Vandit Hedau, Pragya Soni – April 2016 Page No.: 01-04
In India mobile phone was introduced around 1995-1996. In the starting years, it was not so common among people. But as time moves it becomes a necessary component of people’s lives. Now everyone can do their work within a few minutes by using mobile phones. This is the reason of mobile phone growth. In this research work we will ascertain the pattern of mobile subscriber with respect to population, as population increases the subscriber is also increased. To determine the growth of the user it uses Gompertz curve that indicates the shape and growth of mobile subscriber with respect to time here we encounter the upper limit of the curve that is used to fit the data to the model and the model suggests the development of mobile subscriber in near future This work will extrapolate the mobile phone subscriber in the future as well as find out the trend of future subscribers.
- Page(s): 01-04
- Date of Publication: 30 April 2016
- Vandit Hedau
School of Future Studies and Planning,
Devi Ahilya Vishwavidyalaya,Indore, India. - Pragya Soni
School of Future Studies and Planning,
Devi Ahilya Vishwavidyalaya,Indore, India.
References
[1]. Debabrata Das(2012).Growth of mobile phone subscribers in India.Basic Research Journal of Business Management and Accounts ISSN 2315-6899 Vol. 1(1) pp. 14-20.
[2]. Dharmesh Motwani, Khushbu Agarwal(2015).Disseminate Knowledge-International Journal of Research in Management Science and Technology (Disseminate Knowledge-IJRMST) Customer Behavior in Mobile Banking: A Study of Udaipur Region. International Journal of Research in Management Science and Technology DK-IJRMST.Volume 3 Issue 1, ISSN No.2321-1245
[3]. Anabela Botelho, Lıgia Costa Pinto (2004).The diffusion of cellular phones in Portugal. Portugal Telecommunications Policy 28 (427–437).
[4]. Christian M. Dippon 2012,Is Faster Necessarily Better? 3G Take-Up Rates and the Implications for Next Generation Services, Int. J. Communications, Network and System Sciences, 2012, 5, 463-480
[5]. Ahmad, M. I., Alsaadi, M. K, Almamri 2014, Statistical Models for Mobile Telephony Growth in Oman ,Information Management and Business Review Vol. 6, No. 3, pp. 121-127, Jun 2014 (ISSN 2220-3796)
[6]. Christos Michalakelis, Dimitris Varoutas and Thomas Sphicopoulos 2008, Diffusion models of mobile telephony in Greece, Telecommunications Policy 32 (2008) 234–245.
[7]. Sanjay Kumar Singh(2008). The diffusion of mobile phones in India. Telecommunications Policy 32 (642–651).
[8]. Ruchita Gupta, Karuna Jain (2012), Diffusion of mobile telephony in India: An empirical study, Technological Forecasting & Social Change 79 (709–715).
[9]. https://data.worldbank.org/indicator/IT.CEL.SETS.P2/countries.
[10]. https://data.worldbank.org/indicator/SP.POP.TOTL.
[11]. Press Release No. 28/2015. TELECOM REGULATORY AUTHORITY OF INDIA. https://www.trai.gov.in/WriteReadData/PressRealease/Document/PR-TSD-Feb10042015.pdf.
[12]. Annual Report 2013-2014. Department of TelecommunicationsMinistry of Communications & Information Technology Government of India New Delhi.
[13]. Christos Michalakelis, Dimitris Varoutas, Thomas Sphicopoulos(2008). Diffusion models of mobile telephony in Greece. Telecommunications Policy 32 (234–245).
[14]. Barros, P. P., & Cadima, N. (2000). The impact of mobile phone diffusion on the fixed-link network. C.E.P.R. Discussion Paper No. 2598.
[15]. R.L. Chaddha, S.S. Chitgopekar(1971). A generalization of the logistic curves and long-range forecasts (1966–1991) of residence telephones. Bell Journal of Economics and Management Science, 2 (2). pp. 542–560.
[16]. R. Bewley, D. Fiebig(1988) .Flexible logistic growth model with applications in telecommunications. International Journal of Forecasting, 4 (2) pp. 177–192.
[17]. Joseph P.Martino.Technology Forecasting for Decision Making.Third Edition.Research Institute University of Dayton,Ohlo.
