Over the past few years the network-based GPS surveying technique has been widely discussed. The main advantage of the network-based technique is that the distance-dependent errors (i.e, residual atmospheric biases and orbit error effects) can be reduced and the performance of conventional single reference station, carrier phase-based techniques can be extended over longer baselines. However, e...

Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean image patches. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount o...

Machine learning and computer vision have driven many of the greatest advances in the modeling of Deep Convolutional Neural Networks (DCNNs). Nowadays, most of the research has been focused on improving recognition accuracy with better DCNN models and learning approaches. The recurrent convolutional approach is not applied very much, other than in a few DCNN architectures. On the other hand, In...

Many neuroscience applications, including understanding the evolution of brain, rely on neural cell instance segmentation, which seeks to integrate identification and segmentation neuronal cells in microscopic imagery. However, task is complicated by adhesion, deformation, vague outlines, low-contrast protrusion structures, background imperfections. On other hand, existing approaches frequently...