International Journal of Research in Computer & Information Technology

7/24/2019 International Journal of Research in Computer & Information Technology

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International Journal of Research in Computer & Information Technology (IJRCIT)http://www.garph.org

Aim & Scope:International Journal of Research in Computer & Information Technology (IJRCIT)is an online journal in English

published in annually for Academicians, scientist, Engineers, and Research scholars involved in Computer Scienceand Information Technology to publish high quality and refereed papers. Paper reporting original research andinnovative application from all parts of the world is invited. Papers for publication in the IJRCIT are selected throughpeer review to ensure originality, relevance and readability. The aim of IJRCIT is to publish peer reviewed research

and review articles rapidly developing field of Computer Science and Information Technology.

The core vision of IJRCIT is to publish new knowledge and technology from for the benefits of every one rangingfrom the academic and professional research communities to industry practioners in a range of topics in a ComputerScience and Information Technology. It also provides a venue for high caliber research scholars, PhD students,Professionals to submit on-going research and developments in these areas.

Frequency of Publication:One Volume with Four issues per year

Subject Category:all Computer Science and Information Technology

Submission of Manuscripts:

Authors are strongly urged to communicate to Editor-in-chief of the journal through [email protected] only.The final decision on publication is made by the Editor-in-chief upon recommendation of an Associate Editor and oran Editorial Board Member.

Regular Subscription Price:

Within India: Annual INR 2500Outside India:Annual 500 USD

Published by

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2015 Global Advanced Research Publication House, India

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International Journal of Research in Computer & Information Technology (IJRCIT)

Editorial Board

Editor-in-Chief

Dr. Shrinivas P. Deshpande================================================================

Associate Editorial Board Members

Dr. V. M. Thakare

Department of Computer Science and Engineering,Sant Gadge Baba Amravati University, AmravatiMaharashtra, India.

Dr. D. N. Chaudhari

Department of Computer Science and Engineering,Jawaharlal Darda Institute of Engineering &Technology, YavatmalMaharashtra, India.

Dr. S. E. YedeyP. G. Department of Computer Science &Technology, D.C.P.E. H.V.P.M., Amravati,Maharashtra, India.

Prof. P. L. RamtekeDepartment of Information Technology,H.V.P.M College of Engineering & Technology,Amravati,Maharashtra, India

Prof. Anand B. Deshmukh

Department of Information Technology,Sipna College of Engineering & Technology,Amravati,Maharashtra, India.

Prof. Vinay A. RajgureDepartment of Computer Science & Engineering,Prof. Ram Meghe College of Engineering &Management, Bandera,Maharashtra, India

Prof. Bharat S. Kankate

Department of Computer Engineering,Pune District Education Associations College ofEngineering, Manjari, Hadapsar, Pune,Maharashtra, India

Dr. Sandeep R. Shirsat

Department of Computer Science,Shri Shivaji Science College Chikhali,Maharashtra, India.

Dr. Anjali B. Raut

Department of Computer Science & Engineering,H.V.P.M. College of Engineering & Technology,Amravati,Maharashtra, India.

Prof. Sachin DeshpandeDepartment of Computer Engineering,Vidyalankar Institute of Technology, Mumbai,Maharashtra, India.

Prof. Ritesh V. PatilDepartment of Computer Engineering,Pune District Education Associations College ofEngineering, Manjari, Hadapsar, Pune,Maharashtra, India

Prof. Ramanand S. Samdekar

Department of Computer Science & Engineering,S. B. Jain Institute of Technology Management &Research, Nagpur,Maharashtra, India

Prof. Amitkumar S. ManekarDepartment of Computer Science and Engineering,Shri Sant Gajanan Maharaj College of Engineering,Shegaon,Maharashtra, India

Prof. Pritam H. Gohatre

Department of Computer Technology,Laxminarayan Agrawal Memorial Institute ofTechnology, Dhamangoan,Maharashtra, India


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Editorial

Research is a creative work undertaken by applying systematic approach to establish newknowledge or increase the existing knowledge. Research activity includes confirmation ofexisting facts, verify and endorse the results, establish new theory, methods, and approaches. Theresearch undertaken by any individual or a group requires its publication and affirmation by thepeers. Publishing research work in journals and conferences authenticate the work done andefforts taken by the researcher.

There are several journals available in the areas of Computer Science and InformationTechnology having different strategy. IJRCIT aimed at providing an international forum for

scientists, researchers, engineers and developers from a wide range of information science areas

to exchange ideas and approaches in this evolving field. High quality papers in computer science

and information technology areas are solicited and original papers exploring research challenges

will receive especially careful interest from reviewers. Papers that had already been accepted or

currently under review for other conferences or journals were not considered for publications.

This journal publication would not have been possible without help of several individuals

who in one way or another contributed and extended their valuable assistance in the preparation

and completion of journal. My utmost gratitude is to the Editorial Board members and Reviewersfor their sincerity and encouragement. IJRCIT is strongly supported by a dedicated Editorial

Board consisting of renowned scientists. Thus, we ensure the highest quality standards of the

journal and provide prompt, detailed rigorous assessments that allow rapid editorial decisions

and result in significantly improved manuscripts.

We are requesting experts from academia, industry and research groups for active

participation in this publication activity as an editorial committee member, reviewer and

promoter.

IJRCIT Editorial Board

November 2015

www.garph.org


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INDEX

Sr. No Title & Author(s)Page

No

1An Approach For Prediction Of Driver Fatigue.Pritam H. Gohatre

1-7

2

An Improved Image Fusion Algorithm Based On Wavelet Transforms Using

Particle Of Swarm Optimization.Hrishikesh S. Holey

8-14

3SAP Hana Database With NetworkProf. Anup. P. Date , Miss. Namrata S. Mahajan

15-19

4Performance Evaluation Of Database Client Engine Using Modular ApproachProf. Ramanand Samadekar 20-22

5Implementation Image Mosaic Using Phase Correlation And Harris OperatorMs. Kanchan S.Tidke, Pritam H. Gohatre

23-28

6To Study The Types Of Open-Source Applications Of Routing SoftwareHemant Gadbail, Roshan Kalinkar

29-32

7

Universal Gate Application For Floating Point Arithmetic Logic Unit

Prof. Vishwajit K. Barbudhe 33-37

8Review Of Independent Component Analysis Algorithms And Its ApplicationNaresh Nimje

38-42

9A Review On Parallel Programming Models In High Performance ComputingAniket Yawalkar , Ashish Pawar, Amitkumar Manekar

43-47

10Automated Parking Slot Allotter Using Rfid And Nfc TechnologyHarshal Phuse, Sumit Bajare, Ranjit Joshi, Amitkumar Manekar

48-52

11Review Automated Students Attendance Management System Using Raspberry-PiAnd NfcNikhil P. Shegokar, Kaustubh S. Jaipuria,Amitkumar Manekar

53-56

12A Survey Of Biometric Authentication TechniquesAnkush Deshmukh, Poonam Hajare, Rajeshri Kachole, Amitkumar Manekar

57-65

13A Comprehensive Survey On Load Balancing Algorithms In IaasA Gawande, S Jain And K Raut

66-75

14Proposed Automated Students Attendance Management System Using RaspberryPi And NfcMahesh P. Sangewar, Shubham R. Waychol, Amitkumar Manekar

76-79

15My Moments An Android Based Diary ApplicationChetan Patil, Kavita Chaudhari, Snehal Deshmukh, Amitkumar Manekar

80-84

16Comparison Of Particle Swarm Optimization And Genetic Algorithm For LoadBalancing In Cloud Computing EnvironmentK Pathak, G Vahinde

85-96

17A Survey Paper On Tracking System By Using Smart PhonePoonam Hajare, Rajeshri Kachole, Ankush Deshmukh, Amitkumar Manekar

97-100

18Review On Security And Authentication System In Accessing DataMitali Lakade, Ruchi Kela, Ashwini, Amitkumar Manekar

101-105


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I nternational Journal of Research in Computer & Information

Technology (I JRCIT) Vol. 1, I ssue 12015 ISSN:2455-3743

Page 1

AN APPROACH FOR PREDICTION OF DRIVER FATIGUEPRITAM H. GOHATRE

Technocracts Institute of Technology, Bhopal

[email protected]

ABSTRACT:An Approach for Prediction of driver-fatigue monitor. It uses remotely located charge-coupled-device cameras

equipped with active infrared illuminators to acquire video images of the driver. Various visual cues that typically characterize

the level of alertness of a person are extracted in real time and systematically combined to infer the fatigue level of the driver.

The visual cues employed characterize eyelid movement, gaze movement, head movement, and facial expression. The eyes areone of the most salient features of the human face, playing a critical role in understanding a persons desires, needs and

emotional states. Robust eye detection and tracking is therefore essential not only for human-computer interaction, but also for

Attentive user interfaces (like driver assistance systems), since the eyes contain a lot of information about the drivers

condition: gaze, attention level, fatigue. Furthermore, due to their unique physical properties (shape, size, reflectivity), the eyes

represent very useful cues in more complex tasks, such as face detection and face recognition. A probabilistic model is

developed to model human fatigue and to predict fatigue based on the visual cues obtained. The simultaneous use of multiple

visual cues and their systematic combination yields a much more robust and accurate fatigue characterization than using a

single visual cue. This system was validated under real-life fatigue conditions with human subjects of different ethnicbackgrounds, genders, and ages; with/without glasses; and under different illumination conditions. It was found to be

reasonably robust, reliable, and accurate in fatigue characterization.

Keywordsdriver-fatigue, eyelid movement, gaze movement, head movement, visual cues . Fatigue characterization.

1. INTRODUCTION

Fatigue is a dormant physical condition that can bewitnessed right before one falls asleep. Fatigue affects onesreaction time, ability, concentration and generalunderstanding particularly while driving on road adversely.This thing is primarily based on the movement of human

eyelid which distinguished level of alertness. Various visualcauses that generally characterize the level of alertness of a

person are extracted systematically combined to check thefatigue level of the person. A probabilistic model isdeveloped to model human fatigue and to predict fatigue

based on the visual causes obtained. The simultaneous useof multiple visual causes and their systematic combinationyields a much more robust and accurate fatiguecharacterization than using a single visual cause.

The system uses a camera that points directlytowards the persons face and monitors the persons eyes informed to detect fatigue. In such a case when fatigue isdetected, a warning signal is issued to alert the driver. This

system describes how to detect the eyes, and also how todetermine if the eyes are open or closed. The system dealswith using information obtained for the binary version of theimage to find the edges of the face, which narrows the areaof where the eyes may exist. Once the face area is found,then eyes are found by computing the horizontal averages inthe area. After finding the eyes to monitor the eyemovement in the real time capacturing the video in thecamera is specific consecutive frame that gives 10 up to the200 frames. If the eyes are open, it shows eyes in the normalcondition mean Fatigue is not predicted. If the eyes are openand close in some consecutive way it shows the possiblefatigue detection. If the eyes are continuously close for a

while it predicted the fatigue is detected. It gives warning

signal given by the system so it alerts to the user to avoid anaccident.

Driver operation and vehicle behavior can beimplemented by monitoring the steering wheel movement,accelerator or brake patterns, vehicle speed, lateralacceleration, and lateral displacement. These too are non-

intrusive ways of detecting drowsiness, but are limited tovehicle type and driver conditions. The final technique fordetecting drowsiness is by monitoring the response of thedriver. This involves periodically requesting the driver tosend a response to the system to indicate alertness. The

problem with this technique is that it will eventually becometiresome and annoying to the driver.

2. LITERATURE SURVEY & BACKGROUND

[1] In this paper author developed the fatigue detectiontechniques based on computer vision. Fatigue is detected

from face and facial features of driver. By Hybrid method isused for face and facial feature detection. which not onlyincrease the accuracy of the system but also decrease the

processing time.[2]In this paper author proposed, real time machine vision

based system is design for the detection of driver fatiguewhich can be detect the driver fatigue and issue a warningearly enough to avoid an accident. Firstly the face is located

by machine vision based object detection algorithm anddetects eyes and eyebrows.

[3] In this paper author, developed the detect the driverfatigue based on the eye tracking which comes under an

active safety system using ordinary color web camera toinitialize the face detection and eye location and eyetracking.


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[4] In this paper author present the drivers fatigueapproach for real-time detection of driver fatigue. Thesystem consists of a sensors directly pointed towards thedrivers face. The input to the system is a continuous stream

of signals from the sensors.

[5] In this paper author, Developed a real-time driverfatigue detection system based on eye tracking and dynamictemplate matching. Using two new matching functions, theedge map overlapping (EMO) and the edge pixel count(EPC), to enhance matching accuracy.

[6] In this paper author is to developed artificial neuralnetwork has been used to detect the driver drowsiness level.Ever-increasing number of traffic accidents that are due to adiminished drivers vigilance level has become a problem ofserious concern to society. Drivers with a diminishedvigilance level suffer from a marked decline in their

perception, recognition, and vehicle-control abilities.

[7] In this paper author proposed the Eye tracking systemshave many potential applications such as learning emotionmonitoring and driver fatigue detection systems etc..So, howto use an eye tracking system to implement an eye mouse to

provide computer access for people with severe factors. Theeye mouse allows people with severe disabilities to use theireye movements to be manipulated by computers. It requiresonly one low-cost Web camera and a personal computer andfive stage algorithm is developed to estimate the directions

of eye movements and then use the direction information tomanipulate the computer. Several experiments wereconducted to test the performance of the eye trackingsystem.

[8] In this paper author proposed the system for skin, face,eyes detection which together can be used for detectinghuman presence in video. This system is build so that it canapplied to both real-time data although with lower detectionrate and static data i.e. .images and video for in processingwith higher detection rate.

[9] In this paper author proposed the face recognition

techniques (FRT) presented in face the issue and rarely statethe assumptions they make on their initialization; manysimply skip the feature extraction step, and assume perfectlocalization by relying upon manual annotations of the facialfeature positions.

3. PROPOSED METHODOLOGY AND

PROPOSED ARCHITECTRE

In this section discuss the proposed methodology inthis proposed work is to detect closed eyes simultaneouslyto observed and alert the driver on fatigue detection. This isdone with the help of mounting a camera in front of thedriver and continuously captured its real time video using

skin detection, eye detection and Hough Transformalgorithm.The conventional fatigue detectors are mostefficient and successful only on frontal images of faces. The

system can barely cope with 45 face rotation. The detectionarea is both around the vertical and horizontal axis. Anothershortcoming with these processes was that they were

perceptive to lighting conditions. For a few cases the

designed system detected multiple recognition of the sameface, due to overlapping sub-windows.

In this proposed design take test cases up to 200frames. This system describes a method to track the eyesand detect whether the eyes are closed or open. The nextitem to be considered in image acquisition is the videocamera. To demonstrate the project we have used the simpleLaptop camera. To create the video frames used ComputerVision System Toolbox.

The camera uses the functionvision.CascadeObjectDetector creates a Systemobject, detector that detects objects using the Viola-Jonesalgorithm. The Classification Model property controls the

type of object to detect. By default, the detector isconfigured to detect faces. Computer Vision SystemToolbox provides algorithms, functions, and apps for thedesign and simulation of computer vision and video

processing systems. Using this tool box the system canperform object detection and tracking, feature detection andextraction, feature matching, stereo vision, cameracalibration, and motion detection tasks. The system toolboxalso provides tools for video processing, including video fileI/O, video display, object annotation, drawing graphics, andcompositing. Algorithms are available asMATLAB functions, System objects, and Simulink blocks.

The MATLAB script is supposed to do the following:

Step 1.It captures the video and opens a facial view wherethe user has to point his face properly in front of the camera.

Step2. The MATLAB script detects the face and displaysthe image and lets the user place a bounding box around theface.

Step 3.Afterwards one the eye, mouth portions of the frameare recognized, it rescales the eye, mouth portions to 24*24

pixels.The camera uses Viola-Jones algorithm to scan a sub-window capable of detecting faces across a given inputframe. The standard image processing approach would be torescale the input image to different sizes and then run thefixed size detector through these frames.


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PROPOSED MODEL

Figure 1: Proposed model

4.

RESULT ANALYSIS

The system was tested on 15 people and wassuccessful with 12 people, resulting in 80% accuracy. Figure

below shows an example of the step-by-step result offinding the face, eyes and process to detect the fatigue levelof the person using eyelid movement.

I. Input video from camera for processing under theMATLAB when the eyes of the person are opened.

Figure 2: Figure shows the GUI implementation of theprocess when the eyes of the person are open and the eye

area are slice.Outputs:

i) Input video from camera for processing

under the MATLAB

Figure 3: Figure shows the Person place their face in frontof the camera as per location of Head Portion,EyeRegion,Nose Region and Mouth region for processing theoperation

ii)Input frames captured from camera for processingwhen the eyes of the person are opened and closed


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Figure 4: Figure shows eyes of the Person in NoormalCondition.so No Fatigue Predicted

iii) Recognition if the eyes of the person are open or

closed

Figure 5: Recognitising if the eyes of the person are open

or closed

Figure 6: Fatigue Detection process initiated when the eyesof the person are open and the eye area are sliced means itshows the possibale Fatigue Detection.

iv) Recognition if the eyes of the person are open or

closed.

