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Volume-1 Issue-9: Published on August 20, 2013
34
Volume-1 Issue-9: Published on August 20, 2013
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S. No

Volume-1 Issue-9, August 2013, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

N. A. Abdul Latiff, N. I. A Ishak, M. H Yusoff

Paper Title:

Performance Analysis on Peak-to-Average Power Ratio (PAPR) Reduction Techniques in Orthogonal Frequency Division Multiplexing (OFDM) Systems

Abstract: A fundamental wireless transmission system, Orthogonal Frequency Division Multiplexing (OFDM) is widely used recently in wireless communication. However, practical implementation of OFDM introduced a major drawback known as peak-to-average power ratio (PAPR). This paper focused on the most three preferable techniques for reducing high PAPR. In general, Partial Transmit Sequence (PTS), Selective Mapping (SLM) and Clipping and Filtering can improve the PAPR statistic of an OFDM system using 16QAM and 64QAM modulation format regardless the number of subcarriers. Simulation results demonstrate that the techniques can efficiently reduce the PAPR performance based on the number of subcarriers and modulation format that being used in the system.

Keywords:
Partial Transmit Sequence (PTS); Selective Mapping (SLM); Clipping Filtering; Peak-to-Average Power Ratio (PAPR).


References:

1.        Tian Ya-Fei, Ding Rong-hua, Yao Xiao-an, and Tang Hai-wei, "PAPR Reduction of OFDM Signals using Modified Partial Transmit Sequences," 2009.
2.        Verma Seema, Sharma Pawan, Ahuja Shivani, and Hajela Pallavi, "Partial Transmit Sequence with Convolutional Codes for Reducing the PAPR of the OFDM Signal," pp. 70-73, 2011.

3.        Ghassemi,Student Member, IEE Abolfazl and Gulliver, Senior Member, IEEE T.Aoran, "PAPR Reduction of OFDM using PTS and Error-Correcting Code Subblocking," IEEE Transaction on Wireless Communications, vol. 9, no. 3, pp. 980-989, March 2010.

4.        Muller Stefan H. and Huber Johannes B., "OFDM with Reduced Peak-to-Average Power Ratio by optimum Combination of Partial Transmit Sequences," 1997.

5.        Robert W. Bauml, Robert F.H. Fisher, and Johannes B. Huber, "Reducing the Peak-to-average Power Ratio of Multicarrier Modulation by Selected Mapping," IEE Electronics Letters, vol. 32, no. 22, pp. 2056-2060, Oct 1996.

6.        Arun K. Gurung, Fawaz S. Al-Qahtani, Amin Z. Sadik, and Zahir M. Hussain, "Power Savings Analysis of Clipping and Filtering Method in OFDM Systems," ATNAC 2008, pp. 204-208, 2008.

7.        V. Vijayarangan and DR (MRS) R. Sukanesh, "An Overview of Techniques for Reducing Peak to Average Power Ratio and Its Selection Criteria for Orthogonal Frequency Division Multiplexing Radio System," Journal of Theoretical and Applied Information Technology, pp. 25-36, 2005. [Online]. www.jatit.org

8.        Pawan Sharma and Seema Verma, "IJCSI International Journal of Computer Science Issues," Performance Analysis of Peak-to-Average power Ratio Reduction Techniques for Wireless Communication Using OFDM Signals, vol. 7, no. 6, pp. 261-267, November 2010. [Online]. www.IJCSI.org

9.        Mohammad Hossein Ghamat and A. Zolghadrasli, "Iranian Journal of Electrical and Computer Engineering," An overview of PAPR Reduction Techniques for Multicarrier Transmission and Propose of New Techniques for PAPR Reduction, vol. 7, no. 2, pp. 115-120, 2008.

10.     Lee Jae Hong and Han Seung Hee, "Overview of Peak-to-Average Power Ratio Reduction Techniques for Multicarrier Transmission," IEEE Wireless Communications, pp. 56-65, April 2005.

11.     Aeizaal Azman Abdul Wahab and Mohd Fadzil Ain, "Journal of Engineering and Technology Research," Peak to average power ratio reduction in OFDM systems using selected mapping and statistical redistribution, vol. 2, no. 10, pp. 189-194, October 2010. [Online]. http://www.academicjournals.org/JETR


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2.

Authors:

Amarjeet Kaur, M. Sasikumar, Shikha Nema, Sanjay Pawar

Paper Title:

Algorithm for Automatic Evaluation of Single Sentence Descriptive Answer

Abstract: Automation of descriptive answer evaluation process would be helpful for various universities and academic institution to efficiently handle the assessment of exam answer sheets of learners/students. Our objective is to design an algorithm for the automatic evaluation of single sentence descriptive answer. The paper presents an approach to check the degree of learning of the student/learner, by evaluating their descriptive exam answer sheets. By representing the descriptive answer in the form of graph and comparing it with standard answer are the key steps in our approach.

