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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

IJITCE PUBLICATION

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING Vol.1 No.1 January 2011


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

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


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

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.


Contents 1. Measurement of Potassium Levels in the Soil using Embedded System based Soil Analyzer ……….[1] 2. Crowd Safety: A Real Time System For Counting People ……….[6] 3. A Novel design of Electronic Voting System Using Fingerprint…..[12] 4. GPS Tracking Simulation By Path Replaying…………………………[20] 5. Object Oriented Design of E-learning System for Distance Education…[27] 6. On The Relative Character Graph of A Finite Group………………..[31] 7. Detection of Diabetic Retinopathy Using Radial Basis Function….[40] 8. Peristaltic flow of a Williamson fluid in an asymmetric channel through porous medium………………………………………………………[48]


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Measurement of Potassium Levels in the Soil using Embedded System based Soil Analyzer #1

Ms.D. Asha Devi#1, Prof.K.Malakondaiah#2,Mr.M.Suresh Babu#3

Associate Professor, Department Of ECE,Intellectual Engineering College,Anantapur. Professor, Department Of Instrumentation,Sri Krishna Devaraya University, Anantapur,A.P. #3 Principal,Intell Institute Of Science,Anantapur.

#2

Abstract: Potassium is important to the plants for metabolic changes during flowering, and the production of floral clusters. It also promotes general plant-vigor, disease-resistance, and study growth. Hence, in this paper the concentration of potassium ion in aqueous soil samples of Anantapur area is analyzed. Combination of ISE and reference electrode is used to determine the ion activities of potassium ion in aqueous soil samples. This method is very simple and fast when compared with the other methods. The soil samples tested with the embedded system based soil analyzer.

The measured data is represented in the form of web pages, using HTML and the processing of data using Dynamic C functions. The Rabbit is created as

web server and is connected to PC by using Ethernet cross cable (RJ45). Then the Rabbit processor and the PC are in LAN (Local Area Network). Here data is accessed through Rabbit processor, webpage can be viewed on PC, and hence, we can access physical data through LAN.

Keywords: ESBSA (Embedded System Based Soil Analyzer); ISEs (Ion Selective Electrodes); Introduction: The main objective of this research is to develop “EMBEDDED BASED SOIL ANALYZER” which is used to analyze the Potassium levels present in the soil. As Agriculture is one of the most important occupations in INDIA, it is very much essential to know the nutrients present in the soil for a suitable crop. However, in every district only one or two organizations are there for the testing of soil. To increase this facility, adding today’s technology towards agricultural fields, a cost effective soil-testing instrument is developed. In this paper the measurement of Potassium levels present in the soil is demonstrated with the help of ESBSA. Figure.1 consists of two sensors (i.e. temperature, and potassium) which are connected to ADC MCP3208. The main function of analog to digital converter is to convert analog values into digital values. The output of ADC is given to Rabbit Processor (Rabbit 3000). The main reason for using Rabbit Processor is for its excellent feature of inbuilt TCP/IP.

Fig.1. Measurement Of Potassium Embedded system based Soil Analyzer

using

This experiment is to develop and implement an embedded system based soil analyzer, which is an internet based measurement system for analysis of

1


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 different parameters. In this paper the estimation of Potassium present in the soil sample is explained. The ESBSA is connected to local computer via Ethernet port using RJ45 Ethernet cable. Then internet is provided to the local PC, Apache Web Server Software is used to configure the PC and made the local PC as Web Server. Using this software proxy pass and proxy reverse operations are provided to have online communication. Now the local system is ready to communicate the information online. Therefore, with the ESBSA, measured and calibrated data can be accessed globally.

After removing any stones or fresh organic material (roots, twigs, leaves, worms, insects etc), and breaking up any large lumps, soil samples must be air dried by laying out in a thin layer on metal or plastic trays in a current of air at no more than 30°C until dry. Then they must be crushed in a pestle and mortar to pass through a 2mm sieve. About 200g of material should be sufficient for duplicate analysis and storage.

Weigh accurately about 50g of dry soil sample and add exactly 100ml of de-ionized water and shake vigorously for 30 seconds to ensure good dispersion, then leave to stand for 15 mins. After this time, shake again for 5 seconds, and allow to stand again. Repeat this procedure three times before finally allowing to settle. When the solution is clear, take exactly 50 mls (by decanting or pipetting) and mix with 1 ml of buffer solution in a plastic beaker. Procedure for determining the concentration of + POTASSIUM (K ) in SOILS Water-soluble Potassium is extracted from dried soil samples by dispersion in de-ionized water and analyzed by direct potentiometer.

Fig:2 Local PC interfaced with ESBSA

Potassium in Soil Sample measurement technique: 



Prepare two standard concentrations C1 and C2 of the sample potassium.First, dip the Potassium and reference electrodes in the more dilute standard solution of concentration C1 and note down the reading E1 mV. Then rinse both electrodes with double distilled water blot dry with a soft tissue paper. Now dip the electrodes in more concentrated standard of concentration C2, and note the stable reading E2 mV. Now immerse the electrodes in the sample and note down the potential reading Ex mV. Now sample concentration Cx can be calculated from the equation. Cx = C2 / anti-log (∆/S). Where ∆ = E2 – E1, S = Slope of the electrode. FIG 3: Potassium Sensor electrode and reference -3 electrode are immersed in 10 M buffer solution

The Potassium Ion-Selective Electrode has a solid-state PVC polymer matrix membrane which is designed for the detection of potassium ions ( K+ ) in aqueous solutions and is suitable for both field and laboratory applications. The Potassium Ion is a monovalent action. One mole of ( K+ ) has a mass of 39.098grams; 1000 ppm is 0.026 M.

Calibration Before soil sample measurement, the electrodes must be calibrated by measuring a series of known standard solutions, made by serial dilution of the 1000ppm standard solution. For a full calibration, prepare 100ml of solutions containing 1000, 100, 10, 1, and 0.1ppm K. If the approximate range of

Sample Preparation

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 concentrations of the samples is known, and this is within the specified linear range of the ISE, then it is only necessary to make two solutions which span this range: e.g. if samples are known to lie between, say, 30 and 130ppm then we could use standards of 10 and 200ppm or even 20 and 150ppm.2 ml of buffer solution must be added to each 100ml standard and mixed thoroughly to compensate for different activity coefficients between samples and standards.

2. The RCM-3700 has 40 pins out of which we use 7 pins from port-B and 6 pins from port-A for our application. 3. The 4 pins of port-B are configured as input and output pins, 6 pins of port-A are configured for LCD Display. 4. In which PB-5 is used for clock, PB-7 for D-out, PB-4 for D-in, PB-2 for chip select, PA-4, PA-5, PA-6, PA-7 are used for data transfer, PA-0 for R/W, PA-1 for Enabling the LCD.

Sample Measurement Briefly, it is important to note that the electrodes must be washed and dried between each sample, to avoid cross contamination, and sufficient time must be allowed (2 or 3 minutes), before taking a reading after immersion, to permit the electrode signal to reach a stable value. For the highest precision, frequent recalibration is recommended (see operating instructions). The results will be displayed as ppm and mol/l in the solution. Since buffer solution has been added equally to standards and samples, these figures will not need adjusting for this addition. However, the concentration in the solution (in ppm = micrograms per ml) must be multiplied by 100 and divided by the sample weight to give the concentration in the soil (in micrograms per gram).

5. 40th pin is connected to VCC (+5v) and pins 19 and 20 are connected to ground.

Temperature Compensation: The ISE (POTASSIUM) is a temperature dependent one. Hence, in this research a temperature compensated Potassium measurement system is developed. LM35 series are precision integrated-circuit temperature sensors, whose output voltage is linearly proportional to the Celsius (Centigrade) temperature. The LM35 thus has an advantage over linear temperature sensors calibrated in ° Kelvin, as the user is not required to subtract a large constant voltage from its output to obtain convenient Centigrade scaling. The LM35 does not require any external calibration or trimming to provide typical accuracies of ±1⁄4°C at room temperature and ±3⁄4°C over a full −55 to +150°C temperature range. Low cost is assured by trimming and calibration at the wafer level. The LM35’s low output impedance, linear output, and precise inherent calibration make interfacing to readout or control circuitry especially easy. It can be used with single power supplies, or with plus and minus supplies.

Fig.4: Schematic Diagram of Embedded Soil Analyzer

6. LCD INTERFACE: 1, 3, 5, 16th pins are connected to ground; 2,15th pins are connected to 5volts; 4th pin (RS) is connected to PA-0, 6th pin (E) is th connected to PA-1, and 11, 12, 13, 14 pins are connected to PA-4, 5, 6, 7 pins for data transferring.

Hardware Description:

7. In MCP3208, ch-0,1,2,4,5 & 7(not used) Temperature sensor-ch-3, Potassium sensor – ch-5

1. Here, we use rabbit-3000 processor along with RCM3700 development kit.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 8. As written in the software, when input changes the corresponding output is changed and output display is refreshed. 9. Here when enable button is active, it is indicated in Ethernet cable by blinking of light. 10. If we want to end the process, enable is disabled or connection is removed. 11.Appache Web Server software is used to configure the local PC as a server. 12.LCD display is used for standalone results. Results:

S.no.

Soil samples

Measurement

Present study

Fig: 5: On line measurement of potassium sample

M/ppm

Conclusion:

@ Anantapur Soil office 1

6364

20.98

21.33

2

6365

19.42

19.45

3

6366

45.36

45.34

4

6367

23.38

23.14

5

6368

24.48

24.56

By Using “EMBEDD SYSTEM BASED SOIL ANALYSER�, we measure Potassium levels present in the soil at respective temperature and humidity using ION SELECTIVE ELECTRODES. Potassium is absorbed by plants in larger amounts than any other mineral element except nitrogen and, in some cases, calcium. It helps in the building of protein, photosynthesis, fruit quality and reduction of diseases. Potassium is supplied to plants by soil minerals, organic materials, and fertilizer. From the study of potassium ion content in soils, collected from literature values and soil office readings, soils which are having less than 150 Kg/Hectare K2O is poor in potassium deposits. When it is between 150-250 Kg/Hectare K2O of soils are having medium levels of potassium. Finally, the soils, with greater than 250 Kg/Hectare K2O deposits, considered to have sufficient potassium deposits. This system is connected to RABBIT Processor, which has special feature of built in TCP/IP, by which we can upload the data to the Internet. This device is more economical, reliable, and portable. Using this instrument, farmers can access the data through Net from remote area. By this, farmer can evaluate the soil nutrients present in the soil and can analyze the amount of nutrients present in the soil and lack of percentage of nutrients to be added for a specific crop by using predefined data provided. A farmer can have the suggestions from soil analysts or agricultural scientists through Internet, so that he can improve the crop yields in an efficient manner.

Table .1: Potassium levels measured in soil samples

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 12. References [1]www.agric.gov.ab.ca/app21/rtw/index.jsp [2]Advanced sensor technologies, Inc.Toll free: 1-888-wow-ASTI (964-2784) [3]“Computer networks” By Andrew S.Tanenbaum, prentice hall India private limited, third edition 2001. [5] Wireless LAN Medium Access Control (MAC) and Physical Layer(PHY) Specification, IEEE Std. 802.11, 1997 [6] J.J.Lingane, Anal.chem./39,881 (1967) [7] J.J.Lingane, Anal.chem./40,935 (1968) [8] E.Pungor and K.Toth, Anal.chem.Acta, 47,291 (1969) [9] J.W.Ross and M.S Frant, Anal.chem, 49,967(1969) [10] G.A.Rechnitz and N.C.Kenny, Anal.Lett. 2,395(1969) [11] G.kakabadse, Ion-selective Rev., 3,127(1982). [12] R.A.Durst, “Ion Sensitive Electrodes”, [13] Natl.Bur.Stand.Spec.Pub1, 314, Washington D.C., (1969).

[13]

[14] Iodide ion selective electrode manual of pH products company Pvt. Ltd., Hyderabad. [15] A. Hameed, M. A. Goddal, Z. H. Yamini, A. H. Yahya, “Significance of pH Measurements in photo catalytic splitting of water using 355nm UV laser”, Journal of Molecule catalysis A: chemical, 227,241(2005). [16] Australian Journal of Basic and Applied Sciences, 4(5): 922-931, 2010;ISSN 1991-8178;© 2010, INSInet Publication [17] K. Hiiro, A. Kawahara and T. Tanaka, Anal. Chim. Acta,110, 321 (1979). [18]K. Hiiro, A. Kawahara and T. Tanaka, Nippon Kagaku Kaishi,1980, 1447. [19]K. Hiiro, A. Kawahara and T. Tanaka, Bunseki Kagaku, 31, E33 (1982). [20]K. Hiiro, T. Tanaka, A. Kawahara and S. Wakida, Anal. Sci., 2,145 (1986). [21]K. Hiiro, S. Wakida, T. Tanaka, A. Kawahara and M. Yamane, Fresenius' Z. Anal. Chem., 326, 362 (1987). [22] Denshi Kogyo Geppo, 27, 38 (1985).

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

CROWD SAFETY: A REAL TIME SYSTEM FOR COUNTING PEOPLE Prabakaran V #1, Dr.Arthanariee A.M *2, Sivakumar M #3 #1

Assistant Professor, Department of Computer Science, Adharsh Vidhyalaya Arts and Science College for Women, Erode, Tamilnadu, India #2

Director, Bharathidasan School of Computer Applications, Erode, Tamilnadu, India #3 Doctoral Research Scholar, Anna University, Coimbatore, Tamilnadu, India Abstract— Man-made disasters are the outcome means and routes of entrance and exists, of lack of awareness, lack of sensitivity towards the communication, jamming and queuing [3]. safety measures to be taken to prevent unforeseen It is clear that a stampede death never occurs accidents. Large crowds always invite accidents if due to the presence of 1-10 people at a place. It preventive measures are not taken with proper always happens when a huge crowd gathers at planning. When the number of people waits in queue one place for some or other reason. Whenever we before a shopping mall on a special sales day or have experienced any stampede death to our gathering at religious functions or at a sports society the crowd of people has been held gallery, it is very much evident that large crowding responsible. When one person in a crowd starts is part of normal life. This exposes a new problem space of crowd management. Excessive crowding running due to any kind of rumour the entire crowd and poor crowd management can cost loss of follows him or her without having any time for the precious life very easily. Hence the need for second thought to be processed and this causes developing an automated crowd management the deaths on mass level. With the tremendous system will be highly appreciated by the society. growth of population and the facts given that Crowd management involves keeping track of the having a bad system can easily jeopardize the crowd, the space available and balancing between precious life, “Why do crowds need to be the crowd and space. In this paper, we propose a managed?” is becoming a penny worth question prototype for counting the people as a part of and “What is the best way to manage a crowd?” is developing better crowd monitoring system. The system counts people and displays the result in a becoming a million dollar question. The best user friendly interface. The system has been tested reasons for the “Why?” Includes; Big gatherings of in different places and found working fine for people raise the odds of a dangerous occurrence counting people. happening. Secondly, individuals within a crowd always take for granted that others have the responsibility. Thirdly, big crowds or gatherings of Keywords: Crowd Monitoring, LDR & people make changes in action slower and more Programmable Interface Controller complicated. Fourthly, big crowds or gatherings of people make communications slower and more I. INTRODUCTION complicated. And most importantly, big crowds of Crowd monitoring is the process of monitoring people raise the possible number of victims [2]. and controlling large groups of people for their Even though crowd is normal in all over the safety and security. It also provides ways to world, India often faces many problems with crowd efficiently utilize space and reduce cost involved in particularly in religious functions. Religious is the maintenance [1]. It includes different phases like soul of India and its culture. There are number of planning, organizing, guiding and evaluating holy places where the devotees go as pilgrims results of corrective actions. Crowd safety and throughout the year. Whenever there are such security in public areas are primarily the mammoth gatherings, there should be proper organizer’s or operator’s responsibility. A health arrangements to control the people. and safety management system is required to Traditionally, crowd management is performed monitor and control potential crowding risks in by employing extensive closed circuit television public areas. The four interacting elements that system. This involves extensive cost, time and contribute to a better and efficient system are; human effort in setting up and establishing the time, space, information, and energy. The system. As routine monitoring is tedious, the behaviour of the crowd is another attribute that observers are likely to lose concentration. The plays a vital role in efficient crowd monitoring need and necessity of the automatic surveillance system. Other attributes include facility, size,

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

for routine crowd monitoring and controlling is again proven [6] In order to save the life of the public, a model has been developed to count the entry of the people entering into an area. The system has been developed using both the hardware and software implementations. This paper has been organized in the following ways. Section II describes some of the incidents occurred due to overcrowding and Section III discuses previous work done. The Section IV describes prototype of the system and Section V discuses experimental results. Conclusion and future work is given in Section VI.

Examples of these types are turnstiles and mattype foot switches. The above sensors are suitable for counting a few people and are not adequate for crowd monitoring. On the other hand, there are non-contact and non-obstruct sensors. From these types, is CCTV visual camera. In CCTV based research efforts, individuals are first detected by human models or detectors and then tracked in order to count the number of pedestrians. Masoud et al. [2] used difference images to detect moving blobs, and pedestrians are detected by rectangular patches based on their dynamic behavior. Liu et al. [3] proposed a method based on human appearance models to detect pedestrians. In this method, human models are created using a set of low level image features, and the number of people is counted using these human models. Also, Sidla et al. [4] proposed a model-based method to detect the number of humans in scenes. In this method, humans were successfully detected using active shape models (ASM) even if their bodies were partially occluded. However, these methods cannot be applied to multicamera systems or embedded systems because they usually require a great amount of computational power. In this paper, we propose a simple and effective method to count the number of people by using Light Dependent Sensors (LDR). The sensors are fitted in two sides of the doors, so that when light beam is cut, automatically the counting process starts by the program stored in programmable Interface Controller.

II. LIST OF DANGERS ENCOUNTERED Numerous incidents have been recorded in which uncontrolled crowding has resulted in injuries and, in some instances, death. There are inherent dangers associated with every large public gathering. Every year there are reports of overcrowding and crushing incidents from around the world. It is very particular to say that more number of incidents is occurring in India during Temple festivals. Hence there is a growing need in India to develop a very useful automated crowd monitoring system. To put the problem into perspective, the following list highlights just some of the events that have ended in tragedy. January 15, 1999 - 51 Hindus killed and 100 injured in a stampede after part of a shrine collapsed. Over 1.5 million present in a ceremony in Kerala, India.

