A method is provided that includes generating a visual environment for interactive development of a machine learning (ML) model. The method includes accessing observations of data each of which includes values of independent variables and a dependent variable, and performing an interactive exploratory data analysis (EDA) of the values of a set of the independent variables. The method includes performing an interactive feature construction and selection based on the interactive EDA, and in which select independent variables are selected as or transformed into a set of features for use in building a ML model to predict the dependent variable. The method includes building the ML model using a ML algorithm, the set of features, and a training set produced from the set of features and observations of the data. And the method includes outputting the ML model for deployment to predict the dependent variable for additional observations of the data.

A system and a method of determining an absolute position of a first vehicle can be used in restricted areas. The system performs operations of or the method includes receiving image data from a vision system mounted on a second vehicle, determining a first location of the second vehicle using at least positions of stars in the image data, providing the first location to the first vehicle, determining a first relative position between the first vehicle and the second vehicle using at least one signal communicated between the first vehicle and the second vehicle, and determining the absolute position using at least the relative location data and the first location.

A system and a method of determining an absolute position of a first vehicle can be used in restricted areas. The system performs operations of or the method includes receiving image data from a vision system mounted on a second vehicle, determining a first location of the second vehicle using at least positions of stars in the image data, providing the first location to the first vehicle, determining a first relative position between the first vehicle and the second vehicle using at least one signal communicated between the first vehicle and the second vehicle, and determining the absolute position using at least the relative location data and the first location.

A method of displaying, on a screen of a mobile device, time on target information for an aircraft in flight. The method includes displaying a current location of the aircraft on a map, a waypoint between a departure point and a destination point, a symbol indicating the waypoint in a first area of the screen overlapping the map, and a first pop-up window in a second area of the screen overlapping the first area. The first pop-up window displays a current arrival time at the waypoint and an input area to receive an adjustment to the current arrival time.

A system may include a display installed in an aircraft, a hardware button installed in the aircraft, and a processor installed in the aircraft, wherein the processor may be communicatively coupled to the display and to the hardware button. The processor may be configured to: output at least one view to the display; receive a user input from the hardware button; and based at least on the user input, cause a cursor to move to a default position on one of the at least one view.

An aviation advisory module may include processing circuitry configured to receive data indicative of internal factors and external factors related to route optimization of an aircraft. At least some of the external factors may include dynamic parameters that are changeable while the aircraft is in-flight. The processing circuitry may also be configured to generate a guidance output associated with a route of the aircraft based on integration of the internal factors and the external factors to optimize the route for a user-selected cost parameter, and provide a graphical representation of the guidance output along with comparative data or context information associated with the user-selected cost parameter.

An aircraft enhanced vision system includes an electromagnetic sensor comprising at least one group of transmitters and at least one group of receivers. The electromagnetic sensor includes a waveform generation assembly powering each transmitter in order to generate the transmitted signal and a signal capture assembly to capture the signal received by each receiver after reflection off of the ground. The transmitters are distinct and spaced apart from the receivers, being arranged so as to form at least one virtual transmitter/receiver network extending in an elongation direction perpendicular to the observation direction from each transmitter/receiver combination between the group of transmitters and the group of receivers.

Methods and systems are described for new paradigms for user interaction with an unmanned aerial vehicle (referred to as a flying digital assistant or FDA) using a portable multifunction device (PMD) such as smart phone. In some embodiments, a magic wand user interaction paradigm is described for intuitive control of an FDA using a PMD. In other embodiments, methods for scripting a shot are described.

Methods and systems are described for new paradigms for user interaction with an unmanned aerial vehicle (referred to as a flying digital assistant or FDA) using a portable multifunction device (PMD) such as smart phone. In some embodiments, a magic wand user interaction paradigm is described for intuitive control of an FDA using a PMD. In other embodiments, methods for scripting a shot are described.

Systems and methods for coordinating and controlling vehicles, for example heavy trucks, to follow closely behind each other, or linking to form a platoon. In one aspect, on-board controllers in each vehicle interact with vehicular sensors to monitor and control, for example, gear ratios on vehicles. A front vehicle can shift a gear which, via a vehicle-to-vehicle communication link, can cause a rear vehicle to shift gears. To maintain a gap, vehicles may shift gears at various relative positions based on a grade of a road.