Keywords: Descriptive answer, graphical representation, similarity measures, subjective evaluation, word Net.


References:

1.        Hanxiao Shi, Guodong Zhou and Peide Qian (2010), ”An Attribute-based Sentiment Analysis System”, Information Technology Journal,  pp 1607-1614
2.        Papri Chakraborty (2012), ”Developing an Intelligent Tutoring System for Assessing Students’ Cognition and Evaluating Descriptive Type Answer”, IJMER, pp 985-990

3.        Mita K. Dalal, Mukesh A. Zave (2011), “Automatic Text Classification: A Technical Review”, International Journal of Computer Applications, pp.37-40

4.        Asmita Dhokrati, Gite H.R.2, Mahender C.N.3 (2012),”Computation Linguistic: Online Subjective Examination Modeling”, Advances in Computational Research, pp-31-33.

5.        Wael H. Gomaa,Aly A. Fahmy (2013), “A survey of text similarity aproaches”,International Journal of Computer Applications.

6.        Davy Temperley, Daniel Sleator, John Lafferty,”Link Grammar”, Available: http://www.link.cs.cmu.edu/link/submit-sentence-4.html

7.        Meghan Kambli,“Report on Online Examination System”, Available:http://www.cdacmumbai.in/design/corporate_site/override/pdf-doc/meghareport.pdf

8.        Jawaharlal Nehru Technical University, Hyderabad, “Examination and Evaluation System”


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3.

Authors:

Pradeep Kumar Sahu, Rajesh Kumar 

Paper Title:

Demand Forecasting For Sales of Milk Product (Paneer) In Chhattisgarh

Abstract: This paper examines forecasting method for sales of milk product (paneer) in Chhattisgarh. Forecasting method assessed includes single moving average (SMA), double moving average method (DMA), single exponential smoothing (SES), semi average method (SAM) and Naϊve Method. The mean forecast error (MFE), mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE) is used to measure the accuracy of forecasting methods. Based on accuracy, single exponential smoothing (SES) with α=0.3 produces the most accurate forecasting. Method used in this paper is readily transferable to other milk product data sets with weekly demand figures.

Keywords:
Semi average method (SAM) and Naϊve Method.


References:

1.        Heshamk k. Alfares and Mohammad Nazeerudin (2002) “Electric load forecasting: literature survey and classification method,” International journal of system science: volume 33, Number 1, pp 23-24.
2.        Cristiano Cacatto, Patricia Belfiore and Jose Geraldo Vidal Vieira (2012) “Forecasting Practices in Brazilian food Industry,” Journal of logistics management: Vol. 1, No. 4, pp 24-36.

3.        Ryu, Kisang and Sanchez, Alfonso (2003) “The Evaluation of Forecasting Method at an Institutional Foodservice Dining Facility,” Journal of Hospitality Financial Management: Vol. 11: Iss.1, Article 4.

4.        Rachel J.C. Chen, Peter Bloomfield and John S. Fu (2003) “An Evaluation of Alternative Forecasting Method to Recreation Visitation,” Journal of Leisure Research: Vol. 35, No.4, pp 441-454.

5.        Strasheim et al., (1992) “Demand Forecasting for Motor Vehicle Spare Parts,” A Journal of Industrial Engineering, Vol. 6, No. 2, pp 18-19.

6.        Mihaela Bratu et al “The Accuracy of Unemployment Rate Forecasts In Romania and The Actual Economic Crisis”, Scientific Bulletin – Economic Sciences, Vol.11, Issue 2, pp 56-67.

7.        Feridun, M. & Adebiyi,M.A.(2006) “ Forecasting Inflation in Developing Economics: The Case of Nigeria”, International Journal of Applied Econometrics and Quantitative Studies, 1986-1998, Volume 3, Issue 1, pp 18-19.

8.        Floros, Ch., et al., (2005) “Forecasting the UK Unemployment Rate: Model Comparisons”, International Journal of Applied Econometrics and Quantitative Studies,Vol.2, Issue 4, pp 57-72.

9.        Sharma, A.K., Gupta, A and Sharma, U., (2013) “Electricity forecasting of Jammu & Kashmir: A Methodology Comparision”,International Journal of Electrical Engineering & Technology (IJEET), Vol.4, Issue 2, pp 416-426.