IV. PROTOTYPE DESCRIPTION The prototype of the crowd monitoring system includes the following main components in the hardware part. The components are i) Power Unit ii) Step-down Transformer iii) Light Dependent Sensor (LDR) iv) Programmable Interface Controller (PIC) v) LCD Display. The Software part includes development of a system which accepts output from the hardware unit as input and displays the result. The working of prototype is as follows: The 230V power supply is connected to the Transformer. The Step-Down Transformer takes the voltage and converts it into 5V. The 5V supply is connected to Light Dependent Sensors (LDR). The sensor has two hands where 5 mA light is emitted between two hands of the sensor. The two hands are fitted in the left and right sides of the door. Fig. 1 a) shows an entry gate where hands of the sensor are fixed. Fig. 1 b) shows people movement through the gate. The picture shows the entrance of Sri Sangameswarar Temple

August 24, 2005 – 56 die, hundreds of people injured in a stampede at Vaishnavi Devi temple, India September 30, 2008: 147 people were killed during the Chamunda Devi stampede at the Chamunda Devi temple in Jodhpur, India March 4, 2010: At least 71 killed and over 200 injured at Ram Janki Temple in Kunda, India The above listing shows some incidents occurred in India, where there is a growing need for better crowd monitoring system. III. RELATED W ORK In general, the detection methods are broadly classified into two categories. The first one is obstructive and the second is non-obstructive [6]. The first type to detect the number of people requires personal contact, which obstruct the path.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

at Bhavani where a flow rate of people is very high throughout the year.

Fig. 2. a) Passage of people through wooden barriers (Front view)

Fig. 1. a) Entry gate

Fig. 2. b) Passage of people through wooden barriers (Back View)

When a person enters through the door where sensor hands are fixed, the 5mA light passed is cut and the information is passed to the LDR and then to Programmable Interface Controller (PIC). An assembly language program has been written to process the data received from LDR. The assembly language program stored in the PIC is working and increments the count by 1. When the person enters, the light beam is cut, then counter is incremented by 1 and, when a person exits from the area via the door, the counter is decremented by 1. This information is displayed in the Liquid Crystal Display (LCD) and also in software user interface. Fig. 3 provides the prototype of crowd monitoring system.

Fig. 1. b) People flow through the entry gate

For effective counting, the pathway is designed as a single entry by using wooden barriers. The following Fig. 2 a) and 2 b) show the scenario.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

and serial programming and re-programming with flash memory capability. There are five ports in this microcontroller, namely PORT A, PORT B, PORT C, PORT D and PORT E. Each pin in the ports can be used as either input or output pins. V.

In virtue of Visual Basic, the proposed method has been successfully implemented as an actual system for counting people. Comparing to previous methods, the method has higher accuracy in counting the people. The interface designed for displaying output is user friendly. The output shows the number of persons inside a particular area. It also includes date and time. The data collected from this system can be used for various analysis purposes viz. hourly, daily, monthly and yearly report for understanding the crowd behavior in one particular place. The following tables show the result of people counting process held at Sri Sangameswarar Temple, Bhavani. We conducted the experiment by allowing the individual to pass through a queue where the system is installed. The following table depicts the output of our system.

Figure 3. Prototype of crowd monitoring system

The components of the prototype are discussed one by one in the following sections. A. Power Supply Unit Since all electronic circuits work only with low dc voltage it needs a power supply unit to provide the appropriate voltage supply. This unit consists of a transformer, rectifier, filter and regulator. AC voltage typically 230v is connected to the transformer that steps the AC voltage down to the level to the desired AC voltage. A diode rectifier then provides a full wave rectified voltage that is initially filtered by a simple capacitive filter to produce a DC voltage. In our prototype, the power supply unit provides pure DC 5V

EXPERIMENTAL OUTPUTS

Percentage

S. Date

Time

No

Actual

System

Strength

Count

of correctness

1

1/11/10

09:09:10

80

80

100%

2

1/11/10

09:16:20

120

120

100%

3

1/11/10

09:22:50

160

160

100%

4

1/11/10

09:29:40

183

183

100%

5

1/11/10

09:35:50

200

200

100%

6

1/11/10

09:40:59

230

230

100%

7

1/11/10

09:45:10

270

270

100%

B. Step down Transformer Step down transformers are designed to reduce electrical voltage. This kind of transformer "steps down" the voltage applied to it. In our prototype, the step down transformer converts 230 AC to 150-15 AC. C. Light Dependent Resistors LDRs or Light Dependent Resistors are very useful especially in light/dark sensor circuits. Normally the resistance of an LDR is very high, sometimes as high as 1000 000 ohms, but when they are illuminated with light, resistance drops dramatically.

TABLE I PEOPLE COUNTING RESULT OF CROWD MONITORING SYSTEM WITHOUT OCCLUSION

D. Programmable Interface Controller

From the above table, it is clear that the system accurately counts the number of people entering into an area. The following table depicts the result where occlusions are in the crowd while passing through the entry gate

The PIC microcontroller device used in this prototype is PIC16F877A. The PIC16F877 is 40 pin IC. For this prototype development, we used PICs because it is of low cost, wide availability, extensive collection of application notes, availability of low cost or free development tools,

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Percentag e

S. Date

Time

No

Actual

System

Strength

Count

[4]

of correctnes s

[5]

1

1/11/10

10:01:02

18

16

89%

2

1/11/10

10:02:40

24

22

92%

3

1/11/10

10:03:50

30

27

90% [6]

4

1/11/10

10:06:40

35

32

91%

5

1/11/10

10:09:50

42

36

86% [7]

6

1/11/10

10:12:59

51

46

90%

7

1/11/10

10:20:10

59

55

93%

TABLE II PEOPLE COUNTING RESULT OF CROWD MONITORING SYSTEM WITH OCCLUSION

[8]

The above table shows the result of our experiment where the people moving through the entry gate are partially or fully occluded. VI.

[9]

CONCLUSION

In this paper, we suggested a simple and user friendly prototype for counting the number of people entering into a particular area. The system has been developed by keeping in mind that successful counting of people entering into an area as part of the development of full pledged crowd monitoring system. In future work, to resolve the occlusion issue, we try to incorporate a camera in our system and it can also be used to store the images of the public for future references.

[1]

[2]

[3]

Advanced Video and Signal Based Surveillance, pp. 306–311 (2005) Sidla, O., Lypetskyy, Y., Brandle, N., Seer, S.’ Pedestrian Detection and Tracking for Counting Applications in Crowded Situations’ Advanced Video and Signal Based Surveillance, pp. 70–75 (2006) Casburn, L., Srinivasan, M., Metoyer, R. A. and Quinn, M. J. (2005) ‘A Data-Driven Model of Pedestrian Movement’. Proceedings of the Third International Conference on Pedestrian and Evacuation Dynamics (September 2005) M.I.Kamel, M.Fkry, A.Mashat, N.M.Biqami, H.Barhamtoshy, and I.Beedewy, ‘Monitoring, Surveillance and control of the crowds in the holy sites using SCADA System’, The Seventeenth National Computers Conference, Kingdom of Saudi Arabia, April 2004 Fruin, J. J., ‘The Causes and Prevention of Crowd Disasters’. First International Conference on Engineering for Crowd Safety, London, England, March 1993. Document on the web, http://www.crowddynamics.com/Main/Fruin% 20%20causes.htm Still, K., Crowd Dynamics, PhD. Thesis, Mathematics Department University of Warwick, 2000 Tang W., Wan T., Patel, S., ‘Real-time crowd movement on large scale terrains’, Theory and Practice of Computer Graphics, p.p. 146-153, 2003.

Prabakaran V., received his MCA from University of Madras and M.Phil from Bharathiar University. As an enthusiastic researcher, he presented papers in many National conferences and an International Conference. His area of interest includes Object oriented programming, Data mining and Image processing

REFERENCES M.Rossi and A.Bozzoli, ‘Tracking and Counting Moving People’, Proc. Second IEEE International Conference on Image Processing, pp. 212-216, 1994 Masoud, O., Papanikolopoulos, N.P., ‘A Novel Method for Tracking and Counting Pedestrians in Real-time Using a Singe Camera’, Vehicular Technology 50, 1267–1278 (2001) Liu, X., Tu, P.H., Rittscher, J., Perera, A., Krahnstoever, N.: ‘Detecting and Counting People in Surveillance Applications’.

Dr. Arthanariee A.M., holds a Ph.D degree in Mathematics from Madras University as well as Masters Degree in Computer

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Science from BITS, Pilani. He holds a patent issued by the Govt. of India for the invention in the field of Computer Science. He has directed teams of Ph.D researchers and industry experts for developing patentable products. He teaches strategy, project management, creative problem solving, innovation and integrated new product development for last 37 years.

Sivakumar M has 10+ years of experience in the software industry including Oracle Corporation. He received his Bachelor degree in Physics and Masters in Computer Applications from the Bharathiar University, India. He holds a patent for the invention in embedded technology. He is technically certified by various professional bodies like ITIL, IBM Rational, Clear case Administrator, OCP - Oracle Certified Professional 10G and ISTQB

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

A Novel design of Electronic Voting System Using Fingerprint D. Ashok Kumar#1, T. Ummal Sariba Begum#2 #1

#2

Department of Computer Science, V .S.S. Government Arts College, Pulankurichi – 630 405, Sivagangai, Tamil Nadu, India

UGC Research Fellow, V.S.S. Government Arts College, Pulankurichi – 630 405, Sivagangai, Tamil Nadu, India

Abstract— The heart of democracy is voting. The heart of voting is trust that each vote is recorded and tallied with accuracy and impartiality. The accuracy and impartiality are tallied in high rate with biometric system. Among these biometric signs, fingerprint has been researched the longest period of time, and shows the most promising future in real-world applications. Because of their uniqueness and consistency over time, fingerprints have been used for identification over time. However, because of the complex distortions among the different impression of the same finger in real life, fingerprint recognition is still a challenging problem. Hence in this study, the authors are interested in designing and analysing the Electronic Voting System based on the fingerprint minutiae which is the core in current modern approach for fingerprint analysis. The new design is analysed by conducting pilot election among a class of students for selecting their representative. Various analysis predicted shows that the proposed electronic voting system resolves many issues of the current system with the help of biometric technology.

Keywords— Biometric, Electronic Voting.

Fingerprint,

Minutiae,

I. INTRODUCTION Elections allow the populace to choose their representatives and express their preferences for how they will be governed. Naturally, the integrity of the election process is fundamental to the integrity of democracy itself. The election system must be sufficiently robust to withstand a variety of fraudulent behaviours and must be sufficiently transparent and comprehensible that voters and candidates can accept the results of an election. In context of Western democracies' current crisis, electronic voting has become a very popular topic of discussion in academic and technical circles. Voting is a method for a group such as a meeting or an electorate to make a decision or express an opinion—often following discussions, debates, or election campaigns. It is often found in democracies and republics. Electronic voting (also known as evoting) is a term encompassing several different types of voting, embracing both electronic means of casting a vote and electronic means of counting votes.

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For many years, paper-based ballot is used as a way to vote during campus election day. This matter put an inefficient way of voting process as students have to queue up to register their name before they can vote. Furthermore, the traditional way of voting will take a long process and time. So, the novel electronic voting using minutiae will become the best solution for the matters; besides provide easier way of voting. Compared to existing voting system the Electronic voting has several advantages like: Electronic voting system is capable of saving considerable printing stationery and transport of large volumes of electoral material. It is easy to transport, store, and maintain. It completely rules out the chance of invalid votes. Its use results in reduction of polling time, resulting in fewer problems in electoral preparations, law and order, candidate’s expenditure, etc. and easy and accurate counting without mischief at the counting centre. It is also eco friendly [8]. Biometrics is the automated recognition of individuals based on their behavioural and biological characteristics. Biometric recognition means by measuring an individual's suitable behavioural and biological characteristics in a recognition inquiry and comparing these data with the biometric reference data which had been stored during a learning procedure, the identity of a specific user is determined. A fingerprint is an impression of the friction ridges, from the surface of a fingertip. Fingerprints have been used for personal identification for many decades, more recently becoming recognition is nowadays one of the most important and popular biometric technologies mainly because of the inherent ease in acquisition the numerous sources (ten fingers) available for collection, and the established use and collections by law enforcement agencies. Automatic fingerprint identification is one of the most reliable biometric technologies. This is because of the well known fingerprint distinctiveness, persistence, ease of acquisition and high matching accuracy rates. Fingerprints are unique to each individual and they do not change over time. Even identical twins do not carry identical fingerprints. Scientific research in


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

areas such as biology, embryology, anatomy and histology has supported these findings [28]. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for person recognition than traditional token or knowledge based methods. The objectives of biometric recognition are user convenience (e.g., money withdrawal without ATM card or PIN), better security (e.g., difficult to forge access), and higher efficiency (e.g., lower overhead for computer password maintenance). The tremendous success of fingerprint based recognition technology in law enforcement applications, decreasing cost of fingerprint sensing devices, increasing availability of inexpensive computing power, and growing identity fraud/theft have all ushered in an era of fingerprintbased person recognition applications in commercial, civilian, and financial domains. So the Electronic voting system has to be improved based on the current technologies viz., biometric system. There are some previous works which uses fingerprint for the purpose of voter identification or authentication. As the fingerprint of every individual is unique, it helps in maximizing the accuracy. A database is created containing the fingerprint of all the voters in the constituency. Illegal votes and repetition of votes is checked for in this system. Hence if this system is employed the elections would be fair and free from rigging. Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify an individual and verify their identity. Extensive research has been done on fingerprints in humans. Two of the fundamentally important conclusions that have risen from research are: (i) a person's fingerprint will not naturally change structure after about one year after birth and (ii) the fingerprints of individuals are unique. Even the fingerprints in twins are not the same. In practice two humans with the same fingerprint have never been found [7]. In this study, for the fingerprint authentication the minutiae based matching is considered for higher recognition accuracy. Also, the matching accuracy of fingerprint based authentication systems has been shown to be very high. Fingerprint – based authentication systems continue to dominate the biometrics market by accounting for almost 52% of authentication systems based on biometric traits [2]. This paper is organized as follows: The section II describes the issues of the present voting system, section III discusses the fundamentals of finger print authentication system Section III describes the proposed novel application for Electronic Voting Systems, Section IV describes the Experimental Results of a pilot election conducted among a class

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of students to chose their representative and Section V concludes and states the future work plans. II. ISSUES OF PRESENT VOTING SYSTEM There has been several studies on using computer technologies to improve elections [5, 38, 21, 22, and 29]. These studies caution against the risks of moving too quickly to adopt electronic voting system, because of the software engineering challenges, insider threats, network vulnerabilities, and the challenges of auditing. Researchers in the electronic voting field have already reached a consensus pack of following core properties that an electronic voting system should have [30]: Accuracy: (1) it is not possible for a vote to be altered, (2) it is not possible for a validated vote to be eliminated from the final tally, and (3) it is not possible for an invalid vote to be counted in the final tally. Democracy: (1) it permits only eligible voters to vote and, (2) it ensures that eligible voters vote only once. Privacy: (1) neither authorities nor anyone else can link any ballot to the voter who cast it and (2) no voter can prove that he voted in a particular way. Verifiability: anyone can independently verify that all votes have been counted correctly. Collusion Resistance: no electoral entity (any server participating in the election) or group of entities, running the election can work in a conspiracy to introduce votes or to prevent voters from voting. If all entities conspire this property isn’t achieved. So, this characteristic should be measured in terms of the total number of entities that must conspire to guarantee a successful interference in the election. Availability: (1) the system works properly as long as the poll stands and (2) any voter can have access to it from the beginning to the end of the poll. Resume Ability: the system allows any voter who had interrupted his/her voting process to resume it or restart it while the poll stands The existing elections were done in traditional way, using ballot, ink and tallying the votes afterward. But this system prevents the election from being accurate. Problems encounter the usual elections are as follows: • It requires human participation, in tallying the votes that makes the elections time consuming and prone to human error. •

The voter find the event boring resulting to a small number of voters.

Deceitful election mechanism.


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

•

Constant spending funds for the elections staff every year.

So, the proposed electronic voting system has to be addressed these problems. III FUNDAMENTALS OF FINGERPRINT AUTHENTICATION SYSTEM The types of information that can be collected from a fingerprints friction ridge impression can be categorized as Level 1, Level 2, or Level 3 features as shown in fig 1. Level 2 features or minutiae refer to the various ways that the ridges can be discontinuous. These are essentially Galton characteristics, namely ridge endings and ridge bifurcations. A ridge ending is defined as the ridge point where a ridge ends abruptly. A bifurcation is defined as the ridge point where a ridge bifurcates into two ridges. Minutiae are the most prominent features, generally stable and robust to fingerprint impression conditions. The distribution of minutiae in a fingerprint is considered unique and most of the automated matchers use this property to uniquely identify fingerprints. Uniqueness of fingerprint based on minutiae points has been quantified by Galton [7]. Statistical analysis has shown that Level 2 features have sufficient discriminating power to establish the individuality of fingerprints [34].

Fig 1 Fingerprint features at Level 1, Level 2, and Level 3 [36, 23]

Fig 2 Characteristic Attributes of a Minutiae

In a recently published World Biometric Market Outlook (2005-2008), analysts predict that the average annual growth rate of the global biometric market is more than 28%, by 2007 [11]. The technologies that would be included in this are fingerprint technology by 60%, facial & iris by 13%, keystroke by 0.5% and digital signature scans by 2.5% Basically there are two types of fingerprint Recognition System: (1) AFAS ( Automatic Fingerprint Authentication System) (2) AFIS ( Automatic Fingerprint Identification / Verification System ) 1) AFAS (Automatic Fingerprint Authentication System) Components of AFIS are: [40] [10][42] 1. Physical Fingerprint required as input. 2. Input is processed by using various image processing tools and databases and Classification of Fingerprints. The basic fundamental steps of these systems (see Fig (3) are image acquisition, preprocessing segmentation, enhancement etc), feature extraction, matching along with classification through databases. Authentication or verification systems authenticate the person's identity by comparing the own biometric template(s) stored in database (Oneto-One comparison). An identification system recognize an individual by searching the entire templates in database for match (One-to-Many Comparison) [31] [17].

The Fig 2 shows the clear view of minutiae. A minutia is characterized by its location and orientation.

