A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning

This paper presents a computationally efficient approach for mobile subscriber position estimation in wireless networks. A method of data scaling assisted by timing adjust is introduced in fingerprint-based location estimation under a framework which allows for minimising computational cost. The proposed method maintains a comparable level of accuracy to the traditional case where no data scaling is used and is evaluated in a simulated environment under varying channel conditions. The proposed scheme is studied when it is augmented by a hidden-Markov model to match the internal parameters to the channel conditions that present, thus minimising computational cost while maximising accuracy. Furthermore, the timing adjust quantity, available in modern wireless signalling messages, is shown to be able to further reduce computational cost and increase accuracy when available. The results may be seen as a significant step towards integrating advanced position-based modelling with power-sensitive mobile devices.     

Shape-based tracking of left ventricular wall motion

An approach for tracking and quantifying the nonrigid, nonuniform motion of the left ventricular (LV) endocardial wall from two-dimensional (2-D) cardiac image sequences, on a point-by-point basis over the entire cardiac cycle, is presented. Given a set of boundaries, motion computation involves first matching local segments on one contour to segments on the next contour in the sequence using a shape-based strategy. Results from the match process are incorporated with a smoothness term into an optimization functional. The global minimum of this functional is found, resulting in a smooth flow field that is consistent with the match data. The computation is performed for all pairs of frames in the temporal sequence and equally sampled points on one contour are tracked throughout the sequence, resulting in a composite flow field over the entire sequence. Two perspectives on characterizing the optimization functional are presented which result in a tradeoff resolved by the confidence in the initial boundary segmentation. Experimental results for contours derived from diagnostic image sequences of three different imaging modalities are presented. A comparison of trajectory estimates with trajectories of gold-standard markers implanted in the LV wall are presented for validation. The results of this comparison confirm that although cardiac motion is a three-dimensional (3-D) problem, two-dimensional (2-D) analysis provides a rich testing ground for algorithm development