HaiYan Yu

Learn More
This study presents a practical methodology to monitor the spatiotemporal characteristics of urban expansion in response to rapid urbanization at the provincial scale by integrating remote sensing, urban built-up area boundaries, spatial metrics and spatial regression. Sixty-seven cities were investigated to examine the differences of urbanization(More)
Specimens of the genus Cancricepon Giard & Bonnier, 1887 are recorded for the second time from China, and a new taxon, Cancricepon multituberosum n. sp., is described based on specimens parasitising the xanthid crab Liomera laevis (A. Milne-Edwards). Females of the new species can be distinguished from the other seven species of Cancricepon by the presence(More)
The study of brain function by fMRI is currently a cynosure of researchers. In this study, we performed mental calculation as a cognitive task and used MRI for data collection. Activated brain areas were found by the software of SPM. Moreover, the average of BOLD signal (Average-BOLD) and the principal component signal by principal component analysis (PCA)(More)
Dynamic causal modeling (DCM) is a spatio-temporal renewable network model. As an analytical method of causality of functional integration in fMRI, DCM is applied to study the effective connectivity. The neuro-imaging time series of activated regions were put into DCM, and the trial-bound inputs were used as perturbations to the designed model. DCM was used(More)
A digital image watermark embedding and extracting algorithm is presented based on the multi-scale Ridgelet Transform (RT) which can efficiently represent image with linear singularities. RT also has directional sensitivity so that among the transformed coefficients the most significant one represents the most energetic direction of straight edges in an(More)
Disclaimer The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the Department of Transportation in the interest of information exchange. The U.S. Government and California Department of Transportation assume no liability for(More)
  • 1