In this paper, we present Chainsaw, a p2p overlay multicast system that completely eliminates trees. Peers are notified of new packets by their neighbors and must explicitly request a packet from a neighbor in order to receive it. This way, duplicate data can be eliminated and a peer can ensure it receives all packets. We show with simulations that Chainsaw has a short startup time, good resilience to catastrophic failure and essentially no packet loss. We support this argument with real-world experiments on Planetlab and compare Chainsaw to Bullet and Splitstream using MACEDON.
The objective of this paper is to explore an expedient image segmentation algorithm for medical images to curtail the physicians' interpretation of computer tomography (CT) scan images. Modern medical imaging modalities generate large images that are extremely grim to analyze manually. The consequences of segmentation algorithms rely on the exactitude and convergence time. At this moment, there is a compelling necessity to explore and implement new evolutionary algorithms to solve the problems associated with medical image segmentation. Lung cancer is the frequently diagnosed cancer across the world among men. Early detection of lung cancer navigates towards apposite treatment to save human lives. CT is one of the modest medical imaging methods to diagnose the lung cancer. In the present study, the performance of five optimization algorithms, namely, k-means clustering, k-median clustering, particle swarm optimization, inertia-weighted particle swarm optimization, and guaranteed convergence particle swarm optimization (GCPSO), to extract the tumor from the lung image has been implemented and analyzed. The performance of median, adaptive median, and average filters in the preprocessing stage was compared, and it was proved that the adaptive median filter is most suitable for medical CT images. Furthermore, the image contrast is enhanced by using adaptive histogram equalization. The preprocessed image with improved quality is subject to four algorithms. The practical results are verified for 20 sample images of the lung using MATLAB, and it was observed that the GCPSO has the highest accuracy of 95.89%.
Good quality and optically transparent single crystals of pure and doped glycine phosphite (GPI) were grown by both solvent-evaporation and temperaturecooling techniques. Dopants were chosen in different categories, namely transition metals (Cr, Mn, Co, Ni, Zn, Mg, Cd), rare-earth metals (Ce, Nd, La), dyes (rhodamine B, malachite green, fluorescein) and an amino acid (l-proline). The concentration of dopants was chosen depending on the category of dopants and the quality of crystallization during the growth process. The crystalline perfection of the as-grown pure and doped GPI crystals was investigated by high-resolution X-ray diffraction at room temperature. A multicrystal X-ray diffractometer employing a well collimated and highly monochromated Mo K 1 beam and set in the (+, À, À, +) configuration was employed. Most of the crystal specimens show excellent crystalline perfection. However, grain boundaries, low-angle tilt boundaries, and vacancy and interstitial point defects were observed in some crystal specimens.
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