Detection of radioactive sources in urban scenes using Bayesian Aggregation of data from mobile spectrometers

Tandon, Prateek; Huggins, Peter; MacLachlan, Robert A.; Dubrawski, Artur; Nelson, K.; Labov, S. E.

doi:10.1016/j.is.2015.10.006

Cited by 36 publications

(18 citation statements)

References 7 publications

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“…This feature become input for random forest regression model that learns to predict the time since start of bleed (equivalent to the amount of blood lost given fixed bleed rate in the referred experiment), and in the process identifies a subset of all features that jointly yields optimal performance. We also create 1 and 5 min moving averages of these derived variables, as well as often reported measures of HR variability along several domains (time and frequency domains, non-linear measures) 21. The time windows determining the features from the raw electrical signal and output classes will vary according to aims.…”

Section: Methods and Analysismentioning

confidence: 99%

Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study

Hofer¹,

Rinehart

Lee

et al. 2019

BMJ Open

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IntroductionAbout 42 million surgeries are performed annually in the USA. While the postoperative mortality is less than 2%, 12% of all patients in the high-risk surgery group account for 80% of postoperative deaths. New onset of haemodynamic instability is common in surgical patients and its delayed treatment leads to increased morbidity and mortality. The goal of this proposal is to develop, validate and test real-time intraoperative risk prediction tools based on clinical data and high-fidelity physiological waveforms to predict haemodynamic instability during surgery.Methods and analysisWe will initiate our work using an existing annotated intraoperative database from the University of California Irvine, including clinical and high-fidelity waveform data. These data will be used for the training and development of the machine learning model (Carnegie Mellon University) that will then be tested on prospectively collected database (University of California Los Angeles). Simultaneously, we will use existing knowledge of haemodynamic instability patterns derived from our intensive care unit cohorts, medical information mart for intensive care II data, University of California Irvine data and animal studies to create smart alarms and graphical user interface for a clinical decision support. Using machine learning, we will extract a core dataset, which characterises the signatures of normal intraoperative variability, various haemodynamic instability aetiologies and variable responses to resuscitation. We will then employ clinician-driven iterative design to create a clinical decision support user interface, and evaluate its effect in simulated high-risk surgeries.Ethics and disseminationWe will publish the results in a peer-reviewed publication and will present this work at professional conferences for the anaesthesiology and computer science communities. Patient-level data will be made available within 6 months after publication of the primary manuscript. The study has been approved by University of California, Los Angeles Institutional review board. (IRB #19–0 00 354).

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Section: Methods and Analysismentioning

confidence: 99%

Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study

Hofer¹,

Rinehart

Lee

et al. 2019

BMJ Open

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“…Bayesian approaches to detect, classify, and estimate smuggled nuclear and radiological materials are not a new consideration 6,12 , and were extensively studied for the development of the Statistical Radiation Detection System at Lawrence Livermore National Laboratory. This group has used Bayesian model-based sequential statistical processing techniques to overcome the low signal-to-background ratio that complicates traditional gamma spectroscopy techniques with high-resolution HPGe and inorganic scintillation detectors 13,14 . Bayesian approaches have also been applied to radionuclide identification for NaI(Tl) detectors using a wavelet-based peak identification algorithm with Bayesian classifiers 15 , for LaBr 3 (Ce) using a sequential approach 16 , and to HPGe detectors using non-parametric Bayesian deconvolution to resolve overlapping peaks 17 .…”

Section: Algorithms For Rpm Signal Unmixingmentioning

confidence: 99%

Expectation-propagation for weak radionuclide identification at radiation portal monitors

