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Cluster formation and cluster head selection are important problems in sensor network applications and can drastically affect the network's communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In our previous work, in order to deal with this problem, we proposed a power reduction algorithm for sensor...
This paper addresses energy-efficient data gathering issues in wireless sensor networks (WSNs). Leveraging data correlation in densely-deployed sensor networks, we propose an Energy-aware Probability-based Clustering algorithm (EPC), featuring high scalability and flexibility particularly suitable for large-scale WSNs. Unlike most existing data gathering schemes that construct static routing structures...
In this paper, we present a new composite key management technique for key management in ad hoc network. The technique includes hierarchical clustering, partially distributed key management, offline certification authority and mobile agent. We apply the concept of dominating set based clustering for partitioning network into clusters. The cluster head is elected based on the trust ability of the node...
Software clustering is a useful technique to recover architecture of a software system. The results of clustering depend upon choice of entities, features, similarity measures and clustering algorithms. Different similarity measures have been used for determining similarity between entities during the clustering process. In software architecture recovery domain the Jaccard and the Unbiased Ellenberg...
Clustering techniques have been widely employed for software modularization. The clusters formed as a result of the clustering process may be difficult to understand unless they are appropriately labeled. One method to assign labels is to use term weighting schemes from Information Retrieval and Text Categorization which use weights to assign importance to terms in a document. Some of these term weighting...
Prolonged lifetime, improved coverage, and load balancing are important requirements in wireless sensor network applications. In this paper, we propose a new clustering protocol-adaptive clustering algorithm based on energy restriction (ACAER), which periodically selects cluster nodes according to their coverage rate and residual energy. ACAER has no assumptions about the distribution or density of...
This paper mainly studies the complex network detection algorithm, and improves an algorithm based on K-means, Another reference node density properties, this paper puts forward a method community structure detection algorithms (BSTN) based on similarity between the nodes of the complex network, the algorithm greatly reduce iteration times, using the algorithm in the computer generated stochastic...
With the development of the Broadcasting and Video network, the Monitoring System on Digital Video Broadcasting is becoming more and more important. Image recognition technology is widely applied to detect the degraded video in the television observation system. Mosaic block easily occurs in the TV signals, which will degrade the video quality. The conventional mosaic detection algorithm can't distinguish...
A novel model of fuzzy clustering neural network is discussed, which synthesizes unsupervised fuzzy competitive learning algorithm and self-organized competitive network. Based on this model, an algorithm of abrupt video shot boundary detection is presented which is a two-stage clustering on a linear feature space. The experimental results obtained demonstrate that the algorithm is feasible and efficient.
The control of pH process has a vast range of applications in wastewater treatment, biochemical and electrochemical processes, the paper and pulp industry and many other areas. Tight control of pH is also critical in the production of pharmaceuticals. However, the dynamics of pH process is highly nonlinear, time varying with change in gain of several orders. It is very difficult to investigate the...
Majority of the techniques that have been used for pattern discovery from Web Usage Data (WUD) are clustering methods. In e-commerce applications, clustering methods can be used for the purpose of generating marketing strategies, product offerings, personalization and Web site adaptation. A novel Partitional based approach for dynamically grouping Web users based on their Web access patterns using...
An adaptive fuzzy c-means (AFCM) clustering based algorithm was developed and applied to the segmentation and classification of multi-color fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means (FCM) clustering algorithm by introducing a gain field, which...
In vivo parcellation of the cerebral cortex via non-invasive neuroimaging techniques has been in active research for over a decade. A variety of model-driven or data-driven computational approaches have been proposed to parcellate the cortex. A fundamental issue in these parcellation methodologies is the features or attributes used to define boundaries between cortical regions. This paper proposes...
In this paper, a modified fuzzy c-regression model (FCRM) clustering algorithm for identification of Takagi-Sugeno (T-S) fuzzy model is proposed. The FCRM clustering algorithm has considerable sensitive to noise. To overcome this problem, a modified FCRM clustering algorithm is presented. This latter is based to adding a second regularization term in the alternative optimization process of FCRM. This...
The K-Means is a well known clustering algorithm that has been successfully applied to a wide variety of problems. However, its application has usually been restricted to small datasets. Mahout is a cloud computing approach to K-Means that runs on a Hadoop system. Both Mahout and Hadoop are free and open source. Due to their inexpensive and scalable characteristics, these platforms can be a promising...
This paper presents an approach to speed up and enhance matching of virtual network requests to available resources in virtual network provisioning frameworks. The method consists of introducing a weight or score expressing the importance of the resources, their attributes and the values taken by these attributes. The scores are obtained through statistical analysis of the requests for virtual network...
In large wireless sensor networks, low energy consumption is a major challenge. Hence, deployed nodes have to organize themselves as energy efficient as possible to avoid unnecessary sensor and transceiver operations. The energy conserving operations are limited by the task of the network, usually the network has to guarantee complete functionality during its lifetime. The contribution of this paper...
In recent years a flurry of research activity has produced many suggested schemes for clustering wireless sensor networks, but no hard numbers for real-world implementations. This lack of guidance for developers makes it very difficult to effectively and confidently build working networks. This paper partially addresses these concerns by deriving minimal timing bounds on the clustering process in...
For linear plants, IMC have been shown good robustness properties against disturbances and model mismatches. However, when uncertain processes are concerned, the original IMC structure cannot be directly used for control system implementation. In this paper, an internal multiple model control (IMMC) based on linear model's library is introduced. This approach supposes the definition of a set of local...
Populations of healthy older individuals are often highly heterogeneous, as prevalence of various underlying pathologies increases with age. Finding coherent groups of normal older adults may allow to identify subpopulations that are at risk of developing Alzheimer's disease (AD). In this paper, we propose an approach that utilizes longitudinal magnetic resonance imaging (MRI) data to obtain natural...
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