Dr. Meghna Sharma, Prachi Trivedi “Increment of Mobile Subscriber in India : Gompertz Curve’ Buying Behaviour for Green Products” International Journal of Research and Innovation in Applied Science -IJRIAS vol.1 issue 1, pp.01-04 2016
Gurpreet Singh, Gurbinder Singh, Mandeep Singh – April 2016 Page No.: 05-09
Computerization has definitely revolutionized the way banking is done these days. However, even today all banking transactions, especially, financial require our signatures to be authenticated. The identifiable side-effect of signatures is that they are vulnerable to forgery. In order to avoid misuse or manipulations on account of forged signatures the need for research in efficient automated solutions for signature recognition and verification has increased in recent years. A concrete system has to be developed which should not only be able to consider these factors but also detect various types of forgeries. Signature verification approaches using information technology can be categorized according to the acquisition of the data: On-line and Off-line. On-line data records the motion of the stylus while the signature is produced, and includes location, and possibly velocity, acceleration and pen pressure, as functions of time. Online systems could be used in real time applications like credit cards transaction or resource access. While off-Line signature verification systems take as input the 2-D image of a signature. Offline systems are useful in automatic verification of signatures found on bank checks and documents. Artificial Neural Network (ANN) which has been modeled on human brain has been successfully used classifier in numerous fields. The present study focuses on detection of these forgeries using Support Vector Machine with Radial Basis Function (SVM – RBF) kernel. For evaluating our system’s performance and to know the output unit response accuracy we have developed a classification/confusion matrix and kept our performance goal based on mean square error. It was found that while ANN base system had an overall accuracy of 90% the accuracy of SVM –RBF kernel was significantly higher at 95%.
- Page(s): 05-09
- Date of Publication: 30 April 2016
- Gurpreet Singh
Department of CSE,
PTU, AIET, Faridkot, India - Gurbinder Singh
Department of CSE,
PTU, AIET, Faridkot, India - Mandeep Singh
Department of CSE,
PTU, AIET, Faridkot, India
References
[1] A.Piyush Shanker, A.N. Rajagopalan, ―Off-line signature verification using DTW‖, Pattern Recognition Letters 28 (2007) 1407–1414
[2] Alessandro Zimmer and Lee Luan Ling, ―Offline Signature Verification SystemBased on the Online Data‖, EURASIP Journal on Advances in Signal Processing Volume 2008, Article ID 492910, 16 pages.
[3] Alisher Kholmatov, Berrin Yanikoglu. ―Identity authentication using improved online signature verification method‖. Pattern Recognition Letters 26 (2005) 2400–2408
[4] Anil K. Jain, Friederike D. Griess, Scott D. Connell. ―On-line signature veri!cation‖. Pattern Recognition 35 (2002) 2963 – 2972;
[5] Ashwini Pansare, Shalini Bhatia ―Handwritten Signature Verification using Neural Network‖. Volume 1– No.2, January 2012
[6] Bajaj R, Chaudhary S. ―Signature Verification Using Multiple Neural Classifiers‖.
[7] D. Bertolinia, L.S.Oliveirab, E.Justinoa, R.Sabourinc, ―Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers‖, Pattern Recognition (2009), doi:10.1016/j.patcog.2009.05.009.
[8] Danjun Pu, Gregory R. Ball, Sargur N. Srihari ―A Machine Learning Approach to Off-Line Signature Verification Using Bayesian Inference‖ Computational Forensics Lecture Notes in Computer Science Volume 5718, 2009, pp 125-136,
[9] Donato Impedovo and Giuseppe Pirlo. ―Automatic Signature Verification: The State of the Art‖. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol.38,No. 5, September 2008.
[10] H. Baltzakis, N. Papamarkos. ―A new signature veri®cation technique based on a two-stage neural network classifier‖. Engineering Applications of Artificial Intelligence 14 (2001) 95-103
[11] Hao Feng, Chan Choong Wah. ―Online Signature Verification Using a New Extreme Points Warping Technique‖.
[12] J. Coetzer, B. M. Herbst and J. A. du Preez. ―Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model‖. EURASIP Journal on Applied Signal Processing 2004:4, 559–571.
[13] K.R. Radhika, M.K. Venkatesha and G.N. Sekhar, ―Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine‖, Journal of Computer Science 6 (3): 305-311, 2010.
