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Jayalakshmi, J.
- Raindrop Size Distributions of Southwest and Northeast Monsoon Heavy Precipitation Observed over Kadapa (14°4'N, 78°82'E), a Semi-Arid Region of India
Authors
1 Semi-arid-zonal Atmospheric Research Centre (SARC), Department of Physics, Yogi Vemana University, Kadapa 516 003, IN
Source
Current Science, Vol 107, No 8 (2014), Pagination: 1312-1320Abstract
Raindrop size distributions (RSD) of southwest (SW - June to September) and northeast (NE - October to December) monsoon heavy precipitation are measured with PARticle SIze and VELocity (PARSIVEL) disdrometer and Micro Rain Radar (MRR) deployed at Kadapa (14.47°N; 78.82°E), a semi-arid continental site in Andhra Pradesh, India. RSD characteristics stratified on the basis of rainrate showed that the mean values of raindrop concentration of small (medium) drops are less (more) in SW when compared with NE monsoon heavy precipitation. Gamma function applied to heavy precipitation events showed that the mean value of mass weighted mean diameter, Dm (normalized intercept parameter log10 Nw) is higher (lower) in SW monsoon than NE monsoon. Stratiform and convective precipitating cloud fraction observed during SW and NE monsoons revealed that contribution of stratiform precipitation is predominant for the seasonal variation in raindrop size distribution. The coefficient and exponent values of the Z-R relations are higher in SW than NE monsoon in both stratiform and convective precipitation.Keywords
Raindrop Size Distribution, Rainrate, Mass Weighted Mean Diameter.Full Text
- Theoretical Analysis and Performance Measurement of MEMS Based Pressure Sensor
Authors
1 Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai, Tamilnadu, IN
Source
Networking and Communication Engineering, Vol 3, No 9 (2011), Pagination: 589-592Abstract
A humungous technology advancements in the field of pressure sensors starting from metal diaphragm sensors with bonded silicon strain gauges moving to present developments of surface-micro machined, resonant, optical pressure sensors. An Optical Micro Electro Mechanical Systems (MOEMS) pressure sensor with a Mesa membrane having the operating principle of Fabry-Perot (F-P) interference is analyzed in this paper. The idea is to couple light into the sensor (mesa structure diaphragm) through a fiber, which is used to measure pressure by detecting changes of the optical path length or reflectance. The most important criteria in this paper is to compare different mesa structures, say circular and hexagonal .The aim of the paper is to find which structure has a better pressure measurement range and sensitivity. We analyzed the pressure distribution for different structures using ANSYS simulation software and also examined the best application for the specified shapes. Experimental results demonstrate that the circular mesa structure pressure sensor has reasonable linearity, sensitivity, and a wide pressure measurement range.Keywords
Micro Electro Mechanical System, Mesa Membrane, Pressure Sensor.
- An Efficient MRI Brain Image Segmentation and Classification
Authors
1 Department of Computer Science, Mother Teresa Womens University, Kodaikanal, IN
Source
Digital Image Processing, Vol 6, No 2 (2014), Pagination:Abstract
Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. In the field of medical diagnosis an extensive diversity of imaging techniques is presently available, such as radiography, computed tomography (CT) and magnetic resonance imaging (MRI).Medical image segmentation is an essential step for most consequent image analysis tasks. Although the original FCM algorithm yields good results for segmenting noise free images, it fails to segment images corrupted by noise, outliers and other imaging artifact. This paper presents an image segmentation approach using Modified Fuzzy C-Means (FCM) algorithm and Fuzzy Possibilistic c-means algorithm (FPCM). This approach is a generalized version of standard Fuzzy C-Means Clustering (FCM) algorithm. The limitation of the conventional FCM technique is eliminated in modifying the standard technique. The Modified FCM algorithm is formulated by modifying the distance measurement of the standard FCM algorithm to permit the labeling of a pixel to be influenced by other pixels and to restrain the noise effect during segmentation. Instead of having one term in the objective function, a second term is included, forcing the membership to be as high as possible without a maximum limit constraint of one. Experiments are conducted on real images to investigate the performance of the proposed modified FCM technique in segmenting the medical images. Standard FCM, Modified FCM, Fuzzy Possibilistic C-Means algorithm (FPCM) are compared to explore the accuracy of our proposed approach. Support Vector Machine classifiers is used in the proposed approach for classifying segmented image as it is more efficient particularly in dealing with large classification problems.