. A parameter related to the objects may be determined from the extracted features. A spatial density model may be generated, based on the determined parameter and the extracted features, to provide a visual indication of density of distribution of the objects related to a portion of the images, and/or to provide an alert corresponding to the objects related to the portion of the images.

Abstract: An unmanned vehicle (UV) system provides network data connectivity for terminals. UV nodes in the system may transmit and receive terminal data in a network of UV nodes and other nodes according to a routing protocol. The nodes may include fixed nodes or UV nodes with a wired or wireless network connection to a destination terminal. A UV node may receive data from a source terminal for transmission to the destination terminal, and when the UV node is within range of a node in the network connected the destination terminal, the UV node transmits the received data to the node within range for forwarding to the destination terminal via the network.

Abstract: According to examples, inventory, growth, and risk prediction using image processing may include receiving a plurality of images captured by a vehicle during movement of the vehicle along a vehicle path. The images may include a plurality of objects. The images may be pre-processed for feature extraction. A plurality of features of the objects may be extracted from the pre-processed images by using a combination of computer vision techniques. A parameter related to the objects may be determined from the extracted features. A spatial density model may be generated, based on the determined parameter and the extracted features, to provide a visual indication of density of distribution of the objects related to a portion of the images, and/or to provide an alert corresponding to the objects related to the portion of the images.

Abstract: According to examples, inventory, growth, and risk prediction using image processing may include receiving a plurality of images captured by a vehicle during movement of the vehicle along a vehicle path. The images may include a plurality of objects. The images may be pre-processed for feature extraction. A plurality of features of the objects may be extracted from the pre-processed images by using a combination of computer vision techniques. A parameter related to the objects may be determined from the extracted features. A spatial density model may be generated, based on the determined parameter and the extracted features, to provide a visual indication of density of distribution of the objects related to a portion of the images, and/or to provide an alert corresponding to the objects related to the portion of the images.

Abstract: An unmanned vehicle (UV) system provides network data connectivity for terminals. UV nodes in the system may transmit and receive terminal data in a network of UV nodes and other nodes according to a routing protocol. The nodes may include fixed nodes or UV nodes with a wired or wireless network connection to a destination terminal. A UV node may receive data from a source terminal for transmission to the destination terminal, and when the UV node is within range of a node in the network connected the destination terminal, the UV node transmits the received data to the node within range for forwarding to the destination terminal via the network.

Abstract: A computer-implemented method identifies textual statement relationships. Textual statement pairs including a first and second textual statement are identified, and parsed word group pairs are extracted from first and second textual statements. The parsed word groups are compared, and a parsed word score for each statement pair is calculated. Word vectors for the first and second textual statements are created and compared. A word vector score is calculated based on the comparison of the word vectors for the first and second textual statements. A match score is determined for the textual statement pair, with the match score being representative of at least one of the parsed word score and the word vector score.

Abstract: Systems and methods for linking process architectures to technology architectures greatly reduces the time and cost associated with common changing business requirements. The design's robust features allows for simplified integration and management to changing business processes and needs. The system incorporates a method of integrating changing business processes with current and up to date technological systems without having to start over every time a new business process is introduced or a new technology or technology upgrade takes place.

Abstract: A high performance capability assessment model helps a pulp and paper business meet the challenges of the global marketplace. As a result, the pulp and paper business can achieve the clarity, consistency, and well-defined execution of core processes that reduce inefficiencies and waste that result from unnecessary process complexity and exceptions. In addition, the high performance capability assessment model helps the pulp and paper industry business to identify specific areas in which improvements may be made and understand how to make the improvements, and establishes levels of capability along the way to reaching an ultimate capability goal.

Abstract: A high performance capability assessment model helps a chemical industry business meet the challenges of the global marketplace. As a result, the chemical industry business can achieve the clarity, consistency, and well-defined execution of core processes that reduce inefficiencies and waste that result from unnecessary process complexity and exceptions. In addition, the high performance capability assessment model helps the chemical industry business to identify specific areas in which improvements may be made and understand how to make the improvements, and establishes levels of capability along the way to reaching an ultimate capability goal.

Abstract: A computer-implemented method identifies textual statement relationships. Textual statement pairs including a first and second textual statement are identified, and parsed word group pairs are extracted from first and second textual statements. The parsed word groups are compared, and a parsed word score for each statement pair is calculated. Word vectors for the first and second textual statements are created and compared. A word vector score is calculated based on the comparison of the word vectors for the first and second textual statements. A match score is determined for the textual statement pair, with the match score being representative of at least one of the parsed word score and the word vector score.