An effective fuzzy auto-seed cluster means morphological algorithm developed in this work to segment the lung nodules from the consecutive slices of Computer Tomography (CT) images to detect the lung cancer. The initial cluster values were chosen automatically by averaging the minimum and maximum pixel values in each row of an image. The area and eccentricity features were used to eliminate the line like structure and very tiny clusters less than 3 mm in size. The change in centroid analysis was carried out to eliminate the blood vessels. The tissue clusters whose centroid varies much in consecutive slices must be blood vessels. After eliminating the blood vessels, the co-occurrence matrix based texture features contrast, homogeneity and auto correlation were computed on the remaining nodules from the consecutive CT slices to discriminate the calcifications. The extracted centroid shift and texture features were used as the inputs to the Support Vector Machine (SVM) kernel classifier in order to classify the real malignant nodules. This work was carried out on 56 malignant (cancerous) cases and 50 normal cases (with lung infections), which had a total of 56 malignant nodules and 745 benign nodules. Out of these, 60 % of subjects (34 cancerous & 30 non-cancerous) were used for training. The remaining 40 % subjects (22 cancerous & 20 non-cancerous) were used for testing. This work produced a good sensitivity, specificity and accuracy of 100 %, 93 % and 94 %, respectively. The False Positive (FP) per patient was calculated as 0.38.

No abstract

No abstract

No abstract

Abstract:Modeling physical systems usually results in complex high-order dynamic models. It is often desirable to approximate these models by simpler models with reduced order. This study deals with the design of discrete time linear system using a balanced approach reduced order model. The reduced order model retains the desired state variable which contains a significant contribution. A PID controller is designed for the reduced second order model to meet the desired performance specifications by using pole-zero cancellation method. The stabilization of linear discrete time system is achieved by selection of parameters of the PID controller. A numerical example is given to illustrate the design method.

No abstract

No abstract

No abstract