Abstract: Background noise from relevant data sets, including for example over-the-counter sales data, absenteeism data, etc., is subtracted using a background estimation algorithm that outputs residual data. The effects of hypothetical anomalous events, such as a bio-terrorist attack, on the relevant data sets are modeled to create replica data. The replica data may be based on input from epidemiologists and various scenario templates including information on disease manifestation and other intelligence. The residual data and the replica data are then matched using a detector. Types of detectors include for example adaptive matched-filter detectors, change detectors and Bayesian Inference Networks. An alarm is triggered if a real anomalous event similar to a hypothetical anomalous event is detected. A Geographical Information System (GIS) may be used to display data from individual zip codes.

Abstract: A method for calibrating time-of-flight mass spectrometers is provided. The method includes at least modeling a time-of-flight mass spectrometer as a composite operator in accordance with a state space approach. The model is used to perform both symbolic and numeric propagation. To perform symbolic propagation, the model is used to derive closed form equations for the time-of-flight, by symbolically propagating the state of one or more ions modeled as state vectors. To perform numeric propagation, the model is used to calculate the time-of-flight by numerically propagating the state vector representation of one or more ions.

Abstract: A simple statistical model that predicts the distribution of false matches between peaks in matrix-assisted laser desorption/ionization mass spectrometry data and proteins in proteome databases is derived and validated. Given the cluttered and incomplete nature of the data, it is likely that neither simple ranking, nor simple hypothesis testing will be sufficient for truly robust microorganism identification over a large number of candidate microorganisms. In an effort to increase robust microorganism identification, the proteome databases are restricted to include data related to a given set of proteins, and not all proteins. By removing data from the proteome databases, the model is made more robust, i.e., there is a decrease in the number of false matches.

Abstract: Background noise from relevant data sets, including for example over-the-counter sales data, absenteeism data, etc., is subtracted using a background estimation algorithm that outputs residual data The effects of hypothetical anomalous events, such as a bio-terrorist attack, on the relevant data sets are modeled to create replica data. The replica data may be based on input from epidemiologists and various scenario templates including information on disease manifestation and other intelligence. The residual data and the replica data are then matched using a detector. Types of detectors include for example adaptive matched-filter detectors, change detectors and Bayesian Inference Networks. An alarm is triggered if a real anomalous event similar to a hypothetical anomalous event is detected. A Geographical Information System (GIS) may be used to display data from individual zip codes.

Abstract: Method and apparatus for using current-mode analog circuits to compute the correlation between an auditory input signal in the time-frequency domain and a stored binary template. By using massively parallel computation, circuits made of relatively imprecise analog components are able to accurately classify transient events, are competitive with high-performance DSP systems, and operate with much smaller power requirements.