VIGNANA BHARATHI - Bangalore University

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VIGNANA BHARATHI Bangalore University Journal of Science - A bi-annual periodical.

ISSN-0971-6882

Website: http://bangaloreuniversity.ac.in/

JOURNAL INFORMATION Bangalore University, Bangalore, has introduced �The Science Journal� in 1995 and has

been catering to the needs of various disciplines of Science. The journal is published as a

half yearly (with special issues on specific topics in between) and accepts scientific

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http://bangaloreuniversity.ac.in/

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Prof. H.P. Puttaraju Department of Life Science

EDITOR IN CHIEF Bangalore University, Bangalore

Email id : [email protected]

Dr. Sayandip Mukherjee Asst. Prof- Biological Sciences, Bangalore University

Research Scientist (Virology) - SSG ASSOCIATE EDITOR

Hindustan Unilever Research Centre

Dr. S. Sampath Kumar Department of Life Science

ASSOCIATE EDITOR Bangalore University, Bangalore


EDITORIAL BOARD

Prof. Sharath Ananthamurthy Prof. Asha Devi

Department of Physics Department of Zoology

Bangalore University, Bangalore Bangalore University, Bangalore

Email id : [email protected] Email id : [email protected]

Prof. Pradeep Siddeshwar Prof. Chandrappa

Department of Mathematics Department of Chemistry



Prof. Tejavathi Prof. B.C. Prabhakar

Department of Botany Department of Geology



Prof. Hanumanthappa

Department of Computer Application

Bangalore University, Bangalore


mailto:[email protected]









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Editorial policy VIGNANABHARATHI, the bi-annual science journal of Bangalore University aims to publish highquality articles relevant to all those interested in Physical, mathematical and computer sciences, Chemical Science, Biological sciences, Earth and Atmospheric Sciences. This peer-reviewed, semi-annual journal, published by Prasaranga Bangalore University in English, (ISSN number: 0971-6882) will consider original scientific and non-scientific contributions for publication in an Open access format. Research articles, Review articles, reports, and Opinion articles in the areas of Physics, Chemistry, Mathematics, Computer science, Biology and Earth and Atmospheric science will be considered. It is published bi-annual and serves the need of scientific and non-scientific personals involved/interested in gaining knowledge. Indexing: Indexing is under process for Index Copernicus, DOAJ (Directory of Open Access Journals), Scopus and EBSCO, CAS (Chemical Abstracts Service, USA), Open J-Gate, Journal Seek, Science Central, Google Scholar. Instructions to Authors The submission of manuscripts is strongly encouraged, provided that the text, tables, and figures are included in a single Microsoft Word file (preferably in Times New Roman, 12 fonts with double space). Submit manuscripts as e-mail attachment to the Editorial office at: [email protected]. The cover letter should indicate the corresponding author's full address and telephone/fax numbers and should be in an e-mail message sent to the Editor, with the file, whose name should begin with the first author's surname, as an attachment. The authors may also suggest two to four reviewers for the manuscript. Regular articles

These should not exceed 4000 words, 6 display items (tables and figures), should describe new and carefully confirmed findings, and experimental procedures should be given in sufficient detail for others to verify the work. The length of a full paper should be the minimum required to describe and interpret the work clearly. Review articles The review articles should not exceed 6000 words and cited references to be limited to about 100 in number. The article is expected to survey and discuss current developments in a field. They should be well focused and organized, and avoid a general �textbook� style.

mailto:[email protected].

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Short Communications (Technical and Student Articles) A Short Communication is suitable for recording the results of complete small investigations or giving details of new models or hypotheses, innovative methods, techniques or apparatus. Should be less than 1500 words and 2 display items serve to rapidly communicate important new findings. Contributions dealing with technical advances or advances in instrumentation may be published as Technical notes. Manuscript Preparation Manuscripts should be typed (Times New Roman font 12) and double-spaced. The pages should be numbered consecutively, starting with the title page and through the text, reference list, tables and figure legends. Order of manuscript 1. Title page, 2. Abstract, 3. Key words, 4. Text, 5. Acknowledgements, 6. References, 7. Tables, 8. Figures, 9. Legends (on separate page preceding the first figure),

Title page: The Title should be a brief phrase (capitalize first letter of each word in the title). TheTitle Page should include the authors' full names and affiliations, the name of the corresponding Author along with phone, fax and E-mail information. Abstract: The Abstract should be informative and completely self-explanatory, briefly present the Topics, state the scope of the experiments, indicate significant data, and point out major findings and conclusions. No literature should be cited. Abbreviations should be avoided as much as possible. The Abstract should be 100 to 300 words in length. Key words (2-6) should be provided below the Abstract to assist with indexing of the article. Text: All papers should have a brief introduction. The text should be intelligible to readers indifferent disciplines and technical terms should be defined. Tables and figures should be referred to in numerical order. All symbols and abbreviations must be defined, and used only when necessary. Superscripts, subscripts and ambiguous characters should be clearly indicated. Units of measure should be metric or, preferably, SI.

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Acknowledgements should be as brief as possible.

For all manuscript submissions and queries please contact: Dr. H.P.PUTTARAJU, The Editor, Vijnanabharathi Professor and Chairman, Department of Life sciences Bangalore University, Bangalore-560056, India Tel.: +91 80 22961923; Email: [email protected]


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TABLE OF CONTENTS

Sl. No. Title Page No.

01

ARCHIVE OF IMAGE DATA USING DATA MINING

TECHNIQUES

S. Regina LourdhuSuganthi and Hanumanthappa. M

1-12

02

RELIABILITY OF COMPUTER SYSTEMS WITH MINIMAL

REPAIRS

R.Chinnaiyan and V. Ilango

13-17

03

COMBUSTION SYNTHESIS AND CHARACTERIZATION OF

BaTiO3 NANOPARTICLES

M. Kusuma and G. T. Chandrappa

18-24

04

MICROPALEONTOLOGY AND IT�S IMPLICATION IN

PETROLEUM EXPLORATION: AN OVERVIEW

N. Malarkodi

25-30

05

AN OVERVIEW OF SUSTAINABLE RESOURCE

MANAGEMENT OF PERI-URBAN AREAS OF TUMKUR �

IMPLICATIONS FOR SMART CITY DEVELOPMENT PLANS

K. N. Radhika and B.C. Prabhakar

31-39

06

A PERFORMANCE EVALUATION OF F-MEASURE FOR

SYNTACTIC STRUCTURE OF A LANGUAGE USING HFSE

ALGORITHM AND HASHING TECHNIQUE.

Rashmi S, M Hanumanthappa and S Kavitha

40-48

07

SEWAGE WATER INDUCED MITOTIC ABNORMALITIES IN

BRACHIARIA MUTICA A FODDER GRASS

SirangalaThimappaGirisha

49-52

08

ESTIMATION OF TRANSITION PROBABILITIES IN

QUEUING CHAINS

V. SRINIVAS

53-59

09

AMBIENT GAMMA EXPOSURE LEVEL IN AIR AND THE

ASSOCIATED RADIATION DOSE TO THE POPULATION OF

BENGALURU, SOUTH INDIA

N. Nagaiah and N. G. ShivaPrasad

60-68

10

FIELD AND PETROGRAPHIC CHARACTERISTICS OF

GRANITOIDS IN THE SOUTHERN PART OF BANGALORE,

DHARWAR CRATON� SOME CONSTRAINTS ON THEIR

ORIGIN

B.C.Prabhakar, K.N.Radhika, S.V.BalajiManasaRao,

K.M.ShashiKiran, G.Sudarshan, H.J.Pavana, K.B.Naveen and

S.Sunil

69-80

11

ESTIMATION OF MORPHOMETRIC PARAMETERS FROM

ASTER-DEM DATA BY GIS AND REMOTE SENSING

TECHNIQUES � A STUDY ON ELEMALLAPPA

WATERSHED OF DAKSHINA PINAKINI RIVER BASIN,

KARNATAKA, INDIA

K. Satish and H.C. Vajrappa

81-89

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1 ©2016Vijnana bharathi All rights reserved ISSN : 0971-6882; CODEN : VBHAD6

ARCHIVE OF IMAGE DATA USING DATA MINING TECHNIQUES

S. Regina Lourdhu Suganthi and M. Hanumanthappa*

Department of Computer Science and Applications, Bangalore University, Bengaluru,

*Corresponding author: [email protected] Received: 20/01/2016 Accepted: 10/02/2016

ABSTRACT

Commercial Organizations, Agencies and Educational Institutions conduct events round the

year and the iage acquisition activity through the digital devices installed at the event venues

is predominant. An archive of these images with content and context based tags stored in the

databases would enhance future reference and inference. The image archive is created by

focusing on two key aspects namely, effective storage and efficient retrieval. Effective storage

of the captured voluminous image data could be achieved through size reduction, bit-plane

compression and elimination of similar image pairs. Computer vision plays a vital role in

mining image data. Efficient retrieval of the image data is accomplished by classifying and

tagging the images through face detection and object identification.

Keywords:CBIR, RBIR, Haar, HOG, LBP, SSIM, PCA, ROI

1. INTRODUCTION

Organizations conduct various events during the year. No event is complete

without capturing images. The images taken during an event are generally stored in

multiple locations and viewed only over a short period of time and usually till the next

event captures the attention. If the images taken at every event could be organized

with content and context based annotations in an image store, it would provide

meaningful and useful information to prepare organizational reports, edit

departmental newsletter and annual magazines through an efficient query based

retrieval. Image processing applications are largely domain specific. Here the task is to

develop a system to automatically and efficiently tag the images in order to retrieve

them through a simple query.

The paper is organized as follows: Section 2 presents the literature survey of the

related topics. Section 3 presents the proposed framework and the detailed process

flow of the framework. Section4 discusses the experimental results of an event data set

and Section 5 concludes with scope of the work.

2. RELATED WORK

Generally image classification first segments the image into several objects and

the features are extracted from each object. Hierarchical features tree of image objects

is automatically constructed using hierarchical clustering. For each feature tree, an

effective clustering method has been designed to build a hierarchical feature tree. Once


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the hierarchical feature tree is constructed, multi-level association mining approach is

used to generate the classification rules. Given an image, the objects on the image are

mapped on the hierarchical feature tree and using multi-dimensional multi-level

association mining method respective classes are identified6.

Image content can be described as a composition of visual and semantic content.

Visual content itself may be general or specific to a domain. Content Based Image

Retrieval (CBIR) and Region Based Image Retrieval (RBIR) improves the performance

of retrieval by extracting the features of an image after segmenting the image into

regions5. Segmentation is a very challenging area pertaining to human perception9.

Local features such as texture, energy play a significant role in identifying the similarity

between images11. Structural similarity measure (SSIM) is a quantitative measure to

automatically predict the image quality with reference to HVS. SSIM compares local

patterns of pixel intensities that have been normalized for luminance and contrast. It is

used to measure perceived changes in structural information variation.

Object recognition is an integral part of machine vision. To recognize objects from

an image, highly distinctive features that are invariant to image scale and rotation are

extracted. Once the features are extracted the recognition proceeds by comparing these

features to a trained database of features.

In the past decade many face detection techniques have been proposed. Face

comprise of high-dimensional data, referred to as features. Principle Component

Analysis (PCA) discover the dimensionality and yield a compact representation of these

features. One of earlier approach of face recognition based on eigen faces visualized the

problem as a template matching problem10. The first and foremost real-time face

detection system is due to Viola-Jones3,4,7. The system take the input as faces and non-

faces and train the classifier by extracting features to identify faces. Haar features

representing different characteristics of a face are applied to the image to extract face

features. This results in over one sixty thousand features. The redundant features are

eliminated by AdaBoost classifier in the next stage from an integral image by

calculating the linear weighted sum of certain features to decide whether the image

window consists of a facial feature or not. Adaboost reduces the number of relevant

features close to two thousand five hundred features. Another technique known as

cascading is used to determine whether an image is a face or a non-face.

The two classes of image compression techniques are lossy and lossless

compression2. Lossless compression is essential in applications such as medical

imaging, technical drawing and many other such fields. Integer to Integer multi-

wavelet transform based on lifting scheme is used for lossless image compression8.

Many of the image compression algorithms aim at reducing redundancy in order to

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reduce storage and facilitate efficient transmission. Image compression can be

achieved by addressing redundancy1.

3. A TAGGED ARCHIVE OF EVENT IMAGE SET

Image processing techniques are largely application specific. Here, the objective of

creation of an archive of image data set taken during an event is for future reference

with the perspective of organizational documentation. The key focus here is thus

effective storage and efficient query based retrieval. The process flow of the work has

been depicted in Figure 1.

Figure 1 : Process Flow Diagram

3.1 Storage Optimization

Storage efficiency is dealt, by taking into account, the objective. Size reduction and

elimination of similar images minimizes the storage space significantly. Taking

advantage of the fact that the images are used in future for printing, a novel lossy bit-

plane compression technique has been used to achieve storage efficiency.

Event

Digitized

Image Set

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Noise Removal

Image Sharpening

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Storage optimization

Da

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Tagged Archive

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Retrieval

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3.1.1 Size Reduction and Elimination Of Redundant Images

Each image in the entire image set has been reduced to 25% of its original size as

the initial step. Multiple shots of same scene lead to redundancy. These similar and

redundant images occupy great amount of storage space. Identification of multiple

images with same content and its elimination reduce the storage space of the image

set. In this research work, each image has been segmented into 3x3 uniform size parts.

SSIM index is computed for each of the adjacent image pairs. One of the image from

each identified similar pairs is eliminated.

3.1.2 Bit-Plane Compression

The proposed procedure uses spatial information of the image based on pixel

values. Each pixel of an RGB image has a 3 byte representation, one byte each for Red,

Green, and Blue color components. The first five higher order bit planes of each color

component of a pixel are extracted and the remaining three bit planes are ignored as

the loss due to this is negligible with respect to the stated objective. The bit planes of

the three color components are combined in octal form and encoded to a decimal

number. This encoded values require only 2-bytes of storage representation as against

the normal 3-byte per pixel representation. The image is reconstructed without

significant difference on the print quality through the symmetric decoding scheme.

Thus the proposed technique reduces the storage space requirement by one-third. The

compression process is depicted in Figure2.

Original Image Reconstructed Image

Bit Plane Extraction Image Reconstruction

Compression Encoding

Figure 2 : Compression Scheme

Let x be an image pixel with , ,r g b , where 8 7 6 5 4 3 2 1r r r r r r r r r , 8 7 6 5 4 3 2 1g g g g g g g g g and

8 7 6 5 4 3 2 1b b b b b b b b b . The bits extracted are : 8 7 6 5 4 8 7 6 5 4 8 7 6 5 4 , r r r r r r g g g g g g and b b b b b b

It is evident that the maximum loss is just seven units for each color component. The

weighted sum of the extracted bit streams are computed to produce an octal value in

the range 00000 � 77777. Thus the thk octal digit is the result of

Bit Planes

Octal Compressed Image Data

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4 * 2 * k j j jx r g b , 8j k , where , j jr g and jb are the thj bit plane value of the

Red, Green and Blue colors respectively. This octal stream is converted to a decimal

value y, where y is the corresponding pixel of the compressed image. The range of

values resulting from this operation is 0 � 32767 and hence require only two byte

storage space for any pixel x. The image could also be represented as a single matrix of

integer values as against three matrices representing each color component.

The compressed and encoded image can be reconstructed by reversing the

process. The compressed image data stored in the matrix is in the range 0-32767 for

each pixel. This data is first converted to a 5-digit octal number. Secondly, each digit of

the octal number is transformed to a 3-bit number using modulo division. The resulting

higher to lower order bits are associated with Red, Green and Blue bit data. Thirdly, the

Bits corresponding to each of the three color components are combined in higher to

lower order bit stream. This results in a five bit string. Finally, each bit string is

converted to an integer value in the range 0-248. Thus the individual color components

are arrived at with a minimal loss. Though, this procedure is a lossy compression

technique, the human visual perception cannot differentiate this loss in the color

values in print form. Generally peak signal-to noise ratio (PSNR) is used as an

approximation to measure the quality of the reconstructed image through lossy

compression. If I is the original image and J is the compressed image with the size of

the M x N then vzm v m I

210 10. / IPSNR Log MAX MSE where

2

1 1

( , ) ( , ) / *N M

i j

MSE I i j J i j M N

The PSNR for this lossy compression scheme is calculated for a random sample set (

crowded, un crowded, with more color distribution and less color distribution ) eight

images and the results are shown in Table 3.

3.2 Image Tagging

To tag the images in the event image set appropriately, the images must be

classified into predefined classes based on image content and event context. The

image content in the said objective include Chief guest, Celebrity, guest of honour,

Head of the organization and other important personalities. Similarly objects such as

logo, podium, inaugural lamp signify different activities in an event. On the other hand,

event context is extracted using timestamp and the session details. By combining the

event context and the image content, the images are grouped and stored under

different pre-defined classes as an archive to enhance query based retrieval.

3.2.1 Image Content Based Tagging

The first step in this process is person identification. The face is a unique

characteristic of a person, it could be identified after locating them in the images. The

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process include face recognition, face detection and matching. Face detection is

performed by combining face texture, image pyramid and skin-tone. Face recognition

system combine face appearance and geometry. The extracted face features could be

used as a face template. Using a distance measure and an appropriate threshold

obtained from the training data set, the face of a person of interest could be identified

and used for tagging.

3.2.1.1 Face Detection and Identification

Face portion of the person of interest with various postures and uniform size

M x N is cropped from the chosen images. These are converted to gray plane. This set is

used as a training set. If there are P such face images, then each face image is mapped

to an MN x 1 vector resulting in a matrix of size MN x P for P face images. Let v be

represent MNxP face matrix. The features are extracted from these training data set in

terms of eigen-faces. The largest K eigen faces (95%) are used to fix the threshold for

identification. The mean is calculated from the P training face images. Let m

represented the resulting MNx1 vector. The mean is subtracted from each image

resulting in vzm = v - m I, where I is a unit row vector of order P. The eigen faces of

the covariance matrix are computed. These are sorted in descending order. The

required number of K principle components are obtained by setting a threshold.

Each image from the image set is processed using Viola-Jones face detection

technique. The faces detected are represented as a face rectangle resulting in a vector

of size four for each face. To compare and match the face image characteristics with

the trained data, the test image should be centered and must be of same size MxN used

in the training data set. To obtain the test image satisfying the above mentioned

criteria, the first two attributes of the vector that holds the pixel location (x,y) of the

top left corner of a face rectangle is used. The test face images of uniform size M x N is

then cropped. Each of the cropped face images are processed sequentially for

matching characteristics. First the cropped image is mapped to an MN x 1 vector. The

mean of the training image set is then subtracted from this image vector. The resulting

vector is normalized and then compared with the eigen faces. Distance measure is

computed between the trained image set and the test image. The threshold is set as

maximum of the minimum mean subtracted distances of the training face images. If

the distance computed from the test image is less than this threshold, the image is

classified under the pre-set class ( C1, C2, �, Cn ), otherwise, the next face image is

tested and the procedure is continued until all the detected faces from that image are

processed.

3.2.1.2 Object Identification

The next step in this process is object identification. The objects that describe a

particular event attribute could be used to train the classifier. Objects such as logo,

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inaugural lamp, podium and the like, must be chosen to label the event context. When

the aspect ratios of the object do not vary significantly, cascade object detector could

be used to detect the objects.

The Cascade Object Detector is trained in stages using a technique known as

Boosting. The classifier labels the regions by sliding a window of a specific size through

the image and by marking them as positive or negative. If a region is labeled as positive

at a particular stage then that region is passed on to the next stage. The ultimate goal of

training the classifier is to have minimum false negative rate.

The classifier uses two image sets of samples for training. One set consisting of

images with the regions of interest specified in every image to act as positive samples

and the other set with images where the object of interest is not present in order to be

used as negative samples. The number of stages of training could be increased to

achieve accuracy. The three features used by the object detector are Histogram of

Oriented Gradients (HOG), Haar and Local Binary Patterns (LBP).

The high false positive rate in the output of the classifier is addressed by

extracting texture moments from the ROIs of the positive samples. Using a distance

measure and an appropriate threshold, the false positive regions are further processed

to yield either true positive or true negative. Images that are to be tagged are

processed by the object detector one at time. The regions that are detected by the

cascade object detector are reported as bounding boxes. These bounding boxes contain

some false positives. Thus each of the bounding boxes are further processed by

extracting the texture details of the region. The average texture data from the positive

samples used for training the classifier is computed and compared with the extracted

texture feature of each bounding region using a distance measure and an appropriate

threshold to accurately detect and declare only true positives.

3.2.1.2 Decision Tree Based Image Classification

Let E be an event hosted by an organization. If P1,P2, �, Pn are the persons of

interest and O1, O2, �, Om are the objects of interest that distinguish the image context

with C1, C2, �, Cr as pre-determined classes. The classification is done based on the

three attributes namely, Persons, objects and the association A1, A2, �, As between the

object and the person. The splitting attributes are persons, objects and their

associations. The leaves of the decision tree are the classes C1,C2, �, Cr.