Fig 3 Typical Structure for Fingerprint System [16]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

2) AFIS (Automatic Fingerprint Identification/ Verification System) A fingerprint recognition system operates either in verification mode or in identification mode. The various stages in a fingerprint verification system are shown in Fig 4.

Fig 4 Architecture of Fingerprint Verification System

The first stage is the data acquisition stage in which a fingerprint image is obtained from an individual by using a sensor. The next stage is the pre-processing stage in which the input fingerprint is processed with some standard image processing algorithms for noise removal and smoothening. The pre-processed fingerprint image is then enhanced using specifically designed enhancement algorithms which exploit the periodic and directional nature of the ridges. The enhanced image is then used to extract salient features in the feature extraction stage. Finally, the extracted features are used for matching in the matching stage. Data Acquisition: Traditionally, in law enforcement applications fingerprints were acquired off-line by transferring the inked impression on a paper. Nowadays, the automated fingerprint verification systems use live-scan digital images of fingerprints acquired from a fingerprint sensor. These sensors are based on optical, capacitance, ultrasonic, thermal and other imaging technologies. The techniques followed in these sensors are discussed in [2]. Image Pre-processing: The preprocessing steps try to compensate for the variation in lighting, contrast and other inconsistencies which are introduced by the sensor during the acquisition process. The paper [1] discusses the pre- processing steps generally used, which are Gaussian Blur, Sliding-window Contrast Adjustment, Histogram based Intensity Level and etc., Fingerprint Image Enhancement: The Performance of fingerprint feature extraction and matching algorithms relies heavily on the quality of the input fingerprint images. Due to various factors such as skin conditions (e.g., we, dry, cuts, scars and bruises). Non-uniform finger pressure, noise introduced by sensor and inherently poor-quality fingers (e.g., manual workers, elderly people), a

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significant percentage of fingerprint images is of poor quality. In fact, a single fingerprint image may contain regions of good, medium, and poor quality. Thus an enhancement algorithm which can improve the quality of ridge structure is necessary. A survey on different enhancement techniques can be found in [2]. The paper [9] describes the popular enhancement algorithm by Sharat et al. [33], which used contextual filtering in Fourier domain. In paper [24], the enhancement technique like histogram, Fourier and Gabor are compared and best technique gabor is found. There has been lot of interesting work done in enhancing fingerprints. Sherlock [35] proposed enhancing the features in a fingerprint image by directional Fourier filtering. This frequency domain filtering is computationally less expensive than the spatial convolution of the image with filters. The filtered image is usually binarized or thinned for feature extraction. But there has been an effort to extract features from grey scale images, Maio and Maltoni [16] proposed an algorithm to extract features from gray scale images. The feature extraction algorithm has usually been employed on thinned images. Jain [11] and Ratha [27] developed algorithms for thinned images, their approach has involved local neighbourhood based processing on the images. Many authors have identified the need to perform post processing on fingerprint images to remove the false minutiae, Ratha et al., where the minutiae are validated based upon heuristics like distance. Since the fingerprint based system rely on matching between the query fingerprint and the database fingerprint, classification of the database results in the query only searching in a particular class. Many attempts [16] [20] have been made to classify the fingerprints based upon core as well as delta points; these have been point based approach. The matching forms the heart of any fingerprint; the query fingerprint of even a client is usually a transformed version of the database fingerprint. This involved registration of the images before obtaining the match. There have been several prior approaches that addressed this. Ranade and Rosenfield [26] proposed an iterative approach for obtaining point correspondences. The fingerprint enhancement techniques proposed by Chen et al. [27], is based on the convolution of the image with Gabor filters which has the local ridge orientation and ridge frequency. The algorithm includes normalization, ridge orientation estimation, ridge frequency estimation and filtering. The paper [24] evaluates the performance of three types of image enhancement techniques and their impact in minutiae detection. In this work we have taken the account of hough transformation and


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

analyzed the results transformations.

with

previous

referred

to the core point and finger code obtained. The paper [32] proposed a fingerprint matching which is more robust at shift and rotation of the fingerprints while it is of high accuracy. A Survey on Ridge feature based matching techniques is proposed in paper [1]. Minutiae based Matching Let T and Q be the feature vectors, representing minutiae points, form the template and query fingerprint, respectively. Each element of these feature vectors is a minutiae point, which may be described by different attributes such as location, orientation, type, quality of the neighbourhood region, etc. The most common representation of a minutiae is the triplet x, y, θ where x, y is the minutiae location and θ is the minutiae angle. Let the number of minutiae in T and Q be m and n, respectively. i =1…m T=m1, m2,…, mm, mi = xi, yi, θi,

Feature Extraction: In this section I describe various levels of feature in fingerprint. The levels of features which is to be extracted are Minutiae, Pores, Ridge Contour Extraction. Minutiae Extraction The next step after enhancement of the image is the extraction of minutiae. The enhanced image is binarized first in this step. The skeleton of the image is then formed. The minutiae points are then extracted by the following method. The binary image is thinned as a result of which a ridge is only one pixel wide. The minutiae points are thus those which have a pixel value of one (ridge ending) as their neighbour or more than two ones (ridge bifurcations) in their neighbourhood. This ends the process of extraction of minutiae points. Let (x, y) denote a pixel on a thinned ridge, and N0, N1,…, N7 denote its eight neighbours. A pixel (x, y) is a

Q=m’1, m’2,…,m’ni (9)

7

Ridge ending

if ( ∑ Ni ) = 1

Ridge bifurcation

if (

m’j = x’j, y’j, θ ‘j, j=1…n

A minutiae mi in T and mj’ in Q are considered matching, if following conditions are satisfied:

i =0 7

∑ Ni ) > 2 i =0

Pores extraction: Pores are extremely fine details which are lost after the enhancement stage. Kryszczuk et al. [15] and [3] have proposed skeletonization based approach for pore extraction. Jain et al. [13] have proposed a pore extraction technique directly from gray scale image. . A recent study [9] by the International Biometric Group has proposed a new approach for pore extraction which utilizes orientation information of pores along with the location information. Ridge Contour Extraction: Ridge contours can be extracted by using classical edge detection algorithms. Jain et al [13] have proposed an algorithm to extract the ridge contours which used a simple filter to detect ridge contours.

sd ( m’j , mi)=

2

((x’j-xi) +(y’j-yi)

2

≤ ro

dd (m’j , mi) = min ( |θ’j – θi|, 360 - |θ’j – θi|) ≤ θo (10)

Here, r0 and θ0 are the parameters of the tolerance window which is required to compensate for errors in feature extraction and distortions caused due to skin plasticity.

Fingerprint Matching: A variety of automatic fingerprint matching algorithms have been proposed in the pattern recognition literature. A useful literature survey on fingerprint recognition can be found in [2]. One family uses correlation based matching [4]. [6] and [19]. Correlation matching is less tolerant to rotational and translational variances of the fingerprint and of extra noise in the image. Another family uses Minutiae-based matching [37], [10], [14]. Minutiae matching are certainly the most well known and widely used method for fingerprint matching. In general minutiae matching are considered by most to have, higher recognition accuracy. The last family uses Ridge feature based matching [12]. Jain et al [12] proposed a local texture analysis where the fingerprint area of interest is tessellated with respect

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The number of “matching” minutiae points can be maximized, if a proper alignment (registration parameters) between query and template fingerprints can be found. Correctly aligning two fingerprints requires finding a complex geometrical transformation function (map ()), that maps the two minutiae set (Q and T) the desirable characteristics of map () functions are: it should be tolerant distortion; it should recover rotation, translation and scale parameters correctly. For the fingerprint enhancement technique, we compare the four types of fingerprint enhancement technique viz., Histogram, Fourier filter, Hough Transform and Gabor filter and find the best enhancement based on the following measures


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III. A NOVEL ELECTRONIC VOTING SYSTEM The main core of this study is to design an electronic voting system based on fingerprint minutiae is discussed in this section by two phases: i) Enrolment Process and ii) Voting Process. i)

allowed to vote. Otherwise he is rejected and give the beep sound. The person who is authenticated may vote for their beloved one by giving his fingerprint to the fingerprint scanner of corresponding nominee. This is the innovation we made so that no person is allowed to press voting button as it is one of the drawbacks of the present voting machine. After the completion of voting, one can know the status of the nominees by clicking the count button.

Enrolment Process

The Fig 6 shows the enrolment process clearly. The Process involved in using fingerprint scanner for election is very simple. First, the chosen finger for example, the thumb is captured and extracted. The fingerprint template is then enrolled and store in a local repository, a database. This primary process is done during the registration process. After that, the chosen finger can be live scan. The fingerprint template is then processed and extracted. It will subsequently match the scanned fingerprint against the stored template. Upon verification, they will have the access to vote for their desired candidates. Mismatched fingerprint certainly would indicate denial form the access.

IV. EXPERIMENTAL RESULTS In this work, we have conducted the Pilot Election using a Personal Computer with four fingerprint scanners for selecting class representative. For that, we have created the database which consists of the fingerprint of the Computer Science department students with the number of 80 (45 males and 35 females). The database is created based on the digital personal scanner. This primary process is done during the registration process. After that, the chosen finger can be live scan. The fingerprint template is then processed and extracted. It will subsequently match the scanned fingerprint against the stored template. Upon verification, they will have the access to vote for their desired candidates. Mismatched fingerprint certainly would indicate denial from the access. During the voting, the voter first places his thumb on the touch sensitive region. If the fingerprint matches he is allowed to vote. In case the print is not stored before, a single beep is given, so the person cannot vote OR if the same person votes again, the system should give a double beep, so that the security can be alerted. The system is programmed to recognize a fingerprint twice, but to give a beep for more than once. There are three nominees for the selection of representative and each student is asked to vote for the candidates they wish by checking their identity through fingerprint and allowing them to vote by giving thumb impression against the fingerprint scanner of candidate. The Table 6 shows the pilot election results.

Fingerprint Scanner Capture the Fingerprint image

FP Enhancement

Minutiae Extraction

Fingerprint Database Fig 6 Enrollment Process

ii) A Novel Design for E-Voting Process

TABLE 6: PILOT ELECTION RESULT

In the Fig 7, the first process is capture the input image, the captured image is then enhanced by using the best enhancement technique Gabor. The next step after enhancement is the extraction of minutiae. After extracting minutiae, it is compared with the template which is stored in the database based on minutiae based matching as proposed in the previous chapter. If the matching result is true, the person is

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S. No

Name of the Candidate

Count of the Votes Polled

1

M. Jeyaraj

20

2

S. Ashik

15

3

P. Kokila

10

4

P. Krishna

35

Total

80


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Fingerprint Scanner

From the results of Table 6 it is declared that Mr. Krishna has been elected as Representative of the Class of Students.

Capture the Fingerprint image

V. CONCLUSIONS AND FUTURE DIRECTION For over a century, fingerprints have been one of the most highly used methods for human recognition; automated biometric systems have only been available in recent years. This work is successfully implemented and evaluated four different models and PC based electronic voting system under Matlab 7.5. The arrived results were significant and more comparable. It proves the fact that the fingerprint image enhancement step will certainly improve the verification performance of the fingerprint based recognition system. The best enhancement technique Gabor is used to enhance the fingerprints for electronic voting and the report of the pilot study for students’ election shown the better accuracy. By the use of this PC based voting system, the student’s representative is elected in a proper way with high security. Because fingerprints have a generally broad acceptance with the general public , law enforcement and the forensic science community, they will continue to be used with many governments’ legacy systems and will be utilized in new systems for evolving applications that require a reliable biometric. In this work, we counted the spurious minutiae and did not address impact of image enhancement algorithm with spurious minutiae removal algorithms and also we are designed only a PC based electronic voting system. In future, we will design a device with Biometric Technology which can be used as if Indian Electronic Voting Machine.

FP Enhancement

Minutiae Extraction

Referring

Not Allow to False Vote and Give Beep Sound

Match Fingerprint Database

-ing

True (Allow to Vote)

FPS1

Nominee-1

FPS2

Nominee-2

FPS3

Nominee-3

FPS4

Nominee-4

FPSFingerp rint Scanner

Election Result

Fig.7 A Novel Design for E-Voting Process ACKNOWLEDGMENT This work is a part of a Research Project and authors are thankful to UGC for funding the Project (File No. F-38-258/2009 (SR) Dt: 19.12.2009).The authors would like to thank the anonymous reviewers for their thorough reviews, and constructive suggestions which significantly enhance the presentation of the paper. REFERENCES Abishek Rawat,, A Hierarchical Fingerprint Matching System, Indian Institute of Technology, Kanpur, July 2009

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Anil K. Jain and David maltoni. , Handbook of Fingerprint Recognition, Springer-verlag New York, Inc., Secaucus, NJ, USA, 2003 Ashbaugh D. R., Quantitative-Qualitative Friction Ridge Analysis: An Introduction to basic and advanced Ridgeology. CRC Press, 1999 Bahuguna R., Fingerprint Verification using Hologram matched filterings, Biometric Consortium Eighth Meeting, San Jose, CA, 1996 California Internet Voting Task Force. A Report on the Feasibility of Internet Voting, Jan.2000. http://www.ss.ca.gov/executive/ivote/ Coetzee L. and Botha E.C., Fingerprint Recognition in low quality images, Pattern Recognition , Vol.26, No.10, pages. 14411460, 1993 Francis Galton. Fingerprints, Macmillan, London, 1892 Frank Vahid and Tony Givargis, Embedded System: Design A unified Hardware/Software Introduction, John Wiley & Sons, Inc, 2002. International Biometric Group. Analysis of Level 3 features at High Resolutions. http://level3tk.sourcforge.net/, 2008 Jain A. K., Hong L. , Bolle R., Online Fingerprint Verification, IEEE Trans Patt Anal Mach Intell, Vol 19, No. 4., Pages 302-314, 1997 Jain A., Hong, L., Pankanti, S., and Bolle, R. An identity authentication system using fingerprints. In Proceedings of the IEEE ,vol. 85, pp. 1365–1388.Sep 1997 Jain A.K, Prabhakar S., Hong L. , Pankanti S., Filter bank based fingerprint matching, IEEE Trans. Image Processing, 9(5): Pages 846–859, 2000. Jain A.K., Chen Y. and Demirkus M., Pores and Ridges: High Resolution Fingerprint Matching using Level 3 features, PMAI, 29(1):15-27, January 2007 Jie Y., Yifang Y., Renjie Z. and Qifa S., Fingerprint minutiae matching algorithm for real time system, Pattern Recognition , pp. 143–146 , 2006 Kryszczuk K., Drygajlo A. and Morier P., Extraction of Level 2 and Level 3 features for Fragmentary Fingerprints. In proc. Second COST Action 275 Workshop, pages 83-303, 1996. Maio D. and Maltoni D. , Direct gray scale minutia detection in fingerprints. Transactions on PAMI, 19(1), 1997. Maltoni D., Dmaio, Jain A.K., Prabhakar S. , "Hand book of Fingerprint Recognition", Springer, 2003. Mariam BT. Samawi , Web Based Campus Election using Thumb Recognition, Mara University of Technology Faculty of Information Technology and Quantitative Science, May 2006 Marsh R.A., Petty G. S., Optical Fingerprint Correlator, US Patent 5050220, 1991 Meltemp Ballan & F. Ayhan Sakarya & Brian L.Evans, "A Fingerprint Classification Technique Using Directional Images". Mercuri R.. Electronic Vote Tabulation Checks and balances. PhD thesis, University of Pennsylvania, Philadelphia, PA, Oct.2000 National Science Foundation. Report on the National Workshop on Internet Voting: Issues and Research Agenda, Mar.2001. Nieuwendijk H.Y.D, Fingerprints. http://www.xs4all.nl/~dacty/minu.htm, October 2006

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Rajnikannan M., Ashok Kumar D., Muthuraj, Estimating the Impact of Fingerprint Image Enhancement Algorithms for Better Minutia Detection, International Journal of Computer Application, No.1 Article 7, 2010 Raju Sonavane, Dr. B.S. Sawant., Noisy Fingerprint Image Enhancement Technique for Image Analysis: A Structure Similarity Measure Approach, SNS International Journal of Computer Science and Network Security, Vol. 7, No. 9, September 2007 Ranade A. and Rosenfeld A, Point pattern matching by relaxation. Pattern Recognition, 12(2):269–275, 1993. Ratha, N., Chen, S., and Jain, A. Adaptive flow orientation based feature extraction in fingerprint images. Pattern Recognition 28, 11, 1657–1672., 1995 Ridges and Furrows – history and science of fingerprint identification technology and legal issues. http://ridgeand.furrows.homestead.com/fingerprint.html Rubin A.D. Security considerations for remote electronic voting. Communications of the ACM, 45(12):39-44,Dec.2002. Rui Joaquim, André Zúquete, Paulo Ferreira Revs- A Robust Electronic Voting Voting System Instituto Superior Técnico Salil Prabhakar, "Fingerprint classification and matching using filterbank", Ph. D. Thesis, 2001. Shaharam Mohammadi, Ali Frajzadeh A . Matching Algorithm of Minutiae for Real Time Fingerprint Identification System , World Academy of Science, Engineering and Technology 60 ,2009 Sharat Chikkerur, Alexander N. Cartwright and Venu Govindaraju., Fingerprint Enhancment using STFT analysis., Pattern Recognition., 40(1):198-211, 2007 Sharath Pankanti, Salil Prabhakar and Anil K. Jain., On the Individuality of Fingerprints, IEEE Trans. Pattern Anal. Mach. Intell, 24(8):1010-1025, 2002 Sherlock, D. B. G., Monro, D. M., and Millard, K. Fingerprint enhancement by directional Fourier filtering. In IEEE Proc. Vis. Image Signal Processing , vol 141, pp. 87–94., 1994 The Thin Blue Line. http://policensw.com/info/fingerprints/finger06.html, October 2006 Tico M., Kuosmanen P., Fingerprint Matching using an Orientation based minutia descriptor, IEEE Trans. On Patt. Analy and Mach Intell, Vol. 25, No. 8, Pages 1009-1014, 2003 Voting: What Is; What Could Be, July 2001. http://www.vote.caltech.edu/Reports/ WUZHILI, "Fingerprint recognition," Student project, Hong Kong Baptist University, April 2002. Xia X. and O’Gorman, L. Innovations in fingerprint capture devices. Journal of Pattern Recognition, Pergamon Press, Vol. 36, No. 2, pp. 361-370, 2002 Xuejun Tan Bhanu, Yingqiang Lin B. , Fingerprint classification based on learned features, Center for Res. In Intelligent Syst., Univ. of California, Riverside, CA, USA; Aug 2005. Zhou Wang ,Alan Conrad Bovik’sJain,A., “Image Quality Assessment :from error visibility to structure imilarity IEEE transaction On image processing” Vol.13 No4, April 2004


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GPS Tracking Simulation by Path Replaying G. Rajendran#1, Dr. M. Arthanari#2 , M. Sivakumar#3 #1

Assistant Professor of Computer Science, Government Arts College (Autonomous), Salem-636007,India. #2 Director, Bharathidasan School of Computer Applications, Ellispettai-638116, Tamilnadu, India. #3 Doctoral Research Scholar, Anna University, Coimbatore, Tamilnadu, India.