10.     Rachel J.C. Chen, Peter Bloomfield and Frederick W. Cubbage (2008) “Comparing Forecasting Models in Tourism”, Journal of Hospitality & tourism Research, Vol.32, Issue 1, pp 3-21.

11.     Patil, D.P., Shrotri, A.P.  and Dandekar, A.R., (2013) “Management of Uncertainty in Supply Chain”, International Journal of Emerging Technology and Advanced Engineering, Vol.2, Issue 5, pp 303-308.

12.     Carol T. West, et al., (2003) “The Status of Evaluating Accuracy of Regional Forecasts”, The Review of Regional Studies, Vol.33, Issue 1, pp 85-103.

13.     Paul, S.K., et al., (2011) “Determination of Exponential Smoothing Constant to Minimize Mean Square Error and Mean Absolute Deviation”, Global Journal of Research in Engineering, Vol.11, Issue 3, Version 1.0.

14.     Lim, P.Y., and Nayar, C.V., (2012) “Solar Irradiance and Load Demand Forecasting Based on Single Exponential Smoothing Method”, IACSIT International Journal of Engineering and Technology, Vol.4, Issue 4, pp 451-455.

15.     Armstrong, J.S. and Collopy, F., (1992) “Error Measures For Generalizing About Forecasting Method: Empirical Comparisons”, International Journal of Forecasting, Vol.8, pp 69-80.

16.     Padhan, P.C., et al., (2012) “Use of Univariate Time Series Model For Forecasting Cement Production in India”, International Research Journal of Finance and Economics, Issue 83.

17.     Panneerselvam, R., et al., (2009) “Production and Operation Management, 2nd edition, PHI Learning Private Limited, New Delhi (India).

18.     Chopra, S. and Meindl, P., (2010) “Supply Chain Management Strategy, Planning and Operation”, Pearson, 4th Ed, India.

19.     Ramamurthy, P., et al., (2005) “Production and Operation Management”, New Age International (P) Limited, Publishers, New Delhi (India).

20.     Chary, S.N., et al.,(2009) “Production and Operation Management”, Tata McGraw-Hill, New Delhi (India). 


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4.

Authors:

Dipika P. Chanmanwar, Priyanka S. Ghode

Paper Title:

Arbitrary-Ratio Image/Video Resizing Using Fast DCT of Composite Length for DCT-Based Transcoder

Abstract: The most popular image and video compression methods such as JPEG, MPEG 1/2/4, H.261/3/4 use transform domain techniques and in particular the Discrete Cosine Transform (DCT). One application for such images or video sequences is resizing. Resizing is extensively used to meet the requirements of a specific system, to satisfy user’s interests, or to correct spatial distortions. However, a major difficulty encountered when resizing such media is the high computational complexity and the loss of quality caused by the decompression and compression. The purpose of this paper is to implement an arbitrary ratio image resizing scheme in the DCT domain for transcoding of the compressed images. There are several advantages in working in the DCT domain, of these advantages the one that stands out the most is the fact that images are stored in the DCT domain and therefore no initial computation is needed in order to work on the image. The downsizing process in the discrete cosine transform (DCT) domain can be implemented by truncating high-frequency coefficients, whereas the upsizing process is implemented in the DCT domain by padding zero coefficients to the high-frequency part. The implemented method combines a fast inverse and forward DCT of composite length for arbitrary-ratio upsizing or downsizing. The implemented method shows a good peak signal-to-noise ratio and less computational complexity compared with the spatial-domain and previous DCT-domain image resizing methods. Further it will compare several methods offered by different authors for image resizing. The implemented method of arbitrary ratio image resizing improve peak signal-to-noise ratio and reduces computational complexity when compared with other existing methods. This implemented approach of image resizing is extended for video resizing. The PSNR values of the resized video are calculated by using an existing tool. The obtained PSNR values are better when compared with other existing tools.

Keywords:
Arbitrary-ratio image resizing, composite length DCT, transcoder.


References:

1.        R. Dugad, “A fast scheme for image size change in the compressed domain ,” IEEE Trans. Circuits Syst. Video Technol., vol. 11, no. 4, pp. 461–474, Apr. 2001.
2.        J. Mukherjee and S. K. Mitra, “Image resizing in the compressed domain using subband DCT,” IEEE Trans. Circuits Syst. Video Technol., vol. 12, no. 7, pp. 620–627, Jul. 2002.

3.        H. Shu and L. P. Chau, “An efficient arbitrary downsizing algorithm for video transcoding,” IEEE Trans. Circuits Syst. Video Technol, vol. 14, no. 6, pp. 887–891, Jun. 2004.