4. EXPERIMENTAL RESULTS

Event E : Two-day seminar held in an institution Image set : 702 images

The first class hierarchy : Day 1 & Day 2 based on time stamp.

Storage optimization measures:

i) Size reduction: Each image is reduced to 25% of its original size

ii) Elimination of redundant images: Taking advantage of the fact that generally in

events multiple shots of the same scene are captured in continuous intervals, similarity

check is done between two consecutive image pairs. Each image has been segmented

into 3x3 equal segments with padding, each segment pair has been then compared

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with its SSIM index and one of the two similar image is retained and the other is

eliminated. In an image set with 59 images, 16 images were eliminated. Thus the

similarity ratio is 16/59= 0.2712 = 27% .

iii) Bit-Plane Compression : Let p be a pixel in an RGB image with the pixel values

(R,G,B)=(252,129,55), then the first-five higher order bit-plane extraction of p result in

(11111,10000,00110) = (248,128,48). For a pixel p(252,129,55), the bit strings are :

R : 1 1 1 1 1 G : 1 0 0 0 0 B : 0 0 1 1 0

and the compressed octal value using the weighted sum is (4*1+2*1+1*0

4*1+2*0+1*0 4*1+2*0+1*1 4*1+2*0+1*1 4*1+2*0+1*0) = 64554. This octal value is

converted to a decimal 26988 say r. It is evident that any octal value from the extracted

bit stream would be in the range 00000 to 77777 and the corresponding decimal

equivalent is in the range 0 � 32767. Thus the storage required for a pixel is just 2-

bytes as against 3-bytes. The image is reconstructed by first converting the integer r =

26988 to an uniform five length octal string. Thus r is transformed to 64554 using

modulo 8 division and then each digit from the octal string is converted to three bit

form as :6 : 110, 4 : 100, 5 : 101, 5 : 101, and 4 : 100. In the next stage, the first

higher-order bits of each of the 3-bit strings are combined to get the R : 11111, the

second higher-order bits are combined to form G : 10000 and the third bits are

combined to form the B value : 00110. Thus we get the (R,G,B) of the pixel r in the

reconstructed image as (248, 128, 48). For the original uncompressed image named as

original image in Figure 2 of size 201 x 300, the weighted sums of the extracted bits

from the R,G,B components for the portion of the image 55 x 18 to 65 x 24 is shown in

Table 1 and the corresponding decimal values for the same portion of the image is

shown in Table 2.

Table 1 :Octal strings of pixels 55x18 -65x24Table 2 : Decimal values of pixels 55x18 -65x24

77777 77777 77777 77777 77777 77777 7777777777 77777 77777 77777 77777 77777 7776577420 76507 76414 65735 65702 65422 6506607740 06564 64114 64174 64174 64174 6417407700 00006 00421 42340 64114 64174 6417477007 07025 00007 00043 04235 24761 6417477777 77770 70770 00077 00000 00061 0427777777 77777 77777 07700 00654 00007 0000077777 77777 77777 70004 06457 42126 0065677777 77777 77777 70004 04657 42707 4270377777 77777 77777 70077 06453 42707 42707

32767 32767 32767 32767 32767 32767 3276732767 32767 32767 32767 32767 32767 3275732528 32071 32012 27613 27586 27410 27190

4036 3444 26700 26748 26748 26748 267484032 6 273 17632 26700 26748 26748

32263 3605 7 35 2205 17905 2674832767 32760 29176 63 0 49 223932767 32767 32767 4032 428 7 032767 32767 32767 28676 3375 17494 43032767 32767 32767 28676 3375 17863 1785932767 32767 32767 28735 3371 17863 17863

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Image PSNR Value in dB

1 36.3861

2 36.6009

3 36.3824

4 36.5235

5 36.5833

6 36.7271

7 36.5955

8 36.6061

Image Tagging

i) Face detection and identification :

Chief guest of the inaugural session is chosen to be the person of interest.. An

image set consisting of 71 images of the inaugural session has been experimented. The

face portion of the chief guest in various postures has been used as a training set and is

shown in Figure 3.

Figure 3 : Training Face Image Set of the Guest

These face images are of uniform size 51 x 51. Thus M=51, N=51, P=10. Each of

these face images are mapped to a vector of size 2601 x 1 resulting in a 2601 x 10

matrix. Eigen faces of these face images are computed and sorted. The 95% of the

maximum variation was identified in the largest four eigen faces. Hence the number of

principle components are K=4. The mean subtracted matrix multiplied by the principal

components yield a ( P x K ) = (10 x 4 ) matrix cv. This matrix cv is used as required

feature matrix of the face images. To test and identify the chief guest automatically in

all the images, first the faces in each image is detected and the corresponding data is

stored as a 4-dimensional vector comprising of the top-left co-ordinates of the face

rectangle and the length and breadth of the rectangle. This information is used to crop

the face portion of the image. The first two co-ordinates of the 4-dimensional vector is

moderately adjusted using third and the fourth co-ordinates to obtain the face image of

size 51 x 51. If needed the cropped portion is resized to obtain uniform size. This is

Table 3 : PSNR values in dB

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mapped to a 2601 x 1 vector and the mean is subtracted. z=norm(cv-s) is computed

and the minimum of z with an maxmin threshold identifies the presence of the trained

face in the test image. Figure 4, Figure 5, and Figure 6 shows a test image with the

guest, gray-scale cropped face image and the match found in the testing process

In all the images where the face detection algorithm detected the faces, this

template matching with the identified threshold identified all the chief guest faces

resulting in 100% Recall.

ii)Object Detection :

The focus is to by identify the podium logo. Day 2 image set with 457 images are

considered.

To train the classifier the following data sets are used :

Positive samples : One set of 18 images with the logo object specified as ROIs are

chosen ( Figure 7)

Negative samples : To achieve accuracy negative samples have to be more and

hence, a set of 111 images without the logo object are chosen ( Figure 8 )

Figure 7 : Figure 8:

Positive Samples with logo object as ROI Negative Samples without logo Object

The classifier is trained in five stages to achieve accuracy. HOG features have been

extracted from the positive samples and are used to train the classifier. The texture

features from the 18 positive samples are extracted and the mean of few of the

moments are computed for each color component (RGB). The event set E with all the

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702 images have been tested. The detector marks the detected regions in each of the

images with bounding boxes (Figure 9 ).

Figure 9 : Detected Region in the Test Image

The detected region include some false positives, hence to eliminate these false

positives, from each detected region the component wise texture features are extracted

and compared with component wise mean texture features of the positive samples

using a distance measure.

iii) Image Archive :

The images which have the objects and persons of interest are classified into pre-

determined classes by constructing hierarchical decision tree. The outcome of the

resulting tree is labeled and these labels are used to tag the images and the resulting

groups can be maintained as an archive. This archive fastens the search as the large

image sets are grouped into various classes.

5. CONCLUSION

Classification and Tagging of images finds its application in many areas. Old

documents that were written on leaves (Olai Chuvadi) could also be archived using

event context and image content by training the classifier. As the multimedia content is

growing exponentially, tagged image archive of this kind would enhance the

information retrieval and serve as a good resource for future reference.

6. REFERENCES

1. Chen T, K Chuang. (2010) �A Pseudo lossless Image Compression Method�, IEEE

Congress on Image and Signal Processing, Vol. 2, pp 610-615.

2. Krishnan Gupta, Mukesh Sharma, Neha Baweja. (2014) �Three different KG

version for Image Compression�, International Conference on Issues and

Challenges in Intelligent Computing Techniques (ICICT), IEEE

3. Ole Helvig Jensen. (2008) � Implementing the Viola-Jones Face Detection

Algorithm�, thesis, ISBN 87-643-0008,ISSN 1601-233X.

4. Paul Viola, Michael J. Jones. (2004) Robust Real-Time Face Detection,

International Journal of Cumputer Vision 57(2).

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5. Ruba A. A. Salamah. (2010) �Efficient Content Based Image Retrieval�, Thesis

submitted to Islamic University � Gaza, Deanery of Higher Studies.

6. Vincent Shin-Mu Tseng, Ming-Hsiang Wang, Ja-Hwung Su. (2005) � A New

Method for Image Classification by using Multilevel Association Rules�, Data

Engineering Workshop, IEEE, ISBN : 0-7695-2657-8.

7. Yi-Qing Wang. (2014) �An Analysis of the Viola-Jones Face Detection Algorithm�,

Image Processing On Line, 4 pp. 128�148.

8. Yu Shen, Xieping Gao, Linlang Liu, Qiying Cao. (2011) �Integer to Integer

Multiwavelets for Lossless Image Compression�, Proceedings of IEEE IC-

BNMT.

9. Yu-Jin Zhang. (2009) �Image Classification and Retrieval with Mining

Technologies�, Chapter VI, Handbook of Research on Text and Web Mining

Technologies.

10. Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld. A. (2006) � Face recognition:

A literature survey. ACM Computing Surveys�, 35, 399�459.

11. Zhou Wang, Alan Conrad Bovik,, Hamid Rahim Sheikh and Eero P. Simoncelli.

(2004) �Image Quality Assessment: From Error Visibility to Structural

similarity�, IEEE Transactions on Image Processing, Vol. 13, No. 4, April.

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RELIABILITY OF COMPUTER INPUT DEVICES WITH MINIMAL REPAIRS

R.Chinnaiyan*and V.Ilango

Department of Computer Applications, New Horizon College of Engineering, Bangalore.

*Corresponding author: [email protected]

Received: 25/01/2016 Accepted:17/02/2016

ABSTRACT

This paper discusses the reliability of computer input devices and its performance. The

input device�s performance policy undergoes minimal repairs on failures between

replacements is performed with respect to Preventative Maintenance. The Minimal

repairs follow Non-Homogeneous Poisson Process. The Operational characteristics and

Cost Benefit analysis are obtained. The inspection policy minimizing the expected cost

per unit of time for an infinite time span is also discussed.

Keywords: Computerinputdevices, Non-Homogeneous Poisson Process, Minimal

Repairs, Preventative Maintenance, Reliability

1. INTRODUCTION

Computer input devices can undergo minor failures as well as catastrophic ones.

When the former take place in a particular input device, it returns to the right

operating state with slight repairs and at low costs, even if the failure cause the

mechanism to stop working. Consider for instance the case of a computer input device

that presents some difficulties to work due to lack of connection between hardware

devices , or a battery that do not properly supplies power via SMPS. The problem is

easily solved after testing the power connections or the battery level. The catastrophic

failures are those that cause the computer input device to stop working properly but

major repairs and high costs are required. Often, a replacement of the whole hardware

unit or a perfect repair that restore the computer input device to an as-good as- new

condition is to be carried out. For example, failures due to viruses that make the user to

install a new hard disk and, in general, unavoidable over-loads, warming

environments, etc may have serious consequences.

As many users of modern technology know, complex computer input devices

subject to both types of failures are very common to find in practice and maintenance

policies should deal with them. This is the case of computer input devices that can be

affected by inoffensive spy programmes as well as by dangerous viruses.

1.1 Reliability Model for Computer Input Devices

A reliability model for computer input devices represents a clear picture of the

computer input device� s functional interdependencies providing a means to trade-off

design alternatives and to identify areas for design improvement of a computer input


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device. The reliability models are also helpful in: Identifying of critical items and single

points of failure of a computer input device.

(i). Allocating reliability goals to portions of the design of a input device

(ii). Providing a framework for comparing estimated reliability of input device

(iii). Trading-off alternative fault tolerance approaches for input device

Reliability models are derived from, and traceable to, functional requirements of

input device. They represent the required modes of operation, the duty cycles, and are

consistent with a specified definition of what constitutes a input device failure.There

has been continuing interest in the policies for input devices that are subject to

stochastic failures, as the uncalled for failures may prove to be costly and dangerous.

Barlow and Prochan [2] first considered a policy called an age replacement policy

in which system is replaced at age t or at the time of failure whichever occurs first. But

for the complex and expensive systems, it is not advisable to replace the entire system

just because of the failure of one component The minimal repair model introduced by

Barlow and Hunter [3] has been extended in later works that propose maintenance

policies according to the state of the system. Block et al [4] present an interesting

survey concerning maintenance policies with time dependent costs and probabilities.

The text due to Ascher and Feingold [1] constitutes a general framework for repairable

systems and deals, in particular, with the minimal repair model and the underlying

theory on non-homogeneous Poisson processes.

In this paper a policy for the input devices is considered that undergoes minimal

repairs on failures between replacements. Minimal repairs follow Non-Homogeneous

Poisson Process (NHPP).

2. RELIABILITY MODEL ASSUMPTIONS

(a) Computer Input Device is replaced at the time of Preventive Maintenance (PM)

and it undergoes minimal repairs on failures between replacements

(b) Hazard rate of the Input Device is continuous, increasing and is not disturbed by

minimal repairs

(c) All Input Devices maintenance events time are negligible

(d) All failure events for Input Devices are (statistically independent) s-independent

(e) Planning time horizon for Input Devices is infinite

2.1 Notations

F(t) : the failure time cumulative distribution function (cdf) of the

computer input devices with probability density function (pdf)

H(t) : recurrence time cdf from state 0

q(t) : hazard rate of the random life of the computer input devices

Q(t) : cumulative hazard rate

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K(t) : cdf time to PM defined as :

K(t) = 1, if t ≥ T

0, if t< T

Qij (t) : the probability that after transiting from state into the state j, the in

unit amount of time ≤ t.

qij (t) : pdf of Qij (t)

Vij (t) : the expected number of visits to state j during the interval (0, t],

given that the input device starts from the state i.

* : the Laplace Stieltjes convolution

K (t) * L (t) = 0 ∫ t K (u) dL (t-u)

f(s) = LS[f(t)] = 0 ∫ ∞ ℮ -st dF(t)

V(t) : the projected number of failures during the time interval [0,T)

V : the projected number of failures per unit time in the steady state

Vj : the projected number of visits per unit time from the state 0 to the

state j in the steady state

3. DERIVATION OF QIJ(T)

The following transition probabilities have been obtained from makov renewal

processes MRP

Q01 (t) = 0 ∫ 1 ∑ Pj(x) d K(x) = K (t)

With Q10 (t) = 1

Where Pj(x) = [Qj(x)] / j! e �Q(x), j= 0, 1, 2.., denote the probability of j failures in the

interval [0, x].

4. EXPECTED NUMBER OF FAILURES IN THE STEADY STATE

By renewal theoretic arguments, we have

∞ ∞

V(t) = ∑ j pj (t) K (t) +∑j Pj (x) d K(x) + Q01 (t)* Q10 (t) * V (t)

J=0 J=0

Taking LST and simplifying gives way

m(s) = 0∫Te-st dQ (t) /( 1- q01(s)q10 (s))

Thus, the expected number of failures per unit amount of time in the steady state

is given by,

V = lim s m(s) S 0

= Q (T) / T

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5. EXPECTED NUMBER OF VISITS FROM STATE 0 TO STATE 1

Similarly, by renewal theoretic arguments, we have

V01(t) = Q01(t) * Q10(t) * [1+ V01(t) ]+Q01(t) * (1- Q10(t))

Taking LST and simplifying gives

m01(s) = e-sT /( 1-e-sT)

Thus , the expected number of visits per unit amount of time from state 0 to state

1 in the steady state is given by,

V1 = lim s m01(s) S 0

= 1 / T

Thus, the expected cost rate is the steady state (infinite time horizon ) is given by

C (T) = ( C1 Q (T) +C2 ) /T

Where, C1 denotes the cost for each minimal repair and C2 (>C1) denotes the cost

of replacement due to PM . This is the classical Minimal Repair Policy introduced by

Barlow and Hunter[3]. To optimize the cost function C(T) , consider

C (T) = ( C1 Q (T) +C2 ) / T

Differentiating with respect to T , we get,

C 1(T) = TC1q (T) � [C1Q (T) +C2] / T2

For the minimum value of C(T) , C1(T) = 0 , This gives

C1[Tq (T)-Q (T)] � C2 = 0

Or

0∫T [q (T) � q (t)] dt = C2 / C1

Let L (T) = 0∫ T [q(T) � q(t)] dt.Therefore

L (0) = 0< C2 / C1

Since, q(T) is an increasing function of T , one can easily see that L′ (T) >0 , which

implies that L(T) is an increasing function of T and

L(∞) = 0∫∞ [q(∞) � q(t)]dt > C2

/ C1

Thus there exists a unique T which minimizes C(T) such that

Tq(T) � Q(T) = C2 / C1

One may note here that

C′ (T) ≤ 0{ if 0 < T ≤ T′ }

>0{ if T′ < T < ∞ }

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6. CONCLUSION

The model discussed in this paper depicts a clear picture of the computer input

devices functional interdependencies providing a means to trade-off design

alternatives and to identify areas for design improvement for the various input devices.

The models are also supportive in identifying the critical items and single points of

failure, allocating reliability goals to portions of the design of the input device,

providing a framework for comparing the estimated reliability for computer input

device goals and trading-off alternatives for fault tolerance approaches.

7. References

1. Ascher.H, Feingold.H, (1984), Repairable Systems Reliability, Marcel Dekker.

2. Barlow, R. E., F. Proschan (1965), Mathematical Theory of Reliability. John Wiley

and Sons, New York.

3. Barlow R.E and Hunter. L.C, (1960), Optimum preventive maintenance policies,

Operations Research, 8, 90 - 100.

4. R. Chinnaiyan, S. Somasundaram, (2010), �Evaluating the Reliability of

Component Based Software Systems�, International Journal of Quality &

Reliability Management, 27(1), pp.78-87.

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COMBUSTION SYNTHESIS AND CHARACTERIZATION OF BaTiO3

NANOPARTICLES

M. Kusuma and G. T. Chandrappa*

Department of Chemistry, Central College Campus, Bangalore University, Bengaluru � 560 001

*Corresponding author: [email protected] Received: 24/02/2016 Accepted: 11/03/2016

ABSTRACT

In this article, we are reporting the synthesis of barium titanate (BaTiO3) nanoparticles

by using solution combustion reaction of barium nitrate and titanium-peroxo complex as

oxidizers and tartaric acid as fuel. For the first time, the titanium metal powder has been

used as one of the oxidizers. The structure and crystallinity nature has symmetrically

been analysed by Powder X-Ray diffraction (PXRD) and Fourier-transform infrared

spectrum (FTIR). The surface area, morphology and optical properties are studied

through Brunauer-Emmett-Teller method (BET), scanning electron microscopy (SEM)

and UV-Vis diffuse reflectance spectroscopy (UV-Vis DRS). X-ray diffraction pattern shows

pure tetragonal perovskite structure with crystallite size of 26.35 nm. The surface area is

found to be 22.15 m2/g. The morphology of BaTiO3 is observed as a flake like

microstructure. The band gap of BaTiO3 nanoparticles is found to be 3.1 eV.

Keywords: Solution combustion, Barium titanate, Nanoparticle, Perovskite.

1. INTRODUCTION

The perovskite ABO3 type materials have been intensively investigated for at least

half a century1. Perovskite type alkaline earth titanates are well-known as functional

materials, because these have very flexible structure and a variety of foreign cations

can be accommodated in its lattice in different degrees letting a great scope of co-

substitution to tailor properties2. ABO3 type perovskite crystals find application as a

technologically important material, e.g. dielectrical, ferroelectrical, pyroelectrical,

luminescent material, electro-optics, wave guides, laser frequency doubling, high

capacity computer memory cells and high-capacitance capacitors including multilayer

ceramic capacitor, piezoelectric sensors, positive temperature coefficient devices and

printed circuit boards. The electronic structure of perovskites has been recently

calculated from first principles and published by several research groups1,3.

The BaTiO3 is considered as an attractive advanced material which, is the most

studied perovskite system and is being considered as a potential candidate in ceramic

and electronic industry and also used as infrared detectors or transducers due to its

particular characteristics3,4. It is also an important photo-refractive material used for


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applications in distortion correction and in combination with laser power. BaTiO3 has

three phase transitions, from cubic to tetragonal at 393 K, tetragonal to orthorhombic

at 278 K and orthorhombic to rhombohedral at 183 K5.

To date, various precipitation techniques have been reported for the synthesis of

nano-sized BaTiO3 by electrochemical route6, hydrothermal7, microwave8,

combustion9, Williamson-Hall method i.e. three different chemical methods10, low-

pressure spray pyrolysis11, sol-gel12, co-precipitation, polymeric precursor method and

mechanochemical route13.

All these techniques involved, require special chemicals and equipments. We thus

eliminated the complexities of methods mentioned above by establishing novel

solution combustion reaction (SCS). The advantages of SCS over other combustion

methods are:

1. Solution combustion being a solution process, it has control over the

homogeneity and stoichiometry of the products.

2. It is possible to incorporate desired impurity ions in the oxide hosts and can

prepare industrially useful materials such as pigments, phosphors as well as

high Tccuprates and solid oxide fuel cell materials.

3. It is simple, rapid, self-sustained chemical reaction process which allows

effective synthesis of variety nanosized materials.

4. Low processing cost, energy efficiency and it does not require any special

equipment as in other combustion methods14.

In SCS process, there is an advantage of choice of a wide variety of fuels, rapid

cooling leading to nucleation of crystallites without any growth and also has the

potential to scale up. Due to evolution of gas, large particles or agglomerates can be

disintegrated during the process and the products formed are of high purity and high

production rate. The resulting product is very fine particulates of friable agglomerates

that can be easily ground to obtain a much finer particle size15.

In this work, the synthesis of BaTiO3 nanoparticles is carried out by the combustion

of redox mixtures containing tartaric acid as a fuel, barium nitrate and titanium peroxo

complex (obtained by dissolving titanium metal powder in H2O2 and NH3) as oxidizers.

For the first time titanium metal powder has been used in the preparation of BaTiO3.

The effects of calcination temperature, sintering temperature and fuel type on phase,

morphology, crystallite size, band gap and surface area of perovskite BaTiO3 are

investigated.

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2. EXPERIMENTAL METHODS

2.1 Chemicals

Titanium metal powder (~100 mesh), barium nitrate, nitric acid (0.1 M), hydrogen

peroxide (30%), ammonia (30%) and tartaric acid (L (+)) were purchased from Merck

Ltd. and used without further purification.

2.2. Synthesis of BaTiO3 nanoparticles

Titanium metal powder (0.2 gm) is dissolved in a solution mixture of 20 mL H2O2

and 5 mL NH3 to form titanium-peroxo complex16 on continuous stirring for 3-4 h in an

ice bath, which forms a yellow sol. To this sol, 1:5 mole ratio of tartaric acid is added as

a fuel, which on addition forms orange colour. Barium nitrate solution obtained by

dissolving in 1 M dil. HNO3 (Ba:Ti = 1:1) is added to the above solution mixture, which

turns orange colour to a wine red. This solution is heated on a hot plate for the removal

of excess H2O2 and NH3. Later placed in a pre-heated muffle furnace maintained at 550

± 10 oC resulted in foam and fluffy mass of BaTiO3 powder along with the fine particles

of carbon. On calcination for 0.5 hour at the same temperature resulted in carbon free

BaTiO3 nanoparticles.

3. POWDER CHARACTERIZATION

Powder X - Ray Diffraction (PXRD) patterns of BaTiO3 nanoparticles were studied

using PANalytical X�pert PRO X-ray diffractometer with graphite monochromatized Cu

Ká radiation source (ë = 1.541 Å). The diffuse reflectance spectrum (DRS) of the

powder in the wavelength range of 200-800 nm was obtained using UV - Vis

spectrometer (Shimadzu 3101). Fourier- transform infrared spectrum (FTIR) of the

sample was carried out on a Fourier Transform Magna-IR Spectrometer 550 from

Nicolet for which samples were pelletized with KBr powder. Surface morphology was

analysed by scanning electron microscopy (SEM) JEOL- JSM-6490 LV where a thin layer

of platinum had been evaporated on the specimen. BET surface area was performed on

Quantachrome autosorb automated gas sorption system.

4. RESULTS AND DISCUSSION

Figure 1 shows the PXRD pattern of BaTiO3 nanoparticles, could be indexed to the

tetragonal structure (JCPDS card number 5-626). The average crystallite size (D)

estimated based on the line broadening of (110) peak, at 2è = 32.47o using the Debye -

Scherrer equation, D = Kë / âcosè is found to be 26.35 nm. UV-Visible spectrum gives

information about the excitonic and inter-transition of nanoparticles17.

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Fig: 1 - PXRD pattern of BaTiO3 nanoparticles

The UV-diffused reflectance spectrum of BaTiO3 is depicted in Fig. 2 exhibits the

broad reflectance band at 400 nm. The band gap calculated using Tauc equation, E = hõ

is found to be 3.1 eV which matches with the reported band gap range18.

Fig: 2 - UV-Vis spectrum of BaTiO3nanoparticles

The FTIR spectrum (Fig. 3) shows a broad absorption in a wide range from 2400

to 3800 cm-1 indicating the presence of H2O and OH� in the BaTiO3 nanoparticles19. The

absorption bands observed near 1626 cm-1 are due to organic impurities. The sharp

bands observed near 1400 cm-1 and 1100 cm-1 are due to CO bonded to Ti ions. The IR

bands observed at 582 cm-1 and 437 cm-1 corresponds to tetragonal phase.

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Fig: 3 - FTIR spectrum of BaTiO3nanoparticles

The nitrogen adsorption�desorption isotherm and pore size distribution of BaTiO3

nanoparticles is presented in Fig. 4 (a and b).

Fig: 4 - Nitrogen adsorption � desorption of BaTiO3 nanoparticles

The measured Brunauer�Emmett�Teller (BET) surface area and pore volume of

BaTiO3 are respectively found as 22.15 m2/g and ~ 0.03899 cc/g with an average pore

diameter of 70.38 Å. The surface morphology of the combustion derived BaTiO3 as

resulted in Fig. 5 (a and b) shows flake like layered structure. The thermal treatment

for the removal of carbon causes an agglomeration and an increase in the thickness of

the flakes as well as particle size. The pores and voids could be attributed to the gas

escaping during the combustion.

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Fig: 5 - SEM images of BaTiO3nanoparticles

5. CONCLUSION

BaTiO3 nanoparticles are prepared via a simple and cost competitive established

solution combustion. In the present combustion synthesis, for the first time, metallic

powder of titanium has been used as the precursor for the synthesis ofBaTiO3. Rietveld

refinement confirmed that BaTiO3 nanoparticle crystallizes symmetrically in a pure

tetragonal structure at 550±10 oC. The optical property is ascertained to be 3.1 eV with

flake like microstructure. Specific surface area is determined as 22.15 m2/g and ~

0.03899 cc/g of average pore diameter.

6. REFERENCES

1. Piskunov. S, Heifets. E, Eglitis. R. and Borstel. G. (2004). Bulk properties and electronic

structure of SrTiO3, BaTiO3, PbTiO3 perovskites: an abinitio HF/DFT study

Computational Materials Science, vol.29(2).pp.165�178.

2. Mohammadi. M. R and Fray. D. J. (2013). Synthesis of highly pure nanocrystalline and

mesoporous CaTiO3 by a particulate sol-gel route at the low temperature. Journal of

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3. Gaur. A. and Sharma. N. (2013). Structural, Optical and Ferroelectric Properties of

BaTiO3 Sintered at Different Temperatures. International Journal of

Mathematical,Physical, Electrical and Computer Engineering, vol.7(12).pp.1203�

1206.

4. Mohammed. Q. A, Ali. Z. R. and Mijbas. A. F. (2012). Electrical and Optical Properties of

Dielectric BaTiO3 Single Crystals Prepared by Flux Technique. Journal of Babylon

University/ Engineering Sciences, vol.20(1).pp.149-160.

5. Salehi. H, Hosseini. S. M. and Shahtahmasebi. N. (2004). First-Principles Study of the

Electronic Structure of BaTiO3 using Different Approximations. Chinese Journal of

Physics, vol.42(5).pp.619�628.

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6. Tao. J, Ma. J, Wang. Y, Zhu. X, Liu. J, Jiang. X, Lin. B, Ren. Y. (2008). Synthesis of Barium

Titanate Nanoparticles via a Novel Electochemical Route. Materials Research Bulletin,

vol.43.pp.639-644.

7. Qi. H, Zhou. H. and Wang. Y. (2015). Study on the Hydrothermal Synthesis of Barium

titanate nano powders and calcination parameters. Journal of Materials

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8. Fang. C. Y, Wang. C, Polotai. A. V, Agrawal. D. K. and Lanagan. M. T. (2008). Microwave

synthesis of nano-sized barium titanate. Materials Letters, vol.62(17-18)pp. 2551�

2553.

9. Anuradha. T. V, Ranganathan. S, Tanu. M. and Patil. K. C. (2001). Combustion synthesis

of Nanostructured Barium titanate. Scripta Materialia, vol.44.pp.2237-2241.

10. Al-Shakarchi. E. K. (2011). Three Techniques Used to Produce BaTiO3 Fine

Powder.Journal of Modern Physics, vol.02(11).pp.1420�1428.

11. Wang. W. N, Lenggoro. I. W, Terashi. Y, Wang. Y.C. and Okuyama. K. (2011). Direct

Synthesis of Barium Titanate Nanoparticles Via a Low Pressure Spray Pyrolysis

Method. Journal of Materials Research, vol.20 (10).pp.2873�2882.

12. Ashiri. R. (2013). Detailed FT-IR Spectroscopy Characterization and Thermal

analysis of Synthesis of Barium titanate nanoscale particles through a newly

developed process. Vibrational Spectroscopy, vol.66.pp.24�29.

13. Vijatovic. M. M, Bobic. J. D.and Stojanovic. B. D. (2008). History and challenges of

barium titanate: Part I. Science of Sintering, vol.40(2).pp.155�165.

14. Patil. K, Aruna. S. T. and Ekambaram. S. (1997). Combustion synthesis. Current

Opinion in Solid State and Materials Science, vol.2(2).pp.158�165.

15. Aruna. S. T. and Rajam. K. S. (2004). Mixture of fuels approach for the solution

combustion synthesis of Al2O3�ZrO2 nanocomposite. Materials Research Bulletin,

vol.39(2).pp.157�167.

16. Nagabhushana. G. P, Ashoka. S. Chithaiah. P. and Chandrappa. G. T. (2013). An

efficient and a novel route for the synthesis of titania via solution combustion of

peroxotitanic acid. Materials Letters, vol.91.pp.272�274.

17. Reenu.J. and Jayakumari. I. (2014). Band Gap Energy Profile of BSFT (BaSr.9Fe.

1TiO4). International Journal of Scientific and Research Publications, vol.4(12).pp.1�6.

18. Ramakanth. S, Hamad. S, Rao. S. V. and Raju. K. C. J. (2015). Magnetic and nonlinear

optical properties of BaTiO3 nanoparticles. AIP Advances,vol.5(057139).pp.1�11.

19. Ashish. R. T, Kushal. V, Jagdish. D. B. and Hiren H. J. (2012). Synthesis of

Nanostructured Ferroelectric Tetragonal BaTiO3. Journal of Science(Knowledge

Consortium of Gujarat),vol.1.(October-November).pp.3.

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MICROPALEONTOLOGY AND IT�S IMPLICATION IN PETROLEUM EXPLORATION:

AN OVERVIEW

N. Malarkodi

Department of Geology, Bangalore University, Bangalore-560056,

[email protected]

Received: 16/03/2016 Accepted: 6/04/2016

ABSTRACT

Micropaleontological studies have remained largely a tool for exploration arena. Oil

companies, who invest hugely in oil exploration, have rely strongly on biostratigraphic

age control from micropaleontological studies. This is because of the reasonable

accuracy and speed with which the results are delivered.Petroleum explorationists and

field development geologists are being asked to find more oil and develop older reserves.

Therefore, it is a welcoming challenge that geologists should look inward and rediscover

how they can add more value to the exploration and production business. This has led

biostratigraphers, usually the service providers, to evolve new techniques and

approaches, challenging old ones and aligning the science with the business needs.

Microfossils have many applications to petroleum geology. The two most common uses

are: biostratigraphy and paleoenvironmental analyses. Biostratigraphy is the

differentiation of rock units based upon the fossils which they contain.

Paleoenvironmental analysis is the interpretation of the depositional environment in

which the rock unit formed, based upon the fossils found within the unit.

Keywords: Micropaleontology, Microfossils, Biostratigraphy, Paleoenvironment,

Petroleum exploration.

1. INTRODUCTION

Micropaleontology is considered as an independent science which has a wide

application especially in the exploration of petroleum and natural gas.

Micropaleontology study of fossil records of micro-organisms for stratigraphical

classification and correlation were begun over half a century ago. Microfossils are

perhaps the most important group of all fossils; they are extremely useful in age-

dating, correlation and paleoenvironmental reconstruction, and as biostratigraphic

tools. Microfossils, as the name implies, are those fossilized remains that require

specialized methods of preparation and study. They normally cannot be studied by

�naked eyes� and require the use of a microscope. Microfossils useful in hydrocarbon

exploration can be divided into five principal microfossil groups based on the

composition of the shell (test) or hard parts. Calcareous microfossils are

foraminifera, ostracods, calcareous nannofossils; agglutinated foraminifera; siliceous

radiolarians, diatoms and silicoflagellates; phosphatic conodonts; organic walled


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chitinozoans, pollen and spores, acritarchs, dinoflagellates (collectively,

palynomorphs).

Marine microfossils occur in sediments of Precambrian to Recent ages, and in

every part of the stratigraphic column one or more groups can be found useful for

biostratigraphic and paleoecological interpretations1. Fossil marine organisms lived

in almost all the marine environment ranging from tidal pool to deep sea and can

therefore be invaluable in the study of changes in the paleoenvironments.

For instance, Radiolaria, Calcareous Nannoplakton, Petropods, some

Foraminifera and Diatoms are planktonic (free floating) and live in abundance from

0-200m. in the open ocean but diminish rapidly near the continents. These forms are

useful in monitoring past changes in the oceanic environments, particularly changes

in temperature. Other groups such as Ostracodes, Bryozoans and some Foraminifera

and Diatoms are Benthic (adopted to living on the bottom of the sea) since these

forms exhibit distribution patterns linked to depth, sediment type and various

physio-chemical variables in seawater, they are useful in delineating changes in

bottom environment. Spores and pollen, although derived from land plants are

strongly climatic-dependent.

BACKGROUND

To give some historical account, the association of micropaleontology to

petroleum exploration is almost a century old. The earliest use was demonstrated by

Josef Gryzbowski of Poland in 1890 and many recall his pioneering effort in

stratigraphy and correlation of beds. The commercial aspect of their importance was

realized by many geological surveys, oil and gas and coal companies who employed

teams of micropaleontologists to learn more about the rocks they were handling.

These studies have also gained impetus and with systematic documentation world

over by various oil companies, and have proved beyond doubt their predictiveness in

local and geological analyses. Assigning age to the rock is one of the primary

requirements of micropaleontological studies as input to reconstruct stratigraphy.

Biostratigraphy is the study of rock strata using fossils. This important step came

in 1842 by Alcide d�Orbigny. His improvement to earlier principles was the

recognition that unique assemblages of fossils might include many formations

(lithostratigraphic units) in one place and only a single formation in another, leading

to the concept of stage. Albert Oppel conceived the idea of small-scale units defined

by the stratigraphic ranges of fossil species irrespective of lithology. He noted that

some fossils existed for a short geologic time, hence a short vertical range, while

others were quite long. Each of Oppel�s zones was named after a particular fossil

species, called an index fossil. In the late 1800�s, a Polish micropaleontologist, Jozef

Gryzbowski realized that rock samples contained fossils that he could recognize from

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well to well. In addition, he could predict hydrocarbon reservoirs and even identify

structural features, such as faults and folds. The refinement of sequence stratigraphy

by the Exxon Group led to an increased demand for biostratigraphy, because high-

resolution biostratigraphy was a key component of this development. All these pave

the way for applied biostratigraphy in exploration and production.

APPLICATIONS OF MICROFOSSILS TO PETROLEUM GEOLOGY

Microfossils have many applications to petroleum geology 2,3,4. The two most

common uses are: biostratigraphy and paleoenvironmental analyses. Biostratigraphy

is the differentiation of rock units based upon the fossils which they contain.

Paleoenvironmental analysis is the interpretation of the depositional environment in

which the rock unit formed, based upon the fossils found within the unit. There are

many other uses of fossils besides these, including: paleoclimatology, biogeography,

and thermal maturation.

BIOSTRATIGRAPHY

Biostratigraphy plays a critical role in the building of geologic models for

hydrocarbon exploration and in the drilling operations that test those models. The

fundamental principal in stratigraphy is that the sedimentary rocks in the Earth's

surface accumulated in layers, with the oldest on the bottom and the youngest on the

top (Fig.1). The history of life on Earth has been one of creatures appearing, evolving,

and becoming extinct (Fig.2). Putting these two concepts together, we observe that

different layers of sedimentary rocks contain different fossils. When drilling a well

into the Earth's crust in search of hydrocarbons, we encounter different fossils in a

predictable sequence below the point in time where the organism became extinct. In

our simplified case (Fig.1), the extant species C is present in the uppermost layers.

Species B is only found in lower layers. The well does not penetrate any layers

containing fossil A.

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Fig.1-Schematic cross section showing rock units from oldest to youngest with LAD

of hypothetical fossils A, B and C noted.

The point at which you last find a particular fossil is called its LAD (Last

Appearance Datum) (Fig.2). In a simplified case, the LAD in one sequence of rock

represents the same geologic moment as the LAD in another sequence. These are our

points of correlation between wells. Another well drilled in this area should

penetrate the same sequence, but most likely at different depths than the original

well. In addition to the LAD, another useful event is the First Appearance Datum

(FAD). This may be difficult to recognize in a well, because rock from higher in the

well bore may slough off the wall and mix with rock from the bottom of the hole.

However, in studies of rock units exposed at the surface of the Earth and in some

cases from well bores, these FADs are extremely useful biostratigraphic events.

Lastly from fig.2, one can recognize that the range of the three fossils overlap for only

a relatively short period of geologic time. As a consequence, if a sample of rock

contains all three (A, B, and C), it must have been deposited during this interval of

time (Concurrent Range Zone). This is yet another "event" which can be used to

subdivide geologic time into biostratigraphic units.

Fig-2 Schematic range chart showing the range of hypothetical fossils A, B and C.

PALAEOENVIRONMENTAL ANALYSIS

For paleoenvironmental analyses the distribution of living benthic foraminifera 5,6,7 provides an excellent database. Using these data, paleontologists constructed

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models for interpreting past environments using fossil benthic foraminifera 8,9,10 . For

example, wells drilled in Pleistocene and Pliocene age sediments of Gulf Coast

encounter fossils of many extant species of benthic foraminifera and consequently

paleoenvironmental interpretations are made with reasonable confidence. However,

as wells are drilled deeper into older sediments, the percentage of extinct species

encountered rises rapidly. In the older sediments paleoenvironments are more

speculative, but can be inferred. In Gulf Coast, ancient marine environments are

related to interpreted water depths (paleobathymetry). This is an oversimplification

because benthic foraminifera often respond to water conditions (temperature,

salinity, dissolved oxygen, etc.) rather than to depth. However, there are over 40,000

wells drilled in the Gulf. By combining data from existing wells, it is possible to

reconstruct the profile of the continental shelf and slope at various points in geologic

time. Such paleogeographic maps, combined with seismic profiles and other geologic

data sets, are the tools used in the search for hydrocarbons. It is palaeontology that

uniquely explains the element of geologic time and depositional environment to

petroleum geology.

Through the paleoenvironmental analysis the fluctuation in sea level can be

reconstructed by initially inferring the paleobathymetry and then integrating the

same on a regional scale using seismic stratigraphy for reconstruction of

transgressive/ regressive cycles within a time frame. Based on new techniques that

have emerged over two decades the depositional sequences can be inferred. The

sequences thus identified, depending on whether they form part of transgression,

regression or a high stand of sea can be used for depositional models and thereby

giving directions for a successful exploration.

2. DISCUSSION AND CONCLUSION

Micropaleontological studies is an important tool for the Petroleum exploration,

finding practical uses in all stages of the exploration process. Micropaleontological

methods can aid the acquisition of geological field data and enhance the quality of the

reservoir potential assessment by way of sequence stratigraphic correlation,

paleogeographical and facies analysis, depositional and source-rock maturation

determination as well as migration modelling 11. The accuracy and profitability of the

drilling process itself can benefit from micropaleontological monitoring through the

analysis, essentially allowing age determination, correlation of wells, unconformity

evaluation, paleoenvironmental interpretation and lithostratigraphic as well as

depositional sequence characterization. The applications of microfossils are

manifold, including biostratigraphy, paleoenvironmental analysis, biogeography,

paleoclimatology and thermal maturation.

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3. REFERENCES

1. Bilal U.Haq and Anne Boersma (1978). Introduction to Marine

Micriopaleontology, Elseviers, New York. 376pp.

2. Fleisher, R.L. and H.R. Lane. (eds.). In press. Applied Paleontology, In E.A.

Beaumont and N.H. Foster (eds.), Exploring for Oil and Gas Traps, Handbook No.

3, Treatise of Petroleim Geology. American Association of Petroleum Geologists.

3. Ventress, W.P.S. (1991). Paleontology and its application in South Louisiana

Hydrocarbon Exploration, In D. Goldthwaite. (ed.). An Introduction to Central

Gulf Coast Geology. New Orleans, Geological Society. p.85-97.

4. LeRoy, D.O. (1977). Economic Microbiostratigraphy. In L.W. LeRoy, D.O.LeRoy

and J.W. Raese. Subsurface Geology - Petroleum - Mining - Construction.

