Monday, September 5, 2011

D.J-college presented



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AN OPTIMIZED REQUEST/RESPONSE MECHANISM FOR MAP RETRIEVAL IN GEOGRAPHIC INFORMATION SYSTEM

Ramalingam. M*, Thiagarasu.V#, Janarthanam.S*

*Assistant Professors in Computer Science Department,

# Associate Professor in Computer Science Department,
Gobi Arts & Science College, Gobi
E-mail: ramsgobi@gmail.com, gascavt@gmail.com

Abstract — The main aim of the research is to design and develop a prototype for dynamic map handling in mobile agents  The system uses Global Positioning System (GPS) or Mobile Positioning System (MPS) for obtaining the coordinates of the current location. This research paper brings out the necessity of robustness in Map retrieval. Though various Map retrieval methods
are used, the authors felt that the request/response mechanism needs to be strongly implemented. This research work proposes a robust request/response mechanism to know and to verify the Map under the Map Handling. A model has been designed to post the request, to process the received request and to send the Map in an automatic and robust way. This model gives the unavailable Map at the time of request to the client in a robust way. The benefits of request/response mechanism for map
retrieval in Map Handling are discussed with future scope.
Map Handling System has been developed in which the solution is presented for both to the storage problem


Keywords - Dynamic database, Mobile GIS, Map Retrieval, Request/Response Mechanism,
Client/Server architecture and Wireless Network.






I.Introduction
In dynamic map handling, there are limited memory resources in the client devices, but remote access to the server can be assumed. Maps are obtained on-the demand from the client device when available, or automatically through a GSM-network. The idea is that only the necessary amount of data is transmitted. When the map fragments are transmitted in a compressed form, the current network has about enough bandwidth to handle the network traffic.

Map handling is a broader topic that deals with the whole spectrum of
the relationship and networks within government regarding the usage
and application. A critical issue is the way of managing data, Map and knowledge: data most of the time
structured according to data models, often using proprietary formats, leading to consistency problems for the exchanges. The use of international standards is a good way of improving quality of the Map systems used in production management, since they
facilitate interoperability of the software tools used.
Dynamic map handling means that the handling of the maps is invisible to the user: the maps just appear on the screen when needed without any actions by the user, the device indicates the present location to the map handling system, and requests to show a piece of map on the display[8]. If the map does not exist in the memory, request is sent to the server via network. It is a matter of the application to define the logic to automatize the selection of scale and other parameters of the map. The server looks for a proper map sheet from the database and sends the desired piece of the map to the client in a compressed form.

II.GSM AND MAP HANDLING
MPS consists of a server-based Gateway Mobile Positioning Centre (GMPC), a server-based Serving Mobile Positioning Centre and software extensions for the operator's mobile network [6].








Fig. 2 The structure of the mobile terminal

the map, and real-time updating. In the emergent condition, it will save a lot time for the user.
5) Video Module: This part is to complete the tasks: video capture, video compression, and video transmission over Wireless Networking, video decompression, video recovery.
6) Wireless Networking Module: Wireless Networking is the medium of this system. It is needed by the interactive communication between the client and server, video transmission, and some error checking.
In the above six modules there needs among of computing in the Positioning Module and Navigation Module.

IV. REQUEST / RESPONSE MECHANISM

In today's business world, corporations must be able to react to the
changing market needs rapidly, effectively, and responsively. They
must be able to reduce their time to market and adapt to the changing
environments. Decisions must be made quickly and they must be done
right the first time out. Corporations can no longer waits time repeating tasks, thereby prolonging the time it takes to bring new products to market.
Innovations can be enhanced by sharing the resources and maps. To share the maps, it is necessary to introduce a
request/response mechanism. This work is an attempt to develop a general model for map retrieval which can be adapted for some situations. Response is then defined as the process of managing
dependencies between requests. In this context, a theoretical model is presented that allows one to determine how to model software activities and how to detect dependencies between those requests.


