IJITCE June 2011

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: [email protected] Phone: +44-773-043-0249

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NTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.6 JUNE 2011

IJITCE PUBLICATION

INTERNATIONAL JOURNAL OF INNOVATIVE

TECHNOLOGY & CREATIVE ENGINEERING

Vol.1 No.6

June 2011

www.ijitce.co.uk


From Editor's Desk

Dear Researcher, Greetings! Research articles in this issue discusses about Decision Tree, Braille hand glove for the blind, Multi homed Scatter net Network, and Peristaltic pumping. Let us observe research topics made significant progress around the world for this month, It is nice to work with the new Windows 7 OS that has a semi-transparent interface. Scientists have now developed a transparent battery, which could pave way for making cool gadgets. Your mobiles and other devices can now go transparent. LEDs save energy usage and cost. You can find that they are being used widely in many appliances including automobile. A new technology in this is the making of three-dimensional crystals of photons, which enhance the output of these LEDs. These 3D photon crystals can also be used in the manufacture of solar panels. Health wise diabetes is one of the major concerns in the world. Even children are now fallen prey to it. It is however difficult to monitor the glucose levels in real time and administer insulin at the required levels. A new advancement has been made in the insulin pump device that can sense the glucose levels and control the injection of insulin into the body. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technology-related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE


Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia,UPM Serdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at Shangai Jiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin, Faculty of Agriculture and Horticulture, Asternplatz 2a, D-12203 Berlin, Germany Dr. Marco L. Bianchini Ph.D Italian National Research Council; IBAF-CNR, Via Salaria km 29.300, 00015 Monterotondo Scalo (RM), Italy Dr. Nijad Kabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh, Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University, No. 303, University Road, Puli Town, Nantou County 54561, Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Mr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP Project Manager - Software, Applied Materials, 1a park lane, cranford, UK Dr. Bulent Acma Ph.D Anadolu University, Department of Economics, Unit of Southeastern Anatolia Project(GAP), 26470 Eskisehir, TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602, USA.

Review Board Members

Dr. T. Christopher, Ph.D., Assistant Professor & Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar) ,01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. Benal Yurtlu Assist. Prof. Ondokuz Mayis University Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag


33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRua Itapeva, 474 (8° andar), 01332-000, São Paulo (SP), Brazil Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Javad Robati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran Vinesh Sukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. doc. Ing. Rostislav Choteborský, Ph.D. Katedra materiálu a strojírenské technologie Technická fakulta,Ceská zemedelská univerzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Binod Kumar M.sc,M.C.A.,M.Phil.,ph.d, HOD & Associate Professor, Lakshmi Narayan College of Tech.(LNCT), Kolua, Bhopal (MP) , India. Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg.,Hampton University,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A., M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). doc. Ing. Rostislav Chot ěborský,ph.d, Katedra materiálu a strojírenské technologie, Technická fakulta,Česká zemědělská univerzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Amala VijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin Ashokrao B.sc,M.Sc Lecturer in Yeshwant Mahavidyalaya Nanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MEN GG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed . Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-Banglore Westernly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech & PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D.,


Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Rajasenathipathi M.C.A., M.Phil Assistant professor, Department of Computer Science, Nallamuthu Gounder Mahalingam College, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India Prema Selvaraj Bsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. V. Prabakaran M.C.A., M.Phil Head of the Department, Department of Computer Science, Adharsh Vidhyalaya Arts And Science College For Women, India. Mrs. S. Niraimathi. M.C.A., M.Phil Lecturer, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, India. Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., P GDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Mr. R. Vijayamadheswaran, M.C.A.,M.Phil Lecturer, K.S.R College of Ars & Science, India. Ms.S.Sasikala,M.Sc.,M.Phil.,M.C.A.,PGDPM & IR., Assistant Professor,Department of Computer Science,KSR College of Arts & Science,Tiruchengode - 637215 Mr. V. Pradeep B.E., M.Tech Asst. Professor, Department of Computer Science and Engineering, Tejaa Shakthi Institute of Technology for Women, Coimbatore, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Mr. K. Saravanakumar M.C.A.,M.Phil., M.B.A, M.Tech, PGDBA, PGDPM & IR Asst. Professor, PG Department of Computer Applications, Alliance Business Academy, Bangalore, India. Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Research Fellow,Department of Mathematics,National Institute of Technology (REC),Tiruchirappli-620 015, Tamilnadu, India.


Contents 1. Coefficient of Variation based Decision Tree (CvDT) ……….[1]

2. Use Of Braille Database For Design And Implementation Of Braille Handglove For Deafblind People…..[7]

3. Development of Reliable Multihomed Scatternet Network……[13]

4. Peristaltic Pumping Of a Micropolar Fluid In An Inclined Channel…….[22]


1

Coefficient of Variation based Decision Tree

(CvDT) Hima Bindu K #1, Swarupa Rani K #2, Raghavendra Rao C #3

# Department of Computer and Information Sciences, University of Hyderabad Hyderabad, 500046, India

1 [email protected],2 [email protected],3 [email protected]

Abstract— Decision trees are widely used for classification. Several approaches exist to induce decision trees. All these methods vary in attribute selection measures i.e., in identifying an attribut e to split at a node. This paper proposes a novel splitting cr iteria based on Coefficient of Variation and it is named a s Coefficient of Variation Gain (CvGain). The decisio n trees built with CvGain are compared with those built wit h Entropy and Gainfix. Empirical analysis based on standard data sets revealed that Coefficient of Var iation based decision tree (CvDT) has less computational c ost and time.

Keywords: Coefficient of Variation; CvGain; Decision Tree; Splitting Criteria

I. INTRODUCTION Decision trees are well known for classification [6].

Decision trees are easy to interpret and they simplify the complex decision making process. ID3 [10], C4.5 [11], CART [2] are few popular implementations of decision trees. ID3 algorithm is the first decision tree implementation. Building a decision tree follows a greedy approach for choosing the best attribute for splitting at a node. Splitting criteria plays a vital role in building a decision tree. Information Gain, Gain ratio, Gini index, Chi square statistics and Kappa index are the well known splitting criteria. Coefficient of Variation (Cv) [13], is a measure of consistency of a distribution and is used in applied domain. The application of Cv for constructing risk trees in managerial studies is demonstrated in [3]. Cv has not attracted the researchers of data mining as a splitting criteria till date. Cv is a normalized measure of dispersion of a probability distribution. This paper proposes building a decision tree using Cv. ID3, proposed by Quinlan uses Information gain for attribute selection, which is based on information theory. But Information Gain is biased towards multi-valued attributes. C4.5 is a successor of ID3 and uses gain ratio, which is an extension of Information gain. Gain ratio overcomes the biasing for multi-valued attributes by applying some normalization

to Information Gain. But Gain ratio tends to prefer unbalanced splits. The Gini index which considers a binary split for each attribute is used by CART. But Gini index also prefers multi-valued attributes and has a difficulty in dealing large number of classes. The limitations of impurity based measures like Information gain and Gini index are given by [5]. They have proposed a class of attribute selection measures called C-SEP to overcome those limitations.

Reference [7] has applied ID3 on the reduced data obtained by reduct attributes based on rough set theory [9]. Reduct selects only predominant attributes. Thus one can achieve dimension reduction. It is reported that the resulting tree generates fewer classification rules with comparable classification accuracy to ID3. Reference [12] has built a reduct based decision tree where the splitting attributes are selected according to their order of presence in the reduct. To the best of our knowledge, the latest splitting criteria used was based on Kappa index as proposed in [4]. They proposed fixed information gain, called as Gainfix , as the new standard for selecting splitting attributes. Gainfix considers relationship between condition attributes and decision attributes in addition to Information Gain. They claimed that the decision tree (which is named as FID3 by them) built using Kappa achieves better performance and simpler decision tree than ID3.

Since its inception, ID3 has been thoroughly studied by various researchers. ID3 uses Information Gain as the splitting criteria. But Information Gain uses frequencies ignoring its ordinates and is based on the Entropy which invokes logarithmic function several times. The computation of Cv is less expensive as it uses simple arithmetic operations and square root function. This contrasting feature inspired the present study to use Cv for construction of decision trees. The tree built based on the Cv will be called as Cv based Decision Tree, in short CvDT. As the computational complexity of Cv is low, it is expected that Cv based decision tree construction will take less time. This is proved by the hypothesis test performed with paired t-


2

test. Hence it is suitable for agent based applications, where a decision tree has to be built in real time.

The performance characteristics have been tabulated using tenfold cross validation test on the proposed CvDT as well as on ID3 and FID3 methods for evaluation purpose.

This paper is organized as follows: Section 2 illustrates Coefficient of variation and introduces CvGain along with computations. The CvDT algorithm is discussed in Section 3. Section 4 illustrates CvDT construction with a simple example. Section 5 describes the data sets considered for validation and the adopted validation procedure. Section 6 brings out contrasting features of CvDT and ID3. The paper concludes with section 7.

II. CVGAIN

A. Coefficient of Variation Coefficient of Variation [13], [1] is the ratio of standard

deviation σ and mean µ.