Colorado School of Mines. p.212-233.

5. Poag, C.W. (1981). Ecologic Atlas of Benthic Foraminfera of the Gulf of Mexico.

Hutchinson Ross Publishing Co. 174 pp.

6. Pflum, C.E. and W.E. Frerichs. (1976). Gulf of Mexico Deep-Water Foraminifers.

Cushman Foundation for Foraminferal Research, Special Publication No. 14, 125

pp.

7. Phleger, F.B. and F.L. Parker. (1951). Ecology of foraminifera, northwest Gulf of

Mexico. Geological Society of America. Memoir 46, Pt 1. 1-88, Pt. 2, 1-64.

8. Breard, S.Q., A.D. Callender and M.J. Nault. (1993). Paleoecologic and

biostratigraphic models for Pleistocene through Miocene foraminiferal

assemblages of the Gulf Coast basin. Gulf Coast Association of Geological

Societies, Transactions 43: 493-502.

9. Culver, S.J. (1988). New foraminiferal depth zonation of the Northwestern Gulf of

Mexico. Palaios, 3: 69-86.

10. Tipsworth, H.L., F.M. Setzer and F.L. Smith. (1966). Interpretation of depositional

environment in Gulf Coast petroleum exploration from paleoecology and related

stratigraphy. Gulf Coast Association of Geological Societies, Transactions 16:

119-130.

11. Jenkins. D.G. (1993). Applied Micropalaeontology. Kluwer Academic Publishers,

Dordrecht.

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AN OVERVIEW OF SUSTAINABLE RESOURCE MANAGEMENT OF

PERI-URBAN AREAS OF TUMKUR � IMPLICATIONS FOR SMART CITY

DEVELOPMENT PLANS

K. N. Radhika1* and B.C. Prabhakar2

1 Dept. of Civil Engineering, East west Institute of Technology, Bangalore-560091. 2 Dept of Geology, Bangalore University, Bangalore-560056,

*Corresponding author - [email protected]


ABSTRACT

Peri-urban areas emerge due to rapid urbanization. The cities in a radius of 100 -

150 km from Bangalore have witnessed unprecedented growth and caused multifarious

problems and challenges for the town planners and management authorities. The effect

of this rapid growth is evident around Tumkur - a modest city, which has been identified

for developing it as smart city under the central government scheme. Around Tumkur, a

few fringe areas are considered as transition zones which are neither urban nor

completely rural and fall beyond the purview of planners on either side, but continue to

host the spill over population from the urban vicinity. As a result, there are drastic

impacts on the natural resources like water, soil and land. Contamination of surface and

subsurface water, haphazard growth of layouts, crop and farm land destruction has

been happening unabated. The authorities should initiate planning of proper

development of these fringe areas without affecting their ecology, landscape and natural

resources. It cannot be ignored that these fringe areas often become the key producers of

natural resources to the urban areas. Hence, the key perspectives of the peri-urban

interface and the impact of urbanization of Tumkur shall be, in the opinion of authors,

the prime concern in drawing master plan for Tumkur to develop it as smart city.

Keywords: Peri-urban, Urbanization, Kyathsandra, Tumkur.

1. INTRODUCTION

Since 1990s, concerns have been raised about the possible negative impact of

spreading urbanization. The peri- urban interface is a complex region in itself especially

in the developing countries and exhibits the characteristics of transition, but bear the

brunt of urbanization. It is the rural to urban transformation zone where urban and

rural area mix and often clash. The geographic definition of periurban as defined by

Ravetz et al.1 is depicted in Figure 1. During peri-urbanization, many transformations

like physical, morphological, socio-demographic, cultural and economic changes take

place. The rapid urban population growth and expansion of the built-up area,

technological change, economic restructuring etc., combine with each and alter the

interface between �urban� and �rural� quite profoundly2. The environmental impact of


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spreading cities and their effects on the peri-urban has been reviewed and researched

by many workers. Yankson and Gough 3 working on the peri-urban area of Accra, an

African city explain how dramatically the environment has changed with altered water

courses, dried ponds and increased erosion. They also emphasize on the reduction of

fauna and flora in the area. Many challenges arise as cities grow and change and fringe

areas have to bear the spillover effects of rapid urbanization.With the expanding urban

fringes, more and more rural areas become peri-urban. Urbanization and economic

growth are strongly associated, and hence, urban areas, in general, and cities, in

particular, have been identified as the �engines of economic growth�, and �agents of

change�4. Globally, 54 percent of the population lives in urban areas today, and this

trend is expected to increase exponentially. By 2050, the number of people living in

cities is expected to increase to 70 percent and the global population is expected to

increase to 9 billion. This would aggravate the problems of urbanization.

Indian perspectives:

An estimate shows that in India, by 2050, nearly 900 million people will be living

in urban areas5. This rapid expansion is evident in the economic progress that India has

experienced in the last 20 years with an average GDP rate of around 6.5% a year. The

economic progress is expected to keep its pace of growth leading to rapid

industrialization, and population explosion in Indian cities, thus leading to their (cities)

spatial expansion. Sarkar and Bandopadhyay 6 reviewing the dynamics of peri-urban

interfaces in developing countries like India state that there is an acute lack of holistic

understanding of the regions (which are undergoing transition) as a link. Shaw 7 states

that as Indian cities continue to spread outwards, the problems faced by peri-urban

areas will assume greater visibility and importance. In his opinion, policy-makers must

begin to act now and either give such areas more civic autonomy or provide, via the

state governments, the basic environmental services. Local level initiatives can clearly

augment such efforts but local level initiatives with no backing by local

government/State are unlikely to succeed. Research carried out in peri-urban area of

cities like Delhi 8, Bangalore 9 and Chennai 10 explains the environmental stress,

particularly water resources and emphasize the need for comprehensive study of the

peri-urban areas.

Peri-urban scenario in Karnataka:

The peri-urban status of most of the cities in Karnataka is akin to the growth

pattern similar to cities elsewhere in India. Mugali 11 states that Karnataka is in pace

with the national level urban growth with high speed of urbanization in Bengaluru and

opines that a cluster of emerging cities like Tumkur, Mysore, Davanagere etc. are on

their way. He also articulates that there is a need for the development of such secondary

urban clusters as it can reduce the burden on Bangalore alone for the equitable and

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After, Ravetz et al, 2013

Fig.1: The Concept of Peri-urban

Built up

City Center

Inner

Sub Urban

Urban

Urban

Rural

URBAN AREA (Continuous & over

20000 population)

PERI URBAN AREA (Discontinuous & over 40

persons/hectare)

Functional Urban Area

Rural Urban

environment friendly growth of the State. Thus, in Karnataka, the advancement of

urbanization is at a brink in metropolitan and tier one cities and the same is

progressing towards the tier 2 cities like Tumkur, Mysore, Gulbarga, Davanagere,

Belagavi etc. Recently, Indian Government has announced the concept of �smart cities�

with a vision of the need for cities that can cope with inherent challenges of urban lining

and also be the areas for investment to catalyze the local economies. Tumkur is one

among the cities that is recognized for this advancement. However, the concept of smart

cities and the impacts of building cities especially in fringe areas are not clear to

common public and thus authorities should ensure that people have full knowledge of

what is going on around them. The �academia� should also discuss and throw light on

such issues. Tumkur, one of the nearest urban space, has been greatly influenced by the

faster economic growth of Bangalore, and is facing many challenges from the point of

planned and sustainable growth and in this paper, an overview of the aforesaid issues

are brought out and discussed especially from the point of impact of urbanization on

peri-urban areas of Tumkur and its suburbs.

Peri-urban enclaves in the study area:

The peri-urban areas of Tumkur are mainly stretched in 3 directions �

Kyathsandra in the south-east, Sira sector in the north-west and Gubbi in the western

side. While Kyathsandra is closest (5 km), Sira is farthest (30 km) and Gubbi (15 km)

has a modest spatial disposition. Obviously the peri-urban growth is more on

Kyathsandra due to the expanding residential and industrial activities of Tumkur city

(Fig. 2a and 2b) and its proximity to the State capital Bangalore. In this paper the peri-

urban enclave of Kyathsandra is focused as it is likely to witness higher impact of smart

city program. Kyathsandra, with an areal extent of around 10km2 consists of around

1150 households with a population density of 52 people per hectare 12. It

(Kyathsandra) is also well connected with the National highway 4 and rail route. With

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the upgradation of NH4, the town is ever expanding. The presence of Siddaganga Mutt

in Kyathsandra has made the town famous and has also led to the advancement in hotel

industry. The rapid urbanization of Tumkur city (around 40% in the last two decades)

especially along the NH4 has profound impact on the growth of Kyathsandra. Thus, the

area around Kyathsandra has slowly given way to urbanization. In the last two decades,

several industries have come up around Tumkur which include small -scale, semi-major

and major industries. As part of promoting industry, many household and plastic

industries have come up in the southern part of Tumkur city around Kyathsandra.

With the advancement of the Tumkur urban fringe to Kyathsandra and

industrialization in the area, several issues like contamination of surface water bodies,

groundwater quality and quantity depletion, influx of heavy metals etc., and social

issues like inflow of rural population, spurt of slums, sanitation, solid waste

management have cropped up in Kyathsandra. The demographical advancement in the

area is very rapid and this has a direct influence on the sanitation issue and water

resources in the area. These issues have to be focal points while preparing the master

plan for the smart city of Tumkur for a sustainable growth pattern.

Resource constraints due to peri-urban growth around Kyathsandra

Kyathsandra is in the eastern dry zone of Karnataka state with an annual rainfall

of about 700 mm drained by Shimsha River. The streams and tanks (small lakes) of this

area are seasonal where water is available for only 2-3 months during post monsoon

season and are mostly dry in the rest of the year. The infiltration to groundwater is also

less due to the higher runoff rate in the area. The massive hard rock lithology also

contributes to the lesser infiltration as the percolation will take more time. The source

of water for domestic activities is the municipality water supplied from bore wells. The

duration of water supply is usually once in a day or once in couple of days for 1 to 2

hours13. Since the water requirement in the area is ever increasing, several bore wells

have been drilled leading to the depletion of groundwater levels. Recent report14 from

Central Groundwater Board states that the status of groundwater development of

Tumkur is over exploited.

Water quality studies of Tumkur district in general and Tumkur city in particular

has also been attempted by Central Ground water Board and Department of Mines and

Geology and they monitor the groundwater quality at regular intervals and they reveal

signs of quality deterioration. Recent reports from Central groundwater board 14

categorizes this area as over-exploited with respect to groundwater resources. A few

small scale studies also report deteriorating groundwater quality in Tumkur and its

suburbs. Ramakrishnaiah et al.15 have reported high concentration of iron in the area

and suggests for water treatment before drinking. Bhaskar et al.16 report the possible

heavy metal influx to the aquifers due to industrialization. They report high Fe, Cu and

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Ni concentration in the groundwater. Kumar and Belagali 17 have analyzed some

sewage effluents of Tumkur city and report high COD and BOD levels. Ramakrishnaiah

et al.,(2008) 18 while working on the effects of urbanization on Amanikere, a major

water body in Tumkur city, reports that water body is polluted to a greater extent. As

this is a huge water body in the vicinity of study area, its influence on the aquifers of

surrounding areas will be enormous. All these cursory findings call for detailed study of

industries, the chemistry of their effluents and their impact on the environment

particularly on the water resources of the area. The plastic industries, which are located

around Kyathsandra, generally use lead and cadmium pigments and the effluents of

these industries leach to soil and in turn to the groundwater, contaminating both.

Constraints of town planning and socio-economic implications for peri-urban

sector of Kyathsandra:

The urban development authority of Tumkur (TUDA)19 in its master plan of future

urbanization of the city has taken up Kyathsandra and its surrounding areas in District

plan no. 11 (Table 1). The plan covers around 1066 ha land in and around Kyathsandra

including HMT factory and Siddaganga Mutt. Out of this earmarked area 37.06 hectares

is shown to be covered by surface water body, 73.27 hectares of agricultural land, 42.5

Ha as industrial area, 524.90 Ha as land for residential use (both existing and

proposed), around 214.06 for public and transport utility and 61.32 Ha as space for

communication facility. The plan also proposes two new townships in Kyathsandra

area. Hence planning of the water and other natural resources for the present as well as

future becomes vitally important. However, this master plan needs to be reviewed and

merged with the master plan of Tumkur city in the unveil, in order to give a holistic

outlook for the overall development of Tumkur and its surrounding areas.

Recent declaration by Central Govt. of a smart city plan for Tumkur has brought it

and its suburbs in to focal point, as it has raised the hopes of the people in this region

that it would be a hub of industrial and economic growth and bring in enormous

opportunities. At the same time several questions are being raised as to the likely

impact of the implementation of smart city plan, especially on land, surface and sub-

surface water, aesthetic value of the region and social impacts besides, the likely human

conflicts especially related to land rights. The proposed industries too could be cause of

great concern, as they are likely to disturb the otherwise serene environment being

prevailed around Tumkur. Another area of concern is the unprecedented increase in

the municipal solid waste (MSW) being generated in Tumkur and its suburbs. Presently

Tumkur city is producing about 121kg/capita/year of waste. The population of Tumkur

is around 3.5 lakhs and is projected to increase to around 5.5 lakhs by 2045 20. The rate

at which the waste is produced is almost on par with the MSW generated in Bangalore

metropolitan 21. With the fast expansion of Tumkur city, the rate of waste production

will go up and hence it becomes an important issue from the point of drafting elaborate

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management plans. Because, as happened in suburbs of Bangalore, protests (against

MSW dumping) are likely to erupt in peri-urban areas of Tumkur, as already occurred

in Ajjagondanahalli village of Sira sector in 2014. Thus, planning of sanitation,

management of solid waste and sewage effluent, preserving green habitats etc., are also

to be taken care off. As this place (Tumkur and its peri-urban areas) is also not too far

from Bangalore � a mega city in Karnataka, sustainable and planned development of

Kyathsandra assumes greater importance. However, some pertinent queries that arise

are (1) are the problems observed in the metropolitan cities also valid in emerging

cities like Tumkur and suburbs?, (2) how the water quality and quantity threats affect

the environmental sustainability of peri-urban Kyathsandra area? and (3) are there

perceptible plans to ensure preservation and sustainability of natural environment and

resources in this peri-urban sector?

Table 1: Proposed Landuse Analysis : Planning by TUDA (2011)

Sl.No. Land use category Area

(in Hectares) Percentage

1 Residential 542.9 54.88

2 Commercial 31.74 3.32

3 Industrial 42.5 4.45

4 Public & Semi public 61.32 6.42

5 Parks, Open spaces & Burial Grounds 81.16 8.50

6 Public Utility 0.79 0.08

7 Transport & Communication 214.06 22.42

Total 956.47 100.00

8 Water Body 37.06 --

9 Agriculture 73.27 --

Grand Total 1066.80 --

2. CONCLUSIONS

A review of the sparse work carried out so far indicate the vulnerability of the

fringe areas of Tumkur especially Kyathsandra, for their resource depletion and

environmental degradation. To quantify these processes and to develop a rationale for

preventing the adverse impacts and to develop a sustainable development and

management strategies, detailed studies are necessary, especially in the wake of smart

city plan for Tumkur which is in the offing. The authors opine that the detailed studies

should involve, (1) impact assessment of rapid urbanization of Tumkur - Bangalore

corridor on urban fringe areas of Tumkur, (2) modeling of perceptible

landuse/landcover changes in the future, (3) The effect of rural to urban transition on

the water resources of the area which shall include both qualitative and quantitative

assessment like estimation of the available resources and modeling the same for the

future, identification and suggestion of suitable artificial recharge structures in the area,

(4) suggestions for policy interventions and action plan, especially on industrial

establishment (5) social survey of the inhabitants (the main stake holders) of the peri-

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urban areas for the opinion pooling on developmental programs, as their views are

critical for a holistic development.

Figure 2a: Google Earth Image of Tumkur city � in 2004

Figure 2b: Google Earth Image of Tumkur city � 2015

3. REFERENCES

1. Ravetz, J., Fretner, C., & Neilsen, T.S., (2013). Periurban futures: Scenarios and

models for landuse change in Europe, DOI: 10.1007/978-3-642-30529-0-2,

Springer-Verlag Berlin Heidelberg publications, pp.33. Weblink: www.springer.com

http://www.springer.com

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2. Douglas, I., (2006). Peri-urban ecosystems and societies: transitional zones and

contrasting values, in D.McGregor, D.Simon and D.Thompson, (Eds.). The periurban

interface: approaches to sustainable natural and human resource use. Earthscan

VA, USA.

3. Yankson and Gough (1999). The environmental impact of rapid urbanization in the

peri-urban area of Accra, Ghana. Journal of Geografisktidsskrift ,

Weblink:https://tidsskrift.dk/index.php/geografisktidsskrift/article/view/2456/4

349.

4. Mohan, R., & Dasgupta, S., (2005, 15th Jan). The 21st Century: Asia Becomes Urban,

Special Articles, Economic and Political Weekly, 40(3), 213-223.

5. Pathak, H.C., (2013). Urban and Peri-urban agriculture, Policy paper 67, National

academy of agricultural sciences. Pp.12. Weblink: http://www. naasindia.org/

6. Sarkar, S. & Bandopadhyay, S., (2013). Dynamics of the peri-urban interface: Issues

and Perspectives for management. Transactions, 35(1), 49-62.

7. Shaw, A. (2005, 8th January). Peri-Urban Interface of Indian Cities. Growth,

Governance and Local Initiatives. Economic and Political Weekly, 129-136.

8. Narain,V. & Nischal,S. (2007). The peri-urban interface in Shahpur Khurd and

Karnera, India. Environment and Urbanization Journal, 19 (1), 261-273.

DOI:10.1177/0956247807076905.

9. Jeffery, (2011). Peri-urban development and environmental sustainability:

examples of India and China, Project report submitted to Asia-Pacific network for

Global Change Research, Project no. ARCP2009-05CMY. Weblink: http://www.apn-

gcr.org/

10. Srinivasan, V., Seto, K.C., Emersion, R., & Gorelick, S.M., (2013). The impact of

urbanization on water vulnerability: A coupled human-environment system

approach for Chennai, India., Journal of Global environmental change, vol.23,

pp.229-239.

11. Mugali, M.B., (2014). Urban Amenities and the Poor: A Case Study of Belgaum City.

Thesis submitted to Karnatak University, Dharwad. Source:

http://shodhganga.inflibnet.ac.in/.

12. Premasudha, B.G., (2010). Visualization and Spatial Analysis of Healthcare Services

of Tumkur City. Thesis submitted to Educational and Research Institute, Chennai.

Weblink: http://www.drmgrdu.ac.in/

13. RCUE (2011). Slum free plan of action: Tumkur., pp.118 Weblink:

http://rcueshyd.gov.in/

14. C.G.W.B., (2012). Groundwater information booklet, Tumkur district, Karnataka,

pp.32. Weblink: http://www.cgwb.gov.in/

15. Ramakrishnaiah, C.R., Sadashivaiah, C., & Ranganna, G,. (2009). Assessment of

Water Quality Index for the Groundwater in Tumkur taluk, Karnataka State, India.

E-journal of Chemistry. 6 (2), 523-530.

http://www.

http://www.apn-

http://shodhganga.inflibnet.ac.in/

http://www.drmgrdu.ac.in/

http://rcueshyd.gov.in/

http://www.cgwb.gov.in/

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16. Bhaskar, C.V., Kumar, K., and Nagendrappa, G., (2010). Assessment of Heavy metals

in water samples of certain locations situated around Tumkur, Karnataka, India. E-

Journal of Chemistry, 7(2), 349-352.

17. Kumara, K.S. & Belagali, S.L., (2008). A study on correlation coefficient of some

physico-chemical characteristics of Tumkur city sewage. Journal of current world

environment, 3(1), 83-92.

18. Ramakrishnaiah, C.R., Sadashivaiah, C., & Ranganna, G., (2008, Dec.22-24). The

impact of Urbanization of Tumkur on Ammanikere Lake � a case study. Lake 2008 �

Symposium on Conservation and Management of lake and river ecosystems, pp73.

Weblink: http://wgbis.ces.iisc.ernet.in/

19. TUDA, (2011). Tumkur local planning area, Master Plan II, pp.121. Weblink:

www.tuda.co.in

20. Siddharth, S., (2014). Appraisal and evaluation of urban infrastructure � SWM-

Tumkur city, Karnataka, pp.3. Weblink: https://www.academia.edu/6710754

21. Radhika, K.N. & Prabhakar, B.C., (2016). Urban enclaves and their contribution to

global warming through municipal solid waste � innovative approaches in

Bangalore Metropolitan. Proceedings of KSTA 2016 conference on Energy, Climate

change and Environment held on 29th and 30th of January 2016. Pp.11-12.

http://wgbis.ces.iisc.ernet.in/

http://www.tuda.co.in

https://www.academia.edu/6710754

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A PERFORMANCE EVALUATION OF F-MEASURE FOR SYNTACTIC

STRUCTURE USING HFSE ALGORITHM AND HASHING TECHNIQUE.

