Distributed wavelet processing within sensor networks holds promise for reducing communication energy and wireless bandwidth usage at sensor nodes. Local collaboration among nodes de-correlates measurements, yielding a sparser data set with significant values at far fewer nodes. Sparsity can then be leveraged for subsequent processing such as measurement compression, de-noising, and query routing. A number of factors complicate realizing such a transform in real-world deployments, including irregular spatial placement of nodes and a potentially prohibitive energy cost associated with calculating the transform in-network. In this paper, we address these concerns head-on; our contributions are fourfold. First, we propose a simple interpolatory wavelet transform for irregular sampling grids. Second, using ns-2 simulations of network traffic generated by the transform, we establish for a variety of network configurations break-even points in network size beyond which multiscale data processing provides energy savings. Distributed lossy compression of network measurements provides a representative application for this study. Third, we develop a new protocol for extracting approximations given only a vague notion of source statistics and analyze its energy savings over a more intuitive but naïve approach. Finally, we extend the 2-dimensional (2-D) spatial irregular grid transform to a 3-D spatio-temporal transform, demonstrating the substantial gain of distributed 3-D compression over repeated 2-D compression.

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Water-soluble pure hexagonal-phase NaYF(4):Yb(3+),Er(3+)/Tm(3+) nanoparticles were obtained by an ionothermal method for the first time which offers a new alternative in synthesizing such materials.

AIM:To analyze the spectrum and risk factors of gastroesophageal reflux disease (GERD) based on presenting symptoms and endoscopic findings. METHODS:A cross-sectional survey in a cluster random sample was conducted from November 2004 to June 2005 using a validated Chinese version Reflux Disease Questionnaire (RDQ) and other items recording the demographic characteristics and potential risk factors for GERD. Subjects were defined as having GERD symptoms according to the RDQ score (> 12). All subjects were endoscopied and the definition and severity of erosive esophagitis were evaluated by Los Angeles classification. The statistical analysis was performed with SPSS13.0 programs. RESULTS:Of 2231 recruited participants, 701 (31.40%) patients were diagnosed as having GERD while 464 (20.80%) patients had objective findings of reflux esophagitis (RE). Of those 464 patients, only 291 (13.00%) were reported as subjects with GERD symptoms. A total of 528 (23.70%) patients were found to have GERD symptoms, including 19.50% patients with grade A or B reflux esophagitis, 0.90% with grade C and 0.40% with grade D. On multivariate analysis, old age, male, moderate working burden, divorced/widowed and strong tea drinking remained as significant independent risk factors for erosive esophagitis. Meanwhile, routine usage of greasy food and constipation were considered as significant independent risk factors for non-erosive reflux disease (NERD). CONCLUSION:GERD is one of the common GI diseases with a high occurrence rate in China and its main associated factors include sex, anthropometrical variables and sociopsychological characteristics.