Cv = µσ

(1)

Coefficient of Variation is a dimensionless number and hence it is suitable for comparing data in different units or with widely different means. Cv is defined for non zero mean. The computation of Cv is illustrated with Table I data. This data contains two attributes: High School GPA (called as A1) and College GPA (called as A2).

TABLE I GPA DATA

Student A1 A2 D S1 3 2 2 S2 3 1 3 S3 4 3 1 S4 2 1 3 S5 3 3 2

The computations of Cv for each attribute of GPA

data are given below. Cv(A1)=σ(A1)/µ(A1)*100 = (0.6325/3)*100 = 21.0819 Cv(A2)=σ(A2)/µ(A2) *100 = (0.8944/2)*100 = 44.7214 Cv(D)=σ(D)/µ(D) *100 = (0.7483/2.2 )*100 = 34.0151

B. CvGain Let DT be the decision table which is preprocessed

such that Cv can be computed. Let DT = [A1, A2, A3… An, D] where A1, A2….An are

the conditional attributes and D is the decision attribute. Coefficient of Variation of decision attribute D is given

by

Cv(D) = 100*)()(

D

D

µσ

(2)

Coefficient of Variation of D conditioned on Ai having

‘v’ distinct values (a1, a2 … av) is given by

Cv(D|Ai) = )|(1

ji

v

jj aADCvP =∑

=

(3)

Where aj is the jth possible value of Ai with chance Pj And

CvGain ( Ai ) = Cv(D) – Cv(D|Ai) (4)

Using GPA data (Table II), the detailed computations of CvGain are given below along with the conditional tables for Cv (D|A).

TABLE II

CONDITIONAL TABLE WITH A1 = 2

Student A1 D

S4 2 3

From Table II, Cv(D|A1 = 2) = 0/3 * 100 = 0.

TABLE III CONDITIONAL TABLE WITH A1 = 3

Student A1 D

S1 3 2

S2 3 3

S5 3 2

From Table III, Cv(D|A1=3)= 0.4714/ 2.33*100 =

20.20

TABLE IV CONDITIONAL TABLE WITH A1 = 4

Student A1 D

S3 4 1

From Table IV, Cv(D|A1 = 4) = 0/1 * 100 = 0 Assuming that P (Ai = aj) is the probability that attribute Ai takes the value aj, Cv(D|A1)=P(A1=2)*Cv(D|A1=2)+P(A1=3)*Cv(D|A1=3) + P(A1=4)*Cv(D|A1=4) Hence, Cv(D|A1)=1/5*0 + 3/5*20.2031 + 1/5*0 = 12.1219 CvGain( A1 ) = 34.0151 - 12.1219 = 21.8932 With similar calculations for attribute A2,

Cv(D | A2 = 1) = 0 /3 *100 = 0 Cv(D | A2 = 2) = 0/2 *100 = 0 Cv(D | A2 = 3) = 0.5/1.5 *100 = 33.3333 Cv(D|A2) = 2/5*0 + 1/5*0 + 2/5*33.33 = 13.33 CvGain(A2) = 34.0151 - 13.3333 = 20.6818


3

As CvGain(A1) is large when compared with CvGain(A2), A1 is selected as the splitting attribute.

C. Algorithm

Algorithm CvDT : Generate CvDT from the decision table DT. Input: Decision Table DT with attribute_list and decision attribute D. Output: CvDT Method: (1) create a node N; (2) if Cv(D)=0 then (3) return N as a leaf node labeled with the class C, the class of all tuples; (4) if attribute_list is empty then (5) return N as a leaf node labeled with the majority_class in D; //majority voting (6) splitting attribute = max( CvGain ( attribute_list) ) (7) attribute_list = attribute_list – splitting_ attribute; (8) for each value j of splitting_ attribute //partition the //tuples and grow sub trees for each partition (9) DTj = tuples in DT with splitting_ attribute = j ; (10) if DTj = φ then (12) create a leaf node labeled with majority class in DTj and attach it to node N; (13) else attach the node returned by CvDT(DTj, attribute_ list) to node N; (14) end for (15) return N;

III. ILLUSTRATION The popular Weather data set for the concept Play Tennis

[8)] is considered for illustration purpose(Table V).

TABLE V DECISION TABLE FOR THE CONCEPT “PLAY TENNIS”

Outlook Temperature humidity Windy Class

Overcast Hot high False N

Overcast Mild high True N

Overcast Hot normal False N

Overcast Cool normal True N

Rain Mild high False N

Rain Mild high True P

Rain Cool normal False N

Rain Mild normal False N

Rain Cool normal True P

Sunny Hot high False P

Sunny Mild high False P

Sunny Hot high True P

Sunny Cool normal False N

Sunny Mild normal True N

To compute Cv, the mean value need to be non zero.

Hence the data need to be pre-processed in such a way that avoids ‘mean’ to be zero. A simple pre processing which assigns positive integers is used here for illustration. In fact any pre processing technique which gives non zero mean is applicable. Table VI shows the pre-processed data and Table VII shows the CvGain values.

TABLE VI PREPROCESSED DECISION TABLE

Outlook Temperature humidity windy Class

2 3 2 1 2

2 2 2 2 2

2 3 1 1 2

2 1 1 2 2

1 2 2 1 2

1 2 2 2 1

1 1 1 1 2

1 2 1 1 2

1 1 1 2 1

3 3 2 1 1

3 2 2 1 1

3 3 2 2 1

3 1 1 1 2

3 2 1 2 2

TABLE VII CVGAIN VALUES

Attribute CvGain Outlook 5.73 Temperature 0.45 Humidity 2.42 Windy 0.74

From the Table VII, Outlook has maximum CvGain;

hence the attribute Outlook is selected as splitting attribute at root node. Hence the data will be split into three sub tables based on the Outlook values. For Outlook = 1, 2 and 3 the decision tables are shown in VIII, IX and X respectively.

TABLE VIII DECISION TABLE FOR OUTLOOK = 1

Temperature humidity Windy class


4

2 2 1 2

2 2 2 1

1 1 1 2

2 1 1 2

1 1 2 1

TABLE IX DECISION TABLE FOR OUTLOOK = 2

Temperature Humidity windy Class

3 2 1 2

2 2 2 2

3 1 1 2

1 1 2 2

TABLE X DECISION TABLE FOR OUTLOOK = 3

Temperature humidity windy class

3 2 1 1

2 2 1 1

3 2 2 1

1 1 1 2

2 1 2 2

The corresponding building component of the decision

tree is as shown in figure 1:

Fig. 1. Decision tree with Outlook as splitting criteria at root node. With similar computations on tables VIII,IX and X, the

decision tree is obtained as shown in figure 2 with preprocessed codes replaced with their original values.

Fig. 1. Final Decision tree.

IV. EXPERIMENT To examine the effectiveness of our splitting criteria

on decision tree construction, we collected ten datasets from UCI machine learning repository, shown in Table XI.

TABLE XI CHARACTERISTICS OF DATA SETS

S.No Data set Number of Objects

Number of Attributes

1 Iris 150 4 2 Wine 178 13 3 Breast cancer 699 10 4 Blood

Transfusion 748 4

5 Abalone 4177 8 6 Ecoli 336 7 7 Yeast 1484 8 8 Page-blocks 5473 10 9 Wine red 1599 11 10 Pima-Indians 768 8

We built decision trees with three different splitting criteria: Information Gain of ID3, Gainfix of FID3 and CvGain proposed in this paper. The data sets with continuous values are discritized. When the data set is nominal integer codes are used. The data sets are randomly permuted and tenfold cross validation is administered. Each time the same partitions of the data sets are used for building and testing the decision trees. The philosophy of constructing decision tree algorithm is the same for all the three trees, only with difference in the selection criteria. Information Gain, GainFix and CvGain are used as the attribute selection criteria for ID3, FID3 and CvDT respectively. We computed the classification performance and the times taken for training the decision trees as well as for testing them. We performed t-test to verify the statistical significance of our results (we used a standard significance level of 0.05). The characteristics of the datasets are shown in the table XI. The datasets collected contain 150 tuples


5

as the least and 5473 tuples as the highest. The least number of attributes taken is 4 while the highest is 13. The results are shown in table XII.

The advantage of CvGain is revealed in the times

taken for decision tree generation. The generation times of CvDT are statistically significantly low when

compared to the other two methods. In the case of the larger datasets considered in this experiment like Abalone and Page-blocks, the reduction of time is more clearly visible. With Abalone, 338 and 1672 milliseconds of time is saved when compared with ID3 and FID3 respectively. Similarly with Page-blocks they are 118 and 2726 milliseconds. Hence it is expected that CvGain

TABLE XII

COMPARISON OF ACCURACY, TIMES FOR CVDT, ID3 AND FID3

Data Classification Performance Generation Time in ms Testing Time in ms

CvDT ID3 FID3 CvDT ID3 FID3 CvDT ID3 FID3 Iris 97.33 97.33 97.33 20.25 23.30 43.85 0.06 0.05 0.05

Wine 95.56 97.22 96.67 50.71 76.91 226.08 0.07 0.06 0.06

Breast cancer

99.43 99.86 99.71 68.39 92.77 280.32 0.24 0.22 0.23

Blood Transfusion

81.81 81.67 81.81 131.06 153.24 264.64 0.34 0.33 0.33

Abalone 85.80 85.76 85.76 1586.44 1924.62 3258.47 18.01 18.26 21.10

Ecoli 95.88 95.00 95.29 123.71 168.67 319.79 0.13 0.13 0.13

Yeast 92.16 92.30 92.50 792.69 1002.69 1975.08 5.41 5.31 5.27

Page-blocks

97.15 97.28 97.44 785.26 903.79 3511.66 18.95 18.80 18.30

Wine red 95.63 95.19 95.31 756.97 1071.02 2741.15 5.39 5.51 5.07

Pima-Indians

95.97 96.23 96.36 265.04 355.26 840.57 2.34 2.33 2.33

is suitable for applications which require the decision trees to be built in real time.