Figure 7 : Recognitising if the eyes of the person are closethen shows the Fatigue is detected

5. COMPARISON WITH OTHER TECHNIQUES

The simplest method for driver fatigue detection isbased on applying a threshold on each extracted symptom.In the systems presented driver drowsiness was detected byapplying a constant threshold on PERCLOS. In the firststage was driver face identification and then an appropriate

threshold was chosen for the system based on physical andpsychological characteristics of the identified driver. Here,the list down a few techniques and make a comparison ofthem.

5.1 METHODS BASED ON THRESHOLD

In this system the fatigue detection was carried with 5different persons under the age group 30 to 35 years. For atotal of 200 frames, they had opened their eyes for 100frames and closed their eyes for 100 frames and monitoringtheir results is discussed in the table below.

For Open Eye:

No. of

Person

Total

Frames

Eyes

Open

Eyes

Open

Detected

Correct

Detection

Rate

Person1 200 94 94 100%

Person2 200 92 92 100%

Person3 200 91 91 100%

Person4 200 85 85 100%

Person5 200 86 86 100%

Table 1:Open eye detection Rate


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For Closed Eye:

No. of

Person

Total

Frames

Eyes

Close

Eyes

CloseDetected

Correct

DetectionRate

Person 1 200 91 91 100%

Person 2 200 83 83 100%

Person 3 200 88 88 100%

Person 4 200 92 92 100%

Person 5 200 85 85 100%

Table 2: Closed eye detection Rate

6. CONCLUSION

A noninvasive system to locate the eyes and face monitorfatigue when it occurred. The detectors have been testeddifferent faces and eyes under the same lighting situationsand have obtain the result very well considering the amountof parameter adjustment done during the testing. Themonitoring the fatigue if the eyes are opened or closed inseveral continuative way then warning signal is issued. Thesystem is able to automatically detect eyes localizing error

that might have occurred so in case of this type of error thensystem is able to recover and properly localize the eyes. Thefollowing conclusions were made:

Image processing achieves highly accurate andreliable detection of fatigue.

Image processing offers a non-invasiveapproach to detecting fatigue without theannoyance and interference.

A fatigue detection system developed around

the principle of image processingjudges thedrivers alertness level on the basis ofcontinuous eye closures.

7. LIMITATIONS

With 60% accuracy, it is observed that there arelimitations to the system.

The most significant limitation is that it will not

work with people who have very dark skin. This isapparent since the core of the algorithm behind thesystem is based on linearization. For dark skinned

people linearization doesnt work.

Any reflective objects behind the person. The more

uniform the background is, the more robust the

system becomes. For testing, rapid head movementwas not allowed. This may be acceptable, since it

can be equivalent to simulating a tired person. Forsmall head movements, the system rarely losestrack of the eyes. When the head is turned toomuch sideways there were some false alarms.

8. FUTURE WORK

The system does not work in dark skinned individuals. Thiscan be corrected by having an adaptive light source. Theadaptive light source would measure the amount of light

being reflected back. If little light is being reflected, theintensity of the light is increased. Darker skinned individualneed much more light, so that when the binary image isconstructed, the face is white, and the background is black.

Another big improvement would be to includeother salient features in the human face (i.e. the nose or the

mouth). This could introduce new geometrical constraints,but it might provide much better accuracy overall. In thelong run, properly adjusting the parameters, and using a

parallel implementation, this method could actually providegood results for real-time fatigue detection schemes.

9. REFERENCES

[1] Ijaz Khan, Hadi Abdullah, Mohd Shamian Zainal,Shipun Anuar,Hazwaj Mhd, Mohamad Md.VisionBased Composite Approach for LethargyDetection. Md, 2014 IEEE CSPA2014, 7-9

Mac.2014 kuala Lumpur, Malaysia.[2] Amandeep Singh, Jaspreet Kaur, Driver FatigueDetection Using Machine Vision Approach,Robotics and Autonomous Systems, Kaur,978-1-4673-4529-3/12/$31.00@2012 IEEE.

[3] D.J.M.Bomriver, Vision-based Real-time DriverFatigue Detection System for Efficient VehicleControl, nternational Journal of Engineering andAdvance Technology (IJEAT) ISSN: 2249-8958,Volume-2 Issue-1 Oct 2012.

[4] Narendra Kumar, Dr.N.C.Barwar Analysis of RealTime Driver Fatigue Detection Based on Eye andYawning, (IJCSIT) international Journal of

Computer Science and Information TechnologiesVol5 (6), 2014.7821-7826.[5] Narendra Kumar,Dr.N.C.Barwar, Detecting of Eye

Blinking and Yawning for Monitoring DriversDrowsiness in Real Time International journal ofApplication or Innovation in Engineering &Management(IJAIEM) Volume 3,Issue11,November 2014

[6] Mr. Swapnil V. Deshmukh #1, Ms.Dipeeka P.Radake*2, Mr. Kapil N. Hande#3, Driver fatigueDetection Using Sensor Network. InternationalJournal of Engineering Science and Technology(IJEST), NCICT Special Issue Feb 2011.

[7] Yang M. H., Kriegman J. and Ahuja N, DetectingFaces in Images: A Survey. IEEE Transaction on


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Pattern Analysis and Machine Intelligence, Vol. 24,pp. 34-58 (2008).

[8] WEN-BING HORNG, CHIH-YUAN CHEN, JIAN-WEN PENG, CHEN-HSIANG CHEN,

Improvements of Driver Fatigue Detection SystemBased on Eye Tracking and Dynamic TemplateMatching, Department of Computer Science andInformation Engineering Tamkang University, E-ISSN: 2224-3402, Issue 1, Volume 9, January2012.

[9] Er. Manoram Vats1and Er. Anil Garg DETECTIONAND SECURITY SYSTEM FOR DROWSY BYUSING ARTIFICIAL NEURAL NETWORKTECHNIQUE,International Journal of AppliedScience and Advance Technology January-June2012, Vol. 1, No. 1, pp. 39-43.

[10] MU-CHUN SU, KUO-CHUNG WANG, GWO-DONGCHENAn EYE TRACKING SYSTEM AND ITSAPPLICATION IN AIDS FOR PEOPLE WITHSEVERAL DISABILITIES, Department OfComputer Science And Information Engineering,

National Central University, Chung Li, Taiwan, Vol.18 No. 6 December 2006.

[11] Dmitry Golomidov Human detect ion in video,[email protected] Advisor: Jianbo Shi April18, 2008.

[12] Paola Campadelli, Raffaella Lanzarotti andGiuseppe Lipori,Automatic Facial FeatureExtraction for Face Recognition, ISBN 978-3-902613-03-5, pp.558, I-Tech, Vienna, Austria, June2007.

[13] Jung-Ming Wang,DETECTING DRIVERS EYESDURING DRIVING,18th IPPR Conference onComputer Vision, Graphics and Image Processing(CVGIP 2005)

[14] Qiang Ji,Zhiwei Zhu,and Peilin Lan, Real TimeNonintrusive Monitoring and Prediction Of DriverFatigue, IEEE Transaction on VehicularTechnology,VOL-53.No.4 Jul-2004.

[15] Yang M. H., Kriegman J. and Ahuja N., DetectingFaces in Images: A Survey, IEEE Transaction onPattern Analysis and Machine Intelligence, Vol. 24,

pp. 34-58 (2002).[16] FLECK M., FORSYTH D. A., AND BREGLER C.

2002. Finding nacked people. In Proceedingsofthe ECCV, vol. 2, 592602.

[17] BRAND J., AND MASON J. 2000. A comparativeassessment of three approaches to pixellevel humanskin-detection. In Proceeding of the InternationalConference on Pattern Recognition, vol. 1, 10561059.

[18] ZARIT B. D., SUPER B. J., AND QUEK, F.K. H.1999. Comparison of five color models in skin

pixel classification. In ICCV99 Intl Workshop

on recognition, analysis and tracking of faces andgestures in Real-Time systems, 5863.

[19] AHLBERG J. 1999. A system for face localizationand facial feature extraction Tech. Rep.LiTH-

ISY-R-2172, Linkoping University.[20] Z. Zhu, Q. Ji, K. Fujimura, and K. C. Lee, Combining

Kalman filtering and mean shift for real time eyetracking under active IR illumination, presented atthe Int. Conf. Pattern Recognition, Quebec, PQ,Canada, 2002.

[21]Mathworks http://www.mathworks.com[22] http://www.cse.iitk.ac.in/users[23] Duda, R.O. and P.E. Hart "Use of the Hough

Transformation to Detect Lines and Curves inPictures", Comm. ACM, Vol.15, pp.1115(January, 1972).

[24] The Hough Transform,

http://planetmath.org/encyclopedia/HoughTransform.html.

