IJITCE September 2011

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.9 SEPTEMBER 2011

IJITCE PUBLICATION

INTERNATIONAL JOURNAL OF INNOVATIVE

TECHNOLOGY & CREATIVE ENGINEERING

Vol.1 No.9

September 2011

www.ijitce.co.uk


From Editor's Desk Dear Researcher, Greetings! The researcher’s views are rendered through this issue about Reverse redistribution, Fuzzy system for back pain, prudent design of road catering and image transmission in wireless sensor network. Let us review world research focus for the month of September. Pay as you go is a common way of paying for calls on your cellphone. Now the idea could help make solar power a more realistic option for families in Kenya and other African countries. The system, called IndiGo, consists of a low-cost flexible plastic 2.5W solar panel that charges a battery. This is connected to a USB mobile phone charger and an LED lamp that provides around 5 hours of light from one day's charge. Developed by solar energy firm Eight19, based in Cambridge, UK, IndiGo costs $1 a week to run, though the unit itself must be leased for an initial $10 fee. Users add credit by buying a scratchcard that they validate by sending a text message from their phone. India has been drying out for half a century, and air pollution thousands of kilometres away is partly to blame. The monsoon has been weakening since the 1950s. Indian air pollution has been blamed, but now it seems that emissions further afield are also a factor. Air pollution in the form of aerosols can weaken these long-distance wind patterns, however. That's because it reflects sunlight back into space, cooling the polluted area. Thick aerosol pollution over Europe in summer ensures that the northern hemisphere isn't much warmer than the southern hemisphere, so there is nothing to drive the winds – and nothing to trigger the monsoon. Mercury is covered with pits that are unlike anything else in the solar system, new observations from NASA's Messenger spacecraft show. They may have been formed by processes still active today, and change our view of the small rocky planet's history. Levels of radiation in the sea off the Fukushima-Daiichi nuclear plant remain stubbornly high six months after the earthquake and tsunami struck Japan on 11 March. After levels peaked at around 100,000 becquerels per cubic metre of seawater in early April, much of the radioactive iodine, caesium and plutonium from Fukushima was expected to rapidly disperse in the Pacific Ocean. Instead, it seems that the levels remain high. That could be because contaminated water is still leaking into the sea from the nuclear plant, because currents are trapping the material that's already there, or both. Expect the number of mobile device exploits to double by year's end. That prediction comes from a new report released by IBM's X-Force research group, which examined attack trends for the first half of 2011. IBM found that the number of known mobile operating system vulnerabilities, which more than doubled from 2009 to 2010, seems set to increase only slightly from 2010 to 2011. But the number of mobile device exploits--using those vulnerabilities--increased by 400% from mid-2009 to mid-2010, and now seems set to double from 2010 to 2011. All of the above events leave open space for researchers to trigger their minds on experiments and results. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technology-related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE


Editorial Members

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

Review Board Members

Dr. T. Christopher, Ph.D., Assistant Professor & Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar) ,01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. Benal Yurtlu Assist. Prof. Ondokuz Mayis University Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India


Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRua Itapeva, 474 (8° andar), 01332-000, São Paulo (SP), Brazil Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Javad Robati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran Vinesh Sukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. doc. Ing. Rostislav Choteborský, Ph.D. Katedra materiálu a strojírenské technologie Technická fakulta,Ceská zemedelská univerzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Binod Kumar M.sc,M.C.A.,M.Phil.,ph.d, HOD & Associate Professor, Lakshmi Narayan College of Tech.(LNCT), Kolua, Bhopal (MP) , India. Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg.,Hampton University,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A., M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). doc. Ing. Rostislav Chot ěborský,ph.d, Katedra materiálu a strojírenské technologie, Technická fakulta,Česká zemědělská univerzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Amala VijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin Ashokrao B.sc,M.Sc Lecturer in Yeshwant Mahavidyalaya Nanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MEN GG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed . Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-Banglore Westernly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech & PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.


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


Contents 1. Prudent Design of Road Catering to Practical Nuisances ……….[1]

2. Reverse Redistribution Management…..[7]

3. Energy Efficient High Quality Image Transmission in Wireless Sensor Networks……[10]

4. Design and Implementation of Fuzzy Expert System for Back pain Diagnosis…….[16]


1

Prudent Design of Road Catering to Practical Nuisances

Saumil Barolia ,Sunny Goklani School of Building Science and Technology, CEPT University, Kasturbhai Lalbhai Campus,Navrangpura,

Ahmedabad, India [email protected], [email protected]

Abstract —Functionality of the commuting

infrastructure systems well defines the character o f a city.Inefficient road design leads to unorganized t raffic movement and eventually accidents. Extreme cases facing these problems cannot be resolved by implementing traditional solutions like removing encroachments or banning vehicular traffic, as thes e completely dissolve the philosophy of the road. Thi s calls for an innovative yet feasible approach. This paper caters this issue by analysing practical problems in a hol istic manner, thereby suggesting self sustaining tools, w hich can replicate themselves globally. Keywords: Nuisance, Hazard, sustainable, Integrated design.

I. INTRODUCTION

Road or any form of infrastructure sustains when the people using it are safe and secure. The accountability for the same shall be in the hands of the designer and not the users. The road design shall stand true for every user, who has the freedom to utilize it in the manner he wishes to. This philosophy can be potentially beneficial while designing new roads, but is vice versa in case of existing roads.

Indian roads, especially the ones in CBD areas face tremendous traffic problems, which arise due to inefficient road design and ad hoc traffic management. Conventional approaches have been applied and failed as a result of their myopic classification of problems. It is interesting to involve hawkers in the solution system and perceive how innovative sustainable modules can be designed to troubleshoot specific set of problems and bring an intelligent environment friendly solution.

This approach includes perceived nuisances viz. hawkers in the road design itself and creates a safe commuting atmosphere without sacrificing the cultural essence of the road system.

A. Road Systems and hazards: Broadly classifying, an Indian city road consists

existence of three mutually dependent systems, namely:

1. Vehicular movement. 2. People inhabiting along the roads (shopkeepers,

servants, hawkers, etc). 3. Pedestrians who keep the second system

running (customers).

Each one of them are subjected to their own hazards. An ideal road will incorporate factors responsible for all these hazards in its design.

Table I

IDENTIFICATION OF INDIVIDUAL SYSTEM HAZARDS

HAZARDS

Vehicular movement

Roadside temperory inhabitants

Pedestrains

Pedestrians Irrational movement of vehicles

Motorists

Cyclists Hawkers Condition of footpath/walkways

Slow moving vehicles

Anti-social activities Non enforcement of traffic rules

Bullock cart/cattle movement

Illegal parking (blocking interface between the pedestrian and the store)

Junctions

Pavement quality

Filthy environment Inadequate width of walking paths

Junctions Noise as well as air pollution

Irregular parking

Shop extensions

Orientation w.r.t sun rays

Encroachments

Hawkers Authority misuse Crossing the road


2

Complexity of Hazards:

It has been found that hazards are interrelated. Systems and mutual hazards form a vicious circle making it is impossible to incorporate all the hazards and their solutions in a single road system. Hence, the solution lies in identifying nuisances, that are found almost everywhere in any Indian city.

The prime nuisances considered, in order to keep the orientation very specific, are:

1. Pedestrians 2. Cyclists 3. Hawkers

As per statistics [1] , it is clear that considered nuisances are not a cause of the problem, rather are on the receiving end of it.

