Automated analysis of nerve-cell images using active contour models

The number of nerve fibers (axons) in a nerve, the axon size, and shape can all be important neuroanatomical features in understanding different aspects of nerves in the brain. However, the number of axons in a nerve is typically in the order of tens of thousands and a study of a particular aspect of the nerve often involves many nerves. Potentially meaningful studies are often prohibited by the huge number involved when manual measurements have to be employed. A method that automates the analysis of axons from electron-micrographic images is presented. It begins with a rough identification of all the axon centers by use of an elliptical Hough transform procedure. Boundaries of each axons are then extracted based on active contour model, or snakes, approach where physical properties of the axons and the given image data are used in an optimization scheme to guide the snakes to converge to axon boundaries for accurate sheath measurement. However, false axon detection is still common due to poor image quality and the presence of other irrelevant cell features, thus a conflict resolution scheme is developed to eliminate false axons to further improve the performance of detection. The developed method has been tested on a number of nerve images and its results are presented.     

Pseudo software: A mediating instrument for modeling software requirements

A new synthesis of software requirements models called pseudo software is proposed with the aim to cut requirements-related errors. Pseudo software achieves this aim by serving as a mediating instrument to empower stakeholders to participate in requirements elicitation and validation through model construction and manipulation, and to provide guidance to the development team to correctly interpret the requirements in the downstream development activities. Pseudo software obtains its traits as a mediating instrument through the choice of requirements information bits and the use of multimodal representations with tool support to integrate the requirements. Using historical data of fifty projects in the enterprise computing domain, pseudo software is shown to effectively cut the requirements-related errors committed by both the customer and the development team.     

Weight determination in the cross-efficiency evaluation

A neutral data envelopment analysis (DEA) model for cross-efficiency evaluation was recently proposed by Wang and Chin [Wang and Chin (2010b). A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications, 37(5), 3666–3675], which maximinimizes the relative efficiency of each output and effectively reduces the number of zero weights of outputs. Since a large number of zero weights may still exist among inputs, this paper proposes a simultaneously input- and output-oriented weight determination DEA model for the cross-efficiency evaluation. The new DEA model proves to reduce the number of zero weights for both inputs and outputs very significantly, as illustrated by numerical examples. The weights determined by the new DEA model are neutral, neither aggressive nor benevolent.     

An effective 3-in-1 keyword search method over heterogeneous data sources

Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogeneous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.     

Exploring factors influencing consumers’ behavioral intention to adopt broadband in Malaysia

The purpose of this paper is to examine various normative, attitudinal and control factors influencing consumers’ intention to adopt broadband internet in a developing country such as Malaysia. This study is based on empirical data collected using a self-administered questionnaire relating to the normative, attitudinal and control variables. Structural equation modeling analysis is conducted to test the role of numerous variables on consumers’ behavioral intentions to adopt broadband internet in Malaysia. Results revealed that Primary Influences (PI), Relative Advantage (RA), Hedonic Outcomes (HO), Facilitating Conditions Resources (FCR), and Self-Efficacy (SE) are positively associated with consumers’ Behavioral Intention (BI) to adopt broadband in Malaysia. The significance of this research study is twofold. Practically, this research study provide some useful guidelines to industry players, be it the internet service providers (ISPs) or the policy makers, to understand which are the factors (primary influences, self-efficacy, relative advantage, hedonic outcomes and facilitating conditions resources) that can have an influence on consumers’ intention to adopt broadband technology. It was found that primary influences, self-efficacy and relative advantage are the key determinants that influences consumers’ broadband adoption. From here, practitioners could take into consideration our findings when revising and restructuring their marketing strategy. Theoretically, the research framework used in this study is an extension from the past research models used (i.e. TPB, DOI and MATH). With the newly integrated framework, a greater level of comprehension can be attained with regards to the broadband acceptance among the Malaysian consumers.     

Technical importance ratings in fuzzy QFD by integrating fuzzy normalization and fuzzy weighted average

Fuzzy quality function deployment (QFD) has been extensively used for translating customer requirements (CRs) into product design requirements (DRs) in fuzzy environments. Existing approaches, however, for rating technical importance of DRs in fuzzy environments are found problematic, either incorrect or inappropriate. This paper investigates how the technical importance of DRs can be correctly rated in fuzzy environments. A pair of nonlinear programming models and two equivalent pairs of linear programming models are developed, respectively, to rate the technical importance of DRs. The developed models are examined and illustrated with two numerical examples.     

A 1-Local Asymptotic 13/9-Competitive Algorithm for Multicoloring Hexagonal Graphs

In the frequency allocation problem, we are given a mobile telephone network, whose geographical coverage area is divided into cells, wherein phone calls are serviced by assigning frequencies to them so that no two calls emanating from the same or neighboring cells are assigned the same frequency. The problem is to use the frequencies efficiently, i.e., minimize the span of frequencies used. The frequency allocation problem can be regarded as a multicoloring problem on a weighted hexagonal graph. In this paper, we give a 1-local asymptotic 4/3-competitive distributed algorithm for multicoloring a triangle-free hexagonal graph, which is a special case of hexagonal graph. Based on this result, we then propose a 1-local asymptotic13/9-competitive algorithm for multicoloring the (general-case) hexagonal graph, thereby improving the previous 1-local 3/2-competitive algorithm. A preliminary version of this paper appeared in the Proceedings of the 13th Annual International Computing and Combinatorics Conference (COCOON 2007), LNCS 4598, pp. 526–536. Y. Zhang research was supported by European Regional Development Fund (ERDF). F.Y.L. Chin research was supported by Hong Kong RGC Grant HKU-7113/07E. H. Zhu research was supported by National Natural Science Fund (grant #60496321).     

Failure mode and effects analysis using a group-based evidential reasoning approach

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, risks and concerns) can occur. It is a group decision function and cannot be done on an individual basis. The FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information such as complete and incomplete, precise and imprecise and known and unknown because of its cross-functional and multidisciplinary nature. These different types of information are very difficult to incorporate into the FMEA by the traditional risk priority number (RPN) model and fuzzy rule-based approximate reasoning methodologies. In this paper we present an FMEA using the evidential reasoning (ER) approach, a newly developed methodology for multiple attribute decision analysis. The proposed FMEA is then illustrated with an application to a fishing vessel. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties.