Recently, various research efforts have been conducted to develop strategies for accelerating multi-dimensional query processing using the graphics processing units (GPUs). However, well-known multi-dimensional access methods such as the R-tree, B-tree, and their variants are hardly applicable to GPUs in practice, mainly due to the characteristics of a hierarchical index structure. More specifically, the hierarchical structure not only causes frequent transfers of small volumes of data but also provides limited opportunity to exploit the advanced data parallelism of GPUs. To address these problems, we propose an approach that uses GPUs as a buffer. The main idea is that object entries in recently visited leaf nodes are buffered in the global memory of GPUs and processed by massive parallel threads of the GPUs. Through extensive performance studies, we observed that the proposed approach achieved query performance up to five times higher than that of the original R-tree.

Super resolution (SR) reconstruction is the process of fusing a sequence of low-resolution images into one high-resolution image. Many researchers have introduced various SR reconstruction methods. However, these traditional methods are limited in the extent to which they allow recovery of high-frequency information. Moreover, due to the self-similarity of face images, most of the facial SR algorithms are machine learning based. In this paper, we introduce a facial SR algorithm that combines learning-based and regularized SR image reconstruction algorithms. Our conception involves two main ideas. First, we employ separated frequency components to reconstruct high-resolution images. In addition, we separate the region of the training face image. These approaches can help to recover high-frequency information. In our experiments, we demonstrate the effectiveness of these ideas.

We propose a novel scheme that aims to determine the optimal number of clusters based on the field conditions and the positions of mobile sink nodes. In addition, we merge algorithms of tree-based index structures to form an energy-efficient cluster structure. A performance evaluation shows that the proposed method produces highly-balanced clusters that are energy efficient and achieves up to 1.4 times higher survival rates than the previous clustering schemes, under various operational conditions.