USA as Represented by Secretary of the Navy

Other

Susan L Rose-Pehrsson

Mark H Hammond

Sean J Hart

Gregory E Collins

Daniel T Gottuk

Jay W Grate

Michael Paul Malito

Kevin J Johnson

Ronald Eugene Shaffer

Braden C Giordano

Greg E Collins

Cy R Tamanaha

Adam L Lubrano

Ronald E Schaffer

Patricia A Taffe

Daniel Di Lella

Apparatuses and methods for generating trace vapors are provided. The apparatus includes a controller and an oven. The controller includes: a processor, a memory storing at least one control program, a clean solution supply port constructed to output a clean solution, an analyte solution supply port constructed to output an analyte solution, a carrier gas inlet port constructed to receive a carrier gas, and a plurality of carrier gas supply controllers constructed to output the carrier gas. The oven includes a clean manifold, an analyte manifold, a clean solution nebulizer constructed to: receive the clean solution from the clean solution supply port, and the carrier gas from one of the plurality of carrier gas supply controllers, and output a clean solution vapor stream comprising the clean solution and the carrier gas to the clean manifold, an analyte solution nebulizer constructed to: receive the analyte solution from the analyte solution supply port and the carrier gas from another one of the plurality of carrier gas supply controllers, and output an analyte solution vapor stream comprising the analyte solution and the carrier gas to the analyte manifold, a pneumatic valve controllably connected to the processor and communicatively connected to the clean manifold and the analyte manifold, and an output supply port communicatively connected to the pneumatic valve. The controller is configured to operate the pneumatic valve to allow the clean vapor solution or the analyte vapor solution to enter the chamber and be provided to the output supply port.

Methods and apparatuses for generating trace vapors

A method and system of analyzing signal-vector data from first order sensors including providing a training data set, adjusting the training data set using a background adjustment technique, normalizing and transforming the training data set into wavelet coefficients, using an automated analysis of variance feature selection technique and a pattern recognition technique to classify the training data set. The method and system may also include performing these operations on an unknown sample data set collected under unknown conditions and comparing the unknown sample data set to the classification model to provide an identity of the unknown conditions associated with the unknown sample data set. The present invention is also directed to a computer system for analyzing signal-vector data according to this method and a sensing system that includes a sensor and a microprocessor on which is stored a classification model for real-time sensing of unknown sample data sets.

Method and system for analyzing signal-vector data for pattern recognition from first order sensors

A multi-criteria event detection system, comprising a plurality of sensors, wherein each sensor is capable of detecting a signature characteristic of a presence of an event and providing an output indicating the same. A processor for receiving each output of the plurality of sensors is also employed. The processor includes a probabilistic neural network for processing the sensor outputs. The probabilistic neural network comprises a nonlinear, nor-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter. The plurality of data sets includes a baseline, non-event, first data set; a second, event data set; and a third, nuisance data set. The algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discrimination between said data sets, and whether the outputs suffice to substantially indicate the presence of an event, as opposed to a non-event or nuisance situation.

Probabilistic neural network for multi-criteria event detector

A multi-criteria fire detection system, comprising a plurality of sensors, wherein each sensor is capable of detecting a signature characteristic of a presence of a fire and providing an output indicating the same. A processor for receiving each output of the plurality of sensors is also employed. The processor includes a probabilistic neural network for processing the sensor outputs. The probabilistic neural network comprises a nonlinear, nor-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter. The plurality of data sets includes a baseline, non-fire, fist data set; a second, fire data set, and a third, nuisance data set. The algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discrimination between said data sets, and whether the outputs suffice to substantially indicate the presence of a fire, as opposed to a non-fire or nuisance situation.

Identification of fire signatures for shipboard multi-criteria fire detection systems

A solid phase chemical sensor includes a polymer film which has a chemiluminescent reagent immobilized therein. The polymer film and chemiluminescent reagent are chosen to significantly enhance the selectivity of the sensor to the analyte in the gaseous phase to which the sensor is exposed. The sensor is then positioned so that, when exposed to the gaseous mixture, any chemiluminescence generated will be detected by a photomultiplier tube or other photoelectric device, such as a photodiode. The sensor is particularly useful in the detection of O , N H , SO , NO , and halogenated hydrocarbons.

Chemiluminescence chemical detection of vapors and device therefor

A method and apparatus for the detection of hydrazine, monomethylhydrazine, 1,1-dimethylhydrazine in air (or other gas medium) or in an aqueous solution. The detection is accomplished by introducing a stream of air, or other gas medium, or aqueous solution suspected of containing hydrazine, monomethylhydrazine, 1,1-dimethylhydrazine or mixtures thereof into a pH controlled reagent solution containing an aromatic dicarboxaldehyde to react with the hydrazine, monomethylhydrazine, 1,1-dimethylhydrazine or mixtures thereof, respectively, and by exposing the reacted reagent solution to an excitation wavelength range and monitoring an emission from the exposed reagent solution at an emission wavelength range to detect the presence of a hydrazine derivative, monomethylhydrazine derivative, 1,1-dimethylhydrazine derivative, or mixtures thereof (i.e. a derivative formed by the reaction between hydrazine, monomethylhydrazine, 1,1-dimethylhydrazine and the aromatic dicarboxaldehyde) indicating the presence of hydrazine, monomethylhydrazine, 1,1-dimethylhydrazine or mixtures thereof within the stream of air or other gas medium or aqueous solution.

Fluorescent detection of hydrazine, monomethylhydrazine, and

A method and a system using that method are provided which employ a pattern ecognition algorithm to improve sensitivity in detecting hazardous vapors. The algorithm enables the discrimination of vapors of interest from non-hazardous substances at higher concentrations in varying relative humidity. A weight vector is generated corresponding to a N-space representation of a class comprising known vapors of interest, and a N-space representation of the unknown vapor is used to generate an unknown pattern vector. By calculating the dot product of the unknown pattern vector and the weight vector a determination can be made as to whether the unknown vapor is within the class. The weight vector is generated by selecting a training set comprising a subset of the known vapors of interest and background vapors and generating an N-space representation of the training set so as to create an associated weight vector.

Smart sensor system and method using a surface acoustic wave vapor

A solution of various benzaldehyde derivatives dissolved in suitable solvents are used in impinger sampling or as a coating on substrates to provide passive, real-time visual colorimetric indication of the presence of hydrazine and its derivatives.

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Fairfax Station, VA, United States of America

Susan L Rose-Pehrsson