Table II

FAULT ANALYSIS

CAETEGORY RESPONSE

% OF FAULTS / INCIDENTS

Driver 77.91

Design 17.4

Mechanical defect in vehicles

1.4

Bad roads 1.2

Pedestrians 1.36

Category wise fault analysis

Figure I: Cause of Accidents

At the same time, let us consider their importance, and how if they are a nuisance:

Cyclists, pedestrians and bus traffic attract street hawkers. Therefore, Indian city roads are default natural markets for them. Hence, irrespective of space availability, they will block the road sides to set up their stalls. This completely changes the situation on practical front which was never considered during designing then.

B. Methodology: The methodology (explained stagewise in Fig II) is

developed by identifying intermediate phases needed to be achieved as the process flows towards achieving the final objective. The framework developed for this is as follows:

Phase 1: (Nuisance identification and screening)

The process begins with identification of root causes for the entire nuisance developing on roads. By the end of this phase, the actual nuisances on the road under consideration are identified. This is followed by a detailed analysis on each of these nuisances and thus enlisting them as per their impact and importance in day to day life. The screened nuisances thus generated are worked upon in further steps.

Phase 2: (Developing Solutions)

This phase provides a guide way towards developing solutions to mitigate all the screened nuisances. This includes developing specific solution strategies having feasible options, developing prudence through sustainability considerations and design plan as well as implementation planning.

Phase3: (Implementing developed solutions)

There are three possibilities of implementation, namely, Implementation in form of Guidelines; Implementation in form of Pilot application and/or Implementation in form of long term plan.

An important step here is defining roles and responsibilities to the concerned people, so that a level of accountability is maintained throughout the process.


3

Implementation in form of Guidelines may consist of developing guidelines for authority and community. The authority may either implement it for its own purpose or can enforce it as a law on the community.

Implementation in form of Pilot application will include a set of applications that are ready applied currently. Prior to application, approvals shall be received form authority as well as community. To start with, site identification would take place for application of idea; to follow, a reconnaissance survey, would accompany details for the detailed design. The survey process shall include Stakeholder survey, hurdles, authorities involved, feasibility and consequences.

The implementation process follows, as the execution of planned designs take place. Normally, processes and projects end here, which is a wrong practice. Instead, the same should continue with constant reviews and evaluation of the implemented project. If the results are found satisfactory then, long term plans should be accelerated and replication of pilot project should be carried out on wider base. If not then serious reviews and corrections are to be incorporated in the pilot application itself and a major feasibility check should be made on long term proposal.

The methodology ends at a review and correction process, which in nature though, is a recurring process.

Fig II: Methodology process flow diagram


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Case Study:

In order to apply this methodology and make it more pragmatic and specific, the stretch selected for case study is one of the busiest stretches in Ahmedabad city with extremes of considered practical nuisances which is the stretch between Teen Darwaza to Manek Chowk, Length: 400m.

The two sides of the road was divided into Sub stretch 1, From Divergence point (Manek Chowk) to Teen Darwaza and Sub stretch 2 (From Teen Darwaza to Divergence point (Manek Chowk).

The visit was carried out between 1:30 PM and 5:30 PM (4 hours) which also included walk along the stretch. The study also included interaction with vendors/hawkers, pedestrians and shopkeepers, coupled with our own experiences.

Observations: (Sub Stretch 1)

Items that were mainly sold were bags, shoes, household items, sanitary items, ladies accessories, etc. The major things observed were inappropriate and irregular parking, authority misuse, pavement level differences, and inappropriately designed junctions, advertisement on roads, electrical hazards, and hawker mall. An important observation made that was the density of pedestrians increased on approaching Teen Darwaza.

Observations: (Sub Stretch 2)

Items that were mainly sold were kitchen utensils, surgical equipments, perfumes, crockery, etc. apart from there were banks, restaurants, and stationeries present on this stretch. The major things observed were that this stretch had lesser pedestrian density, the ambient temperature was warm yet comfortable, good pavement quality, inappropriately designed junctions, electrical hazards, irregular cycle parking, etc.

C. Solutions: As a solution to incorporate practical nuisances, three stages of solutions are suggested, depending upon the support of authorities.

1) Providing guidelines 2) Pilot Application 3) Long term plan

Albeit, selection depends on extent of authority involvement, planning stage for three of them is kept clear.

1) Guidelines:- No major scope for innovativeness is available

under this option as guidelines are meant to be kept very simple and direct, and also much of the work in this field is done in the past.

We suggest following guidelines provided under IRC 103-1988: which include provisions for[2]:

• Width of sidewalk • Controlled crossings • Zebra crossing • Guardrail • Grade quality • Capacity, etc.

Other similar provisions can be referred from Indian road codes and British codes.

2) Pilot Application Pioneering approach is Proposed under Pilot & Long

term Application plans to develop specific solutions for similar problem facing highly dense & pedestrianised city market areas. Following steps are suggested under Pilot application:

a) Paint roads: To classify parking for two-wheelers & implementing laws to park in specified allocated areas, streetwise. Severe penalty to be proposed for improper orientation. This will organize the traffic, reduce road space wastage & induce better traffic sense in the user community.

b) Retrofit capacities: Existing parking facilities, whose inadequacy leads to illegal parking on roads to be retrofitted through construction of multi-level mechanized parking on the same portion of land where ground-parking only exists.


5

PARCOHUT:

It is a path-breaking concept, as our suggestion could revolutionize the existing system to its optimum activity & capacity in an aesthetic & comfortable manner. It’s a one-pack solution, which is applied in a ‘module’ form and the module can be repeated throughout,(Refer Fig III) irrespective of the stretch-length. A typical module includes Canvas roofing, which encloses the walking space, provides shade to the pedestrian area & keeps temperature cool. Also, this will mark the boundaries for the hawkers, as specific hawker lane will be a part of the module. Moreover, as the major use pattern consists of cycles on the roads, majorly used by workers in shops; the module is equipped with cycle stand at its exterior. Cycles will be parked in an inclined fashion in the cycle-stand, which will also reduce the horizontal road-space. Thus this is a revolutionary module, which when repeated is capable of solving the mammoth problem.

Figure III : Conceptual Sketch of PARCOHUT Module

Advantages:

� Shaded Walking area � Increased walking space � Oriented parking � Less road-space usage in cycle parking � Optimum space utilization

FOLD-HAWK:

It is another such idea which will accompany PARCOHUT, in the hawker lane. Inspiration of the idea is from: Sleeping coaches in Indian railways, which are vertically & horizontally foldable. (Refer Figure IV)

This structure would facilitate accommodating hawkers by selling space to them on running meter basis. This would imply maximum usage of vertical space and thus reducing the horizontal space usage on the hawker lane. Reference: FOLD-HAWK sketch Advantages:

� Hawkers incorporated in design � One-time income for government � Foldable market form � Higher width of Pedestrian walkway.

Figure IV : Conceptual Sketch of FOLDHAWK Module

Both PARCOHUT & FOLD-HAWK are structures that are portable & can be shifted in tandem.

3) Long term Plan Suggested Long term plan requires a couple of things to be assured as pre-requisites:

i) Multilevel parking to be setup at start & end of the stretch.

ii) Hawker mall concept, to be appreciated at junctions.

Once these are done, it should be kept in mind that:

- Road should not be designed for two & four wheelers; rather should be designed primarily as a pedestrian-walkway

- Facilitating relocation of shopkeepers while renovation would take place.

