Note: Interpolation for evaluation of a two-dimensional spatial profile of plasma densities at low gas pressures

An interpolation algorithm for the evaluation of the spatial profile of plasma densities in a cylindrical reactor was developed for low gas pressures. The algorithm is based on a collisionless two-dimensional fluid model. Contrary to the collisional case, i.e., diffusion fluid model, the fitting algorithm depends on the aspect ratio of the cylindrical reactor. The spatial density profile of the collisionless fitting algorithm is presented in two-dimensional images and compared with the results of the diffusion fluid model.     

A study of optimal viewing distance in a parallax barrier 3D display

The optimal viewing distance was proposed as a parameter for designing a parallax barrier 3D display. It can be designed based on simple geometric method and by considering the pitches of image display pixels and parallax barrier, or even including the aperture ratios of the image display pixels and parallax barrier. It can be analyzed by using ray tracing method. By considering the optical refraction index of the cover glass, the angular behavior of the system becomes more realistic; however, the geometric method is difficult to be used. We propose a revised method for estimating the view distance and angular behavior. In this paper, we have demonstrated a designated eye position (DEP) for each view and shown that multiple DEPs make a circular curve around the center of the display. We prove the new concept by comparing the optical ray tracing calculations and optical measurement.     

Image mosaicking for polyhedral scene and in particular singly visible surfaces

Image mosaic construction is about stitching together a number of images about the same scene to construct a single image with a larger field of view. The majority of the previous work was rooted at the use of a single image-to-image mapping termed planar homography for representing the imaged scene. However, the mapping is applicable only to cases where the imaged scene is either a single planar surface, or very distant from the cameras, or imaged under a pure rotation of the camera, and that greatly limits the range of applications of the mosaicking methods. This paper presents a novel mosaicking solution for scenes that are polyhedral (thus consisting of multiple surfaces) and that are pictured possibly in closed range of the camera. The solution has two major advantages. First, it requires only a few correspondences over the entire scene, not correspondences over every surface patch in it to work. Second, it conquers a seemingly impossible task—warping image data of surfaces that are visible in only one of the input images, which we refer to as the singly visible surfaces, to another viewpoint to constitute the mosaic there. We also provide a detail analysis of what determines whether a singly visible surface could be mosaicked or not. Experimental results on real image data are presented to illustrate the performance of the method.     

A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms. Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases. Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing, text mining, XML, and parallel and distributed processing.     

A Randomized Linear-Work EREW PRAM Algorithm to Find a Minimum Spanning Forest

We present a randomized EREW PRAM algorithm to find a minimum spanning forest in a weighted undirected graph. On an n -vertex graph the algorithm runs in o(( log n) ^{1+ ɛ} ) expected time for any ɛ >0 and performs linear expected work. This is the first linear-work, polylog-time algorithm on the EREW PRAM for this problem. This also gives parallel algorithms that perform expected linear work on two general-purpose models of parallel computation—the QSM and the BSP.     

An Adaptive Fuzzy-Inference-Rule-Based Flexible Model for Automatic Elastic Image Registration

In this study, a fuzzy-inference-rule-based flexible model (FIR-FM) for automatic elastic image registration is proposed. First, according to the characteristics of elastic image registration, an FIR-FM is proposed to model the complex geometric transformation and feature variation in elastic image registration. Then, by introducing the concept of motion estimation and the corresponding sum-of-squared-difference (SSD) objective function, the parameter learning rules of the proposed model are derived for general image registration. Based on the likelihood objective function, particular attention is also paid to the derivation of parameter learning rules for the case of partial image registration. Thus, an FIR-FM-based automatic elastic image registration algorithm is presented here. It is distinguished by its 1) strong ability in approximating complex nonlinear transformation inherited from fuzzy inference; 2) efficiency and adaptability in obtaining precise model parameters through effective parameter learning rules; and 3) completely automatic registration process that avoids the requirement of manual control, as in many traditional landmark-based algorithms. Our experiments show that the proposed method has an obvious advantage in speed and is comparable in registration accuracy as compared with a state-of-the-art algorithm.