[14] Karouni A., Daya B., Bahlak S. ―Offline Signature Recognition Using Neural Network Approach‖. Procedia Computer Science, 3 (2011) pp 155-16;
[15] Luan L. Lee, Toby Berger, and Erez Aviczer. ―Reliable On-Line Human Signature Verification Systems‖. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 16, NO. 6, JUNE 1996.
[16] Madasu Hanmandlu, Mohd. Hafizuddin Mohd. Yusof, Vamsi Krishna Madasu. ―Off-line signature verification and forgery detection using fuzzy modeling‖.
[17] Ramachandra A C, Ravi J, K B Raja, Venugopal K R and L M Patnaik, ―Signature Verification using Graph Matching and Cross-Validation Principle‖, International Journal of Recent Trends in Engineering, Vol 1, No. 1, May 2009.
[18] Reza Ebrahimpour, Ali Amiri, Masoom Nazari and Alireza Hajiany, ―Robust Model for Signature Recognition Based on Biological Inspired Features‖, International JournalofComputerandElectrical Engineering, Vol. 2, No. 4, August, 2010.
[19] Sandeep Patil, Shailendra Dewangan. ―Neural Network-based Offline Handwritten Signature Verification System using Hu’s Moment Invariant Analysis‖. International Journal of Engineering and Advanced Technology (IJEAT),ISSN: 2249 – 8958, Volume-1, Issue-1, October 2011.
[20] Upasana Dewan, Javed Ashraf ―Offline Signature Verification Using Neural NetworkOffline Signature Verification Using Neural Networks‖. IJCEM International Journal of Computational Engineering & Management, Vol. 15 Issue 4, July 2012.
Gurpreet Singh, Gurbinder Singh, Mandeep Singh “Signature Verification by using Radial Basis Function (SVM)” International Journal of Research and Innovation in Applied Science -IJRIAS vol.1 issue 1, pp.05-09 2016
Dr. BP Joshi, Dr. SA Gangal –April 2016 Page No.: 10-13
Modern era is witnessing a boom of IT Enabled Services that is converting the world flatter into non-linear functional dimension. Micro-Electro-Mechanical Systems (MEMS), Nano Technology and Genetic Engineering have revolutionised today’s industry. MEMS is an integration of mechanical systems, sensors, actuators and electronics all on a common silicon substrate. MEMS development is recursive in nature, which involves various design and testing cycles until design parameters are met. This would prove to be very expensive but for the predictive design techniques that are applied now a days. The paper discusses two design techniques for the prediction of sensor parameters and how they have been leveraged to achieve flexibility in design evolution of MEMS devices. Employment of such techniques will help MEMS designers optimising design parameters.
- Page(s): 10-13
- Date of Publication: 30 April 2016
- Dr. BP Joshi
Principal
Cummins College of Engineering for Women Nagpur, India - Dr. SA Gangal
ISRO Chair
Savitribai Phule University of Pune, Pune, India
References
[1] Bao Min-Hang, Micromechanical Transducers. Elsevier Science Publication, ISBN: 044450558X, 2000.
[2] Chen K., “A survey of Piezoresistive Semiconductor Accelerometers, Microelectronic sensors.” Technical report; ee663, 2003.
[3] Fedder G., “A Vision of Structured CAD for MEMS” Proceedings of the Fifth ACM/SIGDA Physical Design Workshop. 1996.
[4] Joshi B.P, Study on performance enhancement and design of MEMS Piezoresistive acceleration sensor with special consideration to Sensitivity and Bandwidth optimization, Ph.D. Thesis. 2009.
[5] Joshi B.P., Chaware A.S. and Gangal S.A., “A novel technique for optimising performance
[6] of a cantilever-type Micro-accelerometer sensor.” Defence Science Journal, 2007.
[7] Joshi B.P., Joshi A and Gangal S.A., “Performance improvisation of Cantilever type Silicon Micro Acceleration Sensor using Stress Concentration Regions technique.” Defence Science Journal, 2007.
[8] Roylance L. and Angell J., “A batch fabricated silicon accelerometer.” IEEE Trans. Electron. Devices, ED-26, 1979.