Following steps are to be followed in tandem with each other:

1. Three Walkways to be installed post Massive renovation.


6

2. Massive renovation: Shops on each stretch, on both sides of the road to be reconstructed with G+2 structures instead of standing old & ground structures.

3. Two elevated walkways on each side, arranged at a vertical height separation same as the floor height.

4. Occupation-wise floor differentiation for better oriented & consumer-friendly shopping experience. E.g.: 1st floor on sub stretch 2 to serve only Garments & Utensils.

5. When above step completed on both sides, a ‘STREET ROOF’ concept can be incorporated, as the warm sunlight of continental climate prevails over the shopping area most of the year.

6. As a consideration for the physically handicapped, in the newly constructed G+2 structures, lift at 100m, i.e. 5 nos. to be provided.

7. Thus, 400 m. long walkways would ease the congestion & pressure on existing road by almost two-thirds.

This would transform the experience on the roadside markets from Unorganized Sector to an organized retail one.

Advantages:

� Wider walking space � Road open for motor vehicles � Organized retail experience � Economic opportunities � Hawkers turning shopkeepers � Road design: An All-in-One Solution

Conclusion:

Problems in pedestrianised busy congested market roads can no longer be solved by conventional solutions, which are tried and failed. Rather innovative product design & solutions like these may only be turn out to be solutions of tomorrow. Suggested solutions target root problems and have potential to be applied globally on similar situations successfully.

ACKNOWLEDGMENT

We wish to thank School of Building Science and Technology for providing us a platform to carry out the study and Prof. Anal sheth for encouraging us to broaden our horizons and develop innovative solutions.

REFERENCES [1]. Road Accidents in India: By Transport Research wing,

Ministry of Road Transport and Highways, Government of India [2]. Indian Road Congress Publication : IRC : 103-1988


7

Reverse Redistribution Management Veronika Vitkova #1, Tomas Hladik *2, Petr Tulach #3

* Czech University of Life Sciences Prague, 16521 Prague 6 – Suchdol, Czech Republic 1 [email protected],2 [email protected]

# Logio s.r.o., 160 00 Prague 6, Czech Republic 3 [email protected]

Abstract-The purpose of this paper is to prove meth ods of redistribution management on the model of unsold goods. A research was carried out with data sourced from the aftermarket sector. The research was used to explore basic questions of what are the benefits of unsold goods redistribution and how it should effectively contri bute to inventory management.

The existing information models have focused on forward distribution flows – from suppliers to cons umers. The current information systems are generally in-de pth specialized in forecasting methods of future consum ption while they do not deal with returnable asset contro l. The paper provides a comprehensive description of the redistribution principles tested on the compiled mo del. The basic prerequisite that needs to be fulfilled i n order to reach results, is sharing information between suppl iers and consumers. For application of this method a sha red information platform within the supply chain is req uired.

The expected outcomes of the proposed model are reduced inventory levels and improved structure of the portfolio resulting in higher profits. The redistri bution was conducted with the aim of accelerating inventory tu rnover and minimizing redundant purchasing. Furthermore, a correlation between unsold goods redistribution and higher load vehicle factor was identified.

Keywords: redistribution, inventory management, cost reduction

I. INTRODUCTION

Every production, replenishment and shipment between manufacturers (distribution centres) and every retailer (store) require a particular material flow in the whole supply chain. Current supply chain management demands accurate forecasting of future sales in order to ensure effective logistics system. An accurate demand forecasting and its distribution resource planning (DRP) have several business benefits. The main benefits include lower inventory level and reduced number of stock-outs. Besides the main benefits, there are the so-called hidden reserves, which could be drawn from Flowcasting. Collaborative Flowcasting is based on the inventory planning principles of DRP systems enhanced by:

• Actual store level item demand • Data refreshment based on actual store results

daily • System handling with huge volume of data at

the store level integrated in central ordering system

• Reverse redistribution system from stores The hidden reserves consist of redundant purchases

and enhanced level of distribution costs. Well-functioning and sophisticated ordering system with integrated redistribution model saves not just only hidden reserves, but also reduces risk of stock-outs and on the top of that it saves operating and investment costs. The research objectives were to determine the practices of redistribution management used in Flowcasting.

II. METHODS AND PROCEDURE

Redistribution method was tested in distribution network of an aftermarket distributor. This network constitutes of one central warehouse and 25 stores located in the Czech Republic. Stores act as unique warehouses and keep their individual inventory levels. Distribution between warehouse and stores was established on daily basis. A method was designed to minimize supplies from central warehouse to stores. The aim was to use inventory which is held on other store and is unlikely to be consumed, rather than ordering new inventory batch from supplier to central warehouse. This approach will decrease inventory levels in distribution network, amount of capital locked-up in inventory; will reduce ordering cost and supplier distribution cost.

Basic prerequisites for redistribution:

• Base stock and forecast for each SKU

(Stock keeping unit) on each location must be known.

• Base stock can be calculated by using advanced mathematical methods for


8

demand forecasting (theory of time series) for SKU´s with smooth demand or by probability approach such as bootstrapping (Smart-Willemain 2003) for SKU’s with intermittent demand. Data analysis and evaluation were obtained from Planning Wizard – the system for forecasting and demand planning.

• Limit price value of order must be set in order to avoid inefficient reverse distributions. Limit price value of order include costs for packing and administration workload. Distribution cost is not included, because of using currently established regular transport. All orders valued less than proposed price limit will not be selected for redistribution.

Compiled model covers 25 stores and one central warehouse. At first method used for SKU’s with smooth (constant or normal) demand is presented:

Let: • Dai be demand for SKU a on store i, • Iai be inventory of SKU a on store i, • Faij forecast of SKU a consumption on store i for

next j days • LTai the lead time for SKU a • BSai the base stock for SKU a on location i • Pa the price of SKU a

At first an excessive inventory at stores must be

found. Excessive inventory is determined by base stock calculation. Different approach is used for SKUs with intermittent demand and with smooth demand. Intermittent demand appears randomly with many periods that have no demand. Typical examples of intermittent demand are spare parts or aftermarket. Majority of items have smooth demand which are forecasted by traditional statistical methods such as exponential smoothing and moving averages.

For smooth demand excessive inventory EIai at store is calculated:

EIai = Iai - BSai -2*FaiLTai

Expected consumption for two lead time period is

necessary to secure that item will not be redistributed from one store location to another and that excessive inventory is not likely to be consumed, thus creating demand for order from supplier.

For intermittent demand, the excessive inventory is calculated by means of Smart-Willemain method. The Smart-Willemain method is based on bootstrapping (taking random samples from time series). For defined period, days are randomly selected from time-series many times.

Then these experiments are used to build statistical robust picture of the lead time distribution. These random consumptions are then summarized in histogram, which forms distribution function. For given service level and lead time inventory base-stock level is calculated, by randomly selecting demand for lead time period (for 10 days lead time, chose randomly 10 days) many times.

To find out what inventory is unnecessary to be held at location in next lead time period, double lead time period must be held for Smart-Willemain method. Double lead time is used to secure that algorithm will not suggest frequent movements of inventory from store to store, which will occur if inventory for more than one lead time period is considered excessive.

Let BSaidouble be base stock for doubled lead time period. Than Excessive inventory is calculated:

EIai = Iai - BSaidouble

Next, excessive inventory on stores is ordered

descending by excessive inventory and demand is fulfilled in this order.

