A Self-calibration Algorithm Based on a Unified Framework for Constraints on Multiple Views

In this paper, we propose a new self-calibration algorithm for upgrading projective space to Euclidean space. The proposed method aims to combine the most commonly used metric constraints, including zero skew and unit aspect-ratio by formulating each constraint as a cost function within a unified framework. Additional constraints, e.g., constant principal points, can also be formulated in the same framework. The cost function is very flexible and can be composed of different constraints on different views. The upgrade process is then stated as a minimization problem which may be solved by minimizing an upper bound of the cost function. This proposed method is non-iterative. Experimental results on synthetic data and real data are presented to show the performance of the proposed method and accuracy of the reconstructed scene.     

A transduction-based approach to fuzzy clustering, relevance ranking and cluster label generation on web search results

This paper details a modular, self-contained web search results clustering system that enhances search results by (i) performing clustering on lists of web documents returned by queries to search engines, and (ii) ranking the results and labeling the resulting clusters, by using a calculated relevance value as a degree of membership to clusters. In addition, we demonstrate an external evaluation method based on precision for comparing fuzzy clustering techniques, as well as internal measures suitable for working on non-training data. The built-in label generator uses the membership degrees and relevance values to weight the most relevant results more heavily. The membership degrees of documents to fuzzy clusters also facilitate effective detection and removal of overly similar clusters. To achieve this, our transduction-based clustering algorithm (TCA) and its fuzzy counterpart (FTCA) employ a transduction-based relevance model (TRM) to consider local relationships between each web document. Results from testing on five different real-world and synthetic datasets results show favorable results compared to established label-based clustering algorithms Suffix Tree Clustering (STC) and Lingo.     

Varying SiO2 thickness of core–shell noble-grains on a gradient sensing plate to enhance field Raman scattering

This study demonstrates a strong surface-enhanced Raman scattering (SERS) of crystal violet (CV) dye by using SiO₂ shell/Ag core nanoparticle in gradient-size surface plate. The excitation of CV dye can be enhanced by the localized surface plasmons of Ag core/shell SiO₂ grains due to electromagnetic (EM) enhancement induced. For SERS resonance, the change of dielectric environment of grains results in red shift and magnification of spectra in varying SiO₂ thickness. Herein, the enhanced SERS conducted the core/shell grain with an SiO₂ thickness of 8.7 nm to magnify the intensity about 83 %-fold that is a direct evidence in enhanced charge transport and mutative dielectric environment.

On mean shift-based clustering for circular data

Cluster analysis is a useful tool for data analysis. Clustering methods are used to partition a data set into clusters such that the data points in the same cluster are the most similar to each other and the data points in the different clusters are the most dissimilar. The mean shift was originally used as a kernel-type weighted mean procedure that had been proposed as a clustering algorithm. However, most mean shift-based clustering (MSBC) algorithms are used for numeric data. The circular data that are the directional data on the plane have been widely used in data analysis. In this paper, we propose a MSBC algorithm for circular data. Three types of mean shift implementation procedures with nonblurring, blurring and general methods are furthermore compared in which the blurring mean shift procedure is the best and recommended. The proposed MSBC for circular data is not necessary to give the number of cluster. It can automatically find a final cluster number with good clustering centers. Several numerical examples and comparisons with some existing clustering methods are used to demonstrate its effectiveness and superiority of the proposed method.     

A faster path planner using accelerated particle swarm optimization

The idea of placing small mobile robots to move around in a large building to detect potential intruders has been around for some time. However, there are still two major hurdles to overcome: to locate itself in the environment and to make a decision on how to move around safely and effectively at a reasonable computation cost. This paper describes a mathematical model for developing a scheme for an autonomous low cost mobile robot system using visual simultaneous localization and mapping and accelerated particle swarm intelligent path planner. The results indicated that this system could provide a solution for the problem of indoor mobile robot navigation. Advances in computer technology make this technique a cost effective solution for a future home service robot.     

