Abstract: Medical Image fusion plays a vital role in medical
field to diagnose the brain tumors which can be classified as benign
or malignant. It is the process of integrating multiple images of the
same scene into a single fused image to reduce uncertainty and
minimizing redundancy while extracting all the useful information
from the source images. Fuzzy logic is used to fuse two brain MRI
images with different vision. The fused image will be more
informative than the source images. The texture and wavelet features
are extracted from the fused image. The multilevel Adaptive Neuro
Fuzzy Classifier classifies the brain tumors based on trained and
tested features. The proposed method achieved 80.48% sensitivity,
99.9% specificity and 99.69% accuracy. Experimental results
obtained from fusion process prove that the use of the proposed
image fusion approach shows better performance while compared
with conventional fusion methodologies.
Abstract: Nonlinearity is the inherent characteristics of all the industrial processes. The Classical control approach used for a generation often fails to show better results particularly for non-linear systems and in the systems, whose parameters changes over a period of time for a variety of reasons. Alternatively, adaptive control strategies provide very good performance. The Model Reference Adaptive Control based on Lyapunov stability analysis and classical PI control strategies are designed and evaluated for Continuous Stirred Tank Reactor, which shows appreciable dynamic nonlinear characteristics.