10. AUTHOR PROFILE

Pritam H. Gohatrereceived the Master ofTechonology in SystemSoftware from Rajiv GandhiTechnical UniversityBhopal. Currently he is an

Assistant. Professor inLAMIT, Dhamangoan,india. He has published two

papers in internationaljournals. He is having 7year teaching experienceand his field ofspecialization is softwaredevelopment, Image

processing, Networking.


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AN IMPROVED IMAGE FUSION ALGORITHM BASED ON WAVELET TRANSFORMS

USING PARTICLE OF SWARM OPTIMIZATIONHRISHIKESH S. HOLEY

Patel Institute of Technology, [email protected]

ABSTRACT:Feature based image fusion is new area of research in the field of image fusion. The image fusion used lower

content of image feature. The lower content of image feature such as color texture and dimension. The texture features are very

important component of image. The processing and extraction of texture feature used various transform function such as

wavelet transform function, Gabor transform function and many more signal based transform function. In the process of image

fusion involve two and more image for the process of fusion. The fused image still image pervious quality as well as new feature

and area of improved by new and adopted reference image. In this paper, we proposed a feature based image fusion technique.

The feature based optimization technique also used feature selection and feature optimization process. The feature selection

and feature optimization used particle of swarm optimization technique. The particle of swarm optimization technique selects

the optimal texture feature of both image original image and reference image. The original and reference image find the

optimal feature sub set for the estimation of feature correlation.

KeywordsImage fusion, wavelet transform function, swarm optimization technique, optimal texture.

1. INTRODUCTION

Computers have been widely used in our daily lives, sincethey can handle data and computation more efficiently andmore accurately than humans. Therefore, it is natural tofurther exploit their capabilities for more intelligent tasks,for example, analysis of visual scenes (images or videos) orspeeches (audios), which are followed by logical inferenceand reasoning. For we humans, such tasks are performedhundreds of times every day so easily from subconscious,

sometimes even without any awareness. In computer visionapplications, one of the challenging problems is thecombining of relevant information from various images ofthe same scene without introducing artifacts in the resultantimage. Since images are captured by the use of differentdevices which may have different sensors. Because of thedifferent types of sensors used in image capturing devicesand their principle of sensing and also, due to the limiteddepth of focus of optical lenses used in camera, it is possibleto get several images of the same scene producing differentinformation. Image registration is the process ofsystematically placing separate images in a common frameof reference so that the information they contain can be

optimally integrated or compared. This is becoming thecentral tool for image analysis, understanding, andvisualization in both medical and scientific applications.There are many image fusion methods that can be used to

produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolutionmultispectral images. Starting from the physical principle ofimage formation, Neural network and fuzzy theory is thetwo main methods of intelligence, the image fusion system

based on these two methods of can simulate intelligenthuman behavior, do not need a lot of background

knowledge of research subjects and precise mathematicalmodel, But find the law to resolve complex and uncertaintyissues on the basis of input and output data of objects. Fromthese characteristics and the advantages, it can be seen thatthe use of the approach combined by neural networks andfuzzy theory can better complete the multi-sensor image

pervasive fusion.The goal of proposed system is , the object of

image fusion is to obtain a better visual understanding of

certain phenomena, and to introduce or enhance intelligenceand system control functions. Many advantages of multisensory data fusion such as improved system performance(improved detection, tracking and identification, improvedsituation assessment, and awareness), improved robustness(lessens or redundancy and graceful degradation), improvedspatial and temporal coverage, shorter response time, andreduced communication and computing, can be achieved.

2. LITERATURE SURVEY &

BACKGROUND

[1] In this paper, author proposed a pixel-level

image fusion scheme using multi resolution steerablepyramid wavelet transform. Wavelet coefficients at differentdecomposition levels are fused using absolute maximumfusion rule. Two important properties shift invariance andself reversibility of steerable pyramid wavelet transform areadvantageous for image fusion because they are capable to

preserve edge information and hence reducing the distortionin the fused image. Experimental results show that the

proposed method improves fusion quality by reducing lossof relevant information present in individual images. Forquantitative evaluation, we have used fusion metrics as


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fusion factor, fusion symmetry, entropy and standarddeviation. We proposed a pixel level image fusion schemeusing steerable pyramid wavelet transform. In the proposedmethod, two main steps have to be followed: one, the source

images are decomposed into low pass and high pass sub-bands of different scale using steerable pyramid, andsecondly, low pass sub band is divided into a set of oriented

band pass sub-bands and a low pass sub-band.Thesuitability of the proposed method is tested on multi focusand medical images. For this, we have presented two pair ofimages and their fusion results. The results are also testedon two different conditions; when images are free from anynoise and other when they are corrupted with zero meanwhite Gaussian noise. From experiments, we observed thatthe proposed method performs better in all of the cases. The

performance is evaluated on the basis of qualitative andquantitative criteria.

[2] In this paper, the fusion framework based on data assimilation and genetic algorithm for Multispectral imageand panchromatic image was presented. Data assimilationcan combine the advantage of model operator and observeoperator. Our proposed method can integrate the advantagesof DWT and HIS, construct object function according tosuccessive application to satisfy the aim of adaptivelyadjustment of fusion parameters. Standard deviation andaver-age gradient are chosen as object function. In general,the higher the value, the better the texture information. Andtwo experiments (Spot, Quick bird) validate this framework.The experiment results show that our proposed fusionframe-work is feasible.

[3] In this paper presents a comprehensive framework, thegeneral image fusion (GIF) method, which makes it possibleto categorize, compare, and evaluate the existing imagefusion methods. Using the GIF method, it is shown that the

pixel values of the high-resolution multi spectral images aredetermined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of thehigh-resolution panchromatic image at the low-resolutionlevel.

This paper proposes a framework, the GIFmethod. Under different assumptions on how the LRPI iscomputed and how the modulation coefficients are set,many existing image fusion methods, including, but notlimited to, IHS, BT, HPF, HPM, PCA, ATW, and MRAIM,are shown to be particular cases of the GIF method. The

performance of each method is determined by two factors:how the LRPI is computed and how the modulationcoefficients are defined. If the LRPI is approximated fromthe LRMIs, it usually has a weak correlation with the HRPI,leading to color distortion in the fused image. If the LRPI isa low-pass filtered HRPI, it usually shows less spectraldistortion. If the modulation coefficient is set as a constantvalue, the reflectance differences between the panchromatic

bands and the multispectral bands are not taken intoconsideration, and the fused images bias the color of the

pixel toward the gray. Methods in which the modulationcoefficients are set following the GIF method can preservethe ratios between the respective bands, give more emphasis

to slight signature variations, and maintain the radio-metricintegrity of the data while increasing spatial resolution.[4] This paper addresses the image registration problemapplying genetic algorithms. The image registrations

objective is to define mapping that best match two set ofpoints or images. In this work the point matching problemwas addressed employing a method based on nearest-neighbor. The mapping was handled by affinetransformations. This paper presents a genetic algorithmapproach to the above stated problem of mis-registration.The genetic algorithm is an iterative process whichrepeatedly modifies a population of individual solutions. Ateach step, the genetic algorithm selects individual at randomfrom the current population to be parents and uses them to

produce the children for the next generation. In eachgeneration, the fitness of every individual in the populationis evaluated, multiple individuals are stochastically selected

from the current population (based on their fitness), andmodified (recombined and possibly randomly mutated) toform a new population. The new population is then used inthe next iteration of the algorithm. Over successivegenerations population evolves toward an optimalsolution. The algorithm terminates when either a maximumnumber of generations has been produced, or a satisfactoryfitness level has been reached for the population.

In this paper we have focused on geneticalgorithm for medical image registration. Genetic algorithmis an evolutionary algorithm. There are other methods likesimulated annealing, mutual information theory for image

registration. Apart from this, there are other algorithms likegraph algorithm and sequence algorithms. We canimplement these algorithms and show the comparative studyand get the most suitable for medical applications.

3. PROPOSED METHODOLOGY AND

ARCHITECTRE

In this section discuss the proposed methodology of featurebased image fusion technique based on wavelet transformfunction and particle of swarm optimization, the feature oftransform function passes through feature selection process.The feature selection process used particle of swarmoptimization technique.The particle of swarm optimizationselect the optimal feature of given texture feature matrix. Ifthe correlation coefficient factor estimate the value ofcorrelation is zero then fusion process is done. The processof proposed model divide into two section first section dealswith initially take host image and reference image passesthrough wavelet transform function for feature extractionafter the feature extraction applied optimization task done

by particle of swarm optimization.


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Step feature extraction

a. input the host image and reference image

b.

apply separately Wavelet transform functionfor feature extractionF(x) =I(x, y) is host image F1(x) =I1(x1, y1) isreference imageM (F) = F(x) G(x)The convolution is perform in host imagethrough transform function here M (F) storedthe texture feature matrix of host image.