V. DESIGN OF MAP HANDLING MODEL

The purpose of work is to design an map handling model in GIS
used to execute multiple requests simultaneously. The proposed model
is used to gather maps necessary for multiple executions that
will reflect the data dependencies among the specified requests. It is
necessary to have an execution schedule during the execution of
collection of requests. One of the premises for the execution is that
whenever it is possible to start independent request simultaneously,
all of them must be initiated. This requires that every request
dependency must be known before the
execution. This condition
fundamentally shapes the nature of the model and the mechanism for its
interpretation. All the request specifications must be known before
the execution takes place, and therefore, the Map about all of
them must be stored at the beginning. A proposed algorithm has been
constructed with the following goals:
1.It is intended to be used by non- programmers and should be simple.
2.It should provide mechanism for both explicit and implicit request ordering.


A.MIDDLEWARE TECHNOLOGY STANDARDS
There is a need to introduce the concept of middleware and technology
standards as a tool to develop integral, scalable and robust
map handling solutions, while employing multiple solution providers.
The middleware should support processes involving multi-department and multi-agency workflows. The middleware also should be able to facilitate integration with legacy systems.


Middleware needs to provide services such as identification,
authentication, authorization, directories, and security to all
applications. By promoting standardization and interoperability,
middleware will make advanced network applications much easier to use.
The key middleware components are (a) Map Handling Server, (b)
Inter-application communication and map and collaboration
software


VI. CLIENT/SERVER Architecture

The basic characteristic of the request/response architecture are:
the combination of a client or front-end portion that interacts with
the user, and a server or back-end that interacts with the shared
Map the front-end and back-end with different hardware and software.
the system can be scaled horizontally or vertically.
.











Fig.3. Innovative Map Handling

In this model, major concepts are developed in terms of components of
request /response mechanism, situations of map request,
coordination mechanisms and the map retrieval process.
Stakeholder preferences may be based on the perceived value or urgency
of delivered requirements to the different stakeholders involved. The
technical priorities and individual stakeholder priorities may be in
conflict and difficult to reconcile. This paper provides (i) a method
for optimally allocating requirements to increments; (ii) a means of
assessing and optimizing the degree to which the ordering conflicts
with stakeholder priorities within technical precedence constraints;


(iii) a means of balancing required and available resources for all
increments; and (iv) an overall method aimed at the continuous
planning of incremental software development. The optimization method
used is iterative and essentially based request/response mechanism. A
set of the most promising candidate solutions is generated to support
the final decision.


VII. MAP SEARCH ALGORITHM
A.Definition
An m-way search tree, T, is a tree in which all nodes are of degree <= m. If T is empty (T=0) then T is an m-way search tree. When T is not empty it has the following properties:

(i) T is a node of the type
n0, A0, (K1, A1), (K2, A2)…, (Kn,An)
Where the Ai, 0 < = i <= n are pointers to the sub-trees of T and the Ki, 1 <= i <= n are the key values; and 1 <= n < m.

(ii) Ki < Ki+1, 1 <= i < n

(iii) All key values in the sub tree Ai are less than the key value Ki+1, 0 <= i < n

(iv) All key values in the sub-tree an are greater than Kn

(v) The sub-trees Ai, 0 <= i <= n are also m-way search trees

Pseudo code for the find map procedure
Procedure FindMSearch (Xc, Yc)
//(Xc, Yc)coordinates of the current position
Step1. Get the coordinates of the current position with the help of MPS
Step 2. Identify the indexed (segment) X=(Xc, Yc)

Step 3. MAPSEARCH (T, X)
//search the m-way search tree T residing on disk for the key value X. Individual node format is n, A0, (K1, A1),..,(Kn, An), //n

(i) P<-- T; Ko<-- [-x]; Q<-- 0
(ii) while P != 0 do
(iii) input node P from disk
(iv) let P define n, A0, (K1,A1),...(Kn,An)
(v) Kn+1 <-- [+x]
(vi) Let i be such that Ki < = X < Ki+1
(Vii) if X = Ki then [// X has been found // return(P,i,1)]
(viii) Q<-- P; P<-- Ai
(ix) end
(x) return (Q,i,0)
(xi) end of MAPSEARCH
Step 4.Reterieve the image fragment X
Step 5. Automatic conversion from raster and vector format

//using conversion algorithm (in the section 2)//

Step 6. Identified iamge X (Display X)

