GIS-MAP based Spatial Analysis of Rainfall Data of Andhra Pradesh and Telangana States Using R

The rainfall conditions across wide geographical location and varied topographic conditions of India throw challenge to researchers and scientists in predicting rainfall effectively. India is Agriculture based country and it mainly depends on rainfall. Seasons in India are divided into four, which is winter in January and February, summer is from March to May, monsoon is from June to September and post monsoon is from October to December. India is Agriculture based country and it mainly depends on rainfall. It is very difficult to develop suitable rainfall patterns from the highly volatile weather conditions. In this Paper, it is proposed that Map based Spatial Analysis of rainfall data of Andhra Pradesh and Telangana states is made using R software apart from Hybrid Machine learning techniques. A Study will be made on rainfall patterns based on spatial locations. The Visual analytics were also made for effective study using statistical methods and Data Mining Techniques. This paper also introduced Spatial mining for effective retrieval of Remote sensed Data to deal with retrieval of information from the database and presents them in the form of map using R software

A mean-shift algorithm based autonomous visual tracking for micro aerial vehicles

The main objective is to track the Ground Moving Target continuously with Mean-Shift Visual Tracking Algorithm from On-board Autonomous Visual Tracking System. On-board Autonomous Visual Tracking System contains Gimbaled Camera, Inertial Navigation System / Global Positioning System, Image Tracking Software, Micro Aerial Vehicle Guidance Law, Camera Control Law and Autopilot. A Mean-Shift Algorithm is incorporated in the Image Tracking Software. This Algorithm identifies and tracks the Ground Moving Target based on its 2D colour space histogram. A Graphical User Interface based tool was developed for simulation and test the Autonomous Visual Tracking with Mean-Shift Algorithm performance using MATLAB. The experimental results exhibits that the Mean-Shift Algorithm identifies and tracks the GMT very accuratel

Autonomous visual tracking with Extended Kalman Filter estimator for micro aerial vehicles

\u3cp\u3eThe objective of this paper is to estimate the Ground Moving Target position and track the Ground Moving Target continuously using Extended Kalman Filter estimator. Based on previous target positions in image sequences, this algorithm predicts the target next position in the image sequence. A Graphical User Interface based tool was developed for simulation and test the Autonomous Visual Tracking with Extended Kalman Filter estimator using MATLAB Graphical User Interface Development Environment tool.\u3c/p\u3

A hybrid autonomous visual tracking algorithm for micro aerial vehicles

An efficient image tracking algorithm plays a major role in an autonomous surveillance and monitoring the environment from micro aerial vehicle. A hybrid autonomous visual tracking algorithm is proposed based on cam-shift and extended kalman filter estimator for micro aerial vehicle. The proposed algorithm identifies and tracks the ground moving target continuously, even ground moving target moves quickly, and the color of the other ground moving target or background similar to that of the ground moving target. A MATLAB based simulation tool is developed for determining the proposed algorithm performance. The results exhibit that the proposed algorithm tracks the ground moving target very accurately\u3cbr/\u3e\u3cbr/\u3

A multi-level visual tracking algorithm for autonomous vehicles

A multi-level visual tracking algorithm is proposed for autonomous vehicles based on mean-shift algorithm, cam-shift algorithm and extended kalman filter estimator. The proposed multi-level visual tracking algorithm is implemented and included in simulation to check its performance. Simulation results are captured after applying existing algorithms, proposed multi-level visual tracking algorithm and observed their performance. The simulated results show that the proposed multi-level visual tracking algorithm identifies and tracks the ground moving target efficiently