Abstract—GPS (Global Positioning System) has a variety of applications among which real-time tracking finds significance in day-to-day life. GPS tracking is defined as the measurement of object position and orientation in a given coordinate system using GPS data at different points of time. GPS data are collected from GPS receivers attached to the moving objects and these data are used for tracking objects in real-time. Researchers who work in GPS tracking need GPS databases which contain huge volume of GPS data generated by hundreds of GPS receivers. But the presently available GPS databases are owned by private players and are not available for use by the researchers. This work is an attempt to generate a database of GPS data which can be used by the researchers to develop and test GPS applications. The approach consists of three successive steps: Collecting floating car data (FCD) of each path once in a log file; refining the log file; and replaying multiple instances of several log files simultaneously after replacing some old values with new values to simulate GPS tracking. Thus a single path tracked previously can be used to produce a tracking simulation of a number of moving objects by path replaying and each and every execution of the simulation generates a set of new GPS tracking data of several moving objects. These data are stored in a database and can be used as sample data for developing and testing GPS applications.

Keywords: GPS receiver, GPS simulation, GPS data, Real time tracking. I. INTRODUCTION The applications of GPS tracking in real time have found its place in almost all walks of life, for instance, navigation, map making, land surveying, fishing and trekking. GPS has many technical and economical benefits to almost all industries and nowadays many companies are developing GPS [1] enabled applications and systems. More research is being carried out in this domain as GPS has the unique capability of locating any moving object over the earth in terms of latitude, longitude and altitude with high accuracy.

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Private and Government owned transport, shipping and cargo companies have started using GPS to track their vehicles. They maintain a tracking database of their own vehicles and they do not want to share this database with anybody else because of the fear that these data may be used by their competitors. But researchers need GPS databases generated by hundreds of GPS receivers. This introduces a new problem domain of non-availability of sample data to develop and test GPS applications. Hence the need for a GPS tracking simulator is vital to generate GPS databases. Such a simulator will greatly reduce the expenses in research area and enable us to have more researches carried out in the allocated budgets. This paper presents a GPS tracking simulation process which produces a database of GPS tracking data. GPS simulation has already been carried out by some of the researchers, but with limitations like hardware dependency, involvement of certain cost, complexity, lack of provision for GPS database creation and lack of provision for integration of digital maps. These limitations have been addressed in this work. The remainder of this paper is organised as follows. Section 2 of this paper describes the tracking of moving objects using GPS. Previous work in this area is discussed in Section 3. In Section 4, the simulation scenario is introduced. The results of the simulation along with a comparison of output data items with real-time data are dealt in Section 5. The work is concluded and the possible improvements are discussed in Section 6. The path replaying simulator has been designed using Matlab 7.6. II. TRACKING USING GPS A. A. Global Positioning System There are many thousands of civil users of GPS system world-wide. GPS is a Satellite Navigation System funded, controlled and operated by the U. S. Department of Defense [2, 3]. The GPS system consists of three segments viz., satellites that transmit the position information, the ground stations


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that are used to control the satellites, and finally there is the receiver that computes its location anywhere in the world based on information it gets from the satellites [4]. The satellite segment consists of a minimum of 21 satellites and 3 working spares. The GPS satellites broadcasts two signals, PPS (Precise Positioning Service) which is available for use by military and government and SPS (Standard Positioning Service) which is available for use by public [5]. The Control Segment consists of a system of control stations located around the world. The Master Control facility is located in Colorado. These stations measure signals from the satellites which are incorporated into orbital models which in turn compute precise orbital data and satellites clock corrections for each and every satellite. The Master Control station uploads ephemeris and clock data to the satellites. The satellites then send subsets of the orbital ephemeris data to GPS receivers over radio signals. The Receiver Segment consists of GPS receivers which are used for navigation, positioning, time dissemination and other applications. A GPS receiver receives signals from more satellites than are actually needed for a position fix. The reason for this is that if one satellite becomes unavailable, the receiver knows exactly where to find the best possible replacement. Three satellites are required for two dimensional positioning and four satellites are required for three dimensional positioning. Two dimensional positioning reports position only in terms of latitude and longitude whereas three dimensional positioning reports position in terms of altitude as well. In general, a GPS receiver can provide position information with an error of less than 10 meters, and velocity information with an error of less than 5 meters per second.

Many of the tracking systems combine GPS, GSM technologies. In less than ten years since the first GSM network was commercially launched, it became the world's leading and fastest growing mobile standard, spanning over 200 countries. There is at least one cell tower in every 900m-1000m radius in the high traffic regions (city limits) and in the low traffic regions (high ways) a single cell tower can cover a radius up to 10 km. The operation of GPS tracking is explained in Fig. 1. The GPS receiver captures position data from the satellites, computes the position of the object, say, a vehicle, and sends this information to a central base station, using SMS (Short Message Service) or GPRS(General Packet Radio Service). If the optional storage module is installed, location data can even be stored when the vehicle is out of range of the cellular operator and retrieved later. GSM technology is used to transmit this information which in turn is collected by the server at the base station. The geographical position of the object can be displayed at the base station using a suitable application. B. B. NMEA Specification The NMEA (National Marine Electronics Association) has developed a specification that defines the interface between various pieces of marine electronic equipments. The NMEA standard permits marine electronics to send information to computers and to other marine equipments [6] in predefined formats. GPS receiver communication is defined with NMEA specification. Most computer programs that provide real time position information recognize data that are in NMEA format which includes the latitude, longitude, velocity and time computed by the GPS receiver. In NMEA specification system, data is sent as a line of text, called a sentence which is totally self contained and independent from other sentences. The data is contained within this single line and the data items are separated by commas. The commas act as terminators for the sentences and the programs that read the data should only use the commas to determine the end of a data item. The GPS receivers produce GPS data in the form of standard NMEA sentences. The most important NMEA sentences include the $GPGGA which provides the current fix data, the $GPRMC which provides the minimum GPS sentences information, and the $GPGSA which provides the Satellite status data. The $GPRMC sentence is used for the tracking of moving objects.

Fig. 1 GPS tracking with the help of a GPS receiver fixed in a moving object.

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III. PREVIOUS W ORK GPS simulators help the researchers to carry out their research work with minimal cost and with accurate data which they need. Though some work has been done in this area, more GPS simulators are being developed to suit the custom needs of the hour. A GPS device emulator [6] namely, the GPS Generator PRO, has been designed for providing assistance in developing, testing and debugging programs and equipment working with the NMEA0183 protocol. This emulator generates NMEA messages from different data inputs. It can operate in 2 modes: 1)User select map, start point, heading, speed; 2)User select NMEA log file. The user can change speed and heading during simulation. The generated NMEA sentences can be used by other mapping software. But buying this software involves cost. A device for generating NMEA sequences for testing embedded GPS reception firmware and hardware is described by Sinivee V [7]. This work describes a prototype GPS data simulator designed and built in Department of Physics of Tallinn University of Technology, Estonia. Device can work in standalone mode and also in conjunction with control software. Configuration program can be used to generate test strings without tester hardware as well. First version of the device was limited to generating only one NMEA message and enabled simulation of communication errors. Later versions were developed to a more universal device with control via a GUI running on an ordinary PC. But there is no option provided in this software to create a database of NMEA sentences. Hardware dependent GPS simulators [1, 8] are also available and they operate by generating pseudo GPS signals.

researchers to develop and test GPS applications. This generator can be used not only by software developers, but also by users, who want to learn navigation software before buying GPS receiver. This system generated random directions, but not based on the existing routes available on ground. Thus a few number of GPS tracking simulators are available but with some limitations. Mostly these simulators involve some cost and hardware dependent. Some simulators suffer from lack of support of customized digital maps. Some of the generators require keyboard or mouse control for path creation. In some generators, there is no provision for database creation. The GPS tracking simulator proposed in this work addresses these problems. IV. MODELLING DYNAMIC ENVIRONMENT OF MOVING OBJECTS BY PATH REPLAYING The dynamic environment of moving objects is modelled using the following steps. C. D. E.

Collecting Floating Car GPS data in a log file Log File Pre-processing Replaying multiple instances of several log file.

F. A. Collecting Floating Car GPS data in a log file Nowadays, the main research focus in the community of Intelligent Transport Systems (ITS) is how to acquire real-time and dynamic transportation information. This information can be applied in the transportation area like vehicle tracking, navigation, road guidance and so on. GPS is one such system which is used to provide real time information on moving objects.

A keyboard or mouse controlled NMEA sentence generator, Virace GPS Simulator [9] V0.01, can produce 3 COM port outputs. This simulator supports NMEA sentences like $GPRMC, $GPGGA, $GPGSA and $GPGSV. A lot of defined keys for steering and speed are available in this simulator. It supports three display and input formats of latitude and longitude. The disadvantage of using this is that it does not support GPS track replaying. A work in this area has recently been done by the authors [10] to generate a sequence of NMEA sentences which in turn are used to simulate a GPS tracking environment. The generated data were similar to the data generated in real time by a GPS receiver and this simulator was used to create a database of sample GPS data which can be used by

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Fig. 2 A moving vehicle (Floating car) fixed with a GPS receiver


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The Floating Car (Probe Car) technique is one of the key technologies adopted by the ITS to get the traffic information in recent years [11]. Its basic principle is to periodically record the location, direction, date, time and speed information of the traveling vehicle from a moving vehicle with the data of the GPS as shown in Fig 2. The information can be processed by the related computing model and algorithm so that the floating car data can be associated with the city road in real time [12]. This data can also be used as a source of data for creating research and commercial applications on vehicle tracking and road guidance systems. Mostly, the GPS receivers generate $GPGGA, $GPGSA, $GPRMC, $GPVTG and $GPGSV sentences at a regular time interval. A sample list of NMEA sentences produced by the GPS receiver and stored in a log file when travelled in a road is given in Fig. 3.

07742.4325,E : Longitude 77 deg 42.4325' E 31.6 : Speed over the ground in knots 317.52 : Course over the ground 140510 : Date – 14th of May 2010 A : Autonomous mode *62 : The checksum data, always begins with * TABLE I $GPRMC DATA FORMAT

Data Item

G. B. Log File Pre-processing The log file contains a number of different types of sentences but the $GPRMC (recommended minimum sentence C) provides the essential GPS PVT (Position, Velocity and Time) data. All GPS receivers output this sentence along with some other sentences. This data is used to locate moving objects in terms of latitude and longitude. The moving object, if attached with a GPS receiver, can be located with the help of this NMEA sentence. The $GPRMC data format [13] is given in Table 1.An example of $GPRMC NMEA sentence is given below: $GPRMC,120642.206,A,1118.4253,N,07742.4325, E,31.6,317.52,140510,,,A*62 Where $GPRMC : Recommended Minimum sentence C 120642.206 : Fix taken at 12:06:42.206 UTC A : Status A=active or V=Void. 1118.4253,N : Latitude 11 deg 18.4253' N

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Description

$GPRMC

RMC protocol header.

UTC Time (Coordinated Universal Time)

hhmmss.sss

Fix time to 1ms accuracy.

Status

Char

A Data Valid. V Data invalid.

Latitude

Float

Degrees * 100 + minutes.

N/S Indicator

Char

N=north or S=south.

Longitude

Float

Degrees * 100 + minutes.

Char

E=East or W=West.

Float

Speed Over Ground in knots

Float

Course Over Ground in Degrees

Date

ddmmyy

Current Date

Magnetic Variation

Blank

Not Used

E/W Indicator

Blank

Not Used

Mode

Char

A Autonomous

Checksum

*xx

2 Digits

Message Terminator

<CR><LF>

ASCII 13, ASCII 10

E/W Indicator

Fig. 3 Log file of floating car GPS data with $GPGGA, $GPGSA, $GPRMC, $GPVTG and $GPGSV sentences

Format

Message ID

Speed Ground

over

Course Ground

over

Hence the next step in the simulation process is to refine the log file by removing other sentences in such a way that it contains the $GPRMC sentences only as shown in Fig 4. This refined log file now contains the path of the probe car in terms of latitude and longitude at an interval of one second per sentence. In this context, replaying means picking the $GPRMC sentences one by one from the log file and plotting the latitude and longitude position of the object continuously in a map. During replay, new $GPRMC strings are also generated for the moving object as described below. For instance, consider the following sentence in refined log file.


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$GPRMC,120642.206,A,1118.4253,N,07742.4325, E,31.6,317.52,140510,,,A*62

$GPRMC,111315.773,A,1120.8973,N,07743.0879, E,0.00,286.94,240410,,,A*6F

Fig. 4 Refined Log file of $GPRMC sentences

H. C. Replaying Multiple Instances of Several Log Files During simulation, the values in the date and time fields in the above sentence are replaced with system date and time values as shown below. The microtime in the time field is left unaltered because of its insignificance. The check sum field is also left unaltered as it is insignificant in simulation. The remaining values are treated as current values for simulation. So the newly generated sentence for simulation will be $GPRMC,151245.206,A,1118.4253,N,07742.4325, E,31.6,317.52,221110,,,A*62 The different fields of the newly generated sentence for the currently moving object can be extracted and stored in the database. V. GPS TRACKING SIMULATION RESULTS Wonde-X series GPS receiver (ZX4125) was used in order to produce log files once for each path. The GPS receiver is fixed in a moving car and the NMEA sentence generated by it are stored in the log file in a laptop kept in the moving car. The log-file is then refined and replayed to produce simulation of one moving object. During replay, new $GPRMC sentences are produced out of the sentences present in log file. For instance, the first ten sentences originally available in the log file for a particular path is given below.

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$GPRMC,111316.773,A,1120.8972,N,07743.0879, E,0.00,286.94,240410,,,A*6D GPRMC,111317.773,A,1120.8972,N,07743.0879, E,0.00,286.94,240410,,,A*6C $GPRMC,111318.773,A,1120.8972,N,07743.0878, E,0.00,286.94,240410,,,A*62 $GPRMC,111319.773,A,1120.8972,N,07743.0878, E,0.00,286.94,240410,,,A*63 $GPRMC,111320.772,A,1120.8972,N,07743.0877, E,0.00,286.94,240410,,,A*67 $GPRMC,111321.772,A,1120.8974,N,07743.0872, E,1.85,275.60,240410,,,A*6E $GPRMC,111322.772,A,1120.8979,N,07743.0860, E,4.79,292.27,240410,,,A*6F $GPRMC,111323.772,A,1120.8988,N,07743.0842, E,6.96,294.28,240410,,,A*6A $GPRMC,111324.771,A,1120.8996,N,07743.0819, E,8.56,290.47,240410,,,A*60 The new $GPRMC sentences produced from the above sentences during simulation are listed below. $GPRMC,040210.773,A,1120.8973,N,07743.0879, E,0.00,286.94,251110,,,A*6F $GPRMC,040211.773,A,1120.8972,N,07743.0879, E,0.00,286.94, 251110,,,A*6D $GPRMC,040212.773,A,1120.8972,N,07743.0879, E,0.00,286.94, 251110,,,A*6C $GPRMC,040213.773,A,1120.8972,N,07743.0878, E,0.00,286.94, 251110,,,A*62 $GPRMC,040214.773,A,1120.8972,N,07743.0878, E,0.00,286.94, 251110,,,A*63 $GPRMC,040215.772,A,1120.8972,N,07743.0877, E,0.00,286.94, 251110,,,A*67 $GPRMC,040216.772,A,1120.8974,N,07743.0872, E,1.85,275.60, 251110,,,A*6E $GPRMC,040217.772,A,1120.8979,N,07743.0860, E,4.79,292.27, 251110,,,A*6F $GPRMC,040218.772,A,1120.8988,N,07743.0842, E,6.96,294.28, 251110,,,A*6A $GPRMC,040219.771,A,1120.8996,N,07743.0819, E,8.56,290.47, 251110,,,A*60


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Fig. 6 GPS database extracted from new $GPRMC sentences produced by the simulator

Fig. 5a

Thus each moving object is associated with new $GPRMC sentences generated out of old sentences available in refined log file. The digital map built in the earlier work [14] of authors is used to plot objects in a 2D-plane. Multiple instances of the log-file are replayed in parallel to get simulation of several moving objects as shown in Fig. 5a and 5b. The values of fields in the new strings are stored in the database as shown in Fig. 6. It can be noted that only the significant values in the $GPRMC sentence is stored in the database along with an object identification field, say, vehicle number.

Thus, it is observed that the new $GPRMC sentences generated during simulation resemble with the sentences generated by actual tracking. Besides, a replay of multiple instances of the previously tracked path produces a GPS tracking simulation of moving objects on ground. The significant values are stored in the GPS database. VI. CONCLUSION AND FUTURE WORK This paper introduces a GPS tracking simulation process which is used to simulate a number of moving objects by path replaying. It is found that the generated sentences are similar to the data generated in real time by a GPS receiver and they are found to fit within standards. This GPS tracking simulator has eliminated the limitations of the previous work carried out in this area. The data generated by this software are used to create a database of sample GPS data which can be used by researchers to develop and test GPS applications. This generator can also be used by software developers as well as the novice users of GPS to learn navigation software. At present this system simulates a fixed number of vehicles initially set during the execution. In future, this work can be extended to simulate random number of moving vehicles at any point of time. ACKNOWLEDGMENT

Fig. 5b

The authors would like to thank Hashprompt Logistics Management India Pvt. Ltd., for their support to perform demonstration on GPS tracking using their resources.

Fig. 5a, 5b Moving objects simulated and plotted in the map at different instances of time.