Fig. 1: Redistribution procedure


9

III. RESULTS AND DISCUSSION

Based on the methodology described above, an analyzed portfolio was about 100,000 unique items from the aftermarket sector. Inventory management in the aftermarket deals with intermittent demand, which is the reason for redistribution implementation.

This item portfolio is used in one central warehouse and 25 stores located in the Czech Republic. Stores act as warehouses and keep their unique inventory level. Entire portfolio was tested in two intervals with a difference of three months, a period that corresponds to the longest lead time (delivery time) for supply from China.

The entire portfolio is from the perspective of inventory management divided into three main groups – distribution centre (central warehouse), new items, and stores.

Model prerequisites: new items are those with sales history not longer than two months. These items need to be eliminated for further steps due to the significant errors in forecasting. New products represented the amount of approximately 9 thousands of unique items (2%) in the value of 0.14 M € (4%). According to the procedure above, 2 stages were simulated. In the first stage the redistribution functionality is not used; in the second stage redistribution is applied. The obtained results are shown in the tables below.

TABLE I MODEL WITHOUT REDISTRIBUTION FUNCTION: PORTFOLIO VALUE IN € AND

IN NUMBER OF PIECES (QUANTITY)

€ % number of pieces %

Distribution centre 2.503 M € 67% 0.255 M 60%

Stores 1.086 M € 29% 0.159 M 38%

Total 3.589 M € 96% 0.414 M 98%

value of inventory number of pieces (quantity)Portfolio classification according to

inventory allocation

TABLE III

MODEL WITH REDISTRIBUTION FUNCTION: PORTFOLIO VALUE IN € AND IN NUMBER OF PIECES (QUANTITY)

€ % number of pieces %

Distribution centre 2.517 M € 69% 0.257 M 62%

Stores 0.984 M € 27% 0.147 M 36%

Total 3.501 M € 96% 0.404 M 98%

Portfolio classification according to

inventory allocation

value of inventory number of pieces (quantity)

The presented tables compare value of inventory

without redistribution function (table I) and with redistribution function (table II). As shown in the tables, the difference of 0.088 M € in the inventory value and 10,000 difference in quantity (number of pieces ) per month was achieved as a result of redistributio n function .

IV. CONCLUSION

Nowadays, redistribution is an important issue for each business management dealing with its own distribution network providing transport of goods to stores. Effective redistribution management within its own distribution network is a source of savings in inventory (capital) and minimizing redundant purchasing. Other benefit is the flexibility of customer service within self-served network rather than staying dependent on external suppliers. Redistribution means serving customers in a sustainable way.

Based on the presented research, a reduction of inventory value by 0.088 M € per month was achieved due to effective redistribution management. Redistribution management system implies the use of forward-direction distribution network, thus does not produce extra transportation cost.

In addition to financial savings, it is necessary to mention the contributions in a global system of sustainable development. Due to efficient inventory consumption the liquidation of unused (technologically obsolete and unsalable) inventory is minimized. The future issue will be to manage redistribution not just within own distribution network, but in the whole supply chain.

References

[1]. Christopher, M.: Logistics and Supply chain management:

Creating Value-Adding Networks. Prentice Hall, 2005, ISBN: 978-0-273-68176-2Sabri, E. – Shaikh, S.: Lean and agile value chain management: A Guide to the Next Level of Improvement. J.Ross Publisihing, 2010, ISBN: 978-1-60427-025-9 Sixta, J. – Zizka M.: Logistika – Metody používané pro řešení logistických projektů (in English: Methods used for logistics solution). 1.vyd. Brno: Computer Press, 2009, ISBN 978-80-251-2563-2


10

Energy Efficient High Quality Image Transmission in Wireless Sensor

Networks Ms.Santha devi.P1 M.C.A., M.Phil., Doctoral Research Scholar,Mother Teresa Women’s University, Kodaikanal.

DR.ARTHANARIEE A.M, DIRECTOR, BHARATHIDHASAN SCHOOL OF COMPUTER APPLICATIONS, ERODE.

Mr.Sivakumar.M3 M.C.A., Doctoral Research Scholar, Anna University of Technology,Coimbatore.

Email: [email protected], [email protected], [email protected]

Abstract - A Simple Wavelet Compression (SWC) processing is proposed to maximize compression and minimize energy cost in WSN (Wireless Sensor Network). Most of the current work, utilize lossy image compression techniques to minimize the resource consumption. The lossy image compression technique reduces the size of the image to a great extent, however the quality of the image being reproduced needs appreciation. The existing work presented, an improved polyomines lossless compression technique, which increases the quality of image at receiving end of the wireless communication by reducing the Peak to Signal Noise ratio and mean square error. In Wireless Sensor Networks, reducing transmission energy consumption is one of the important critical issues.

In this paper, we propose a novel Energy Efficient High Quality Image Transmission scheme (EEHQIT) to achieve energy efficient image transmissions in WSNs. EEHQIT scheme is compelling due to its ability of saving individual power consumption over multiple sensors by spreading total transmission consumption. Individual packets describing an embedded wavelet-encoded image exhibit a significantly unequal contribution towards image quality. By leveraging this fact we develop a strategy of appropriately selecting the number of interactive sensors for each packet transmission in order to achieve the highest possible image quality with minimal transmission power consumption. Experimental results show that our proposed mechanism can provide about 2dB higher image quality under the same power budget compared with other transmission approach.

Simulation results show up to 85% reduction in the total power consumption achieved by using the proposed strategy.

Keywords: Wireless communication, Image compression, interactive transmission, image quality.

I. Introduction

Large-scale networks of sensors with wireless communication capability have drawn the attention of researchers for the last few years. Most of the applications are centered towards harvesting information from the physical environment, performing a simple processing on the extracted data and transmitting it to remote locations. In general, most of the applications require a small bandwidth demand and usually transmission delay is not a major concern. These devices normally are equipped with multi-hop capabilities, self-healing, automatic-management and self configuration. These attributes make WSNs suitable for a wide range of application ranging from home automation, surveillance to industrial process control. The idea of including image processing capability into the sensor mode not only will enhance the existing applications but also will enable new ones.

The characteristic of wireless multimedia communication which can be used to overcome the bandwidth and energy bottlenecks is that the conditions and requirements for mobile communication vary. Variations in wireless channel conditions may be due to user mobility, changing terrain, etc. For example, the Signal to Interference Ratio (SIR) for cellular phones was found to vary by as much as 100dB for different distances from the base-station. Moreover, the Quality of Service (QoS)


11

– such as transmission latency or bit error rate (BER) – and Quality of Multimedia Data (QoMD) – including image/video quality – required during multimedia communication changes depending on the current multimedia service. For example, the QoS (latency) and QoMD requirements of transmitted data are different between video telephony and web browsing.

The usual method for transmitting images over the Internet is to first compress the images using a lossy scheme such as JPEG, and then to transmit them across the intrinsically lossy Internet using the lossless TCP/IP protocol. JPEG and related lossy schemes are very sensitive to bit errors and hence require lossless transmission. The price paid for lossless transmission over a lossy medium is excessively lengthy transmission times due to retransmissions of lost packets. A more efficient means of transmitting the data is via some form of redundant transmission (forward error correction) which will make serious transmission errors unlikely. Redundancy must be applied selectively, however, since the addition of redundancy increases the amount of information to be transmitted. Lossless transmission schemes are even more problematic for Internet video broadcasting. Retransmission is impractical with broadcasting because the receivers will not in general experience the same losses. A broadcaster attempting to respond to all of these different losses will quickly be overwhelmed. Again, what we need to cope with packet losses is some form of forward error correction.

