Sensors for chemical weapons detection

Murray, George M.; Southard, Glen E.

doi:10.1109/mim.2002.1048978

Cited by 27 publications

(27 citation statements)

References 19 publications

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“…In this case the threshold level of the decision is related to the count of these particles. Likewise, methods for detection of chemical agents, currently sarin, cyanide, and pesticides, may be applied at other inspection stations using different sensors [4].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of a Port-of-Entry Inspection Policy

Young

Zhu

et al. 2010

IEEE Trans. Automat. Sci. Eng.

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At the port-of-entry containers are inspected through a specific sequence of sensor stations to detect the presence of nuclear materials, biological and chemical agents, and other illegal cargo. The inspection policy, which includes the sequence in which sensors are applied and the threshold levels used at the inspection stations, affects the probability of misclassifying a container as well as the cost and time spent in inspection. In this paper we consider a system operating with a Boolean decision function combining station results and present a multi-objective optimization approach to determine the optimal sensor arrangement and threshold levels while considering cost and time. The total cost includes cost incurred by misclassification errors and the total expected cost of inspection, while the time represents the total expected time a container spends in the inspection system. An example which applies the approach in a theoretical inspection system is presented.

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Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of a Port-of-Entry Inspection Policy

Young

Zhu

et al. 2010

IEEE Trans. Automat. Sci. Eng.

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“…For example, ambient air over landmines will be drastically different from that near the body of a person boarding an aircraft hiding a bomb or a busy market place threatened with a hidden bomb 6,7 . Several alternate sensor technologies have been developed to meet this challenge [7][8][9][10][11] . The most important of these are based on gravimetric, optical, and chemoresitive principles implemented on varied platforms.…”

Section: Introductionmentioning

confidence: 99%

Development of Surface Acoustic Wave Electronic Nose

Jha

Yadava

2010

DSJ

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The paper proposes an effective method to design and develop surface acoustic wave (SAW) sensor array-based electronic nose systems for specific target applications. The paper suggests that before undertaking full hardware development empirically through hit and trial for sensor selection, it is prudent to develop accurate sensor array simulator for generating synthetic data and optimising sensor array design and pattern recognition system. The latter aspects are most time-consuming and cost-intensive parts in the development of an electronic nose system. This is because most of the electronic sensor platforms, circuit components, and electromechanical parts are available commercially-off-the-shelve (COTS), whereas knowledge about specific polymers and data analysis software are often guarded due to commercial or strategic interests. In this study, an 11-element SAW sensor array is modelled to detect and identify trinitrotoluene (TNT) and dinitrotoluene (DNT) explosive vapours in the presence of toluene, benzene, dimethylmethylphosphonate (DMMP) and humidity as interferents. Additive noise sources and outliers were included in the model for data generation. The pattern recognition system consists of: (i) a preprocessor based on logarithmic data scaling, dimensional autoscaling, and singular value decomposition-based denoising, (ii) principal component analysis (PCA)-based feature extractor, and (iii) an artificial neural network (ANN) classifier. The efficacy of this approach is illustrated by presenting detailed PCA analysis and classification results under varied conditions of noise and outlier, and by analysing comparative performance of four classifiers (neural network, k-nearest neighbour, naïve Bayes, and support vector machine).

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“…Detection of NBC threats benefits from the large number of sensing mechanisms and sensor systems that have been studied recently (i.e., [10][11][12]). Still, design and deployment of a reliable and accurate network of these sensors over a large area remains a challenge [13,14].…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement for Urban Homeland Security Applications

Hamel

Chwastek

Garcia

et al. 2010

International Journal of Distributed Sensor Networks

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We simulated a sensor network that detects and tracks the release of harmful airborne contaminants in an urban environment. The simulation determines sensor placement in that environment. The effort required integration of models from computational fluid dynamics (CFDs), combinatorial optimization, and population mobility dynamics. These CFD models, coupled with population mobility models, facilitate estimation of the effect of released contaminant on civilian populations. We studied the effects of a contaminant, chlorine gas, as a function of urban environment, prevailing winds, and likely attack locations. The models predictions optimized sensor node locations, providing mitigation of contaminant effects on human population. Results show that higher fidelity dispersal predictions increase sensor placement effectiveness. Incorporation of civilian evacuation models helps to minimize the overall impact of an attack when compared to a static population. Moreover, results show the benefits of using seasonal sensor configurations to maximize detection capabilities, taking into account prevailing seasonal wind conditions.

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Sensors for chemical weapons detection

Cited by 27 publications

References 19 publications

Multiobjective Optimization of a Port-of-Entry Inspection Policy

Multiobjective Optimization of a Port-of-Entry Inspection Policy

Development of Surface Acoustic Wave Electronic Nose

Sensor Placement for Urban Homeland Security Applications

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