Greenray Industries, Inc.

Esterline Research and Design LLC

John Esterline

Steven J Fry

Wayne Bolton

Alan Snavely

Terry Hitt

The present disclosure provides for a system and method for compensating an electronic oscillator for one or more environmental parameters. A method may comprise segmenting test data received from an output signal of the oscillator and generating at least one correction voltage to thereby compensate the oscillator for one or more environmental parameters. A system may comprise at least one multi-function segmented array compensation module configured to receive one or more output signals from an oscillator and generate one or more correction voltages to thereby compensate the oscillator for environmental parameters. The system may also comprise one or more sensors and a user EFC.

System and method for multifunction segmented array compensation for oscillators

A crystal oscillator having a plurality of quartz crystals that are manufactured so that the directional orientation of the acceleration sensitivity vector is essentially the same for each crystal. This enables convenient mounting of the crystals to a circuit assembly with consistent alignment of the acceleration vectors. The crystals are aligned with the acceleration vectors in an essentially anti-parallel relationship and can be coupled to the oscillator circuit in either a series or parallel arrangement. Mounting the crystals in this manner substantially cancels the acceleration sensitivity of the composite resonator and oscillator, rendering it less sensitive to vibrational forces and shock events.

Crystal oscillator with reduced acceleration sensitivity

Systems and methods for controlling frequency output of an electronic oscillator to compensate for effects of a parameter experienced by the oscillator incorporate artificial neural network processing functionality for generating correction signals. A neural network processing module includes one or more neurons which receive one or more inputs corresponding to a parameter of an electronic oscillator, such as temperature. Weights are calculated and applied to inputs to the neurons of the neural network as part of a training process, wherein the weights help shape the output of the neural network processing module. The neural network may include a linear summation module configured to provide an output signal that is at least partially based on outputs of the one or more neurons.

Neural network frequency control

Systems and methods of using an artificial neural network processing module to compensate a control voltage and create a linear output response for an electronic oscillator to produce a target frequency. The artificial neural network processing module includes one or more neurons which receive one or more inputs corresponding to the control voltage. The artificial neural network processing module is configured to provide a correction based at least in part on the control voltage and pre-calculated DAC values. The pre-calculated DAC values are determined in part by predetermined or predefined pull ranges and linear control voltage transfer functions. The artificial neural network processing module can preferably achieve a control voltage tuning linearity better than 0.5% linearity over an entire tuning range of +&#x2212;75 ppm.

Neural network frequency control and compensation of control voltage linearity

Systems and methods for controlling frequency output of an electronic oscillator to compensate for effects of one or more parameters experienced by the oscillator incorporate artificial neural network processing functionality for generating correction signals. A neural network processing module includes one or more neurons which receive one or more inputs corresponding to parameters of an electronic oscillator, such as temperature and control voltage (or correction voltage). One or more sets of weights are calculated and applied to inputs to the neurons of the neural network as part of a training process, wherein the weights help shape the output of the neural network processing module. The neural network may include a linear summation module configured to provide an output signal that is at least partially based on outputs of the one or more neurons.

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Marysville, PA, United States of America

John Esterline