The Classification Performances are more or less equal for all the three trees. The observations based on the experiment are as follows:

1. Basically all the three trees are working on the

same partitions of the data in each the ten folds used in the experiment.

2. The same procedure is used to build the decision tree, with the variation in the splitting criteria.

3. The gain values of the attributes are different values with CvGain, Information Gain and GainFix. But the attribute with maximum gain value is the same with all the three methods for some of data sets. It is different for some of the data sets also.

4. It is possible that more than one attribute can have the maximum Gain value, and one of them is selected arbitrarily.

5. When the decision trees are verified, the decision trees built are the same for few data sets ( Tom Mitchell, Iris, Blood Transfusion, ) but different with all the other data sets. For some of the data sets even though the decision trees are different, it is observed that some sub trees are being the same. And few Attributes have interchangeable behavior in terms of selection for splitting.

The algorithm of the FID3 paper is also

implemented in Matlab environment, to make the readings comparable. Thus the Classification Performances reported in the table XII are not the same as reported in [4]. The time taken for testing the CvDT is also same as ID3 and FID3. But the time taken for

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-711) VOL.1 NO.6 JUNE

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generating decision tree using CvDT is significantly less when compared to ID3 and FID3. The paired t-test on generation times reveals statistical significance, indicating that CvDT construction time is significantly lower than ID3 and FID3. In fact CvDT is outperforming the other two methods in terms of generation time when the data sets are large in size. The following figure 3 with the data sets along the X-axes with increasing sizes reveals this.

Fig. 3. Comparison of Times taken for generating(TG) the three

decision trees X. CONCLUSION

The criterion for splitting a node in a decision tree decides the efficiency of a decision tree. So far Information Gain, Gain ratio, Gini index, Chi square statistic and Kappa index are used as the splitting criteria. CvGain is proposed and demonstrated as another splitting criteria in this paper. Coefficient of Variation (Cv), which is a measure of consistency of a distribution is used to compute CvGain. It has been observed that decision tree based on CvGain has the same performance as ID3 and FID3, but at less computational cost.

ACKNOWLEDGMENT

We thank Dr. Rajeev Wankar and P.S.V.S Sai Prasad

of University of Hyderabad, for their thoughtful comments and support.

REFERENCES

[1] Blake Ian F, (1979). “An Introduction to Applied Probability”, John Wiley & Sons.

[2] Breiman L., Friedman J., Olshen R., and Stone C (1984).. Classification and Regression Trees.Wadsworth International Group.

[3] Damghani K.Khalili, Taghavifard M.T., Moghaddam R. Tavakkoli (2009), Decision Making Under Uncertain and Risky situations, 2009 ERM Symposium, www.ermsymposium.org/2009/pdf/2009-damghani-decision.pdf

[4] Baoshi, Zheng Yongqing, Zang Shaoyu (2009), A New Decision Tree Algorithm Based on Rough Set Theory, IEEE, 2009 Asia-Pacific Conference on Information Processing.

[5] Fayyad U. M. and Irani K. B.( 1992), The attribute selection problem in decision tree generation. In Proc. 1992 National Conference on Artificial Intelligence (AAAI’92), pages 104–110, AAAI/MITPress.

[6] Han Jiawei and Kamber Micheline (2006), “Data mining Concepts and Techniques”, 2nd edition, Morgan Kauffman Publishers.

[7] Minz Sonajharia and Jain Rajni (2003), “Rough Set based Decision Tree Model for Classification”, LNCS 2737 Springer.

[8] Mitchell Tom (1997), Machine Learning. McGraw-Hill. [9] Pawlak Nsjn Zdzislaw (1991), Rough Sets – Theoretical Aspects

and Reasoning about Data, Kluwer Academic Publications. [10] Quinlan R. (1986),“Induction of decision trees”, Machine

Learning, Vol. 1, No. 1, pp.81-106. [11] Quinlan R. (1993),“C4.5: Programs for Machine

Learning”,Morgan Kaufmann Publishers. [12] Ramadevi Y, Rao C.R. (2008), Reduct based Decision Tree

(RDT), IJCSES International Journal of Computer Sciences and Engineering Systems, Vol.2,No.4

[13] Snedecor George W., Cochran William G, (1989), “Statistical Methods”, Eighth Edition, Iowa State University Press.


7

Use Of Braille Database For Design And Implementation Of Braille Handglove For

Deafblind People

M.Rajasenathipathi , Arthanariee A. M, and M.Sivakumar

Assistant professor in Computer Science , N.G.M. College-642001, Tamilnadu, India Director,Bharathidasan School of Computer Applications,Ellispettai-638116, Tamilnadu, India Doctoral Research Scholar, Anna University, Coimbatore, Tamilnadu , India

Abstract : Braille hand glove is one of the communication methods for the deaf blind. The Braille hand glove produces the vibration on the six position of the right hand of deaf blind. These six positions are matched to six values of Braille code. Here the user input is translated into Braille code by a conversion algorithm and the same is sent to hand glove to operate the corresponding vibration motors inside the glove. So instead of touching the raised dots in Braille sheet, this Braille hand glove produces vibration based on English character value. The hand glove vibration method sees to be most suitable medium for real-time communication for the benefit of deaf and blind people, who prefer to work in computer environment.

Keywords : Braille, Glove, Vibration, Cell

I INTRDUCTION

People who have both sight and hearing impairments are known as deaf blind. Because of their impairments they face many problems in their normal daily life. It is particularly difficult for totally deaf and blind people to acquire vital and sufficient information necessary for daily living, compared with sighted hearing people. To obtain information for living, Braille glove vibration method is one de`vice for the benefit of deaf-blind people, who work in computer environment. There are several communication methods that involve tactile sensation, such as finger Braille, manual alphabets and the print on palm method. However, some problems arise in such conversion, such as lack of privacy for deaf blind people and not suitable for computer environment. Therefore focus has been on vibration in six different positions which matches to Braille code.

II THE BRAILLE SYSTEM

The Braille code was adapted by Louis Braille in the early part of the nineteenth century from a military system which used raised dots to send messages at night. After competition with other raised systems earlier this century, it has become the main system for

the majority of those blind people who read and write using tactile means, and can be found in many countries around the world. Braille uses the raised dots in groups of six which are arranged in three rows of two and which are numbered from 1 to 6.

Fig 1. Braille cell Fig 2. letter M value

These six positions which can be raised or flat, are used in combination to give just 64 different Braille characters. This clearly means that there cannot be one to one correspondence between Braille characters and text Braille code.

1 4

2 5

3 6


8

As mentioned earlier Braille generally consists of cells of six raised dots arranged in a grid of two dots horizontally by three dots vertically. The dots are conventionally numbered 1,2 and 3 from the top of the left column and 4,5 and 6 from the top of the right column. The presence or absence of dots given the coding for the symbol. English Braille is used to code the letters, punctuation symbol, some double letter signs and word signs directly but capital letters and numbers are dealt with by using a prefix symbol as follows

III BRAILLE CODE TRANSLATION

The steps which are followed when any English text is converted to Braille code are as follows

1. Read the input value up to the enter key 2. Separate the words on the basis of blank space 3. Break the corresponding word into corresponding

letter 4. Access the Braille database based on the following

major condition

(i) Input value is between ‘a’ to ‘z’ (ii) Input value is between ‘A’ to ‘Z’ (iii) Input value is between ‘0’ to ‘9’ (iv) Input value is in special symbol list (v) If character matches, then print

corresponding Braille code as it is (vi) Repeat steps 4 and 5 until all the

characters of input values are matched with database. If match does not occur then appropriate error messages are generated.

By following the above mentioned steps, we will be able to convert English to Braille code. This conversion is totally based on one to one matching. The flowchart for the same is as follows

.

Fig 3: Braille Alphabet

Flow chart for conversion of English text to Braille code


9

If str[i] between

If str[i]

Betwee

If str[i]

Betwee

If str[i]

In symbol

Print lowercase Braille code

Print uppercase Braille code

Print digits Braille code

Print symbols Braille code

No

Yes Yes Yes Yes

No No No

No

Yes

Assign L = 1

Read Input values in Str

Access character Str[i]

L=L+1

End

start

Print error message

If str[i]==NULL


10

IV SOFTWARE IMPLEMENTATION

In standard Braille, all sixty four cells will correspond to a letter of the roman alphabet. When we convert English text to Braille code then the above conversion chart is used as the database and the input text is matched for the corresponding Braille representation, Braille is displayed. Input and output window will look like as in fig no.

