R. B. Dubey ¹, M. Hanmandlu ², S. K. Gupta ³, S. K. Gupta ⁴

Affiliations
1 Dept. of E & IE, Apeejay College of Engg., Sohna, Gurgaon, IN
2 Dept. of EE, IIT, Delhi, IN
3 Vaish College of Engg., Rohtak, Haryana, IN
4 Dept. of EE, DCRUST, Murthal, Sonepat, Haryana, IN

Abstract

The tumor volume is a significant prognostic factor in the treatment of malignant tumors. Manual segmentation of brain tumors from MR images is a challenging and time consuming task. A semi-automated region growing segmentation method is proposed to segment brain tumor from MR images. The proposed method can successfully segment a tumor provided that the parameters are set properly. This method is applied to 8-tumor contained MRI slices from 2 brain tumor patients' and satisfactory segmentation results are achieved.

Keywords

Brain Tumor, MRI, Imaging, Segmentation

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R. B. Dubey ¹, M. Hanmandlu ², S. K. Gupta ³

Affiliations
1 Dept. of E & IE, Apeejay College of Engg., Sohna, Gurgaon
2 Dept. of EE, IIT, Delhi, IN
3 Vaish College of Engg, Rohtak, Haryana, IN

Abstract

Breast cancer is the leading cause of death among woman. Currently X-ray mammography is the most widely used method for early detection of breast cancer. Many computer aided techniques are available to assist the radiologist in taking crucial decisions. A method uses level set for segmentation of masses in digital mammograms is introduced. This method uses the Gaussian filter for smoothing the image and noise reduction. Level set methods offer a powerful approach for the medical image segmentation since it can handle any of the cavities, concavities, convolution, splitting or merging. However, this method requires specifying initial curves and can only provide good results if these curves are placed near symmetrically with respect to the object boundary. The results of experimental study indicate that our scheme can provide useful contour extraction for mass structure.

Keywords

Breast Cancer, Level Set, Mammograms, Gaussian Filter

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Saurabh Diwaker ¹, S. K. Gupta ², Neeraj Gupta ³

Affiliations
1 Department of Information Technology, Krishna Institute of Engineering and Technology, Ghaziabad - 201206, Uttar Pradesh, IN
2 Department of Computer Science, Bundelkhand Institute of Engineering and Technology, Jhansi - 284128, Uttar Pradesh, IN
3 Department of Electrical and Electronics, Krishna Institute of engineering and Technology, Ghaziabad - 201206, Uttar Pradesh, IN

Abstract

Objectives: In this paper, we have evaluated the effectiveness of classification of Electroencephalogram (EEG) signals using the correlation between channels as a method of features selection. Methods/Statistical Analysis: First data is broken sample wise, then correlation coefficient between channel pair for each sample is calculated. After that mean of the correlation coefficient of all channel pair for each class over all samples is calculated and in a similar manner, standard deviation from the mean is also calculated. For feature selection we have plotted a pair of the Gaussian curves between channels of two separate classes and choose those channels which give us lower misclassified area as features. Then these features are used for training purpose of Support Vector Machine (SVM). Findings: Most of the previous researches follow either signal processing approach or machine learning approach while we emphasized upon the nature of the signal propagation amongst the neurons. The basic idea behind the feature selection is taken from the way the signals propagate from one neuron to the other. In our work we assume that EEG signals follow the normal distribution and verify the fact using chi-square test. On applying SVM the accuracy of classification on testing data confirms that correlation among channels can be used for feature selection. Application/Improvements: The results can be improved by improving the pre-processing of EEG signals. It can be used to develop a Brain Computer Interaction (BCI) system.

Keywords

Correlation Coefficient, Electroencephalogram (EEG), Support Vector Machine (SVM).

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Manju Aggarwal ¹, Madhusudan Singh ², S. K. Gupta ¹

Affiliations
1 Department of Electrical Engineering, DCRUST, Murthal - 131039, Haryana, IN
2 Department of Electrical Engineering, Delhi Technological University, Delhi - 110042, IN

Abstract

Objectives: In this paper, dynamic performance of Wind Energy System (WES) comprising of Squirrel Cage Induction Generator (SCIG) is analyzed for grid connected operation with DSTATCOM. Methods/Statistical analysis: In the present work, modeling, analysis and working of a low voltage distribution system supported with wind energy conversion system is analyzed. A Distribution STAtic COMpensator (DSTATCOM) has been modeled and simulated using MATLAB. The effectiveness of the proposed system has been verified by simulation and analytical results. The indirect current control technique is employed for present analysis as it is simple, robust and favourable as compared to other methods. Findings: The DSTATCOM improves the performance of Wind Energy System (WES) under transient condition i.e. change in wind speed and sudden change in load. The DSTATCOM augmented with wind energy system addresses the power quality and grid stability issues. Application/Improvements: The DSTATCOM in present study provides transient voltage support and also maintains the stability of a WES.When wind penetration increases beyond 30%, the system loses stability. A detailed simulations study is carried out on the application of DSTATCOM to improve the stability of the system

Keywords

DSTATCOM, Squirrel Cage Induction Generator (SCIG), Wind Energy System (WES), Wind Generator, Grid Code

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