Ijciis May 2011 Vol. 2 No. 5

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International Journal of

Computational Intelligence and

Information SecurityISSN: 1837-7823

May 2011

Vol. 2 No. 5

IJCIIS Publication

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International Journal of Computational Intelligence and Information Security, May 2011 Vol. 2, No. 5

2

IJCIIS Editor and Publisher

P Kulkarni

Publishers Address:

5 Belmar Crescent, Canadian

Victoria, Australia

Phone: +61 3 5330 3647

E-mail Address:[email protected]

Publishing Date: May 31, 2011

Members of IJCIIS Editorial Board

Prof. A Govardhan, Jawaharlal Nehru Technological University, India

Dr. A V Senthil Kumar, Hindusthan College of Arts and Science, India

Dr. Awadhesh Kumar Sharma, Madan Mohan Malviya Engineering College, India

Prof. Ayyaswamy Kathirvel, BS Abdur Rehman University, India

Dr. Binod Kumar, Lakshmi Narayan College of Technology, India

Prof. Deepankar Sharma, D. J. College of Engineering and Technology, India

Dr. D. R. Prince Williams, Sohar College of Applied Sciences, Oman

Prof. Durgesh Kumar Mishra, Acropolis Institute of Technology and Research, IndiaDr. Imen Grida Ben Yahia, Telecom SudParis, France

Dr. Himanshu Aggarwal, Punjabi University, India

Dr. Jagdish Lal Raheja, Central Electronics Engineering Research Institute, India

Prof. Natarajan Meghanathan, Jackson State University, USA

Dr. Oluwaseyitanfunmi Osunade, University of Ibadan, Nigeria

Dr. Ousmane Thiare, Gaston Berger University, Senegal

Dr. K. D. Verma, S. V. College of Postgraduate Studies and Research, India

Prof. M. Thiyagarajan, Sastra University, India

Dr. Manjaiah D. H., Mangalore University, India

Dr.N.Ch.Sriman Narayana Iyengar, VIT University ,India

Prof. Nirmalendu Bikas Sinha, College of Engineering and Management, Kolaghat, India

Dr. Rajesh Kumar, National University of Singapore, Singapore

Dr. Raman Maini, University College of Engineering, Punjabi University, India

Dr. Seema Verma, Banasthali University, India

Dr. Shahram Jamali, University of Mohaghegh Ardabili, Iran

Dr. Shishir Kumar, Jaypee University of Engineering and Technology, India

Dr. Sujisunadaram Sundaram, Anna University, India
mailto:[email protected]:[email protected]:[email protected]:[email protected]

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Dr. Sukumar Senthilkumar, National Institute of Technology, India

Prof. V. Umakanta Sastry, Sreenidhi Institute of Science and Technology, India

Dr. Venkatesh Prasad, Lingaya's University, India

Journal Website:https://sites.google.com/site/ijciisresearch/
https://sites.google.com/site/ijciisresearch/https://sites.google.com/site/ijciisresearch/https://sites.google.com/site/ijciisresearch/https://sites.google.com/site/ijciisresearch/

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Contents

1. Comparative Analysis Of Different Image Sharpening Techniques Using Different

Quality Matrics (pages 5-16)

2. Data Hiding for Medical Images: Issues and Challenges (pages 17-26)

3. A Survey on Ontology-Based Approach for Context Modelling and Reasoning (pages

27-36)

4. A proposed scheme for implementation of password authentication mechanism in the

security architecture of MANETs (pages 37-42)

5. A Survey on Recovery Techniques in Self-Healing Systems (pages 43-54)

6. Software Defect Prediction Based On Data Mining Techniques and Statistical Models

(pages 55-61)

7. Intrusion Response System with Self-Healing Intelligence (pages 62-70)

8. Fractal Geometry of Polynomial Surfaces pages (71-80)

9. Survey On Self-Adaptation In Context-Aware Systems (pages 81-89)

10. Reliability Forecast For Sugar Plant With Standby Redundant Boiler (pages 90-99)

11. Experimental Results Of Multilevel Inverter Based Statcom (pages 100-105)

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COMPARATIVE ANALYSIS OF DIFFERENT IMAGE SHARPENING

TECHNIQUES USING DIFFERENT QUALITY MATRICS

G. P.Hegde1

and Dr. I.V. Muralikrisna2

1Assistant Professor, SDMIT, Ujire

Email:[email protected],

2Retd. Professor, JNTU, Hyderabad

Email: [email protected]

Abstract

In this paper we focus on pan-sharpening algorithms especially for the remote sensing satellite imaging

application and employ experimental testing to compare their performance. Four different image sharpening

techniques were applied to fuse higher resolution panchromatic and lower spatial resolution multispectral

images of SPOT satellite. The pan sharpening results were evaluated according to five measures of performance,

such as Mannons quality index, Difference quality index, Objective measure, Mutual information, Image

quality index. Finally quality evaluation of fused image was carried and the experimental results show that

PHLST proposed image sharpening technique yields more information from pan-sharpened images.

Keywords: Image sharpening, Performance metric, Polyharmonic local sine transform (PHLST), NSCT.
mailto:[email protected]:[email protected]

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1. Introduction

Image sharpening or fusion is the process by which two or more images are combined into a single image

retaining the important features from each of the original images. It aims at the integration of complementary data

to enhance the information apparent in the images as well as to increase the reliability of the interpretation. The

successful fusion of images acquired from different modalities or instruments is of great importance in many

applications such as remote sensing, medical imaging, microscopic imaging, computer vision, and robotics.

Now a days, with the rapid development in high-technology and modern instrumentations, satellite imaging has

become a vital component of a large number of applications, including remote sensing, space research, and military

applications. In order to support more accurate earth and space information satellite images were properly

registered and corrected for evaluation and image sharpening was carried out by combining the features of high

resolution SPOT panchromatic (SPOT-PAN) images with multispectral (SPOT-XS) image. These remote sensing

satellite images usually provide complementary and occasionally conflicting information. The SPOT-PAN sensed

images can provide dense structures like water area and trees with less distortion, but it has poor spectral changes,

while the SPOT-XS image can provide spectral more information but it cannot support the high resolution spatial

information. In this case, only one kind of image may not be sufficient to provide accurate analysis of earth

observation and study for the researchers and astronomers. Therefore, the pan-sharpening or fusion of the

multimodal remote sensing satellite images is necessary and it has become a promising and very challenging

research area in recent years[3].

This paper presents 4 different pan-sharpening or fusion techniques. In section 2 the four suitable image pan-

sharpening techniques will be introduced. In section 3, introduction of five methods of evaluation parameters are

given. Section 4 presents quantitative analysis and experimental results of applying these image fusion techniques

to SPOT-PAN and SPOT-XS images.