[9] Walter Patrick L., “Trends in accelerometer design for military Accelerometers.” Sensors online Magazine, 1999.
[10] Willoughby, The Gen, Spring, 2008.
Dr. BP Joshi, Dr. SA Gangal “Leveraging Performance Parameters in Predictive Design Evolution of MEMS” International Journal of Research and Innovation in Applied Science -IJRIAS vol.1 issue 1, pp.10-13 2016
Jair Mu López, José G. Vargas-Hernández- April 2016 Page No.: 14-19
This work seeks to analyze if there is an impact from the adoption of technology used for online transactions, starting from the theories of adoption of electronic commerce as well as the strategies that companies choose to develop a competitive advantage, the models on economic interactions, the barriers on the part of companies to be included in the electronic marketplace, Mexico data are revised in recent years to review the trend that has been e-commerce and exploratory.
- Page(s): 14-19
- Date of Publication: 30 April 2016
- Jair Mu López
University Center for economic and Managerial Sciences
University of Guadalajara Zapopan, Jalisco, México - José G. Vargas-Hernández
University Center for economic and Managerial Sciences
University of Guadalajara Zapopan, Jalisco, México
[1] AMIPCI. (2014). Estudio sobre el comercio electrónico en México 2013.https://www.amipci.org.mx/estudios/comercio_electronico/131028_-_Comunicado-Estudio_Comercio_Electronico2013-Proyecto-V3_-_REV.pdf
[2] David, F. R. (2003). Conceptos de administración estratégica. Pearson Educación.
[3] DiMaggio, P., & Powell, W. W. (1983). The iron cage revisited: institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.
[4] Figueroa González, E. G., Hernández Cantú, F. I., González Herrera, M. B., & Arrieta Díaz, D. (2013). COMERCIO ELECTRÓNICO COMO FACTOR COMPETITIVO EN LAS MICRO, PEQUEÑAS Y MEDIANAS EMPRESAS DEL SECTOR COMERCIAL EN EL ESTADO DE DURANGO. Revista International Administración&Finanzas, 6(3), 29-44.
[5] González, O. (2015).Prioritario invertir en TI en el sector Retail. MundoLogístico. 11, 69.
[6] Grewal, R., Comer, J. M., & Mehta, R. (2001).An investigation into the antecedents of organizational participation in business-to-business electronic markets.Journal of Marketing, 65(3), 17–33
[7] Helgueros, Y. (2010). Comercio electrónico como estrategia impulsora de las exportaciones en México. RevistaInternacionalAdministración&Finanzas, 3(3). Pp. 81-90
[8] Hofstede, G. 1993. “Cultural constraints in management theories.”Academy of Management Executive 7, 81–94, 1993.
[9] Klijn, E., Reuer, J., Buckley, P. and Glaister, K. (2010), “Combinations of partners’ joint venture formation motives”, European Business Review, Vol. 22, pp. 576-590.
[10] Kumar, R., Raghavan, P., Rajagopalan, S., & Tomkins, A. (1999, September).Extracting large-scale knowledge bases from the Web.In VLDB Vol. 99, pp. 639-650.
[11] Lancioni, R. A., Smith, M. F., &Schau, H. J. (2003). Strategic Internet application trends in supply chain management. Industrial Marketing Management, 32(3), 211–217.
[12] Laudon, K. C., &Traver, C. G. (2013).E-commerce 2013.
[13] Mahoney, J., &Thelen, K. (2010). A theory of gradual institutional change.Explaining institutional change: Ambiguity, agency, and power, 1-37.
[14] Mata, F. J., Fuerst, W. L., & Barney, J. B. (1995). Information technology and sustained competitive advantage: a resource-based analysis. MIS Quarterly, 19(4), 487–505.
[15] Moreno, M. P. (2014). Comercio electrónico y su impacto en la globalización.Observatorio de la EconomíaLatinoamericana, (201).
[16] Owens, J. (2006) “Electronic business: A business model can make the difference,” Management Services, No. 50, pp. 16-24
[17] Perez, L., &Cambra-Fierro, J. (2015). Value generation in B2B contexts: the SMEs’ in the State of Durango. Revista International Administración&Finanzas, 6(3), 29-44.
[18] Porter, M. E. (2008). The five competitive forces that shape strategy. Harvard business review, 86(1), 25-40.