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REFERENCES [1] Yun Young-sun, Park Sung-min and Kee Chang-don, "Test of GBAS Integrity Monitoring System Using GPS Simulator," Wuhan University Journal of Natural Sciences, vol. 8, no.2B, pp. 697-704, 2003. [2] Parkinson, B. W. and Spilker, J. J., "Global Positioning System: Theory and Applications," American Institute of Aeronautics and Astronautics, Washington, 1996. [3] Interface control document, Navstar GPS Space Segment (Navigation User Interfaces), 2000. [4] Asoke K Talukder and Roopa R Yavagal, "Mobile ComputingTechnology, Applications and Service Creation," Tata McGraw Hill Publishing Company, 2005. [5] Goran M. Djuknic and Robert E. Richton., "Geolocation and Assisted GPS," Computer, vol. 34, no. 2, pp.123-125, 2001. [6] http://avangardo.com accessed on 10-10-2010. [7] Sinivee V., "Simple yet efficient NMEA sentence generator for testing GPS reception firmware and hardware," Novel Algorithms and Techniques in Telecommunications and Networking, Springer Netherlands, pp.207-210, 2010. [8] Kou Yanhong, Yang Dongkai and Zhang Qishan, "GPS Satellite Simulator Signal Estimation based on ANN," Journal of Electronics(China), vol. 22 no.5, pp.458-464, 2005. [9]http://www.gpspassion.com/forumsen/topic.asp?TOPIC_ID=114 933 accessed on 10-10-2010. [10] Rajendran G., Arthanari. M., and Sivakumar M. "A Simplified NMEA Sentence Generator for the Simulation of GPS Tracking," Global Journal of Computer Science and Technology, vol. 10, no. 14, November 2010. [11] X. W. Dai, M.A. Ferman, “A simulation evaluation of a realtime traffic information system using probe vehicles,” IEEE ITSC, vol. 1, pp. 12-15, 2003. [12] R. Kuehne, R.P. Schaefer and J. Mikat, “New approaches for traffic management in metropolitan areas,” IFAC CTS, 2003. [13] http://www.gpsinformation.org/dale/nmea.htm accessed on 10-10-2010. [14] Rajendran G., Arthanari. M., and Sivakumar M., "Customized Digital Road Map Building using Floating Car GPS Data," International Journal of Computer Science and Information Security, vol. 8, no. 3, pp. 21-29, June 2010.

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Object Oriented Design of E-learning System for Distance Education #1

#2

#3

S. Mukhopadhyay , S. Dan , A.K.Singh , Department of Computer Sc.Unversity of Burdwan, India #2 Registrar, University of Burdwan, India #3 Department of Computer Sc. Netaji Mahavidayalaya(B.U) Arambag , India #1

is a continuous process where the learner deprived to attend formal mode of education.

Abstract: E-Learning, an important component of EEducation, is a novel system for communication between learners and teachers. In this paper we have tried to design an Object Oriented UML based ELearning system for distance education of an institute with the objective to enhance the quality of teaching learning process in distance mode. The various objects participating in the system have been identified and shown by USE CASE diagrams and the object oriented development process, object to object communication among the objects, is shown by sequence diagram.

E-Learning mechanism involves a variety of professionals such as Instructional designers, Course Writers/ Content Creators, Reviewers, Graphic Designers and Knowledge Organizers/Library, and Information Professionls. Vental and Prakash [4] have described the following figure of e-education environment for higher education.

Key words: E-Education, Distance Education, Digital library, O-O Design Course Writers

Instructional Designers

Content Organiser

I. Introduction

Knowledge Organisers/Library and Information

E-Learning allows learners to study from home, minimizes the distance between learners and teachers, and enables institutes to provide a high quality education with minimum cost through distance mode.

Partners in E-learning System

Content Creators

Graphic Visualize Graphic Designer

Digital Libraries

Distance education is really a golden opportunity for those who are not in a position to continue their education in normal mode, attending classes regularly, due to some reasons. Some disadvantages of distance education which does not include direct provision of regular interaction between a student and a teacher, no question of revision, etc may be solved by e-learning. In e-learning, a component of eeducation, instructions are imparted with the help of computer and communication technology.

E-Learning System/ E-Universities

Internet Resources BBS Network Newsgroups Electronic Conferences Discussion Forums

Non-Print Media Microfilms Floppies Slides Audio Tapes Video Tapes Data Cartridges CD-ROMS

The web is a powerful information delivery mechanism. It has lots of information, which can get to the learner fast. The crucial issue is there must be a real value to the information that is being posted on the web. The web is conceptually a kind of library, so called Digital Library, a storehouse of information to go to, in order to get something to read and something to do.

The ultimate objective of e-learning is to guide the learners in every corner including remote place of the country and thereby providing education for all. E-Learning covers a wide set of application and processes. It involves delivery of content of resources for learning via different communication protocols. It

27


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Information collection, organization and dissemination greatly affected by technology thereby making/the predictions of Lancaster’s paperless society to reality. Digital Library collection contains fixed , permanent documents. Not only that current Libraries have more dynamic collections, but digital environment will enable of quick handling. Digital Libraries [1] are based on digital technologies. These will break all the physical barrier of data transfer. It can store the large amounts of information in various form i.e. text, audio, video and graphic material. Learners can make effective search for the information in digital libraries with sophisticated search engines. Learner can browse required material and can easily download into his/her system. Overall the Digital Libraries will greatly support the eLearning environment.

II. Objective of E-learning The main objective of e-learning is to guide the student / learner of every corner of the country. Elearning provides education all times so there is notes question of day and night . Students are free to access their notes / tutorials and assignments any time.

III. Benefits of E-learning in Distance Education The benefits of e-learning in distance education is summaried below

• •

Typical e-learning web content one normally finds on the web:

Minimize the distance between the learner and teacher . e-learning allows people to study from home. e-learning provides a high quality and efficient educations with minimum cost. e-learning provide education in 24X7 mode.

Read text: press button for next page. IV. Objects for e-Learning system and Use cases

Read text: make choice from numbered list; receive score. Read question: answer; get feedback; read next question.

Learner: End users of the system as shown in Use Case 1 of learner.

Read lots of text; answer questions at end.

Course Designer: Responsible for designing course curriculum.

Take test immediately; learn score; get feedback. These examples are simply imitating the commonly accepted notion of what education looks like-which does not involve the way people really learn. “People really learn by doing a task they care about, failing and redoing it until they get it right”.

Content writer: Responsible for writing the course materials for a particular course. E-Course library/ Digital Library: Output of Course Designers and Content writers. Administrative Manager: He/She is in-charge of over all administrative functions.

Object oriented system design using UML [2,3] is an important research activity now-a-days. Different steps of O-O design are design of classes, identification and construction of different objects, and ultimately establishing object to object communication.

Briefly we describe the use cases as follows: Use Case 1: Learner-Different Cases are Enrollment, View course, Browse, and Examination.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Use Case 4: E-Course Library/ Digital Library - Five possible Use Cases are provided here, namely, Login, course, Internet resource, non print media and Update Library. Learners have to login to the system first. Digital Library contains several course materials in digital form may be available in CDs/Pen drives or through Internet. Digital Library must be updated regularly.

Enroll

Exam inatio

Bro

Login

Course

Vie Use Case 1 Use Case 2: Course Designer- Four different cases for course designer are Update course, setting question papers, updating model questions, monitor users ( learners) performances.

Inte rne

Update Library

No Update

Use Case 4 Monitor User

Question for

Use Case 5: Administrative Manager- Six different Cases have been proposed for administrative manager. These are Guideline to be given to the learners, Form Design, supervise the course may be named as Course Monitoring, fees collection, controlling examination activities, presenting results.

Examinat

Upd ate Use Case 2

Guidel ine

Use Case 3: Content Writer- Two cases are related to Content Writer. These are write contents of the course and updating the contents as and when needed.

Form Design

C

Con Fee Collectio

Update

Result

Use Case 5

Use Case 3

29

Exam Control


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Sequence Diagram demonstrates the dynamical behaviour of objects in the system in a use case by describing the communication among objects by message passing. In our proposed system content

V. Sequence Diagram Lea

E-

Cont

Cour

Admi nistr

VI. Conclusion: E-Learning is an important component of EEducation and education for all is possible if eLearning is implemented in distance education. O-O implementation of E-Learning is simple and easy to implement for software development point of view.

References [1] A. M. Midda and S. Mukhopadhyay, “Information and Communication Technology in E-Education”, Journal of Library and Information Technology, Vol 2, No. 1, 2006 ,pp 42-47. [2] S Banerjee, D E Booth, S Ghosh, S Mukhopadhyay, “A prototype Design for Intellectual property Right Management in ECommerce-A UML Based Approach”, Journal Of Computer Society of India, Vol 36, No 4. , 2006, pp 12-17. [3] A.K Singh and S Mukhopadhya, “E-Learning in Distance Education using UML”, Int. Journal of Information and Computing System, Vol 12, No 2, 2009, pp 30-35. [4] R. M. vental, K Prakash, “Introducing Electronic Information Resources , through E- learning Mechanism : A Study with Reference to Distance Education in its Environment” University News, Vol.42(14), April 05-11,2004 [5] L. Vernal, M.U. Paily, “ICT in Teacher Education” , University News , Vol.42 (39), September 27-03 October , 2004.

writer constructs the contents of digital library. Learners log into the e-course library, login is verified and a confirmation is passed to the learners. Then learners can browse from the E-Library and access tutorial sessions. If a learner does not find the desired information, reports to the content writer. Then content writer communicates with course designer for update the library. Request may also come from administrative manager from time to time to the course designer for updating the library. Course designer then updates the course and instructs the content writer for updating. Content writer then updates the e-Library and reports to the designer. Course designer reports to the Administrative Manager. Finally Administrative Manager informs the learners about updation.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

ON THE RELATIVE CHARACTER GRAPH OF A FINITE GROUP Dr.K.T.NAGALAKSHMI#1

Dr.A.V. JEYAKUMAR#2

Department of Mathematics Department of Mathematics K.L.N.College of Information Technology Periyar Maniammai University Pottapalayam-630 611,Sivagangai District Vallam,Tanjore District Tamil Nadu, INDIA Tamil Nadu, INDIA For the basics of character theory of G, we shall refer to [4] . The universal notations such as IrrG, χ H, θ G and [ χ , ψ ] will stand for the complete set

Abstract—This paper,in a sense, is a sequel to an

earlier construction by T.Gnanaseelan of a graph Γ (G, H) for any finite group G and a subgroup H using (complex) irreducible characters (See [3]). We construct another graph

of irreducible characters of G, the restriction of a character χ of G to a subgroup H, the induction of a

Ω (G, H), which is structurally

quite different from Γ (G,H). However, we prove that for the special case of the sequence of subgroups Sn-1 ⊂ Sn ⊂ Sn+1 (where Sn is the symmetric group on n

letters), Γ (Sn, Sn-1) and isomorphic.

Ω (Sn+1,

character of H to G and the scalar product

1 O (G )

Sn) are indeed

χ (s) ψ (s −1 )

(of

course,

all

S ∈G

representations are finite dimensional taken over the complex field C / ).

Key words: Character Theory, Graph Theory, Group Theory, and Representation Theory. 1) Definition : The relative character graph Γ (G, H) of G with respect to a subgroup H has IrrG as its vertex set and two distinct χ , ψ in IrrG are adjacent

I. INTRODUCTION From the time of R.Brauer, various finite graphs have been constructed using mostly irreducible characters (both complex and p – modular) of a finite group G. These graphs in general give a pictorial representation of the intricate nature of irreducible characters of G. For instance, the famous Brauer graph has as vertex set the full set of complex irreducible characters of G and two distinct vertices are incident if and only if their reduction mod p (where p is a prime dividing O (G)) contains atleast one p-modular irreducible character in common.

χ H and ψ H have atleast one element of IrrH in common. This is equivalent to saying that [ χ , ψ ]H > 0. Clearly Γ (G,H) is a simple graph in graph if and only if

theoretic sense, that is, it has no double edges and self-loops. To begin with the following easy observations can be made. 2) Γ (G, H) is the null-graph if and only if H = G.

Quite recently, T.Gnanaseelan in his Ph.D. thesis [17] has constructed a new finite graph for any subgroup H of G, which he calls the relative character graph of G over H and denotes by Γ (G, H). In the next section, we shall define Γ (G,H) and recount some of the salient properties as proved in [ 17] .

3) If H is the trivial subgroup, then Γ (G,H) is a complete graph, but even for certain types of nontrivial subgroups H, Γ (G,H) can be complete. For instance, if H is a cyclic group generated by x and if all the matrices ρ i (x), where ρ i runs through a full set of inequivalent irreducible representations of G, have 1 as eigen value, then Γ (G, H) turns out to be complete. (This can be of some interest in the representation theory of finite Chevalley groups).

II. THE GRAPH Γ (G, H)

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namely {1}, { θ 2, θ 3} and { θ 4}. Let 1G, χ 2, χ 3, χ 4

4) If H and K are two subgroups of G such that K H, then Γ (G,H) is a subgraph of Γ (G,K). 5) If,

x ∈G

χ 5 be the irreducible characters of S4 with

and

then Γ (G, H) = Γ (G, Hx), where Hx

degrees 1, 1, 2, 3 and 3 respectively.Then

θ G2 = θ 3G = χ 3 and

1GH = 1G +

is the conjugate of H under x.

χ 2,

6) Γ (G, H) is connected if and only if core GH = (1).

conclude that Ω (L,G) has three connected components, namely, {1G, χ 2}, { χ 3} and { χ 4, χ 5}.

Ω (G,H) are not

Naturally, Γ (G,H) and isomorphic.

7) A connected graph Γ (G, H) is a tree if and only if G is a Frobenius group NH, and the kernel N is a unique elementary abelian normal p-Sylow subgroup for some prime p with order pm and O(H) = m P - 1.

III. THE GRAPH

θ G4 = χ 4 + χ 5. We

We do not propose to study the graph Ω (G,H) systematically here such as the connectivity properties etc, as was done by Gnanseelan for his graph Γ (G,H). However, taking cue from the last example, we make some beginning which partially compares with results of Gnanseelan mentioned in [7].

Ω (G, H).

We shall now construct another finite graph, again with reference to a subgroup, which, in a sense, will be dual to the graph Γ (G, H).

2) Proposition : If H is a normal subgroup of G, then Ω (G,H) is disconnected. (If H is trivial Ω (G,H), is just a dot, whereas Γ (G, H) is complete).

1) Definition : The vertex set of Ω (G, H), is IrrH

Proof : Let O1, O2, . . . ., Os be the complete set of distinct orbits of IrrH under the conjugate action of G an IrrH. Then by Clifford’s theorem it is clear that two distinct θ, φ, ∈ IrrH are adjacent in Ω (G,H) if and

θ and φ are adjacent if and only if G G the induced characters θ and φ have atleast one element of IrrG in common,in other words[ θ G, φ G]G > 0. The structural properties of Ω (G, H) differ in many ways to those of Γ (G, H). To begin with, even

and two distinct

only if

θ

and

φ

lie in the same orbit Oi for some i.

Since is disconnected.

the vertices sets of both the graphs are different.

H

∆ G, s >1 and hence Ω

(G,H)

Infact, Ω (G,H) has exactly s components each of which is complete.

But there is one situation wherein the vertices sets of both Γ (G, H) and Ω (G, H) coincide. This occurs when we have a sequence of groups and subgroups of the form H ⊂ G ⊂ L. Here the vertices sets of both Γ (G, H) and Ω (L,G) coincide. But these graphs need not be the same, as can be seen from the following example. Consider the sequence H ⊂ G ⊂ L , where, L is the symmetric group S4, G is the Alternating subgroup A4 and H is the subgroup consisting of the two elements {(1), (12)(34)}. Since core GH = maximal normal subgroup of G contained in H is trivial, using the criterion for connectivity obtained in [3], Γ (G,H) is a connected graph. Using Clifford’s theorem, it is quite easy to see that Ω (L,G) is not connected. To see this, let 1H, θ 2, θ 3

We shall push a little further in the compare and contrast syndrome vis-à-vis Γ (G, H) of Gnanaselan. As per (1.3), Γ (G, (1) ) is complete But in Ω (G,H) completeness can be achieved in quite different situations. First we shall recall Mackey’s subgroup theorem. 3) G

and

x∈D

and θ 4 be the four distinct irreducible characters of A4 of degrees 1, 1, 1 and 3. The conjugate action of S4 on IrrA4 breaks these 4 characters into 3 orbits,

Theorem (Mackey [1]) : if

θ, φ, ∈

(θ, x φ) H ∩

x

H

IrrH,

If H is a subgroup of then

(θ , G

φ G)

=

, where D is a set of double coset

representatives of H in G. We now prove the following theorem providing a sufficient condition for the completeness of Ω (G,H) 32


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

upper triangular matrices, (q = pn for a fixed prime p) and also G = PSL (2, q) and B = Borel subgroup. For a brief reference of the representation theory of G, we refer to [1]. We straightaway go to the following.

4) Theorem ; Let H be a subgroup of G such that for some x ∈D , the set of (H, H) - double coset representatives of H, H

∩x H

= (1). Then Ω (G,H)

is complete. Proof : By Mackey’s subgroup theorem

(θ, φ) H ∩ x

x∈D

assumption, H (θ, is

x

H

for any two

∩x H

θ, φ, ∈

= (1) for some x

θ G, φ G =

7)

Then [φ

IrrH,. By

x

8)

denotes the characters

φ.

Proposition: When G = GL (2, q), the graph

0 1  (7) gives all the σ =  1 0 characters θ m, n of Ω which can be

Proof is clear. 6) Corollary : If G = NH is a Frobenius group with complement H, then Ω (G,H) is complete. = (1) for all a

Where W is

Proof : As already mentioned, W consists of just two elements namely, 1 and the element =

5) Corollary : If H is non-normal and prime cyclic, then Ω (G,H) is complete.

∩a H

w

Ω (G, B) is connected.

Ω (G,H) is complete.

Proof : By definition, H

, φG ] = | w ∈ W | φ w = φ |

obtained by the conjugate action of w on

φ )(1) > 0, which implies ( θ G, φ G) > 0. Since this true for any two distinct θ, φ, ∈ IrrH, it follows

that

G

the weyl group of G and φ

∈D. Hence

Let φ, θ ∈ , IrrB.