S Rashmi 1, M Hanumanthappa 1* and S Kavitha2 1*Dept. of Computer science and Applications, Bangalore University, Bangalore-56. 2Dept. of Computer Science & Applications, Dayananda Sagar College of Science, Bangalore-78.



ABSTRACT

For years now, writing efficiently adapts professional, innovative, unique skills with

grammatical knowledge being impeccable. The field of linguistic has its conventional

importance especially among anthropologists. In this regard, the Linguistic knowledge

has thus proven to be systematic, meticulous, robust and in short, �Scientific Discipline�.

The precision in the formulation of new theories hence are envy upon the exact

grammatical statements with impressive linguistic descriptions. The English- syntax and

linguistic theory has rigorous attempt to construct self-evident detailed level of �Phrase

structure� & �Dependency Structure�. These structures are dispensed under Syntactic

Representation, a branch in Natural language Processing (NLP). The need for Syntactic

anatomy is explained and presents a prototype for �Phrasal, Dependency & Parser

Structures�. In this paper a new algorithm called �Hash Fold Spell Error� (HFSE) using

public hash key technique is proposed to arrive at all the possible errors for a given

keyword. The obtained results were tested on 2050 words and Recall & Precision was

evaluated. It is observed that the algorithm attains high and improved recall and

precision in contrast with the existing ones.

Keywords: Syntactic structure, Phrase structure, Dependency structure, Hashing,

Hash Function, Hash Table, Hash Algorithm, Natural Language Processing.

1. INTRODUCTION

Errors, especially featuring typing or spelling, are huge in the user entered

electronic texts. All the search engines are restricted to the query entered by the user.

If the entered query has spelling errors then the search is hindered or hampered. In

order to overcome this we require a word-matching algorithm to locate the errors in

the query. The error checking algorithm serves in many ways such as reduces

misspend computational processing, time and achieves resilient full bodied structure

and support in recognizing the entailed information.

During the past twenty five years from now, linguistics has stood as one man army

that governs the entire community of Natural Language Processing. The descriptions

of any Natural Languages are distinguished among various aspects in machine

learning. The early development in NLP shows ballistic growth in the upcoming trends

of machine learning. The paramount feature of NLP is the series of steps that need to


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be evaluated before the knowledge is generated. The sequence of steps includes

Syntactic, Morphological, Discourse, Phonetics, & Semantics [i]. The present vogue of

Linguistics is to provide the mechanical procedure for identifying the correct syntax of

the Grammar for the given corpus under any of the Natural Language. These days there

are innumerable ways to identify the scrupulous Grammar structure that gives the

result which are error-free and the accuracy rate are also high. The present genre is

striving hard to connect the viaduct of scientific knowledge and mechanical procedure

in order to come up with advanced proficiency that includes statistical methods,

counting techniques, ultimately the meta-theory of the Natural Languages and the

structure and root cause of any Natural Languages.

Endeavoring to build the syntactic structure, one can divide the phases as;

i) Generating all possible error lists for a particular word: This step involves the

hashing mechanism to construct the word list that contains feasible alternatives

for a given word. The output of this phase would be a set of errors for any distinct

word.

ii) Build the spell check that identifies the spelling mistakes in the given sentence:

The user enters a sentence and the spell check will identify if there are any

spelling mistakes in the given sentence.

Using i) & ii), a framework can be built to enhance the spell-check to a huge corpus, file

or a document.

In this research paper, we develop phase i) and phase ii). The methodologies of this

implementation are discussed in the upcoming sections.

2. IMPLEMENTATION

2.1) Phase I

A list that contains errors for any particular word has many uses. A list of

spelling errors can be kept as a key to find out the possible spelling mistake for a

word.

The hashing mechanisms are used to implement the creation of spelling errors.

Hashing is a transformation mechanism to convert the long length string of

characters into shorter length key. It is used to ensure data integrity and efficiently

identifies the original objects [ii]. The hashing algorithm makes use of hash function.

The hash function is used to map the key word (key word is a word for which the

spelling errors are generated by the algorithm) into the hash table. The address

generated by the hash function (home address) is used as a reference to map onto

the hash table. This is shown in figure 1.

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Figure 1

The storage location in the hash table is also called as prime area. The prime

area stores the generated spelling errors for the given key word. The algorithm

(2.1a) creates all the attainable spelling errors for a given word. The algorithm uses

the fold method of the hashing technique. According to the author J.S Park et al, the

promising applications for empowering the attained ad-hoc manner of traversal can

be resolved using Public-key hash algorithm which uses the concepts of Signature

Seeking Drive (SSD). SSD collects the nearly similar traversal as a vehicle to drive the

alternative path in the traversal network. As the vehicle of culmination moves

making its mark as a legitimate advertisement for the strangers of the path, it defrays

the cost of its travel and subscribes the incentives as Virtual cash. The virtual cash

hashes the traversal routes into the appropriate key address [iii]. The said

mechanism is adopted in our algorithm 2.1a plus the folding mechanisms which are

proven to be efficient enough to generate the random spelling errors for a given

keyword. Six kinds of transformations of the folding method are used in order to

implement the above said mechanism.

2.1.1) Fold shift & fold Boundary:

The given keyword is kept in an array of [n] location. Then we calculate the mid by

using the formula (2.1.1)

Mid= (Low + High)/2 ------ (2.1.1)

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Where Low is the location of the first letter in the array and High is the location of the

last letter in the given keyword.

Keeping the mid letter as center of reference, the extreme left and right letters are

folded in an arbitrary way. This gives rise to a new combination of a given keyword

(Figure 2).

Example:

Low=0 & High=4, Mid = (0+4)/2= 2. Therefore the following possible combinations

are.

EHLOL, EHLOL, HELOL, LOLHE

2.1.2) Swap Characters : in this step, the adjacent characters are swapped

2.1.3) Replicate characters: In this step, the letters are repeated as if the keyboard were stuck

2.1.4) Eliminate characters: A character is omitted on purpose.

2.1.5) Replace characters: A character is modified into another character

The above steps have been showcased in the following algorithm 2.1a

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Figure 3

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When the keyword say �Computer� is passed onto the above algorithm, the hash

function generates the outputs shown in figure 1. The same has been shown in the

figure 3

The output of the algorithm 2.1a is shown in figure 4

Figure 4

The above figure (Figure 4) shows the use of hash mechanism to generate the

possible error combination for the keyword �Computer�. The algorithm efficiently

calculates the spelling error by filing the hash codes and thus induces the random

combination of foster words for any given keyword.

2.2) Phase 2

Spell-Checker algorithm is developed in this phase. The different spelling

generated for a particular word that was shown in phase (i) is used as the filter in

order to identify the misspelled word. The author Yuan Zhong has show the

generation of pseudonyms which is obtained by a series of collaborating nodes.

Whenever a node is launched for further processing, a pseudonym is enabled. The

third part vendor and the node itself can open these pseudonyms [iv]. Adapting what

Yuan Zhong, the new algorithm which complements the effort of the authors [iv] and

introducing the new ideology of expressing the hash technique. Using the hashing

techniques, an algorithm is proposed to identify the spelling mistakes in the sentence

entered by the user. The hashing proficiency used in this phase is Modulo-Division

Method.

Explanation:

Modulo-Division Method is one of the algorithms that are used inside hash

function. Here we divide the key by the array size and the remainder is considered as

the new home address in the hashed list. Equation (1) indicates the calculation of a

new address in the hash table

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H(K)=K mod M ------(1)

Where K= the size of the Key word that we are passing to the hash function.

M= Memory size of the hash table. Here it is 100 blocks.

For example, if the Key= 8 (Computer), Array size=100, then by (1) we have,

8 mod 100 = 8.

Hence, first alternative for the keyword �Computer� is stored at 8th location in the

hash table. The procedure is carried out for all the spell-errors for the given keyword

as explained in phase I.

The above said methodology employs hashing algorithm 2.2a to mandate

misspelled word in a given sentence. At the inception of the algorithm, it coils around

the array of known errors that was generated in phase 1. Then it calculates a hash

code for each error looking upon the errors of phase 1 as indicated in the algorithm

2.1a. The algorithm 2.2a stores each spell errors on a flat string array. When it is

looping in through the input sentence, it enumerates the same hash code that was

calculated earlier. In the next step it quests through the already known misspellings

and then declares as error found. Modulo division method is used to scatter the

elements in a more superior way and also avoid the out-of-range condition or the

stack overflow condition. The size of the hash table is kept minimal however this can

be increased to a much larger size, say 49000.

Below figure (Figure 5) shows the output generated using the algorithm 2.2a.

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Figure 5

3. EVALUATION OF EXPERIMENTAL RESULTS

This section has been split into two parts. The first part deals with calculating the F-

measure for �Hash Fold Spell Error� (HFSE) algorithm. The HFSE algorithm in phase I

generates possible spelling errors for a given �keyword�. It makes use of public key

generation hashing technique that goes into six transformations and then recursively

builds errors for the �keyword� (Section 2.1.1-2.1.5). The second part is dealt with

phase II and its associated F-measure. The phase II makes use of the �Hash Fold Spell

Error� (HFSE) algorithm and is evaluated on a sentence to check if it correctly

identifies the error present. The following are the results of the above said

calculation:

Precision=True Positive/(True Positive + False Positive) -----[1]

Recall= True Positive/(True Positive + False Negative) ------[2]

Phase I:

Using [1],

Precision = 1700/ (1700+200) =0.894

Using [2],

Recall = 1700/ (1700+150) =0.918

Phase II:

Using [1],

Precision = 740/ (740+85) =0.896

Using [2],

Recall = 740/ (740+25) =0.967

4. DISCUSSION

With HFSE algorithm, tests were conducted separately for phase I &phase II. In

phase I, we tested 2050 words individually and its evaluation for accuracy is

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discussed in Result Evaluation section. We focused on both lengthy words and also

short length words. With the promising result from phase I, we developed phase II on

850 sentences changing the �keyword� every time. The highlight in this paper has

been on the HFSE algorithm. The algorithm outperforms the other techniques and

tools and increased the result of overall Recall and Precision. The F-measure also

shows its applications in various methods to develop �Phrasal and Dependency

Structures� for Natural Language Processing. With the proven results the objectives

were met.

5. CONCLUSION

Spelling errors can be easily identified by a word-processing program. But in

computer field many correct words are considered as an error. This becomes a

problem when dealing with huge repository of information in an electronic form. For

example: The word �StringBuilder� is inevitably spotted as a spell-error and as

noticed, underline appears indicating as misspelled word. This is an inception of new

kind of problems which indeed are more problematic. Each document has several

false errors increasing the rate of false negative. This makes the actual error hard to

spot. One way to deal with the problem is to find out the known errors and

traversing through the characters from beginning to end and then build the list of

known errors and prefixes. This makes the algorithm more powerful. On the offset of

the paper, one can design a new method to recognize some of the style errors such as

slangs and profane words and can avoid obscenity on the internet usage.

6. REFERENCES:

1. Rashmi, S., and Hanumanthappa M, (2013). �Processing of Natural Languages

Semantically � A Detailed Survey�,International Journal of Engineering

Research and Technology (IJERT)ISSN: 2278 - 0181, Vol. 2 Issue 12, .

2. Long Chen (2008). �An Efficient Piecewise Hashing Method for Computer

Forensics�. Published in IEEE Knowledge Discovery and Data Mining,.WKDD

2008. First International Workshop on, 23-24 Jan. 2008 page no:635 � 638

Print ISBN:978-0-7695-3090-1, DOI: 10.1109/WKDD.2008.80

3. Joon-Sang Park, (2014). �Securing one-way hash chain based incentive

mechanism for vehicular ad hoc networks�, et al, DOI: 10.1007/s12083-012-

0178-y, Print ISBN 1936-6442 Peer-to-Peer Networking and Applications,

Volume 7, Issue 4, pp 737-742, Published by Springer US.

4. Yuan Zhong, (2012). A Hash-Chain Based Anonymous Incentive Mechanism for

Mobile Ad Hoc Networks�, et al, Advanced Technology in Teaching -

Proceedings of the 2009 3rd International Conference on Teaching and

Computational Science (WTCS 2009) Advances in Intelligent and Soft

Computing Volume 116, 2012, pp 441-450, Published by Springer US.

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SEWAGE WATER INDUCED MITOTIC ABNORMALITIES IN BRACHIARIA

MUTICA A FODDER GRASS

Sirangala Thimappa Girisha

Dept. of Microbiology and Biotechnology, Bangalore, University, Bangalore,

[email protected]


Abstract

The genotoxicity of sewage water was investigated in one of the important commercial

fodder grass B. mutica and the aberrations were significantly observed, the plants

which grow in sewage water root meristems were squashed and the cell exhibited

cytological variations and many cytological manifestations were recorded, the number

of chromosomes varied from 2n = 36 to 46. Sometimes chromosomes were clumped

and somatic segregation at metaphase and anaphase was not normal. Chromosomal

stickiness and chromatin bridge formation were also observed.

Key words: Sewage water, Mitotic abnormalities, Brachiaria mutica

1. INTRODUCTION

Sewage water is used in agriculture by many countries over a considerable

period of time. It is reported that sewage is used to improve the organic content of

agricultural soils1 and the chemical composition depends on origin of wastewater,

generally it is a compound rich in organic matter and essential nutrients for plants.

Sewage water contains a number of potentially toxic elements such as arsenic,

cadmium, chromium, copper, lead, mercury, zinc, etc., however, many of these are

mutagens and some are carcinogenic for humans. Apart from this municipal sewers

also contain genotoxic compounds2 and White and Rasmussen3 reported that these

genotoxic substances are N-nitroso compounds, aromatic amines and poly-aromatic

hydrocarbons, the study of cytotoxic and mutagenic effects of these compounds

provides studies related to environmental monitoring by plants4 and the effects of

metals on plants can be usefully applied to other systems as well5. The limitation

during utilization of sewage in agriculture refers to the presence of metals and other

persistent pollutants which have phytotoxic effect6. Thus, scientific studies have

detected toxic and genotoxic effects caused by the liquid effluents dumped into the

environment, in consonance with the issues raised, the present study was

undertaken.

2. MATERIALS AND METHODS

Prior to commencing this research investigation the details studies of physico

chemical properties of sewage water was studied, the present investigation has

been carried out to evaluate the genotoxic effect of sewage water on fodder grass B.


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mutica, the details of experimental procedure used in this research work have been

explicitly elaborated here. Emerged root tips from B. mutica grown in sewage water

were collected and overnight fixed in 1:3 acetic acid � ethyl alcohol mixture and

then 5 - 7 minutes treat the root tips in 45% acetic acid. Then root tips were

hydrolyzed in 1 (N) HCl at 600 C for 5 minutes, followed by staining with 2% aceto-

orcein following the methods described by Sharma and Sharma7.

After fixation, staining and squash, chromosome plates were observed under

light microscope. To determine the effects on mitotic index 100 cells were scored in

control and treated group. The mitotic index (MI) was calculated by number of cells

in division / total number of cells and cytological abnormalities were observed and

scored both in sewage water and normal water irrigated plant root tips.

Photomicrographs of cells showing chromosomal aberrations as well as showing

normal mitosis were taken using microscope and statistical analysis was done to

estimate standard error (SE) of the results.

3. RESULTS AND DISCUSSION

The present results deals with literature that includes research findings

pertaining to the present research work and the observations clearly indicate the

lowering of mitotic index and spindle abnormalities. Mitotic index lowering has

been achieved by the inhibition of DNA synthesis at S-phase. Stickiness of

chromosomes observed in the treated cells reveal the depolymerisation effect of

effluent on the nucleic acid of the chromosomes.

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Figure 1 - a, b, metaphase �I (40 -50 chromosomes depicting variation) c. Clumping

of chromosomes, d. Somatic bridge all figures X 1280

Microscopic examination of squashed root tip meristem cells showed that sewage water

induced a number of mitotic abnormalities when compared with control. The plants which

grow in sewage water root meristems were squashed and the cell exhibited cytological

variations and many cytological manifestations were recorded. Even number of

chromosomes varied from 36 to 46 (Figure 1 � a and b). Sometimes chromosomes were

clumped (Figure 1 � c) and somatic segregation at metaphase and anaphase was not

normal. Mitotic index is an acceptable measure of cytotoxicity for all living organisms8, in

the present study it was 0.80 and 0.92 in control and tread plant respectively (Table 1).

Table 1.Effects of sewage water on phenotypic indices and chromosome aberration

Phenotypic indices Chromosome aberration

Type Total

number of

cells

Number of

cell in

mitosis

Mitotic

index

Stickiness Clumps Fragments Bridges

Control 100 80 0.80 0 0 0 0

Treated 100 92 0.92 12 36 1 18

Phenotypic indices and chromosomal aberrations were scored per slide

According to Lazareva et al.,9 high toxicity of pollutants reflects on chromosomal

abnormalities due to inhibition of spindle formation and chromosomal stickiness is

characterized by chromosomal clustering during any phase of the cell cycle. In this

study, occurrence of several types of chromosomal abnormalities, such as stickiness

(12), clumping (36), fragmentation (1) and bridges (18) in root tip cells clearly

shows that effect of sewage water results inactivation of spindle formation,

deformation of non - histone chromosomal proteins and mutation of the structural

genes (Figure 1 � d). Soheir et al.,10 reported a high level of chromosomal stickiness

in metaphase stage after herbicide treatment and Patil & Bhat11 reported that

stickiness is a type of physical adhesion involving mainly the proteinacious matrix of

chromatin material. The formation of bridges could be attributed to stickiness of

chromosome12. Salam et al.,13 reported that induction of bridges and breaks may

lead to loss of genetic material and binucleate cells were found at higher

concentrations may be due to the inhibition of cell wall development at telophase.

The precocious movement of the chromosomes might have been caused by the early

terminalisation and stickiness of chromosome14. From the above observation it is

evident that sewage water is a potential genotoxic agent, the abnormalities

observed were sticky anaphases, precocious movement, lagging fragments, multiple

anaphase bridges and chromatid gaps. From the fore going results and discussion it

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is clear that sewage water is highly genotoxic to the fodder grass B. mutica hence the

present work was undertaken as research work.

4. REFERENCES

1. Grisolia. C.K, de Oliveira. A.B, Bonfim. H and Guimarmes. M.K. (2005) Gen Mol

Biol, vol.2.pp.334 � 338.

2. Jha N.A, Hutchinson T.H, Macklay J.M, Elliot B.M. and Dixons D.R. (1997).

Evaluation of the genotoxicity of muncipal sewage effluent using the marine

worm Platynereis dumerilli (Polychaeta: Nereidae). Mutation Res,vol.

391.pp.179-188.

3. White P and Rasmussen J. B. (1998) The genotoxic hazard of domestic wastes

in surface waters. Mutation Res, vol.410.pp. 223-236.

4. Fiskesjo. G. (1988) The Allium cepa-test an alternative in environmental

studies, the relative toxicity of metal ions. Mut. Res, vol.19.pp.243-260.

5. Ivanova. E, Staykova. T and Velcheva. I. (2008) Cytotoxicity and genotoxicity

of heavy metal and cyanide-contaminated waters in some regions for

production and processing of ore in Bulgaria, Bulgarian Journal of

Agricultural Science, vol.14.pp.262-268.

6. Cintya Ap. Christofoletti, Annelise Francisco and Carmem S. Fontanetti

(2012) Biosolid Soil Application: Toxicity Tests under Laboratory Conditions,

Applied and Environmental Soil Science, vol.2.pp.1-9.

7. Sharma A. K. and Sharma A. (1980) Chromosome Techniques: Theory and

practice. 3rd Edition, Butterworths and Co. Ltd., London.

8. Smaka-Kinel V, Stegner P, M Lovka and Toman M. J. (1996) The evaluation of

waste, surface and ground water quality using the Allium test procedure.

Mutation Research, vol.368.pp.177-179.

9. Lazareva E. M, Polyakov V. Y, Chentsov Y. S and Smirnova E. A. (2003) Time

and cell cycle dependent formation of heterogeneous tubulin arrays induced

by colchicines in Triticum aestivum root meristem cell. Biology International,

vol.27.pp. 633-646.

10. Soheir E, Antoinette H and Atif H. (1989) Cytological effects of herbicide

Garlon-4 on root mitosis of Allium cepa.Cytologia, vol.54.pp.465-472.

11. Patil B. C and Bhat G. I. (1992) A comparative study of MH and EMS in the

induction of chromosomal aberrations on lateral root meristem in Clitoria

ternata L. Cytologia, vol.57.pp.259-264.

12. El-Khodary. S, Habib. A and Haliem. A. (1990) Effect of the herbicide tribunil

on root mitosis of Allium cepa. Cytologia, vol.55.pp.209-215.