Using Delta Range for Fault Detection/Exclusion on GPS Positioning

Conventionally, the well‐known position‐velocity‐acceleration (PVA) model is adopted as the dynamic model of the Kalman filter for global positioning system (GPS) positioning. Nevertheless, when a moving vehicle corners in the situation of accelerated motion, the conventional PVA model without using extra sensors is no longer adequate for describing the motion of the vehicle. Thus, the positioning result of the vehicle is less accurate during accelerated motion, and normalized innovation squared is no longer suitable as the test statistic measurement for fault detection and exclusion on GPS positioning. Therefore, in this paper, the delta range (DR) equation is proposed to accurately model the dynamic behavior of a maneuvering vehicle. We propose both the conventional PVA model and the delta range model on the Kalman filter for a maneuvering vehicle to measure its position. Simulation results show that the proposed delta range can perform both positioning and fault detection and exclusion well as the vehicle maneuvers. Finally, we successfully perform two experiments to demonstrate that the DR model has a better positioning effect than the PVA model for a maneuvering vehicle. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Fabrication of imprinted photonic films via predesigned multiple UV-polymerizations and their ability to detect solvents and metal ions in aqueous solution

To synthesize photonic films without a chiral dopant, a predesigned multiple photopolymerization process was carried out. The photonic films were prepared by the photopolymerization of a mixture of chiral nematic liquid crystals. After polymerization, the chiral dopant, CB15, was removed and recycled. The imprinted photonic polymer films showed Bragg reflection without the presence of the chiral dopant. Upon the sensing of solvents in aqueous solution, significant color changes and peak shifts were observed by the naked eye and ultraviolet–visible spectroscopy, respectively. A linear calibration curve between the central wavelength of the reflection band of the fabricated imprinting film and the volume ratio of 1,4-dioxane in water was observed. Furthermore, the sensing of chloroform content in methanol, ethanol, and acetone via the imprinted film were also investigated. The results suggest that the synthesized imprinted photonic films can detect different kinds of mixed solvents. The sensing properties of the photonic films were further improved by copolymerization with a rhodamine-derived monomer. The synthesized modified photonic films can detect heavy metal ions in aqueous solution. This study reports a novel, recyclable, and easy approach to detect organic solvents and copper ions in aqueous solution.     

2005 EDOC: meeting the challenges of enterprise computing

An increasing demand for interoperable applications exists, sparking the real-time exchange of data across borders, applications, and IT platforms. To perform these tasks, enterprise computing now encompasses a new class of groundbreaking technologies such as Web services and service-oriented architecture (SOA); business process integration and management; and middleware support, like that for utility, grid, peer-to-peer, and autonomic computing. Enterprise computing also influences the processes for business modeling, consulting, and service delivery; it affects the design, development, and deployment of software architecture, as well as the monitoring and management of such architecture. As enterprises demand increasing levels of networked information and services to carry out business processes, IT professionals need conferences like EDOC to discuss emerging technologies and issues in enterprise computing. For these reasons, what started out as the Enterprise Distributed Object Computing (EDOC) conference has come to encompass much more than just distributed objects. So this event now used the name International EDOC Enterprise Computing Conference, to recognize this broader scope yet also retain the initial conference's name recognition.     

A system for matching moving vehicles across different camera sites in outdoor environments

A method for identifying the same running vehicle from images captured at different camera sites is proposed. This is achieved through an image matching process. With cameras installed at the inlets and exits of highways and at crossroads in a road network, the system can be used for tracking the routes of individual vehicles and computing the times of traveling from one site to another. The system has the advantages of being low cost and not requiring sophisticated imaging hardware. Contributions include (i) a novel background removal method that employs the robust RANSAC algorithm for estimation, (ii) an elaborate shadow removal algorithm, and (iii) an effective license plate matching method applicable to poor quality images.© 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 211–219, 2005     

An Information Service for Grid Virtual Organization: Architecture, Implementation and Evaluation

Information Service is the heart of the entire Grid software infrastructure. It maintains various information in a Grid environment, and provides this information to users on request. We present an Information Service architecture for information capturing, aggregation, and provisioning in a Grid Virtual Organization (VO). This Information Service is a hierarchical structure which consists of VO layer, site layer and resource layer. The architecture defines downloadable and pluggable information sensors for portability and flexibility of information capturing. The meta-data mechanism is incorporated into our Information Service for information organization and management. Experiments were conducted to evaluate the performance of the Information Service, and the results show that the Information Service presents satisfactory scalability with number of users and number of information amount. Our Information Service has been implemented based on the Globus Toolkit as a Grid service compliant to the Open Grid Services Architecture (OGSA) specifications.