Indra Gupta

DG Integrated Approach for Service Restoration Under Cold Load Pickup

The additional power demand caused by cold load pickup (CLPU) condition restricts the simultaneous restoration of all the loads of a network due to excessive loading on the network elements and violation of limits. Therefore, step-by-step restoration is the most adopted approach. However, it requires long time for the complete restoration. The main cause of CLPU problem is the loss of diversity amongst the loads which causes the enduring phase of CLPU condition. The conservation of load diversity reduces the demand during restoration. In the proposed approach, the diversity is conserved by using distributed generation (DG) for quick restoration and the reduction of additional power demand caused by CLPU condition. The capacity required for DG is evaluated on the basis of the required additional power demand and the load-diversity preserved. The proposed approach utilizes the genetic algorithm for determination of the optimal size and location of the required DG. The approach is demonstrated on a 33-bus, 12.66-kV primary distribution network.

ANN-based estimator for distillation using Levenberg-Marquardt approach

In modern chemical industries the purity of the distillate is the main objective and time to estimate the distillate composition is also the constraint. In the present paper, the Levenberg-Marquardt (LM) approach is proposed for predictive inferential control of distillation process. The developed estimator using LM approach predicts the composition of distillate using column pressure, reboiler duty, and reflux flow along with the temperature profile of the distillation column as inputs. In complex chemical industries where the output depends on many parameters, Steepest Descent Back Propagation (SDBP) algorithm does not work properly for estimating the composition of distillate, which results in saturated outputs and differs from the desired results. To overcome such type of situation, LM approach is used in developed estimator. The estimated results are compared with the simulation results and it is observed that the results obtained from LM approach are significantly improved than the results obtained from SDBP algorithm. To enhance the accuracy of the estimated results, the pressure, reflux flow and heat input with temperature profile of the column are used as input to train the neural network.

A novel content-based active contour model for brain tumor segmentation

Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation.

Combinational domain encryption for still visual data

Image data has distinct regions of different importance. This property of image data has extensively been used to develop partial encryption techniques, but it is still unnoticed for total encryption. Providing similar security level to data of varied significance consumes more computational resources. This necessitates the development of an encryption framework that considers data significance while implementing total encryption. This article proposes a new framework of combinational domain encryption that encrypts significant data in spatial domain and insignificant data in wavelet domain. Experiments have been performed to analyze the effect of proposed framework as compared to encryption technique in a single domain. Significant reduction in computational time has been observed without compromising on the security. Medical applications or security applications requiring fast computation would be benefitted by implementation of the proposed technique.

On Demand Routing Protocols for Mobile Ad Hoc Networks: A Review

A mobile ad hoc network is a multihop wireless network with dynamically and frequently changing topology. The power, energy and bandwidth constraint of these self operating and self organized systems has made routing a challenging problem. Number of routing protocols has been developed to find routes with minimum control overhead and network resources. Extensions are done on the conventional protocols to improve the throughput by further reducing the control overhead. This paper gives an overview of the existing on demand routing protocols and a parametric comparison is made with the recently developed protocols, proposed in the literature, These protocols are the multipath extensions of Ad Hoc On Demand Distance Vector routing protocol (AODV) such as AODV with break avoidance (AODV-BR), Scalable Multipath On Demand Routing (SMORT) etc.

Classification of brain tumors using PCA-ANN

The present study is conducted to assist radiologists in marking tumor boundaries and in decision making process for multiclass classification of brain tumors. Primary brain tumors and secondary brain tumors along with normal regions are segmented by Gradient Vector Flow (GVF)-a boundary based technique. GVF is a user interactive model for extracting tumor boundaries. These segmented regions of interest (ROIs) are than classified by using Principal Component Analysis-Artificial Neural Network (PCA-ANN) approach. The study is performed on diversified dataset of 856 ROIs from 428 post contrast T1- weighted MR images of 55 patients. 218 texture and intensity features are extracted from ROIs. PCA is used for reduction of dimensionality of the feature space. Six classes which include primary tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), child tumor-Medulloblastoma (MED) and Meningioma (MEN), secondary tumor-Metastatic (MET) along with normal regions (NR) are discriminated using ANN. Test results show that the PCA-ANN approach has enhanced the overall accuracy of ANN from 72.97 % to 95.37%. The proposed method has delivered a high accuracy for each class: AS-90.74%, GBM-88.46%, MED-85.00%, MEN-90.70%, MET-96.67%and NR-93.78%. It is observed that PCA-ANN provides better results than the existing methods.