Proposition ( [1 ], p.22):

non-principal

pulled back to give all non-principal irreducible characters of B. It can be easily seen that

∉H .

that only when m

The result now follows from the above theorem. Having made the above observations, it is perhaps worthwhile to point out the significance of both Γ (G, H) and Ω (G, H) both graph theoretically and grouptheoretically. As was mentioned earlier, Gnanaseelan has highlighted many graph – theoretic properties of Γ (G, H). Further study of Γ (G,H) in graph – theoretic aspects such as colouring domination etc may throw more light on the study of character theory of G itself. It is interesting to note that the vertex sets of Γ (G,H) and the Brauer graph are one and the same and therefore comparisons are possible.

θσ m , n

=

θ n ,m . (Note

≠ n the pull backs are irreducible.)

Hence by (7) ( θm , n , θ n , m ) = |w G

G

∈W| θ mw ,n

=

θ n ,m |

=1

Hence (T( θ m , n ), T ( θ n , m ) is non zero for all non principal irreducible characters of B and therefore by the definition of the graph Ω (G, B) all the nonprincipal characters of B are adjacent. Since

Turning to our Ω (G,H) we observe that this graph has a strong bearing on the character theory of algebraic groups and the related finite groups. The reasons are obvious. The study of the character theory of these groups heavily rests on the so-called ‘Harish – Chandra Induction’ and all the fascinating theory governing these aspects can be naturally fitted graph theoretically into our graph Ω (G,H). We propose to take up this study systematically in our future works, but for the moment, we shall briefly outline the construction of Ω (G,B), where G = GL (2,q) and B = the Borel subgroup, the subgroup of

1GB

have some irreducible other than 1G we have B) is connected.

must

Ω (G,

n

The case G = PSL (2, q), (q - p for a prime p), the Projective Special Linear Group of rank 1. The subgroup that we choose is as before the Borel subgroup B - TU, where T consists of the 2 x 2 matrices of determinant 1 and U denotes a p Sylow subgroup (the unipotent subgroup). We have O (T) =

33

q −1 2

when p is odd and q-1 when p is 2,


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

q (q 2 − 1) 2

O(U) = q and O (G) =

χ 2 },

I (1B) = {1G, when q is odd and

q(q2-1) when p is 2. For the complete table of (Complex) irreducible representations of G, we refer to [2] (of course, we should pick out the irreducibles of G from this table, which is quite easy). Now B is a Frobenius group with kernel U and complement T.

(1)

I ( α1 ) = { χ 2 ,

χ 3 , {ψ j }

I ( α2 ) = { χ2 ,

χ 4 , {ψ j }

I( φ i) =

ψj

1≤ i ≤

q −3 , 2

1≤ j≤

(for some j) (2

q −3 2

≤ i≤

We should distinguish two cases now, the p odd case and the p = 2 case.

1≤ k ≤

, { ψ 'k }

q −3 2

1≤ k ≤

q −1 ) 2

}

q −3 2

}

(2)

(3)

(4)

We have now proved the following theorem, whose proof can be gleaned from the equations (1), (2), (3) and (4).

I. The odd prime case. Case 1. q

{ ψ 'k }

≡ -1 (mod 4)

In this case, these are

q+5 2

9) Theorem : The graph connected.We shall draw the graph case p = 7.

distinct

Ω (G,H) is Ω (G,B) for the

irreducible cases of G which are denoted as follows :

χ1

(= the trivial character),

Steinberg character of degree q),

χ 2 (= χ 3, χ 4,

1B

the the

q −1 q −3 , the 2 4 ψ1 , ψ 2 , .... ψ q −3 each for

irreducible characters of degree irreducible characters

α1

4

q −3 4

degree q+1 and the

ψ '1 , ψ ' 2 , .... ψ ' q −3

α2

irreducible characters

each of degree q-1.

4

From the Frobenius groups, we can

q+3 2

exactly

{Φ i }

1≤ i ≤

and

θ

q −1 2

character theory of easily see that B has

φ1

irreducible characters denoted by ( φ 1 = trivial character

α 2 each of degree

≠ 1B), α 1

Case 2 : q

q −1 . 2

denoted

, for any

θ ∈ IrrB . We write

down the induced degrees

covers I ( θ ) as follows :

34

1 (mod 4)

q+5 irreducible characters of G are 2 by χ 1, χ 2 (= Steinberg), χ 3, χ 4, of q +1 q −5 , the irreducible characters 2 4

The

We can very easily calculate the character G

φ1


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

ψ 1, ψ 2, . . . . , ψ

q −1 4

q −5 4

, each of degree q+1 and the IV. THE GRAPHS

characters

ψ '1 ,... ψ q −1

each of degree q-1.

4

We have already noted in (2) that even in the special case of groups given by the sequence H ⊂ G ⊂ L, Γ (G,H) and Ω (L,G) need not be isomorphic. However, it is interesting to note that in the case of the sequence of groups given by Sn-1 ⊂ Sn ⊂ Sn+1, Γ (Sn, Sn-1) and Ω (Sn+1, Sn) are indeed isomorphic. The rest of the paper deals with a proof of this statement.

The irreducible characters of B in this case also are 1B,

α 1, α 2 and {φ i }

1≤ i ≤

q −1 . 2

The induced covers in this case are given below.

I (1B) = {1G,

χ 2 },

I ( α 1) = { χ 2 ,

I ( α 2) = { χ 2 ,

It is well known that the study of irreducible representations of Sn can be made through partitions of n.

(1)

χ 3 , {ψ j }

1≤ i ≤

q −5 , 4

χ 4 , {ψ j }

1≤ j≤

q −5 4

{ ψ 'k }

, { ψ 'k }

1≤ k ≤

q −1 } 4

1≤ k ≤

q −1 } 4

Γ (G,H) AND Ω (G, H) FOR G – THE

SYMMETRIC GROUP SN.

(2)

1) Partitions of N : A decreasing sequence of positive integers i.e λ ≥ λ ≥ . . . ≥

λ = ( λ 1, λ 2, . . . ., λ r)

(3)

1

λ r ≥ 1 is called a partition of n if λ 1+ λ 2+ ....+ λ r = n. We denote this by the symbol λ

I( φ i) =

ψj

(for some j) (2

≤ i≤

λ n The λ i’s are called the parts of the partition λ , and the

q −1 ) 2

We shall draw the graph p = 5.

µ (µ 1 , . . . . r = s and λ 1 = µ i

Two partitions λ = ( λ , . . . . ., λ ) and = 1

r

, µ s) of n are said to be equal if

i.e

integer r is called the number of parts or length of λ .

(4)

We have 10) Theorem : The graph

2

for all i.

(G, B) is connected.

Ω (G, B) for the case when

We shall omit several details regarding the wellknown theory of representations via partitions as developed by Frobenius and Schur and just state the basic facts needed in the sequel.

1B

2)

The set of all conjugacy classes of Sn is naturally bijective with the set of all partitions of n. Since the set of all conjugacy classes of Sn is

α1

naturally bijective with the set of all complex irreducible characters of Sn, it immediately follows that the set of all complex irreducible characters of Sn is naturally bijective with the set of all partitions of

α2

n.

λ of n, one can associate an irreducible module V λ for Sn. The family (V λ ) λ n is a complete set of 3) (Frobenius - Schur) :

φ1

35

To every partition


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Proof : Let [ λ ] ↑ and [ µ ] ↑ have a common

mutually inequivalent irreducible representations of Sn over C /.

irreducible constituent. (Note that we have simplified the notation, since there is no danger of confusion).

λ is a

4) The Branching Theorem ( [ 5 ]): If

partition of n, we shall denote by [ λ ] the unique

(i) Suppose that |r-s| > 1.

irreducible representation V λ associated to λ . We shall simultaneously denote the irreducible (complex) character associated to V λ also by [ λ ]. Given a

Assume that r = s + k, k > 1. That is, λ has s + k parts and µ has s parts with k > 1. By Branching Theorem, the partition corresponding to

θ ∈ IrrH χ ∈ IrrH respectively, we shall denote by θ ↑ GH

subgroup H of a finite group G, and given and

the induced character of

χ

restriction of

θ

to G and by

χ ↓GH

any irreducible constituent of [ λ ] ↑ will have atleast s + k parts and the partition corresponding to any irreducible constituent of [ µ ] ↑ will have atmost s+1

the

to H.

parts.

irreducible

5)Branching theorem for Sn.

Let S

S

S

( λ 1,...., λ j

n

n+1

constituent

± 1,..... λ r) of n ±

1 according as λ

λ j or λ j > λ j+1.

j-1

i

[ λ ] ↑Snn +1 S

equals

≤i≤

>

First let r = s.

i

Suppose

any

r.

µ i - λ i > 1.

Let λ = ( λ ,. . . . ., λ ,. . . ., λ ) and 1

[λ]↓

[λ]↑

(ii) Assume that | λ - µ | > 1 for some i, 1

Then we have Sn Sn −1

of

Hence no

irreducible constituent of [ µ ] ↑ , which proves (i).

in a natural way. Given

λ = ( λ 1, . . . . , λ r ) n, let λ j ± denote the partition n-1

But s + 1 < s + k as k > 1.

=

µ

⊕ j [λ ] and j−

λ j > λ j+1

=

i

r

= ( µ , ..... λ + k , ....., 1

i

i

µ r), ki > 1.

Then, in the partitions corresponding to each irreducible constituent of [ λ ] ↑ , the ith part is either λ or λ +1 whereas the ith part of the partitions

⊕ j [λj+ ] λ j−1 > λ j

i

i

corresponding to each irreducible constituent of [ µ ] ↑ contains entries which are atleast

6) Lemma :

λ = ( λ 1, . . ., λ r) and µ = ( µ 1, . . . .,

Let

µ s)

be two partitions of n. Then [ λ ] ↑

Sn +1 Sn

and

Hence no two irreducible constituents of [ λ ] ↑ and [ µ ] ↑ will coincide.

λ

have a common irreducible constituent if and only if

µi > 1 and µ r = 0

Similar argument holds when λ i

i)

|r-s|

ii)

| λ - µ | ≤ 1 for every i (taking i

for some i. When r = s + 1, λ r = 1 and when s = r + 1, µ s = 1 and λ s = 0, we can prove (ii) using similar arguments, which completes the proof of (ii).

1, i

r = s+1 and

λ

r+1

µ s+1 = 0 when

= 0 when s = r +1

respectively. iii)

|λ -µ | i

i

λ i+ ki.

(iii) Suppose that | λ i

= 1 for exactly two distincts i’s

Σλ i

=

Σµ i

= n, if

some j

= 0 for the remaining i’s.

36

µ i |=1for k

i’s, k > 2. Since

λ i = µ i -1, then there exists


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µ j +1 to maintain parity.

such that λ = j

µ j. Therefore, ( λ 1, . . . . , λ j-1, µ j +1 . . . . . . . , λ k +1, . .

By

..

Branching theorem, only one part of [ λ ] and [ µ ] will

[µ ]↑

respectively.

Therefore

λ k-1 > λ k. λ k+1, . . . .

λ j-1, µ j +1 . . . . λ k-1,

λ s) is constituent of [ λ ] ↑ as well. This completes the proof of the lemma.

increased by at a time, which constitute the parts of and

is a constituent of [ µ ] ↑ . Again

Therefore ( λ 1, . . . .

be

[λ]↑

λ s)

no

λ r) and µ

= (µ ,

be partitions of n. Then [ λ ] ↓

and

7) Lemma : Let

irreducible . . . . ,

µ s)

λ

= (λ , . . . . 1

Sn Sn −1

1

constituent of [ λ ] ↑ will coincide with any irreducible

[ µ ] ↓ Snn −1 have a common irreducible constituent if

constituent of [ µ ] ↑ . That is, [ λ ] ↑ and [ µ ] ↑ will

and only if

S

not have

a common arguments

constituent.

we can prove the result when

Using

µ i = λ i - 1.

similar

i)

|r-s|

ii)

|λ -µ | i

i

µj µ (λ

= 0 when

= 0 when

s = r + 1

i

i

= 0 for the remaining i’s.

1

Proof :Let [ λ ] ↓ and [ µ ] ↓ have a common

= 1. By iii) there exists some j such that

irreducible constituent (as before, we have simplified the notations).

We can take

= λ +1 and for all other i, λ = j

i

µ i.

That means,

(i) Suppose that |r-s| > 1.

, λ , . . . . λ , 1 ) and µ = i-1 i s λ j-1 , λ j+1 , . . . . λ s, 0). Now,

= (λ , . . . . . λ 1

1

s+1

| λ - µ | = 1 for exactly two distinct i’s

iii)

s+1

r+1

µ

µ s+1 = 0 and hence by

. . . . . , µ s). s+1

1 for every i (taking

respectively).

Case (i) : Let | r – s |=1. It is enough to prove the result for r = s+1. Then λ = ( λ , . . . . λ ) and µ = ( µ , (ii) λ

r = s+1 and λ Hence the

result. Conversely, let the three conditions hold.

1

1,

, . . . .

λ j−1 ≥ λ j

+ 1. That

is

λ j−1 > λ j

Assume that r = s+k, k>1. By Branching theorem, the partition corresponding to any irreducible constituent of [ λ ] ↓ will have atleast s+k1 parts and the partition corresponding to any

. Therefore

( λ 1, . . . ., λ j+1, . . . λ s, is a constituent of [ λ ] ↑ clearly, replacing 0 by 1 in 1) [ µ ] ↑

we get ( λ , . . . .,

λ s, 1)

1

λ j-1, λ j+1,

irreducible constituent of [ µ ] ↓ will have almost s parts. Since k > 1, s + 1 < s + k.

Hence no

irreducible constituent of [ λ ] ↓ is equal to any

. . . .

irreducible constituent of [ µ ] ↓ which proves (i).

2) is a constituent of [ µ ] ↑ as well. Case (ii) :

(ii) Assume that | λ i

λ = ( λ 1, . . . . , λ s) and = ( µ 1, . . . . , µ s). By (iii), there exists some j and k such that λ j = µ j+1 and µ k = λ k +1 and λ j = µ i for all other i’s. That is, λ = ( λ 1, . . . . , λ j-1, µ j + 1, . . . , λ k, . . . . . , λ s) and µ = ( λ 1, . . . . , λ j-1, µ j, . . . . , λ k +1, . . . . λ s) Then λ j-1 > r = s. Then

µ

| >1 for some i, 1

i

≤i≤

r.

µ i - λ i > 1. Let λ = ( λ 1, . . . ., λ r ) and µ = ( µ 1, . . . . . µ i + ki, .....,

Let r = s Suppose

λi , . . . . ,

µ r),

k > 1. Then in the partitions corresponding to i

each irreducible constituent of [ λ ] ↓ , the ith part is either λ or λ -1 whereas the ith part of the i

37

i


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

partitions

corresponding

to

each

Therefore, ( λ , . . . . , λ , λ , . . . .0) is a constituent

irreducible

1

[ µ ] ↓ contain entries which are

constituent

of

atleast

λ i + ki

j-1

j

of [ µ ] ↓ clearly the above is a constituent of [ λ ] ↓

- 1. But ki > 1.

as well. Case (ii) :

Hence no two irreducible constituents of [ λ ] ↓

Let r = s. Then

λ i - µ i > 1 for some

1. The cases r = s +1 and s = r + 1 can be dealt with similarly, completing the proof of ii). Suppose that | λ -

(iii)

µ i |=1 for k i’s, k > 2. Σλ i = Σ µ i = n, if λ i = µ i + 1, then some j such that λ = µ -1 to j j i

Since there exists

Again

k

λ k-1 -1, λ k,

[ λ ] ↓ and [ µ ] ↓

k

upon restriction.

Γ (Sn, Sn-1) are isomorphic.

Thus in each part of [ λ ] ↓ and

n

will not have any common constituent.

[ λ ] ↑ and [ µ ] ↑ contain an irreducible character of

µ i = λ i-1.

S

n+1

Sn-1 in common. Again by the two lemmas, the converse statement also holds. Hence Γ (S , S )

Case (i) :

and

Let | r – s | =1. It suffices to prove the result for r = s+1. Then λ = ( λ , . . . . λ ) and µ = ( µ , . . . . ., 1

We can take

µ s+1

s+1

=

(S

n-1

, S ) are isomorphic graphs.

n+1

n

V.CONCLUSION The graphs Γ (G, H) and Ω (L,G) are generally different for H ⊂ G ⊂ L, as we observed earlier. Taking the cue from our result for Sn, it would be interesting to find conditions (in general) on H, G and L so that Γ (G,H) and Ω (L,G) are isomorphic.

λ s+1 = 0. By iii) there exists some j such that = λ -1 and for all other i, λ = i

µ i.

That is,

λ = ( λ 1, . . . . . λ i-1 , λ i , . . . . λ s, 1 ) and µ = ( λ 1, . . . . . λ j-1 , λ j-1 , . . . . λ s, 0). Now,

Also a deep study of Ω (G,H) in the case of algebraic groups and suitable subgroups may throw more light on the representation theory of such groups.

- 1. since otherwise in λ = ( λ 1, . . . . λ j-1,

λ j, . . . .), contradiction. Hence

n

1

1 and hence by (ii)

j

in common. Then by Lemma 5 and Lemma 6

[ λ ] ↑ and [ µ ] ↑ contain an irreducible character of

Conversely, let the three conditions hold.

λ j−1 ≥ λ j

S ) and

Proof :Let [ λ ] and [ µ ] belong to IrrSn and let

≠ µ k. That

Hence the result.

µj

Ω (Sn+1,

is, [ λ ] ↓

k

Similarly we can prove this result when

µ s).

is a constituent of [ λ ] ↓

8)Theorem : The two graphs

parts will not be touched

[ µ ] ↓ , there will be some λ and [ µ ] ↓

λ s)

. . . .

This completes the proof of the lemma. We are now in a position to prove our main theorem.

k

≠ µ k and these

λ k -1, . . . . λ s) is a constituent of [ µ ] ↓ . λ k-1 > λ k. Therefore ( λ 1, . . . . λ j-1, µ j . . . .

also.

respectively. Hence if k > 2, there will be parts say λ , µ in [ λ ], [ µ ] respectively (k ≠ 1,2) such that λ

1

.......,

maintain parity. By Branching theorem, only one part of [ λ ] and [ µ ] will be increased by 1 at a time, which constitute the parts of

= (µ , . . . . ,

µ s). By (iii), there exists some j and k such that λ j = µ j-1 and µ k = λ k - 1 and λ i = µ i for all other i’s. That is, λ = ( λ , . . . . , λ , µ - 1, . . . , λ k, . . . . ., 1 j-1 j λ s) and µ = ( λ 1, . . . . , λ j-1, µ j, . . . . , λ k -1, . . . . λ s) Then λ j-1 > µ j. Therefore, ( λ 1, . . . . , λ j-1- 1, µ j

and [ µ ] ↓ will coincide. Similar argument holds when

λ = ( λ 1, . . . . , λ s) and µ

λ j-1 > λ j. 38


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

[9] A.Turull,“ The Schur Indices of the Irreducible Characters of the Special Linear Groups,” J.Algebra., 235(1), pp. 275-314,2001.