2. Image Pan-sharpening Techniques

In this paper we mentioned Poly Harmonic Local Sine Transformation (PHLST) as a proposed image pan-

sharpening or fusion technique; it is compared with other fusion techniques qualitatively and quantitatively.

2.1 Wavelet Transformation Technique

The two-dimensional Discrete Wavelet Transform (DWT) is one of the standard pan-sharpening technique,

computed by successive lowpass and highpass filtering of the digital images. Its significance is in the manner it

connects the continuous time multiresolution to discrete-time filters. The principle of image fusion using

wavelets is to merge the wavelet decompositions of the two original images using fusion methods applied to

approximations coefficients and details coefficients [13]. The figure 1 shows the process of image fusion tomerges two different images leading to a new image.

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The wavelet transform decomposes the image in to low-high, high-low and high-high spatial frequency bands at

different scales and the low-low band at the coarsest scale. The L-L band contains the average image

information whereas the other bands contain directional information due to spatial orientation. Higher absolute

values of wavelet coefficients in high bands correspond to salient features such as edge or lines.

Fig. 1.Block diagram of a DWT based image fusion approach

2.2 Spatial Frequency (SF) Techniques

Spatial frequency (SF) is used to measure the overall activity level of an image [11] [16]. For anMNimage

F, with the gray value at pixel position O(m, n) denoted by F(m, n), its spatial frequency is defined as

2 2SF CF RF= + (1)

WhereRFand CFis row frequency and column frequency

( )2

1 2

1( , ) ( , 1)

M N

m n

RF F m n F m nMN = =

= (2)

( )2

1 2

1( , ) ( , 1)

N M

n m

CF F m n F m nMN = =

= (3)

The basic algorithm may be written as follows: (i) Decompose the source images into blocks of size MN;

(ii) Compute the spatial frequency for each block; (iii) Compare the spatial frequencies of two corresponding

blocksAi andBi, and construct the ithblockFi of the fused image as

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(4)

Where TH is the threshold and (iv) Verify and correct the fusion result in step (iii) with saliency checking. In

this case the aim of this process is to avoid isolated blocks the process is illustrated in figure 2.

Fig.2. Flow chart of the technique with SF as a parameter of clarity of images.

2.3 (NSCT+HIS) Techniques

The Non-Subsampled Contourlet Transform (NSCT) combines nonsubsampled pyramids and non-

subsampled directional filter bank (DFBs.). The pyramids provide multiscale decomposition and the DFBs

provide directional decomposition. This process is iterated repeatedly on the lowpass subband outputs of

nonsubsampled pyramids resulting in the non-subsampled contourlet transform. [6]. In this paper a fusion

method based on NSCT combining with HIS is briefly explained. This method is especially yields better results

for edges and contours than DWT technique. [7]. The core of the NSCT is the non-separable two-channel

nonsubsampled filter banks. It is easier and more flexible to design the needed filter banks that lead to a NSCT

with better frequency selectivity and regularity when compared to the counterlet transform. Based on mapping

approach and ladder structure fast implementation, the NSCT frame elements are regularity, symmetric and the

frame is close to a tight frame. The multiresolution decomposition of NSCT can be realized by nonsubsampled

pyramid (NSP), which can each the subband decomposition structure similar to Laplacian pyramid [23].

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A general scheme for the NSCT+ HIS fusion methods is shown in figure 3. This method can be performed in

the following steps:

Step 1: Perform HIS on the SPOT-XS image and get saturation, hue and intensity components;

Step 2: Apply histogram matching between the SPOT-PAN image and intensity to get a histogram-matched

PAN image.

Step 3: Employ NSCT on intensity and the histogram-matched SPOT-PAN image, and get low frequent

subband and high frequent subbands.

Step 4: Fuse the intensity and the histogram-matched SPOT-PAN image. The fused low frequent data employ

the low frequent coefficient of intensity. The fused high frequent coefficient adopt Maximum the region-

energy for every coefficient of each subband of SPOT-PAN image and intensity get by step 3.

Step 5: Apply NSCT reconstruction with new coefficient to obtain the new intensity.

Step 6: Perform the inverse HIS transform to obtain the fused image.

Fig 3. Image fusion flow chart of NSCT+HIS

2.4 PHLST Based Pan-sharpening Technique

In this paper a proposed fusion technique such as Polyharmonic Local Sine Transform is briefly explained.

A more detailed description of polyharmonic local transform may be found in [20]. AssumeI(x,y) is a spatial-

domain image. The main idea of PHLST is that an image I(x,y) can be divided into two parts: p which we call

the polyharmonic component ofI(x, y) and r which we call the residual ofI(x, y). P is a polynomial. R is a

geometric series. P represents base or trend or predictable part of the original image, whereas rstands for

texture or fluctuation or unpredictable part of the original image. This method coincides with the

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characteristic of human visual system. Human beings first focus on the noticeable parts of an image. The

noticeable parts are the fluctuation of an image. So, we extract texture which is in favor for subsequent

manipulation. I(x,y) is an rectangular image. Let Iinterr be the interior ofI(x,y),Ibou be the boundary ofI(x,y).

For simplicity, 0 x 1, 0 y 1. By solving polyharmonic equation (5) with given boundary conditions (6),

we can obtain the polyharmonic component

np = 0 in Iinter, n =1,2,.. (5)

kl I

kl p

_______=

_______on Ibou, l =0,m-1 (6)

nkl n

kl

where kl= 2l, the even order normal derivatives. We need not to consider the odd order normal derivatives

because this is automatically guaranteed [7]. The k0 = 0, which means that p = f(x, y) on the boundary. These

boundary values and normal derivatives ensure the function values and the normal derivatives of orders k1, ,

kn-1 ofp along the boundary to match those of the original imageI(x,y) over there.

For n = 1, we obtain the following Laplace equation with the Dirichlet boundary condition:

p= 0 in Iinter

p =I(x,y) on Ibou (7)

For n = 2, Eq. (4.1) becomes biharmonic equation with the mixed boundary condition:

2p =0 in Iinter

(8)

p =I(x,y) , 2p 2I on Ibou

______=

______

n2 n

2

We use the Laplace/Possion equation solver proposed by AVERBUCH et al. [2,3] to solve Eqs. (7) and (8).

