14018437
Parallel SOR Iterative Algorithms and Performance Evaluation on a Linux Cluster
Abstract : The successive over-relaxation (SOR) iterative method is an important solver for linear systems. In this paper, a parallel algorithm for the red-black SOR method with domain decomposition is investigated. The parallel SOR algorithm is designed by combining the traditional red-black SOR and row block domain decomposition technique, which reduces the communication cost and simplifies the parallel implementation. Two other iterative methods, Jacobi and Gauss-Seidel (G-S), ate also implemented in parallel for comparison. The three parallel iterative algorithms are implemented in C and MPI (Message Passing Interface) for solving the Dirichlet problem on a Linux cluster with eight dual processor 2.6ghz 32 bit Intel Xeons, totaling 16 processors. The performances of the three algorithms are evaluated in terms of speedup and efficiency.
6853058
Markov chain correlation based clustering of gene expression data
An efficient Markov chain correlation based clustering method (MCC) has been proposed for clustering gene expression data. The gene expression data is first normalized and Markov chains (MC) are constructed from the dynamics of the gene expressions, in which the behavior of the genes at each step of the experiment can be taken into account. Based on the correlation of one-step Markov chain transition probabilities, an agglomerative method is employed to group the series that have similar behavior at each point. The proposed MCC clustering method has been applied to four gene expression datasets to obtain a number of clusters. The results show that the MCC method outperforms the commonly used K-means method and produces clusters that are more meaningful in terms of the similarity of the grouped genes. Another advantage of the proposed method over the existing clustering methods is that the knowledge of the group number is not required.
38318651
Parallel Virtual Screening for Prediction of Antimicrobial Activity Using Fourier Transforms
2272663
Refactoring Middleware with Aspects
Middleware platforms, such as Web services, J2EE, CORBA, and DCOM, have become increasingly popular during the last decade. They have been very successful in solving distributed computing problems for a large family of application domains. The architecture of middleware systems have gone through many significant cycles of evolution, both in terms of the completeness of functionality and the range of adoptions for different types of platforms. However, at the same time, it is getting increasingly difficult to achieve and to maintain a high level of adaptability and configurability because the structure of the middleware architecture is becoming overly complicated and rigid. We attribute that problem to the limitations of traditional software decomposition methods. Aspect-oriented programming, on the contrary, has introduced new design perspectives that permit the superimpositions of different abstraction models on top of one another. This is a very powerful technique for separating and simplifying design concerns. In our effort of applying principles of aspect orientation to the middleware architecture, we first pragmatically analyze the use of aspects in the middleware architecture. We then show that aspects are the correct remedy for the above outlined middleware problems by quantifying crosscutting concerns in the legacy implementations of several prominent middleware systems. Our aspect analysis results strongly indicate that modularity of middleware architecture is greatly hindered by the wide existence of tangled logic. To go one step further, we factor out a number of crosscutting concerns identified in the mining process, reimplement them as aspects, and superimpose them back into the refactored architecture. This allows us to use a set of software engineering metrics to quantify the refactorization in terms of changes in the structural complexity, modularity, and performance of the resulting system. This aspect-oriented refactoring proves that aspect orientation is capable of composing orthogonal design requirements. The final "woven" system is able to correctly provide both the fundamental functionality and the "aspectized" functionality with negligible overhead and an overall leaner architecture. Furthermore, the "aspectized" feature can be configured in and out during compile-time, which greatly enhances the configurability of the architecture.
1804026
PRISM is research in aSpect mining
Aspect oriented programming (AOP) provides programmers with a new level of modularization capability. Although improving the aspect oriented languages seems to be the most eminent task, we recognize that it is equally important for (professional) software developers to properly leverage the power of AOP and to apply AOP at large scales. As many researchers have realized, one of the best ways to assess the practicality of AOP is to retrofit AOP onto legacy software systems [6, 7, 1]. One of the important activities in doing so is aspect mining. Aspect mining aims at finding non-localized programming concerns of very large software systems, which in turn provide opportunities for aspect oriented modularization and aspect oriented refactoring. Existing work of aspect mining focuses on aspect visualization and aspect browsing [3, 2]. These tools often involve tedious human interactions for complex pattern definition over large code base. Inspired by AMT [3], AMTEX [5] is a step towards building an aspect mining environment which facilitates either autonomous or assisted aspect discovery. Prism is a major extension of AMTEX, which is designed for IDE integration, cross-language mining support, and multimodal analysis. Prism is also an extensible mining platform, which is open for supporting new mining techniques. The current implementation of Prism is built as an Eclipse plugin. It supports Java systems and partially supports C# programs. Through Prism, users can define complex lexical, semantic, and syntactical patterns. The Prism engine
15005856
Security analysis for key generation systems using face images
In this paper, we analyze the security problem of user information associated key generation (UIAKG) systems. We consider three kinds of attacks from the hacker: the exhaustive search attack, the authentic key statistics attack and the device key statistics attack. Under each attack, we give the estimate of the number of guesses the hacker has to make in order to access the system. Such analysis provides useful guidelines in designing a UIAKG system. The analysis also suggests that a user-dependent UIAKG is more secure than a user-independent one. Two UIAKG systems using face images as inputs are designed and compared to support the above theoretical analysis.
34951978
A Top-Down Fingerprint Image Enhancement Method Based on Fourier Analysis
We present a top-down fingerprint image enhancement method which is based on Fourier analysis Our method takes advantage of two filtering methods in Fourier domain proposed previously and employs a top-down iteration filtering technique, which enables the enhancement procedure not to rely severely on an accurate estimation of ridge frequency and orientation and also enables us to make use of more than the local information in local fingerprint filtering Consequently, our method is robust to handle low quality fingerprint images Results of enhancement are presented for some representative fingerprint images.
9616437
Image database retrieval utilizing affinity relationships
Recent research effort in Content-Based Image Retrieval (CBIR) focuses on bridging the gap between low-level features and high-level semantic contents of images as this gap has become the bottleneck of CBIR. In this paper, an effective image database retrieval framework using a new mechanism called the Markov Model Mediator (MMM) is presented to meet this demand by taking into consideration not only the low-level image features, but also the high-level concepts learned from the history of user's access pattern and access frequencies on the images in the database. Also, the proposed framework is efficient in two aspects: 1) Overhead for real-time training is avoided in the image retrieval process because the high-level concepts of images are captured in the off-line training process. 2) Before the exact similarity matching process, Principal Component Analysis (PCA) is applied to reduce the image search space. A training subsystem for this framework is implemented and integrated into our system. The experimental results demonstrate that the MMM mechanism can effectively assist in retrieving more accurate results from image databases.