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- W4387447334 abstract "We present results where multi-layer neural networks were used to predict temperature-induced frequency fluctuations in a low size, weight, and power ultra-stable oscillator (USO). Our results demonstrate that a multi-layer USO utilizing inputs from multiple temperature sensors can reduce the modified Allan deviation of the oscillator below 10 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>–12</sup> at all timescales between 1 and 1000 seconds. We also show that a direct digital synthesize (DDS) can be used to correct the frequency fluctuations predicted by the neural network while maintaining a low-noise and frequency stable output signal." @default.
- W4387447334 created "2023-10-10" @default.
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- W4387447334 date "2023-05-15" @default.
- W4387447334 modified "2023-10-11" @default.
- W4387447334 title "Multi-Layer Neural Network for the Stabilization of Ultra-Stable Oscillators" @default.
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- W4387447334 doi "https://doi.org/10.1109/eftf/ifcs57587.2023.10272127" @default.
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