Abstract: A transformable Unmanned Aerial Vehicle (UAV), can operate as a coplanar hexacopter or as an omnidirectional multirotor based on different operation modes. The UAV has 100% force efficiency for launching or landing tasks in the coplanar mode. In the omnidirectional mode, the UAV is fully actuated in the air for agile mobility in six degrees of freedom (DOFs). Models and control design are developed to characterize the motion of the transformable UAV. Simulation results are presented to validate the transformable UAV design and the enhanced UAV performance, compared with a fixed structure.

Abstract: A transformable Unmanned Aerial Vehicle (UAV), can operate as a coplanar hexacopter or as an omnidirectional multirotor based on different operation modes. The UAV has 100% force efficiency for launching or landing tasks in the coplanar mode. In the omnidirectional mode, the UAV is fully actuated in the air for agile mobility in six degrees of freedom (DOFs). Models and control design are developed to characterize the motion of the transformable UAV. Simulation results are presented to validate the transformable UAV design and the enhanced UAV performance, compared with a fixed structure.

Abstract: A transformable Unmanned Aerial Vehicle (UAV), can operate as a coplanar hexacopter or as an omnidirectional multirotor based on different operation modes. The UAV has 100% force efficiency for launching or landing tasks in the coplanar mode. In the omnidirectional mode, the UAV is fully actuated in the air for agile mobility in six degrees of freedom (DOFs). Models and control design are developed to characterize the motion of the transformable UAV. Simulation results are presented to validate the transformable UAV design and the enhanced UAV performance, compared with a fixed structure.

Abstract: Methods, apparatus, systems and articles of manufacture for producing generic Internet Protocol (IP) reputation through cross-protocol analysis are disclosed. An example apparatus includes a data collector to gather a first data set representing IP telemetry data for a first protocol, the data collector to gather a second data set representing IP telemetry data for a second protocol different from the first protocol. A label generator is to generate a training data set based on records in the first data set and the second data set having matching IP addresses, the training data set to include combined label indicating whether each of the respective matching IP addresses is malicious. A model trainer is to train a machine learning model using the training data set. A model executor is to, responsive to a request from a client device, execute the machine learning model to determine whether a requested IP address is malicious.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to analyze network traffic for malicious activity. An example apparatus includes a graph generator to, in response to obtaining one or more internet protocol addresses included within input data, generate a graph data structure based on one or more features of the one or more internet protocol addresses in the input data, a file generator to generate a first matrix using the graph data structure, the first matrix to represent nodes in the graph data structure and generate a second matrix using the graph data structure, the second matrix to represent edges in the graph data structure, and a classifier to, using the first matrix and the second matrix, classify at least one of the one or more internet protocol addresses to identify a reputation of the at least one of the one or more internet protocol addresses.

Abstract: Methods, apparatus, systems and articles of manufacture for producing generic Internet Protocol (IP) reputation through cross-protocol analysis are disclosed. An example apparatus includes a data collector to gather a first data set representing IP telemetry data for a first protocol, the data collector to gather a second data set representing IP telemetry data for a second protocol different from the first protocol. A label generator is to generate a training data set based on records in the first data set and the second data set having matching IP addresses, the training data set to include combined label indicating whether each of the respective matching IP addresses is malicious. A model trainer is to train a machine learning model using the training data set. A model executor is to, responsive to a request from a client device, execute the machine learning model to determine whether a requested IP address is malicious.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to analyze network traffic for malicious activity. An example apparatus includes a graph generator to, in response to obtaining one or more internet protocol addresses included within input data, generate a graph data structure based on one or more features of the one or more internet protocol addresses in the input data, a file generator to generate a first matrix using the graph data structure, the first matrix to represent nodes in the graph data structure and generate a second matrix using the graph data structure, the second matrix to represent edges in the graph data structure, and a classifier to, using the first matrix and the second matrix, classify at least one of the one or more internet protocol addresses to identify a reputation of the at least one of the one or more internet protocol addresses.

Abstract: Methods, apparatus, systems and articles of manufacture for producing generic Internet Protocol (IP) reputation through cross-protocol analysis are disclosed. An example apparatus includes a data collector to gather a first data set representing IP telemetry data for a first protocol, the data collector to gather a second data set representing IP telemetry data for a second protocol different from the first protocol. A label generator is to generate a training data set based on records in the first data set and the second data set having matching IP addresses, the training data set to include combined label indicating whether each of the respective matching IP addresses is malicious. A model trainer is to train a machine learning model using the training data set. A model executor is to, responsive to a request from a client device, execute the machine learning model to determine whether a requested IP address is malicious.

Abstract: Methods, apparatus, systems and articles of manufacture for producing generic Internet Protocol (IP) reputation through cross-protocol analysis are disclosed. An example apparatus includes a data collector to gather a first data set representing IP telemetry data for a first protocol, the data collector to gather a second data set representing IP telemetry data for a second protocol different from the first protocol. A label generator is to generate a training data set based on records in the first data set and the second data set having matching IP addresses, the training data set to include combined label indicating whether each of the respective matching IP addresses is malicious. A model trainer is to train a machine learning model using the training data set. A model executor is to, responsive to a request from a client device, execute the machine learning model to determine whether a requested IP address is malicious.

Abstract: A device for holding and organizing items includes an anchor for fastening the device to an object; a strap adapted to secure the items to the device; and a connector, having an upper piece and a lower piece that pivot relative to one another, the upper piece being connected to the anchor and the lower piece being connected to the strap.

Abstract: The apparatus includes a first end bar having first and second ends, the first bar having first and second spacers mounted on the first and second ends. A second end bar is included having first and second ends. At least one intermediate bar between is positioned between the first and second end bars, the at least one intermediate bar having first and second ends, with first and second spacers mounted on the first and second ends thereof. The first and second ends of the first and second end bars and the at least one intermediate bar have first and second apertures extending through. First and second stretchable cords have first and second ends attached to said first and second end bars and extending through the fist and second apertures in the first and second end bars and the at least one intermediate bar.

Abstract: An electrical system according to various aspects of the present invention includes a supply configured to provide a signal at a desired level. The supply monitors the output signal and compares the output signal to multiple thresholds. If the signal crosses a coarse-adjustment threshold, the supply coarsely adjusts the output to the load to quickly drive the signal toward the target level. If the signal crosses a fine adjustment threshold, the supply finely adjusts the output.