The ABIV method provides more accurate solutions than those based on the finite difference (FD)[5,15]. There

are several versions of the ABIV method. We choose the simplest and most practical one to solve (7) that does

not need to estimate any derivative. It follows the recipe

p(x,y)=p1(x,y)+ {p2k(1)

gk(x,1-y)+ p2k(2)

gk(y,1- x) + p2k(3)

gk(x,y)+ p2k(4)

gk(y,x)} (9)

Wherep1(x,y) is a harmonic polynomial that matchesI(x,y) at the four corner points of the image. And its

simplest form is:

p1(x,y)=a3xy+a2x+a1y+a0 (10)

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Letp1(0,0)=I(0,0), p1(0,1)= I(0,1), p1(1,0)= I(1,0), p1(1,1)= I(1,1), we have

I(0,0) = a0

I(0,1) = a1+ a0

I(1,0) = a2+ a0

I(1,1) = a3+ a2+a1+ a0 (11)

By solving (11), we can easily obtain the parameters ai. The function gk(x,y) is defined as follows:

sinh( )( , ) sin( )

sinh( )k

ky x y kxg

k

= (12)

and2

( )k ip , i = 1, 2, 3, 4, are the k-th 1D Fourier sine coefficients of boundary functions I(x, 0) p1(x, 0),I(0,

y) p1(0,y),I(x, 1) p1(x, 1), andI(1,y) p1(1,y), respectively, where 0 x 1, 0 y 1. Subtractingp(x,y)

fromI(x,y), we obtain r(x,y). It can be written as:

1 1ij

( , ) sin( )sin( )si j

r x y i x j y

= (13)

where sij is the 2D Fourier sine coefficients ofr(x,y).

For a more precise approximation ofI(x,y), we can segment an imageI(x,y) into a set of rectangular blocks

(of different sizes possible) using the characteristic function. There is no overlap between adjacent patches, but

adjacent patches may share the boundaries. Then, we decompose each patch into two components: the polyharmonic componentp and the residual r, according to the foregoing method.

2.4.1The Image Pan-sharpening Scheme

Figure 4 shows a schematic diagram of the basic structure of the image fusion scheme proposed. For

simplicity, we make an assumption that there are just two source images,I1 andI2, and the fused image is F.

Fig. 4. Block diagram of PHLST, proposed method of fusion method

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2.4.2 Pan-sharpening or Fusion Rules

The objective of image pan-sharpening or fusion is to combine multiple source images of the same scene and

obtain a better quality image. The straightforward approach to image fusion is to compute the pixel by pixel

average of the input images. Although image averaging is a simple method, a major drawback is that it can

cause a decreased image contrast. To avoid a loss of detail, the basic strategy here is to fusep and rseparately to

construct a fused PHLST representation from the PHLST representations of the original data. P represents

base of the original image. We use the simplest method to compute p averaging.R represents the detail or

texture of the source image. The larger values in rcorrespond to the sharper brightness changes and thus to

the salient features in the image, such as edges, lines, and region boundaries. Therefore, a good integration rule

is to conserve r of the two source images at each point. So, we compute the composite r by the following

equation.

1 2( )

F I Ir r r = + (14)

where and represent rs, fromI1andI2, respectively, rFis the composite r.

Subsequently, a composite image is constructed by performing an inverse PHLST. Since the PHLST

provides spatial localization, the effect of the direct summing fusion rule can be illustrated in the following two

aspects. If the same object appears more distinctly (in other words, with better contrast) in image I1 than in

imageI2, after fusion the object in imageI1 and in imageI2 will be preserved with better contrast than inI2; in a

different scenario, suppose an object appears in the image I1, while being absent in image I2, after fusion theobject in imageI1 will be preserved and the contrast of the composite image will be enhanced.

3. Evaluation Parameters

Performance measures are essential to determine the possible benefits of fusion as well as to compare

results. Computational objective fusion metrics are an efficient alternative as they need no display equipment or

complex organization of an audience. Recent proliferation of image fusion algorithms has prompted the

development of reliable and objective ways of evaluating and comparing their performance for any given

application [9],[18], [5]. Five different measures are used to evaluate the performance of the algorithms under

investigation. These measures are: Difference quality index (QD), Objective measure (E), Mannons quality

index(QM), Mutual information(MI), Image quality index(Qp). Detailed equations of these measures can be

found in the literature. Objective measure is used here to measure the average objective edge information

between fused and reference images.

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4. Qualitative Analysis and Experimental Results

In this section, we verify the significant performance of the image fusion method proposed by comparing it

with four different image fusion methods using five image fusion metrics. The first algorithm is a Discrete

Wavelet Transform fusion algorithm [10], where the source images are decomposed using DWT, the

coefficients of the integrated image are computed by choosing the corresponding coefficients of input images

with the largest amplitude in high frequency bands and by averaging the coefficients of base band. Second

algorithm is Spatial Frequency measures overall activity level in an image. The third fusion algorithm is a Non-

Subsampled Contourlet Transform and Hue Intensity Saturation (NSCT+HIS) fusion algorithm [18],. The fourth

fusion Polyharmonic Local Sine Transform (PHLST) algorithm is a proposed fusion method gives more

quantitative information [12].

Satellite images consist of much information like river, agriculture land, urban area, forest area, sea, roads.

Extracting the information of both urban and rural features are important work. High resolution SPOT-PAN

image of 1024x1024 resolution and high spectral low resolution multispectral image of 256x256 resolution

images were used in this study for image sharpening. The four fusion techniques were applied to different cases.

Results were compared both qualitatively and quantitatively [5].

Images used in this study are from Kammam Dist. Hyderabad and its vicinity with both urban and rural

features. Two images are geometrically corrected using ground control points extracted from the maps and both

these images are fused together using conventional and non conventional methods of images fusion. Beforefusion of images it must be properly co-registered and resampled. There are several image sets are tested, but in

this paper only two data set image are shown. Figures 5 and 7 shows data set 1 and data set 2 images

respectively. Where a and b are the SPOT-XS and SPOT-PAN input images, c is the pan-sharpened or fused

image obtained by Spatial Frequency fusion technique, d is the fused image using Discrete Wavelet

Transformation method, e is the fused image using NSCT+HIS method, f is the fused image using PHLST

method.

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Fig. 5. Image Fusion of Data set 1 images (a-SPOT-XS image, b-SPOT-PAN image )

The quantitative assessments of fused images are listed in Table. 1. From this table, we can observe that the

performance of the proposed algorithm is best according to all metrics. The fused images are illustrated in Figs.

5c5f. .

Table. 1. Experimental results of the pan-sharpened images of Data Set 1

Fig. 6. Image Fusion of Data set 2 images (a-SPOT-XS image, b-SPOT-PAN image )

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Table 2.The experimental results of pan-sharpened images of Data Set 2

5. Conclusions

The aim of this study is to select the best image sharpening techniques by evaluating qualitative and

quantitative parameters. In this study four image sharpening algorithms have been applied to remote sensing

satellite images. They are based on the Spatial Frequency, DWT, NSCT+HIS and PHLST. Five meaningful

performance evaluation quality metrics based on Mutual information, Image quality index, Mannnons quality

index, Objective measure, Difference quality index were used to access the effectiveness of different image

fusion algorithms. Results of different dataset images have proved that entropy and mutual information is more

in PHLST fused image. Hence this method preserves large amount of information of both SPOT-XS and SPOT-

PAN images. It is hoped that the techniques can be extended for different bands of SPOT-XS multispectral

images and for fusion of multiple sensor images.

Reference

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[2] AVERBUCH A., Israeli M., Vozovoi L., A fast Poisson solver of arbitrary order accuracy in rectangular

regions, SIAM Journal on Scientific Computing 19(3), 1998, pp. 933952.

[3] Alparone, L., et al., 2004. A global quality measurement of pan-sharpened multispectral imagery. IEEE

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[5] Chen, H. and P. K. Varshney: 2007, A human perception inspired quality metric for image fusion based on

regional information.Image Fusion, 8, 193207.

[6] D. A. Bluemake et al. Detection of Hepatic Lesions in Candidates for Surgery: Comparision of

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[7] E. Braverman, M. Israeli, A. Averbuch, and L. Vozovoi. A fast 3D Poisson solver of arbitrary order

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[8] F. Maes, D. Vandermeulen, and P. Suetens, Medical image registration using mutual information,

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[9] Firooz Sadjadi, Comparative Image Fusion Analysis, Proceedings IEEE Computer Society Conference

on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03, 2005.

[10] Gonzalez and Woods, 2001, Digital Image Processing , Prentice Hall , 2nd edition.

[11] J.L. Johnson, M.L Padgett, PCNN models and applications, IEEE Trans. Neural Networks, Vol.10, pp.

480-498, 1999.

[12] L. Da Cunha, J. Zhou, and M. N. Do "The Nonsubsampled Contourlet Transform: Theory, Design, and

Applications," Image Processing, IEEE Transactions on, vol.15, no.10, pp.3089-3101, Oct. 2006

[13] Li, H., B.S. Manjunath, and S.K. Mitra, Multisensor image fusion using the wavelet transform, Graph.

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transform (PHLST) Optica Applicata, Vol. XXXIX, No. 2, 2009

[15] M. N. Do and M. Vetterli, The contourlet transform: An efficient directional multiresolution image

representation,IEEETrans. Image Proc., 2005, to appear

[16] M.A. Mohamed and B.M. El-Den2, Implementation of Image Fusion Techniques Using FPGA ,IJCSNS

International Journal of Computer Science and Network Security, VOL.10 No.5, May 2010

[17] N. Saito and J.-F. Remy. The polyharmonic local sine transform: A new tool for local image analysis and

synthesis without edge effect.Applied and ComputationalHarmonic Analysis, 20(1):4173, 2006.

[18] Nedeljko Cvejic, Artur oza, David Bull and Nishan Canagarajah A Similarity Metric for Assessment of

Image Fusion Algorithms International Journal of Information and Communication Engineering 2:3 2006

[19] O. Rockinger, Pixel-level fusion of image sequences using wavelet frames., in Proceedings in Image

Fusion and Shape Variability Techniques, Mardia, K. V., Gill, C. A., and Dryden, I. L., Ed., pp. 149154.Leeds University Press, Leeds, UK, 1996.

[20] Saito N., Remy J.-F., The polyharmonic local sine transform: A new tool for local image analysis and

synthesis without edge effect, Applied and Computational Harmonic Analysis 20(1), 2006, pp. 4173.

[21] Thomas Lehmann, Walter Oberschelp, Erich Pelikan and Rudolf Repges Medical Image processing

Publisher: Springer Verlag, 1997.

[22] V. Bara and J.-Y. Boire, A general framework for the fusion of anatomical and functional medical

images, NeuroImage, vol. 13, no. 3, pp. 410424, 2001.

[23] Y. Jiaa, M. Xiao Fusion of Pan and Multispectral Images Based on Contourlet Transform. July 57,

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Data Hiding for Medical Images: Issues and Challenges

J.Samuel Manoharan1

Dr.Kezi Selva Vijila2

A.Sathesh3

D.Narain Ponraj4

1,3,4Asst.Professor, ECE Dept, Karunya University, SouthIndia

2Professor, Christian College of Engineering, SouthIndia

[email protected] [email protected]

[email protected] [email protected]

Abstract

Data Hiding is an age old technique and has been gaining wide spread attention and significance with

increasing threat of insecured data transmission and reception and also data hacking. Data Hiding in medical

images are of great significance as they are multipurpose based like copyright protection, reduction of

bandwidth, telediagnosis etc., Medical Image Data hiding has to be carefully dealt with as there cannot be any

compromise on the accuracy of data hiding as it may result in wrong diagnosis and ultimately to severe

consequences. An extensive survey has been carried out in a pool of transform based techniques for medical

image hiding of patient information in an attempt to bring out an ideal choice of transform for appropriate

applications.

Keywords: Robustness, Fidelity, Embedding Capacity, Correlation Coefficient, Geometric Attacks
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1. Introduction

Data Hiding is an ancient technique and still widely used for concealing vital information inside another

image, audio or a video sequence. It may serve the purpose of content authenticity, copyright protection,

fraudulent and data manipulation detection etc., apart from the above mentioned data security applications, it

also serves as a medium of transmitting a secret data or a code inside the host image, audio or video for stego

applications and at the same time for bandwidth reduction applications. Due to its wide range of applicationsespecially in the fields of data security and communication, several tehcniques are being brought about to bring

out an optimal embedding and recovery procedures and algorithms with respect to many parameters. A basic

Data Hiding system for medical images is shown below in Figure 1 where the medical image which may be a

retinal image, MRI or Cranial Image is used as the cover or host image. The data which is usually the patient

information (Electronic Patient Information EPR) as well the diagnosis report is used as the watermark which

has to be embedded in the cover image.

Figure 1: A General Data Embedding and Retreival System

The cover image is transformed into frequency domain using any choice of transforms (T) selected using

certain criteria and a suitable embedding algorithm is used to embed the text inside the cover image. The

Embedded Image is then transmitted, received and subjected to the same transform as used in the transmitter

side and the patient information and the cover image are retrieved independently. Figure 2 illustrates the

different medical images that could be used as cover images where the first one is a retinal image and the latter acranial image and figure 3 illustrates the different watermarks that could be embedded inside the cover images.

The former is a doctors digital signature which could be embedded inside the cover image to serve the purpose

of copyright protection while the latter is a patient information or diagnosis report which could be embedded

inside the cover image to aid in tele diagnosis.

Figure 2: Medical Cover Images used

Cover

EPR

Embedde

d

Cover

EPR

T

T

T

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Figure 3: Watermarks used

Though the general system shown in Figure 1 may appear to be a simple mechanism,

the optimality of the embedding and retreival techniques used highly depend on the choice of various factors

which have been surveyed and deeply dealt with in the preceding sections.

2. Literature Review

Basically, any data hiding technique is broadly classified into Spatial domain technique based and

frequency domain technique based. Spatial Domain technique based data hiding involve manipulation of pixel

values while frequency domain techniques involve manipulation of the frequency coefficients. While each have

their own merits and demerits, almost all data hiding techniques revolve around certain key factors like

robustness, fidelity, embedding capacity, method of retrieval etc., Generally, after the data embedding process,

the embedded medical image is sent through a communication channel which might be a wired or wireless

media. In both the cases, there are always some components present which tend to degrade the watermarked

image. Commonly, these components are termed to be noise. Some predominant forms of noise are random

noise, Gaussian noise, Impulse noise, Speckle noise etc., when the embedded image along with the noise added

during the transmission time is subjected to retrieval at the receiver side, the extracted information or the

watermark does not exactly resemble the original watermark before embedding which means that the

embedding algorithm is not sufficiently strong enough to tackle or withstand the noise. Here, robustness is the

parameter used as a measure as to how much the embedded image withstands the attacks where attacks may

comprise intentional which may be cropping, rotating, filtering, compression etc., and unintentional which may

be noise. Fidelity is used to describe the degree of resemblance of extracted image to the original image. The

closer it resembles the better the embedding algorithm. It is usually measured in terms of a parameter known ascorrelation coefficient which lies in the interval of [0,1]. A value towards 1 indicates strong embedding

algorithm while values towards 0 indicate weakness in the embedding algorithms. Another important criteria is

the embedding capacity which is a measure of how much data could be packed inside a cover image without

causing any distortion to the embedded image.

Another method of classification is its division into robust, fragile and semi fragile. While

robust watermarks are able to withstand any external attacks, fragile watermarks get destroyed when exposed to

attacks. While robust watermarks could serve the purpose of secret message transmission, copyright protection,

fragile watermarks on the other hand serve the purpose of tamper detection. Another classification is based on

the method of extraction of watermarks at the receiver side. If the original image is needed at the receiver side

for extraction, it is known as a Non - Blind extraction process and if it does not require an original image for

extraction,. Such an extraction is called as Blind watermarking. The survey has been carried out taking into

account certain key factors like robustness, fidelity etc., in terms of PSNR and cross correlation coefficient.

2.1 Review of Spatial Domain Technqiues

The work in watermarking has commenced since the late 1980s with

Ingemar J. Cox et als [1] technique for Secure Spread Spectrum Watermarking for Multimedia which had the

property of tamper resistance followed by Jiri Friedrich [2], who utilized the complementary robustness

properties of both low frequency watermarks and spread spectrum generated watermarks to obtain a

watermarked image capable of surviving an extremely wide range of severe image distortions. Brian Chen et al

[3], was able to establish a tradeoff between the embedding capacity and quality of watermarked image through

his Quantization Index Modulation methods (QIM). With the advancements in technology, a fuzzy based

watermarking method was proposed by Pankaj Lande et al [4] enabling it to be applicable for ownership and

copyrights protection. Shaomin Zhu et al [5]proposed a scheme for tamper identification but a fragile system

showing poor tolerance towards high frequency attacks.Srdjan Stankovic et al [6] introduced a Radon basedapproach to incorporate translation invariance properties to the watermark. Following these developments, the

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research in watermarking areas has taken a turn, to exploit both spatial and frequency domain properties to

achieve the desire robustness and Image Quality. Frank et al [7] introduced a watermarking scheme to increase

the watermarking capacity and also to provide a double kind of protection to the watermarking through his

watermark splitting approach.Hsien et al [8]provided with a vector quantization based method to reduce the

storage and transmission time. Navneet Mandhani et al [9] introduced a code division multiple access scheme

for hiding data in monochrome images. Phen Lan et als [10] hierarchical digital watermarking used the method

of average intensity comparison and provided to be storage effective. A genetic codebook partition scheme wasproposed by Feng-Hsing et al [11] which proved have a good encoding time, good imperceptibility and strong

robustness towards attacks.A region of Interest based data hiding scheme was introduced by Amit Phadikar et

al [12] where the regions were selected using the quad tree decomposition method. But it was a non blind

approach during extraction and but was translation invariant. Ming-Chiang Hu proposed a blind, lossless and

two phase data embedding method [13] in the spatial domain which exhibited good tolerance towards various

attacks especially to Geometric attacks. A block based approach was proposed by Ju-Yuan Hsiao et al, [14]

where the image was divided into two areas with one being used for data embedding and other for auxillary

information embedding based on edge prediction. This method proved to increase the embedding capacity. A

further improvement in embedding capacity was shown by Shih-chieh Shie et al [15] by using Compressed VQ

Indices of Images. Xiang-Yang Wang et al [16], utilized the pseudo Zernike moments and Krawtchouk

moments to develop a robust image watermarking algorithm to specifically address geometric distortion. A

recent advancement in the spatial domain methods are the utilization of Luminance values of an image proposedby Jamal Hussein [17] which exhibited good tolerance towards JPEG compression and rotation attacks.

2.2 Review of Frequency Domain Techniques

Even though the above mentioned spatial domain techniques provide a good fidelity and

embedding capacity increase, the quality of image tends to degrade with increasing aggressive image processing

operations such as increased compression, scaling, filtering and increased levels of noise as spatial domain

techniques tend to operate on raw pixel values as such. Hence, in attempt to overcome the above said

drawbacks, there was a shift towards frequency domain techniques where the image pixels are converted into

frequency domain coefficients before embedding. Normally the transformation divides the image into high

frequency and low frequency components with mid band frequency components in between. This

decomposition or separation of frequencies also provides the user increased flexibility in choice of an ideal

embedding location depending on the application. If the watermarked image tends to be compressed during its

path, the watermarks could be embedded into the low or mid frequency components. On the other hand, if the

watermarked image tends to be passed through a channel prone to high levels on noise, then it is desirable to

embed in the low frequency components of the image. The heart of any frequency domain watermarking is the

transform used for decomposition and reconstruction. Many transforms exist such as the Fast Fourier Transform

(FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Contourlet Transform (CT),

Ridgelet Transform (RT), Shearlet Transform (ST) etc., Each transform is unique in the sense that DWT

provides increased levels of decomposition but cannot be used for image with sharp discontinuities whereas CT

can be utilized for smooth contoured images while RT could be used for fingerprint watermarking and

reconstruction. Hence, choice of appropriate transform for specific application is truly a challenge to obtain

optimal embedding results. Most of watermarking in Frequency domain utilize the robustness property and

presence of mid band coefficient characteristics of Discrete Cosine Transform (DCT).

After a DCT is performed on the image to get the coefficients, a pseudo random sequencecorresponding to the watermark may be embedded into the DCT coefficients as proposed by Mauro Barni et al

[18]. The resulting watermarked image proved to be robust towards aggressive image processing operations like

compression, medial filtering etc., A blind and translation invariant frequency domain watermarking approach

was put forward by Joseph et al [19], by utilizing the modulation of magnitude components in Fourier space. Yi

Ta et al [20] proposed a adjusted purpose watermarking technique where the user can vary a parameter known

as quantity factor so as to make the resulting watermarking technique to be fragile, semi fragile or robust

watermarks. Keeping in view the security parameter in the watermarking system, an Arnold iteration transform

was utilized by Rongrong et al [21] and the resulting watermark was found to be robust against some spatial

attacks like contrast changing, scribbling, low pass and high pass filtering and JPEG processing. A blind

approach was proposed by Dimitar et al [22] by use of a visual mask generated from the image content and Jieh

Ming et al [23] and Chin Chen Chang et al [24] proposing a semi blind approach by using singular value

decomposition method (SVD) with the watermarked image to be strongly resistant towards attacks and alsocould be used for tamper detection applications. A middle band coefficient exchange system was introduced by

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that Curvelet transforms [51] [59] and Contourlet [60] - [63] have a significant edge over the other

conventional techniques. Works of Chen et al, have shown that optimal coefficients in fingerprint images [64]

could be extracted using the complex ridgelet transforms. A further advancement of the Curvelet transforms is

the Shearlet transforms put forward by Wang Q. Lim and Sheng Yi et al, which are used to predict the behavior

of edges [65] - [66] towards multiscale representations. Contourlet Transforms exploited by Ibrahim et al,

Guiduo et al, Akhaee et al, Haohao et al and Minh Do et al put forward another transform for efficient

directional multiresolution representation through the Contourlet transform [67] which is capable of bringing outthe directional properties of each of the coefficients.

2.3 Attacks and Embedding Capacity

Once the choice of transform has been done suitable and compatible to the application, the

most important requirement following it is that the embedding algorithm should be stable. The stability is best

when the embedded image or content is able to withstand against Intentional and Unintentional attacks that

intervene in the communication channel. A review of Voloshynovskiy et al, Jonathan et al, Frank Hartung et al,

Claude Desset et al and Raphael et al s work [68] - [71] show a wide range of attacks predominant in the

transmission channel. Noise is a common obstacle present which is classified as an unintentional attack while

cropping, filtering, scaling, rotating [72] , compression are classified as intentional attacks as they are done on

the embedded image in an attempt to destroy the watermark or retrieve the information in some way or theother. Hence, it is necessary to test the stability of the embedding algorithm by subjecting the watermarked

image to all the above attacks and measuring its robustness. As mentioned in previous sections, normalized

cross correlation coefficient is mostly used to evaluate the robustness where a value towards 1 indicates a strong

embedding algorithm while values toward 0 indicate weakness in the algorithm. A set of images subjected to the

above attacks have been shown below in Figure 4.

Figure 4: Lena Images subjected to Noise, Rotation and Compression

Another critical criterion is the estimation of embedding capacity which is a measure of how much of

information could be packed or embedded inside the image without causing any visual degradation or affecting

the fidelity. Pierre Moulin and M Krvanc Mihcak [73] used a statistical model comprising of auto regression,

wavelet statistical models and block DCT while Fan Zhang exploited the relationship between Watermark

Capacity and Watermark average energy to achieve a tradeoff.

3. Prospects and Applications

With all the above aspects discussed so far, the area of Digital watermarking is proven to bean evergreen field as long as the security of data transmitted or received is an issue. Since Multimedia content

are always subject to hacking and attacks, and also increase in bandwidth requirements for communication, data

embedding along with encryption stands to be one of the solutions for protection, reduction of bandwidth, time

and storage spaces, and also detection of attacks. A recent extension of data hiding towards medical imaging

[74] has invited considerable interests from researchers all over due to its significant benefits ranging from

telemetry to telediagnosis. Rajendra Acharya et al [75] introduced a technique where in the electronic

information of the patient commonly termed as the Electronic Patient Information (EPR) which contains the

name and personal details of the patient is being embedded into the medical image thus saving storage space and

also providing a high class of electronic security and also preventing any attempt of tampering. Following this,

Jason Dowling et al, put forward a comparative analysis [76] between the DCT and DWT techniques for

medical image embedding of EPR and obtain critical inferences after exposing them to some common

prevailing attacks. The above thoughts could be extended for embedding the entire patient diagnosis reportavailable in the form of text inside the medical image thus reducing the storage space. The text could be

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compressed thus facilitating the requirement of lesser bandwidth for transmission. Once transmitted, the doctor

on the receiver side could extract the report, analyze, modify and re- embed and transmit the medical image

along with the report thus aiding in tele diagnosis and tele medicine. Since, no compromise can be made on the

fidelity criteria of the embedded medical image, since even the smallest change would bring about a wrong

diagnosis, all the above parameters play a very critical role to bring about a perfect precision with which the

report is embedded. Hence, appropriate transforms for medical images could be investigated and incorporated to

bring about an optimal embedding in medical images.

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A Survey on Ontology-Based Approach for Context Modelling and

Reasoning

R.Shyamala, R.Sunitha, G.Aghila

Department of Computer Science

School of Engineering and Technology

Pondicherry University, India.

[email protected]

[email protected]

Abstract

Computing becomes increasingly mobile and pervasive in todays scenario; this implies that the

applications should adapt to the dynamic environments. Context aware infrastructure requires an efficient

context model. There are several approaches for modelling context; object oriented models, key-value, markup

scheme, graphical, logic-based, spatial model and ontology based model. The most efficient approach isontology based model which is used to represent concepts and their relationships. In this paper we present a

comparative study of different ontology based models for context modelling and reasoning.

Keywords: Context modeling, Ontology, Pervasive computing.
mailto:[email protected]:[email protected]:[email protected]:[email protected]

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ambiguity. There are many types of ontology in literature which includes: Domain ontology, Generic ontology,

Metadata ontology, Representation ontology, Task ontology, Method ontology.Domain ontology is designed to

represent knowledge relevant to a certain domain type, e.g. medical, mechanical etc. Generic ontology is one

which has general concepts that can be applied to various technical domains. Representation ontology

formulates general representation entities without defining what should be represented e.g. Frame Ontology.

Task ontology provides specific terms for a particular task. Method ontology provides specific terms for a

particular problem solving method. Ontology-based model uses OWL-DL (Web Ontology Language Description Logic) to represent context information. OWL-DL is used to model a particular domain by defining

classes, individuals, characteristics of individuals (data type properties), and relations between individuals

(object properties) and it is supported by number of reasoning services. OWL-DL ontological models are used in

several architectures like Context Broker Architecture (CoBrA), Service Oriented Context Aware Middleware

(SOCAM) etc.

3.1 Advantages and Disadvantages

The most promising assets for context modelling is found in ontology-based models [1,4], because it

meets the six requirements dominant in pervasive environments: (1) distributed composition, (2) partial

validation, (3) richness and quality of information, (4) incompleteness and ambiguity, (5) level of formality, and

(6) applicability to existing environments. They clearly outperformed the key-value, markup scheme, graphical,

logic-based, and object oriented models in terms of expressiveness and interoperability. The big challenge

remains the right usage of the ontology tools and languages. If ontology consists of large number of individuals

then online execution of ontology reasoning poses scalability issues.

4. Context Modeling and Reasoning

Literature works for ontology based context modelling can be classified as works related to ontology for

context aware applications, architecture using ontologies to model context and domain specific ontologies as

shown in Figure 1.

4.1 Ontologies

CONON (CONtext ONtology) [5] is Web Ontology Language (OWL) encoded context ontology formodelling context in pervasive computing environments, and for supporting logic based context reasoning.

CONON context model is divided into upper ontology and specific ontology. Upper context ontology captures

general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a

hierarchical manner. Upper ontology consists of abstract classes describing a physical object including Person,

Activity, Computational Entity and Location, as well as a set of abstract sub-classes. Each entity is associated

with its attributes (owl: DatatypeProperty) and relations with other entities (owl:

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Figure 1: Classification of Ontology based approaches

ObjectProperty). Specific ontology is a collection of ontology set which define the details of general concepts

and their features in each sub-domain. A number of concrete sub-classes are defined to model specific context ina given environment (e.g., the abstract class IndoorSpace of home domain is classified into four sub-classes

Building, Room, Corridorand Entry). Logic reasoning is used in order to perform consistency checks and to

deduce high-level context knowledge from explicitly given low-level context information. There are two distinct

ways to perform reasoning with CONON: Ontology reasoning by description-logic rules which are integrated in

the OWL semantics, e.g. for transitive and inverse relations. User-defined reasoning is done by creating user

rules using first-order-logic. For e.g. to find whether the user is sleeping or not, the rule is (? u locatedIn

Bedroom) ^ (Bedroom lightLevel LOW) ^ (Bedroom drapeStatus CLOSED) => (? u situation SLEEPING).

Similar to CONON is the SOUPA [6] ontology Standard Ontology for Ubiquitous and Pervasive

Applications, it is designed using the Web Ontology Language (OWL) to model and support pervasive

computing applications, and includes modular component vocabularies to represent intelligent agents with

associated beliefs, desires, and intentions, time, space, events, user profiles, actions, and policies for security and

privacy. SOUPA consists of two distinctive but related set of ontologies: SOUPA Core SOUPA Extension. The

set of the SOUPA Core ontologies attempts to define generic vocabularies for expressing concepts that are

associated with person, agent, belief-desire-intention (BDI), action, policy, time, space and event that are

universal for different pervasive computing applications. The set of SOUPA Extension ontologies, extended

from the core ontologies, define additional vocabularies for supporting specific types of applications and

provide examples for the future ontology extensions. The SOUPA Extension ontologies are defined with two

purposes: (i) define an extended set of vocabularies for supporting specific types of pervasive application

domains, and (ii) demonstrate how to define new ontologies by extending the SOUPA Core ontologies.

An ontology created by merging publicly available ontological content into a single, comprehensive,

and cohesive structure is called the SUMO [17, 18] (Suggested Upper Merged Ontology). SUMO is a large,

free, upper ontology in first order logic. SUMO provides definitions for general-purpose terms and acts as a

foundation for more specific domain ontologies. It is increasingly being used as a resource in natural language

understanding research. SUMO hasbeen used as thebasis for an interchange language, to resolve the meaning

of terms in web search, to express the deep semantics of restricted natural language sentences, and as a

repository of pragmatics and world knowledge to support question answering. The language used in SUMO to

Ontology Architecture DomainSpecific

CALA-ONT

GCOM

COBRA

CROCOON

SUMO

SOUPA

COBRA-

CONO

CROCO

CASP

OWL&SWRL

CALA

Home Healthcare

Ontology based approaches

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represent knowledge is a version of KIF (Knowledge Interchange Format).

CoBrA-ONT [7] is an ontology model developed with the help of OWL and other building tools for

CoBrA. CoBrA-ONT is a collection of OWL ontologies for context-aware systems. CoBrA-ONT models the

basic concepts like people, places, agent etc in the environment. CoBrA-ONT consists of four sub-ontologies:

Place, Agent, Agents Location and Agents Activity. In Place ontology the central concept is Place with

attributes such as latitude and longitude to describe its location and related concepts like Atomic place and

Compound place. In Agent ontology the central concept is Agent with specializations like person, software

agentand has attributes like name, email address and assigned roles like speaker, audience. Agents Location

ontology adds the locatedIn relation to the agent concept to capture the agents location i.e. in atomic place or

compound place. From the locatedIn property, two sub properties are derived locatedInAtomicPlace and

locatedInCompoundPlace which has sub properties like locatedInRoom, locatedInRestroom, locatedInBuilding,

and locatedInCampus etc. Agents Activity ontology describes the events happen at places and events which are

attended by agents. Current event is represented using the class EventHappeningNow. Every event has a

schedule; PresentationSchedule is a class for presentation event with properties like startTime, endTime,

location.

The next ontology is the CroCoON [9] (Cross-application Context Ontology) which is a generic

ontology based context model developed for the architecture CroCo. CroCo is an ontology-based context

management service that allows for cross-application context gathering, modelling, and provision. CroCoON

allows for the integration of domain-specific knowledge to facilitate the usage of CroCo in diverse applications.CroCoON consist of upper ontology and several sub-ontologies. Upper ontology is used to extend the model

and to integrate domain specific knowledge for diverse applications. These extensions are called Ontology

Profiles. Sub-ontology models several aspects of context like place, person, activity, time, device, software,

space, documents etc. These concepts are reused from ontologies like SOUPA, PROTON, and W3C Time

Ontology. CroCoON uses OWL and RDF for representing the context and it uses Jena Semantic Web

Framework which provides Jena rules and rule reasoner for reasoning purpose.

