The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is closest to the initial configuration.
Rotating shafts experience a an elliptical motion called whirl. It is important to decompose this motion into a forward and backward whil orbits. The current function makes use of two sensors to generate a bi-directional spectrogram. The method can be extended to any time-frequency distribution
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％ compute the forward/backward Campbell/specgtrogram
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％ INPUT:
％ y (n x 2) each column is measured from a different sensor
％ ///////
％ __
％ |s1| y(:,1)
％ |__|
％ __
％ / \ ________|/
％ | | | s2 |/ y(:,2)
％ \____/ --------|/
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％ Fs Sampling frequnecy
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％ OUTPUT:
％ B spectrogram/Campbel diagram
％ x x-axis coordinate vector (time or Speed)
％ y y-axis coordinate vector (frequency [Hz])
