This paper presents a formal definition of stable peers, a novel method to separate stable peers from all peers and an analysis of the session sequences of stable peers in P2P (Peer-to-Peer) systems. This study uses t...
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This paper presents a formal definition of stable peers, a novel method to separate stable peers from all peers and an analysis of the session sequences of stable peers in P2P (Peer-to-Peer) systems. This study uses the KAD, a P2P file sharing system with several million simultaneous users, as an example and draws some significant conclusions: (1) large numbers of peers with very short session time usually possess few sessions;(2) the stable peers is about 0.6％ of all peers;(3) the 70％ of stable peers possess very long total session time ensured by a large number of sessions, and possess large difference between session time;(4) the 30％ of stable peers, whose average session time is 1.8 times of the former, possess long total session time, a small number of sessions and high availability. We believe that these two types of stable peers can be used for different functions to solve the churn problem in the hierarchical P2P systems.
Using historical time-series data, we test for convergence and common trends in real per capita output for New Zealand and her four major trading partners. Both bivariate and multivariate time-series methods are used,...
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Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical appli...
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ISBN:
(纸本)9781479980826
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical applications. Here in this paper, we here supposed to propose a novel image segmentation using iterative partitioning mean shift clustering algorithm, which overcomes the drawbacks of conventional clustering algorithms and provides a good segmented images. Simulation performance shows that the proposed scheme has performed superior to the existing clustering methods.
This study proposes an integrated Decision Support System (DSS) with Multi-criteria Decision-Making (McDM) to evaluate trainers in organizations and choose the most suitable one(s) for a training program. The clusteri...
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Urine color is an indicator of health status, especially for patients undergoing medical intervention treatment of urinary catheterization. Urinary tract infections are the most frequently developed infections among p...
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Urine color is an indicator of health status, especially for patients undergoing medical intervention treatment of urinary catheterization. Urinary tract infections are the most frequently developed infections among patients receiving treatment in medical institutions. Such infections can be detected from urine color, like in the case of the purple urine bag syndrome. However, it is a difficult task for non-nursing care star and even the nursing staff to correctly conduct naked-eye identification without proper tools. To better assist both nursing and non-nursing care staff with the detection of infection signs in urine bag patients, a urine color automatic identification device has been developed. The device is based on microcontroller framework and color quantization algorithm. A hybrid color quantization algorithm and two features were proposed to identify the urine color. The identified color, as query data instead of human-described color keyword, can be used to retrieve the information from the database and then find possible symptoms for early warning. Instead of the nursing sta r, the device can automatically identify the patient's urine color. From experimental results, the device with the proposed algorithm shows its capability and feasibility of the urine color automatic identification.
In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks....
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In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.
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