Then a feature vector is constructed using mn and mn asfeature components:

f= [00 00 0101. mn mn] .. (1)We obtain a numerical vector of 60 dimensions for 10

orientations and 6 scales changes. This moment featurevalue stored in M (F) matrix.

N (F) =F1(x) G(x)

The convolution is perform in host image through transformfunction here (F) stored the texture feature matrix of hostimage.

Then a feature vector is constructed using 1mn and 1mn asfeature components:

f= [100 100 01101. 1mn 1mn] ..(2)

We obtain a numerical vector of 60 dimensions for 10orientations and 6 scales changes. This moment featurevalue stored in N (F) matrix.

1. Both the feature matrix convent into feature vectorand pass through particle of swarm optimization

2. step two used here particle of swarm optimizationfor classification of pattern Transform data to theformat of an SVM that is X is original data R is

transform data such that Xi d here d isdimension of data.

Conduct scaling on the data

here is scaling factor and m is total data point and k istotal number of instant and sim find close point of data.

Consider the RBF kernel K(x; y)

H(x) =

this is kernel equation of plane.Use cross-validation to 2nd the best parameter C andUse the best parameter C and to train the whole training set

Ro=

where Ro is learning parameterof kernel function.

Generate pattern of similar and dissimilar pattern of bothimage.3. Estimate the correlation coefficient of both patterns using

persons coefficient.

Estimate the feature correlation attribute as

Here a and b the pattern of host

image and reference image.

The estimated correlation coefficient data check the total

value of MSE

Create the relative feature difference value

If the relative pattern difference value is 0

4. Fusion process is done

5. Calculate PSNR value of fused image

6. Calculate IQI value of fused image

7. Calculate fusion MSER of fused image.

PROPOSED MODEL

Figure 1: Proposed model of image fusion technique basedon feature optimization


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DESCRIPTION OF MODEL

In this section describe the process of proposed model. Theproposed model contain with wavelet transform function

and particle of swarm optimization. The swarm optimizationused for the feature optimization process. Here discuss step

of proposed model.

Step 1.Initially put the original image and reference image

for the processing of feature extraction

Step 2. After processing of image discrete wavelettransform function are applied for the texture featureextraction

Step 3. After the texture feature extraction calculate themaximum value of feature using mean standard formula.

Step 4the maximum value of feature set is global value offitness constraints of particle of swarm optimization

Step 5. The particle of swarm optimization select the allfeature as particle and measure value of difference andmove according to feature direction for the processing ofoptimalStep 6. The selection of optimal feature in both imageestimate the correlation coefficient function of value R.Step 7.If the value of R is 0 image are going on process ofimage fusion.Step 8. If value of R not equal to 0 the processing going to

estimation function.

4. RESULT ANALYSIS

To investigate the effectiveness of the proposed method forimage fusion based on wavelet transform function and

particle of swarm optimization. We used MATLABsoftware 7.14.0 and some reputed image used forexperimental task such as the name given head image, headCT image, head MRI image, Heart image and Hand image.This all image is gray scale image size is 512 * 512. The

performance measuring parameter is MSER, PSNR and IQI.Here we are using various types of image fusion techniques

such as wavelet and Particle of swarm optimization.

IMAGE

NAME

Name of

methodMSER PSNR IQI

Head DWT 22.03 18.30 0.955

Head DWT-POS 26.18 20.17 0.947

Table 1:Shows that the comparative result analysis for theHead image with using DWT and DWT-POS method and

we find the value of MSER, PSNR and IQI.

IMAGE

NAME

Name of

Method

MSER PSNR IQI

Head CT DWT 17.38 15.82 1.96

Head CT DWT-

POS

23.67 18.29 0.953

Table 2:Shows that the comparative result analysis for theHead CT image with using DWT and DWT-POS methodand we find the value of MSER, PSNR and IQI.

IMAGE

NAME

Name of

Method

MSER PSNR IQI

Head MRI DWT 15.89 14.43 1.964

Head MRIDWT-

POS22.15 16.84 0.957

Table 3:Shows that the comparative result analysis for the

Head MRI image with using DWT and DWT-POS method

and we find the value of MSER, PSNR and IQI.

IMAGE

NAME

Name of

MethodMSER PSNR IQI

Heart DWT 22.17 18.43 0.954

HeartDWT-

POS26.53 21.01 0.943


Heart image with using DWT and DWT-POS method and


IMAGE

NAME

Name of

Method

MSER PSNR IQI

Hand

image

DWT 24.76 20.86 0.948

Hand

image

DWT-

POS

29.06 23.41 0.940


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Hand image with using DWT and DWT-POS method and


COMPARATIVE RESULT GRAPH

Figure 2: Shows that the comparative result graph for Head

image with using DWT and DWT-POS image fusion

method and find the value of MSER, PSNR and IQI.


CT image with using DWT and DWT-POS image fusion



MRI image with using DWT and DWT-POS image fusion


Figure 5: Shows that the comparative result graph for Heart



0

5

10

15

20

25

30

MSER PSNR IQI

Comparative result graph for Head image

with using DWT and DWT-POS image

fusion method and find the value of MSER,

PSNR and IQI

DWT

DWT-POS

0

5

10

15

20

25

MSER PSNR IQI

Comparative result graph for Head CT image

with using DWT and DWT-POS image fusion

method and find the value of MSER, PSNR

and IQI

DWT

DWT-POS

0

5

10

15

20

25

MSER PSNR IQI

Comparative result graph for Head MRI

image with using DWT and DWT-POS

image fusion method and find the value of

MSER, PSNR and IQI

DWT

DWT-POS

0

5

10

15

20

25

30

35

MSER PSNR IQI

Comparative result graph for Hand image

with using DWT and DWT-POS image

fusion method and find the value of MSER,

PSNR and IQI

DWT

DWT-POS


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Figure 6: Shows that the comparative result graph for Hand



5. CONCLUSIONIn this dissertation proposed a feature based image fusiontechnique for the improvement of quality of image ofdistorted and damage image. The process of proposedalgorithm used wavelet transform function for the featureextraction process. The wavelet transform function extractthe lower content of texture feature. The lower content oftexture feature used for the process of feature optimization

process. The feature optimization process done by particleof swarm optimization. Particle of swarm optimization isdynamic population based optimization technique. Thecorrelation coefficient factor estimate the relation oforiginal image and reference image. If the value ofcorrelation is 0 then image are fused. If the value ofrelation is not equal to zero the estimation factor recall.Measure the quality of fused image measures areconsidered. These measures play an important role invarious Image Processing applications. Goal of imagequality assessment is to supply quality metrics that can

predict perceived image quality automatically. Whilevisual inspection has limitation due to human

judgment, quantitative approach based on the evaluation ofdistortion in the resulting fused image is moredesirable for mathematical modeling. The goals of thequantitative measures are normally used for the result ofvisual inspection due to the limitations of human eyes. InMathematical modeling, quantitative measure is desirable.One can develop quantitative measure to predictperceivedimage quality. In this dissertation used PSNR, IQI andMSER for estimation of quality of image.

6. FUTURE WORK

The proposed method of image fusion is very efficient forthe process of image quality improvement. The process of

fusion produces good result in term of quantitative analysis.But it still need some improvement in IQI parameter. Themaximum value of IQI is 1. But in this dissertation onlyreached 97-98% for quality factor. In future improve thevalue of IQI up to 1. For this used two or more featurecombined with texture feature.

7. REFERENCES

[1] Om Prakash, Arvind Kumar, Ashish Khare, Pixel-levelimage fusion scheme based on steerable pyramid wavelettransform using absolute maximum selection fusion ruleIEEE, 2014. Pp 765-771.

[2] Liang HongKun YangXianchun Pan, Multispectral

and panchromatic image fusion Based On GeneticAlgorithm and Data Assimilation IEEE, 2011. Pp 156-160.[3] Zhijun Wang, Djemel Ziou, Costas Armenakis, DerenLi, and Qingquan Li, A Comparative Analysis of ImageFusion Methods IEEE TRANSACTIONS ONGEOSCIENCE AND REMOTE SENSING, VOL. 43, 2005.Pp 1391-1402.[4] V. T. Ingole, C. N. Deshmukh, Anjali Joshi, DeepakShete, MEDICAL IMAGE REGISTRATION USINGGENETIC ALGORITHM Second InternationalConference on Emerging Trends in Engineering and

Technology, 2009. Pp 63-67.[5] Won Hee Lee, Kyuha Choi, Jong Beom Ra, FrameRate Up Conversion Based on Variational Image FusionIEEE TRANSACTIONS ON IMAGE PROCESSING,VOL. 23, 2014. Pp 399-413.[6] De-gan Zhang, Chao Li, Dong Wang, Xiao-li Zhang, A

New Method of Image Data Fusion Based on FNN SixthInternational Conference on Natural Computation, 2010. Pp3729-3733.[7] Chaunt W. Lacewell, Mohamed Gebril, Ruben Buaba,and Abdollah Homaifar, Optimization of Image FusionUsing Genetic Algorithms and Discrete WaveletTransform IEEE, 2010. Pp 116-121.