Fig 4 : Screen layout for Braille code conversion

V DESIGN OF BRAILLE HAND GLOVE

Fig 5: Hand glove with six positions

Braille hand glove principle is based on six dots. The six dots forming the cell permit sixty three different patterns of dot arrangements. It is matched with

lphabets, numbers and special symbols of the English language. The Braille glove contains six vibration motors. These are fixed in five fingers and center palm. The basic technique used in the hand glove is based on the ASCII value of English letter from the user typed input in the keyboard. It is converted into Braille value and it activates the corresponding motors. So based on the position of vibration the blind person can understand the value of the letter. For example if the user types the letter “r”, it is converted to Braille value as 1,2,3,5 and this value activates the corresponding motors in Braille hand glove. This conversion program is written in hi tech C language and it is recorded in micro controller of the hand glove. Any blind person can wear this glove in right hand, and understand the English letters through vibration instead of touching the Braille sheet. Similarly the whole word or sentence is converted into Braille vibration and send to blind person. Based on this method the visible person and deaf and blind person can communicate effectively.

VI THE DESIGN CONCEPT

The Braille Hand glove comprises the following key components

1. 89C51 Micro controller 2. RS 232 C 3. Relay Driver and Relay 4. power supplies 5. Vibrator motor in hand glove

Fig. 6 Block diagram of Braille hand glove

89C51

PC

Relay

driver- 6

Relay - 6 Vibrator -

6

RS 232 C


11

Table 1 ASCII and Binary value for Braille

VII HARDWARE IMPLEMENTATION

The main component in Braille glove is vibration motor. it is configured in coin type motor, is a simple brush motor with a traditional axial design. The eccentric movement of the weight attached to the rotor provides vibration during operation. The amount of vibration is directly proportional to the voltage applied to the motor. Cylinder motors are manufactured in high volumes and are fairly inexpensive. An electrical current applied to the coil in the direction of the arrow generates upward force on the left side of the coil and downward force on the right side, causing the coil to revolve clockwise.

The basic technique used in the hand glove based is based on the ASCII value of English letter from the user typed in input box of the editor. After clicking the serial or parallel mode in the editor, the input English characters are converted into Braille value and activate the corresponding motors. So based on the position of vibration the blind person can understand the value of English letter. Any blind person can wear this glove in right hand, and can understand the English letters through vibration

.Fig 7: Hardware prototype

VIII TESTING OF THE SYSTEM

Both hardware and software Translation programs were written in an incremental fashion, testing and verifying each section of code. This starts with reading the rules from a text file, separating them into fields and matching each field and applying the rule. Consequently debugging and corrections were made to the code at all steps of development. Both programs work well and have not had any fatal errors. The execution time of the programs in software part and vibrations in hardware are done under a few seconds making it acceptable for the Blind people to use.

For the testing of the system we have translated English newspapers heading into the corresponding Braille text and Braille vibrations. We found that it is with 100% accuracy for Braille text conversion and Braille glove is working with 100% accuracy in vibration for corresponding position. Also every conversion is done automatically. The

ASCII

Value

ASCII Character

Braille

Character

Binary Representati

on

D6

D5

D4

D3

D2

D1

32 (space

) 0 0 0 0 0 0

33 ! ! 1 0 1 1 1 0

34 “ " 0 1 0 0 0 0

35 # # 1 1 1 1 0 0

36 $ $ 1 0 1 0 1 1

37 % % 1 0 1 0 0 1

38 & & 1 0 1 1 1 1

39 ‘ ' 0 0 0 1 0 0

40 ( ( 1 1 0 1 1 1

41 ) ) 1 1 1 1 1 0

42 * * 1 0 0 0 0 1

82 R R 0 1 0 1 1 1


12

Translation of English text to Braille vibration is shown in the table 2.

Table 2 : Two modes in Braille Vibration

The little poor accuracy of the translation in serial mode was mainly due to mixing of digits and alphabets. It can be rectified by increasing the buffer size and by decreasing the speed of vibration in hand glove .

IX CONCLUSION AND FURTHER WORK SUGGESTED

The development of low cost Braille hand glove is necessary for visually impaired community. The same Translation technique can be used in various languages like Bengali, Hindi, Tamil ,French, etc., Also it proposes a new approach to blind persons to know about computer oriented technologies. The feedback from visually impaired community is that Braille hand gloves are the best kit for two way communication . This technology if upgraded can prove to be a boon for the visually impaired community so that they can perform better and on par with the visible people

The reverse engineering process for the same problem which produces Braille signal to English text, if constructed can prove to be an effective two way communication tool in online chatting and new effective teaching methodology for physically impaired people.

X REFERENCES

[1] Pradip k Das and Atal Chaudhuri,” A computerized Braille Tran scripter for the Visually handicapped ” ,IEEE-EMBS 1995

[2] Kenneth R Ingham, “Braille, the language, its machine Translation and Display”, IEEE Transactions and Man Machine systems 1998.

[3]. c. Moore and I Murray, “An Electronic design of a low cost Braille type writer” , International Conference, Perth, western Australia, 18-21 November 2001

[4] Paul Blenkhorn,” A system for converting print into Braille”, IEEE on Rehabilitation Engineering. Vol5 No2 ,June 1997

[5] Paul blenkhorn and Gareth evans, “Automated Braille production from word processed documents”, IEEE transactions on neural systems and rehabilitation engineering,Vol9 No1, March 2001

[6] Murray and T Dias, “A portable device for optically recognizing Braille”, International Conference, Perth, western Australia,18-21 , November 2001

[7] F.E.Sandne and Y.P.huand, “C hord level error correction for portable Braille devices”, Electronics letters , vol 42,No 2 January 2002

[8] Gael Dias and Bruno conde, “Towards web browsing for Visually impaired people”, International Conference of information Technology (ITNG,07)- 2007

[9] Gael Dias and Bruno conde, “Towards web browsing for Visually impaired people”, International Conference of information Technology (ITNG,07),IEEE,UK,2007

[10] Myung-chul cho and Hoo-gon choi, “Pair of Braille based chord gloves,” IEEE computer soci ety, 2002

[11] Yasuhrio Matsuda and Isomura, “ Finger Braille Recognition system for people who communicate with deaf”, Proceeding of IEEE international conference on 2008

[12] Anupam Basu and soumitro Banerjee, “A Pc based Braille library system for the sightless”, IEEE Transactions on Rehabilitation Engineering vol 6 no1 March 1998

[13] Makoto Tanaka and Hideaki Goto,” Text Tracking wearable camera system for visually impaired people”, IEEE 2008

[14] Mu chun su and Yu chine wang, “Portable communication aid for deaf-blind people”, Computing and control Engineering journal February 2001

[15] David A Fisher and C.Bond,” A single handed Braille chord system for computer Keyboard input”, IEEE 1992

[16] Paul blenkhron, “A system for converting Braille into print” IEEE Transactions on rehabilitation Engineering vol3 no 2 June 1995[17] Lgmo Koo and Ryel Choi, “Wearable Fingertip Tactile Display”,SICE International Conference , Korea, 2006

[18] Manip and Akria,” Analysis of prosody in finger Braille using electromyography”, IEEE EMBS International Conference, US Aug 30 sep 3 ,2006

MODE Total number of words

Percentage of words translated correctly

software Hardware

Serial mode 876 99.62 99.12

Parallel mode

992 100 100


13

Development of Reliable Multihomed Scatternet Network R.Dhaya, Dr.V.Sadasivam, Dr.R.Kanthavel

Lecturer, National Engineering College, Kovilpatti, Tamilnadu ,India. Professor, M.S.University, Tirunelveli, Tamilnadu, India.

Teaching Research Associate, Govt. College of Engineering, Tirunelveli, Tamilnadu, India.

Abstract— Since wireless networks are movable and

flexible, the conventional protocols are standing b ehind fault tolerance problems. A new Stream control transmission protocol (SCTP) is a transport layer p rotocol which is reliable, message-oriented data trans port protocol that supports multiple streams to pr event head-of-line blocking and multihoming for end-to-end network fault-tolerance. A host is multi homed if it can be addressed by multiple IP addresses. SCTP multihoming allows connections, or associations to remain alive even when an endpoint’s IP addre ss become unreachable. In a multihomed host there wil l be at least two IP addresses. SCTP uses one IP for a p rimary path and the other IP for secondary path. Initial ly, SCTP uses the primary path for transmission of data. If the primary path fails then the secondary path is chosen for further transmission. Similarly if the secondar y path fails then the primary path is chosen for furth er transmission. On the other hand Bluetooth Scatterne t refers to a collection of Bluetooth piconets. The p roposed Bluetooth Scatternet system uses the multihoming concept of SCTP for effective fault tolerance durin g data transmission.

Keywords: Multihoming, Scatternets, Piconets,Bluetooth.

