Abstract: Methods and systems for depicting avionics data anomalies in an aircraft. Time series data is received from the avionics data source, a future time is predicted when a first anomaly threshold will be crossed based on the time series data, and the future time when the first anomaly threshold will be crossed is depicted on a display device associated with the aircraft.

Abstract: A plurality of metrics records, including some records indicating metrics for which anomaly analysis has been performed, is obtained. Using a training data set which includes the metrics records, a machine learning model is trained to predict an anomaly analysis relevance score for an input record which indicates a metric name. Collection of a particular metric of an application is initiated based at least in part on an anomaly analysis relevance score obtained for the particular metric using a trained version of the model.

Abstract: A plurality of metrics records, including some records indicating metrics for which anomaly analysis has been performed, is obtained. Using a training data set which includes the metrics records, a machine learning model is trained to predict an anomaly analysis relevance score for an input record which indicates a metric name. Collection of a particular metric of an application is initiated based at least in part on an anomaly analysis relevance score obtained for the particular metric using a trained version of the model.

Abstract: Methods and systems for depicting avionics data anomalies in an aircraft. Time series data is received from the avionics data source, a future time is predicted when a first anomaly threshold will be crossed based on the time series data, and the future time when the first anomaly threshold will be crossed is depicted on a display device associated with the aircraft.

Abstract: Methods and systems are provided for monitoring sensors and other data sources and detecting data anomalies. One exemplary method involves determining a probable range for a metric influenced by a behavior a sensor based at least in part on historical data associated with the sensor, identifying an anomalous condition with respect to the sensor based on a relationship between a current value for the metric indicative of a current behavior of the sensor and the probable range, and providing a graphical indication of the anomalous condition on a display device.

Abstract: Methods and systems are provided for calculating and displaying flight safety analytics for an aircraft. The method comprises first receiving historical flight data for the aircraft from a flight data quick access recorder (QAR) located onboard the aircraft. The flight data is then processed to identify safety events and store the identified safety events in an events database. The contents of the events database are analyzed to determine statistical data regarding the identified safety events. An onboard predictive model is applied that utilizes the statistical data to predict the likelihood of a safety event based on current flight data received from the aircraft. The predicted likelihood of a safety event is displayed on a flight display of the aircraft.

Abstract: A method for creating and using a contextual Artificial Intelligence (AI) model to analyze flight data for one or more aircraft, by a central computer system, is provided. The method obtains a set of aggregate contextual data comprising at least aircraft and flight-specific data, airport and air traffic control (ATC) data, weather data, and human factor data associated with flight crew members of the one or more aircraft; creates the contextual AI model using the set of aggregate contextual data, by the at least one processor; applies the contextual AI model to a set of flight data, to perform a statistical analysis; generates a set of results based on the statistical analysis, by the at least one processor, wherein the set of results comprises at least one of probable causes of aircraft performance events and probable aircraft performance events resulting from current conditions; and presents the set of results.

Abstract: Methods, systems and apparatuses are provided to perform a continuous-to-continuous mapping of neural signal data received from one or more body sensors connected to an user wherein the one or more body sensors monitors at least neural activities of the user of a sub-vocalized voice at a sensory level and sends the neural signal data to a processor. The processor receives the neural signal data in an iterative closed loop to train the processor and to generate a sufficiently large data set in the neural signal domain to link to a produced voice domain. The processor constructs a common feature space which associates the neural signal domain with the produced voice domain wherein the common feature space implicitly extracts features related to audio communications for linking neural signal domain data to the produced voice data without requiring any prior feature classification of the received neural signal data.

Abstract: An adaptive non-invasive alternating current brain stimulation system and method includes supplying transcranial alternating current stimulation (tACS) from a tACS source to a person, and receiving electroencephalogram (EEG) signals from EEG sensors disposed on the person, where the EEG signals including stimulation interference. The received EEG signals are processed using an adaptive model to estimate the stimulation interference in the EEG signals. The estimated stimulation interference is subtracted from the EEG signals to estimate neural oscillations of the person. The estimated neural oscillations are processed through an autoregressive model to generate predictions of future neural activity of the person and, based on the generated predictions, one or more electrical characteristics of the tACS being supplied to the person are varied to thereby modulate the neural oscillations of the person.

Abstract: Methods and systems are provided for monitoring sensors and other data sources and detecting data anomalies. One exemplary method involves determining a probable range for a metric influenced by a behavior a sensor based at least in part on historical data associated with the sensor, identifying an anomalous condition with respect to the sensor based on a relationship between a current value for the metric indicative of a current behavior of the sensor and the probable range, and providing a graphical indication of the anomalous condition on a display device.

Abstract: An apparatus and method for detecting under performance of a current takeoff of an aircraft by predicting at least one takeoff performance characteristic of an aircraft prior to takeoff for a current flight is provided. The apparatus includes: at least one processor deployed on the aircraft, the at least one processor being programmed, when a model of thrust based on a lookup table is unavailable, to implement a trained model of thrust of the aircraft during a takeoff having a first component based on sensor data contributed from the current flight takeoff and having a second component based on derivative data contributed from a prior flights takeoff wherein the first and second components used in the model of the thrust are based on one aircraft takeoff characteristics of: acceleration from thrust, friction from slope, and drag from friction of the aircraft during the takeoff.

Abstract: Methods, systems and apparatuses are provided to perform a continuous-to-continuous mapping of neural signal data received from one or more body sensors connected to an user wherein the one or more body sensors monitors at least neural activities of the user of a sub-vocalized voice at a sensory level and sends the neural signal data to a processor. The processor receives the neural signal data in an iterative closed loop to train the processor and to generate a sufficiently large data set in the neural signal domain to link to a produced voice domain. The processor constructs a common feature space which associates the neural signal domain with the produced voice domain wherein the common feature space implicitly extracts features related to audio communications for linking neural signal domain data to the produced voice data without requiring any prior feature classification of the received neural signal data.

Abstract: An adaptive non-invasive alternating current brain stimulation system and method includes supplying transcranial alternating current stimulation (tACS) from a tACS source to a person, and receiving electroencephalogram (EEG) signals from EEG sensors disposed on the person, where the EEG signals including stimulation interference. The received EEG signals are processed using an adaptive model to estimate the stimulation interference in the EEG signals. The estimated stimulation interference is subtracted from the EEG signals to estimate neural oscillations of the person. The estimated neural oscillations are processed through an autoregressive model to generate predictions of future neural activity of the person and, based on the generated predictions, one or more electrical characteristics of the tACS being supplied to the person are varied to thereby modulate the neural oscillations of the person.