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An absolute position detector that interprets, rather than avoids, abnormal sensory states. Different combinations of sensors in an array are activated as a marker travels along a path. The current activation/deactivation state of the array is converted into a digital signal that is reliably indicat

An absolute position detector that interprets, rather than avoids, abnormal sensory states. Different combinations of sensors in an array are activated as a marker travels along a path. The current activation/deactivation state of the array is converted into a digital signal that is reliably indicative of the current absolute position of the marker along the path. In a preferred embodiment, a dynamic magnetic field is provided whose current condition represents the current absolute position of a moving marker. As the marker moves, the condition of the field changes to activate different groups of Hall Effect devices in an array. The activated groups may comprise one or more Hall Effect devices in the array, depending on the condition of the field as created by the position of the marker. The current activation/deactivation state of the array is then converted, advantageously via truth table logic, into a digital signal representative of the current position of the marker.

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An absolute position detector that interprets, rather than avoids, abnormal sensory states. Different combinations of sensors in an array are activated as a marker travels along a path. The current activation/deactivation state of the array is converted into a digital signal that is reliably indicat

An absolute position detector that interprets, rather than avoids, abnormal sensory states. Different combinations of sensors in an array are activated as a marker travels along a path. The current activation/deactivation state of the array is converted into a digital signal that is reliably indicative of the current absolute position of the marker along the path. In a preferred embodiment, a dynamic magnetic field is provided whose current condition represents the current absolute position of a moving marker. As the marker moves, the condition of the field changes to activate different groups of Hall Effect devices in an array. The activated groups may comprise one or more Hall Effect devices in the array, depending on the condition of the field as created by the position of the marker. The current activation/deactivation state of the array is then converted, advantageously via truth table logic, into a digital signal representative of the current position of the marker. ive spectrometers. 10. The instrument of claim 1 wherein the linear least squares computing means comprises means for generating a model matrix, multiplying the model matrix by its transpose, inverting the resulting matrix, multiplying this inverted matrix by the transpose of the model matrix, and storing the second resulting matrix. 11. The instrument of claim 10 wherein the linear least squares computing means additionally comprises means for using the stored matrix to multiply a sequence of observed spectra to obtain a sequence of concentration measurements. 12. The instrument of claim 11 wherein the generating, multiplying, inverting, multiplying, and storing means comprises a first processor, and the means for using the stored matrix comprises a second processor. 13. The instrument of claim 12 wherein the second processor comprises a processor selected from the group consisting of digital signal processors and microcontrollers. 14. The instrument of claim 1 wherein the linear least squares computing means comprise model spectra computed at a number of environmental conditions and means for selecting the known spectrum as the model spectrum whose environmental conditions most closely match those of the analyte. 15. A spectrographic method comprising the steps of: a) providing a spectrometer; b) digitizing a sample spectrum in a spectral range that includes at least one feature of an analyte; and c) comparing, via a linear least squares computation, the sample spectrum to a reference spectrum known to closely approximate the sample spectrum at a reference condition and to one or more derivatives of the known reference spectrum taken with respect to one or more parameters of the reference condition; and wherein the comparing step comprises employing a derivative taken with respect to a center wavenumber of a reference spectral feature. 16. The method of claim 15 wherein the comparing step comprises employing fit coefficients corresponding to terms of the one or more derivatives to correct a concentration determined by the fit coefficient corresponding to the known spectrum. 17. The method of claim 15 additionally comprising the step of adjusting spectral interval of the spectrometer employing a weighting factor corresponding to a derivative with respect to wavenumber and negative feedback to stabilize a relative wavenumber position of a spectral peak by adjusting average position of the spectral interval. 18. The method of claim 15 additionally comprising the step of adjusting spectral interval of the spectrometer employing a weighting factor corresponding to a derivative with respect to wavenumber divided by a weighting factor corresponding to a spectral feature, with the resulting ratio being used with negative feedback to stabilize a relative wavenumber position of a spectral peak by adjusting average position of the spectral interval. 19. The method of claim 18 wherein the providing step comprises providing a diode laser spectrometer. 20. The method of claim 15 wherein the comparing step comprises employing a derivative taken with respect to a line width of a reference spectral feature. 21. The method of claim 15 wherein the comparing step comprises employing a derivative of the reference spectrum taken with respect to a reference analyte temperature. 22. The method of claim 15 wherein the comparing step comprises employing a derivative of the reference spectrum taken with respect to a reference analyte pressure. 23. The method of claim 15 wherein the providing step comprises providing a spectrometer selected from the group consisting of diode laser spectrometers, Fourier transform spectrometers, and dispersive spectrometers. 