[8] Ling Tao, Zhi-Yu Qian, An Improved Medical ImageFusion Algorithm Based on Wavelet Transform eventhInternational Conference on Natural Computation, IEEE2011. Pp 76-80.[9] LU Xiaoqi, ZHANG Baohua, GU Yong, MedicalImage Fusion Algorithm Based on clustering neuralnetwork IEEE, 2007. Pp 637-640.[10] Vikas Kumar Mishra, Shobhit Kumar, Ram KailashGupta, Design and Implementation of Image FusionSystem International journal of computer science &engineering, Vol-2, 2014. Pp 182-186.[11] S. Bedi, Rati Khandelwal, Comprehensive and

Comparative Study of Image Fusion TechniquesInternational Journal of Soft Computing and Engineering,Vol-3, 2013. Pp 300-304.

0

5

10

15

20

25

30

35

MSER PSNR IQI

Comparative result graph for Hand image

with using DWT and DWT-POS image fusion

method and find the value of MSER, PSNR

and IQI

DWT

DWT-POS


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[12] Jamal Saeedi, Karim Faez, Infrared and visible imagefusion using fuzzy logic and population-based optimizationApplied Soft Computing, Elsevier ltd. 2012. Pp 1041-1054.[13] P. Shah, S. N. Merchant, U. B. Desai, "Fusion of

surveillance Images in Infrared and visible band usingcurvelet wavelet and wavelet packet transform"International Journal of Wavelets, Multire solution andInformation Processing Vo1.8, No.2, 2010. Pp 271-292.[14] P. Shah, S. N. Merchant, U. B. Desai, "An efficientspatial domain fusion scheme for multi focus images usingstatistical properties of neighborhood" Multimedia and Expo(ICME), 2011, Pp 1-6.[15] V. P. S. Naidu, R. Raol, "Pixel-level image fusionusing wavelets and principal component analysis" DefenceScience Journal, Vo1.58, no.3, 2008, Pp 338-352.

8. AUTHOR PROFILE

Prof. Hrishikesh S. Holey

received the Master ofTechnology in SoftwareEngineering from RajivGandhi Technical University,Bhopal. Currently he is anAssistant Professor in HVPMCollege of Engineering &Technology, Amravati, India.He has published two papers in

international journals. He ishaving 6 year teachingexperience and his field ofspecialization is ImageProcessing, Digital SignalProcessing, SoftwareEngineering, Computer

Network and NetworkSecurity..


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I nternational Journal of Research in Computer & I nformation


Page 14

SAP HANA DATABASE WITH NETWORK

PROF. ANUP. P. DATE1, MISS. NAMRATA S. MAHAJAN

2

1Assistant Professor, Department of CSE, KGIET Darapur,

Sant Gadge Baba Amravati University, Maharashtra, India.

[email protected],2Department of CSE. KGIET Darapur,

Sant Gadge Baba Amravati University, Maharashtra, India

[email protected]

ABSTRACT:SAP HANA Database is stand for system, application and processing on database with High performance AnalyticAppliance. SAP HANA core database can serve real-time, complex queries and multi structured data needs. Enterprise

Requirement may increase day by day. In SAP HANA database no of user operate same data simultaneously. The goal of the SAP

HANA database is the reduced the problem related with storage and workload within management system. By this reasons it

have modern hardware with multiple processor, large main memory, and caches. SAP HANA database have various method for

compression of database content [1].

Keywords:SAP HANA, real time, multiple processor, cache, main memory.

1. INTRODUCTION

It is a combination of hardware and software made to

handle both high transaction rate and complex query

processing. SAP HANA previously called SAP High-

Performance Analytic Appliance. In addition to database engine

HANA include an embedded web server, Trex search engine an

in memory column-oriented search engine [2]. SAP HANA has

the cloud platform for the data storage. And hence the data isstored in secure and efficient way [3].

The SAP HANA has two type of connection to network:

1) Inbound connection.

2) Outbound connection

Inbound connections: In inbound connection for database

clients and data provisioning it used the protocols SQLDBC by

using port no.3xx15, 3xx17 and SAP HANA Studio operate

administrative functions by using port no.5xx13, 5xx14, 1128,1129.for the web base access of SAP HANA us done by using

HTTP and HTTPS for this port no.80xx, 43xx is used[3].

Outbound connections:Outbound connection is used by the

solution manager for the purpose of diagnostics agent

installed on each system.

2.THE SAP HANA DATABASE WITH NETWORK

CONNECTIVITY

Network is defined as the set of two or more number of

computers that shared the information, resources to each

other.

An SAP HANA data centre database can running range

from the single host to a complex distributed system having

multiple hosts location.

2.1 High availability and disaster recovery in SAP

HANA: SAP HANA is fully designed have high

availability. It supports recovery used for the detection of

error related to the software and other fault. High

availability in SAP HANA IS derived from set of

techniques, practices of engineer and design principles that

fulfil the requirement of business continuity [4].

For achieved High availability in SAP HANA first it

eliminating single points of failure as quickly and

providing the ability to repair the fault. Fault recovery isthe method of detection and correction of the fault. In

disaster recovery it take a backup of data. The backup is

used at the time of data is loss due to some reasons like due

to viruses, power supply fault.

2.2 Connectivity to Network: SAP HANA database has

the client-server model of connectivity. SAP HANA is

transaction-oriented databases because it used the

replication services.

The setup of an SAP HANA depends on following things.


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Technology (I JRCIT) Vol. 1, I ssue 1, 2015 ISSN: 2455-3743

Page 15

It Support to traditional database clients, Web-baseduser, and admin.

The number of client used to the SAP HANA system,

ranging from a single-client system to a complexdistributed system is responsible for provides ainformational framework to use the most efficientchannels of transmission information with a low errorrate .with multiple client.

Support for high availability through the use of

multiple technique, and support for disaster recoverydatacenters with recovery methods.

SAP HANA has number of network communicationchannels with the different SAP HANA setups.

There is separate channels used for external access toSAP HANA function operate by end-user clients,administration clients, application servers, and for

data providing via SQL or HTTP protocols.For purpose of separate external and internal

communication, SAP HANA hosts use a separate network

adapter .network adapter means it is a board or PCMCLA

card that have RAM, DSP chips and link interface with a

separate logical address for each of the different networks.

In addition, SAP HANA can be configured to use SSL

(secure socket lock protocols for secure communication.

3. NETWORK ZONES

3.1 Client zone: In the simple means client is persons that

use the facility provides by server. The is used by SAPapplication servers, by clients used client zone by SAP

HANA studio or Web applications running With help of

SAP HANA Extended service server, and for the storing

historical data of user it used data warehouse[8].

3.2 Internal zone:This zone covers the inter host network

between hosts which are in a distributed system and the

SAP HANA system replication technique.

3.3 Storage zone: it is responsible data backup purpose.

Figure 1:Network Zone.

3.4 Connections used by Database Clients and Web Clients

to connect to the SAP HANA.

Connection is as follows

Administrative purposes connection.

Data provisioning (providing) connection.

Clients Connections from database that access thedatabase by using SQL/MDX interface.

4. PROTOCOLS USED BY DATABASE FOR

CONNECTING TO NETWORK

Protocols: A protocol is rules and regulation that governthe communication. Without protocols there is no

communication.For the database connectivity SAP HANA database usedfollowing protocols.4.1. SQLDBC: It stands for the SQL databaseconnectivity. By using this protocol we can connect thedatabase to the network. This protocol again has to typeJBDC and OBDC. This protocols used by client andadminister to database for connection.

JDBC: Stand for JAVA database connectivity. Thisprotocol is responsible for the client connects andaccess to the database. It used for the updating thedatabase contains. The stored procedures are invoked byJDBC connection.

ODBC: Stand for open database connectivity. Thisapplication which is written using the ODBC is portedto both client and server side. It used concept of DNSthat is data source names. DNS collect the additionalinformation about the particular source connection.

4.2 HTTP/HTTPS: HTTP is tends for hypertext transferprotocols and S is for the Secure. SAP HANA USED theseprotocols for connecting web application to the database.HTTP is reliable data transfer. HTTP used the portno.80xx.HTTP protocols used the TCP connection [9].4.3 SSL:SSL stand for the secure sockets layer protocols.This is used for the secure communication between SAPHANA database and client. This protocol provides theauthentication to server and data encryption. SSL used the

port no 5xx13 and 5xx14.


