Networked sensing systems for detecting people carrying radioactive material

Chandy, Mani; Pilotto, Concetta; McLean, Ryan

doi:10.1109/inss.2008.4610916

Cited by 33 publications

(12 citation statements)

References 8 publications

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“…[8] is concentrated only the detecting the radiation efficiently and [9] emphasis the details of detecting the error free radioactive signal form the radioactive source. But the proposed work innovatively introduced the Basien stochastic estimates used for high hit radiation area and noised adjusted PCA combined together for wireless radioactive sensor which will reduce highly correlated ambient noise in the radiation filed.…”

Section: Discussionmentioning

confidence: 99%

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An efficient Ba[Pt(CN)<inf>4</inf>] wireless radioactive sensor with noise adjusted principal component analysis

Suganya¹,

Anand

2011

2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering

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Detection of Radioactive rays is essential research in Nuclear Physics field. This paper deals with the design of radioactive wireless sensor by a novel approach of Principal component analysis (PCA) and with a realizable scintillator design. Former, very few radioactive sensor sensed rays only from radioactive element by wired network. But innovatively, the proposed wireless radioactive sensor senses , rays from the hazardous radiation and transmit the sensed signal by wireless network. The past works of radioactive sensor (RAS) are not realizable and exist only as a prototype. Hence our work towards the RAS is realizable and reliable. Innovatively, this wireless radioactive sensor (WRAS) based on Barium Platino Cyanide Ba[Pt(CN) 4 ] scintillator and Photo multiplier tube (PMT) with Charge coupled devices (CCD).The photon particle from the scintillator is typically measured with a PMT. PMT is attached with small signal voltage amplifier to amplify photo-electric signal. The photo electric signal is used to count , and rays. If count is in the acceptable range then that will be transmitted to the destination with the wireless network. Moreover Number of sensor used to detect the hazards is calculated in optimized way by Basien stochastic priori estimates of target location and source intensity. Noise from the environment mixed with the transmitted signal is eliminated by principal component analysis. After PCA, the received signal is accurately decoded this will decrease error floor performance of wireless network. Hence the proposed wireless radioactive sensor is realizable and optimized in count with noise adjusted will yields increase the degree of performance in detection of radioactive material.

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Section: Discussionmentioning

confidence: 99%

“…The proposed work give the the sensor in optimistic way by Basien s estimates. [8]. A radiation source generat Poisson manner at some rate μ.…”

Section: Sensor Optimization In Numentioning

confidence: 99%

An efficient Ba[Pt(CN)<inf>4</inf>] wireless radioactive sensor with noise adjusted principal component analysis

Suganya¹,

Anand

2011

2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering

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“…The non-directional detectors were often used to map environments in terms of radiation intensity, or a network of embedded non-directional detectors was used to find stationary or mobile sources. In [31,32,33], a network of fixed radiation sensors was used to identify radioactive stationary or mobile sources amidst background radiation. Baidoo-Williams [34] has defined the smallest number of fixed sensors to theoretically localize sources based on their gamma-ray count.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Exploration for Radioactive Hotspots Localization Taking Account of Sensor Limitations

Ardiny

Witwicki

Mondada

2019

Sensors

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Effective radioactive hotspot localization and detection is limited by sensor characteristics (i.e., the long acquisition time and poor angular resolution AR of a gamma camera) that significantly degrade the performance of autonomous exploration in terms of the completion time and accuracy. The goal of this research is to study effective exploration algorithms that take into account these specific sensor limitations. These exploration algorithms are adapted and implemented based on behaviour-based and multi-criteria decision making MCDM approaches on an autonomous robot. The algorithms were also tested in simulation and validated by experiments performed on a real robot. According to the results, the algorithms demonstrate the ability to mitigate the unfavourable effects of the limitations.

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“…Among the methods utilized to recover the information about the source properties, Bayesian techniques are popular. They include direct application of the Bayes' theorem or Markov chain Monte Carlo methods . Other common methodologies employ maximum likelihood estimators , cumulative sum with triangulation , mean of estimates , and Kalman filters .…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem

Ştefanescu

Schmidt

Hite

et al. 2017

Int. J. Numer. Meth. Engng

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In this investigation, we propose several algorithms to recover the location and intensity of a radiation source located in a simulated 250 m × 180 m block in an urban center based on synthetic measurements. Radioactive decay and detection are Poisson random processes, so we employ likelihood functions based on this distribution. Due to the domain geometry and the proposed response model, the negative logarithm of the likelihood is only piecewise continuous differentiable, and it has multiple local minima. To address these difficulties, we investigate three hybrid algorithms comprised of mixed optimization techniques. For global optimization, we consider Simulated Annealing (SA), Particle Swarm (PS) and Genetic Algorithm (GA), which rely solely on objective function evaluations; i.e., they do not evaluate the gradient in the objective function. By employing early stopping criteria for the global optimization methods, a pseudo-optimum point is obtained. This is subsequently utilized as the initial value by the deterministic Implicit Filtering method (IF), which is able to find local extrema in non-smooth functions, to finish the search in a narrow domain. These new hybrid techniques combining global optimization and Implicit Filtering address difficulties associated with the non-smooth response, and their performances are shown to significantly decrease the computational time over the global optimization methods alone. To quantify uncertainties associated with the source location and intensity, we employ the Delayed Rejection Adaptive Metropolis (DRAM) and DiffeRential Evolution Adaptive Metropolis (DREAM) algorithms. Marginal densities of the source properties are obtained, and the means of the chains' compare accurately with the estimates produced by the hybrid algorithms.

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Networked sensing systems for detecting people carrying radioactive material

Cited by 33 publications

References 8 publications

An efficient Ba[Pt(CN)<inf>4</inf>] wireless radioactive sensor with noise adjusted principal component analysis

An efficient Ba[Pt(CN)<inf>4</inf>] wireless radioactive sensor with noise adjusted principal component analysis

Autonomous Exploration for Radioactive Hotspots Localization Taking Account of Sensor Limitations

Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem

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