Step 7. End of FindMSearch

B.Algorithm description
In order to search for any key value X in this tree, first look into the root node T=a and determine the value of i for which Ki <= X < Ki+1 (for convenience to use K0=[-x] and Kn+1=[ +x ] where [-x] is smaller than all legal key values and [+x] is larger than all legal key values). In case X=Ki then the search is completed. If X! = Ki then by the definition of an m-way search tree X must be in sub-tree Ai if it is in the tree. When the n (no. of key nodes) is larger, the search for the appropriate value of i above may be carried out using binary search.
Algorithm MAPSEARCH searches an m-way search tree T for key value X using the scheme describes above. In practice, when the search tree represent an index , the tuples (Ki, Ai) in each of the nodes will really be 3-tuples (Ki, Ai, Bi) where Bi is the address in the field of the record with key Ki.

VIII. CONCLUSIONS
This research work describes the methods for dynamic map handling in mobile environment with low computing and storage resources. The maps are stored in compressed raster formats in separated logical binary layers and to dynamically build map from smaller image fragments. In this way, design low resource dynamic map handling system for the client device, which is capable of composing maps from smaller image blocks independent on their location and the original format.
Map Handling has been developed in which the solution is presented for both to the storage problem and to the real-time requirement of the system.  To achieve higher flexibility and to better satisfy actual customer
requirements, there is an increasing tendency to develop and deliver
Map in an incremental fashion. In adopting this process,
requirements are delivered in releases and so a decision has to be
made on which requirements should be delivered in which release. Three
main considerations that need to be taken account of are the technical
precedences inherent in the requirements, the typically conflicting
priorities as determined by the representative stakeholders, as well
as the balance between required and available effort.

References

[1] P. G. Howard and J. S. Vitter, “Fast and efficient lossless image compression,” in Proceedings of the IEEE Data Compression Conference (DCC- 93),

[2] A. Amir and G. Benson. Efficient two-dimensional compressed matching. In J. A. Storer and J. H. Reif, editors, Proc. Data Compression Conference, pages 279-288, IEEE, 1992.

[3] D. A. Huffman. “A method for the construction of minimum redundancy codes”. In Proc.Inst. Electr, Radio Eng., pages 1098-1101, 1952.

[4] “Pattern Matching in Compressed Raster Images” Renato Pajarola Peter Widmayer Department of Computer Science Institute of Theoretical Computer Science ETH Zurich, Switzerland

[5] P. G. Howard and J. S. Vitter. “Fast and efficient lossless image compression”. In J. A. Storer and J. H. Reif, editors, Proc. Data Compression Conference, pages 351-360, IEEE, 1993.
[6] R. Pajarola and P. Widmayer. “Spatial queries on compressed raster images”: How to get the best of both worlds. Technical Report 240, Dept. of Computer Science, ETH Zurich, 1995.

[7] “DATA COMPRESSION ALGORITHM IN LOCALIZATION PROBLEMS “Andreu Urruela and Jaume Riba Department of Signal Theory and Communications. Universitat Polit`ecnica de Catalunya. Campus Nord, Ed. D5, J ordi Girona 1 i 3, 08034, Barcelona (Spain).

[8] Charles E. Perkins, David B. Johnson, “Mobility Support in IPv6,” Proceedings of the Second Annual International Conference on Mobile Computing and Networking (MobiCom’96), November 1996

[9] “New Efficient Methods of Image Compression in Digital Cameras with Color Filter Array” Chin Chye Koh, S tudent Member, IEEE, Jayanta Mukherjee, Member, IEEE, and Sanjit K. Mitra, Life Fellow, IEEE IEEE Transactions on Consumer Electronics, Vol. 49, No. 4, NOVEMBER 2003

[10] C. Weerasinghe, I. Kharitonenko and P. Ogunbona, “Method of color interpolation in a single sensor color camera using green channel separation”, IEEE Int. Conf. Acoustics, Speech and Signal Processing 2, vol. 4, pp. 3233-3236, May 2002

[11] J.F. Hamilton and J.E. Adams, “Adaptive color plane interpolation in single sensor color electronic camera”, U.S. Patent 5,629,734, 1997.