REFERENCES

[10] M.Guralnick,r, K.Magaard, J.Saxl,, and P.H.Tiep,“ Cross Characteristic Representations of Sympletic and Unitary Groups,” J.Algebra., 257(2),pp.291-347,2202.

[1] C.W.Curtis,, and I.Reiner,“Methods of Representation Theory,” Volume I., Wiley Interscience Publications., Newyork,1981.

[11] R.A.Ferraz, “Simple Components and Central units in Group Algebras,” J.Algebra., 279(1), pp. 191-203,2004.

[2] i.M.Isaacs,“Character Theory of Finite Groups,” Academic Press., New York,1976.

[12] G.James,and A.Kerber, “The Representation Theory of the Symmetric Group” J.Algebra., 284(1),pp. 326-332,2005.

[3] C.Musili,“Representations of Finite Groups,” Hindustan Book agency., New Delhi,1993.

[13] M.Guralnick,r, K.Magaard, J.Saxl,, and P.H.Tiep,“ Cross Characteristic Representations of odd Characteristic Sympletic Groups and Unitary Groups,” J.Algebra., 299(1), pp. 443446,2006.

[4] J.P.Serre,J “Linear Representations of Finite Groups,” Springer Verlag., Newyork,1971. [5] Narsingh Deo, “Graph Theory with applications to Engineering and Computer science,”Prentice-Hall,1974.

[14] F.Szechtman, “Irreducible Characters of Sylow Subgroups of Symplectic and Unitary Groups,” J.Algebra., 303(2), pp. 772730,2006.

[6] F.Harary,F., 1988, “Graph Theory,” Narosa Publishing House, New Delhi.

[15] I.M.Isaacs,“Character Kernels and Degree Ratios in Finite Groups,” J.Algebra., 322(6), pp. 2220-2234.2009.

[7] N.Chigira,,Y.Takegahara, andH.Yamaki, “On the number of homomorphisms from a finite group to a general linear group,” J.Algebra., 232(1), pp, 236-254,2000.

[16] H.Fukushima, “Irreducible Products of Characters of Solvable Groups,” J.Algebra., 321(1), pp. 312-315,2009.

[8] H.Mitsuhashi, “The q-analogue of the Alternating group and its Representations,” J.Algebra., 240(2), pp. 535-558,2001.

[17] T.Gnanaseelan, “Studies in Algebraic groups and representations,” Ph.D.Thesis,Madurai Kamaraj University,1999.

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DETECTION OF DIABETIC RETINOPATHY USING RADIAL BASIS FUNCTION Mr. R. Vijayamadheswaran#1, Dr.M.Arthanari#2, Mr.M.Sivakumar#3 #1

#2

Doctoral Research Scholar,Anna University, Coimbatore. Director,Bharathidhasan School of Computer Applications, Ellispettai, Erode. #3 Doctoral Research Scholar, Anna University, Coimbatore.

Abstract â&#x20AC;&#x201D; Retinal exudates classification and identification of diabetic retinopathy to diagnose the eyes using fundus images requires automation. This research work proposes retinal exudates classification.

distribution of exudates and microaneurysms and hemorrhages[3], especially in relation to the fovea can be used to determine the severity of diabetic retinopathy

Approach: Representative features are obtained from the fundus images using contextual clustering (CC) segmentation methods. The number of features obtained is two. The radial basis function (RBF) network is trained by the features. Final weights are obtained and subsequently used for testing.

Hard exudates are shinny and yellowish intraretinal protein deposits, irregular shaped, and found in the posterior pole of the fundus [4]. Hard exudates may be observed in several retinal vascular pathologies. Diabetic macular edema is the main cause of visual impairment in diabetic patients. Exudates are well contrasted with respect to the background that surrounds them and their shape and size vary considerably [5]. Hard and soft exudates can be distinguished because of their color and the sharpness of their borders. Various methods have been reported for the detection of Exudates. Efficient algorithms for the detection of the optic disc and retinal exudates have been presented in [6][7].

Results: The presence of exudates is identified more clearly as the CC uses neighbourhood information. By knowing the outputs of RBF during testing, accurate diagnosis and prescription for treatment of the affected eyes can be done. One hundred fundus images are used for testing. The performance of RBF is 96%(48 images are classified). Conclusion: Simulation results show the effectiveness of RBF in retinopathy classification. Very large database can be created from the fundus images collected from the diabetic retinopathy patients that can be used for future work

Keywords: Diabetic retinopathy, fundus image, exudates detection, radial basis function, contextual clustering I. INTRODUCTION Diabetic Retinopathy (DR) cause blindness [1]. The prevalence of retinopathy varies with the age of onset of diabetes and the duration of the disease . Color fundus images are used by ophthalmologists to study eye diseases like diabetic retinopathy [2]. Big blood clots called hemorrhages are found. Hard exudates are yellow lipid deposits which appear as bright yellow lesions. The bright circular region from where the blood vessels emanate is called the optic disk. The fovea defines the center of the retina, and is the region of highest visual acuity. The spatial

40

Thresholding and region growing methods were used to detect exudates [8][9], use a median filter to remove noise, segment bright lesions and dark lesions by thresholding, perform region growing, then identify exudates regions with Bayesian, Mahalanobis, and nearest neighbor (NN) classifiers. Recursive region growing segmentation (RRGS).[10], have been used for an automated detection of diabetic retinopathy Adaptive intensity thresholding and combination of RRGS were used to detect exudates,[11], [12], combine color and sharp edge features to detect exudate. First they find yellowish objects, and then they find sharp edges using various rotated versions of Kirsch masks on the green component of the original image. Yellowish objects with sharp edges are classified as exudates.


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segmentation is typically used to locate data in a vector. The result of contextual data segmentation is a set of regions that collectively cover the entire data. Each value in a data is similar with respect to some characteristics. Adjacent regions are significantly different with respect to the same characteristics. Several general-purpose algorithms and techniques have been developed for data segmentation. Contextual clustering algorithms segments a data into one category (ω0) and another category (ω1). The data of the background are assumed to be drawn from standard normal distribution. II. MATERIALS AND METHODS

1. Define decision parameter Tcc (positive) and weight of neighbourhood information β (positive). Let Nn be the total number of data in the neighbourhood. Let Zi be the data. 2. Initialization: classify element of data with Zi>Tcc to ω1 and element of data to ω0. Store the classification to C0 and C1. 3. For each element of data ‘i’, count the number of data ui, belonging to class ω1 in the neighbourhood of data ‘i’. Assume that the element of data outside the data area belong to ω0. 4. Classify element of data with

This research work proposes contextual clustering (CC) and Radial Basis Function (RBF) network. CC is used for feature extraction. The extracted features are input to the RBF network. In order to achieve maximum percentage of identification of the exudates, proper data input for RBF, optimum topology of RBF and correct training of RBF with suitable parameters is a must. A large amount of exudates and non exudates images are collected. Features are extracted from the images using contextual clustering segmentation. The features are input to the RBF and labeling is given in the output layer of RBF. The labeling indicates the exudates. The final weights obtained after training the RBF is used to identify the exudates. Figure 1 explains the overall sequence of proposed methodology.

zi +

N β (u i − n ) > Tα to ω1 and other Tcc 2

element of data to ω0. Store the classification to variable C2. 5. If C2 ≠C1 and C2 ≠ C0, copy C1 to C0, C2 to C1 and return to step 3, otherwise stop and return to C2.[13] IV.RADIAL BASIS FUNCTION

III.CONTEXTUAL CLUSTERING Radial basis function neural network (RBF) is a supervised neural network. The network has an input layer, hidden layer (RBF layer) and output layer. The 2 features obtained are used as inputs for the network and the target values for training each exudate is given in the output layer.

Image segmentation is a subjective and context-dependent cognitive process. It implicitly includes not only the detection and localization but also the delineation of the activated region. In medical imaging field, the precise and computerized delineation of anatomic structures from image data sequences is still an open problem. Countless methods have been developed, but as a rule, user interaction cannot be negated or the method is said to be robust only for unique kinds of images.

Training RBF is done as follows: 1. Finding distance between pattern and centers . 2. Creating an RBF matrix whose size will be (np X cp).(Figure 2) , where np = number of pattern used for training and cp is number of centers which is equal to 10. The number of centers chosen should make the RBF network learn the maximum number oftraining patterns under consideration.

Contextual segmentation refers to the process of partitioning a data into multiple regions. The goal of segmentation is to simplify and / or change the representation of data into something that is more meaningful and easier to analyze. Data

41


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3. Calculate final weights which is inverse of RBF matrix[14][15][16][17][18] multiplied with Target values. 4. During testing, the performance of the RBF network, RBF values are formed from the features obtained from CC and processed with the final weights obtained during training. Based on the result obtained, the image is classified to have a type of exudate or not.

Fig.3 Hardexudates

V. EXPERIMENTAL WORK

Color retinal images obtained from Aravind Hospitals, Madurai (India). According to the National Screening Committee standards, all the images are obtained using a Canon CR6-45 Non-Mydriatic (CR6-45NM) retinal camera. A modified digital back unit (Sony PowerHAD 3CCD color video camera and Canon CR-TA) is connected to the fundus camera to convert the fundus image into a digital image. The digital images are processed with an image grabber and saved on the hard drive of a Windows 2000 based Pentium -IV.

Figure 2 shows sample images of eyes in good condition. Figure 3 shows sample images of eyes with hard exudates.

The Sample images of normal (Figure 2) and abnormal types (Figure 3)are given.

VI. RESULTS AND DISCUSSION

For template matching and comparison purposes, representative exudates are isolated from the original retinopathy images in order to create exudates templates which are presented in

Fig. 2 Normal fundus images

Figure 4. Fig. 4: Segmented pictures of hard exudates

(a)Sample Hard Exudates

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Figure 4a shows the sample templates out of one hundred templates collected. Each template has varied scattering of the exudates. Figure 4b shows , the segmented exudates by the CC method. The black indicates the background of the image and the white shows the hard exudates. CC does effective segmentation. Statistical features for the hard

(b) Segmented hard exudates by CC

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Fig.5c Plane 2 of the image in Figure 5a

exudates templates are found. The statistical features considered are ‘Convex Area’, ’Solidity’,’ Orientation’ and ‘Filled Area’. In Fig. 5a, different stages of outputs of CC are given. The entire image processing included here involves normalizing, histogram equalization, segmentation.

Fig.5a A portion of the original true color image

Fig.5d Plane 3 of the image in Figure 5a

Figure 5a presents a portion of the original diabetic retinopathy image in true color. The plane-1 information of the original image is shown in Figure 5b. The plane-2 (Figure 5c) and plane-3(Figure 5d) are shown. Identification of exudates is done using plane-2 information.

Fig.5e Plane 2 of the image segmented by using CC

Fig.5b Plane 1 of the image in Figure 5a

The hard exudates are found scattered in the retinopathy image. The segmented image of CC shows more noise. Noise is present in CC segmented image. Figure 6a presents 9 pixel values summed versus the window number during scanning the image to be segmented. The average summed number is above 1500 which is an indication of slight white background appearance as can be seen from Figure 5c (plane 2).

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Fig.6a Total pixel values

an object is greater than a value of 500, then it is treated as optic disc. Using the boundingbox concept, this object is filled with black. Hence the remaining objects could be either the noise or the exudates. Figure 7 shows the edydisc removed by applying statistical features.

2500

Summation of the window

2000

1500

1000

Fig.7 Eyedisc removed 500

0

0

2

4

6 8 3 X 3 window position

10

12 4

x 10

Fig.6b Mean of each window 300

200

6000

5000

150 Statistical area of the objects

Mean of the window

250

100

4000

3000

2000

1000

50

0

0

0

2

4

6 8 3 X 3 window position

10

12 4

x 10

Fig.6c Contextual values calculated for each window 300

Contextual value of window

250

200

150

100

50

0

2

4

6 8 3 X 3 window position

10

50

100

150 Object numbers

200

250

300

The sample outputs of statistical area of the imfeature is shown in Table 1.

The mean (Figure 6b) and the contextual values (Figure 6c) are shown.

0

0

12 4

x 10

The property of imfeature is applied to the segmented image. The area of the labeled objects in the segmented image are obtained. The optic disc in the image is removed by using a threshold. If the area of

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Implementation of RBF Table 1 Data for training RBF Training Inputs Area

VII. CONCLUSION Target outputs Labeling

Solidity

Orientation

55

Filled Area 59

0.6111

-16.5837

1

59

59

0.7024

-29.5294

1

61

61

0.5980

43.1644

1

64

64

0.5161

-4.1202

1

69

70

0.6970

20.1090

1

75

75

0.7732

7.9202

1

78

80

0.6393

82.4571

1

89

91

0.5973

84.0033

1

100

101

0.5587

-39.8444

1

104

108

0.7324

-12.7048

2

109

109

0.8790

42.4872

2

139

139

0.7128

81.1306

2

165

165

0.9016

45.6726

2

167

180

0.5860

55.3490

2

214

219

0.7431

40.4485

2

251

251

0.6452

80.2676

2

5108

5117

0.8913

91.3917

2

The main focus of this work is on segmenting the diabetic retinopathy image and classify the exudates. Segmentation is done using contextual clustering and classification of the exudates is done using radial basis function (RBF) network. The performance classification of exudates by using RBF and CC is better than that of using only CC. The proposed RBF classifies the segmented information of the image into hard exudates or not. 1. All the fundus images in this work have to be transformed to a standard template image condition. This corrects in the illumination effect on the images. 2. Only when the fundus image is taken with good quality, detection of exudates is more accurate. REFERENCES

[1] XU Jin, HU Guangshu, HUANG Tianna, HUANG Houbin CHEN Bin “The Multifocal ERG in Early Detection of Diabetic Retinopathy” - Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4, 2005 [2] Akita K. and H. Kuga. A computer method of understanding ocular fundus images. Pattern Recognition, 15(6):431–443,1982. [3] Lili Xu and Shuqian Luo, A novel method for blood vessel detection from retinal images, BioMedical Engineering OnLine 2010, 9:14 doi:10.1186/1475-925X-9-14 [4] Walter, T.; Klevin, J.C.; Massin, P.; et al. A Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy — Detection of Exudates in Color Fundus Images of the Human Retina. IEEE Transactions on Medical Imaging 2002, 21, 1236–1243

The RBF is trained with the data given in Table 1. Each row made up of 4 variables. A labeling is given in the last column. Eighteen patterns are considered for training RBF. These 18 patterns are taken from the segmented image given in Figure 5e. Additional hard exudate images can also be considered from which additional patterns can be obtained. A topology of 4(nodes in the input layer) X 5(nodes in the hidden layer) X 1(node in the output layer) is used for training RBF. The final weights obtained is used for classification of the segmented exudates from the noise present in the segmented image.

[5] Akara Sopharak and Bunyarit Uyyanonvara, “Automatic Exudates Detection From Non-Dilated Diabetic Retinopathy Retinal Images Using FCM Clustering Sensors 2009, 9, 21482161; doi:10.3390/s90302148 [6] Xiaohui Zhang, Opas Chutatape School Of Electrical & Electronic Engineering Nanyang Technological University, Singapore, Top-Down And Bottom-Up Strategies In Lesion Detection Of Background Diabetic Retinopathy. Proceedings Of The 2005 IEEE Computer Society Conference On Computer Vision And Pattern Recognition (CVPR’05), 2005. [7] Vallabha, D., Dorairaj, R., Namuduri, K., and Thompson, H., Automated detection and classification of vascular abnormalities in diabetic retinopathy. Proceedings of ThirtyEighth Asilomar Conference on Signals, Systems and Computers. 2:1625–1629, 2004. [8] Liu, Z.; Chutatape, O.; Krishna, S.M. Automatic Image Analysis of Fundus Photograph. IEEE Conf. on Engineering in Medicine and Biology 1997, 2, 524–525. [9] Christopher E. Hann, James A. Revie, Darren Hewett, Geoffrey Chase and Geoffrey M. Shaw, Screening for Diabetic Retinopathy Using Computer Vision and Physiological Markers, Journal of Diabetes Science and Technology Volume 3, Issue 4, July 2009

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[10] Sinthanayothin, C.; Boyce, J.F.; Williamson, T.H.; Cook, H.L.; Mensah, E.; Lal, S.; et al. Automated Detection of Diabetic Retinopathy on Digital Fundus Image. J. Diabet. Med. 2002, 19, 105–112. [11] Usher, D.; Dumskyj, M.; Himaga, M.; Williamson, T.H.; Nussey, S.; et al. Automated Detection of Diabetic Retinopathy in Digital Retinal Images: A Tool for Diabetic Retinopathy Screening. J. Diabet. Med. 2004, 21, 84–90. [12] Sanchez, C.I.; Hornero, R.; Lopez, M.I.; et al. Retinal Image Analysis to Detect and Quantify Lesions Associated with Diabetic Retinopathy. IEEE Conf. on Engineering in Medicine and Biology Society 2004, 1, 1624–1627. [13]Contextual clustering for analysis of functional fMRI data, IEEE Transaction on Medical imaging, 20(2001),pp403-411 [14] Purushothaman S., Sambasiva Rao Baragada, Ramakrishna S., Rao M.S., Polynomial Discriminant Radial Basis Function for Steganalysis, International Journal of Computer Science and Security [15] García M, Sánchez CI, Poza J, López MI, Hornero R., Detection of hard exudates in retinal images using a radial basis function classifier, Ann Biomed Eng. 2009 Jul;37(7):1448-63. Epub 2009 May 9. [16] J. Anitha; C. Kezi Selva Vijila; D. Jude Hemanth, Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images (Proceedings Paper) , Second International Conference on Digital Image Processing, Proceedings Vol. 7546, 26 February 2010, DOI: 10.1117/12.852746 [17]García M, Sánchez CI, López MI, Abásolo D, Hornero R. ,Neural network based detection of hard exudates in retinal images, Comput Methods Programs Biomed. 2009 Jan;93(1):9-19. Epub 2008 Sep 7. [18] M. García, M. I. López, R. Hornero, A. Díez and J. Poza, Utility of a Radial Basis Function Classifier in the Detection of Red Lesions in Retinal Images, World Congress On Medical Physics And Biomedical Engineering, September 7 - 12, 2009, MUNICH, GERMANY IFMBE Proceedings, 2009, Volume 25/11, 21-24, DOI: 10.1007/978-3-642-03891-4_6