I. INTRODUCTION

Bluetooth is a wireless protocol for exchanging data over short distances from fixed and mobile devices, creating personal area networks (PANs). It can connect several devices, overcoming problems of synchronization. Bluetooth uses a radio technology called frequency-hopping spread spectrum, which chops up the data being sent and transmits chunks of it on up to 79 frequencies. It can achieve a gross data rate of 1 Mb/s [2]. Bluetooth provides a way to connect and exchange information between devices such as mobile phones, telephones, laptops, personal computers,

printers, Global Positioning System (GPS) receivers and digital cameras.

Bluetooth protocols assume that a small number of units will participate in communications at any given time. These small groups are called piconets, and they consist of one master unit and up to seven active slave units. The master is the unit that initiates transmissions, and the slaves are the responding units[13].

As with piconets, where multiple Bluetooth devices are able to connect with each other in an ad-hoc manner, so too can multiple piconets join together to form a larger network known as a scatternet [12]. Bluetooth devices must have point-to-multipoint capability in order to engage in scatternet communication, and several piconets can be connected to each other through one scatternet [4]. Furthermore, a single Bluetooth device may participate as a slave in several piconets, but can only be a master in one piconet.

II. PROPOSED SOLUTION

The main objective of this paper is to perform the simulation of SCTP in Bluetooth Scatternets which of course, is a wireless network. A Bluetooth Scatternet consisting of two piconets is established. Each piconet has one master and one slave. The slave called “Slave-bridge” connects the two piconets. “Connects” here means that this slave-bridge acts as the medium through which data transmission will take place between the two Masters. This suggests that whenever there is a path breakage, SCTP will automatically detect the path


14

failure and an alternate path can be used by means of heartbeat signals.

A. DEVICE DETECTION

In mobile ad hoc environments, devices initially have no information about their surrounding environment or the devices that operate within their range. There is no centralized instance to query about the environment. Therefore, a protocol must exist that provides means for detecting devices and enables devices to set up a connection, Bluetooth uses the Base band protocol for this task . Two procedures are used in the device discovery procedure; inquiry and page.

B. PROXIMITY PROCESS

In order to set up a connection, a device must detect what other devices are in range. This is the goal of the inquiry procedure. The process is initiated by the unit that wishes to collect device information or create a connection. To conserve power and coexist with other link activity, inquiry is always initiated by higher level control protocols. The inquiry procedure must overcome the initial frequency discrepancy between devices. Therefore, inquiry only uses 32 of the 79 hop frequencies [3]. Typically a device enters inquiry mode periodically. Similarly, a device that wishes to be visible to inquiring units enters inquiry scan in certain intervals. In order to find each other, one device must be in Inquiry state and one (or more) device must be in Inquiry Scan sub-state simultaneously.

C. CONNECTION ESTABLISHMENT

In Bluetooth the connection establishment is handled by the page process. The page process requires knowledge of the BD_ADDR of the device with which the connection is to be established. Furthermore the device being paged must be in Page Scan sub-state, i.e. listening for page messages. At the end of the page process a connection has been set up, the paging device becomes the master and the paged device becomes the slave. As with inquiry a device typically enters Page state periodically and a device that wishes

to be able to connect to paging units enters page scan in certain intervals.

D. SCATTERNET USERCASE

In this scenario the mobile phone functions as both a master and a slave . In order for this to work, regardless of data speed, an effective scatternet implementation is required.

E. PICONET VS SCATTERNET:

A piconet is the type of connection that is formed between two or more Bluetooth-enabled devices. However, when a piconet is formed between two or more devices, one device is dynamically elected to take the role of 'master', and all other devices assume a 'slave' role for synchronization reasons. Piconets have a 3-bit address space, which limits the maximum size of a piconet to 8 devices (23 = 8), i.e. 1 master and 7 slaves [3]. A piconet allows one master device to interconnect with up to seven active slave devices (because a three-bit MAC address is used). Up to 255 further slave devices can be inactive, which the master device can bring into active status at any time. A piconet typically has a range of about 10 m and a transfer rate between about 400 and 700 kbit/s depending on whether synchronous or asynchronous connection is used.

A scatternet is a type of ad-hoc computer network consisting of two or more piconets. A scatternet is a number of interconnected piconets that supports communication between more than 8 devices . Scatternets can be formed when a member of one piconet (either the master or one of the slaves) elects to participate as a slave in a second, separate piconet. The device participating in both piconets is known as slave-bridge which can relay data between members of both ad-hoc networks. Using this approach, it is possible to join together numerous piconets into a large scatternet, and to expand the physical size of the network beyond Bluetooth's limited range.

III. EXISTING PROBLEM

Besides all the explanations mentioned above, SCTP still has some existing shortfalls. The existing problem is, however not involved with fixed network with


15

fixed hosts i.e., the normal connections that involves connections using cables. The existing problem is mainly with the manipulation of data transmission in fixed network with mobile hosts and with wireless networks [10].

Presently TCP is mainly used in wireless networks. But TCP does not support multihoming. It supports only one IP per host that greatly reduces the fault tolerance level of the connection. Also there is a great possibility for congestion to occur[11]. The same problem occurs when we use UDP as the transmission protocol.

Introduction

IV. STREAM CONTROL TRANSMISSION PROTOCOL

Stream Control Transmission Protocol (SCTP) is an end-to-end transport protocol that provides services heretofore unavailable from either of the workhorse transport protocols[9].

A. NEED FOR SCTP

TCP has performed immense service as the primary means of reliable data transfer in IP networks. However, an increasing number of recent applications have found TCP too limiting, and have incorporated their own reliable data transfer protocol on top of UDP [8]. The limitations which users have wished to bypass include the following:

• TCP provides both reliable data transfer and strict order-of transmission delivery of data. Some applications need reliable transfer without sequence maintenance, while others would be satisfied with partial ordering of the data [5]. In both of these cases the head-of-line blocking offered by TCP causes unnecessary delay.

• The stream-oriented nature of TCP is often an

inconvenience. Applications must add their own record marking to delineate their messages, and must make explicit use of the push facility to ensure that a complete message is transferred in a reasonable time.

• The limited scope of TCP sockets complicates

the task of providing highly-available data transfer capability using multihomed hosts.

• TCP is relatively vulnerable to denial of service

attacks, such as SYN attacks. Transport of PSTN signalling across the IP network is an application for which all of these limitations of TCP are relevant[6] .

Two key problems surfaced in the use of TCP:

• Head-of-line blocking - a problem where sending independent messages over an order-preserving TCP connection causes delivery of messages sent later to be delayed within a receiver’s transport layer buffers until an earlier lost message is retransmitted and arrives thus resulting in undesirable call setup failure[5].

• Multihoming - where a host with multiple points

for redundancy purposes, does not want to wait for a routing convergence to communicate critical messages to its peer communication endpoint. For call control signalling, such delay is unacceptable when an alternate available path exists[1]. A TCP connection only binds a single point of attachment at either end point.

TABLE 1: COMPARISON OF SCTP SERVICES AND FEATURES WITH THOSE OF TCP AND UDP.

SERVICES/FEATURES SCTP TCP

UDP

Connection – oriented Yes Yes No

Full Duplex Yes Yes Yes

Reliable Data Transfer Yes Yes No

Partial Reliable Data Transfer

Optional No No


16

Flow control Yes Yes No

TCP Friendly Congestion Control

Yes Yes No

ECN Capable Yes Yes No

Ordered data delivery Yes Yes No

Unordered data delivery Yes No Yes

Path MTU discovery Yes Yes No

Message fragmentation Yes Yes No

Message bundling Yes Yes No

Multistreaming Yes No No

Multihoming Yes No No

Reachability check Yes Yes No

SCTP monitors the paths of the association using a built-in heartbeat [7] as shown in Table 1; upon detecting a path failure, the protocol sends traffic over the alternate path. It's not even necessary for the applications to know that a failover recovery occurred.

V. EXPERIMENTAL WORKS :BASIC STRUCTURE OF THE PROPOSED SYSTEM

The experiment consists of:-

• Two Piconets, each with a Master and a Slave • First Piconet consists of Master1 with two IP

addresses: IP 0.1 and IP 0.2 • Second Piconet consists of Master2 with two IP

addresses: IP 6.1 and IP 6.2 • Slave Bridge connects both the Piconets • Primary path is (if node 1 is to be failed)

o Master1 IP 0.1 Slave Bridge IP 6.1 Master2

• Alternate path is o Master1 IP 0.2 Slave Bridge

IP 6.1 Master2 • Path for acknowledgement is

o Master2 IP 6.2 Slave Bridge IP 0.2 Master1

A. WORKING OF THE PROPOSED SYSTEM

The main working of this paper is as stated above. According to the node selected, the primary path is assigned. For example, if we select node 1 to fail, the path 01346 will be selected as the primary path. If we select node 5 to fail, the path 02356 will be selected as the primary path. Similarly, the heartbeat also depends on the node to be failed. When we select the node to be failed, the heartbeat will start flowing through its complementary IP address of the corresponding host. For example, if node 1 is selected then heartbeat signals will flow through 32 and 20 (Node 2 is the complementary IP address for node 1).

