Abstract Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with greatest need often surprisingly challenging in developing countries. Due logistical challenges traditional household surveying, official statistics can be slow updated; estimates that exist coarse, a consequence prohibitive costs and...

Despite the fact that environmental variables often exhibit spatial correlation, this is not typically accounted in calibration models for remote sensed observations. Another feature in many remote sensing instruments is that the derived predictor variables are highly correlated. Both of these affect the prediction accuracy, especially when the training set for model calibration is small. We pr...

Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for estimation is an important means estimating regional concentration. However, due to lack of consideration uncertainty in methods, based on have certain overfitting problems process estimation. In response this problem, paper designs a Bayesian model that takes into account mul...

ultrasound images suffer of multiplicative noise named speckle. different de-speckling algorithms run either in spatial domain or in transformed domain. in this paper, an adaptive filter in spatial domain according to assume the nakagami distribution as the statistic of log-compressed ultrasound images is used. for de-speckling in transformed domain, the non-sub sampled shearlet transform is us...