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Particle Swarm Optimization based effective clustering in Wireless Sensor Networks is proposed. In the existing OEERP, during cluster formation some of the nodes are left out without being a member of any of the cluster which results in residual node formation. Such residual or individual nodes forward the sensed data either directly to the Base Station or by finding the next best hop by sending many control messages hence reduces the network lifetime. The proposed E-OEERP reduces/eliminates such individual node formation and improves the overall network lifetime when compared to the existing protocols. It can be achieved by applying the concepts of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for cluster formation and routing respectively. For each Cluster Head, a supportive node called Cluster Assistant (CA) node is elected to reduce the overhead of the Cluster Head. With the help of PSO, clustering is performed until all the nodes become a member of any of the cluster. This eliminates the individual node formation which results in comparatively better network lifetime. With the concept of GSA, the term force between the cluster heads is considered for finding the next best hop during route construction phase. The performance of the proposed work in terms of Energy Consumption, Throughput, Packet Delivery Ratio and Network Lifetime are evaluated and compared with the existing OEERP, LEACH, DRINA, BCDCP protocols. The work is simulated using NS-2 simulator. The results prove that, the proposed E-OEERP shows better performance in terms of lifetime.

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Background: This paper reviews segmentation techniques for 2D ultrasound fetal images. Fetal anatomy measurements derived from the segmentation results are used to monitor the growth of the fetus. </P><P> Discussion: The segmentation of fetal ultrasound images is a difficult task due to inherent artifacts and degradation of image quality with gestational age. There are segmentation techniques for particular biological structures such as head, stomach, and femur. The whole fetal segmentation algorithms are only very few. Conclusion: This paper presents a review of these segmentation techniques and the metrics used to evaluate them are summarized.

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