II. Related Works

Wireless Multimedia Sensor Network (WMSN) is defined as a network of wireless embedded devices that allow retrieving video and audio streams, still images and scalar sensor data from the physical environment which can be understood as a convergence between the concept of WSN and distributed smart cameras [3].Literature survey in [1], [2], [4] addressed various issues regarding the challenges faced by research community in realizing WMSN. Even with the availability of CMOS camera which is low cost, low power and small form factor, current WSN constraints still prohibit the implementation of effective and efficient multimedia data into it. A new paradigm is needed in order to realize WMSN in the aspect of hardware design, algorithms, protocols and

techniques to deliver multimedia content over a large-scale network given the nature of the wireless sensor network which has a very tight resource constraint.

The early research efforts in wireless sensor networks did not investigate the issues of node collaboration, focusing more on issues in the design and packaging of small, wireless devices [5], more recent efforts (e.g. [6], [7]) have considered node collaboration issues such as data “aggregation” or “fusion”. Our approach of distributed image compression falls within the domain of techniques that apply the concept of network processing, i.e. processing in the network by computing over the data as it flows through the nodes. It is worth noting that current aggregation functions (e.g., “maximum” and “average” [7]) are limited to scalar data. Our approach can be viewed as an extension to vector data aggregation.

Previous distributed signal processing/compression problems (e.g. [8], [9]) exploit correlations between data at close-by sensors in order to jointly compress or fuse the correlated information resulting in savings in communication energy. In parallel distributed computing theory [10], a problem (or task) is divided into multiple sub-problems (or sub-tasks) of smaller size (in terms of resource requirements). Every node solves each sub problem by running the same local algorithm, and the solution to the original problem is obtained by combining the outputs from the different nodes. Our approach to the design of distributed image compression is similar in concept, in that we distribute the task of image encoding/compression to multiple smaller image encoding/compression sub-tasks. However, a key difference is that distributed computation theory typically focuses on maximizing the speed of execution of the task while our primarily concern here is reducing the total energy consumption subject to a required image quality. Thus, our proposed approach of image compression intersects with the literature on lossy and lossless compression, which primarily focuses on polyomino technique.

W. Yu Z. Shinoglu and A.Vetro. in [11] proposes an optimized joint source channel coding (JSCC) scheme to achieve minimized total distortion for multiple images over lossy channels simultaneously. The layer-based dependency as well


12

as distortion reduction expectation is well modeled, and combined total distortion is minimized subject to total rate constraint. Hamzaoui et al. survey recent advances in forward error correction (FEC) based scalable image coder in [13], and propose a local-search-based rate-distortion optimization solution. Li et al. in research [14] develop a real-time link layer retry limit adaptation algorithm for robust video streaming over 802.11-based wireless networks. Multiple video layers are unequally protected by different link layer retry limits. van der Schaar and Turaga in [12] propose cross-layer optimized packtization and retransmission strategies for delay sensitive video delivery over WLANs.

The cross-layer optimization problem is formulated as distortion minimization given delay constraints, and significant multimedia quality gain is reported by packtization and retransmission optimization. The aforementioned works are mainly delaydistortion or rate-distortion optimization algorithms suitable for general wireless networks; it is hard to be directly used in WSNs due to the limited energy rather than bandwidth resource in WSNs.

III. Energy Efficient High Quality Image Transmission in Wireless Network

3.1. Lossless Image Compression

There are two types of image compression: lossless and lossy. After decompression the original image is recovered. Compressing an image is significantly different than compressing raw binary data. The general purpose compression is used to compress images, but the result is less than optimal. This is because images have certain statistical properties which can be exploited by encoders specifically designed for them. This also means that lossy compression techniques can be used in this area.

An integer-to-integer wavelet transform produces an integer-valued transform from the grey-scale, integer-valued image [11]. Since n loops in Bit-plane encoding reduces the quantization error to less than T0/2n, it follows that once 2n is greater than T0, and there will be zero error. In other words, the bit-plane encoded transform will be exactly the same as the original wavelet transform, hence lossless encoding is achieved Lossless compression involves with compressing data which, when decompressed,

will be an exact replica of the original data. This is the case when binary data such as executables, documents etc. are compressed. They need to be exactly reproduced when decompressed.

3.2 Error Metrics In Wavelet based image compression

Two of the error metrics used to compare the various image compression techniques are the Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR). The MSE is the cumulative squared error between the compressed and the original image, whereas PSNR is a measure of the peak error. The mathematical formulae for the two are

MSE= +MN

PSNR = 20 * log10 (255 / sqrt(MSE))

where I(x,y) is the original image, I'(x,y) is the decompressed image and M,N represents dimensions of the images. A lower value for MSE means lesser error, and as seen from the inverse relation between the MSE and PSNR, this translates to a high value of PSNR. Logically, a higher value of PSNR is good because it means that the ratio of Signal to Noise is higher.

The signal is the original image, and the noise is the error in reconstruction. It is highly required to evaluate a compression scheme having a lower MSE (and a high PSNR).

Fig 1: Image transmission


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3.3 Wavelet Image Compression Wavelet based image compression

introduces no blocky artifacts in the decompressed image. The decompressed image is much smoother and pleasant to eyes. We can also achieve much higher compression ratios regardless of the amount of compression achieved. By adding more and more detail information we can improve the quality. This feature is attractive for what is known as progressive transmission of images. Another compression scheme developed for image compression is the lossy image compression scheme (fig 1).However the lossy image compression is very complex and time consuming.

The filter components are reduced their size by half either by rejecting the even or odd samples thereby the total size of the original signal is preserved. The low pass filter component retains almost all distinguishable features of the original signal. And the high pass filter component has little or no resemblance of the original signal. The low pass component is again decomposed into two components. The decomposition process can be continued up to the last possible level or up to a certain desired level. As the high pass filter components have less information discernible to the original signal, we can eliminate the information contents of the high pass filters partially or significantly at each level of decomposition during the reconstruction process. It is this possibility of elimination of the information contents of the high pass filter components that gives higher compression ratio in the case of wavelet based image compression.

3.4 Energy Efficient High Quality Image Transmission scheme

In this paper, we propose image transmission scheme driven by energy efficiency considerations in order to be suitable for wireless sensor networks. Wavelet image transform provides data decomposition in multiple levels of resolution, so the image can be divided into packets with different priorities, the packets are ready to be sent.

Fig 2: Interactive Image Transmission Energy and Time Approach

Figure 2 shows the diagrammatic representation of our proposed Interactive Image Transmission Energy and Time Approach. The source sensor transmits the packets starting by those with the highest priority, and then continues with those of the next lower priority, and so on. Since it is not mandatory to receive all the priority levels at the sink, except the basic level 0, in order to play out a version of the image, packets of subsequent priorities are only forwarded by intermediate nodes if their battery state-of-charge is above a given threshold. This choice is motivated by the scarce energy in the context of sensor networks. In fact, the hop-by-hop transmission is handled as reliable, i.e., the data packet is always acknowledged and retransmitted if lost, whereas the end-to-end transmission is handled as semi-reliable, i.e., the intermediate node decides to forward or discard a packet according to the battery's state-of-charge and the packet's priority. This is carried out using a threshold-based drop scheme where each of the p priorities is associated to an energy level.