24. The method of claim 15 wherein the comparing step comprises the computing steps of generating a model matrix, multiplying the model matrix by its transpose, inverting the resulting matrix, multiplying this inverted matrix by the transpose of the model matrix, and storing the second resul ting matrix. 25. The method of claim 24 wherein the comparing step additionally comprises using the stored matrix to multiply a sequence of observed spectra to obtain a sequence of concentration measurements. 26. The method of claim 25 wherein the computing steps comprise executing the computing steps on a first processor, and the step of using the stored matrix comprises multiplying on a second processor. 27. The method of claim 26 wherein the step of using the stored matrix comprises multiplying on a second processor selected from the group consisting of digital signal processors and microcontrollers. 28. The method of claim 15 additionally comprising the steps of computing model spectra at a number of environmental conditions and selecting the known spectrum as the model spectrum whose environmental conditions most closely match those of the analyte. 29. An instrument comprising a spectrometer, means for digitizing a sample spectrum in a spectral range that includes at least one feature of an analyte, and linear least squares computing means for comparing the sample spectrum to a reference spectrum known to closely approximate the sample spectrum at a reference condition and to one or more derivatives of the known reference spectrum taken with respect to one or more parameters of the reference condition, and wherein the spectrometer comprises a spectrometer selected from the group consisting of diode laser spectrometers, Fourier transform spectrometers, and dispersive spectrometers. 30. An instrument comprising a spectrometer, means for digitizing a sample spectrum in a spectral range that includes at least one feature of an analyte, and linear least squares computing means for comparing the sample spectrum to a reference spectrum known to closely approximate the sample spectrum at a reference condition and to one or more derivatives of the known reference spectrum taken with respect to one or more parameters of the reference condition, and wherein the linear least squares computing means comprises means for generating a model matrix, multiplying the model matrix by its transpose, inverting the resulting matrix, multiplying this inverted matrix by the transpose of the model matrix, and storing the second resulting matrix. 31. The instrument of claim 30 wherein the linear least squares computing means additionally comprises means for using the stored matrix to multiply a sequence of observed spectra to obtain a sequence of concentration measurements. 32. The instrument of claim 31 wherein the generating, multiplying, inverting, multiplying, and storing means comprises a first processor, and the means for using the stored matrix comprises a second processor. 33. The instrument of claim 32 wherein the second processor comprises a processor selected from the group consisting of digital signal processors and microcontrollers. 34. A spectrographic method comprising the steps of: a) providing a spectrometer; b) digitizing a sample spectrum in a spectral range that includes at least one feature of an analyte; and c) comparing, via a linear least squares computation, the sample spectrum to a reference spectrum known to closely approximate the sample spectrum at a reference condition and to one or more derivatives of the known reference spectrum taken with respect to one or more parameters of the reference condition; and wherein the providing step comprises providing a spectrometer selected from the group consisting of diode laser spectrometers, Fourier transform spectrometers, and dispersive spectrometers. 35. A spectrographic method comprising the steps of: a) providing a spectrometer; b) digitizing a sample spectrum in a spectral range that includes at least one feature of an analyte; and c) comparing, via a linear least squares computation, the sample spectrum to a reference spectrum known to closely approximate the sample spectrum at a reference condition and to one or more derivatives of the known reference spectru m taken with respect to one or more parameters of the reference condition; and wherein the comparing step comprises the computing steps of generating a model matrix, multiplying the model matrix by its transpose, inverting the resulting matrix, multiplying this inverted matrix by the transpose of the model matrix, and storing the second resulting matrix. 36. The method of claim 35 wherein the comparing step additionally comprises using the stored matrix to multiply a sequence of observed spectra to obtain a sequence of concentration measurements. 37. The method of claim 36 wherein the computing steps comprise executing the computing steps on a first processor, and the step of using the stored matrix comprises multiplying on a second processor. 38. The method of claim 37 wherein the step of using the stored matrix comprises multiplying on a second processor selected from the group consisting of digital signal processors and microcontrollers. polarization period being part of the atrial cycle time. 21. The system of claim 20, wherein the processor is further capable of determining an atrial depolarization period, the atrial depolarization period being part of the atrial cycle time. 22. The system of claim 9, wherein the processor is capable of determining a ventricular depolarization period, the ventricular depolarization period being part of the ventricular cycle time. 23. The system of claim 22, wherein the processor is capable of determining a ventricular repolarization period, the ventricular repolarization period being part of the ventricular cycle time. 24. The system of claim 9, wherein the processor is configured to perform digital signal processing. 25. The system of claim 9, wherein the processor is capable of predicting an arrhythmia based upon the comparison of the hemodynamic baseline ratio and the active ratio. 26. The system of claim 9, wherein the processor includes an algorit