This study extended the computation of GLCM (gray level co-occurrence matrix) to a three-dimensional form. The objective was to treat hyperspectral image cubes as volumetric data sets and use the developed 3D GLCM computation algorithm to extract discriminant volumetric texture features for classification. As the kernel size of the moving box is the most important factor for the computation of ...

Kernel machines such as kernel SVM and kernel ridge regression usually construct high quality models; however, their use in real-world applications remains limited due to the high prediction cost. In this paper, we present two novel insights for improving the prediction efficiency of kernel machines. First, we show that by adding “pseudo landmark points” to the classical Nyström kernel approxim...

A new fast multipole formulation for solving elliptic difference equations on unbounded domains and its parallel implementation are presented. These difference equations can arise directly in the description of physical systems, e.g. crystal structures, or indirectly through the discretization of PDEs. In the analog to solving continuous inhomogeneous differential equations using Green’s functi...

Physical simulations provide a rich source of time-variant three-dimensional data. Unfortunately, the data generated from these types of simulation are often large in size and are thereby only experienced through pre-rendered movies from a fixed viewpoint. Rendering of large point cloud data sets is well understood, however the data requirements for rendering a sequence of such data sets grow l...

UNLABELLED Genome-wide proximity ligation assays, e.g. Hi-C and its variant TCC, have recently become important tools to study spatial genome organization. Removing biases from chromatin contact matrices generated by such techniques is a critical preprocessing step of subsequent analyses. The continuing decline of sequencing costs has led to an ever-improving resolution of the Hi-C data, result...

Current drug-eluting stent (DES) technology is not optimized with regard to the pharmacokinetics of drug release, more research on the <span style="font-size: 12pt; color: #000000; font-style: normal; ...

This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find ago...

Kernel functions can be viewed as a non-linear transformation that increases the separability of the input data by mapping them to a new high dimensional space. The incorporation of kernel function enables the K-Means algorithm to explore the inherent data pattern in the new space. However, the recent applications of kernel KMeans algorithm are confined to small corpora due to its expensive com...