Example 1:If node 1 fails, then the Primary path (01346) will fail. This means that no data transmission can occur through the primary path. Now stop the heartbeat signals through 32 and 20. As soon as the heartbeat signals stop, data transmission will continue through the secondary path (02346). Note here that only the IP address of the failure node is replaced with its complementary IP address (1 is replaced with 2), but not necessarily for the other host (4 is not replaced with 5). After all data transmissions are over, acknowledgement signals will be started from the Receiver (Master2) to the Sender (Master1) via Slave-bridge (65320).

Example 2:If node 2 fails, then the Primary path (02356) will fail. This means that no data transmission can occur through the primary path. Now stop the heartbeat signals through 31 and 10. As soon as the heartbeat signals stop, data transmission will continue through the secondary path (01356). Note here also that only the IP address of the host that fails is replaced with its complementary IP address (2 is replaced with 1), but not necessarily for the other host (5 is not replaced with 5). After all data transmissions are over, acknowledgement signals will be sent from the Receiver (Master2) to the Sender (Master1) via Slave-bridge (65320).


17

Fig 1: Proposed Bluetooth Scatternet with two piconets

Figure 1 shows our proposed network having two piconets. Node-0 and node-6 are the masters in each piconet. As we can see, the slave-bridge lies in the intersection of the two piconets. This means that it lies within the data transmission range of Master1 as well as Master 2. Note that from the piconets, Master1 and Master 2 are not in each others range for data transmission.Node-1 and node-2 are the IP addresses of Master1, node-4 and node-5 are the IP addresses of Master 2. Thus we have justified that both Master1 and Master 2 are multihomed. Data transmission takes place through one of the IP addresses of Master1, passes through the slave-bridge, and then reaches Master2 through one of its IP addresses. This is referred to as the “Primary path”. When path breakage occurs, data transmission will take place by replacing the failed IP address with its complementary IP address of the corresponding host.

This is illustrated in the following algorithm proposed in

figure 2:-

Fig 2: Proposed algorithm

V. EXPERIMENTAL RESULTS

A. INITIALIZATION

Node 0 checks for other nodes which are within its range and is illustrated in Fig 3. Here Node 1, Node 2 and Node 3 are within the range of Node 0. So Node 0 can transmit data to these three nodes.

Fig 3: Node 0 inquires its neighbouring nodes

B. DATA TRANSFER THROUGH PRIMARY PATH

Step 1 : Initialize the Bluetooth Scatternet.

Step 2 : Start Data transfer through Primary Path.

Step 3 : Start Heartbeat signals through alternate

path.

Step 4 : If Primary path fails-

• Stop Data transfer through

Primary path.

• Search for alternate path using

Heartbeat signals.


18

After initialisation the data starts transferring from the sender to the receiver through the Primary Path. Here the Primary Path is 01346 as shown in the Figure 4. Here the Primary Path is 01346 because the node to be failed is selected as Node 1.

Fig 4: Data transfer through Primary Path

C. HEARTBEAT SIGNALS THROUGH 32 AND 20

After data starts transferring through the Primary Path the Heartbeat signals are sent through the paths 32 and 20 which is shown in Figure 5. The heartbeats check whether these two paths are alive or not. This is done because when the Primary Path fails these two paths will serve as the alternate path.

Fig 5: Heartbeat signals through alternate path

D. PATH FAILURE DUE TO NODE 1 (PACKET LOSS)

When data transfers through the Primary Path, there occurs a path failure due to Node 1 which results in Packet Loss in Node 1 and which is shown in Figure 6. Data can no longer transfer through the Primary Path.

Fig 6: Path failure due to node 1

E. DATA TRANSFER THROUGH SECONDARY PATH

Due to the Primary Path failure, we cannot transfer any data through it. So we use the Secondary Path as shown in Figure 7 for transferring the remaining data.The Secondary Path transfers all the remaining data from the sender to the receiver. Here the Secondary path is taken as 02346.

Fig 7: Data transfer through Secondary Path


19

F. ACKNOWLEDGEMENT FROM RECEIVER TO SENDER

After all the data transmissions are over, we send the acknowledgements through a separate path. This is illustrated in Figure 8. Here the path 65320 is used for transferring acknowledgements from receiver to sender.

IV. GE Fig 8: Acknowledgement from Receiver to Sender

G. X-GRAPH : NODE VS TRANSMISSION TIME (PRIMARY AND SECONDARY PATH)

The Figure 9 graph compares the efficiency of

data transfer through Primary and Secondary path.

Fig 9 : Node Vs Transmission Time (Primary and Secondary path)

TABLE 2: NODE VS TRANSMISSION TIME (PRIMARY PATH)

X-AXIS (NODE)

Y-AXIS

(TRANSMISSION TIME)

0 0.221539

1 0.456455

3 0.556162

4 0.682935

6 0.691748

In Table 2, Node(N) is taken in X-axis and Transmission Time is taken in the Y-axis. Node(N) specifies the nodes forming the Primary Path. Transmission time specifies the time at which every node receives a particular packet that is sent from sender to receiver. Here the time taken by Primary Path to send a single packet from sender to receiver is around 0.46.

TABLE 3: NODE VS TRANSMISSION TIME (SECONDARY PATH)

X-AXIS

(NODE)

Y-AXIS

(TRANSMISSION TIME)

0 3.086787

2 3.111769

3 3.190826

4 3.262293

6 3.273045

In the above Table 3, Node(N) is taken in X-axis and Transmission Time is taken in the Y-axis. Node(N) specifies the nodes forming the Secondary Path. Transmission time specifies the time at which every node receives a particular packet that is sent from sender to receiver. Here the time taken by Secondary Path to send a single packet from sender to receiver is


20

around 0.20. So from the tables we can find that Secondary Path is more efficient than Primary Path.

H. 5.8.2 NODE VS TRANSMISSION TIME (PATH FAILURE)

This Figure 10 shows the path failure due to node 4. In x-axis we take the nodes forming the primary path. In y-axis, we take the transmission time from sender to receiver. As we can see from the graph, when node 4 fails the transmission through the Primary path is ended.

Fig 10: Node Vs Transmission Time (Path failure)

TABLE 4: NODE VS TRANSMISSION TIME (PATH FAILURE)

X-AXIS

(NODE)

Y-AXIS

(TRANSMISSION TIME)

0 1.91539

1 2.156455

3 2.356162

4 2.8023

6 ---------

In the above Table 4, we take Node (N) in X-axis and Transmission Time is taken in the Y-axis. Node (N) specifies the nodes forming the Primary Path 01346. Data starts transferring through the Primary path. It reaches Node 4 at the time 2.8023. At

the meantime, Node 4 fails due to packet loss. So no more data can be transferred through Node 4.Hence the data does not reaches Node 6. This is the reason why Node 6 has no transmission time.

VI. CONCLUSION

A Bluetooth Scatternet has been established. Multi-homing facility of SCTP has been implemented in the established Bluetooth Scatternet. Hence congestion is avoided in our Proposed System. From the experimental result it is seen that secondary path is more efficient than Primary path. Secondary path transfers data two times faster than the Primary path. So even if Primary path fails, data transmission through Secondary path will be very efficient and reliable. SCTP thus increases the fault tolerance level during data transmission in Bluetooth Scatternets. Increased ACK traffic due to large amount of data transmission can be avoided by providing separate path for ACK. In our Proposed System, we provided a separate path for ACK. Hence ACK traffic has been avoided.

In our proposed system we have two IP addresses per host. But we can increase the number of IP addresses more than two for each host so as to obtain more than two paths for data transmission in order to increase the fault tolerance level.

REFERENCES [1] Abd EI AI ,Saadawi, and M. Lee., LS_SCTP: A Bandwidth Aggregation Technique For Stream Control Transmission Protocol. Computer Communication, Vol 27, No 10, pp 1012-1024,2004.

[2] Baatz, M. Frank, C. Kühl, P. Martini and C. Scholz, “Bluetooth Scatternets: An Enhanced Adaptive Scheduling Scheme”, Proc. IEEE INFOCOM’02,pp 789-790, 2005.

[3] Basagni, Bruno and Petrioli, A Performance Comparison of Scatternet Formation Protocols for Networks of Bluetooth Devices, Proc. IEEE International Conference on Pervasive Computing and Communications (PerCom) Texas, pp 93-103, 2005.

[4] Bhagwat and Segall A, A routing vector (RVM) for routing in Bluetooth scatternets, Proc. IEEE Int. Workshop on Mobile Multimedia Communications MoMuC,pp 375-379, 1999.

[5] caro A, Amer P ,Iyengar J and R Stewart, Retransmission policies with transport layer multihoming In IEEE ICON 2003, pp 255-260, 2003.

[6] Daoud K. , Guillouard, K., Herbelin, P. and Crespi, N, A Network-Controlled Architecture for SCTP Hard Handover,IEEE Conference on Vehicular Technology, pp 1-5,2010. [7] Iyengar J , shah K,Amer P, and Stewart Concurrent multipath transfer using SCTP multihoming. In SPECTS 2004, San Jose.Califonia,pp 74-81, 2004.


