Abstract: A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions and to cooperatively pass their data through the network to a Base Station. Due to the increase in the quantity of data across the world, it turns out to be very complex task for analyzing those data. Categorize those data into remarkable collection is one of the common forms of understanding and learning. This leads to the requirement for better data mining technique. These facilities are provided by a standard data mining technique called Clustering. Clustering can be considered the most important unsupervised learning technique so as every other problem of this kind; it deals with finding a structure in a collection of unlabeled data. This paper reviews four types of clustering techniques- K-Means Clustering, LEACH, HEED, and TEEN. K-Means clustering is very simple and effective for clustering. It is appropriate when the large dataset is used for clust

• Publication Date: 25-May-2014
• DOI: 10.15224/978-1-63248-028-6-01-02
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Abstract: There are various applications of image processing like satellite imaging, biomedical imaging, remote sensing and radar imaging where the size of the image and quality of the image is most important but it requires a lot of space to store at the places due to the high bandwidth of the communication of the original image. In these applications we apply the image compression techniques to store the data and reduce its space for storage time. There are various factors which affecting the compression like spatial resolution, bit depth, noise, image sizing, viewing distance, etc. Biomedical imaging focuses on the capture of images for both diagnostic and therapeutic purpose. The biomedical images can be displayed by the high bit resolution and we have to convert the high bit resolution into the low bit resolution for displaying the images. This problem is occurs mostly on the low-cost or small devices. In this paper, we capture the 2D images for resolving. The resolution of the 2D images is

• Publication Date: 25-May-2014
• DOI: 10.15224/978-1-63248-028-6-01-04
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