4.        H. W. Park, Y. S. Park, and S. K. Oh, “L/M-fold image resizing in block-DCT domain using symmetric convolution,” in IEEE Trans. Image Process., vol. 12, Sep. 2003, pp. 1016–1034.

5.        C. L. Salazar and T. D. Tran, “On resizing images in the DCT domain,” in Proc. IEEE Int. Conf. Image Processing, Singapore, Oct. 2004, pp. 2797–2800.

6.        C. Loeffler, A. Ligtenberg, and G. S. Moschytz, “Practical fast 1-D DCT algorithms with 11 multiplications,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, vol. 2, May 1989, pp. 988–991.

7.        G. Bi and L. W. Yu, “DCT algorithms for composite sequence length,” IEEE Trans. Signal Process, vol. 46, no. 3, pp. 554–562, Mar. 1998.

8.        Y. S. Park and H. W. Park, “Design and analysis of an image resizing filter in the block-DCT domain,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 2, pp. 274–279, Feb. 2004.

9.        Y. H. Chan and W. C. Siu, “Mixed-radix discrete cosine transform,” IEEE Trans. Signal Process., vol. 41, no. 11, pp. 3157–3161, Nov. 1993.


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5.

Authors:

Tamkanath Sabeeha, Siva Yellampalli

Paper Title:

Design of a Data Collection and Transmission System Based on AD9284

Abstract: This paper introduces a high speed dual channel data collection system based on AD9284 which can transmit data to PC by USB interface chip CYUSB3014. USB is more used than some traditional inter-PC Bus such as PCI due to the high speed and agility and also provides properly convenient communication interface for A/D conversion. This paper not only focusing on the characteristics of the AD9284, but also explains the interface circuit. By controlling the variation of the signal from analog to digital, the system achieves the point of the high-speed dual-channel data collection and real time monitoring, It can be primly used as a Spectrum analyzer or Oscilloscope in back-end receiver system.

Keywords:
AD9284; Data collection; PLL IC AD9510; USB3.0.


References:

1.        J.N. Chengalur, Y. Gupta and K.S. Dwarakanath, “Low frequency radioAstronomy,”Third edition, National Centre for Radio Astrophysics, Tata Institute of Fundamental research, Pune, India, 2007.
2.        Duan Guangyun. Design of high-speed multiple Channel data Collection System based on   AD7865.Journal of Qinghai University (Nature Science).Vol. 26(2). Pp.37-40, 2008.

3.        Cypress Semiconductor Corporation. EZ-USB FX3 technical Reference Manual version2.2 [EBOL]. USA. May12, 2013, http://www.icpdf.com

4.        B. Brannon and J. Hall, “Understanding state-of-the-art in ADCs,” Analog Devices, 2007, www.analog.com/everywhere.

5.        N. Gray, “ABCs and ADCs,” National Semiconductor, August-2004.

6.        http://www.analog.com/static/importedfiles/datasheets/AD928.pdf.

7.        http://www.cypress.com/?docID-39478.pdf.

8.        http://www.usb.org/developers/docs.pdf.

9.        Ma Juntao. Li Zhenyu. Data communication between CY7C68013 and FPGA in slave FIFO mode. Journal of Communication Univetrsity of China (Science of Technology).Vol.16 (2), pp. 38-41, 2009.

10.     F. Daneshgaran, M. Laddomada, and M. Mondin, ‘A High-Resolution CMOS Time-to-Digital Converter and Quantization Noise Cancellation’, IEEE JSSC, vol. 35, no. 2, pp.240-247, 2008.

11.     H. Johnson and M. Graham, “High-speed digital design: A handbook of black magic,” Prentice hall, 1993.

12.     B. Brannon, “Sampled Systems and the effects if clock phase noise and jitter, “application Note AN-756, Analog Devices Inc.

13.     V. Blaschke “Cognitive Radio Receiver Supporting Wide-Band Sensing,” IEEE Communications Society, in proc on ICC workshop, IEEE May 2008.

14.     J.N. Chengalur, Y. Gupta and K.S. Dwarakanath, “Low frequency radio Astronomy,” Third edition, National Centre for Radio Astrophysics, Tata Institute of Fundamental research, Pune, India, 2007.

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6.

