12587822
Clustering Research Based on Density Gradient
Cluster analysis, intending to help users understand the natural grouping or structure in a data set, has received a lot of attention in the last few years. Aimed to solve difficult problems in clustering with irregularly distributed data set, a new clustering algorithm based on density gradient is provided. With analysis of density of each point and its neighbors, the algorithm searches points with the maximum density and takes them as centers of original clusters. Then it combines some little clusters into larger clusters according to the distribution of boundary points between neighbor clusters. Experimental results show that the new algorithm has better performance than DBSCAN.
34094601
DNA-Based Algorithm for 0-1 Planning Problem
Biochemical reaction theory based DNA computation is of much better performance in solving a class of intractable computational problems such as NP-complete problems, it is important to study the DNA computation. A novel algorithm based on DNA computation is proposed, which solves a special category of 0-1 planning problem by using the surface-based fluorescence labeling technique. The analysis show that our new algorithm is of significant advantages such as simple encoding, low cost and short operating time, etc.
16518214
Key Distribution for Group-based Sensor Deployment Using a Novel Interconnection Graph
In this paper, we propose a pairwise key distribution scheme based on a novel interconnection graph termed Hierarchical Hypercube. Motivated by the fact that sensor nodes are often deployed in groups (for example, dropped from an airplane at different locations) and hence the whole network is composed of multiple such groups, we design Hierarchical Hypercube as a two layer topology, where each group is modeled by an inner hypercube and connections between the groups are modeled using an outer hypercube. we propose a topology termed Hierarchy Hypercube. While retaining the desirable properties already shown by existing pairwise schemes, by using Hierarchical Hypercube, direct communication from a node to any other nodes is not required, either within a group or among groups, and hence this topology can effectively and realistically reflect the connectivity of the physical sensor network deployed in groups. Furthermore, we propose key pre-distribution scheme based on this novel topology and show that the new scheme possesses high probability of direct key establishment, low memory overhead, and resilience in the presence of broken communication links and compromised nodes, and thus still retain the desirable properties even when the ideal logical connection are distorted in the real deployment.
33446676
Voronoi Tessellation Based Rapid Coverage Decision Algorithm for Wireless Sensor Networks
The coverage problem is a fundamental issue in the research of sensor network. A novel Voronoi tessellation based rapid coverage decision algorithm is proposed, in which the region of interest is partitioned into some square grids firstly, and then the complex area coverage problem is transformed into simple vertices coverage problem, on the basis of the good characteristics of Voronoi tessellation such as being able to divide the planner area into a set of simple geometric simplexes. Theoretical analysis and simulation results show that, the new algorithm can decrease the computation complexity of networks, especially for sensor networks with n different sensors, it can solve the coverage problem correctly for any given region of interest with time costs of O(nlogn) only.
6464793
Ringed Petersen spheres connected hypercube interconnection networks
On the basis of the short diameter of Petersen graph, and high connectivity of hypercube, a novel interconnection networks, RPSCH(n) (ringed Petersen spheres connected hypercube) is proposed, and whose characteristics are studied simultaneously. It is proved that RPSCH(n) is not only of regularity and good extensibility, but also of shorter diameter and better connectivity than those previous well-known interconnection networks such as Q/sub n/, RP(n) and HP(n). In addition, the unicasting and broadcasting routing algorithms are designed for RPSCH(n), whose communication efficiency are proved to be n-2 and n-2 respectively.
41889531
Research on Pairwise Key Establishment Model and Algorithm for Sensor Networks
Security schemes of pairwise key establishment, which enable sensors to communicate with each other securely, play a fundamental role in research on security issue in wireless sensor networks. A new kind of pairwise key pre-distribution model for sensor networks is proposed. And in addition, based on which, an efficient dynamic key path establishment algorithm is designed. Theoretic analysis and experimental figures show that the new algorithm has better performance than those previous related works.
14688887
Data Compression Algorithm Based on Hierarchical Cluster Model for Sensor Networks
A new distributed algorithm of data compression based on hierarchical cluster model for sensor networks is proposed, in which, the whole sensor network is mapped into a kind of hierarchical clusters model firstly, and then different wavelet transform models are used to commit data compression in inner and super clusters respectively. Theoretical analyses and simulation results show that, the above new methods have good performance of approximation, and can compress data and reduce the amount of data efficiently.
23743946
Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model
Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.
2862393
Online Constrained Pattern Detection over Streams
Online pattern detection poses a challenge in many data-intensive applications, including network traffic management, trend analysis, intrusion detection, and various intelligent sensor networks. These applications have to be time and space efficient while providing high quality answers. Meanwhile, far less attention has been paid for detecting constrained patterns, that cannot be simply matched because there is no available pattern for prediction. This paper presents our research effort in efficient pattern detection with constraint. We propose a new method named Online Pattern Detection with Constraint (OPDC) to detect constrained patterns over evolving data stream, taking into account various user-defined constraints. To ensure that the constrained patterns are representative, we extend regular expression in a simple but powerful way. Our experimental results on real data sets demonstrate the feasibility and effectiveness of the proposed scheme.
14823691
MRF parameter estimation by MCMC method
Abstract Markov random field (MRF) modeling is a popular pattern analysis method and MRF parameter estimation plays an important role in MRF modeling. In this paper, a method based on Markov Chain Monte Carlo (MCMC) is proposed to estimate MRF parameters. Pseudo-likelihood is used to represent likelihood function and it gives a good estimation result.
6828357
Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance