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Volume 8 Issue 4 April - June 2014

Research Paper

OpenC: Extending C with Computational Reflection

Songqing Yue*

Department of Mathematics and Computer Science, University of Central Missouri, Warrensburg, USA.

Yue ,S.(2014). OpenC: Extending C with Computational Reflection. i-manager’s Journal on Software Engineering, 8(4), 1-8. https://doi.org/10.26634/jse.8.4.3046

Abstract

Computational Reflection has shown much promise for improving the quality of software by providing programming language techniques to address issues of modularity, reusability, maintainability, and extensibility. The Meta-Object Protocol (MOP) is a powerful tool to provide the capability of computational reflection by means of object oriented and reflective techniques to organize a meta-level architecture. It provides a set of interfaces for developers to access the underlying implementation of programs in order to automate the source-to-source program translations. In this paper, the author describes how to bring the power of computational reflection to C through a MOP, named OpenC, which offers a framework to build arbitrary source-to-source program transformation libraries for large software systems written in C. The design focus of OpenC is to automate program transformations in a straightforward and transparent way through techniques of code generation, so that client users only need to add a simple annotation to their code to be manipulated, while removing the need to know the details on how the transformations are performed. The paper provides a general motivation for using reflection and explains briefly the design and implementation of the OpenC framework. In addition, this paper will show an example, how OpenC can be used to build a simple profiling library that can be employed to analyse the distribution of execution time among all functions in a project by recording the amount of time spent on executing each function.

References

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[6]. Maes, P. (1988). Computational reflection. The Knowledge Engineering Review, Vol.3(01), pp.1-19.

[7]. Kiczales, G. (1991). The art of the metaobject protocol. MIT press.

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[14]. Scott, M. L. (2000). Programming language pragmatics. Morgan Kaufmann.

[15]. Myers, G. J., Sandler, C., & Badgett, T. (2011). The art of software testing. John Wiley & Sons.

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Research Paper

Detecting Hidden Data Using Higher Order Empirical Transition Matrix

Swagota Bera* , Monisha Sharma**

* Associate Professor, Department of Electronics and Telecommunication, Shri Shankaracharya Institute of Engineering & Technology, Durg.

** Professor, Department of Electronics and Telecommunication, Shri Shankaracharya College of Engineering and Technology, Bhilai.

Bera,S., and Sharma,M. (2014). Detecting Hidden Data Using Higher Order Empirical Transition Matrix. i-manager’s Journal on Software Engineering, 8(4), 9-16. https://doi.org/10.26634/jse.8.4.3047

Abstract

Steganalysis is the art of detecting the presence of hidden data in any common data. Universal steganalysis is general class of steganalysis techniques which can be implemented with any steganographic embedding algorithm, even an unknown algorithm. In this paper, the detection technique is based on the fact that there occurs variation in the feature vectors in an image before and after hiding. The whole image is divided in blocks of 8x8. There exists interdependency among the pixel values within the image blocks known as intrablock dependency. The statistical feature is calculated on the basis of the transition in the pixel value. The features used here is the transition probability matrix calculated by using the markov statistical process. If the transition probability matrix is found out by considering the transition in the pixel value of second pixel w.r.t to first one and so on, then it is known as one step transition probability matrix. If the transition probability matrix is found out by considering the transition in the pixel value of third pixel w.r.t to first and second one simultaneously and so on then it is known as two step transition probability matrix. Further the values of the quantized DCT pixel is restricted in -4 to 4 values which is known as thresholding. This way a feature set is calculated with optimum dimension for the classification between the cover image and the stego image.

References

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[8]. Bera, S. & Sharma, M. (2007). Survey on Steganographic Techniques & Steganalysis. (2007, 28-29 Oct). National Conference in Advances in electronics & Telecommunication Technologia. vision- 2020.

[9]. Bera, S. & Sharma, M. Steganalysis of Real Time Image by Statistical Attacks.(2010). International Journal of Engineering Science and Technology, Vol. 2(9), pp.4397- 4406.

[10]. Monisha Sharma and Swagota Bera (2011). Steganalysis of the Image by Visual & Statistical Attacks, imanager’s Journal on Electronics Engineering. 1(2) Dec- Feb, 2011 Print ISSN: 2229-7286, E-ISSN: 2249-0760, pp,48-55.

