Comparative Alpha Tracks Counting Using an Optical Microscope and a Spark Counter

In the metrology of radon, an environmental lung carcinogen, the integrated measurements necessary for epidemiological studies are made very often using the tracks detector LR 115 type 2. For dosimetric analysis, the etched tracks from radon alpha particles on this detector are usually counted by means of an optical microscope or a spark counter. An optimal reading of the track densities which must be converted into radon concentrations, can’t be done without a good mastery of the mode of operation and use of these devices. Furthermore, investigations to know as to whether or not each of those can be used to determine radon concentration are necessary. These are the objectives of the present work in which LR 115 samples exposed to radon for at least 3 months, were chemically developed under standard conditions and read. The track densities obtained with the microscope are very much higher than those of the counter for each sample. These results are consistent with those published by other authors. However, each of these devices can be used interchangeably for alpha tracks counting, as both provide radon concentrations with a very good linear correlation coefficient of 0.95 taking into account their respective calibration factors for the reading of this detector. In addition, the saturation phenomenon for the spark counter reading of LR 115 detector occurs beyond 11,000 tr/cm2, a density never reached during our environmental radon measurements.

Kernel density estimation and marginalized-particle based probability hypothesis density filter for multi-target tracking

In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.

Multiple extended target tracking algorithm based on Gaussian surface matrix

In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.

A novel variable-lag probability hypothesis density smoother for multi-target tracking

It is understood that the forward-backward probability hypothesis density （PHD） smoothing algorithms proposed recently can significantly improve state estimation of targets. However, our analyses in this paper show that they cannot give a good cardinality （i.e., the number of targets） estimate. This is because backward smoothing ignores the effect of temporary track drop- ping caused by forward filtering and/or anomalous smoothing resulted from deaths of targets. To cope with such a problem, a novel PHD smoothing algorithm, called the variable-lag PHD smoother, in which a detection process used to identify whether the filtered cardinality varies within the smooth lag is added before backward smoothing, is developed here. The analytical results show that the proposed smoother can almost eliminate the influences of temporary track dropping and anomalous smoothing, while both the cardinality and the state estimations can significantly be improved. Simulation results on two multi-target tracking scenarios verify the effectiveness of the proposed smoother.

Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance

In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.

The impact on alpha emission rates of varying distances between a CR-39 detector and alpha-emitting bone samples

Background Changing the distance between a sample and an exposed solid-state nuclear track detector affects the number of alpha tracks recorded by the detector.This concept had been used to distinguish high-energy alpha particles by computing the alpha emission rate at two distances(0 and 2 cm)from animal bone ash.Materials and methods Two Cr-39 detectors were placed in a container with bone ash inside to measure the alpha emission rate when the detectors were in contact with the ash and hung at a distance of 2 cm from the ash.Results The alpha emission rate was found to be 62.7×10^(−4) Bq cm^(−2) when the detector was in contact with the sample(a small exposure area)and 324.4×10^(−4) Bq cm^(−2) when the sample was placed 2 cm away(a larger exposure area).A mathematical equalization of the exposure areas was conducted(the area of the detector exposed to the alpha emitter sample at a distance of 2 cm was equalized to the area exposed when in contact with the sample).After equalization,a reduction in the average value of the alpha emission rate from 324.4×10^(−4) to 17.4×10^(−4) Bq cm^(−2) was observed.Conclusion The increase in distance between the sample and the detector allowed only high-energy alpha particles with a range greater than the traveled distance to reach the detector.Thus,this system can distinguish the type and number of nuclides in the sample by changing the distance between the detector and the sample according to each nucleus range.Additionally,the results show that the alpha emission rates in these bone samples are higher than the local values.