Naveen Choudhary

STG-NoC: A Tool for Generating Energy Optimized Custom Built NoC Topology

on Chip (NoC) has emerged as a viable solution to the complex communication requirements of constantly evolving System on Chip (SoC). The communication centric architecture of NoC can be optimized across a variety of parameters as per the design requirements. With the development of customized application the inclination has shifted from regular architectures to irregular topology which leaves researchers with larger spectrum of optimization parameters. Many heuristic methods have been explored as the optimization problems encountered are NP-hard. This paper presents a customized topology generator STG-NoC which implements a heuristic technique based on simulated annealing for achieving the objective of energy optimization.

Scrambled hybrid wavelet-singular domain digital image watermarking

Digital watermarking has attracted substantial attention as it is the most commonly applied method for the copyright protection of the intellectual property rights of digital media. In conformity with the characters of human vision, the objective of the paper is to be focused on the performance of proposed algorithm on the basis of invisibility, interference and robustness to attacks using scrambled hybrid wavelet-singular domain watermarking system. By the use of MATLAB 7.7.0, the efficiency of the watermarking scheme has been demonstrated via the experimental results. The results show the proposed algorithm can embed and distill digital watermark more effectively in comparison.

Classification of brain tumor types using multiclass kernel-based Hellinger decision method for HD-Tree and HD-Forest

Currently, the radiologist needs to distinguish the medical imaging with their multiple classes. In this paper, we work on several steps: segmented ROI, feature extraction of ROI and classification. In this work, we proposed a multiclass kernel based Hellinger decision method HD-Tree and HD-Forest for the classification of brain tumor classes with respect to classification time and accuracy. The calculated features like patient symptoms, centroid, shape, etc. are used in the classification scheme. Total 97 MRI brain tumor images (Astrocytoma (22), Ganglioglioma (6), Glioblastoma (23), Epidermoid (3), Mixed Glioma (5) and Meningnet (41)) were used for the experiment. The Experimental result shows that kernel-based Hellinger HD-Tree was found to be 96.50 % of accuracy and HD-Forest was found to be 99.9%. In this paper, we compare our proposed method LA-SVM method. LA-SVM was found to be 96% of accuracy. We can see that HD-forest gives the best accuracy result.

Automatic localization and level set based energy minimization for MRI brain tumor

Automatic segmentation of tumor abnormality is a very difficult task for the radiologist. In this research, we proposed a located brain tumor with automatic seed point localization and no need to initially select the location of the region which is to be infected. Estimation of the abnormalities for initial bounding box after this, we proposed the segmentation of tumor called automatic level set minimization function with a new technique that is localization based energy minimization of MRI brain tumor. The performance of localization is evaluated using based on the level of detection and radiologist analytical results. Total 100 FLAIR, T1, and T2-weighted MRI brain tumor images (Astrocytoma (22), Ganglioglioma (6), Glioblastoma (23), Epidermoide (3), Mixed Glioma (5) and Meningnet (41)) (5type of tumors) were used for the experiment. Experimental results show that the method has successfully localized the brain tumors with 97% accuracy.

Dual secured hybrid image watermarking in wavelet and singular domain

These days there is an increased risk of copyright violation of multimedia data that has become an important issue for content owners and service providers. Watermarking technology aims at asserting Intellectual Property Rights (IPR) of digital images by inserting a copyright identifier in the contents of the image, to achieve copyright protection. The dual scrambled watermark is embedded on the elements of singular values of the scrambled cover images DWT middle frequency sub-band and the image scrambling is done by Arnold and Scrambling Sequence. The security levels are increased by using a key value and scaling factor for the embedding and extraction process. Various standard database gray scale images of size 512×512 and watermark of size 64×64 have been used and the simulation was performed on MATLAB 7.7.0. The correlation factor for Salt and pepper noise, Gaussian noise, JPEG compression, Gaussian low pass filter and Cutting methods used by Rui-mei et al. and Wang et al. were compared with proposed method using hybrid dual scrambled watermark scheme. The proposed method enhanced the robustness and imperceptibility of digital images.

Video Traffic in Wireless Sensor Networks

The primary goal of clustering in wireless sensor networks is to efficiently manage the energy consumption of among the sensory nodes using multi-hop communication with particular cluster. This proposed approach will reconsider the concept of leveling, sectoring and adaptive clustering for video traffic. Partitioning the entire network into clusters would manage the video traffic required for the data dissemination to the base station using multi-hop communication. Making both the cluster head selection and cluster size dynamic has been the central idea of this routing protocol. In this paper the simulation for transmission of videos over wireless networks is performed in MATLAB and the results are analyzed on the basis evaluation of configuring parameters such as PSNR, Network Life time, and Average Power Consumption. The Proposed mechanism improved the PSNR, Network Life time, and Average Power Consumption.

An Efficient Secure Routing Protocol in MANET Security - Enhanced AODV (SE-AODV)

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