[11]. Bera, S. & Sharma, M. (2012). A Review on blind still image Steganalysis Techniques Using Features Extraction and Pattern Classification Method. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), ISSN:2231-3117(Online);2231- 3605(print). Vol.2(3), pp.117-135.

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Research Paper

Clustered Micro Calcification Detection Based On the Texture Feature Extraction Using Adaptive Bilateral Filter Of Mathematical Morphology

B. Sridhar* , K.V.V.S. Reddy, A.M. Prasad*

* Faculty, Department of ECE, Lendl Institute of Engineering and Technology, Vizianagaram, India.

Professor, Department of ECE, Andhra University, Visakhapatnam, India.

* Professor, Department of ECE, JNT University, Kakinada, India.

Sridhar.B., Reddy.K.V.V.S., and Prasad.A.M. (2014). Clustered Micro Calcification Detection Based On the Texture Feature Extraction Using Adaptive Bilateral Filter of Mathematical Morphology. i-manager’s Journal on Software Engineering, 8(4), 17-24. https://doi.org/10.26634/jse.8.4.3048

Abstract

A morphological adaptive bilateral filter based scheme has been proposed for textures segmentation of small field digital mammograms. Mathematical morphology offers flexible operations for extraction of micro calcifications. Unlike the traditional texture filters, adaptive bilateral filter is applied to remove the noise and improve the quality of the selection of the different texture regions. The proposed method uses an adaptive threshold selection, which can remove unwanted textures region from image. The texture regions extract the features of micro calcification and noise boundaries are smoothing again by adaptive bilateral filter. Segmentation results are displayed by inclusion of textures with input image. The proposed method is experimented on 10 micro calcification images and the quality of the method is evaluated.

References

[1]. N. Riyahi Alam, F. Younesi, M. S. Riyahi Alam (2009). 'Computer-Aided Mass Detection on Digitized Mammograms using a Novel Hybrid Segmentation System', International Journal of Biology and Biomedical Engineering, Volume 3, Issue 4, 51.

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Research Paper

Discount Analysis for Decision Indication in Retail Mart

K. Lavanya* , J. Shaik Mohamed, S. Ramakrishna*

* MCA Department, SITAMS, Chittoor, A.P.

Assistant Professor, MCA Department, SITAMS, Chittoor, A.P.

* Professor, Department of Computer Science, SVUCCMCS, S.V. University, Tirupati, A.P.

Lavanya.K.,, Mohamed,S.J., and Ramakrishna.S. (2014). Discount Analysis for Decision Indication in Retail Mart. i-manager’s Journal on Software Engineering, 8(4), 25-32. https://doi.org/10.26634/jse.8.4.3049

Abstract

Businesses use discount pricing strategy to sell the products in high quantities. With this strategy, it is important to cut costs and stay competitive. Large retailers are able to demand price discounts and make a discount pricing strategy effective. The shopping mart in general gives different kinds of discounts such as one plus one offer, cost reduction, bulk discount and seasonal offers on near expiry products and low sales products. In this study, the Discount Offering algorithm is proposed and implemented to help the retailer to make effective decision by listing out the products with its discount details. This algorithm automatically calculates the combination of products for selling one plus one offer, cost reduction techniques, bulk discount offer and seasonal offer by comparing cost, expiry date and lowest sales of all the products in the retail mart. The main advantage of this approach is that it attracts attention and boost up sales for retailer.

References

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Review Paper

Guidelines to Improve Software Engineering Process Using Artificial Intelligence Techniques

Hema Shankari* , R. Thirumalai Selvi**

Assistant Professor, Department of Computer Science, Women's Christian College, Chennai.

Assistant Professor, Department of Computer Science, Govt. Arts College (Men) (Autonomous), Chennai.

Shankari,H.K., and Thirumalaiselvi.R. (2014). Guidelines to Improve Software Engineering Process Using Artificial Intelligence Techniques. i-manager’s Journal on Software Engineering, 8(4),33-43. https://doi.org/10.26634/jse.8.4.3050

Abstract

Software Engineering and Artificial Intelligence are the two important fields of the Computer Science. Artificial Intelligence is about making machines intelligent, while Software Engineering is knowledge intensive activity, requiring extensive knowledge of the application domain and to target on software itself. This study intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The goal of this paper is to give some guidelines to use the artificial intelligence techniques that can be applied in solving problems associated with software engineering processes. This paper also find out the exact AI technique is likely to be fruitful for particular software development process.