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Database Client Access:

Client Protocols used

by client

TCP port

number

1. Client used

the SAP HANA

database as

application.

Example:

Business

Warehouse.

2. The user used

SAP HANA

directly

Example:

Microsoft Excel.

3. SAP HANA

studio:

SAPHANA

studio is

administrative

block

The protocol

used for

database client

access is

SQLDBC.

3 xx 15

3 xx 17

Administrative Tasks:

Client Protocols and

other

information

Port no.

1)SAP support

2)SAP HANA

studio

The internal SAP

protocol is used.The protocols is

used by studio is

SQLDBC. here

the connection

agent play role

of administrator

3xx09

5xx13 5xx14

(SSL)

5. SAP HANA SERVER

Server: Server is the any computer program or

machines that accept the client request and give response.

Main motto of server is to share resources and hardware

between the clients.

The SAP HANA has the following server.

Index Server: It is responsible for the storing of actualdata and the engines for processing the data. It acts also as

main server. User all the other servers coordinate by the

server

Name Server: It is having responsibility to store the

topologyof Distributed SAP HANA database name server.

The SAP HANA used by distributed system by using the

instances number. Name server has information about the

running component of SAP HANA database.

Statistics Server: It has information about Status about the

Performance and Resource Consumption from all the other

server components. For the access the Statistics Serverthere is used of the SAP HANA Studio. It monitors the

entire server working.

Pre-processor Server: It is used for to find Text Data and

extracting the information on which the text search

capabilities are based [2].

6. NETWORK SECURITY

6.1 Authentication and Authorization: SAP HANA

supports an authentication method. Authentication is

method to provide a way of identifying a user. The most

basic is username/ password combinations for database

users created and maintained through the SAP HANA

Studio [4].

6.2Encryption: ENCRYPTION Is method of data hiding

in the network .The secure sockets layer (SSL) protocol be

used to encrypt client-server traffic and internal

communications in SAP HANA. SSL is not invulnerable

(not have any weakness). SSL proxies are widely available

and can be used to encrypt and decrypt packets passed

between endpoints within a network. Root encryption keys

are stored using the SAP Net Weaver secure storage file

system (SSFS). Keys should be periodically changed using

the sql command alter system persistence encryption create

new key followed by alter system persistence encryption

apply current key [5].

6.3Auditing And Logging: An audit also called audit log.

The meaning of audit means recording the log file. Once

enabled, audit policies should be configured to log actions

that include SELECT, INSERT, UPDATE, DELETE, And

EXECUTE and other quires when combined with specific

conditions. Policies can be configured for specific users,

tables, views and procedures. It is recommending auditingall actions performed by privileged users including the


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SYSTEM user and actions that impact sensitive database

objects. SAP HANA database supports to recovery. In it

database have one copy of original data if anyone make

changes in it then data is match with copy [5].

7. CONCLUSION

The requirement of every organization is to have the

powerful database for storage the SAP HANA provides all

things that make organization best. The SAP HANA has

one speciality that thousands of users may read or update

records of the same data. SAP HANA provides the best

security to data. As all organization give important to have

the powerful network of database the SAP HANA fulfill all

the requirement of organization. SAP HANA database with

network connectivity give benefits to the organization.

9. REFERENCES

[1]F Farber, N.May, W.Lehner, P.Grobe The SAP HANA

DATABASE ARCHITECTUCTURE OVERVIEW

IEEE Data Eng-Bull 35(1), 2012.

[2]Tusal patel, Priti Gupta, Nishant Khatri Distributed

SAP HANA DATABASE FOR EFFICIRNT

PROCESSING. International Journal of advanced

Research in computer communication Engineering, volume

2 issue 6 June 2013

[3] T. Benson, A. Akella, A. Shaikh, and S. Sahu.

Cloudnaas: a cloud networking platform for enterprise

applications. In SOCC, 2011.

4]H. Plattner. A common database approach for OLTP and

OLAP using an in-memory column database. In

Proceedings of the 35th SIGMOD international conference

on Management of data, SIGMOD '09.

[5] SUSE Linux Enterprise Server 11 SP3 Security and

Hardening, June 2013, SUSE LLC, SUSE Linux Enterprise

Server 11 SP3 Security Guide, July 2013, SUSE LLC.

[6] F. Farber, S. K. Cha, J. Primsch, C. Bornhovd, S.Sigg, and W. Lehner. SAP HANA Database - Data

Management for Modern Business Applications. SIGMOD

Record, 40(4):4551, 2011.

[7] T. Legler, W. Lehner, and A. Ross. Data mining with

the sap netweaver bi accelerator. In VLDB, pages 1059

1068, 2006.

[8]P. Costa, M. Migliavacca, P. Pietzuch, et al. NaaS:Network-as-a-Service in the cloud. In HotICE, 2012.

9]D. D. Clark, J. Romkey, and H. C. Salwe. An analysis of

TCP processing overhead. In LCN, 1988.

[10] SAP AG or an SAP affiliate company, SAP HANA

Introduction, Participant Handbook, 2013, pp. 2-18.

10. AUTHOR PROFILE

Prof. Anup. P. Date received

the MBA in ComputerManagement from Universityof Pune. Currently he is anAssistant Professor in KGIETDarapur, India. He has

published three papers ininternational journals and twonational conferences. He ishaving 6 year teachingexperience and his field ofspecialization is softwareEngineering, softwaremanagement, computer

networking, databasemanagement, artificialintelligence, web baseengineering, Image

processing.

Miss. Namrata S. MahajanCurrently pursing Bachelor ofEngineering in ComputerScience and Engineeringfrom Kamalatai GawaiInstitute of Engineering &Technology, Amravati, India.


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PERFORMANCE EVALUATION OF DATABASE CLIENT ENGINE USING MODULAR

APPROACH

PROF. RAMANAND SAMDEKAR

S.B. Jain Institute of Technology Management & Research, Nagpur, India

[email protected]

ABSTRACT:Materialized view creation is an important aspect for large data centric applications. Materialized views create anabstraction over the actual database tables to the users. The MV view creation and selection is based on the various parameters

like access frequency, base update frequency etc. Database client engine use cluster base approach to create materializes view to

reduce query execution time.

Keywords:Database client engine, cluster base, Data Warehouse, Threshold, materialized views.

1.

INTRODUCTIONBy observing the use of heterogeneous data in

Data warehouses are designed to facilitate reporting andanalysis of data, focuses on data storage. The datawarehouse is intended to provide decisions support servicesfor large volumes of data. So how to rapidly respond toquery request is a great challenge in data warehouse. Quickresponse time and accuracy are important factors in thesuccess of any database.

This paper proposes an approach of grouping inbroader sense clustering the similar queries depending oncertain parameters like access frequency to find the resultfrom MV. The proposed work explores the area of a) query

clustering for the selection of materialized view to decreasethe response time and storage space deploymentenvironment b) Ease network goals c) Enabling data sub-setting d) Enabling disconnected computing. To achievethese benefits, a methodology is proposed in this paper toform a quantitative optimize total query response timeunder a disk space constraint for data warehouseapplications presented in [1] [3].

2. RELATED WORKS

Ordinary views are loaded with data every time it iscalled. Thus in real life applications materialized views arefound to be more suitable to reduce query execution time.

Materialized view creation involves several issues toconsider. However, the main concern is to ensureavailability of higher amount of user requested data directlyfrom materialized views. Automated selection [13] ofmaterialized views in large data oriented application isdesirable for dynamic changes. A very few research workhas been done about selection of materialized view usingclustering approach. A significant work about dynamicclustering of Materialized view is done by [1].Paper [5]

proposes a greedy algorithm BPUS based on the latticemodel. And paper [6] discusses the issue of materializedview selection with the B-tree index. Paper [7] proposesPBS algorithm which make the size of materialized view as

selection criteria. Paper [8] proposes preprocessor ofmaterialized view selection, which reduces the

Search space cost and time complexity of staticmaterialized view selection algorithm. These algorithms are

based on the known distribution of query, or uniformdistribution under the premise, which essentially are staticalgorithms. However, the query is random in actual OLAPsystem, so materialized view set which static algorithmgenerates cannot maximally enhance the query response

performance in data warehouse. In order to improve furtherquery response performance in data warehouse, paper [9]

proposes dynamic materialized view selection algorithm,FPUS algorithm, which is based on query frequency in unitspace. Relationship among several attributes in the form of a

Quantitative metric using a robust mathematical model, whichis implemented here using line fitting algorithm. Thisquantitative measure guides to construct the materializedviews.

3. PROPOSED METHODOLOGY

Our solution is an approach based on user behavior andtheir interactions with the system, particularly the distributionof their queries, to create the set of views to materialize.Materialized views are able to provide the better performancefor DW queries. However, these views have maintenancecost, so materialization of all views is not possible. Animportant challenge of DW environment is materialized view

selection because we have to realize the trade-off betweenperformance and view maintenance is needed to considerfollowing things.

