• Volume 4,Issue 2,2010 Table of Contents
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    • >Regular Papers
    • Distance-Based Classifier via the Kernel Trick

      2010, 4(2):121-133.

      Abstract (5273) HTML (0) PDF 426.99 K (7196) Comment (0) Favorites

      Abstract:For classifying large data sets, we propose a discriminant kernel that introduces a nonlinear mapping from the joint space of input data and output label to a discriminant space. Our method differs from traditional ones, which correspond to map nonlinearly from the input space to a feature space. The induced distance of our discriminant kernel is Eu- clidean and Fisher separable, as it is defined based on distance vectors of the feature space to distance vectors on the discriminant space. Unlike the support vector machines or the kernel Fisher discriminant analysis, the classifier does not need to solve a quadric program- ming problem or eigen-decomposition problems. Therefore, it is especially appropriate to the problems of processing large data sets. The classifier can be applied to face recognition, shape comparison and image classification benchmark data sets. The method is significantly faster than other methods and yet it can deliver comparable classification accuracy.

    • Introducing Gravitational Force into Affinity Propagation Clustering

      2010, 4(2):135-149.

      Abstract (4598) HTML (0) PDF 1.93 M (3458) Comment (0) Favorites

      Abstract:Clustering has long been an important data processing task in different applications. Typically, it attempts to partition the available data into groups according to their underlying distributions, and each cluster is represented by a center or an exemplar. In this paper, a new clustering algorithm called gravitational-force-based affinity propagation (GAP) is proposed, based on the well-known Newton's law of universal gravitation. It views the available data points as nodes of a network (or planets of a universe) and the clusters and their corresponding exemplars can be obtained by transmitting affinity messages based on the gravitational forces between data points in a network. While GAP is inspired by the recently proposed affinity propagation (AP) clustering approach, it provides a new definition of the similarity between data points which makes the AP process more convincing and at the same time facilitates the differentiation of data points' importance. The experimental results show that the GAP clustering algorithm, with comparable clustering accuracy, is even more efficient than the original AP clustering approach.

    • Classification of Fatty and Cirrhosis Liver Using Wavelet-Based Statistical Texture Features andNeural Network Classifier

      2010, 4(2):151-163.

      Abstract (9305) HTML (0) PDF 2.15 M (5801) Comment (0) Favorites

      Abstract:Computational methods are useful for medical diagnosis because they provide additional information that cannot be obtained by simple visual interpretation. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. The study and development of Probabilistic Neural Network (PNN), Linear Vector Quantization (LVQ) Neural Network and Back Propagation Neural Network (BPN) for classification of fatty and cirrhosis liver from Computerized Tomography (CT) abdominal images is reported in this work. Neural networks are supported by more conventional image processing operations in order to achieve the objective set. To evaluate the classifiers, Receiver Operating Characteristic (ROC) analysis is done and the results are also evaluated by the radiologists. Experimental results show that PNN is a good classifier, giving an accuracy of 95% by holdout method and giving an accuracy of 96% by 10 fold cross validation method for classifying fatty and cirrhosis liver using wavelet based statistical texture features.

    • Approximate K-Median of Location Streams with Redundancy and Inconsistency

      2010, 4(2):165-182.

      Abstract (4031) HTML (0) PDF 1.17 M (3341) Comment (0) Favorites

      Abstract:Data streams produced by positioning systems such as Global Positioning System (GPS) or RFID readers can be considered as location streams[12]. Location streams are usually generated in a distributed fashion by a large scale distributed system covering a wide range of areas. Computing on distributed location streams is both practically useful and theoretically challenging. The results of computation could be used to schedule the traffic in a metropolis to avoid traffic jam, dispatch taxis to serve the passengers more quickly and display the current position of goods in supply chain management, etc. Since location streams are usually generated with very high rate in uncertain ways over hostile environments, the collected updates of location are probably redundant and inconsistent in a wide positioning system. To process distributed location streams with redundancy and inconsistency, this paper proposes a novel method based on min-wise hash. With this method, redundant updates of distributed location streams can be effiectively filtered out, while the true location could be derived from inconsistent ones. Consequently, globally uniform samples can be obtained. Based on the uniform samples, an algorithm for computing the approximate k-median of huge number of moving objects is presented in this paper. Furthermore, it is demonstrated that sketch-based methods are not necessarily effiective in processing location streams with redundancy and inconsistency. In addition to theoretical analysis, some extensive experiments are conducted to validate the efficiency and effiectiveness of the proposed approach.

    • An Energy-Aware Geographic Routing Protocol for Mobile Ad Hoc Networks

      2010, 4(2):183-196.

      Abstract (8319) HTML (0) PDF 1.56 M (5640) Comment (0) Favorites

      Abstract:Mobile ad hoc networks (MANET) are characterized by multi-hop wireless links and resource constrained nodes. To improve network lifetime, energy balance is an important concern in such networks. Geographic routing has been widely regarded as efficient and scalable. However, it cannot guarantee packet delivery in some cases, such as faulty location services. Moreover, greedy forwarding always takes the shortest local path so that it has a tendency of depleting the energy of nodes on the shortest path. The matter gets even worse when the nodes on the boundaries of routing holes suffer from excessive energy consumption, since geographic routing tends to deliver data packets along the boundaries by perimeter routing. In this paper, we present an Energy-Aware Geographic Routing (EGR) protocol for MANET that combines local position information and residual energy levels to make routing decisions. In addition, we use the prediction of the range of a destination's movement to improve the delivery ratio. The simulation shows that EGR exhibits a noticeably longer network lifetime and a higher delivery rate than some non-energy-aware geographic routing algorithms, such as GPSR, while not compromising too much on end-to-end delivery delay.