[12] R. Kimmel, Demosaicing:” Image reconstruction from color CCD samples”,IEEE Trans. Image Processing, vol. 8, issue 9. pp. 1221- 1228, Sep 1999.

[13] S.C. Pei, I.K. Tam, “Effective color interpolation in CCD color filter array using signal correlation”, IEEE Int. Conf. Image Processing, vol. 3, pp. 10-13, Sep 2000.

[14] S.Y. Lee and A. Ortega, “A novel approach of image compression in digital cameras with a Bayer color filter array”, IEEE Int. Conf. Image Processing 2001, vol. 3, pp. 482-485, Oct 2001.

[15] D. Greer and G. Ruhe, Information and Software technology, Volume
6,No.4,March-2004.

[16] Prabavathi G.T. and Thiagarasu V., Intelligent Agents for
information retrieval in distributed Systems, Proceeding of the
National Conference on Information Technology (NCCIT-2001), pp.129-140, Sep.2001.

Map Handling system


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Dynamic Map Handling and Retrieval in Mobile Navigation System

Ramalingam. M*, Prabavathi.G.T*, Narendran. P# ,
* Lecturers in Department of computer Science,
# Associate Professor & Head, Department of computer Science,
Gobi Arts & Science College, Gobi



Abstract The main aim of the research is to design and develop a prototype for dynamic map handling in mobile agents with lstorage resource. TThe proposed method combines the map dynamically from smaller image fragments retrieved on the demand basis. The system uses Global Positioning System (GPS) or Mobile Positioning System (MPS) for obtaining the coordinates of the current location.

The function of this system is that the maps are stored in server-side database and spatial views are generated for the client side application using compressed raster image formats and also organized to support zooming and panning requirements.

Keywords Raster image formats, Dynamic database, Mobile GIS, Client/Server architecture and Wireless Network.
P SEARCH ALGORITHM
    1. Definition
An m-way search tree, T, is a tree in which all nodes are of degree <= m. If T is empty (T=0) then T is an m-way search tree. When T is not empty it has the following properties:

(i) T is a node of the type
n0, A0, (K1, A1), (K2, A2)…, (Kn,An)
Where the Ai, 0 < = i <= n are pointers to the sub-trees of T and the Ki, 1 <= i <= n are the key values; and 1 <= n < m.

(ii) Ki < Ki+1, 1 <= i < n

(iii) All key values in the sub tree Ai are less than the key value Ki+1, 0 <= i < n

(iv) All key values in the sub-tree an are greater than Kn

(v) The sub-trees Ai, 0 <= i <= n are also m-way search trees

    1. Pseudo code for the find map procedure
Procedure FindMSearch (Xc, Yc)
//(Xc, Yc)coordinates of the current position
Step 1. Get the coordinates of the current position with the
help of MPS
Step 2. Identify the indexed (segment) X=(Xc, Yc)
Step 3. MAPSEARCH (T, X)
//search the m-way search tree T residing on disk for
the key value X. Individual node format is
n, A0, (K1, A1),..,(Kn, An),
//n
found at node P, key Ki, Else j = 0 and P is the node
into which X can be inserted.