21

8] Iyengar J, Amer P and Stewart R, Concurrent multipath transfer using transport layer multihoming: performance under varying band width proportions. In Milcom Vol 1,pp 238-244, 2003.

9] Jinsuk Baek Fisher, P.S. Minho Jo and Hsiao-Hwa Chen, A Lightweight SCTP for Partially Reliable Overlay Video Multicast Service for Mobile Terminals , IEEE Transactions on Multimedia, Vol 12,No 7,pp 754 - 766 ,2010. [10] Riccione, RG-SCTP: Using the relay gateway approach for applying SCTP in vehicular networks,The IEEE symposium on Computers and Communications, pp 234-239,2010.

[11] UC Berkeley, LBL,USC/ISI, AND Xerox parc.ns2 documentation and software, version 21b8,2001,

[12] Wang Y , Stojmenovic I and LI X Y, Bluetooth Scatternet Formation for Single-hop Ad Hoc Networks Based on Virtual Positions, in Proc. 9th IEEE Symposium on Computers and Communications ISCC’2004,pp 1-17 , 2006.

[13] Wang Y , Stojmenovic I and LI X Y, Partial Delaunay, Triangulation and Degree Limited Localized Bluetooth Multihop, Scatternet Formation, IEEE Transactions on Parallel and Distributed, Systems, Vol 15, No 4, pp 350-361, 2006.


22

Peristaltic Pumping Of a Micropolar Fluid In An Inclined Channel

*R.Hemadri Reddy, *A.Kavitha, **S.Sreenadh, **P.Ha riprabakran

*School of Advanced Sciences, VIT University, Vellore- 632014,T.N, INDIA.

**Department of Mathematics, Sri Venkateswara University, Tirupati- 517502.A.P.INDIA.

Abstract-Peristaltic pumping of a non-Newtinian micropolar fluid in an inclined channel is studied.

The analysis has been carried out in the wave frame of reference with long wavelength and zero Reynolds number assumptions. The velocity distribution and stream functions are obtained. The pressure rise (drop) over one wavelength is obtained. The velocity field, the stream function, , the volume flux and the pressure rise over one cycl e of the wave and frictional force are obtained and o n the flow quantities are discussed graphically.

Keywords: Peristaltic transport; micropolar fluid; volume flow rate; pressure rise.

I. INTRODUCTION

Peristaltic pumping is a form of fluid transport, generally from a region of lower to higher pressure, by means of a progressive wave of area contraction or expansion which propagates along the length of a tube-like structure. Peristalsis occurs naturally as a means of pumping biofluids from one place of the body to another. This mechanism occurs in the gastrointestinal, urinary and reproductive tracts and many other glandular ducts in the living body. The early reviews of Ramachandra Rao and Usha [1] , Jaffrin and Shapiro[2] , Manton [3], Brasseur et al. [4], Srivastava and Srivastava [5], Provost and Schwarz [6], Shukla and Gupta [7], Misra and Pandey [8] ,Rao and Rajagopal [9],Kavitha et.al[10] Vajravelu et al. [11-15], Subba Reddy[16&17], Srinivas [18,19] deal with the peristaltic transport of viscous fluids through tubes and channels having impermeable flexible walls.

In classical continuum theory a body is assumed to be a dense collection of point masses in which there is no internal structure. In the motion of a volume element

v∆ it is assumed that the individual motions of material points coincide with the motion of centre of mass of the

volume element v∆ . In this case the density ρ of the

volume element v∆ is independent of the size of v∆ and independent on it’s location in space and the time t. Eringen [20] reported that this is not true

as v 0∆ → .The density ρ shows an increasing

dependence on the size of the v∆ , when v∆ is less

than a critical value v *∆ . Classical continuum theory cannot explain the mechanical behavior of rheologically complex fluids, such as liquid crystals, colloidal fluids and blood. Due to this fact a new approach was necessitated. There are several approaches to the formulation of microcontinuum theories of fluids such as simple deformable directed fluids, dipolar fluids, polar fluids, simple micro-fluids, micropolar fluids, etc. All these consider the existence of couple stresses and body couples.

Eringen [20,21] reported the theory of micropolar fluids in which the fluid micro elements undergo rotations without stretching. Micropolar fluids are superior to the Navier-Stokes fluids and they can sustain stresses and body couples. Here the micro particles in

the volume v∆ rotate with an angular velocity about the centre of gravity of the volume in an average sense and is described by the micro rotation vector

.Ω The micropolar fluids can support stress and body

couples and find their applications in a special case of


23

fluid in which micro rotational motions are important. Airman and Cakmak [22] discussed three basic viscous flows of micropolar fluids. They are Couette and Poiseuille flows between two parallel plates and the problem of a rotating fluid with a free surface. The results obtained are compared with the results of the classical fluid mechanics. Srinivasacharya et al.[23] made a study on the peristaltic pumping of a micro polar fluid in a tube. The gravitational effects are also important in peristaltic pumping. In view of this, we have considered the peristaltic pumping of a micropolar fluid in an inclined channel. This mathematical model may be useful to have a better understanding of the physiological systems such as blood vessels. The velocity field, the stream function, the volume flow rate and the pressure rise are obtained and results are discussed through graphs.

II.MATHEMATICAL FORMULATION and SOLUTION

Consider the peristaltic pumping of a micropolar fluid in an inclined channel of half-width ‘a’. A longitudinal train of progressive sinusoidal waves takes place on the upper and lower walls of the channel. For simplicity we restrict our discussion to the half-width of the channel as shown in figure. (1)

The wall deformation is given by

( ) ( )2H X, t a bsin X ct

π= + −λ

(1)

Where b is the amplitude, λ is the wavelength and c is

the wave speed.

Figure 1. Physical model

EQUATIONS of MOTION

Under the assumption that the channel length is

an integral multiple of the wavelength λ and the

pressure difference across the ends of the channel is a constant, the flow becomes steady in the wave frame

( )x, y moving with velocity c away from the fixed

(laboratory) frame ( )X, Y . The transformation between

these two frames is given by

X= X-ct; y= Y; u(x, y)=U(X-ct,Y); v(x,y)=V(X-ct,Y) (2)

Where U and V are velocity components in the laboratory frame and u, v are velocity components in the wave frame. In many physiological situations it is proved experimentally that the Reynolds number of the flow is very small. So, we assume that the flow is inertia-free. Further, we assume that the wavelength is infinite.

USING the NON-DIMENSIONAL QUANTITIES.

2u x y pa a Hu ; x ; y ; p ; ; h

c a c c a

Ω= = = = Ω = =λ λ µ

The non-dimensional form of equations governing the motion (dropping the bars) is


24

( )2

2

u pN 1 N sin 0

y y x

∂ ∂Ω ∂+ − − + η α =∂ ∂ ∂

(3)

2

2 2

2 N u2 0

m y y

− ∂ Ω ∂− − Ω =∂ ∂

(4)

Where k

Nk

=µ +

is coupling number

Ω is the micro rotation velocity

u is the velocity

µ is the viscosity of the fluid

k is the micropolar viscosity

m is the micropolar parameter

p is the fluid pressure

The non-dimensional boundary conditions are

u

0y

∂ =∂

at y = 0 (5)

0y

∂Ω =∂

at y = 0 (6)

u = -1 at y= h(x) (7)

0Ω = at y= h(x) (8)

SOLUTION

The general solution of (3) and (4) is given by

Nu

m

−= ( ) ( )( )

1

2 3 42

21 N P sin 2AA sinhmy A coshmy y A

2 N my

− −η α+ + + +

−

(9)

Where p

Px

∂=∂

( )( )

12 3 2

1 N p sin AA coshmy A sinhmy y

2 N m

− −η αΩ= + − − −

(10)

using the boundary conditions (5) to (8) in (9) and (10),we obtain the velocity of the fluid and micro rotation velocity as

( )( )

21 2

1 N p sin Nu Ssinhmy coshmy y S 1

2 N m

− −η α = − + + − − (11)

where 11 2

3

2N DS

m S

−=

21 12 2 2

3 3

D 2ND2N sinhmh h NcoshmhS h

m S S mm m = + − + −

3S 2cosh mh N= −

1D mh sinh mh= −

( )( )

12 3 2

1 N P sin AA coshmy A sinhmy y

2 N m

− −η αΩ= + − −

− (12)

where

( ) [ ]( )( )1

mN 1 N P sin mh sinh mhA

2 N 2coshmh N

− − η α − =− −

( ) [ ]( )( )2

2 1 N P sin mh sinhmhA

m 2 N 2coshmh N

− −η α − =− −

( )( )3

1 N P sinA

2 N m

− − η α=

−


25

Integrating the equation (11) and using the condition

0ψ = at y 0= , we get the stream function as

( )( )

31

2

1 N p sin S coshmy Nsinhmy yS y y

2 N m m m 3

− −η α ψ= − + + − −

(13)

The volume flux q through each cross-section in the wave frame is given by

h

0

q u dy= ∫ (14)

where

11 2

3

2N DS

m S

−=

( )( )

