Studying Different Etching Methods Using CR-39 Nuclear Track Detector

In this research we try to investigate the optimum etching time for the tracks originate in (CR-39) solid state nuclear track detector after irradiated with alpha source (241Am) using three different etching techniques: the traditional method (water bath), microwaves and ultrasound devices. The track etching parameters: bulk etch rate (VB), track etch rate (VT), track etch rate ratio evaluates (V), critical angle (θC), and etching efficiency (η) were calculated in this research. It’s seen that the optimum etching time was ranging with (60 - 150 min), (20 - 30 min) and (60 - 120 min) when etching with water bath, microwave and ultrasound respectively. Also we observed that the critical angle was (24.29) when etching CR-39 detector with microwave. This value is lower than the critical angles values for the detector etched with water bath or ultrasound;thus it can be the optimum magnitude because its decrease leads to increasing the number of the tracks appeared in the detector and the etching efficiency.

Identification of traffic object's motion behavior based on track

To extract the track parameters of a traffic object in traffic video and identify its motion behavior,a new method is proposed based on CamShift(Continuously Adaptive Mean Shift)and HMM(Hidden Markov Model).First,an object entering the video scene is located and tracked by the CamShift based algorithm,then its track parameters are obtained.Next,the track parameters are processed to form the observation sequence of HMM,and the motion behavior modeling and probability evaluation are implemented based on HMM.At last,the behavior identification and behavior statistics of the tracked traffic object in video are achieved.Experiments show that this method can be used to sort and recognize the motion behavior of the traffic object by its corresponding behavior track,and to do some statistics or corresponding process schemes.

Non-fragile switching tracking control for a flexible air-breathing hypersonic vehicle based on polytopic LPV model

This article proposes a linear parameter varying （LPV） switching tracking control scheme for a flexible air-breathing hypersonic vehicle （FAHV）. First, a polytopic LPV model is constructed to represent the complex nonlinear longitudinal model of the FAHV by using Jacobian linearization and tensor-product （T-P） model transformation approach. Second, for less conservative controller design purpose, the flight envelope is divided into four sub-regions and a non-fragile LPV controller is designed for each parameter sub-region. These non-fragile LPV controllers are then switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a specified performance criterion. The desired non-fragile LPV switching controller is found by solving a convex constraint problem which can be efficiently solved using available linear matrix inequality （LMI） techniques, and robust stability analysis of the closed-loop FAHV system is verified based on multiple Lypapunov functions （MLFs）. Finally, numerical simulations have demonstrated the effectiveness of the proposed approach.

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.