Abstract: A method for determining a threat substance encountered by a time-of-flight mass spectrometer (TOF-MS) using a pre-computed threat library is described. The method comprising the steps of acquiring a spectrum of a test substance, wherein the acquired spectrum is an average of individual spectra acquired from a plurality of laser shots on the analyte; identifying mass/charge (m/z) values corresponding to each of a plurality of spectral peaks of the acquired spectrum; assigning a corresponding ranking code to the acquired spectrum based on the plurality of its spectral peaks and troughs, wherein a peak presence is indicated by a numeral 1, while peak absence is indicated by a numeral 0, relative to each of a set of substances in a threat library; comparing the assigned rankings of the acquired spectrum over all threat substances stored in the threat library; and identifying the threat substance as that which produced the highest ranking.

Abstract: A method of diagnosing pathologic heart conditions in which a time series of heart sounds is filtered and parsed into a sequence of individual heart cycles. A systolic interval as well as systolic sub-intervals are identified for each heart cycle. The systolic intervals and ECG peaks are then digitally filtered to optimize for click detection. For each heartcycle, systole time limits are determined, a time series of the transform at specific wavelet scales are input to a Neyman-Pearson “constant false alarm rate” (CFAR) detector to identify anomalously high wavelet coefficients, and a vector of detections vs. time is created. The series of anomalously high detections (one series for each heart cycle) are then assembled into a matrix and convolved with an averaging vector yielding detection statistics across heart cycles and time intervals consistent with an observed spread of click occurrence times.

Abstract: A method of diagnosing pathologic heart conditions in which a time series of heart sounds is filtered and parsed into a sequence of individual heart cycles. A systolic interval as well as systolic sub-intervals are identified for each heart cycle. An energy value is computed for the systolic sub-interval of one or more heart cycles. The energy value computed is proportional to the energy level associated with the filtered series of heart sounds. A composite energy value is then computed for the systolic sub-intervals of one or more heart cycles and compared to a threshold level in order to distinguish between a normal heart and a pathologic heart. The system corresponding to the method is comprised of a portable computing device that manages data collection and stores data collected from new patients, and analyzes data.

Abstract: A controller that processes the mass spectrum of a sample provided by a detector of a mass spectrometer, for example, by a field portable mass spectrometer system. The controller provides a constant false alarm rate (CFAR) processing of the mass spectral data received. The CFAR processes the mass spectral data to determine noise included in the mass spectral data and outputs spectral peaks when the mass spectral data exceeds a threshold that reflects the noise included in the spectral data. The output peaks are compared with spectral peaks for known threats stored in a database and a notification that a known threat is present in the sample is provided if there is a correspondence between one or more output spectral peaks and one or more spectral peaks of a known threat as stored in the data base.

Abstract: A method of diagnosing pathologic heart conditions in which a time series of heart sounds is filtered and parsed into a sequence of individual heart cycles. A systolic interval as well as systolic sub-intervals are identified for each heart cycle. The systolic intervals and ECG peaks are then digitally filtered to optimize for click detection. For each heartcycle, systole time limits are determined, a time series of the transform at specific wavelet scales are input to a Neyman-Pearson “constant false alarm rate” (CFAR) detector to identify anomalously high wavelet coefficients, and a vector of detections vs. time is created. The series of anomalously high detections (one series for each heart cycle) are then assembled into a matrix and convolved with an averaging vector yielding detection statistics across heart cycles and time intervals consistent with an observed spread of click occurrence times.

Abstract: A method of diagnosing pathologic heart conditions in which a time series of heart sounds is filtered and parsed into a sequence of individual heart cycles. A systolic interval as well as systolic sub-intervals are identified for each heart cycle. An energy value is computed for the systolic sub-interval of one or more heart cycles. The energy value computed is proportional to the energy level associated with the filtered series of heart sounds. A composite energy value is then computed for the systolic sub-intervals of one or more heart cycles and compared to a threshold level in order to distinguish between a normal heart and a pathologic heart. The system corresponding to the method is comprised of a portable computing device that manages data collection and stores data collected from new patients, and analyzes data.

Abstract: A controller that processes the mass spectrum of a sample provided by a detector of a mass spectrometer, for example, by a field portable mass spectrometer system. The controller provides a constant false alarm rate (CFAR) processing of the mass spectral data received. The CFAR processes the mass spectral data to determine noise included in the mass spectral data and outputs spectral peaks when the mass spectral data exceeds a threshold that reflects the noise included in the spectral data. The output peaks are compared with spectral peaks for known threats stored in a database and a notification that a known threat is present in the sample is provided if there is a correspondence between one or more output spectral peaks and one or more spectral peaks of a known threat as stored in the database.