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011

Peristaltic flow of a Williamson fluid in an asymmetric channel through porous medium A.Kavitha1

R.Hemadri Reddy*2

1

S.Sreenadh1

R.Saravana1

Department of Mathematics, Sri Venkateswara University, Tirupati-517 502 2 * School of Advanced Sciences, VIT University, Vellore, TamilNadu release of liquid and solid gastric content into the Abstract:Peristaltic transport of a Williamson fluid small intestines where nutrient uptake occurs. The in an asymmetric channel through porous MRI image of the stomach has revealed its medium is studied under long wavelength and asymmetry nature. Another physiological system low Reynolds number assumptions. The namely uterus is also modeled as an asymmetric nonlinear governing equations of the peristaltic channel by Eytan and Elad [17]. These facts will flow are solved using perturbation method. The explain the necessity of considering the physiological solution for the stream function is obtained by system to be asymmetric ducts also. Motivated by neglecting inertia and curvature effects. The these facts, it will be interesting to study the peristaltic velocity distribution, the volume flow rate and the transport of Williamson fluid through an asymmetric pressure rise are also determined. channel filled with porous material. In this paper peristaltic pumping of Williamson Key words: Williamson fluid, Reynolds number, fluid through a porous medium in an asymmetric Peristalsis, Velocity and Pressure rise. channel with flexible walls is investigated. Using the wave frame of analysis, boundary value problem is solved and the results are discussed I. Introduction Peristalsis is a well-known mechanism for through graphs. pumping biological and industrial fluids. Even though it is observed in living systems for many centuries; the mathematical modeling of peristaltic transport has II. Mathematical Formulation begun with the important works by Fung and Yih [1] Let us consider the peristaltic transport of an using laboratory frame of reference and Shapiro et incompressible Williamson fluid in a two dimensional al.[2] using wave frame of reference. Many of the channel of width d1 + d 2 . The flow is generated by contributors to the area of peristaltic pumping have sinusoidal wave trains propagating with constant either followed Shapiro or Fung. Most of the studies speed c along the channel walls. The geometry of the on peristaltic flow deal with Newtonian fluids. The wall surfaces are defined as complex rheology of biological fluids has motivated  2π  investigations involving different non-Newtonian Y = H1 = d1 + a1 cos  ( X − ct ) (upper wall), fluids. Peristaltic flow of nonNewtonian fluids in a tube λ  was first studied by Raju & Devanathan [3].  2π  Peristalsis is a mechanism adopted by many Y = H2 = −d2 − b1 cos  ( X − ct ) +φ (lower wall), λ physiological systems and mechanical peristaltic   pumps. Most of the physiological systems may be (1) approximated as symmetric ducts. In view of this, where a 1 and b1 are the amplitudes of the Peeyush Chandra [4], Sarojamma et al [5], Ramachandra Rao and Usha [6], Misra & Pandey [7], waves, λ is the wave length, d 1 + d 2 is the width of Vajravelu et al. [8-11], Subba Reddy et al. [12, 13] the channel, c is the velocity of propagation, t is the and Srinivas et al.[14,15] made detailed studies on time and X is the direction of wave propagation. The peristaltic pumping through tubes and channels. phase difference φ varies in the range 0 ≤ φ ≤ π in Brassuer and Anupampal (vide Chengel & Cimbala, [16]), made experiments on the mechanical which φ = 0 corresponds to symmetric channel with functioning of the stomach using MRI (Magnetic waves out of phase and φ = π the waves are in Resonance Image). They observed that the stomach is a mixer, a grinder, a storage chamber, and a sophisticated peristaltic pump that controls the

48


a1 , b1 , d1 , d 2 and φ

phase, further condition a

2 1

and low Reynolds number, neglecting the terms of order δ and higher, equations (5) and (6) take the form  ∂u   ∂u  ∂p ∂  (7) = 1 + We     − σ 2 (u + 1) ∂x ∂y   ∂y   ∂y 

satisfies the

+ b 21 + 2a1b1 cos φ ≤ (d1 + d 2 ) 2 .

∂p =0 ∂y

The corresponding boundary conditions in wave frame of reference are given by u= -1 on y=h1(x) u= -1 on y=h2(x) (9) Elimination of pressure from equations (7) & (8) yields  ∂u   ∂u  dp ∂  (10) = 1 + We     − σ 2 (u + 1) dx ∂y   ∂y   ∂y  The volume flow rate q in a wave frame of reference is given by

Figure 1.Physical Model Introducing a wave frame ( x , y ) moving with velocity c away from the fixed frame transformation,

( X , Y ) by the

h1 ( x )

q=

x = X −ct , y =Y ,u =U −c ,v =V and

(8)

P(x) = P(X,t).

udy

h2 ( x )

(11) The instantaneous flow Q(x, t) in a fixed frame is

(2) and defining

x y u v c h h λ d x= , y= ,u= ,v= ,t = t , h1 = 1 , h2 = 2 , τxx = τxx, τxy = 1 τxy λ d1 c c λ d1 d2 µ0c µ0c

Q(x,t) =

h1(x)

h2 (x)

ρcd γ&d d d d Γc τyy = 1 τyy, δ = 1 , Re= 1 ,We= , P= P, γ& = 1 µ0c λ µ0 d1 cλµ0 c

h1(x)

h1(x)

h2 (x)

h2 (x)

(u +1)dy = ∫ udy +

∫ 1dy=q+(h -h ) 1

2

2 1

(12) The time average flux Q of the peristaltic wave is

(3) and using the above non-dimensional quantities, the resulting governing equations become (Nadeem[18]),

∂u ∂v + =0 ∂x ∂y

(4)

 ∂u ∂u  ∂p 2 ∂τ xx ∂τ xy + v  = − −δ − −σ 2 (u +1) x y x x y ∂ ∂ ∂ ∂ ∂   ∂τ ∂τ  ∂ v ∂ v ∂p δ 3 Re u + v  = − − δ 2 xy − δ yy

δ Re u

 ∂x

∂y 

∂y

∂x

where ∈ k σ2 = , Da = 2 , ∈= porosity Da a τ xx = −2 [1 + Weγ& ]

T

Q=

∂y

(6)

 

(13)

u = u0 + Weu1 + 0(We 2 ), P = P0 + WeP1 + 0(We 2 ),

q = q0 + Weq1 + 0(We 2 ), (14) ∂p where P = , Substituting above expressions in ∂x

and k = permeability

∂u , ∂x

equation (10) and boundary conditions (9), we get the following system.

∂v ∂v   ∂u + δ 2  , τ yy = −2 [1 + Weγ& ] , ∂y ∂ x ∂ x   2

 ∂u   ∂u 2 ∂v  2  ∂v   + 2δ    + +δ ∂x   ∂x   ∂y  ∂y 

γ& =  2δ 2 

1

1 1 Qdt = ∫ ( q + h1 − h2 )dt = q + 1 + d ∫ T 0 T 0

III. Perturbation solution Since, equation (10) is non-linear; its exact solution may not be possible. Therefore, we expand u, P and q as

(5)

τ xy = − [1 + Weγ& ]  2

over one period T (= λ / c )

2

System of order

We

0

dp0 ∂ 2u0 = 2 − σ 2 (u0 + 1), ∂y dx

1 2

  . 

and the respective boundary conditions are u 0 = −1 for y = h 1 , u 0 = −1 for y

Here δ, Re, We represent the wave, Reynolds and Weisseing numbers, respectively. Under the assumptions of long wavelength δ << 1

(15)

= h2 (16)

49


System of order

We1

and the volume flow rate 2

dp1 ∂ 2u1 ∂  ∂u0  2 = +   − σ u1 dx ∂y 2 ∂y  ∂y 

u 1 = 0,

q1 =

(17)

for y = h 1 , u 1 = 0, for y = h 2

σ h1

A −2σ h1  e e  D 

σ h2

−e

σ

q1 is given by

 A −2σ h1  e −σ h1 − e−σ h2 + e  σ  D 

 eσ h1 − eσ h2  + Ee −σ h1   +H σ  

(18)

0

(23) where ( B+C) e−2σh1 eσh1 −eσh2 + F−G e−σh1 eσh1 −eσh2 +( B+C) e−σh1 −e−σh2  H= )    (    D  σ   σ  D  σ 

Solution for system of order We Solution of Eq. (15) satisfying the boundary conditions (16) can be written as

 p  1 −σ h  σ y −σ y  1)  e u0 = 0  (1 − ke + ke −1 −1   σ 2  eσ h1  

F 2σh1 2σh2 dp0 k2 −2σh1 −2σh2 ( e −e ) − dx 3σ ( e −e ) 2σe2σh1

(19) where

   σ h2 σ h1  −e  e   , and the volume flow    k =  eσ ( h2 −h1) − eσ ( h1−h2 )     

2

Substituting equations (21) and (24) in to equation (14) and using the relation (14), we get

dp σ3  L  (   = ( q+( h1 −h2 ) ) −σ6We2 q2 +h12 +h22 −2hh 1 2 +2q( h1 −h2 ) 2  dx J  J   

(20)

25)

σ ( q0 + ( h1 − h2 ) ) dp0 = −σh1 dx e (1− ke−σh1 )( eσh1 − eσh2 ) − k ( e−σh1 − e−σh2 ) −σ ( h1 − h2 ) 3

where

(21) 1

Solution for system of order We Substituting the zeroth-order solution (19) into (17), the solution of the resulting problem satisfying the boundary conditions take the following form. ( A+ B+C) e−σh1 + D( E + F −G) e−σh1  σy  A+ B+C −σy 1  dp1  u1 =  e − 2  e − D σ  dx   D   

−Fe−2σh1e2σy +

2  dp0  2 −2σy   ke 3σ3  dx 

where

A

−2σ h1 σ ( 2 h2 − h1 )

e

2

)

(26) where

e

−e

A1 =

A2 =

)

, E = 1  dp1  , σ 2  dx 

2

2 F = 2  dp0  1 − ke−σ h 2 , G = )  ( 3 

3σ  dx 

1

Integrate above equation over

2 3σ

3

(1 − ke ) D

−σ h1 2

 e −2σh1 e σ(2 h 2 −h1 ) − e σh 2   

 , 

− eσ h2   3σ 3 D σ  σ h1 σ h2 2  e −e  2 A3 = 3 (1 − ke −σ h1 ) e −σ h1  , 3σ σ   σ h1 σ h2 e −e  2 A4 = 3 k 2 e−2σ h1   3σ σ  

− eσ h2 ,

(

I , D

L = A1 + A2 + A3 − A4 + A5 + A6 − A7 − A8

 eσ h1 − eσ h2  σ 

(22) = E ( eσ h1 − eσ h2 ) ,B =

2  dp 0  2 σ ( h2 − 2 h1 ) σ h −2h k e − e ( 1 2) 3σ 3  dx  σ ( h2 − h1 ) σ ( h1 − h2 )

J=

one wavelength, we get (21) 1 1 dp L  σ3    ∆P=∫ dx =∫ ( q+( h1 −h2 ) ) −σ6We2 q2 +h12 +h22 −2hh 1 2 +2q( h1 −h2 ) 2 dx dx J J    0 0

2

D=

I

I = ( eσh1 −eσh2 ) e−2σh1 +( eσh1 −eσh2 )( e−σh1 −e−σh2 ) −σD( h1 −h2 ) + De−σh1 ( eσh1 −eσh2 )

From Equation (20), we get

C=

( q1 − H ) D (24)

dp0  1  −σh1 −σh1 σh1 σh2 −σh1 −σh2   3 e (1−ke )( e −e ) −k( e −e ) −σ( h1−h2)−( h1 −h2)  dx σ 

(e

3

where

q0 =

F

dp1 σ = dx

From equation (23), we get

q 0 is given by

rate

  − E ( h1 − h2 ) 

2  dp0  2 −2σ h1   k e 3σ 3  dx 

50

2

(

σ ( h2 − 2 h1 )

k2 e

σ ( h1 − 2 h2 )

−e

σ h1

)  e


A5 =

2

A6 =

2

(1 − ke ) ( e D −σ h1 2

3σ  e −σ h1 − e −σ h2    σ   3

3σ 3 D

(

σ ( h2 − 2 h1 )

k2 e

−2σ h1 σ ( 2 h2 − h1 )

e

σ ( h1 − 2 h2 )

−e

)  e

− eσ h2

−σ h1

)

− e −σ h2   σ 

e −2σ h1 ( e 2σ h1 − e2σ h2 ) , 6σ k2 A8 = 4 ( e −2σ h1 − e −2σ h2 ) 3σ A7 =

2

(1 − ke )

−σ h1 2

4

Fig.2. The variation of

∆P

with Q for different values of

for a fixed a = 0.8, b =0.5,

φ

σ

=0, d =2; We = 0.03

IV. Results and Discussions From equation (26) we have calculated the pressure difference as a function of Q for different values of permeability parameter σ and different phase differences φ for a fixed a=0.8, b=0.5, d=2, We=0.03 and is shown in figures (2) and (3).We observe that the larger the parameter σ the greater the pressure rise against which the pump works. We observe that for a given ∆P , the flux Q increases with increasing σ .For free pumping there is no

difference in flux Q for increase in σ .We also observed that the pressure rise increases with increasing phase difference φ . The variation of pressure rise with time averaged flow rate is calculated from equation (26) for different values of We and different phase differences for a fixed a=0.8,b=0.5,d=2, σ =1.5 and is shown in figures (4) to (6). We observe that the larger the We, the smaller the pressure rise against which the pump works. We observe that for a given ∆P ,the flux Q decreases with increasingWe. For a given flux Q the pressure rise decreases with increase We. The variation of ∆P with time averaged flow rate is calculated from equation (26) for different values of the phase difference φ , for a fixed a=0.8, b=0.5,d=2, σ =0.5, We=0.03 and is shown in figure (7). We observe that the larger the phase difference φ , the greater the pressure rise against which the pump works. We observe that for a given ∆P , the flux For a given flux

Fig.3. The variation of ∆P with Q for different values of for a fixed a = 0.8,b =0.5,

Fig.4. The variation of

Q increases with increasing φ .

increasing φ .

51

=

π /6, d =2; We = 0.0

∆ P with Q for different values of We

for a fixed a = 0.8,b =0.5,

Q the pressure rise increases with

φ

σ

φ = 0, d =2; σ

= 1.5.


REFERENCES

Fig.5. The variation of ∆P with

Q for different values of We

for a fixed a = 0.8, b =0.5,

Fig.6. The variation of

∆P

φ

=

π / 6 , d =2, σ

[1].

Fung, Y.C. and Yih, C.S. Peristaltic transport, Trans ASME, E.J. App. Mech, 35, pp.669-675, 1968.

[2].

Shapiro, A.H., Jaffrin, M.Y and Weinberg, S.L. Peristaltic pumping with long wavelengths at low Reynolds number, J. Fluid Mech. 37,pp. 799-825, 1969.

[3].

Raju, K.K. and Devanathan, R. Peristaltic motion of a non-Newtonian fluid part –I Rheol. Acta. 11,pp. 170-178, 1972.

[4].

Peeyushchandra,G.Radhakrishnamacharya, Viscous Flow through a Permeable Tube of Varying Cross-section with Reference to Proximal Renal Tubule Physiological Fluid Dynamics-I, SCPFD Publications, Madras PP.113,1985.

[5].

Sarojamma, G., Haritha, P. and Nagarani, P. Peristaltic Transport of a Viscous Fluid in a Channel under the Influence of a Transverse Magnetic Field, Proc. of AP Academy of Sciences, Vol. 7, No. 1, pp.25-32, 2003,

[6].

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

[7].

Misra, J.C. and Pandey, S.K. Peristaltic transport of a non-Newtonian fluid with a peripheral layer, Int. J.Engg Sci., 37,pp. 18411858, 1999.

[8].

Vajravelu, K. Sreenadh, S. and Ramesh Babu, V. Peristaltic pumping of a Herschel-Bulkley fluid in a channel, Appl. Math.&Computation, 169,pp. 726-735, 2005a.

[9].

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,pp. 83-90, 2005b.

= 1.5.

with Q for different values of We

for a fixed a = 0.8, b =0.5,

φ

=

π / 3 , d =2,

σ

= 1.5.

[10]. 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,No.4,pp.593-604, 2006.

Fig.7. The variation of

∆P with Q for different values of

for a fixed a = 0.8, b =0.5, We =0.03, d =2,

σ

[11]. 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 Fluid Mechanics Research, 36 (3),pp. 244-254,2009.

φ

= 0.5.

52


[12]. Subba Reddy, 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,pp. 824-827, 2005. [13]. 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,pp. 11531161, 2006. [14]. Kothandapani. M, Srinivas. S , Non-linear peristaltic transport of a Newtonian fluid in an inclined asymmetric channel through a porous medium, Physics Letters A, Volume 372, Issue 8, pp. 1265-1276,2008. [15]. Srinivas.S, Kothandapani. M, Peristaltic transport in an asymmetric channel with heat transfer International Communications in Heat and Mass Transfer, Volume 35, Issue4,pp.514522,2008. [16]. 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,pp.495-519, 1987. [17]. Eytan, O. and Elad, D. Analysis of Intra â&#x20AC;&#x201C; Uterine fluid motion induced by uterine contractions, Bull. Math. Bio.,61,pp.221-238, 1999. [18]. Nadeem.S ,Akram Safia, Influence of inclined magnetic field on peristaltic flow of a Williamson fluid model in an inclined symmetric or asymmetric channel, Mathematical and Computer Modelling, Volume52,Issue1-2, pp.107-119, 2010 [19]. Nagarani, P., Sarojamma, G. and Girija. Jayaraman, Effect of boundary absorption in dispersion in Casson fluid flow in a tube, Annals of Biomedical Engineering, Vol. 32, No. 5,pp.706-719, 2004.

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IJITCE Jan 2011  

International Journal of Innovative Technology and Creative Engineering January 2011 issue No.1 Vol.1

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