Method and apparatus for detecting and identifying a condition
OwnerFrederic M. Ham, Glenn M. Cohen +1inventors
InventorsFrederic M. Ham, Glenn M. Cohen, Samuel P. Kozaitis
Life of this patent
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Abstract
A method and apparatus for sensing and classifying a condition of interest in a system from background noise in which a parameter representative of the condition of interest is sensed and an electrical signal representative of the sensed parameter is produced. The electrical signal is converted into a digital signal, this digital signal containing a signal of interest representative of the condition of interest and background noise. The digital signal is received by an artificial neural network which filters out the background noise to produce a filtered signal from the digital signal, and classifies the signal of interest from the filtered signal to produce an output representative of the classified signal.
Claims25
A method of filtering and classifying a signal of interest from background noise, comprising:
- receiving in an artificial neural network a digital signal containing a signal of interest and background noise;
- filtering out the background noise to produce a filtered signal from the digital signal in a feedforward perceptron of the artificial neural network;
- and
- classifying the signal of interest from the filtered signal in a counterpropagation network of the artificial neural network.
A method of sensing and classifying a condition of interest in a system from background noise, comprising:
- sensing a parameter representative of the condition of interest and producing an electrical signal representative of the sensed parameter;
- converting the electrical signal into a digital signal, said digital signal containing a signal of interest representative of the condition of interest and background noise;
- receiving in an artificial neural network the digital signal containing the signal of interest and the background noise;
- filtering out the background noise in the artificial neural network to produce a filtered signal from the digital signal;
- and
- classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
A device for filtering and classifying a signal of interest from background noise, comprising:
- means for receiving in an artificial neural network a digital signal containing a signal of interest and background noise;
- means for filtering out the background noise from the digital signal to produce a filtered signal;
- and
- means for classifying the signal of interest from the filtered signal.
A device for sensing and classifying a condition of interest in a system from background noise, comprising:
- means for sensing a parameter representative of the condition of interest and producing an electrical signal representative of the sensed parameter;
- means for converting the electrical signal into a digital signal, said digital signal containing a signal of interest representative of the condition of interest and background noise;
- means for receiving in an artificial neural network the digital signal containing the signal of interest and the background noise;
- means for filtering out the background noise in the artificial neural network to produce a filtered signal from the digital signal;
- and
- means for classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
A device for measuring a condition of a biological system, comprising:
- a sensor that senses a parameter representative of the condition of interest in the biological system, said sensor producing a signal representative of the sensed parameter and containing background noise;
- a converter coupled to the sensor to convert the signal produced by the sensor to a digital signal, said digital signal containing a signal of interest and background noise;
- and
- an artificial neural network coupled to the converter, said artificial neural network filtering the background noise from the digital signal to produce a filtered signal and classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
Dependencies are inferred from the claim text.