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Subject: search.filters.subject.Fuzzy C-Means

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    Fingerprint-based technique for indoor localization in wireless sensor networks using Fuzzy C-Means clustering algorithm
    (2011-12-01)
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    ;
    Sooraksa, Pitikhate
    ;
    Takada, Jun Ichi
    ZigBee as IEEE 802.15.4 standard has been using for main research topic in the wireless sensor network (WSN) applications. The new method of radio frequency (RF) fingerprint-based technique for indoor localization is proposed. The received signal strength indicator (RSSI) is used as database values which correspond to the location of the sensor nodes. Fuzzy C-Means (FCM) clustering algorithm is applied as the experiment data cluster method. FCM algorithm is deployed to cluster the obtained feature vectors into several classes corresponding to the different amount of RSSI values. The results show that FCM can cluster the target node in a group of the fingerprint database. The location of target node is arranged in various forms to validate the accuracy of the clustering technique. Euclidean distance is used as the parameter to compare the similarity between fingerprint database and the target location. The results show that the new method is simple and effective method to reduce the complexity and to support the low power and to reduce the time using in the fingerprint-based localization technique. © 2011 IEEE.
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    Medical Image Compression Using Tree-Structured Vector Quantization and Fuzzy C-Means
    (2002-01-01)
    Supot, Sookpotharom
    ;
    Yuttana, Kitjaidure
    ;
    Manas, Sangworasil
    Compression of magnetic resonance images (MRI) has proved to be more difficult than other medical imaging modalities. In an average sized hospital, many tera bytes of digital imaging data (MRI) are generated every year, almost all of which has to be kept. Compression of medical images is currently being performed by using different algorithms. In this paper, Fuzzy Clustering Method is used for the image Tree Structure Vector Quantization (TSVQ). First, MR image is used for the feature vector. Then use this feature vector to design a classification tree by Fuzzy C-Means (FCM) algorithm to split two clusters. At every nonterminal, the centroid of the feature vectors clustered in each child node is computed to be the testing vector. At every leaf, the centroid of the training image blocks corresponding to their feature vectors falling on the same terminal node is calculated to be the codevector. All codevectors in the leaves are composed of a codebook. By doing so, the algorithm can preserve the edge of image, make good image quality, and reduce the processing time while constructing Tree Structured Codebook.