Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

A novel method of brain tumor segmentation using extensive feature set

Abstract: Brain tissue Segmentation from the MRI images is having significance in the medical research field. The accurate Segmentation of the normal as well as the abnormal tissues is the complex assignment in this process. Because of the inconsistency and difficulty of abnormal tissues, MRI Brain Image Segmentation turned into more hard procedure. In this paper, a technique is proposed for segmenting the abnormalities such as Tumor and Atrophy in the MRI Brain images. (1) Feature extraction (2) Classification (3) Segmentation are the three stages offered in this work. At first, the features such as energy, entropy, homogeneity, contrast and correlation from MRI Brain Images are extracted. Next, by utilizing Neuro-Fuzzy classifier, the Classification process is carried out and for this process, the feature set is specified as the input. From the outcome of Classification, the images are categorized into normal as well as abnormal. The further procedure Segmentation is performed according to this outcome only. The abnormal MRI images are segmented into abnormal tissues like Tumor and Atrophy using Region Growing method. Utilizing MATLAB platform the implementation of the proposed technique is made. The experimentation is carried out on the MRI Brain Images by BrainWeb data sets. The performance of our proposed technique is assessed with the help of the metrics namely FPR, FNR, Specificity, Sensitivity and Accuracy. Therefore, using our proposed technique with enhanced classification, the abnormal tissues of MRI Brain images are segmented accurately.

Design and Analysis of Planar Array with Horn Antenna Beams

Abstract: The planar array antennas are widely used in the communication systems. Planar array antennas consist of many numbers of microstrip patch antennas that give high directionality and high gain. The symmetrical patterns are provided with low sidelobes and directional beams. The horn antennas are used in large antenna structures. The elements are not resonated so, they can operate over wide ranges of frequency. These horn antennas are less weight, high directionality and high gain which will also reduce the multipath effects. For transmission and reception of microwave signals, horn antennas are mainly used. In the horn antenna, we have an E plane and H plane for getting high directionality. Horn antennas are available in pyramidal and conical forms. Horn antennas have less gain which approximately equals directivity. Therefore, the planar array antenna structure along with the horn gives high efficiency and high directionality.

Tree Crown Delineation from High Resolution Satellite Images

Abstract: This paper presents the comparison of two different algorithms for detection and counting of individual tree crown from high resolution satellite images. Trees can be detected from the high resolution satellite images using the different algorithms like K-means clustering, Circle Unification, Watershed segmentation and Normalized Difference Vegetation Index (NDVI). In this paper Watershed segmentation algorithm and NDVI performance are improved for the detection and counting the trees in the satellite images. The fundamental process of algorithms and results are compared to find the better approach for the detection of the trees.

Analysis and Evaluation of Discriminant Analysis Techniques for Multiclass Classification of Human Vocal Emotions

Abstract: Many of the classification problems in human computer interaction applications involve multi class classification. Support Vector Machines excel at binary classification problems and cannot be easily extended to multi class classification. The use of Discriminant analysis how ever is not experimented widely in the area of Speech emotion recognition. In this paper Linear Discriminant Analysis and Regularized Discriminant Analysis are implemented over Berlin and Spanish emotional speech databases. Prosody and spectral features are extracted from the speech database and are applied individually and also with feature fusion. Based on the results obtained, LDA classification performance is poor than RDA due to the singularity problem. The results are analysed using ROC Curves.

Comparative analysis of contrast enhancement techniques between histogram equalization and CNN

Abstract: Contrast enhancement is one of the primary aspects in computer vision In order to understand the image, the contrast of the image should be clear In many scenarios, especially in biomedical images, security and surveillance, the visual quality of source images or video is not up to the expected quality There exist many algorithms such as histogram equalization, genetic algorithms and neural networks to improve the contrast of the images In this work, we summarized the state of the art and made comparative study among contrast enhancement techniques Comparisons are done in two cases: one among the histogram based techniques, another between histogram based techniques and method using Cellular Neural Networks (CNN) The method using CNN proved to perform better than the conventional techniques