In order to cluster and detect anomalies for the trajectory data collected by video surveillance equipment, a novel trajectory abstraction algorithm was proposed. Trajectories were firstly resampled by utilizing the Jensen-Shannon Divergence (JSD) measurement to improve the accuracy of similarity measurement between trajectories. Resampled trajectories in equal length, i.e. with the same number of sampling points, were required by the following non-local denoising. The similarity thresholds of the trajectory were determined adaptively, and the non-local means were used to cluster the trajectory data and identify the abnormal trajectory data. From the perspective of signal processing, the grouping trajectory data was filtered by the hard-thresholding method to get the summary trajector. The proposed algorithm was insensitive to the order of input trajectories and provides visual multi-scale abstractions of trajectory data. Compared with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, the proposed algorithm performs better in terms of precision, recall and F1-mearsure.

In practical engineering applications, using traditional CPU serial computation for combustion numerical simulation can hardly meet the requirements on simulation speed. This paper took the advantage of GPU which has more computing capability than CPU, by discretizing the combustion physical equations on staggered grid, solving the discrete equations with preconditioned bi-conjugate gradient stabilized (PBiCGSTAB) method, exploring the parallel algorithm of GPU-oriented matrix vector multiplication and the parallel algorithm of inverse matrix vector multiplication. Hence a feasible method for numerical calculation of laminar diffusion combustion on GPU was offered. The experimental results indicate that the parallel program on GPU achieved more than 10 times speedup relative to serial program on CPU. Since the calculated results are in line with the actual situation, the method is feasible and efficient.