16399618
Fast filter-and-refine algorithms for subsequence selection
Large sequence databases, such as protein, DNA and gene sequences in biology, are becoming increasingly common. An important operation on a sequence database is approximate subsequence matching, where all subsequences that are within some distance from a given query string are retrieved. This paper proposes a filter-and-refine algorithm that enables efficient approximate subsequence matching in large DNA sequence databases. It employs a bitmap indexing structure to condense and encode each data sequence into a shorter index sequence. During query processing, the bitmap index is used to filter out most of the irrelevant subsequences, and false positives are removed in the final refinement step. Analytical and experimental studies show that the proposed strategy is capable of reducing response time substantially while incurring only a small space overhead.
20063786
Implementation of a graphics design framework on the Web
We propose a client-server graphics design framework and implement a room design system using CORBA. We have integrated technologies used in distributed computation (Java and CORBA) and computer graphics (VRML). To support multiuser collaborative design, we propose a supplier-consumer communication approach and implement it using the event service in CORBA. Our system can be applied in electronic commerce to improve the existing systems by allowing customers to select furniture and to visualize the whole furnished room in 3D.
15916460
An approach to security and privacy of RFID system for supply chain
Radio frequency identification (RFID) is expected to become pervasive and ubiquitous, as it can be embedded into everyday items as smart labels. A typical scenario of exploiting RFID is supply chain. The RFID based supply chain management yields convenience, efficiency and productivity gains. However, RFID systems create new risks to security and privacy. We briefly present the current solutions to RFID security and privacy. A new approach is then proposed, which exploits randomized read access control and thus prevents hostile tracking and man-in-the-middle attack. In addition, compared with current schemes that achieve the similar security level, the proposed approach dramatically decreases the computation load. Another benefit is that it is suitable for RFID systems with a large number of tags
9889663
Feature Similarity Based Redundancy Reduction for Gene Selection
this paper we propose a feature similarity based redundancy reduction (FSRR) algorithm for high-dimensional gene expression data analysis. FSRR has two steps. First, the relevance of each feature is evaluated. Second, based on the relevance, the redundant features are removed by feature similarity. The efficiency and effectiveness of our algorithm is established through an experimental study using gene expression data. Four state-of-art feature ranking algorithms and three feature similarity measures are compared and discussed in our work. The results indicate that our algorithm has the capability of finding a well-suited feature set and improving the classification accuracy.
15452882
On Cellular Capacity with Base Station Cooperation
This paper is concerned with cellular systems with rate constraints in fading environments. We consider a simple but asymptotically optimal matched filtering/successive interference cancellation (MF-SIC) scheme. We derive several concise and closed-form bounds for the power and spectral efficiencies of various base station cooperation strategies, which provide some useful insights into cellular systems.
45158244
Discovering pattern-based subspace clusters by pattern tree
Traditional clustering models based on distance similarity are not always effective in capturing correlation among data objects, while pattern-based clustering can do well in identifying correlation hidden among data objects. However, the state-of-the-art pattern-based clustering methods are inefficient and provide no metric to measure the clustering quality. This paper presents a new pattern-based subspace clustering method, which can tackle the problems mentioned above. Observing the analogy between mining frequent itemsets and discovering subspace clusters, we apply pattern tree - a structure used in frequent itemsets mining to determining the target subspaces by scanning the database once, which can be done efficiently in large datasets. Furthermore, we introduce a general clustering quality evaluation model to guide the identifying of meaningful clusters. The proposed new method enables the users to set flexibly proper quality-control parameters to meet different needs. Experimental results on synthetic and real datasets show that our method outperforms the existing methods in both efficiency and effectiveness.
27793638
Wiener Indices of Balanced Binary Trees
We study a new family of trees for computation of the Wiener indices. We introduce general tree transformations and derive formulas for computing the Wiener indices when a tree is modified. We present several algorithms to explore the Wiener indices of our family of trees. The experiments support new conjectures about the Wiener indices.