An ontology context model developed for learning environments is called CALA-ONT [10] (Context

Aware Learning ArchitectureONTology) which is designed to use within the CALA - Context Aware Learning

Architecture, CALA is developed to support a context aware learning service that employs knowledge and

reasoning of context and share this information in intelligent learning services in ubiquitous learning

environments. In CALA-ONT context information is represented in first order predicate logic and context model

is defined in OWL-DL. CALA-ONT consists of four top-level classes and sub-classes, and twelve mainproperties which describe the relations between individuals in top level class and its sub properties. XML, RDF

Schema and OWL are a part of CALA-ONT model. For an intelligent school spaces, the four top-level classes

are Person, Place, Computational Entity and Activity. Each top-level class has its sub-classes. For e.g. the class

Person may have sub-classes like Student, Teacher, Office staff etc. The twelve main object properties related to

top-level class are presentIn, hasUsage, hasComEntity, isUsedBy etc. Each property represents the binary

relationship linking an individual in the domain to an individual in the range. There are two ways to perform

reasoning with CALA-ONT: Ontology reasoning using first order predicate logic of the class relationship,

property characteristics, and limitations. Ontology reasoning of the context reasoning engine is expressed in first

order predicate logic for a transitive relation is subClassOf (?A rdfs:subClassOf ?B), (?B rdfs:subClassOf ?C) -

> (?A rdfs:subClassOf ?C). Rule-based reasoning is one where new context is reasoned based on information

about various other contexts using Boolean algebra. The AND operator is used to connect information of two

contexts and a new context is reasoned.

4.2 Architectures using ontology for context modelling

In literature there are number of distributed systems developed to support pervasive computing like

Intelligent rooms [14], Cooltown [15] and Context Toolkit [16] etc. These architectures dont support

knowledge sharing and context reasoning because they do not have common ontologies. CoBrA [7] - Context

Broker Architecture addresses the drawbacks like support for knowledge sharing and context reasoning by using

common ontology defined using Semantic Web languages. CoBrA is agent based architecture for context ware

computing in intelligent spaces. Physical spaces (e.g. living rooms, meeting rooms) embedded with intelligent

systems that provide computing services to users are called intelligent spaces. CoBrA-ONT an ontology model

is developed for use within CoBrA architecture (discussed in 4.1). Context information is acquired from agent,

sensors and then it is integrated into a coherent model and shared among the devices and agents. CoBrA

Architecture consists of three components: a context broker, context aware agents, and context aware devices.

Agents and devices can contact the context broker and exchange information by the FIPA Agent Communication

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Language. Context Broker is an important component in CoBrA which maintains and manages the shared model

of context. Context Broker acquires context from two sources (1) external sources like information servers,

semantic web services, database, (2) intelligent spaces (data from sensors). Context Broker has the responsibility

of (i) acquiring contexts from heterogeneous information sources and maintaining the consistency of the overall

context knowledge through reasoning, (ii) helping distributed agents to share context knowledge through the use

of ontologies, agent communication languages and protocols, and (iii) protecting the privacy of users by

establishing and enforcing user defined policies while sharing sensitive personal information with agents in thecommunity. Context reasoning is done using logic inference engine. The problem of the broker agent being a

bottle-neck in distributed systems is solved by a so-called broker-federation, which is a network of context

broker agents.

GCoM [8] is a generic context management model that supports collaborative reasoning by providing

structure for contexts, rules and their semantics in a multi-domain pervasive context-aware application. In

GCoM context is represented using upper level and lower level ontology and rules are used for reasoning

purpose which are represented using ontology compatible rule language. Context is divided into semi-

independent components i.e. static and dynamic context instance, which makes GCoM dynamic and reusable in

pervasive computing environment.GCoM model consists of three components: Context Ontology, Context Data

and Context related Rules. Ontology represents semantics, concepts and relationships in the context data.

Ontology component is formed by integrating the Generic ontology and Domain specific ontology. This

ontology is then stored in a Context-Onto repository. Context data represents instances of context that exist inthe form of profiled data or in the form of context instances obtained from the sensors. Sensed context is to be

communicated to GCoM using RDF/XML triple representation format. Sensed context is stored in a repository

and then converted into ontologies. Rules represent certain axioms that are used by context-aware systems to

reason out and derive decisions. These rules have two sources; rules that are explicitly given by the users

through the user interface and rules that are implicitly learnt by the system itself. Semantic mapping and

delivery module is responsible for mapping and conversion between rules and context-onto repository so as to

deliver a data that is ready for reasoning using the Jena generic rule language.

Context management services for heterogeneous environments should support generic and flexible

mechanisms for cross application context handling, reasoning, security and privacy. One such service is the

CroCo [9] an ontology-based, cross-application context service which allows cross-application context

gathering and modelling for heterogeneous and networked environments. A generic, ontology-based context

model, called CroCoON (Cross-application Context Ontology), is developed for the use within CroCo(discussed in 4.1). CroCo allows arbitrary context providers to submit, and context consumers to request context

data via specific service interfaces, it follows the Blackboard model, which promotes a data-centric approach

enabling easy addition of new context providers and consumers. CroCo consists of three modules: Context

management module, Consistency checking and reasoning module, and Context data update and provision

module. Context management module consists of three layers: Context History (CH) consists of history of

updates to the context model, Consistent Context (CC) represents the currently valid, consistent contextual data,

and Inferred Knowledge (IK) layer consists of all derived information, i.e. reasoned from the current context

information. Consistency checking and reasoning module consists of a Consistency Manager (CM) and

Reasoning manager (RM). Consistency manager is triggered whenever new context is added and consistency

enforces within the manager is responsible for consistency checks and conflict detection. Reasoning manager is

similar to Consistency manager which invokes the reasoners to start the reasoning process when relevant data

changes. Context data update and provision moduleprovides two services: Update service and Query service.

Update service enables data update and changes in the model. Query service enables to retrieve context

information from CroCo. Privacy Enforcers ensure security to data. There are three additional mechanisms in

CroCo which enables efficient consistency check and reasoning: Confidence value, Variability and Reputation.

Each context provider is given a consistency value indicating the accuracy and reliability. A users name may be

static while his location may be dynamic; this is called Variability which is stored in Aging Knowledge Base.

Each context provider is given a reputation depending on the data quality, if the provider sends inconsistent data

continuously its Reputation decreases resulting in lower consistency value.

Ubiquitous computing leads to ubiquitous learning environments, where various embedded

computational devices will be pervasive and interoperate to support learning, and introduces context-aware

learning service that employs knowledge and reasoning to understand the local context and share this

information in support of intelligent learning services. CALA [10] (Context Aware Learning Architecture) a

context-aware manager based architecture developed to support a context aware learning service for ubiquitous

learning environments like intelligent school spaces. CALA-ONT - Context Aware Learning Architecture ONTology is an ontology context model designed to use within the CALA architecture (discussed in 4.1).

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CALA architecture consists of five components: Personal agent, Computing entity, Physical sensor, Activ

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