Quantifying the change in streamflow complexity in the Yangtze River.

Hydrological processes of the Yangtze River have changed over the past decades due to environmental change and human activity. This paper uses sample entropy to investigate the spatial distribution and dynamic change in streamflow series complexity in the Yangtze River, China. In this study, the complexity of the streamflow series is quantified by entropy analysis. Daily streamflow series for four stations located in the mainstem and two control stations of the two largest freshwater lakes were analysed for the past 60 years. The results showed that the complexity of the streamflow series showed an obvious spatial difference and an increasing trend from upstream to downstream in the Yangtze River. There was a negative relationship between the annual streamflow and the corresponding sample entropy, and their peak-to-valley values showed well-corresponding relationships. The complexity of the runoff series at the Cuntan, Yichang, and Datong stations showed a continuous increasing trend, while that of the Hankou station showed a decreasing trend. The Three Gorges Dam changed the streamflow series complexity in the middle reach of the Yangtze River during the initial impoundment stage, while it had only slight impacts during the fully operational stage. Compared to the mainstem reaches, the streamflow series complexity of the two lakes showed no obvious change. The complexity of the streamflow series in the mainstem of the Yangtze River has been influenced by dam construction. The study could provide a scientific reference for understanding the flow dynamic evolution in the Yangtze River.

Efficient parallel implementation of active appearance model fitting algorithm on GPU.

The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.