The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios. © 2012 Elsevier Ltd. All rights reserved.

Comparison of relative flexoelectric coefficients measured by different techniques

While it is well known that it is possible to determine the effective flexoelectric coefficient of nematic liquid crystals using hybrid cells [1], this technique can be difficult due to the necessity of using a D.C. field. We have used a second method[2], requiring an A.C. field, to determine this parameter and here we compare the two techniques. The A.C. method employs the linear flexoelectrically induced linear electro-optic switching mechanism observed in chiral nematics. In order to use this second technique a chiral nematic phase is induced in an achiral nematic by the addition of a small amount of chiral additive (∼3% concentration w/w) to give helix pitch lengths of typically 0.5-1.0 μm. We note that the two methods can be used interchangeably, since they produce similar results, and we conclude with a discussion of their relative merits.

The cross-entropy method for blind multiuser detection

We consider the problem of blind multiuser detection. We adopt a Bayesian approach where unknown parameters are considered random and integrated out. Computing the maximum a posteriori estimate of the input data sequence requires solving a combinatorial optimization problem. We propose here to apply the Cross-Entropy method recently introduced by Rubinstein. The performance of cross-entropy is compared to Markov chain Monte Carlo. For similar Bit Error Rate performance, we demonstrate that Cross-Entropy outperforms a generic Markov chain Monte Carlo method in terms of operation time.

Assessing the relative road damaging potential of HGVs

Assessing the road damaging potential of heavy vehicles is becoming an increasingly important issue. In this paper, current vehicle regulations and possible future alternatives are reviewed, and are categorized as tests on individual axles and whole vehicles, and 'direct' and 'indirect' tests. Whole vehicle methods of assessing road damaging potential accurately are then discussed. Direct methods are investigated (focussing on using a force measuring mat), and drawbacks are highlighted. Indirect methods using a transient input applied to individual axles are then examined. Results indicate that if non-linearities are accounted for properly, indirect methods of assessing whole vehicle road damaging potential could offer the required accuracy for a possible future test procedure.