A node can discard packets even if it has sufficient energy to forward them, if it knows that a node further down the path has an insufficient amount of energy. Of course, the node does not initially know the state-of-charge of the other nodes. This knowledge is gradually obtained from received acknowledgment packets. Thus feedback is used to report the lowest energy level currently available in others nodes. The delay induced by the feedback is


14

proportional to the distance between the concerned nodes.

IV. Result and Discussions on the Performance of Energy Efficient Image Transmission scheme

In this section, we apply the energy consumption models to evaluate and compare energy performance of image transmission schemes in various scenarios. A monochrome image of 128X128 pixels, is used as a test image. This one is 8 bits per pixel originally encoded. That means a data length of 16394 bytes, including the image header of 10 bytes. Numerical values adopted for the input parameters of energy models are described below. Then, we present the results of numerical application.

To get a reference, we evaluated the consumed energy by transmitting the whole image (37249 bytes) reliably without applying WT or compression algorithms. In the following, we call that the "the original scenario". The amount of energy dissipated to transmit the original image is 15J per hop. Afterwards, we applied WT once and then twice without compression. When WT is applied once, we obtained a resolution 0 of 4106 bytes and a resolution 1 of 12288 bytes. Similarly, when WT was applied twice, we obtained 1034, 3072 and 12288 bytes for resolutions 1, 2 and 3 respectively. We computed the average energy consumption to transmit the image for scenario (Interactive Image Transmission Energy and Time WT). Figure 3 shows the average consumed energy per node as a function of the number of intermediate nodes. We see that the consumed energy when WT is applied is clearly lower compared to the case without WT.

02

468

10

1214

37249 56753 75173 105422 117331

Image Size (bytes)

Ene

rgy

cons

umpt

ion

(joul

es)

proposed Image Transmission Energy and Time Mechanism

existing approach

Fig 3: Image size Vs energy Consumption

Figure 3 shows the comparison of our proposed Interactive Image Transmission Energy and Time Mechanism with existing approach. For simulation the image size is taken as bytes. There are five images taken for experimentation (37249, 56753, 75173, 105422, 117331). As the image size increases, energy also gets increased. When compared to existing approach our proposed Interactive Image Transmission Energy and Time approach consumed lower energy.

0

20

40

60

80

100

37249 56753 75173 105422 117331

Image Size (Bytes)

Exe

cutio

n T

ime

(sec

)Proposed Image Transmission Energy and Time Mechanism Existing Approach

Fig 4: Image size Vs Execution Time

Figure 4 depicts the execution time for transmitting image by using our Interactive Image Transmission Energy and Time approach. For experimental works, the image size is taken as bytes. Execution time is measured in terms of seconds. Here five images are taken for simulation (37249, 56753, 75173, 105422, and 117331). As the image size increases, execution time also gets increased. By the comparison of our approach with existing work, proposed Interactive Image Transmission Energy and Time Mechanism have better performance.

V. Conclusion

The proposed work presented an improved wavelet based inductive methods for lossless image compression, lossy image compression and the wavelet image compression which can be effectively deployed in the transmission of wireless communication. The proposed scheme has selected two parameters of the JPEG image compression algorithm to vary, and gives the results of modifying the parameters on quality of image, and computation and communication efficiency with respect to energy utilization.


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This paper has presented image transmission scheme driven by energy efficiency considerations, based on wavelet image transformation also to achieve with high quality transmission. It achieves high-energy efficiency and time consumption. The results obtained by our analytical model of the energy and time consumption are promising since the time and energy savings are significant and communication protocols are of low complexity. The energy savings is about 85% in our Interactive Image Transmission Energy and Time Mechanism, with a guarantee for the image quality to be lower-bounded. Consequently, we argue that our proposals are suitable for WSN.

References

[1] Dr. Arthanariee A.M., Santha devi p. and Sivakumar M., “Efficient Wavelet based Image Compression Technique for Wireless Communication”, International journal of Innovative Technology & Creative Engineering , vol. 1 No.3, March 2011.

[2] Zaruba.G and Das.S, Off-the-shelf enablers of ad hoc networks. New York: IEEE Press Wiley, 2003.

[3] Ephremides .A, “Energy concerns in wireless networks,” IEEE Wireless Communications, vol. 9, no. 4, pp. 48-59, August 2002.

[4] Gamal.A .E,. Trends in CMOS image sensor technology and design,. in Proceedings of IEEE International Electron Devices Meeting, San Francisco, CA, December 2002.

[5] Hendessi .F, Sheikh A.U, and Hafez R. M., “Co-Channel and Adjacent Channel Interference in Wireless Cellular Communications”, Wireless Personal Communications, vol. 12, pp. 239–253, March 2000.

[6] Kallel .S, Bakhtiyari .S, and Link .R, “An Adaptive Hybrid ARQ Scheme”, Wireless Personal Communications, vol. 12, pp. 297–311, March 2000.

[7] Liu, C.-M., Lee, C.-H., and Wang, L.-C. Power-efficient communication algorithms for wireless mobile sensor networks. in 1st ACM International Workshop on Performance Evaluation of Wireless, Ad Hoc, Sensor and Ubiquitous Networks, 2004 pages 121-122.

[8] Min Wu and Chang Wen Chen. Collaborative image coding and transmission over wireless sensor netowrks. EURASIP Journal on Advances in Signal Processing, 2007. Article ID 70481.

[9] Magli, Mancin, M., and Merello .L., “Low complexity video compression for wireless sensor networks” in Proceedings of 2003 International Conference on Multi- media and Expo, 2003 pages 585-588.

[10] Schurgers .C, Aberthorne .O, and Srivastava .M.B, “Modulation scaling for energy aware communication systems,” in Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED ’01), pp. 96–99, Huntington Beach, Calif, USA, August 2001.

[11] Wang .W, Peng .D, Wang .H, Sharif .H, and Chen H.H., “Optimal image component transmissions inmultirate wireless sensor networks,” in Proceedings of the 50th Annual IEEE Global Communications Conference (GLOBECOM ’07), Washington, DC, USA, November 2007.

[12] Wagner .R, Nowak .R, and Baraniuk .R. “Distributed image compression for sensor networks using correspondence analysis and super-resolution” in

Proceedings of IEEE International Conference on Image Processing (ICIP’03), volume 1, pages 597–600, Barcelona, Spain, September 2003.

[13] Yu .W, Sahinoglu .Z, and Vetro .A, “Energy efficient JPEG 2000 image transmission over wireless sensor networks,” in Proceedings of IEEE Global Telecommunications Conference (GLOBECOM’ 04), vol. 5, pp. 2738–2743, Dallas, Tex,USA,November- December 2004.

[14] Zhang .W, Deng .Z, Wang .G , Wittenburg .L, and Xing .Z, “Distributed problem solving in sensor networks,” in Proceedings of the first international joint conference on Autonomous agents and multiagent systems. ACM Press, 2002, pp. 988–989.

