Informatics Publishing Limited

B. Rama Devi ¹, K. Kishan Rao ², M. Asha Rani ³

Affiliations
1 Department of E.C.E., Kakatiya Institute of Technology and Science, Warangal, Telangana, IN
2 Department of E.C.E., Vaagdevi College of Engineering, Warangal, Telangana, IN
3 Department of E.C.E., Jawaharlal Nehru Technological University, Hyderabad, Telangana, IN

Abstract

Vehicular ad-hoc networks require development of new safety applications to prevent collisions and save lives. We consider two cases: (i) Road with Multiple Lanes - Unidirectional Traffic (ii) Road with Single Lane- Bidirectional Traffic. The basic concept is to reduce speed of vehicles, which follow behind the accident-prone vehicle when an accident occurs (emergency event) to avoid further collisions and to pass on the information regarding a route diversion. Further Cooperative relays proposed for usage of multihop communication and to pass information over a control channel for avoidance of vehicular collision and path diversion. We pass the information to the junction for diverting traffic, and the information is passed for an ambulance service and to the police station for emergency help. Further collision between vehicles can be controlled using threshold safety distance by generating a distance warning message to alert the driver. IEEE 802.11p Dedicated Short Range Communication standards are used with OFDM technique. Along with a collision avoidance technique lane number, vehicle number, time of incident occurrence, vehicular position, route diversion is to be passed on and broadcasted using control channel. Further simulation results were analyzed for both unidirectional and bidirectional traffic conditions using cooperative relay collision avoidance and path diversion technique.

Keywords

Cooperative Relay, Collision Avoidance, Dedicated Short Range Communication (DSRC), OFDM, Vehicular Ad Hoc Networks (VANETs).

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Bobbala Sreedevi ¹, T. Anil Kumar ², K. Kishan Rao ³

Affiliations
1 Department of ECE, Vaagdevi College of Engineering, Bollikunta , Warangal – 506005, Telangana, IN
2 Department of ECE, MVSR Engineering College, Hyderabad –501510, Telangana, IN
3 Sreenidhi Institute of Science and Technology,Technology, Hyderabad – 501301, Telangana, IN

Abstract

Objectives: The objective of this work is to study denoising and segmentation methods to extract brain tumor area from the MRI image, implemented using MATLAB2013b and to examine its performance metrics. Methods/Statistical Analysis: As preprocessing stage is essential for better segmentation as it removes noise that makes images having similar qualities so that tumor area can be shown and extracted with great accuracy. An anisotropic diffusion filter with 8-connected neighborhood is employed for noise removal and Fast Bounding Box (FBB) for exactly showing tumor area on MRI images. Finally Support vector machine classifies the boundary and extracts the tumor from the MRI image. Findings: Brain tumor is the major cause of cancer deaths in human which is due to uncontrollable cells growth in brain portion. Prior detection, diagnosis and accurate healing of brain tumor are the primary work to prevent human death. Image segmentation can also be done in several approaches like thresholding, region growing, watersheds and contours. Specialists with their basic knowledge do manual segmentation, which is time consuming process, where this limitation can be overcome by our fully automatic proposed method. Employing of an isotropic diffusion filter with 8-connected neighborhood compare to 4-connected neighborhood results in considerable improvements in terms of lower identical error rates. Our proposed Fast Bounding Box (FBB) method is applied that exactly shows tumor area on MRI images and its central region is selected judiciously to have sample points required for functionality of one class SVM classifier training. To achieve optimal classification level there is necessity of SVM with optimum efficiency, so that we adapted Support vector machine that immediately stops its operation once all the points are separated. Application/Improvements: Segmented tumor obtained with precision are very useful for radiologists and specialists to had good idea of estimating tumor position and size with great dealt with ease and without any prior information.

Keywords

Brain Tumor, Feature Extraction, Fast Bounding Box, MRI, Support Vector Machine, Tumor Extraction.

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