Authors:

Mohamad Firdaus Che Abdul Rani, Nor Azlina Abd Rahman, Khalida Shajaratuddur Harun

Paper Title:

Intelligent Travel Advisor System (ITAS)

Abstract: This paper is discussing on Intelligent Travel Advisor System (ITAS) Framework. The purpose of this system is to help the tourist to plan their trip based on budget, tourist spots or any criteria that they want to base on. Several similar systems reviewed such as TripAdvisor, Priceline and Expedia Inc to identify the functionalities, strength and weaknesses of the existing system. Overall ITAS system architecture discussed that includes user terminals, ITAS and payment Agencies. ITAS components are highlighted which are web crawling, database and secure network. The process that involve in ITAS divided into three parts which are input process that accept the criteria of searching, system process that match the input with certain websites by using web crawler and output process that will display the information that match with user’s input. Besides that this paper is also discussing on the impact of ITAS to the tourists and society.

Keywords:
Advisor; intelligent; trip; tourist; Web Crawler.


References:

1. G PR Newswire US, 2013.“TripAdvisor Launches Powerful, Free Review Collection Service For Businesses”. Available at:<http://search.proquest.com/docview/1355877545/fulltext/13E6EF961314716DAB2/81?accountid=46052/> [Accessed 7  June  2013] 
2. Google Analytics, worldwide data, March 2013. Available at: <http://ehis.ebscohost.com.newdc.oum.edu.my/eds/detail?sid=d190fc7d-ae84-4e81-8d93-735111c244a6%40sessionmgr10&vid=1&hid=8&bdata=#db=bwh&AN=201305070700PR.NEWS.USPR.NE08143> [Accessed 12 June 2013]

3.  Chris Anderson, 2012. “Cornell Hospitality Report, The Impact of Social Media on Lodging Performance”. [online] Available at:< [http://search.proquest.com/docview/1348784659/13EEEF577A936971523/16?accountid=46052>[Accessed 13 June 2013]

4.        PR Newswire New York,  2013. Available at:<
http://search.proquest.com/docview/1314689003/fulltext/13EEEF577A936971523/6?accountid=46052> [Accessed 15 June 2013]
5. Priceline.com Incorporated Company Information. Available at:<http://www.hoovers.com/company-information/cs/company-profile.pricelinecom_Incorporated.5423b84d32cc8f47.html> [Accessed 18 june 2013]

6.        Sanders, 2013. “How Does Priceline.com work?”. Available at:<http://www.ehow.com/how-does_4740562_priceline_com-work_.html> [Accessed 20 June 2013]

7.        PR Newswire New York,  2013. Available at:
<http://search.proquest.com/docview/1362141679/13E91BE80F96DECC133/17?accountid=46052> [Accessed 19 June 2013]
8.        PR Newswire New York, 2013. “Expedia Launches "Expedia Viewfinder(TM)" Blog - Featuring Globetrotting Travel Bloggers - to Give Summer Travelers Every Tip and Trick They Need”. Available at:
<http://search.proquest.com/docview/1355877545/fulltext/13E6EF961314716DAB2/81?accountid=46052> [Accessed 23 June 2013]
9. PR Newswire New York, 2003.“Expedia.com Lends a Helping Hand to Holiday Travelers”. Available at: <http://search.proquest.com/docview/451828520/fulltext/13E17702E907F0C06CD/15?accountid=46052> [Accessed 25 June 2013]

10.     A Brief History of the Online Travel Industry Pioneer. Available at:<http://www.expediainc.com/company.cfm> [Accessed 1 July 2013]

11.     Symantec, “Secure Sockets Layer: How it works”. [online] Available at: <http://www.symantec.com/page.jsp?id=how-ssl-works>[Accessed 7 May 2013]

12.     Javvin Company. “S-HTTP: Secure Hypertext Transfer Protocol” .[online] Available at: <http://www.javvin.com/protocolHTTPS.html> [Accessed 7 May 2013]

13.     Soumen Chakrabarti. “Focused Web Crawling”. Encyclopedia of Database Systems. Available at: http://www.springerreference.com/docs/html/chapterdbid/63300.html

14.  Luciano B., Srinivas B., Vivek Kumar S. R., “Crawling Back and Forth: Using Back and Out Links to Locate Bilingual Sites”.  Available at http://www.research.att.com/techdocs/TD_100391.pdf

15.     Page, Larry, “PageRank: Bringing Order to the Web”, Stanford Digital Library Project, talk. August 18, 1997 (archived 2002)

16.     “PageRank sculpting”. Mattcutts.com. 2009-06-15. [Accessed 1 July 2013]

17.     “Focused Crawling”. Available at <http://www.scaleunlimited.com/about/focused-crawler/> [Accessed 9 August 2013]

18.     “Internet History - Search Engines (from Search Engine Watch)”, Universiteit Leiden, Netherlands, September 2001.

19.     Gandal, Neil (2001). “The dynamics of competition in the internet search engine market”. International Journal of Industrial Organization 19 (7): 1103–1117.


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