Altmann

Fulvio

Paff

et al. 2020

Sci Rep

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We propose a sparsity-promoting Bayesian algorithm capable of identifying radionuclide signatures from weak sources in the presence of a high radiation background. the proposed method is relevant to radiation identification for security applications. In such scenarios, the background typically consists of terrestrial, cosmic, and cosmogenic radiation that may cause false positive responses. We evaluate the new Bayesian approach using gamma-ray data and are able to identify weapons-grade plutonium, masked by naturally-occurring radioactive material (noRM), in a measurement time of a few seconds. We demonstrate this identification capability using organic scintillators (stilbene crystals and EJ-309 liquid scintillators), which do not provide direct, high-resolution, source spectroscopic information. Compared to the EJ-309 detector, the stilbene-based detector exhibits a lower identification error, on average, owing to its better energy resolution. organic scintillators are used within radiation portal monitors to detect gamma rays emitted from conveyances crossing ports of entry. the described method is therefore applicable to radiation portal monitors deployed in the field and could improve their threat discrimination capability by minimizing "nuisance" alarms produced either by noRMbearing materials found in shipped cargoes, such as ceramics and fertilizers, or radionuclides in recently treated nuclear medicine patients.The growing terrorism threat based on the use of special nuclear materials (SNMs), i.e., highly enriched uranium (HEU), weapons-grade plutonium (WGPu), or high-activity radiological sources has reinforced the need for improved population protection mechanisms. Nuclear security aims to deter and detect the smuggling of these materials across state borders. One major defense mechanism involves the installation of radiation portal monitors (RPMs) at border crossings. These RPMs typically consist of 3 He proportional counters embedded in polyethylene for neutron detection, and slabs of polyvinyl-toluene (PVT) scintillators for gamma-ray detection. Only a tiny fraction of the millions of vehicles and cargo containers entering a country like the United States are likely to be carrying radiological contraband. The International Atomic Energy Agency's Incident and Trafficking Database (ITDB) merely counts a few dozen reported successful interdictions of nuclear and radiological materials globally per year 1,2 . The ITDB provides only a partial picture of the number of smuggling attempts. The reported figures should be considered a lower bound of the number of successful interdictions, because they include only successful interdictions, voluntarily reported by the member states.Complicating matters, the radiological contraband might be well shielded. In 2017, the United Nations Conference on Trade and Development estimated the global container port throughput at over 750 million 20-foot equivalent units 3 . As a consequence, RPMs are limited in measurement time to minimize unnecessary impediments to the fl...

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“…Bayesian aggregation (BA) [22] is used to combine information from multiple measurements to produce a single score for source presence as a function of location. BA is applied to both CEW and PID in the same fashion.…”

Section: Bayesian Aggregationmentioning

confidence: 99%

“…We use measurement based simulations to test the performance of detectors with modest spectral resolution (NaI scintillators) both with and without a coded aperture uniform redundant mask providing imaging capability. We analyze the data using two fusion methods, an analytic weighted projection, and Bayesian Aggregation [22] of evidence from multiple spectral measurements. We also consider alternative, weakly supervised methods of scoring individual observations that rely on spectral, spatial, and spatio-spectral analysis.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of coded aperture radiation detectors using a Bayesian approach

Miller

Huggins

Labov

et al. 2016

Nuclear Instruments and Methods in Physics Research Section A:

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Introduction. We investigate the utility of coded aperture (CA) for roadside radiation threat detection applications. With coded aperture, information in the form of photon quantity is traded for directional information.Whether and in what scenarios this trade-off is beneficial is the focus of this study. We quantify the impact of a masking approach by comparing performance with an unmasked approach in terms of both detection and localization of a roadside nuclear threat. We simulate many instances of a drive-by scenario via Monte Carlo using empirical observations from the RadMAP project [1] to obtain background photons and synthetic injection of threat source. Simulation results suggest that at 0.1%false positive rate (FPR) the masked detector suffers 20-50% loss of detection probability for weak sources, but only 1-1.5% for moderate source intensities. The masked approach also demonstrates consistent improvement in localization performance ranging from 0.7-2.2m across all source intensities investigated.

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Detection of radioactive sources in urban scenes using Bayesian Aggregation of data from mobile spectrometers

Cited by 36 publications

References 7 publications

Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study

Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study

Expectation-propagation for weak radionuclide identification at radiation portal monitors

Evaluation of coded aperture radiation detectors using a Bayesian approach

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