References

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i-manager's Journal on Software Engineering (JSE)

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Volume 8 Issue 4 April - June 2014

OpenC: Extending C with Computational Reflection

Songqing Yue*

Department of Mathematics and Computer Science, University of Central Missouri, Warrensburg, USA.

Yue ,S.(2014). OpenC: Extending C with Computational Reflection. i-manager’s Journal on Software Engineering, 8(4), 1-8. https://doi.org/10.26634/jse.8.4.3046

Abstract

References

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Detecting Hidden Data Using Higher Order Empirical Transition Matrix

Swagota Bera* , Monisha Sharma**

* Associate Professor, Department of Electronics and Telecommunication, Shri Shankaracharya Institute of Engineering & Technology, Durg. ** Professor, Department of Electronics and Telecommunication, Shri Shankaracharya College of Engineering and Technology, Bhilai.

Bera,S., and Sharma,M. (2014). Detecting Hidden Data Using Higher Order Empirical Transition Matrix. i-manager’s Journal on Software Engineering, 8(4), 9-16. https://doi.org/10.26634/jse.8.4.3047

Abstract

References

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Clustered Micro Calcification Detection Based On the Texture Feature Extraction Using Adaptive Bilateral Filter Of Mathematical Morphology

B. Sridhar* , K.V.V.S. Reddy**, A.M. Prasad***

* Faculty, Department of ECE, Lendl Institute of Engineering and Technology, Vizianagaram, India. ** Professor, Department of ECE, Andhra University, Visakhapatnam, India. *** Professor, Department of ECE, JNT University, Kakinada, India.

Sridhar.B., Reddy.K.V.V.S., and Prasad.A.M. (2014). Clustered Micro Calcification Detection Based On the Texture Feature Extraction Using Adaptive Bilateral Filter of Mathematical Morphology. i-manager’s Journal on Software Engineering, 8(4), 17-24. https://doi.org/10.26634/jse.8.4.3048

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Discount Analysis for Decision Indication in Retail Mart

K. Lavanya* , J. Shaik Mohamed**, S. Ramakrishna***

* MCA Department, SITAMS, Chittoor, A.P. **Assistant Professor, MCA Department, SITAMS, Chittoor, A.P. *** Professor, Department of Computer Science, SVUCCMCS, S.V. University, Tirupati, A.P.

Lavanya.K.,, Mohamed,S.J., and Ramakrishna.S. (2014). Discount Analysis for Decision Indication in Retail Mart. i-manager’s Journal on Software Engineering, 8(4), 25-32. https://doi.org/10.26634/jse.8.4.3049

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Guidelines to Improve Software Engineering Process Using Artificial Intelligence Techniques

Hema Shankari* , R. Thirumalai Selvi**

Assistant Professor, Department of Computer Science, Women's Christian College, Chennai. Assistant Professor, Department of Computer Science, Govt. Arts College (Men) (Autonomous), Chennai.

Shankari,H.K., and Thirumalaiselvi.R. (2014). Guidelines to Improve Software Engineering Process Using Artificial Intelligence Techniques. i-manager’s Journal on Software Engineering, 8(4),33-43. https://doi.org/10.26634/jse.8.4.3050

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* Associate Professor, Department of Electronics and Telecommunication, Shri Shankaracharya Institute of Engineering & Technology, Durg.

** Professor, Department of Electronics and Telecommunication, Shri Shankaracharya College of Engineering and Technology, Bhilai.

B. Sridhar* , K.V.V.S. Reddy, A.M. Prasad*

* Faculty, Department of ECE, Lendl Institute of Engineering and Technology, Vizianagaram, India.

Professor, Department of ECE, Andhra University, Visakhapatnam, India.

* Professor, Department of ECE, JNT University, Kakinada, India.

K. Lavanya* , J. Shaik Mohamed, S. Ramakrishna*

* MCA Department, SITAMS, Chittoor, A.P.

Assistant Professor, MCA Department, SITAMS, Chittoor, A.P.

* Professor, Department of Computer Science, SVUCCMCS, S.V. University, Tirupati, A.P.

Assistant Professor, Department of Computer Science, Women's Christian College, Chennai.

Assistant Professor, Department of Computer Science, Govt. Arts College (Men) (Autonomous), Chennai.