Cloud computing is growing day by day and becoming popular in today’s era. A large number of organizations are shifting their business on cloud and paying more attention towards the cloud. This scenario shows that before the adoption of cloud computing business model, there should be an evaluation. This evaluation can be done only with the help of the simulators. This paper presents the study and comparative analysis of existing cloud simulator and shows the comparison that helps the end user for selecting the suitable simulator.

Today’s one of the most profitable and popular aquaculture is ornamental aquaculture which is breeding Lou Han. This Louhan is in many types with some standards. The standard Louhan is sales at high cost with high demand. Nowadays there is various aqua farms in different countries are involved themselves in breeding them. The main problem in behind of this breeding Louhan is selecting the quality parents for raising the juveniles. We are providing the systematic solutions for this selection of the parents. The standard or quality is identified by the shape and size of the Kok, color and pearl (strains) of the Louhan. This standard has been varied with types of Louhan. We have proposed the image processing techniques for selecting the best parents for breeding in the captivity. We can also use this method for selecting the best fishes in the competition and also use as the guide for the unfamiliar person of the Louhan field.

The main goal of this project is to simulate the localization of a car-like autonomous vehicle when the Global Positioning System on the vehicle fails. GPS is an important component that helps in the locating the position of the vehicle in the world. Sometimes the GPS may fail to lead to accidents or erratic motion in the case of autonomous vehicles. An effective methodology is required to locate the vehicle in the world even after the failure of the GPS and for parking the car in a safe location nearby. The Vehicle uses a Lidar from which the point cloud of obstacles surrounding the vehicle is obtained. Using several filters, only the static obstacles like the Traffic signal post are clipped. The vehicle is assumed to be at the stop line whose global coordinates are known. From the filtered point cloud data, the coordinates of the static reference points with respect to the vehicle are obtained. By using Parallelogram Law of Vectors, the global coordinates of the reference points are calculated. Then as the vehicle moves, the global coordinates of the vehicle is calculated from the corresponding local coordinates of the reference points with respect to the car. The Autonomous vehicle is modeled and imported in simulation software called Gazebo which runs alongside Robot Operating System. The Autonomous vehicle is mounted with a Velodyne HDL-32E Lidar, which is also modeled and imported in Gazebo. Point Cloud Library provides various filters required for processing the point cloud data. The Programming language used is python. Robot Operating System bridges between Gazebo and the algorithm developed.