Background/Objectives: Diesel engine’s optional fuel known as Biodiesel holds fatty acids alkyl monoesters from oils of vegetable or fats of animals. It can be formed from renewable sources namely vegetable oils, restaurant waste oil and fry oil. Bio Diesel may be cost effective if produced from feedstock of low cast namely restaurant animal fats, waste oil, and fry oil, which contains of free fatty acids having high amount (FFA). Methods/Statistical Analysis: When processing these oils that are low cost problem occurs and fats are those they regularly possess huge quantity of Free Fatty Acids (FFA) which is impossible for conversion as biodiesel by means of an alkaline catalyst. In this work, a technique has been described for reducing the free fatty acids content of this feedstock’s utilizing pretreatment of an acid catalyzed to esterify the free fatty acids earlier to transterifying the triglycerides with catalyst of an alkaline to fulfill the reaction. Chief principle of this work was to expand a two-step production technique of biodiesel from pork waste as a raw material. The variables were methanol to oil ration, base catalyst and acid concentration. With particular attention for optimizing, the first step was the acid catalyst esterification to reduce the free fatty acid content and the second step was alkali catalyzed Transeseterification to convert fatty acid methyl ester. Experiments established the RSM model validity. Maximum percentage of fatty acid methyl ester under optimum conditions of the variables was 93%. Findings: Optimum condition for Transeseterification was 13:1 of methanol to oil, 0.4gm sodium hydroxide concentration and 90min of reaction time. Optimum condition for the acid catalyzed esterification was found to be 1.5v/v. ANOVA analysis has been executed for studying the effect of the variables and response surfaces were plotted. Experiments established the RSM model validity. Applications/Improvements: Experiments are going to be establishing the RSM model validity along with tuning with the help of intelligent algorithms. Keywords: Adaptive Steganography, Enhanced Canny Operator, Ensemble Classifier, Least Significant Bit, Positive Predictive Rate

The objective of this work is to determine the performance of magnetic brakes in small wind turbines for various speeds. Finally, the quality of electric power produce can be enhanced by exact control of turbine`s rotor speed given by the magnetic brakes. Findings: Simulations have done using the various controllers comprising Proportional Integral Derivative (PID) Controller, Proportional Integral Controller, Fuzzy Logic (FL) Controller and Non Linear Controller in Mat lab Simulink. A comparative study is then made for the above simulations and obtained a conclusion that PID is the best choice. Besides this, a turning is made for PID controller using bacterial Fragmentation Algorithm and the best fit results are obtained. With such a controller inserted in our system, the system can have its optimum performance even at stall conditions. Applications/Improvements: With such a controller inserted in our system, the system can have its optimum performance even at stall conditions. Keywords: Control System, Fuzzy Logic Controller, Magnetic Brake, Torque, Wind Turbine

This paper attempts to the overview of design, conceptual analysis and implementation of coordinated multipurpose robot system with smart sensors. The aim of this paper is to show how to design and integrate various sensors in a single module and create a data retrieval system which can be used as a black box and an investigation device.

Medical image segmentation has become an essential technique in clinical and research-oriented applications.MRI provides accurate anatomical brain images without use of ionizing radiation. For clinical use, the estimated values must be reliable and accurate,but many techniques fail on these criteria in an unrestricted clinical environment. Automated Magnetic Resonance Imaging (MRI) segmentation systems classify brain voxels into one of three main tissue types such as Gray Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). Volumetric analysis of different parts of the brain is useful in assessing the progress or remission of various diseases such as Alzheimer's disease, epilepsy, multiple sclerosis, and schizophrenia. In the existing methods segmentation was done based on the intensity value of the voxels. Thus using intensity information alone has proven to be insufficient for a reliable automated segmentation of the brain tissues. Hence an adaptive mean-shift methodology is utilized in order to classify brain voxels where the MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. This proposed method clusters the joint spatial-intensity feature space thus extracting a representative set of high-density points within the feature space otherwise known as modes. The resultant output of an Adaptive means shift consists of several modes. A mode pruning step is undergone to reduce the number of modes. After the pruning step, intensity based clustering technique such as K-means algorithm is used for the classification of tissues. In this paper instead of K-means algorithm, Fuzzy C-means algorithm is implemented in order to achieve better segmentation. The final output consists of three regions which are used for the analysis of various neural diseases. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.