[15] Digital pixel sensor,. 2004. [Online]. Available: http://www-isl. stanford.edu/_abbas/group/


16

Design and Implementation of Fuzzy Expert System for Back pain Diagnosis

Mohammed Abbas Kadhim#1, M.Afshar Alam#2, Harleen Kaur#3 #Department of Computer Science, Hamdard University

Hamdard Nagar, New Delhi-110062, India [email protected],[email protected],[email protected]

Abstract —Decision support through information technology become a part of our everyday lives. In this paper we produce a Fuzzy Expert System (FES) to diagnosis of back pain disease based on the clinica l observation symptoms using fuzzy rules. The clinica l observation symptoms which processed by fuzzy expert system may be used fuzzy concepts to describ e that symptoms such as (little, medium, high). To de al with fuzzy concepts in clinical observation symptom s we should be used fuzzy rules to hold this concepts . The parameters used as input for this fuzzy exp ert system were Body Mass Index (BMI), age, and gender of patient as well as the clinical observation symp toms. The proposed expert system can help to diagnosis of back pain disease and produce medical advice to the patient. The system implemented and tested using clinical data that is correspond to 20 patients wit h different back pain diseases. The proposed system implemented using Visual Prolog programming language ver. 7.1.

Keywords: Fuzzy expert system, fuzzy logic, fuzzy rules, back pain diagnosis

I. INTRODUCTION

Medical diagnosis is the art of determining a person's pathological status from an available set of findings. Why is it an art? Because it is a problem complicated by many and manifold factors, and its solution involves literally all of a human's abilities including intuition and the subconscious [1]. Nowadays the methods of Artificial Intelligence (A.I.) have largely been used in the medical applications. In the medicine area, many expert systems were designed to diagnose and treatment the disease. Hence, a rule-based fuzzy expert system that simulates an expert-doctors behavior for diagnosis of the disease is developed. Fuzzy logic is a true extension of conventional logic, and fuzzy logic controllers are a true extension of linear control models. Hence anything that was built using

conventional design techniques can be built with fuzzy logic, and vice-versa [2]. Expert system is one of the most common application of A.I., it is a computer program that simulates the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field. Typically, such a system contains a knowledge base containing accumulated experience and a set of rules for applying the knowledge base to each particular situation that is described to the program[3]. A fuzzy expert system is a collection of membership functions and rules that are used to reason about data. Unlike conventional expert systems, which are mainly symbolic reasoning engines, fuzzy expert systems are oriented toward numerical processing. The part of the rule between the "if" and "then" is the rule's _premise_ or _antecedent_ . This is a fuzzy logic expression that describes to what degree the rule is applicable [2]. There are two general types of fuzzy expert system: fuzzy control and fuzzy reasoning. Although both make use fuzzy sets, they differ qualitatively in methodology. It accepts numbers as input, then translates the input numbers into linguistic terms such as Slow, Medium, and Fast (fuzzification). Rules then map the input linguistic terms onto similar linguistic terms describing the output. Finally, the output linguistic terms are translated into an output number (defuzzification). The syntax of the rules is convenient for control purposes, but much too restrictive for fuzzy reasoning; fuzzification and defuzzification are automatic and inescapable [4]. The advent of computers and information technology in the recent past has brought a drastic change in the fuzzy medical expert system. Information gathered from the domain experts must be transferred to knowledge and must be used at the right time [5] .These Knowledge can be incorporated in the form of fuzzy expert system in the diagnosis of back pain disease in specific.


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In this study, we present a Fuzzy Expert System (FES) to diagnosis of back pain diseases depend on medical observation symptoms which represented as fuzzy rules or linguistic rules, the fuzzy rule is a rules which take the condition part linguistic values. There are other parameters can effect on back pain diseases such as patient’s history, Body Mass Index (BMI), age, and gender of patient. The input age, BMI and symptoms for the patient converted it to lingustic values using fuzzification process, the lingustic values and corresponding membership function have been determined by the aid of the experts.

II. RELATED WORKS Most of researchers develop many methods to diagnosis medical diseases based on clinical symptoms such as neural networks, rule-based systems, expert systems, and fuzzy expert systems. In [2], a fuzzy expert system is designed for diagnosis of hypertension risk for patients aged between 20’s, 30’s and 40’s years and is divided into male and female gender. The input data is collected from a total of 10 people which consists of male and female with different working background. In [1], A fuzzy expert system has been designed for learning, analysis and diagnosis of liver disorders. Required data has been chosen from trusty data base (UCI) that has 345 records and 6 fields as the entrance parameters and rate of liver disorder risks is used as the system resulting. In [6], the authors produce a Knowledge based diagnosis of abdomen pain using fuzzy Prolog rules the main objective of the system is to assist doctors, assistants and social workers in their decision making process and create awareness in the area especially where trained manpower is in scarce. To impart the fuzziness of the domain, modified Prolog rule format is used, which is illustrated in a case of appendicitis. In [7], A fuzzy expert system for diagnosing, and learning purpose of the prostate diseases is described. HIROFILOS is a fuzzy expert system for diagnosis and treatment of prostate diseases according to symptoms that are realized in one patient and usually recorded through his clinical examination as well as specific test results.

In [8], an expert system for diet recommendation in this study they proposed a case-based approach for diet recommendation. Based on this approach, we are going to construct an expert system which is intended to be employed in a health record management system. Their approach is based on ripple down rules (RDR), however, a special representation is also needed for patient attributes and rule actions. In [9], enhanced fuzzy rule based diagnostic model for lung cancer using priority values which design a fuzzy rule based medical model to detect and diagnose lung cancer. The disease is determined by using a rule base, populated by rules made for different types of lung cancer. The algorithm uses the output of the rule base (i.e. the disease name) and the symptoms entered by the user; it also uses the priority and severity values to determine the stage of cancer the patient is in.

III. STRUCTURE OF THE SYSTEM After selecting the domain that we want to build expert system, knowledge acquisition is started which involves the acquisition of knowledge from human experts, books, or documents. The knowledge may be specific to the problem domain or to the problem solving procedures, it may be general knowledge, or it may be metaknowledge (by metaknowledge, we mean information about how experts use their knowledge to solve problems and about problem-solving procedures in general). We formally verified that knowledge acquisition is the bottleneck in ES development today [10]. Acquired knowledge is organized to will be ready for use, in an activity called knowledge representation. This activity involves preparing and encoding of knowledge in the knowledge base. The proposed system used production system method to represent acquired knowledge which are sets of: IF antecedents THEN consequent The model of proposed system is given in figure (1) which represent the components of the system. The Knowledge Base (K.B) contains the problem solving knowledge (information about back pain diseases). the knowledge of the expert in the decision-making can be represented in various forms. The knowledge of expert can be easily represented into rule-based


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Fig. 1. Structure of the system

Knowledge base

Inference Engine

Patient’s history

User Interface

Symptoms & other information Advises & diagnosis

D.B

Working memory

format as a set of conditional rules. Rules may be chained according to the knowledge it represents[6].

