Kalpana Saini

Utility of SCADA in power generation and distribution system

SCADA is an acronym for Supervisory Control and Data Acquisition. SCADA systems are used to monitor and control a plant or equipment in industries such as telecommunications, water and waste control, energy. A typical SCADA system comprises of I/O signal hardware, Controllers, software, network & communication. Supervisory control and data acquisition system is a system in which message or commands that are individual are sends to the external world. Provides a host control functions for the supervisor to control and define settings. The basic natural sources like Coal, gas, Diesel, atomic etc are single time useable due to which the quantity of these sources is decreases day by day. The emissions of these fuels are also responsible for air pollution. On the other hand if we use the water power and solar power to generate the electricity. We can reduce the pollution as well as the generation cost of the energy. The quantity of the water is varies in the rivers according to the season and the sunlight intensity is vary according to the day hours. So we have developed a system in which consumers are connected to different type of power plant via a grid. The grid load and plants are monitored and controlled by the SCADA system. This provides the uninterrupted power supply at minimum power generation cost.

Designing of a Virtual System with Fingerprint Security by considering many Security threats

In these days there is a requirement of reliable security system for process industry, in banks, homes etc. We have taken one of the Biometric modality i.e. fingerprints for this purpose which is the most reliable security tool for security of the system. Fingerprint recognition has long been favored among many biometric identification technologies due to its uniqueness and permanence. Nowadays, fingerprint recognition is considered to be the best choice for most applications from network security systems to compact devices, due to its accuracy, speed, reliability, non-intrusive interfaces, and cost-effectiveness. We have made a fingerprint matching system using MATLAB. After collecting the database we provide a security to a virtual process that has been designed in LABVIEW and make an efficient HMI.The fingerprint recognition system may suffer attacks at different points during the authentication process. Somtimes the fingerprints gives 100% matching but it may be false fingerprint.The most common attacks occur by the use of fake fingerprint during the capture of image. The transmission channel between the feature extractor and matching may also be intercepted and the fingerprint feature may be stored for the later use. The main problem relies on how to differentiate a live finger from that one made of some synthetic material. In This Paper we disscuss the all such type of attacks as well as some other interseting and other topics related to fingerprints like these may be damage or stolen by some personand effect of illlness to them. This paper gives an approch to remove such type of attacks by using a virtual system. Along with this virtual system some aspects related to secure fingerprint is also be considered in this paper.

A fast region-based active contour model for boundary detection of echocardiographic images.

This paper presents the boundary detection of atrium and ventricle in echocardiographic images. In case of mitral regurgitation, atrium and ventricle may get dilated. To examine this, doctors draw the boundary manually. Here the aim of this paper is to evolve the automatic boundary detection for carrying out segmentation of echocardiography images. Active contour method is selected for this purpose. There is an enhancement of Chan–Vese paper on active contours without edges. Our algorithm is based on Chan–Vese paper active contours without edges, but it is much faster than Chan–Vese model. Here we have developed a method by which it is possible to detect much faster the echocardiographic boundaries. The method is based on the region information of an image. The region-based force provides a global segmentation with variational flow robust to noise. Implementation is based on level set theory so it easy to deal with topological changes. In this paper, Newton–Raphson method is used which makes possible the fast boundary detection.

Segmentation of mitral regurgitant jet using the combination of wavelet and watershed transformation

This paper concentrates on automatic segmentation of the regurgitation jet from the color Doppler echocardiographic image during the mitral regurgitation. Segmentation of color jet area is not an easy task especially when the background image is also color image. In this case it is not possible to do pixel classification for the separation of the color area. Jet area is directly related to the severity of mitral regurgitation. Here the main objective is to segment the color mosaic pattern from the color Doppler image. For this a multistep algorithm has been applied. The first stage of the proposed algorithm is dual-tree complex wavelet transform. Next stage is the watershed transform and then similarity measure has been done to get the final segmented image. Jet area obtained with the proposed method satisfies the clinical results.

Comparative Study of Edge Detectors in case of Echocardiographic Images

In this paper we compare different edge detectors based on peak signal to noise ratio on Echocardiographic images. Edge detection is a critical element in image processing, since edges contain a major function of image information. The function of edge detection is to identify the boundaries of homogeneous regions in an image based on properties such as intensity and texture.We have taken Perwitt edge detector, Robarts edge detector, LoG edge detector, Canny edge detector, and Sobel edge detector for this comparison and study.

Modified nonlinear complex diffusion filter (MNCDF)

BackgroundSpeckle noise removal is the most important step in the processing of echocardiographic images. A speckle-free image produces useful information to diagnose heart-related diseases. Images which contain low noise and sharp edges are more easily analyzed by the clinicians. This noise removal stage is also a preprocessing stage in segmentation techniques.MethodsA new formulation has been proposed for a well-known nonlinear complex diffusion filter (NCDF). Its diffusion coefficient and the time step size are modified to give fast processing and better results. An investigation has been performed among nine patients suffering from mitral regurgitation. Images have been taken with 2D echo in apical and parasternal views.ResultsThe peak signal-to-noise ratio (PSNR), universal quality index (Qi), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) have been calculated, and the results show that the proposed method is much better than the previous filters for echocardiographic images.ConclusionsThe proposed method, modified nonlinear complex diffusion filter (MNCDF), smooths the homogeneous area and enhances the fine details.

Spatial enhancement and modified log transformation for Echocardiographic images

This paper presents the spatial enhancement of Echocardiographic images. We have also developed a modified log transformation, which gives better results than that obtainable using conventional log transformation. Results thus obtained demonstrate that log transformation, as already existing in the literature, does not work well with Echocardiographicimage but modified log transformation, which is now presented in this paper, gives better results. Better balanced contrasted images are obtained, and these are both important and useful for manual processing as well as for assessment by a clinician, and also for computer analysis.

Statistical Analysis of speckle noise reduction techniques for echocardiographic Images

Echocardiography is the safe, easy and fast technology for diagnosing the cardiac diseases. As in other ultrasound images these images also contain speckle noise. In some cases this speckle noise is useful such as in motion detection. But in general noise removal is required for better analysis of the image and proper diagnosis. Different Adaptive and anisotropic filters are included for statistical analysis. Statistical parameters such as Signal‐to‐Noise Ratio (SNR), Peak Signal‐to‐Noise Ratio (PSNR), and Root Mean Square Error (RMSE) calculated for performance measurement. One more important aspect that there may be blurring during speckle noise removal. So it is prefered that filter should be able to enhance edges during noise removal.