Honeywell International Inc.

Umut Orhan

Michael Dillard

Stephen D Whitlow

Santosh Mathan

Mohammad Moghadamfalahi

Michael Pavel

Steve Johnson

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.

Methods and systems for depicting avionics data anomalies

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.

Methods and systems for detecting data anomalies

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.

Methods, systems and apparatuses for inner voice recovery from neural activation relating to sub-vocalization

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.

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Kirkland, WA, United States of America

Umut Orhan