(i) P<-- T; Ko<-- [-x]; Q<-- 0
(ii) while P != 0 do
(iii) input node P from disk
(iv) let P define n, A0, (K1,A1),...(Kn,An)
(v) Kn+1 <-- [+x]
(vi) Let i be such that Ki < = X < Ki+1
(Vii) if X = Ki then [// X has been found // return(P,i,1)]
(viii) Q<-- P; P<-- Ai
(ix) end
(x) return (Q,i,0)
(xi) end of MAPSEARCH
Step 4. Reterieve the image fragment X
Step 5. Automatic conversion from raster and vector format
//using conversion algorithm (in the section 2)//
Step 6. Identified iamge X (Display X)
Step 7. End of FindMSearch
    1. Algorithm description
In order to search for any key value X in this tree, first look into the root node T=a and determine the value of i for which Ki <= X < Ki+1 (for convenience to use K0=[-x] and Kn+1=[ +x ] where [-x] is smaller than all legal key values and [+x] is larger than all legal key values). In case X=Ki then the search is completed. If X! = Ki then by the definition of an m-way search tree X must be in sub-tree Ai if it is in the tree. When the n (no. of key nodes) is larger, the search for the appropriate value of i above may be carried out using binary search.
Algorithm MAPSEARCH searches an m-way search tree T for key value X using the scheme describes above. In practice, when the search tree represent an index , the tuples (Ki, Ai) in each of the nodes will really be 3-tuples (Ki, Ai, Bi) where Bi is the address in the field of the record with key Ki.
  1. CONCLUSIONS
This research work describes the methods for dynamic map handling in mobile environment with low computing and storage resources. The maps are stored in compressed raster formats in separated logical binary layers and to dynamically build map from smaller image fragments. In this way, design low resource dynamic map handling system for the client device, which is capable of composing maps from smaller image blocks independent on their location and the original format.
MISS has been developed in which the solution is presented for both to the storage problem and to the real-time requirement of the system. The developed prototype minimizes the storage size and memory requirement of the mobile device.
    References
    [1] P. G. Howard and J. S. Vitter, “Fast and efficient lossless image compression,” in Proceedings of the IEEE Data Compression Conference (DCC- 93),
    [2] A. Amir and G. Benson. Efficient two-dimensional compressed matching. In J. A. Storer and J. H. Reif, editors, Proc. Data Compression Conference, pages 279-288, IEEE, 1992.
    [3] D. A. Huffman. “A method for the construction of minimum redundancy codes”. In Proc.Inst. Electr, Radio Eng., pages 1098-1101, 1952.
    [4] “Pattern Matching in Compressed Raster Images” Renato Pajarola Peter Widmayer Department of Computer Science Institute of Theoretical Computer Science ETH Zurich, Switzerland
    [5] P. G. Howard and J. S. Vitter. “Fast and efficient lossless image compression”. In J. A. Storer and J. H. Reif, editors, Proc. Data Compression Conference, pages 351-360, IEEE, 1993.
    [6] R. Pajarola and P. Widmayer. “Spatial queries on compressed raster images”: How to get the best of both worlds. Technical Report 240, Dept. of Computer Science, ETH Zurich, 1995.
    [7] “DATA COMPRESSION ALGORITHM IN LOCALIZATION PROBLEMSAndreu Urruela and Jaume Riba Department of Signal Theory and Communications. Universitat Polit`ecnica de Catalunya. Campus Nord, Ed. D5, J ordi Girona 1 i 3, 08034, Barcelona (Spain).
    [8] Charles E. Perkins, David B. Johnson, “Mobility Support in IPv6,” Proceedings of the Second Annual International Conference on Mobile Computing and Networking (MobiCom’96), November 1996
    [9] New Efficient Methods of Image Compression in Digital Cameras with Color Filter Array” Chin Chye Koh, S tudent Member, IEEE, Jayanta Mukherjee, Member, IEEE, and Sanjit K. Mitra, Life Fellow, IEEE IEEE Transactions on Consumer Electronics, Vol. 49, No. 4, NOVEMBER 2003
    [10] C. Weerasinghe, I. Kharitonenko and P. Ogunbona, “Method of color interpolation in a single sensor color camera using green channel separation”, IEEE Int. Conf. Acoustics, Speech and Signal Processing 2, vol. 4, pp. 3233-3236, May 2002
    [11] J.F. Hamilton and J.E. Adams, “Adaptive color plane interpolation in single sensor color electronic camera”, U.S. Patent 5,629,734, 1997.
    [12] R. Kimmel, Demosaicing:” Image reconstruction from color CCD samples”,IEEE Trans. Image Processing, vol. 8, issue 9. pp. 1221- 1228, Sep 1999.
    [13] S.C. Pei, I.K. Tam, “Effective color interpolation in CCD color filter array using signal correlation”, IEEE Int. Conf. Image Processing, vol. 3, pp. 10-13, Sep 2000.
    [14] S.Y. Lee and A. Ortega, “A novel approach of image compression in digital cameras with a Bayer color filter array”, IEEE Int. Conf. Image Processing 2001, vol. 3, pp. 482-485, Oct 2001.