Monday, September 5, 2011

Map Handling system


Know more about Operating System....Click!



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.


5 comments: