RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics

Image inpainting refers to filling missing places in images using neighboring pixels. It also has many applications in different tasks of image processing. Most of these applications enhance the image quality by significant unwanted changes or even elimination of some existing pixels. These changes require considerable computational complexities which in turn results in remarkable processing time. In this paper we propose a fast inpainting algorithm called RIBBONS based on selection of patches around each missing pixel. This would accelerate the execution speed and the capability of online frame inpainting in video. The applied cost-function is a combination of statistical and spatial features in all neighboring pixels. We evaluate some candidate patches using the proposed cost function and minimize it to achieve the final patch. Experimental results show the higher speed of 'Ribbons' in comparison with previous methods while being comparable in terms of PSNR and SSIM for the images in MISC dataset

Image Inpainting by Hyperbolic Selection of Pixels for Two Dimensional Bicubic Interpolations

Image inpainting is a restoration process which has numerous applications. Restoring of scanned old images with scratches, or removing objects in images are some of inpainting applications. Different approaches have been used for implementation of inpainting algorithms. Interpolation approaches only consider one direction for this purpose. In this paper we present a new perspective to image inpainting. We consider multiple directions and apply both one-dimensional and two-dimensional bicubic interpolations. Neighboring pixels are selected in a hyperbolic formation to better preserve corner pixels. We compare our work with recent inpainting approaches to show our superior results

PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Convolutional Neural Networks

Abstract Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model

Tyre-road adherence conditions estimation for intelligent vehicle safety applications

It is well recognized in the automotive research community that knowledge of the real-time tyre-road friction conditions can be extremely valuable for intelligent safety applications, including design of braking, traction, and stability control systems. This paper presents a new development of an on-line tyre-road adherence estimation methodology and its implementation using both Burckhardt and LuGre tyre-road friction models. The proposed strategy first employs the recursive least squares to identify the linear parameterization (LP) form of Burckhardt model. The identified parameters provide through a Takagi-Sugeno (T-S) fuzzy system the initial values for the LuGre model. Then, it is presented a new large-scale optimization based estimation algorithm using the steady state solution of the partial differential equation (PDE) form of LuGre to obtain its parameters. Finally, real-time simulations in various conditions are provided to demonstrate the efficacy of the algorithm

Patrick-Murray Administration Issues South Coast Rail Executive Order and Awards Technical Assistance

BACKGROUND:The migration of healthcare specialists from developing countries has increased in recent years. This has caused a rapid reduction in the access to and quality of healthcare services in such countries. The aim of this study is to evaluate the factors affecting the migration of specialist human resources in Iran's healthcare system. METHODS:This is a qualitative study, which was carried out through semi-structured interviews between 2015 and 2016. For sampling, purposive sampling method with maximum variation sampling was used. Further, data saturation was observed by conducting 21 interviews, and data analysis was performed using the MAXQDA10 content analysis software. RESULTS:Factors affecting the migration of specialists were classified into five key themes, including structural, occupational, personal, socio-political and economic factors. These themes consisted of 12 categories and 50 subcategories. The most important factors affecting the migration of our study population were structural issues, occupational problems, and personal concerns. CONCLUSION:Identification of factors influencing migration is the first step to prevent the migration of specialist human resources. Implementing the recommendations proposed in this study would assist to prevent migration of medical professionals