OBJECTIVEThis report summarizes the basic and Brownian Reber search theories which have been used to model the performance of the Advanced Unmanned Search System. It is intended to serve as a reference to the system modeling which influenced the design of the system. It should also serve as a foundation for the design of optimal searches. APPROACHReber's original theory has beer modified to incorrcrate navigation and control fcatures typical of autonomous vehicle systems; the natural modification involves modeling vehicle trajectories as fractional Brownian paths. The general structure of the Brownian Reber theory is identical to the structure of the basic Reber theory, so that the usual figures of merit, such as mean time to detection, are calculable in terms of system measurables, such as navigation error and lateral range function. RESULTSThe basic and Brownian theories are described in some detail, and their similarities and differences are explicitly analyzed. The form of the expression for mean time to detection is provided. The important parameters in the Brownian theory are determined numerically for a significant range of system variables, and these results are presented in a series of figures.

No abstract

No abstract

Nowadays medicinal treatment is highly depending on medical images. There are variety of multi modal medical images are available. Each images are having certain pros and cons. Hence, there is a need of image fusion of multiple images to get a single informative image. In this paper, SPECT (Single Photon Emission Computed Tomography) and MRI (Magnetic Resonance Image) images are taken as source images. They are the images of Alzheimer disease affected patients. In this proposed method, the Adaptive Blood Flow Weight, ABF WEIGHT for each image is calculated based on the severity of the disease. The blocks of each image is applied contourlet transform, so that it yields Low Frequency Coefficients (LFC) and High Frequency Coefficients (HFC) separately. The High Frequency coefficients are fused using Guided Filter algorithm. The Lower Frequency coefficients are fused using the proposed Adaptive Blood Flow (ABF) fusion rule. Finally inverse transform is applied to get fused image. The work is implemented in MATLAB. It has been proved that the proposed method provides better result. The quality of fused images are computed using the performance measures like entropy, Peak Signal to Noise Ratio (PSNR), Edge strength (Q) and Fusion Factor (FF).