31

2

1 N P sinq

2 N

S coshmh Nsinhmh hSh h

m m m 3

− −η α=

−

− + + −

21 12 2 2

3 3

D 2ND2N sinhmh h NcoshmhS h

m S S mm m = + − + −

3S 2cosh mh N= −

1D mh sinh mh= −

The pressure gradient is obtained from equation (14)

( )( )( ) 3

1 2

q h 2 Ndp 1sin

coshmh Nsinhmh hdx 1 NS Sh

m m m 3

+ −

= +η α − − + +

(15)

The time averaged flow rate is

Q q 1= + (16)

THE PUMPING CHARACTERISTICS

Integrating the equation (15) with respect to x over one wavelength, we get the pressure rise (drop) over one cycle of the wave as

( )( )( )

1

03

1 2

Q 1 h 2 Np dx sin

1 N

1

coshmh Nsinhmh hS Sh

m m m 3

− + −∆ = +η α

−

− + +

∫

(17)

The pressure rise required to produce zero

average flow rate is denoted by 0P∆ . Hence 0P∆ is

given by

( )( )( )

1

0

0 3

1 2

h 1 2 Np dx sin

1 N

1


m m m 3

− −∆ = +η α

−

− + +

∫

(18)

The dimensionless frictional force F at the wall across one wavelength in the inclined channel is given by

1

0

dpF h dx

dx = −

∫

( )( )( )

1

0

3

1 2

h 1 2 Nh dx sin

1 N

1


m m m 3

− −= − +η α

−

− + +

∫

(19)


26

III. DISCUSSION OF The RESULTS

From equation (17), we have calculated the

pressure difference as a function of Q for different

values of coupling number N with a 0.4,= m=2,

2η = and for different )2

,4

,0.(ππαα =ei and is

shown in figure (2-4). In figure( 3) It is observed that for chosen parameters the pumping curves intersect at a point in the first quadrant closure to Q 0 .1 8 .≈ This

may be due to the inclination of the channel is horizontal. For Q 0 .1 8< we observed that the

pressure rise increases with the coupling number N.

The behavior is otherwise when Q > 0.18. For free

pumping the Q decreases with the increasing N. For

free pumping we observe that the flux Q increases

with increasing 02

π α < α <

. The same behavior

is observed for the pressure difference p∆ when

Q 0.=

The variation of pressure rise with time averaged flow rate is calculated from equation (17) for different values of micropolar parameter ‘m’, and is shown in figures (5) to (7) for fixed a 0 .4 ,=

n 0.2,= 2η = .It is observed that the pumping

curves that the pumping curves meet at a point between Q 0 .1= and Q 0 .2= , this value is

estimated as Q 0.12= , when Q 0 .1 2< the

pressure rise decreases with increasing m. The opposite behavior is noticed for Q 0 .1 2> . For free

pumping we observe that the flux Q increases with

increasing inclination of the channel0

2

π ≤ α ≤

.

From equation (17), we have calculated

pressure rise as a function of Q for different values of

02

π α ≤ α ≤

and is shown in figure (8) for fixed

n 0.2,= 2η = , m 2= and a 0 .4 ,= . It is observed

that for an inclined channel, the peristaltic wave passing over the channel wall pumps against more pressure rise compared to a horizontal channel ( )0α = . For free

pumping the flux Q increases with increasing angle of

inclination α between 0 and2

π .

The variation of frictional force with time averaged flow rate is calculated from equation (19) for different values of N, m, α for a fixed a = 0.4 and is shown in figure (9) to (13) it is observed that the frictional force F has the opposite behavior compared to pressure rise. For horizontal channel it is observed that the given N, the frictional force increases with the

flux Q .

Figure 2.The variation of p∆ with Q for different values

of N with a 0.4,= m=2, 2η = , α =0

Figure 3. The variation of p∆ with Q for different values


27

of N with a 0.4,= m=2, 2η = , 4

πα =


of N with a 0.4,= m 2= , 2η = , α =2

π


of m with a 0.4,= n=0.2, 2η = , α =0


of m with a 0.4,= n=0.2, 2η = , α =Pi/4


of m with a 0.4,= n=0.2, 2η = , α =Pi/2


28


of α with a 0.4,= m=2, 2η = , n=0.2

Figure 9.The variation of F with Q for different values

of N with a 0.4,= m=2, 2η = , 4

πα =

Figure 10.The variation of F with Q for different values

of N with a 0.4,= m=2, 2η = , α =Pi/2

Figure11.The variation of F with Q for different values

of N with a 0.4,= m=2, 2η = , α =0


29


of α with a 0.4,= m=2, 2η = , n=0.2

Figure 13. The variation of F with Q for different values

of m with a 0.4,= n=0.2, 2η = , α =Pi/2

REFERENCES

[1] Ramachandra Rao, A., and Usha, S. Peristaltic transport of two immiscible viscous fluid in a circular tube, J. Fluid Mech., 298(1995), 271-285.

[2] Jaffrin, M.Y. and Shapiro, A.H. Peristaltic Pumping, Ann. Rev. Fluid Mech., 3(1971), 13-36.

[3] Manton, M.J. Long-Wave length peristaltic pumping at low Reynolds number, J. Fluid Mech. 68(1975), 467-476.

[4] Brasseur, J.G., Corrsin, S. and LU, Nan Q. The influence of a peripheral layer of different viscosity on peristaltic pumping with Newtonian fluids, J. Fluid Mech., 174(1987), 495-519.

[5] Srivastava, L.M. and Srivastava, V.P, Peristaltic transport of blood: Casson model II, J. Biomech, 17(1984), 821-829.

[6] Provost, A.M. and Schwarz, W.H. A theoretical study of viscous effects in peristaltic pumping, J. Fluid Mech., 279(1994), 177-195.

[7] Shukla, J.B. & Gupta, S.P. Peristaltic transport of a power-law fluid with variable viscosity. Trans. ASME. J. Biomech. Engg. 104, (1982) 182-186.

[8] Misra, J.C. and Pandey, S.K. Peristaltic transport of a non-Newtonian fluid with a peripheral layer, Int. J. Engg Sci., 37(1999), 1841-1858

[9] Rao, I.R. & Rajagopal, K.R., 1999, Some simple flows of a Johnson- Segalman fluid, Acta Mech.132,209-219.

[10] Kavitha. A, Hemadri Reddy. R, Sreenadh. S, Saravana. R, Srinivas. A. N .S. Peristaltic flow of a micropolar fluid in a vertical channel with longwave length approximation, Advances in Applied Science Research, 2011, 2 (1): 269-279

[11] Vajravelu, K. Sreenadh, S. and Ramesh Babu, V.Peristaltic pumping of a Herschel-Bulkley fluid in a channel, Appl. Math. And Computation, 169(2005a),726-735.

[12] Vajravelu, K. Sreenadh, S. and Ramesh Babu, V.Peristaltic pumping of a Herschel-Bulkley fluid in an inclined tube, Int. J. Non-linear Mech. 40(2005b), 83-90.

[13] Vajravelu, K. Sreenadh, S. and Ramesh Babu, V.Peristaltic pumping of a Herschel-Bulkley fluid in contact with a Newtonian fluid, Quarterly of Appl. Math.64, (2006) No.4,593-604.

[14] Vajravelu. K, Sreenadh. S, Hemadri Reddy. R, and Murugeshan.K, Peristaltic Transport of a Casson fluid in contact with a Newtonian Fluid in a Circular Tube with permeable wall, International Journal of luid Mechanics Research, 36 (3), (2009), 244-254.

[15] Vajravelu, K. Sreenadh,S. Lakshmi Narayana, P. The influence of heat transfer on peristaltic transport of a Jeffrey fluid in a vertical porous stratum, (accepted) Communications in Nonlinear Science and Numerical Simulation (2010),

[16] Subbareddy, M.V. Manoranjan Mishra, Sreenadh, S. and Ramachandra Rao, A. Influence of lateral walls on peristaltic flow in a Rectangular ducts, Journal of Fluids Engineering, 127 (2005), 824-827.

[17] subba Reddy.M.V, A.Ramachandra Rao and S.Sreenadh, Peristaltic motion of a power-law fluid in an asymmetric channel, International Journal of Nonlinear Mechanics, 42, 1153-1161, 2006.

[18] Srinivas, S. & Kothandapani, M., 2009 The influence of heat and mass transfer on MHD peristaltic flow through a porous space with complaint wall, Appl. Math. Comput. 213, 197-208.

[19] Srinivas, S., Gayathri, R. & Kothandapani, M., 2009 The influence of slip conditions, wall properties and heat transfer on MHD peristaltic transport, Computer Physics Communications 180, 2115-2122.

[20] Eringen, A.C. Theory of Micropolar fluid ONR Report.1965 [21] Eringen, A.C. Theory of micropolar fluids, J.Math. Mech.,16

No.1(1966), 1-18. [22] Ariman. T and Cakmak. A.S., Some Basic viscous flows in

Micropolar fluids, Rheologica Acta, Band 7, Heft 3 (1968), 236-242.

[23] Srinivasacharya, D., Mishra, M. and Ramachandra Rao .A.Peristaltic pumping of a micropolar fluid in a tube, Acta Mechanica, 161(2003), 165-178.


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