13. Salam A. Z, Hassan H. Z, Badawy, Fatma, M. I and Abdel-Naby Waffaa M.

(1993) The mutagenic potentialities of three pesticides on three biological

systems. Egypt. J. Genet. Cytol, vol.22.pp.109-128.

14. Permijit K and Grover I. S. (1985) Cytological effects of some

organophosphorus pesticides II. Meiotic effects. Cytologia, vol.50.pp.199-211.

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ESTIMATION OF TRANSITION PROBABILITIES IN QUEUING CHAINS

V. SRINIVAS

Department of Statistics, Bangalore University, Bengaluru-560056, Karnataka, India

Corresponding author email:[email protected]


ABSTRACT

In the Imbedded Markov Chain (IMC) analysis of the M/G/1 queuing system , the

random variables X1,X2, . . . ,Xn, . . . form a sequence of independent and identically

distributed (i.i.d) random variables where Xn denotes the number of customer arrivals

during the service time of nth customer . For the M/M/1 and M/D/1 queuing systems,

the distributions of common random variables are the geometric and Poisson

distributions, respectively, with mean ñ, the traffic intensity parameter. Utilizing these

facts Maximum Likelihood (ML), Uniformly Minimum Variance Unbiased (UMVU) and

Bayes, relative to Squared Error Loss (SEL) and Lomax prior distribution, estimates of

transition probabilities of IMC are derived based on a sample x=(x1,x2, . . . ,xn), where xn

is the realization of Xn, for the M/M/1 queuing system. For the M/D/1 queuing system,

only the Bayes estimators of transition probabilities of IMC, relative to SEL and

conjugate gamma prior distribution are derived.

Key Words: Transition probabilities of queuing chains,Maximum likelihood

estimates, Uniformly minimum variance unbiased estimates, Bayes estimates.

1. INTRODUCTION

The various problems of estimation have drawn the attention of researchers from

classical as well as Bayesian schools of thought. In particular, the problems of estimation

of queuing parameters and different measures of performance have attracted

statisticians, applied queuing theorists and others. Clarke1 and Muddapur2 seem to be

among the early researchers in this area of research and the corresponding

contemporary literature in this century has been enriched by contributions from Armero

and Conesa3, Butler and Huzurbazaar4, Zheng and Seila5, Huang and Brill6, Ausin et al.7-

8,Mukherjee and Chowdhury9, Chu and Ke10-11, Choudhury and Borthakur12, Ramirez et

al.13-14, Jayakrishna Udupa15, Kiessler and Lund16, Srinivas et al.17 , Xu et al.18, Chowdhury

and Mukherjee19 and Srinivas and Jayakrishna Udupa20.

A specific problem of importance is that of estimation of transition probabilities of the

Imbedded Markov Chain (IMC) arising in the analysis of / /1 queuing system. We

shall refer to this chain as the queuing chain. This problem is of importance in its own

right and because of potential applications. However we shall consider special cases of / /1 and / /1 queuing systems instead of the more general ȀܩȀͷ queuing


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system as they are amenable to parametric estimation. For parametric estimation, the

sample data used will be explained in the next section. Further the plan of this article is

detailed now. In Section 2, estimation of transition probabilities of / /1 queuing chain

is the objective with both classical, in the sense of Maximum Likelihood (ML) estimation

and Uniformly Minimum Variance Unbiased (UMVU) estimation, and Bayesian

estimation relative to Squared Error Loss (SEL) being considered. The purpose of

Section 3 is that of Bayesian estimation of transition probabilities of the queuing chain

arising in the study of / /1 queuing system. However, the prior distributions of the

queuing analyst for the parameter, which is the traffic intensity, will be mentioned in

sections 2 and 3. Final section is for concluding the discussion.

2. ESTIMATION IN M/M/1 QUEUING CHAIN

In the IMC analysis of M/G/1 queuing system, 1, 2,⋯ , ,⋯ forms a sequence

of i.i.d random variables with a compound Poisson probability mass function (p.m.f).

For the special case of / /1 queuing system, the p.m.f can be derived and it takes the

following form: ( ) = 1

1 + 1 + , = 0,1,2,⋯, (2.1)

which is the well known geometric distribution involving a single parameter ñ, the

traffic intensity, and thus belongs to power series family of distributions and

consequently belongs to Koopman-Darmois family of distributions. Utilizing this fact,

we have the random sample = ( 1, 2,⋯ , ), where denotes the number of

customer arrivals during the service time of the th customer. The transition

probabilities of the IMC arising in the IMC analysis of / /1 queuing system are given

by

= ⎩⎪⎨⎪⎧ 1

1 + 1 + , = 0, ≥ 0,1

1 + 1 + 1 , > 0, ≥ − 1, 0 , Otherwise,

(2.2)

following Gross and Harris21. As mentioned in the previous section, the objective is

that of estimation of the transition probabilities in (2.2). For ML estimation of these

transition probabilities we derive the ML estimate of the traffic intensity ñ based on

the sample = ( 1, 2,⋯ , ), where xn is the realizationof Xn . To derive we construct

the log-likelihood function, which is given by

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ℒ( ; ) = − log(1 + ) +1

[log − log(1 + )]. The ML estimator of , , is the solution of the following log-likelihood equation:

−1 + +

1

1 − 1

1 + = 0. Interestingly, the ML estimate is , the sample mean. This was mentioned in Srinivas

et al.22 Now using the invariance property of ML estimator, the ML estimates of

transition probabilities of M/M/1 queuing chain are obtained by plugging for in

(2.2).

To derive the UMVU estimators of transition probabilities in (2.2), we note this

estimation problem is equivalent to UMVU estimation of probabilities of geometric

distribution in (2.1). The UMVU estimators can easily be derived using Rao-Blackwell

and Lehmann-Scheffe theorems. The UMVU estimates of ( ) in (2.1) are ( ) =1 / 1

. Using this, the UMVU estimates of transition probabilities are

obtained. The estimates are

We derived the classical estimates. In variation we now intend Bayesian estimation.

For this the sample data is the one that was used for classical estimation and the loss

function is ሺߩො െ ሻߩ ൌ ሺߩො െ .ሻ, where denotes an estimate of , the SEL functionߩ

The other non-sample information is the queuing analyst�s prior distribution, which is

the well known Lomax distribution with the functional form

( | ) = 1

1 + 1 , 0 < < ∞ ( > 0). The Bayes estimates of transition probabilities in (2.2) are now derived relative to SEL

function and Lomax prior distribution. The Bayes estimates are

ൌ ቀା௧ͷ௧ ቁ൫ା௧ͷ௧ ൯ , = 0,1,2,⋯ ,= 1

1, = − 1, , + 1,⋯ ,

= 0, otherwise.

(2.3)

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=⎩⎪⎪⎨⎪⎪⎧ ( + + 1, + + 1)( + 1, + ) , = 0, ≥ 0,

( + − + 2, + )( + 1, + ) , > 0, = − 1, ,⋯ , 0 , Otherwise.

(2.4)

3. ESTIMATION IN M/D/1 QUEUING CHAIN

For the / /1 queuing system, ͷǡ ǡڮ ǡ ǡڮ is a sequence of i.i.d random

variables with the p.m.f of common random variable given by ( ) = ! , = 0,1,2,⋯, (3.1)

which is the popular Poisson distribution with mean , the traffic intensity, which also

belongs to PSD and hence to Koopman-Darmois family of distributions. Exploiting this

we conceive the random sample to be = ( 1, 2,⋯ , ), where represents the

number of customer arrivals during the service time of th customer. The transition

probabilities of the queuing chain arising due to IMC analysis of / /1 queuing

system are given by

= ⎩⎪⎨⎪⎧ ! , = 0, ≥ 0,

1( − + 1)! , = 0, ≥ − 1, 0 , Otherwise.

(3.2)

The objective in this section is that of estimation of transition probabilities in (3.2). It is

well known that the ML estimator of Poisson mean is the sample mean. Substituting

this sample mean for in (3.2) we obtain the ML estimators of transition probabilities

in (3.2), which is the application of invariance property of ML estimators.

To derive the UMVU estimates of transition probabilities in (3.2) is equivalent to

deriving the UMVU estimates of Poisson probabilities based on a sample from (3.1).

Working in the framework of PSD, following Lehmann and Casella23, the UMVU

estimates of Poisson probabilities are binomial probabilities , 1, where is the

realization of complete sufficient statistic. This is a well known class room example.

Thus the UMVU estimates of transition probabilities in (3.2) are easily obtained as

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=⎩⎪⎪⎨⎪⎪⎧ 1

1 − 1 , = 0, ≥ 0,− + 1

11

1 − 11 , > 0, ≥ − 1,

0 , Otherwise.

(3.3)

These ML and UMVU estimators of transition probabilities were derived by Srinivas

and Kale (2016) and are mentioned here for the sake of completeness in presentation.

The Bayes estimates of transition probabilities relative to SEL function and the

conjugate gamma prior ( , ) are the posterior expectations of transition

probabilities. Thus the Bayes estimates of transition probabilities can be derived easily

and turn out to be

00 = , 1 = +∝+∝ +1

0 = | + +| + ∙ ( +∝) ( +∝ +1) ∙ 1! , = 1,2,⋯

= | + + − + 1| + ∙ ( +∝) ( +∝ +1) 1∙ 1( − + 1)! , > 0,= − 1, , + 1,⋯ = 0, Otherwise.

(3.4)

4. CONCLUSION

The estimates derived in sections 2 and 3 are based on different doctrines of

decision making for the problem of estimation of transition probabilities in / /1

and / /1 queuing systems, respectively. The fact that more than one estimator is

proposed leads us to a natural question. The question is: which estimator is good and

in what sense? This question can be answered. However we shall refrain from doing so

as there are an infinite number, although countable, of transition probabilities being

estimated. The ML, UMVU and Bayes estimators of transition probabilities of IMC in

M/Er/1 queuing system can similarly be derived and this is done in a different context

and will be part of a different communication.The results in this article indicate the

interplay between research and class room teaching which is the synergy being talked

about in the context of higher education in India.

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5. ACKNOWLEDGEMENT

The author is thankful to Council of Scientific and Industrial Research (CSIR), India, for

the initial research support and to the reviewer.

6. REFERENCES

1. Clarke. A. B. (1957) Maximum likelihood estimates in a simple queue. The Annals of

Mathematical Statistics, vol.28.pp. 1036-1040.

2. Muddapur. M .V. (1972) Bayesian estimates of parameters in some queueing models.

Annals of the Institute of Statistical Mathematics, vol. 24 (1).pp. 327-331.

3. Armero. C. and Conesa. D. (2000) Prediction in Markovian bulk arrival queues.

Queueing Systems, vol. 34(1).pp. 327-350.

4. Butler. R. W. and Huzurbazaar. A. V. (2000) Bayesian prediction of waiting times in

stochastic models. Canadian Journal of Statistics, vol. 28.pp. 311-325.

5. Zheng. S. and Seila. A. F. (2000) Some well-behaved estimators for the M/M/1 queue.

Operations Research Letters, vol. 26(5).pp. 231-235.

6. Huang. M. L. and Brill. P. (2001) On estimation in M/G/C/C queues. International

Transactions of Operations Research, vol. 8.pp. 647-657.

7. Ausin. M. C., Wiper. M. P. and Lillo. R. E. (2004) Bayesian estimation for the / /1

queue using a phase type approximation. Journal of Statistical Planning and Inference,

vol. 118.pp.83-101.

8. Ausin. M. C., Wiper. M. P. and Lillo. R. E. (2008) Transient Bayesian inference for short

and long-tailed / /1 queueing systems. Statistics and econometrics series 05,

Universidad Carlos III De Madrid. pp. 05-35.

9. Mukherjee. S. P. and Chowdhury. S. (2005) Bayesian Estimation of traffic intensity.

IAPQR transactions, Vol.30 (2).pp. 89-100.

10. Chu. Y. K. and Ke. J. C. (2006) Confidence intervals of mean response time for an / /1

queueing system: boot strap simulation. Applied Mathematics and Computation, vol.

180.pp. 255-263.

11. Chu. Y. K. and Ke. J. C. (2007) Interval estimation of mean response time for a / /1

queueing system : empirical Laplace function approach. Mathematical Methods in the

Applied Sciences, vol. 30.pp.707-715.

12. Choudhury. A. and Borthakur. A. C. (2008) Bayesian Inference and Prediction in the

single server Markovian queue. Metrika, vol. 67.pp. 371-383.

13. Ramirez. P., Lilla. R. E. and Wiper. M. P. (2008) Bayesian analysis of a queueing system

with a long-tailed arrival process. Communications in Statistics, Simulation and

computation, vol. 37.pp. 697-712.

14. Ramirez. P., Lillo, R. E. and Wiper. M. P. (2008a) Inference for double Pareto lognormal

queues with applications. Statistics and econometrics series 02, Universidad Carlos III

De Madrid. pp. 08-04.

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15. Jayakrishna Udupa. H. (2009) Estimation of steady state probability distribution of

system size in M/M/1 queue. Mapana Journal of Sciences, Vol. 8(2).pp. 20-28.

16. Kiessler. P. C. and Lund. R. (2009) Technical Note: Traffic Intensity Estimation. Naval

Research Logistics, vol. 56.pp. 385-387.

17. Srinivas. V., Subba Rao. S. and Kale. B. K. (2011) Estimation of measures in / /1

queue. Communications in Statistics, Theory and Methods, vol. 40(18).pp. 3327-3336.

18. Xu. X., Zhang. Q. and Ding. X. (2010). Hypothesis Testing and Confidence Regions for

the Mean Sojourn Time of an / /1 Queueing System. Communications in Statistics -

Theory and Methods, vol. 40(1).pp. 28-39.

19. Chowdhury. S. and Mukherjee. S. P. (2013) Estimation of traffic intensity based on

queue length in a single ȀܯȀͳ queue. Communications in Statistics - Theory and

Methods, vol. 42(13).pp. 2376-2390.

20. Srinivas. V. and Jayakrishna Udupa. H. (2014) Best Unbiased Estimation and CAN

Property in / /1 Queue. Communications in Statistics - Theory and Methods, vol.

43(2).pp. 321-327.

21. Gross. D. and Harris. C.M. (1985) Fundamentals of Queueing Theory, Second Edition,

New York, John Wiley and Sons.

7. Srinivas. V. and Kale. B. K. (2016) ML and UMVU estimation in the M/D/1 Queuing

System. Communications in statistics- Theory and Methods, DOI:

10.1080/03610926.2014.950750.

8. Lehmann. E.L. and Casella. G. (1998). Theory of Point Estimation. Springer-Verlag,

New York, Berlin Heidelberg.

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AMBIENT GAMMA EXPOSURE LEVEL IN AIR AND THE

ASSOCIATED RADIATION DOSE TO THE POPULATION OF

BENGALURU, SOUTH INDIA

N. Nagaiah1* and N. G. ShivaPrasad2

1Department of Physics, Bangalore University, Bengaluru-560056, Karnataka, India 2Department of Physics, Government First Grade College, Srirangapatna-571438, Karnataka, India

*Corresponding author:[email protected]


ABSTRACT

The ambient gamma exposure level at different locations of Bengaluru & its

surrounding region was measured using environmental radiation dosimeter. The result

reveals that, the dose rate is found to range from 61.4�201.7 nGyh-1 with a mean of

117.2nGyh-1. The observed variation is attributed to the geological features of the study

area. From the measured dose rates, the associated radiation parameters like, annual

effective dose equivalent, mean annual effective dose and the life time cancer risk were

evaluated. The details of which are discussed in the paper.

Keywords: Background radiations, Dosimeter, Gamma dose rate, Life time dose

1. INTRODUCTION

Our World is radioactive and has been since it was created. Everyone on this

planet are exposed continuously to the background radiations which are of both

terrestrial and extraterrestrial origin. Terrestrial sources of radiation are due to the

radioactive elements U238, Ra226, Th232 and K40 present in the earth�s strata, whereas,

the extraterrestrial sources are due to cosmic rays. These radioactive elements are

found naturally in air, water and soil and their concentrations are found to vary from

place to place on the globe. Human beings are exposed to radioactive nuclides in a

number of ways i.e., through ingestion of food & water, inhalation of air and externally

from soil. Depending upon their concentrations, the radioactive radiations can induce

different biological effects in human beings such as somatic effects, bone cancer, lung

cancer, thyroid cancer, genetic effects, cataracts, etc.1. It has been established that, the

average annual radiation dose that the World population receive from the natural

sources of radiation is about 2.4mSv 2. The contribution of annual exposure of natural

and artificial sources of radiation to the population of the World is shown in figure 1.

It is evident from figure 1 that, the natural sources of radiation contributes almost

81% of the total radiation dose, out of which about 55% is only from the inhalation of

radon and its progeny. In view of the biological effects of these ionizing radiations, it is

of prime importance to know the presence of these radiations and their levels in our

living environment.


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Fig. 1. Sources of Background Radiations & their % of contribution

Bangalore is one of the major cities of India and is located in the Southern part of

Karnataka state. It extends between 120 151 and 130 301 N latitude and 770 31 and 770

561 E longitude with an average elevation of 920m from the sea level. This includes

both Bangalore rural and urban districts and covers an area of 5814 sq km and 2191sq

km respectively. It is a fast growing city with different types of industries like

metallurgical, chemical, aircraft, leather, paper, textiles etc. Geologically, the area

belongs to peninsular gneisses, younger and older granites, dolerite and amphibolite

dykes, laterite, charnockite, amphibolite and pelitic schist. The soil variation of

Bengaluru includes clayey, fine loamy and red laterite soils which are highly porous

and sandy in nature3. Large number of granite quarries have been established in and

around the city and several thousands of people are engaged in quarrying activities.

These quarrying activities leads to inhalation of dust particles through air and water

which inturn leads to the intake of radon and its progeny. In addition, the residences

in the study area are being built with granite stones, bricks, mud etc. All these lead to

the exposure to the natural radiations. If the radiation dose exceeds the permissible

level which is different for different radionuclides, then a serious concern has to be

taken. Therefore, a systematic and detailed study was undertaken to establish the

background radiation level by measuring the concentration of various natural

radioactive elements present in soil, water and air in the environment of Bengaluru,

South India. The present paper deals with the measurement of ambient gamma

exposure level and the resultant radiation parameters to the population of Bengaluru

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& its surrounding region. The study was carried out by selecting 33 locations which fall

within 30km radius from Bengaluru city & are as shown in following figure 2.

2. EXPERIMENTAL METHOD

The external gamma dose rates in air were measured in different locations of the

study area using a portable G M tube based Environmental Radiation Dosimeter

(Model ER709, Nucleonix Systems Pvt. Ltd., Hyderabad, India). This dosimeter was

exclusively designed to serve as a low-level survey meter. It was factory calibrated

using gamma survey instruments calibrator (Model 773 AEA Technology, U.S.A). The

instrument is capable of measuring gamma dose rates in the range 0-10 mR h-1. The

accuracy and the sensitivity of the dosimeter are ± 15% and 1ìR h-1 respectively.

Gamma exposure levels were recorded at a height of 1m above the ground level4 at the

sites that are open, undisturbed level ground surfaces free from sheltering, vegetation

and away from the public roads & buildings. The exposure level recorded by this

instrument includes both terrestrial and cosmic ray components. The measured

exposure rate (in µR h-1) was converted into absorbed dose rate (in nGyh-1) using the

conversion factor of 1 µR h-1 = 8.77 nGy h-1 which stems from the definition of the

Roentgen5.

Figure 2. Study area

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2.1 Annual effective dose equivalent

The annual effective dose equivalent was evaluated by using the outdoor gamma

dose rates for each person and hence to the population living in the study area using

the following relation2.

TOFCFADSvE )(

Where, E is the annual effective dose equivalent

AD is the absorbed dose rate in air

CF is the conversion factor which is 0.7 Sv Gy-1

OF is the outdoor occupancy factor which is 0.2, which converts the absorbed

dose rate in air to the effective dose received by a person

T is the time in hours for 1 y, i.e. 8760 h.

2.2 Life time gamma dose

The life time gamma dose received by the population of the region was estimated

from the annual effective dose equivalent using the relation,

ALESvLD )(

Where, LD is the life time gamma dose

E is the annual effective dose equivalent

AL average life time of human beings i.e., 70 y.

2.3 Life time cancer risk

There is a linear relation between the lifetime relative risk of all cancers and

background gamma radiations. Studies have been conducted to examine the risks of

cancer in areas of high natural background radiation6,7. From these studies it was

found that at doses of 100 mSv or less, statistical limitations make it difficult to

evaluate the cancer risk in humans8. A comprehensive review of available biological

and biophysical data led the committee to conclude that the risk would continue in a

linear fashion at lower doses without a threshold and that the smallest dose has the

potential to cause a small increase in the risk to humans. This assumption is termed as

the �linear-no-threshold� model8. Comparative studies on some groups exposed to

different levels of natural background radiation do not, however, have the statistical

power to detect significant effects on cancer incidence2. Generally, lower doses would

produce proportionally lower risks. Thus, it is important to examine the lowest levels

of dose at which a significantly elevated level of radiation-induced cancer has been

observed in human population. In this study, the risks associated with the outdoor

gamma radiations were assessed for the population of the Bengaluru environment. The

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risk of cancer (R) due to outdoor gamma radiation exposure from natural

radionuclides was calculated using the following relative risk equation.

