Kakatiya Institute of Technology and Science

About: Kakatiya Institute of Technology and Science is a based out in . It is known for research contribution in the topics: Image segmentation & Cluster analysis. The organization has 400 authors who have published 454 publications receiving 3325 citations.

A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter

Abstract: The performance of pulse oximeters is highly influenced by motion artifacts (MAs) in photoplethysmographic (PPG) signals. In this paper, we propose a simple and efficient approach based on adaptive step-size least mean squares (AS-LMS) adaptive filter for reducing MA in corrupted PPG signals. The presented method is an extension to our prior work on efficient use of adaptive filters for reduction of MA in PPG signals. The novelty of the method lies in the fact that a synthetic noise reference signal for an adaptive filtering process, representing MA noise, is generated internally from the MA-corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Thus, the generated noise reference signal is then applied to the AS-LMS adaptive filter for artifact removal. While experimental results proved the efficacy of the proposed scheme, the merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal. In addition to arterial oxygen saturation estimation, the artifact reduction method facilitated the waveform contour analysis on artifact-reduced PPG, and the conventional parameters were evaluated for assessing the arterial stiffness.

Brain Tumor Classification Using Convolutional Neural Networks

Abstract: The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation (i.e) accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. The deeper architecture design is performed by using small kernels. The weight of the neuron is given as small. Experimental results show that the CNN archives rate of 97.5% accuracy with low complexity and compared with the all other state of arts methods.

Optimization of process parameters in drilling of GFRP composite using Taguchi method

Abstract: a b s t r a c t The objective of the present work is to optimize process parameters namely, cutting speed, feed, point angle and chisel edge width in drilling of glass fiber reinforced polymer (GFRP) composites. In this work, experiments were carried out as per the Taguchi experimental design and an L9 orthogonal array was used to study the influence of various combinations of process parameters on hole quality. Analysis of variance (ANOVA) test was conducted to determine the significance of each process parameter on drilling. The results indicate that feed rate is the most significant factor influencing the thrust force followed by speed, chisel edge width and point angle; cutting speed is the most significant factor affecting the torque, speed and the circularity of the hole followed by feed, chisel edge width and point angle. This work is useful in selecting optimum values of various process parameters that would not only minimize the thrust force and torque but also reduce the delimitation and improve the quality of the drilled hole.

Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems

Abstract: Distributed generator (DG) resources are small scale electric power generating plants that can provide power to homes, businesses or industrial facilities in distribution systems. Power loss reductions, voltage profile improvement and increasing reliability are some advantages of DG units. The above benefits can be achieved by optimal placement of DGs. Whale optimization algorithm (WOA), a novel metaheuristic algorithm, is used to determine the optimal DG size. WOA is modeled based on the unique hunting behavior of humpback whales. The WOA is evaluated on IEEE 15, 33, 69 and 85-bus test systems. WOA was compared with different types of DGs and other evolutionary algorithms. When compared with voltage sensitivity index method, WOA and index vector methods gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf.

Effect of Alkali Treatment on the Properties of Century Fiber

Abstract: Century natural fibers (Agave americana L.) have superior mechanical properties. For their usage as an effective reinforcement in biocomposites, they require surface modification. In the present study, these fibers were treated with 5% aq. NaOH solution for optimized 1-h period. The effect of alkali treatment on the mechanical, thermal, and morphological properties was been studied. The results indicated thinning and surface roughening of the fibers and removal of hemicellulose on alkali treatment. The tensile strength and elongation at break improved on alkali treatment. X-ray studies indicated increase of crystallinity for alkali-treated fibers.