1) Classification of queries: it is to determine thecategories of data which the user is interested.[11]

2) Classification of attribute groups:it is to determine thegroups of attributes for each class.

3) Merging classes: merge the data classes to make theclasses that are most compact.

A clustering method is suggested in which

similar queries will be clustered according to theirquery access frequency to select the materialized views


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that will reduce the execution time and storage space.When the query is posed, it will be compared withalready clustered or existing query, and the

precomputed MV will be returned as a result whichwill reduce the execution time of the query. In thisapproach, a framework is created which will reduce theexecution time of query when posed to thisframework.[9]Algorithm for Database client view

The steps of the algorithm are as below.

I) Generation of random set of records for giventables in database by record generator.

II) Extraction or generation of all possible set ofqueries resolved by system on above createdrecords.III) Optimization of above set of queries according totheir access frequency.IV) Creation of MV according to query accessfrequency called as Threshold Value and according toMaximum Cluster Area Threshold % According toabove step a of MV creation, 3types of MV arecreated as follows.1) Single query to Multi table MV. In this responseof single query is obtained from multiple MV table.2) Single query to single table MV. In this responseof single query is obtained from single MV table.[10]3) Multiple queries to single table MV. In thisresponse of multiple similar queries will be obtainedfrom single MV table.4) After creation of these 3 different types of MV, wewill store these MV. Creating candidate views formaterialization in our approach, we assumed that adata pattern is present in user queries, i.e. certaincategories of data will be queried more frequentlythan others. Thus, it will be very useful to extractthese patterns given the basis of which we will createthe candidate views for materialization. Extracting theattributes of interest. Generally in a mediation system,a global schema representing the domain of use is

provided. It is in terms of the latter are expressed theuser queries. We analyze these queries to determine,among all the attributes of this schema, those inWhich users are interested, i.e. the most frequent

attributes.4. Algorithm Materialized_View_Creation

Begin

Step 1.

/* In this step, construct a (2m) matrix calledImportant Attribute and Affinity Matrix (IAAM) from

the array Total_Use and the matrix AAM to computethe degree of importance of attributes. */Call method IAAM_Computation. [6]

Step 2.

/* In this step, the views are created one after one by

taking the attributes from IAAM in descending orderof importance. It takes as input numbers of viewsuser want to create and also the corresponding sizeof each view. */

Call method Materialized_View_Creation.End

5. AN ILLUSTRATIVE EXAMPLE

Consider an example of a query set where 10 queriesare participating and 10 attributes are used in thesequeries. Say the name of the attributes is A1, A2, and

A10. Execution of the algorithmAttribute_Affinity_Scale is shown below.A. Attribute_Affinity_ Scale:Step 2.

/* In this step, the views are created one after one bytaking the attributes from IAAM in descending orderof importance. It takes as input numbers of views userwant to create and also the corresponding size of eachview. */Call method Materialized_View_Creation.End

6.

Selection of views to materialize

The views created in the first phase of our approachcannot be all materialized. Indeed, the space formaterialization, the frequency of update and the costof access to sources is critical.

The frequency of change: the views that rarelychange are good candidates for materialization.

The size of views: the views of small sizes arefavored

For materialization than large ones.

The availability of sources: The views, whose datare sides in sources that are rarely available, should bematerialized.

The cost of access: the materialization of viewswhose data resides in sources with a high cost ofaccess will improve the system performance. Thus, aview will be materialized, if it satisfies at least twocriteria. [7]


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7. CONCLUSIONS

Thus the paper proposes algorithm for the

materialized view design problem, e.g., how to selectthe set of views to be materialized so that the cost ofprocessing a set of queries and storage space requiredstoring the data for the materialized views isminimized. This approach realizes on analyzing thequeries so as to derive common intermediate resultswhich can be time and to eliminate the need forcreation of same MV for the query. The proposedalgorithm for determining a set of materialized viewsis based on the idea of reusing temporary results fromthe execution of the global queries. The cost modeltakes into consideration of both query accessfrequencies and % threshold. The work presented

here is the first stage research in selection of querieswith high access frequencies, clustering them andcreation of Materialized Views for the same. Thesehigh access frequency queries are further analyzed forrequired cluster area to create MV.

8. REFERENCES

[1] A Gong, Weijing Zhao, Clustering-basedDynamic Materialized View SelectionAlgorithmProceedings of Fifth InternationalConference on Fuzzy Systems and KnowledgeDiscovery, 2008, China, pp391- 395.

[2]

Jian Yang, Kamlakar Karlapalem, QuingLi,Algorithms for materialized view design indata warehousing environment. K. Aouiche,P.Emmanuel Jouve, and J.Darmont

[3] , Clustering-Based Materialized View Selectionin Data WarehousesTechnical Report, Universityof Lyon, 2007.

[4] Shukla A, Deshpande PM, Naughton JF,Materialized view selection for multidimensionaldatasets, Proceedings of the 24th International

[5] Conference on VLDB, Morgan KaufmannPublishers, San Francisco, 1996, pp. 51.

[6] Shukla A, Deshpande P M, Naughton JF,Materialized View Selection orMultidimensional Datasets, Proceedings of the

24th VLDB Conference, 1998, pp. 488-499.[7] Dynamic Materialized View Selection AlgorithmProceedings of Fifth International Conference onFuzzy Systems and Knowledge Discovery, 2008,China,pp391- 395

[8] Hadj Mahboubi, Kamel Aouiche and JrmDarmont, Materialized View Selection by QueryClustering in XML Data WarehousesFourthInternational Conference on ComputerScience and Information Technology-Jor dan.

[9] Gupta H, Harinarayan V, Rajaraman A, et al,Index Selection for OLAP, Proceeding of

International Conference on DataEngineering,1997, pp. 208-219.

[10]Mistry H, Roy P, Sudarshan S, et al, Materializedview selection and maintenance using multi-query

optimization, Proceedings of SIGMOD'01, 2001,pp. 307-3.S.Agarawal, S.Chaudhuri, V.NarasayyaAutomated Selection of Materialized Views andIndexes for SQL Databases Proceedings of 26thInternational Conference on Very LargeDatabases, Cairo, Egypt, 2000

[11]P.A.Larsen, Jingren Zhou Efficient Maintenanceof Materialized Outer-Join Views 23rdInternational Conference on Data Engineering(ICDE 2007), Istanbul, Turkey, April 2007.

[12]Xia Sun, Wang Ziqiang An Efficient MaterializedViews SelectionAlgorithm Based on PSO.Proceeding of the International Workshop onIntelligent Systems and Applications, ISA 2009,Wuhan, China, May 2009

[13]S.H. Talebian, S.A. Kareem Using Genetic

Algorithm to Select Materialized Views Subject toDual Constraints. InternationalConference onSignal Processing Systems, Singapore, May 2000

9. THOR PROFILE

Prof. Ramanand

Samdekar received theMaster of Technology inComputer Science andEngineering RastrasantTukodoji Maharaj Nagpuruniversity, Nagpur.

Currently he is anAssistant. Professor in S.B.Jain Institute ofTechnology Management& Research, Nagpur, India.He has published three

papers in internationaljournals, two papers innational journal and one innational conference. He ishaving 7 year teachingexperience and his field ofspecialization is Software

development, Data mining,Software System, and

Network Security.


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IMPLEMENTATION IMAGE MOSAI USING PHASE CORRELATION AND HARRIS

OPERATOR1MS. KANCHAN S.TIDKE,

2PRITAM H. GOHATRE

1GH Raisoni Amravati, [email protected]

2LAMIT, Dhamangoan (Rly), India

[email protected]

ABSTRACT:Image mosaic is a technique used to composite two or more overlapped images into a seamless wide-angleimage through a series of processing and it is widely used in remote sensing areas, military applications, etc. When taking these

photos, it's difficult to make a precise registration due to the differences in rotation, exposure and location. The image mosaic

techniques are widely used in remote sensing, medical imaging, and military purposes and so on. Now days, many smart phones

are equipped with the mosaicing application which helps user in many different ways. The image mosaicing technique can be

broadly classified into feature-based and frequency-based techniques. Feature-based method uses the most similarity principle

among images to get the parameters with the help of calculation cost function. Method based on the frequency domain

transforms the image from spatial domain to frequency domain, and get the relationships of translation, rotation and zoomfactor through Fourier transformation. In frequency domain there are methods like phase-correlation, Walsh transform, etc.

Keywords:Image mosaic, remote sensing, medical imaging, Feature-based method, frequency domain transforms, phase-correlation, Walsh transform.

1. INTRODUCTION

International Journal of Research in Computer & Information Technology

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