RFALER Where, E is the annual effective dose equivalent

AL is the average lifetime (70 y)

RF is the risk factor (0.0582 Sv-1), fatal cancer risk per Sievert8.

3. RESULTS & DISCUSSION

The results of the external gamma dose rates in air measured at different locations of

the study area are presented in Table 1.

Table 1: Measured gamma dose rate in air

Sl. No. LOCATION DOSE (nGy/h)

1 Kannahalli 122.8

2 Kengeri 114

3 Mallathalli 114

4 Katriguppe 175.4

5 Machohalli 105.3

6 Lalbagh 201.7

7 Doddakallasandra 105.2

8 Kumbalagodu 114

9 Tavarekere 114

10 Bannerughatta 175.4

11 Agara 70.2

12 Hebbal 131.6

13 Kambasandra 140.32

14 Beemanakuppe 105.3

15 Hejjala 105.3

16 Ragihalli 114

17 Kudlu 114

18 Kalkere 140.3

19 Tarabanahalli 70.2

20 Mallasandra 131.6

21 Shivanahalli 149.1

22 Bidadi 122.8

23 Mummenahalli 122.8

24 Nelamangala 87.7

25 Kannur 122.8

26 Varthur 70.89

27 Harohalli 87.7

28 Madapura 96.5

29 Magadi 140.32

30 Kukkanahalli 61.4

31 Baglur 149.1

32 Kadugodi 105.3

33 Morasur 87.7

Range 61.4 � 201.7

Median 114

Mean 117.2

SD 31.4

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The dose rates are found to vary in the range of 61.4 � 201.7 nGy h-1 with the mean

value of 117.2 nGy h-1. The lowest dose rate is observed at Kukkanahalli and the

highest at Lalbagh. The absorbed dose rate in air observed in the present study is

compared with the values reported for other environments5,8-19 in Table 2. The mean

value of the absorbed dose rate observed in Bengaluru environment (117.2 nGy h-1) is

slightly higher when compared to the Indian national average values of 80.7 nGy h-1

and 88.5 nGy h-1 as reported by Mishra and Sadasivan8 and Nambi et al.5, respectively.

It is two times higher than the world average16 value of 57 nGy h-1. Again, the absorbed

dose rate observed in the present study is nearly two times higher than that of Spain17,

but is very much less than that of Turkey18.

Table 2.Comparison of (measured) external gamma absorbed dose rate

in air (nGyh-1) with other environments.

From the absorbed dose rate, the annual effective dose equivalent to the population of

this region was estimated2 and the same has been presented in figure 3. The annual

effective dose equivalent varies in the range 75.3 � 247.4 µSv with a mean value of 143.8

µSv. The mean annual effective dose equivalent is two times higher than the global

average value of 73.6 µSv. The lifetime dose received by the population of this area was

estimated by considering the average life time2 of human beings as 70 years & is found to

vary in the range 5.3 � 17.3 mSv with a mean value of 10.1 mSv. However, the worldwide

average2 is 4.9 mSv. The results of life time dose received by the population of the region

are shown in figure 4.

Present work Literature value Region Reference

61.4-201.7 80.7 All India Average Mishra and Sadashivan

(117.2) (1971)

88.5 All India Average Nambi et al. (1987)

57 (18-107) World average UNSCEAR (1999)

87 Switzerland Herbst (1964)

94 (24-270) Germany Ohslen (1971)

73 (20-1100) Norway Strenden (1977)

49 (50-100) Japan Abe et al. (1980)

56.6 (9-230) Spain Baeza et al. (1994)

66.1 (42-87) Kaiga Karunakara et al.

(2001)

64 (36-101) Goa Avadhani et al. (2001)

253 (205-337) Turkey Merdanoglu and

Altinsoy. (2006)

12.65 (4.34-25.31) Agababu Omoniyi Isinkaye

(2008)

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The risk of cancer due to outdoor gamma radiation exposure was calculated using

the relative risk relation provided by UNSCEAR (2000) & the results are presented in

figure 5. The cancer risk varies in the range of 3.1x10-4 � 10.1 x10-4 with a mean value

of 5.9x10-4. This is two time higher than the global average2 of 2.99x10-4. These results

show that the population of the Bangalore environment are receiving relatively high

gamma radiation dose.

4. CONCLUSIONS

The mean absorbed gamma dose rates measured in the environment of Bangalore

is slightly high when compared with the Indian national average values and is two

Figure 5. Life time cancer risk to the population of the region

0 5 1 0 1 5 2 0 2 5 3 00

2

4

6

8

1 0

Life

Tim

e C

ance

r R

isk

(10-4

)

L o c a tio n

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times higher than that of the world average value. The observed trend of variation is

attributed to the geological features of the study area. The mean annual effective dose

equivalent to the population of the region is two times higher than the global average

value. The mean lifetime dose received by the population of the region is two times

higher than the global average value. The risk of cancer due to the outdoor gamma

exposure was estimated and the mean cancer risk found is two times higher than the

global average value.

5. REFERENCES

1. Merril Eisenbud. (1987) Environmental radioactivity, Academic press, INC.

2. UNSCEAR, United Nations Scientific Committee on the Effect of Atomic

Radiation. Sources and Effects of Ionizing Radiation. (2000) Report to the

General Assembly with Scientific Annexes, United Nations. Annexure B. pp.97�

105.

3. District Planning Map series (1997) Bangalore Rural and Urban Map.

Government of India Copy right.

4. Environmental Measurement Laboratory procedure manual. (1983). 26th

edition. New York.

5. Nambi. K. S. V., Bapat. V. N., David. M., Sundram. V. K., Sunta. C. M. and Soman.

S. D. (1987) Country wide environmental radiation monitoring using

thermoluminesence. Radiation Protection Dosimetry, vol.18(1).pp.31-38.

6. Ghiassi-nejad. M., Mortazavi. S. M. J., Cameron. J. R., Niroomand-rad. A. and

Karam. P. A. (2002) Very high background radiation areas of Ramsar, Iran:

preliminary biological studies. Health Physics, vol.82(1).pp.87�93.

7. Krishnan Nair. M., Nambi. K. S. V., Sreedevi Amma. N., Gangadharan. P.,

Jyalekshmi. P., Jayadevan. S., Cherian. V. and Nair Reghuram. K. (2002)

Population study in the high natural background radiation area in Kerala,

India. Radiation Research, vol.152(6).pp.145�148.

8. BEIR VII. (2006) National Research Council of the National Academies. Health

Risks from Exposure to Low Levels of Ionizing Radiation. Estimating Cancer

Risk. BEIR VII Phase 2 (Washington DC: National Academies Press) pp.267�

312.

9. Mishra. U. C. and Sadasivan. S. (1971) Natural radioactivity levels in Indian

soils. Journal of Scientific and Industrial Research, vol.30.pp.59-62.

10. Herbst. W. (1964) Investigation of environmental radiation and its variability.

In: Adams. J. A. S. and Lowder. W. M eds. Natural radiation environment.

Chicago, University Chicago press. pp. 781-796.

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11. Ohlsen. H. (1971) Determination of the mean population burden from natural

external radiation in the German Democratic Republic. SZE-14/69, also

translation AEC-tr-7216.

12. Stranden. E. (1977). Population doses from environmental gamma radiation in

Norway. Health Physics, vol.33.pp.319-323.

13. Abe. S. and Fujitaka. K. (1980)Natural Radiation in Japan. In: Gesell. T. F.,

Lowder. W M., eds. Natural Radiation Environment III. Vol.2, Oak Ridge; TN:

Technical Information Centre; United States Department of Energy; CONF-

780422.pp.1034-1048.

14. Karunakara. N., Somashekarappa. H. M., Avadhani. D. N., Mahesh. H. M.,

Narayana. Y. and Siddappa. K. (2001) Radium-226, 232Th and 40K distribution

in the environment of Kaiga of South west coast of India. Health Physics,

vol.80(5).pp. 470�476.

15. Avadhani. D. N., Mahesh. H. M., Somashekarappa. H. M., Karunakara. N.,

Narayana. Y. and Siddappa. K (2001). Natural radioactivity in the environment

of Goa of south west coast of India. Journal of Radiation Protection and

Environment, vol.24(1&2).pp.S136�S142.

16. UNSCEAR, United Nations Scientific Committee on the Effect of Atomic

Radiation. Sources and Effects of Ionizing Radiation. (1999) Report to the

General Assembly with Scientific Annexes, United Nations, New York.

17. Baeza. A., Del Rio. M., Miro. C. and Paniagua. J. M. (1994) Natural Radionuclides

Distribution in soils of Cacers, Spain, Dosimetry Implification. Journal of

Environmental Radioactivity, vol.23.pp.19-39.

18. Merdanoglu. B. and Altýnsoy. N. (2006) Radioactivity concentrations and dose

assessment for soil samples from Kestanbol granite area, Turkey. Radiation

Protection Dosimetry, vol.121(4).pp.399�405.

19. Omoniyi Isinkaye. M. (2008) Radiometric assessment of natural radioactivity

levels of bituminous soil in Agbabu, southwest Nigeria. Radiation

Measurements, vol.43.pp.125�128.

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FIELD AND PETROGRAPHIC CHARACTERISTICS OF GRANITOIDS IN THE

SOUTHERN PART OF BANGALORE, DHARWAR CRATON � SOME

CONSTRAINTS ON THEIR ORIGIN

B.C.Prabhakar1*, K.N.Radhika2, S.V.Balaji Manasa Rao1, K.M.Shashi Kiran1, G.Sudarshan1,

H.J.Pavana1, K.B.Naveen1 and S.Sunil1

1Department of Geology, Bangalore University, Bangalore-56 2Department of Civil Engineering, East West Institute of Technology, Bangalore � 91

* Correspondingauthor: [email protected]

Received:22/04/2016 Accepted: 30/06/2016

ABSTRACT

Complexity is the hall mark of gneissic rocks. With older protoliths, juvenile additions, crustal

reworking, migmatisation, metamorphic and synkinematic structural imprints, gneisses

represent one of the intriguing geochronological problems in most of the cratonic blocks of

the world. Episodic growth, repeated remobilisation accompanied by metamorphic history

and granitic intrusions presents one of the fascinating sites for the study of these rocks in

Dharwar craton, where they occupy vast areas dating back to 3.3 Ga to 2.5 Ga events. Their

outcrops in southern part of Bangalore provide one of the excellent areas for their study.

Gneissic varieties like foliated grey granite, banded gneiss, coarse biotite gneiss and

migmatites along with their enclosed mafic bodies reflect complex processes operated during

their evolution. These rocks along with their mafic enclaves have been helpful in deciphering

their evolution due to the melting of basaltic protolith in a subduction environment followed

by juvenile additions and crustal reworking to generate granitic plutons.

KEY WORDS: Granitoids, Peninsular gneiss, Dharwar craton, Granite intrusions.

1. INTRODUCTION

A variety of gneisses and granites together constitute granitoids and are the chief rocks

(constituting ~75% by volume) in Karnataka. Out of them, gneisses are the most

predominant ones and are generally known as Peninsular Gneiss1. However, they do not

represent a single petrologic, structural or temporal entity, rather are a complex assembly

of almost similarly looking, yet compositionally variable rocks formed during 4.0-2.5 Ga

time2, through an episodic synkinematic magmatism and migmatization in diverse

geodynamic setting. The enclaves within gneisses and their fabric patterns also reveal

different stages and regimes of tectonics that prevailed during the evolution of the

granitoids. Compositionally, the gneisses are broadly referred to as TTGs-a host of tonalite,

trondhjemite and granodiorite components in them 3,2.


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The gneissic domains of Karnataka, show strong imprint of migmatization and they are

broadly coeval with isoclinal first folding, followed by near co-axial refolding and non-co

axial upright folding4. Thus, a major cycle of metamorphism and migmatization associated

with isoclinal folding have produced extensive remobilised basement5. Relics of the

gneisses generated during early cycle rarely retained their identity because of

superimposed deformation. Episodic anatexis led to migmatization which obliterated the

memory of early events. The gneissic foliation as seen in most areas of Peninsular gneiss is

N-S, but a few areas show E-W indicating episodic gneiss formation coupled with

differential tectonic regimes. Atleast three important periods of gneiss formation had taken

place in Dharwar cration, viz. 4.0 � 3.2 Ga gneiss as found near Gorur, Holenarasipura and

Kunigal areas; 3.0 � 2.9 Ga gneiss found around Bangalore and 2.7 to 2.5 Ga gneiss

occurring in northern part of Karnataka as determined by several workers through Rb-Sr

and 207Pb/206Pb methods. Thus, the history of gneissic rocks is a story of long period of

evolution from 4.0 to 2.5 Ga involving polyphaser thermal process, melting and reworking,

juvenile additions and tectonic interventions. Thus, no surprise that these lithologies

continue to invoke interest amongst geologists for their vastness, fascinating structures,

compositionally wide ranging enclaves, varied litho compositions (from dioritic to

adamellite) and geochronology for unravelling the metamorphic and igneous processes

through time which have shaped up these great rocks. Within the vast TTG gneisses

formed in a broad Archaean time � span, the late Archaean (2.7 � 2.5 Ga) is a period of

accretion super event which produced large quantities of mantle � derived cale-alkaline

magmas resulting in granite plutons6. They range in composition from leucogranites to

sinukitoids and form distinct terrainal features in Karnataka. These granite bodies with the

basement TTG gneisses form a typical granitoid terrain in southern part of Bangalore. In

the backdrop of this, gneissic and associated rocks of the southern part of Bangalore,

especially around Jnanabharathi campus and NICE road sections have been studied for

their field and petrographic characteristics in order to decipher their evolutionary history.

2. Field and petrographic study

For the present study, as mentioned above, two adjacent corridors were selected where

outcrops of gneisses, granites and their enclaves are exposed both in natural outcrops, as

well as in the road cuttings (Fig.1).One of them is Jnana Bharathi Campus, where good

outcrops with structural features are conspicuous in several spots. The outcrops are mostly

at ground level and few are in elevated spots.

The other area of present study is NICE road corridors. Vast stretch of land has been

excavated/ quarried for making road between Mysore road and Kanakapura road and

Mysore road and Outer Ring road connecting Banashankari. This road section has provided

good access of fresh rocks and to study their structural features.

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The gneissic rocks in the study area occur at ground level, small mound like outcrops and

rarely as small hills are occur ocassionally. Their out crops are also found in quarry

sections and valley cuts. The structural and textural characters are mostly seen as outcrop

scale features. Typical foliation and compositional banding are conspicuously seen at many

places. Migmatized gneiss is no less common. Metatexitic features where the migmatitic

gneiss components (leucosomes) traversing the foliation of early phases of gneiss are

commonly observed. They are normally net-textured ranging from few millimetre to

centimetres. The foliated gneiss generally shows N-S strike with moderate to steep dip

towards east. In some places, apophyses of leucosomes and melt accumulation is

prominently seen. Pockets of tonalites and granodiorites are also observed within gneisses.

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Hornblende schilerns are also observed frequently. Their structural and textural features

are obscured in most of the natural outcrops due to weathering. However quarry

cuts/faces display good features.

Traverses were made in two areas in a week�s time, the areas were mapped and samples

were collected for petrographic study. The sections of all the variety of rocks were

prepared and studied under Zena Lab Research Microscope. Their photographic features

were recorded, photographs of important petrographic features were taken. Both field and

petrographic features were synthesised and presented here. Based on their conspicuous

field characteristics and mineral composition different phases of granitoids and their

enclaves have been identified and presented here.

Foliated grey gneiss - is the most predominant phase with moderate to strongly foliated

fabric along N-S. The foliation is conspicuous with elongated hornblende grains and

schilerns roughly parallel to felsic minerals. Huge enclaves measurable in meters, of

amphibolite with angular to sub angular boundaries are infrequently observed within this

gneiss (Fig.2). Later quartzo-feldspathic veins have permeated these enclaves. Under

microscope, it (gneiss) displays idioblastic to crudely linear arrangement of quartzo

feldspathic minerals with slender hornblende grains. Quartz (25-30%), plagioclase (35-

40%) and K-feldspar (20-25%) are the chief minerals in it with hornblende constituting

about 10-12% of volume. The deformation of grains is indicated by prominent strain

shadow effect on quartz, where quartz becomes extinct under crossed polars. It�s margins

are strained and fractured giving pathways to the fluids generated during alteration

especially biotite alteration (Fig.9). K-feldspar is commonly perthitic. Small crystals of

zircon, sphene and allanite are the commonly noticed accessory minerals.

Very-coarse grained biotite gneiss: Thisvariety of gneiss is locally observed. It is very

coarse, the mineral grains measurable in centimetre and is buff coloured. At times it

resembles pegmatite. Large flakes of biotite mica are very conspicuous and are aligned

along the foliation trending similar to foliated grey gneiss. Very coarse grains of idioblastic

quartz, microcline and relatively lesser plagioclase form the predominant mineral

composition. Microcline is mostly perthitic in character. The alteration of biotite leading to

iron oxide leaching and chlorotization is seen under microscope. Accessories like sphene,

ilmenite and zircon are commonly observed.At no place, it reveals the presence of any

enclave within it, probably pointing at its later/anatectic origin.

Strongly banded gneiss: This gneissic variety occurs closely with other gneissic phases

and the contacts are transitional. In this phase, abundant quartzo-feldspathic bands make

alternating banding with thin mafic bands. At places, they appear like fine-laminations,

coarse bands measurable in centimetres are also observed (Fig. 3).The bands show

contortions in some places which is due to folding and shearing. Blastic growth of quartzo -

feldspathic grains is also commonly observed. The mafic mineral band is chiefly made up of

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hornblende and less commonly biotite. Shearing and syn-melt intrusion along the shear

planes at places has resulted in stretching and dragging of bands (Fig. 4). These melts are

mostly the products of incipient melting of the basement gneisses. Shearing is mostly of

sinistral type with parallel shear planes displacing the gneiss bands.

Migmatic Gneiss: Sporadic occurrence of migmatitic gneiss is commonly noticed in the

study areas. The migmatitic outcrops display exotic structures and textures with varied

colours of the two components involved in mixing during high grade metamorphism. These

two components are arrested at stages as seen now in the outcrops. Strongly migmatized

leucosomes/ melts of pink quartzo - feldspathic material is closely interwoven with grey

coloured contorted quartzo-feldopathic material (Fig. 5). The leucosome components show

ptygmatic folding and local shearing and folding. Initially, the leucosomes appeared to have

occupied the foliation planes of the gneiss undergoing remobilization. As the melts/

leucosomes increased in their volume, they swell and broke the foliation and hybridization

became imminent, thus producing migmatite. Isoclinical folds, drag structure, melt

accumulation, restite and metatexite structures are commonly observed. The leucosomes

are K-feldspathic predominant, whereas the older phase of gneiss (which melted)

prominently consisted of quartz, orthoclase and plagioclase with minor amounts of

hornblende. Areas of intimate mixing show two generations of K-feldspars and plagioclase.

On an average, mineral composition, migmatitic gneiss is constituted by quartz (15-20%),

plagioclase (30-38%) and K-feldspar (25 -30%) and hornblende (10 � 12%). Sphene,

zircon and magnetite are the common accessory minerals. Plagioclase is strongly altered to

sericite. Dragging and rotation of minerals is commonly observed.

Fig.2. Field photograph showing enclaves of amphibolite in the NICE-road cut.

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Fig.3. Field photograph of strongly Fig.4. Field photograph of syn-melt

banded gneiss in Jnana Bharathi area. intrusion along a shear plane in

banded gneiss

Fig.5.Field photograph showing migmatitic Fig.6.Field photo showing diorite enclave in

gneiss in Jnana Bharathi area. Gneiss. Note the felsic material from

gneiss entering diorite.

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Fig.7.NICE-road cut exposing a vertical Fig.8. A shear- fault displacing dolerite dyke

dolerite dyke intruding grey gneiss as exposed in the NICE-road cut

Granites:

The gneissic rocks have, at several places are, co-existing with granite bodies in the study

area. The structural and textural features of them invariably make a contrasting

appearance from that of gneisses. However, at places they so closely match in their

appearance that distinction becomes difficult, unless closely inspected. Both with

transitional and sharp contacts, the granite bodies occur within the gneisses. Sharp

contacts sometimes with wedging effects on gneisses, are common. Assimilation of gneisses

along the contacts, veining into gneisses are possibly the indicators of syn- to semi-

contemporaneous nature of granite evolution with gneisses. Quite intriguingly gneisses at

places also contain granite bodies as enclaves. These granite enclaves are huge, bouldery in

appearance, with the surrounding gneisses showing flowage or thinning out of gneissic

foliation. Probably, such granitic boulder enclaves within the gneisses suggest the early

crustal segment through which the gneissic material was pervaded, or represent a locally

remelted component of the gneissic rocks. The rheology of such process can only be

ascertained though geochronological studies. The important granitic varieties found in the

gneissic domain are discussed below for their field and petrographic aspects.