The knowledge here represented as a set of fuzzy rules which is extracted from human experts (doctors) and back pain diseases documents, the fuzzy rules example can be represented in Prolog format as: diagnosis(“slipped disc”,3):- (age(young);age(middle);age(old)),(bmi(medium) ;bmi(high)), back_pain(very high), leg_pain(high), leg_narcotize (little), foot_senseless(medium). The fuzzy rule consists of two parts, head of rule (consequent) and body of rule (antecedents). The head of rule here consist of two arguments, disease name and region number (1-5) as well as to predicate name (diagnosis). The body of rule consists of all symptoms and their severity which stored in K.B. The other component of K.B is treatment of the diseases

which represented as facts for medical advices which may be name of drugs, surgery operation, or take some rest, for example for these facts can be represented in Prolog format as: treatment(“slipped disc”, ”take some drugs like Tilcotil20mg ،Arcoxia 90 mg , if the situation continue do surgery operation“ ). The treatment facts consist of two arguments the first one represent disease name and the other represent the appropriate medical advice for that disease. The Inference Engine (I.E) makes use of fuzzy logic to map the given input in this case the symptoms to an output the possible disease a patient can have. Based on this we can make decision and the treatment for a particular patient [9] also it makes use of membership function, If-then rules and logical operators for making these decisions. The inference engine contain the strategies of reasoning process, its carries out the reasoning process by links the contains of the knowledge base with the symptoms which input by users through user interface to capture appropriate decision, the working memory contains all the temporary results during the reasoning process, the backward chaining strategy is used in inference engine of proposed system. I.E performs fuzzification on the inputs and determine the degree to which the input belongs to the fuzzy set. The user can interact with fuzzy expert system through User Interface (U.I) , the U.I must be friendly of user and hide the other complex components of fuzzy expert system, the questions and answers method used in building of user interface for the system. When the user input his preliminary information (ID, name, age, gender, weight, height for the patient), the proposed system check all this data in database if any, then retrieves the patient’s history otherwise called registration procedure to stored it in database, that mean the database contain all data about registered patients and their diseases, the patient’s history is very important for decision making because of most diseases may be overlapping with each other (especially back pain diseases with other diseases) the structure of database can be illustrated by table I:


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Table I Structure of database for proposed system

ID Name of patient

gender Birth date

BMI

Kg/

m2

Disease

1 Tom m 1978 20.3 Rupture and contraction

2 Sarah f 1967 19.5 Spinal injury

. … … … … …

. … … … … …

20 John m 1980 22.1 Slipped disc

IV. CALCULATING MEMBERSHIP FUNCTION A membership function defines how each point in the input space is mapped to a degree of membership between 0 and 1 [5]. In our system we calculate the membership function for age and BMI input variables. For each of them we can calculate the membership function, we use three linguistic variables young, middle, and old for age input value as in figure(2) and table II, and we use three linguistic variables low, medium, and high for BMI value as in figure(3) and table III.

Fig. 2. Linguistic variables and membership function of ‘Age’

table II

Classification of age input variable

Input variable Range Fuzzy Sets

Age <25

22-40

38>

Young

Middle

Old

Fig. 3.Linguistic variables and membership function of ‘BMI’

table III Classification of BMI input variable


BMI <18

19-25

24>

Low

Medium

High

Also we can calculate the membership function for the input symptoms based on the severity experienced by the patient we scale the range [0-100], for each symptom can calculate the member function, we use four linguistic variables little, medium, high, and very high for symptom input value as in figure (4) and table IV.

Table IV Classification of symptom input variable


Any symptom <20

18-60

58-80

78>

Little

Medium

High

Very high

Fig. 4. Linguistic variables and membership function of ‘symptom’

little


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V.HOW THE SYSTEM WORKS

In general the following algorithm represent the main steps of proposed system: Algorithm 1) Take input from user (patient) as (a1,a2,a3…a7)

which represent (ID, name, age, gender, weight, height, and region pain number for the patient) respectively, and calculate BMI=a5/(a6)

2 2) Select N number of symptoms and choose their

severity and assign some membership values to linguistic variables, symptomi=little| medium| high| very high, where i=1 to N .

3) IF the patient is new THEN register all his data in database ELSE retrieval his data from database.

4) Matching the symptomi and their severity (input in step 2) against the antecedents part of fuzzy rules in knowledge base to make decision as disease name.

The rules that used by experts can be developed using decision tree by maintaining the decision sequence this is illustrated in figure (5) that describe the structure of decision making for the slipped disc disease, the backbone of human being can be divided into five regions as shown in figure (6).

VI.IMPLEMENTATION OF CLINICAL DATA

The tests of the proposed system were performed using real clinical data that correspond to 20 patients, 13 males and 7 females taken from Max hospital, New Delhi, India during the year 2010, The accuracy of the system diagnosis is evaluated by comparing with the diagnosis of specialist (doctor), the system accuracy diagnosed is 90% of cases which tested it. Patients distributed according to age groups is illustrated in figure(7), and figure (8) show the distribution of patients according to gender and age group.

Fig. 7. distribution of patients according to age

To evaluate the working of the proposed system, we will produce real case for the proposed system to show the steps of system carry out to produce the

Fig.8.distribution of patients according to age and gender group

diagnosis and advices for that case, the figures (9), (10), and (11) illustrated that steps.

Fig. 5. decision tree for slipped disc disease

Pain in backbone

Cervical thoracic lumber sacrol coccygeal Region region region region region

Legs narcotize Y N Legs pain foot narcotize Y N Foot senseless Y N

Fig. 6. regions of backbone human being

Legends 1-Cervical region 2-Thoracic region 3-Lumber region 4-Sacrol region 5-Coccygeal region


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When the proposed system carry out this case study, it will invoked the fuzzy rules concern with region number 3 for backbone regions and neglected the fuzzy rules for other regions, therefore we should be mentioned to region number with the head of fuzzy rules. We can illustrate the sample of knowledge base contains (set of fuzzy rules for different regions to make diagnosis and set of treatment facts to produce advices for that disease using Prolog language format) as in figure (12).

Fig. 12. Sample of knowledge base contains

VII. CONCLUSION This paper discuss the architecture of fuzzy expert system that used fuzzy rules to represent the diseases of backbone for human being, it can be conclude there is no doubt whether fuzzy expert system should be applied for diagnosis of back pain diseases and produce an advice for patient based on the symptoms which represented as fuzzy concepts in antecedents of fuzzy rules. Fuzzy logic systems are a very good tools for handling of ambiguous and imprecise information especially in medical diagnosis. The accuracy of the proposed system diagnosis was evaluated by comparing it to diagnosis indicated by specialist (doctor), the system accuracy diagnosed 90% of cases which tested it

1 2 3 4

Fig. 9. Primarily information for the patient

Fig. 10. Detail symptoms for the input patient

Fig. 11. Make decision and produce advices for the patient

Rule1:diagnosis(“rupture and contraction”,2):-((age(young);age(middle); age(old)) , (bmi (medium);bmi(high)), long_back_pain(very high), difficult_breath(high),cough(high).

Rule2:diagnosis(“osteoporosis”,2):-age(old),(bmi(medium);bmi(high)),

back_pain(high),smoke(high),drink(high). Rule3:diagnosis(“slipped disc”,3):-((age(young);age(middle);age(old))

, (bmi (medium);bmi(high)), back_pain(very high), leg_pain(high), leg_narcotize (little), foot_senseless(medium).

Rule4:diagnosis(“arthritis of vertebae”,3):-((age(old);age(middle);

age(old)) , (bmi (medium);bmi(high)), back_pain(very high), leg_pain(high), foot_narcotize (little).

Rule5:diagnosis(“osteoporosis”,4):-age(old),(bmi(medium);bmi(high)),

back_pain(high),smoke(high),drink(high). Fact1:treatment(“rupture and contraction”,” heating the region and the

use of patches with palliative treatments like Voltaren”). Fact2:treatment(“osteoporosis,”Bisphosphonates or Calcitonin”). Fact3:treatment(“slipped disc ”, ”take some drugs like Tilcotil20mg ،

Arcoxia 90 mg , if the situation continue do surgery operation“ ). Fact4:treatment(“arthritis of vertebrae”,”Panadol and Nsaids”).


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IJITCE September 2011