Images are normally degraded by some form of impulse noises during the acquisition, transmission and storage in the physical media. Most of the real time applications usually require bright and clear images, hence distorted or degraded images need to be processed to enhance easy identification of image details and further works on the image. In this paper we have analyzed and tested the number of existing median filtering algorithms and their limitations. As a result we have proposed a new effective noise adaptive median filtering algorithm, which removes the impulse noises in the color images while preserving the image details and enhancing the image quality. The proposed method is a spatial domain approach and uses the 3×3 overlapping window to filter the signal based on the correct selection of neighborhood values to obtain the effective median per window. The performance of the proposed effective median filter has been evaluated using MATLAB, simulations on a both gray scale and color images that have been subjected to high density of corruption up to 90% with impulse noises. The results expose the effectiveness of our proposed algorithm when compared with the quantitative image metrics such as PSNR, MSE, RMSE, IEF, Time and SSIM of existing standard and adaptive median filtering algorithms.

Image Enhancement is one of the major research areas in digital image processing. The main intention of image enhancement is to process the image so that the result is more compatible than the original image for a specific application. Many images like satellite images, medical images, aerial images and even our photographs suffer from poor contrast and noises due to various reasons such as lighting, weather or equipment that has been used to capture the image. It is necessary to enhance the contrast and remove the noises to increase the image quality by using image parameters. Image enhancement techniques differ from one field to another according to its objective. The noises such as Gaussian noise, Salt and Pepper noise and Speckle noise affect most of the images. This paper discusses the advantages and disadvantages of various image enhancement techniques and the metrics which have been used for quantitative measures. Finally it decides which techniques are most appropriate for the real-time image enhancement.

Clustering is an important descriptive model in data mining. It groups the data objects into meaningful classes or clusters such that the objects are similar to one another within the same cluster and are dissimilar to other clusters. Spatial clustering is one of the significant techniques in spatial data mining, to discover patterns from large spatial databases. In recent years, several basic and advanced algorithms have been developed for clustering spatial datasets. Clustering technique can be categorized into six types namely partitioning, hierarchical, density, grid, model, and constraint based models. Among these, the density based technique is best suitable for spatial clustering. It characteristically consider clusters as dense regions of objects in the data space that are separated by regions of low density (indicating noise).The clusters which are formed based on the density are easy to understand, filter out noise and discover clusters of arbitrary shape. This paper presents a comparative study of different density based spatial clustering algorithms, and the merits and limitations of the algorithms are also evaluated.

Wireless Sensor Network (WSN) is a collection of autonomous sensor nodes which are low cost hardware components consists of sensor nodes with constraints on battery life, memory size and computation capabilities to monitor physical (or) environmental conditions. WSN is deployed in unattended and unsecure environments, so it is vulnerable to various types of attacks. One of the physical attacks is node replication attack (or) clone attack. An adversary can easily capture one node from the network and extract information from captured node. Then reprogram it to create a clone of a captured node. Then these clones can be deployed in all network areas, they can be considered as legitimate members of the network, so it is difficult to detect a replicated node. WSN can be either static (or) mobile, in that centralized and distributed clone attack detection methods are available. In this paper we analysis various centralized and distributed protocols in the static and mobile environments. We review these protocols and compare their performance with the help of witness selection, communication and memory overhead, detection probability of replicated nodes, resilience against adversary's node compromise. General TermsClone attack, Clone attack detection approach, Static nodes, and Mobile nodes.

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