Grey granite - Medium to coarse grained grey hornblende granite occurs as a discordant

phase with gneiss. It does not exhibit any foliation and thus has sharp contacts with well

foliated gneiss. The field relation suggests that it could be a younger phase of granite

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activity, occurring as apophyses of a major intrusive activity. This grey granite is made up

of perthitic K-feldspar (25-35%), (20 � 30%) quartz (20-30%) and plagioclase (25 � 38%)

Homblende (2 � 10%) is the chief mafic mineral. Zircon and sphene are observed as

accessory minerals. Strong myrmeckitization (Fig. 10), perthite growth (Fig .11), suggest

the solid- solution process while cooling of this granite.

Leuco-granite - White to buff-coloured leuco-granite commonly occurs as a late phase of

granite activity. It occurs as patches and veins traversing through all the earlier

gneissic/granitic phases. It almost resembles micro granite, but its grain-size is coarser to

make it a normal granite. It is medium grained with typical hypidiomorphic texture,

consisting of quartz, plagioclase and perthitic k-feldspar with minor amounts of

hornblende. Zircon and sphene are the common accessory minerals noticed. Epidote-

biotite-sphene-ilmenite association is also seen at places along the contacts of leuco

granite, the effects of assimilation could be seen, whereas the core portions are unaffected.

Mafic enclaves in gneisses - Mafic components are frequently found as enclaves within

the gneissic rocks and they are important to decipher the precursor history of the granitoid

evolution during Archaean period. They range in size from a centimetre wide (across)

xenoliths and bondinaged bouldery bodies measurable in meters. Compositionally also

they show variations. Here the pattern of enclaves and their relationship to their host is

discussed.

Amphibolite enclaves - Amphibolite enclaves are commonly noticed in gneisses (Fig. 2).

They are fine to medium grained and strongly permeated by quartzo-feldspathic material

(migmatized). The contacts are normally sharp indicating the older rheology of

amphibolites to which the gneiss/ granitic material invaded and assimilated, the remnants

being present as enclaves. No signature of either mixing or mingling is noticed along the

contacts. Thus, it is possible that amphibolite or its igneous parent served as a precursor

for gneissic rocks. Mineralogically, it is chiefly made up of honblende and plagioclase (Fig.

12) with minor pyroxene. Biotitization / Chloritization of hornoblende is commonly

observed. Most of the amphibolite enclaves are granular and do not show any alignment

with gneissic foliation.

Dioritic enclaves:

Small oval to wedge shaped dioritic enclaves are seldom found within the foliated grey

gneisses. They are dark coloured and show sharp contact with gneisses. Occasionally the

gneissic material has been wedged into diortic bodies indicating concomitant development

of both diorte and gneisses (Fig. 6). Thus, the diorite enclave could be the remanant of an

earlier crustal segment which has been remobilised, deformed and entrailed with the

gneissic body.

Intrusives:

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Gneisses and granites are frequently intruded by dykes of different dimensions. A road cut

along the NICE �road corridor has displayed spectacular features of them. They are mostly

vertical to slightly inclined (Fig. 7). Many of the dykes also display off-shoots producing

mini-dykes as thin as a centimetre. They commonly show chilled boundaries along the

contact. The effect of wedging, bridging are also noticed. Compositionally, these dykes are

doleritic in composition with pyroxene and plagioclase crystals showing sub-ophitic

texture.

Frequent dyke-activity displayed in the study area suggests that post-gneiss, post-granite

activity witnessed large scale rupturing of the crustal rocks around 2.24 Ga enabling dyke

activity. Post �dyke deformational activity is also seen in the area where shear � faults at

many places have displaced the dykes (Fig. 8).

Fig.9. Strain-shadow effect in quartz in Fig.10. Development of myrmeckite in

foliated grey gneiss. granite.

Fig.11.Flame perthite development in Fig.12. Amphibolite showing moderately

leuco granite. foliated hornblende with granular

plagioclase

3. Discussion and conclusions

TTG gneisses and granitic rocks have been known to be produced in different geodynamic

settings like collision, subduction and mantle plume tectonic regimes. Krogstad et al.

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(1995)7, Hansen et al. (1995)8 and Chadwick et al. (2000)9 have proposed subduction by

lateral action as responsible for producing granitoids in eastern Dharwad craton. The

southern part of Bangalore area is predominantly constituted by TTG gneissic basement

formed similar to many cratonic blocks of the world (Windley, 1995) episodically, i.e

melting of the oceanic basaltic crust. The prominently foliated grey gneiss has been formed

during pervasive regional metamorphism of the felsic rocks already produced by a

subduction process. Very coarse grained biotite gneisses have been formed from volatile-

rich leuco-components after the formation of foliated grey gneisses. Remelting and anatexis

of the precursor rocks (early phases of gneisses) generated new melts and these melts

mixing with both the mafic enclaves (amphibolites) within the gneisses and gneisses

proper, produced the hybrid rocks i.e. migmatites. Local to regional scale shearing was

prevalent during the entire period of gneiss formation resulting in both sinistral and

dextral shearing and remobilisation of gneissic components. The strong banding in gneiss

appears to have developed during intense N-S shearing and fabric stretching. The mafic

enclaves in the gneisses could be the precursors or the representatives of early formed

crust, mostly of basaltic character. During the high grade regional metamorphism they have

been remobilised, fractured and permeated by leucosomes, yet resisted the complete

assimilation during the prolonged episodic activity involving folding, shearing and

migmatization.

The apophyses, stocks and bosses of granites within the gneiss are indicative of significant

felsic intrusive activity in the terminal stages of gneiss formation. Chadwick et al. (2000)

envisages accretion of granites due to convergent plate setting with subduction from west

to east to account for juvenile granitoids in Dharwar craton. Reworking of the gneissic

basement could have also contributed for the formation of granites. Leucogranites in the

study area indicate this possibility. Studies of Archaean cratons by Choukroune et al.,

(1994) and Condie (1989) slow that wide-spread juvenile calc �alkaline granite

magmatism accreted during 2.7 � 2.5 Ga and this was spatially associated with crustal

reworking (Jayananda et al. 2006) and culminated in granite magmatism and cratonization

of the Archaean crust (Friend, 1983).This juvenile continental accretion led to

emplacement of large scale granite emplacement and intrusions during a tectano-

metamorphic event around 2.51 Ga that affected the craton at the close of the Archaean

(Peucat et al., 1993 ; Krogstad et al., 1991;Chardon et al., 2002; Mahabaleswar et al., 1995).

The distinct structural/textural characteristics of granites and their contact relationship

are suggestive of an episode of granite formation in this (study area) southern part of

Dharwar craton. Many of these juvenile granites, represent the youngest mantle derived

components at the time of cessation of magmatic/metamorphic activity and stabilization of

the crust.

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4. ACKNOWLEDGEMENT

Dr. Mohammed Shareef, Sr. Geologist, Geological Survey of India, Bangalore is thanked for

helping in taking photomicrographs of the thin sections.

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12. Jayananda, M., Chardon, D., Peucat, J.J., Capdevila, R. and Martin, H., (2006). 2.61

Gapotassic granites and crustal reworking, Western Dharwar craton (India): tectonic,

geochronologic and geochemical constraints. Precambrian Res., v.150, pp. 1-26.

13. Friend, C.R.L., (1983). The link between charnockite formation and granite production:

evidence form Kabbaldurga, Karnataka, South India. In: M. Atherton and C.D. Gribble

(Eds.), Migmatites, Melting, Metamorphism. Shiva Press, Nantwich, pp.264 -276

14. Peucat, J.J., Mahabaleswar, B. and Jayananda, M., (1993). Age of younger

tonaliticmagmatism and granulite metamorphism in the amphibolite �granulite

transition zone of southern India (Krishnagiri area): comparison with order peninsular

gneisses of Gorur-Hassan area. Jour. Metamorph. Geology, v.11, pp. 879-888.

15. Krogstad, E.J, Hanson, G.N and Rajamani, V. (1991). U�Pb ages of zircon and sphene for

two gneiss terrains adjacent to the Kolar Schist Belt, south India: Evidence for separate

crustal evolution histories; J. Geol. 99 801�816. 16. Chardon, D., Peucat, J.-J., Jayananda, M., Choukroune, P., and Fanning, C. M. (2002).

Archean granite-greenstone tectonics at Kolar (South India): interplay of diapirism and

bulk inhomogeneous contraction during magmatic juvenile accretion, Tectonics, 21, 10-

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17. Mahabaleshwar, B., Jayananda, M., Peucat, J.J and ShadaksharaSwamy, N., (1995).

Archaean gneiss complex form Satnur-Halgur-Shivasamudram area: petrogenesis and

crustal evolution. Jour. Geol. Soc. India, v.45, pp.33-49

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ESTIMATION OF MORPHOMETRIC PARAMETERS FROM ASTER-DEM

DATA BY GIS AND REMOTE SENSING TECHNIQUES � A STUDY ON

ELEMALLAPPA WATERSHED OF DAKSHINA PINAKINI RIVER BASIN,

KARNATAKA, INDIA

K.Satish1*andH.C. Vajrappa2

Department of Civil Engineering, UVCE, Bangalore University, Bangalore-56 2Department of Geology, Bangalore University, Bangalore-56



Abstract

The morphometric analyses have been carried out for Elemallappa Watershed of

Dakshina Pinakini River Basin. Manual measurement of watershed morphometric

parameters for analyses is expensive and time consuming. Hence, Advanced Spaceborne

Thermal Emission and Reflection Radiometer Digital Elevation Model (ASTER-DEM) has

been used as data source for extracting all complex morphometric parameters of the

watershed. Morphometric parameters such as stream order, number of streams of each

order, stream length, bifurcation ratio, drainage density, stream frequency, etc. have been

estimated through DEM derived from ASTER (30 m) in GIS environment. By knowing these

parameters study of groundwater potential zone and sites for groundwater recharge is

easier in a watershed study.

KEYWORDS: ASTER-DEM, Elongation Ratio, Infiltration Number, Groundwater Recharge

1. INTRODUCTION

Since the mid-1980s digital elevation models (DEMs) have been used to delineate

drainage networks and watershed boundaries, to calculate slope characteristics, and to

produce flow paths of surface runoff.1�4 In this study, it has been found that Advanced

Spaceborne Thermal Emission and Reflection Radiometer Digital Elevation Model

(ASTER-DEM) has been very useful data to extract the morphometric characteristics of

watersheds accurately in very less time, which helps in locate the sites for groundwater

recharge and proper watershed planning. The morphometric studies involve the

evolution of stream parameters through the measurement of various stream

properties.5An attempt has been made to evaluate morphometric parameters derived

from ASTER-DEM (30 m). Modern technologies such as Geographic Information System

(GIS), have gained significant importance over the last decade in their applications

pertaining to distributed hydrologic modelling. GIS is suitable for the analysis of spatially

referenced data. GIS can handle both spatial and aspatial data effectively and efficiently.

Nowadays, GIS is widely used for resources planning in watershed.6,7 Using the presently


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available GIS techniques one has to go through tedious steps for generating these

characteristics.

2. STUDY AREA

This study is conducted on Elemallappa Shetty watershed of Dakshina Pinakini

River Basin � a tributary of river Cauvery, which falls between 77 32 and 77 47 E

longitude and between 13 0 and 13 10 N latitude, covering an area of 292.1 Km2 (Fig.

1).

3. MATERIALS AND METHODS

In this study ASTER images obtained from the website

http://www.gdem.aster.ersdac.or.jp are used. By using the ArcGIS 10.2.1, the drainage

network from ASTER-DEM has been extracted (Fig. 1) adopting the standard

procedures.8�11

Fig. 1 Drainage Map of Elemallappa

Watershed evaluated through ASTER-

DEM

http://www.gdem.aster.ersdac.or.jp

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The steps followed to obtain streams and watershed parameters based on ASTER-

DEM are

a) Fill the sinks in the ASTER-DEM

b) Apply the flow direction function

c) Apply the flow accumulation

d) Apply a threshold condition

e) Obtain a streams grid

f) Obtain the stream links grid

g) Obtain watershed grid

h) Vectorize the streams grid

i) Vectorize the watershed grid.

4. MORPHOMETRIC ANALYSIS

Morphometric analyses of the watershed carried out by using three

parameters viz, basic, derived and shape parameters.

(i) Basic Parameters

The first is perimeter (P), which is the total length of the watershed boundary.

The perimeter of the watershed is 80.7 Km. The total area of watershed is 292.1 Km2.

The length of the watershed(L) measured parallel to the main drainage line. It is clearly

noticed that the accurate elineation is possible when a higher resolution image is used.

The values of watershed length are as shown in Table 1.

Stream Order � Stream order or classification of streams is a useful indicator of stream

size, discharge and drainage area. 12The number of streams (N) of each order(u) is

presented in Table 1. Th e wa te rs he d is fifth order basin, it is observed that decrease

in stream frequency as the stream order increases (Fig. 2).

Stream Length � The number of streams of various orders in watershed is counted and

their lengths are measured. The stream length characteristics of the watersheds

confirm Horton�s second law� law of stream length� which states that the average length

of streams of each of the different orders in a watershed tends closely to approximate a

direct geometric ratio.13 In general, the total length of stream segments is high in first

order streams and decreases as the stream order increases (Fig. 3). In t h i s case, the

stream segments of various orders show variation from general observation. When

stream length h a s b e e n calculated for t h e watershed, it has been found that it

follows a general pattern for ASTER, that is, stream length is maximum in the first order

and decreases with the least at fifth order. The values of length (Lu) and total stream

length (Lt) are shown in Table 1.

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The maximum and minimum height (H ,h) corresponds to the highest and

lowest points of the watershed. The maximum and minimum heights of watershed are

920 and 820 m, respectively.

Table 1 Morphometric Parameters Derived from ASTER-DEM

Stream

Order

Stream

Count

Total No. of

Streams

Stream

Length

Total Lengt

h

Mean Lengt

h Rb Rbm Dd Fs Re Rf Rc If

1 385

525

310.76

542.2

0.8 3.63

4.6 1.85 1.79 0.7 0.38 0.56

3.33

2 106 127.67 1.2 4.07 3 26 52.78 2.03 3.71 4 7 26.5 3.78 7 5 1 24.51 24.51

Fig. 2. Relation of no. of Streams to Fig. 3. Relation of Stream Order to

Stream Order. Stream Length.

(ii) Derived Parameters

Bifurcation R atio -The bifurcation ratio (Rb) ranges between 3.63 and 7 when the

influence of geological structures is negligible. 14Themean Rb may be defined as the

average of bifurcation ratios of all orders (Table 1). In this study the mean Rb is 4.6,

which indicates mature stage of development.

ub

u +1

NR

N

0

50

100

150

200

250

300

350

1 2 3 4 5

No

. of S

trea

ms

Stream Order

0

50

100

150

200

250

300

350

1 2 3 4 5

Stre

am L

engt

h

Stream Order

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Where, Nu=Total no. of stream segments of order �u� and Nu+1=No. of segments of the

next higher order.

Drainage Density - The value of Dd is 1.85 Km/Km2, which reveals that the watershed

is composed of permeable subsurface material, vegetation cover and low relief. This

reflects that this watershed has more infiltration capacity and is the good site for

groundwater recharge (Table 1).

ud

u

=L

DA

Where, Lu=Total stream length of all the orders and A=Area of the watershed (Km2).

Stream Frequency - Horton introduced stream frequency (Fs) or channel

frequency.15The total number of stream segments of all orders per unit area. Fs is

related to permeability, infiltration capacity and relief of watershed. The watershed

shows medium Fs (1.79), which corresponds to permeable strata.

NF

A

where, N=Total no. of streams and A=Total area of the watershed.

Drainage Texture � Drainage texture (Dt) is an expression of the relative channel

spacing in a fluvial dissectedter rain. It depends on a number of natural factors such as

climate, rainfall, vegetation, rock and soil type, infiltration capacity, relief and stage of

development of a watershed. 16The value of Dt is 6.5, which indicates fine drainage texture.

According to Horton, Dt is the total number of stream segments of all orders per

perimeter of that area.13 The drainage density <2 indicates very coarse, between 2 and 4

is related to coarse, between 4 and 6 is moderate, between 6 and 8 is fine and > 8 is very

fine drainage texture.

ut

NR

P

Where, Nu=Total no. of streams of all orders and P=Perimeter (Km).

Infiltration Number - Infiltration number is the product of the drainage density and

stream frequency. It plays a significant role in observing the character of watershed. It is

inversely proportional to the infiltration capacity of the watershed. The result reveals

that watershed has high infiltration capacity, composed of fractured subsurface and

undulating to low relief (Table 1).

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f d sI D F

Where, Dd=Drainage density and Fs=Stream Frequency.

(iii) Shape Parameters

Elongation Ratio � Schumm defined elongation ratio(Re) as the ratio between the

diameter of the circle of the same are as the watershed and the maximum length of the

watershed (L).17 Result of Re exhibits that the watershed is more or less elongated in

shape with the value of 0.7. A circular watershed is more efficient in the discharge of

run off than an elongated watershed.18 The value of Re varies from 0 (in highly

elongated shape) to the unity, that is, 1 (in circular shape).

ue

b

2 /AR

L

Where, A=Area of the watershed (Km2); =3.14; and Lb= watershed length.

Circularity Ratio - Circularity ratio (Rc) is the ratio of the area of watershed to the area

of a circle having the same circumference as the perimeter of the watershed.18Rc is

influenced by the length and frequency of streams, geological structures, land use/land

cover, climate and relief of the watershed. The Rc of the watershed is 0.56. The result

shows that the watershed is more or less elongated and characterized by moderate-to-

low relief.

c 2

4 AR

P

Where, =3.14; A=Area of the watershed (Km2) and P2=Square of the perimeter (Km).

Form Factor - Form factor (Rf) is the ratio of the watershed area to the square of

watershed length. It is used as a quantitative expression of the shape of watershed form.

Rf value indicates that the watershed is less circular in shape with lower value of 0.38,

where we can expect more groundwater recharge.

uf 2

b

=A

RL

Where, A=Area of the watershed (Km2) and Lb2=Square of watershed length.

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5. RESULTS AND DISCUSSIONS

The study reveals that the maximum frequency in case of first order stream is

found in the watershed, that is 385, 106 second, 26 third, 7 fourth and 1 fifth order

streams found in the watershed (Table 1). It is noted that stream frequency decreases as

the stream number increases. Plot of number of streams against the order of streams

indicate linear correlations. It means that number of streams usually decreases in

geometric progression as the stream order increases (Fig. 2). The variations in rock

structures of watersheds are responsible for inequalities in stream frequencies of each

order. The results show that the total length of stream segments is maximum in case of

first order streams, that is, the first order has length of 310.76 Km, second order 127.67

Km, third order 52.78 Km, fourth order 26.5 and fifth order has length of 24.51 Km. This

indicates that as the stream length decreases the order increases (Fig. 3). Thus, stream

length characteristics of the watershed confirm Horton�s second law �law of stream

length�.13 The value of drainage density 1.85 Km2/Km indicates that region under this

watershed is composed of permeable subsurface material, vegetation cover and low

relief. This indicates that the Elemallappa Shetty watershed has more infiltration

capacity and can be the good site for groundwater recharge. Stream frequency is related

to permeability, infiltration capacity and relief of watershed. Stream frequency reveals

that the watershed is covered by vegetation and having very good infiltration capacity.

The form factor value varies from 0 (in highly elongated shape) to the unity, that is, 1 (in

perfect circular shape). Hence, higher the value of form factor more circular the shape of

the watershed and vice-versa. The shape parameters (Rf, Rc& Re) reveal that the

watershed is more or less elongated (Fig. 1 & Table 1). This is the indication of more

ground water recharge and good site for groundwater recharge.

6. CONCLUSIONS

The morphometric analysis shows dendritic drainage pattern. The drainage

density of Elemallappa Shetty watershed reveals that the watershed is composed of

permeable subsurface material, vegetation cover and low relief. The value of stream

frequency reveals that the watershed is covered by vegetation and having very good

infiltration capacity. Shape parameters (Rf, Rc & Re) obtained for this watershed indicate

that the watershed is more or less elongated. This reflects that the watershed has more

infiltration capacity and can be considered as good site for groundwater recharge. If

watershed characteristics studied thoroughly, the groundwater recharge area can be

located. Hence, a systematic analysis of morphometric parameter switch in the

drainage network using ASTER-DEM data can provide a significant value in

understanding the watershed characteristics. Overall results of Elemallappa Shetty

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watershed show that the morphometric parameters derived from the ASTER data

provide good and satisfying results. The results will be more efficient when the DEM cell

size is smaller or the resolution of the image is higher.

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