A method is provided for calculating the estimated navigation performance prediction for a trajectory associated with a list of segments of a flight plan. A method for displaying the navigation performance in a corridor trajectory so as to guarantee compliance with the navigation performance requirements while offering immediate viewing of the navigation latitude in a corridor is also provided.

Embodiments include devices and methods for navigating an unmanned autonomous vehicle (UAV) based on a measured magnetic field vector and strength of a magnetic field emanated from a charging station. A processor of the UAV may navigate to the charging station using the magnetic field vector and strength. The processor may determine whether the UAV is substantially aligned with the charging station, and the processor may maneuver the UAV to approach the charging station using the magnetic field vector and strength in response to determining that the UAV is substantially aligned with the charging station. Maneuvering the UAV to approach the charging station using the magnetic field vector and strength may involve descending to a center of the charging station. The UAV may follow a specified route to and/or away from the charging station using the magnetic field vector and strength.

Various embodiments of the present technology generally relate to unmanned aerial vehicle (UAV) scale rendered analysis, orthomosaic, and 3D mapping and landing platform systems. More specifically, some embodiments relate to systems, methods, and means for the collection and processing of images captured during a UAV flight sequence. In some embodiments, the UAV landing platform retrieves flight information and initial map information over a unidirectional virtual private network from a multitenant cloud-based scheduling application. The UAV landing platform sends the initial map information to a UAV over a WiFi, Bluetooth, or radio frequency network and initiates a drone flight sequence once the drone flight sequence has been approved by a local user. The UAV landing platform receives property image data from a UAV after a UAV flight sequence has ended and transmits the received property image data back to the cloud application.

Using power line rights of way for UAV routing provides a direct, uninterrupted, aerially clear path to the vast majority of lots and buildings from nearby substations and generating stations. Segmenting or separating the UAV traffic by airframe glide ratio improves safety for people on the ground and utilization of the limited airspace. Further segmenting UAV traffic by airframe speed and size allows greater traffic throughput.

A method of optimizing the operation of a fleet of gas turbine engines is provided. The method comprises the steps of: (a) measuring respective values for plural control actuator settings within each of the gas turbine engines; (b) deriving, based on data external to the operation of the gas turbine engines, a desired performance modification of the gas turbine engines; (c) determining, based on the measured control actuator settings, one or more respective trim signals for varying selected of the control actuator settings to achieve the desired performance modification; and (d) transmitting the trim signals to respective electronic controllers of the engines to vary the selected control actuator settings accordingly.

The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.

An aircraft control system (100) including an input interface (102), an output interface (114) and a processing engine (108) having a classifier (110) that applies input data (104) generated by the input interface (102) to generate output control data (112). The classifier (110) has a plurality of parameters which represent a control policy for operating the aircraft (800). The output interface (114) generates control outputs to control the aircraft (800) based on the output control data (112). A machine learning system (900) for training the classifier (110) including an environment (902), a pathway evaluation engine (904), storage (906), and a training engine (908). The machine learning system (900) generates training data (912) by selecting a pathway representing an operating procedure using the pathway evaluation engine (904). The training engine (908) trains the classifier (110) using the training data (912).

A method for validating a terrain database includes a step of simulating flights based on trajectory data for an aircraft, the flight simulations being speeded up, a step of determining terrain collision risks by means of a system for signalling terrain collision risks based on the speeded-up flight simulations, a step of determining the origins of terrain collision risks with a view to validating or not validating the terrain database.

Systems and methods for correlating a Notice to Airman (NOTAM) with a chart displayed on an avionic display in a cockpit of an aircraft. The method includes receiving a flight plan (FP); receiving a position and location of the aircraft; and, rendering an avionic display comprising a chart page or a NOTAM page. Responsive to receiving a NOTAM, the method determines whether the NOTAM is relevant. The NOTAM is relevant when the NOTAM is concurrently related to the FP and references a chart of a plurality of pre-loaded charts onboard the aircraft. Responsive thereto, a selectable visual indicator of the chart is rendered on the NOTAM page of the avionic display. The NOTAM is also relevant when it is related to a chart page displayed on the avionic display; responsive thereto, a selectable visual indicator of the NOTAM is rendered on the chart page displayed on the avionic display.

Disclosed are methods, materials and devices for approximation of blood volume in a fluid, such as in a biological fluid collected during a surgical procedure. The method and devices may include a RBC flocculant, such as polyDADMAC, and provide for an approximate blood hematocrit for a selected type of animal, as well as provide for a calculated RBC packing ratio corresponding to the collection device receiving the fluid. Also provided is a Blood Indicator Panel (BIP), comprising a series of markings calculated from an observed red blood cell sedimentation volume, the average animal type hematocrit, and a calculated RBC packing ratio “η” value for the collection device. Pediatric (about 200 ml or 250 ml size container), adult human (about 1,000 ml-1,500 ml) and veterinary (about 500 ml-2,500 ml) collection containers are also disclosed, that may include a RBC flocculant, for use in approximating blood volume in a fluid. Methods of detecting blood in a sample, such as a fluid sample, and kits for performing the methods, are also provided.