Multimedia Encryption: A Brief Overview

The augmentation in the field of communication technology has lead to an extensive use of multimedia applications. Multimedia applications, especially, over wireless networks, can easily be intercepted, thus, making its security an essential and challenging issue. Multimedia encryption is the core enabling technology that provides confidentiality and prevents unauthorized access of the content. Real time constraints, large amount, and unique characteristics of multimedia data inhibits the use of traditional cryptographic algorithms over multimedia data. Recent years have witnessed an astounding development in the direction of format compliant, perceptual, and scalable encryption techniques that support advanced functionalities. This chapter gives a snapshot of the conventional encryption, and an up-to-date treatise of the principles, techniques, attacks, and advancements of multimedia encryption techniques developed to meet desired goals in specific applications.

FPGA-based PID controller for DC-DC converter

Proportional Integral Derivative (PID) controller is the most preferable controller in industries that does not require precise analytical model of the system to be controlled. This paper presents a design and implementation of PID (Proportional-Integral-Derivative) controller based on FPGA (Field-Programmable Gate Arrays) for low voltage synchronous buck Converter. Matlab/Simulink environment is used for the PID controller design to generate a set of coefficients associated with the desired controller characteristics. These controller coefficients are then included in VHDL that implements the PID controller on to FPGA. The two architectures of PID controller are considered with their device utilization and power dissipation reports to show the resource utilization and power dissipation of selected FPGA. The architectures are implemented in FPGA Virtex-5(ML505) XC5VLX50T-1FF1136 (-1 speed grade) device.

Auto-tuned, discrete PID controller for DC-DC converter for fast transient response

Ziegler-Nichols tuned PID controllers performances usually are not acceptable for applications requiring precise control. In this paper an improved discrete auto-tuning PID scheme is developed for DC-DC converters where large load changes are expected or the need for fast response time. The algorithm developed in this paper is used for the tuning discrete PID controller to obtain its parameters with a minimum computing complexity and is applied to Synchronous buck converter to improve its performance. To improve the transient response and rise time of the Converter, the controller parameters are continuously modified based on the current process trend. For its implementation a synchronous buck converter is designed and its MATLAB/Simulink model with non-linear parameters is developed and considered. Also, the non-linear effects such as S/H, quantization, delay, and saturation are considered in the close loop model. The simulation results demonstrate the effectiveness of the developed algorithms.

Multiclass Brain Tumor Classification Using GA-SVM

The objective of this study is to develop a CAD system for assisting radiologists in multiclass classification of brain tumors. A new hybrid machine learning system based on the Genetic Algorithm (GA) and Support Vector Machine (SVM) for brain tumor classification is proposed. Texture and intensity features of tumors are taken as input. Genetic algorithm has been used to select the set of most informative input features. The study is performed on real 428 post contrast T1-weighted MR images of 55 patients. Primary brain tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), Meningioma (MEN), and child tumor-Medulloblastoma (MED) along with secondary tumor-Metastatic (MET) are classified by GA-SVM classifier. Test results showed that the GA optimization technique has enhanced the overall accuracy of SVM from 56.3 % to 91.7%. Individual class accuracies obtained are: AS-89.8%, GBM-83.3%, MEN-96%, MET-91.8%, MED-97.1%. A comparative study with earlier methods is also done. The study reveals that GA-SVM provides more accurate results than earlier methods and is tested on more diversified dataset.