[19] Porter, M. E. (2001). Strategy and the internet. Harvard business review, 1.
[20] Renna, P.&Argoneto, P. (2010). Production planning and automated negotiation for SMEs: An agent based e-procurement application. International Journal of Production Economics, 127(1), 73-84.
[21] Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York: Free Press.
[22] Sila, I. (2013). Factors affecting the adoption of B2B e-commerce technologies.Electronic commerce research, 13(2), 199-236.
[23] Swanson, E. B. (1994). Information systems innovation among organizations.Management Science, 40(9), 1069–1092.
[24] Tornatzky, L. G., & Fleischer, M. (1990).The processes of technological innovation.Lexington: Lexington Books.
[25] UNCTAD. (2015).Informe sobre la economía de la información 2015.UNCTAD.https://unctad.org/es/PublicationsLibrary/ier2015overview_es.pdf
Jair Mu López, José G. Vargas-Hernández “Strategies for the Adoption of E-Commerce” International Journal of Research and Innovation in Applied Science -IJRIAS vol.1 issue 1, pp.14-19 2016
Renu Malsaria – April 2016 Page No.: 20-21
Linearity improvement is one of the most critical issues in the development of high-power, high frequency TWT. Higher linearity allows utilizing more compact and less expensive power supplies. Furthermore, high-efficiency Travelling wave tubes can operate more reliably and have longer lifetime due to reduced collector loading. While the high-power outputs and wide gain-band widths make TWTs ideally suited for these purposes, the nonlinearity of these devices results in amplitude, phase and spectral distortion. Nonlinear distortion products appear as harmonics and for multi-carrier operation also as intermodulation products, at the output of the amplifier thus limiting the usable bandwidth of the amplifier and degrading fundamental efficiency. In this paper, design and development of a Ku-band 140W Helix TWT for improved linearity and high efficiency will be presented.
- Page(s): 20-21
- Date of Publication: 30 April 2016
- Renu Malsaria
Scholar,
Central Electronics Engineering & Research Institute (CEERI) Pilani, Rajasthan, India
References
[1] V Srivastava, et.al. “Design of High Efficiency Space TWT,” (March-April 1999) IETE Tech Review, vol.16, no.2,, pp.249- 254.
[2] V Srivastava, et.al “Design of helix SWS for high efficiency TWTs” (Dec.2000) IEEE Trans., vol.ED-47, no.12, pp.2438-44.
[3] https://www.cst.com
[4] MK Alaria, A Bera & AK Sinha & V Srivastava, ‘Analysis of Helix Slow Wave Structure for High Efficiency Space TWT’, Journal of Infrared Millimetre, and Terahertz Waves, Vol. 30, No. 3, March, 2009, Page 211-216.
[5] V Srivastava and SN Joshi” One-dimensional nonlinear model for helix TWTs” J. Instn. Of Electronics and Telecommunications Engineers (JIETE), India, vol.6, no.6, pp500-507, Nov-Dec.1989.
[6] V Srivastava and SN Joshi ‘Improved non-linear model for multi-signal analysis of helix TWTs’, IEEE Proceedings (UK), vol.139, Pt.H, no.2 Pp.129-134, April 1992
[7] V Srivastava,’2.5-Dimensional multi-signal large Signal analysis of helix TWTs’, IETE Journal of Research (India), vol.49, no.2, July-August, 2003, pp 239-246
[8] R.O.Jenkins and R.G.Carter Lancaster university “Design of pitch profile for high linearity helix TWT amplifiers” (May 2001) displays and vacuum electronic conference, Garmisch- Patenkirchen, Germany .pp49-54.
Renu Malsaria “High linear Traveling Wave Tube” International Journal of Research and Innovation in Applied Science -IJRIAS vol.1 issue 1, pp.20-21 2016
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Kamsali Nagaraja, Manikiam B and S. Ganapathy Venkatasubramanian –April 2016 Page No.: 22-27
The Earth is at a distance of 150 million kilometres from the Sun and still the radiation emitted by the Sun drives the Earth’s climate system. Variations in the composition and intensity of incident solar radiation hitting the Earth may produce changes in global and regional climate which are both different and additional to those from man-made climate change. In the current epoch, solar variation impacts on regional climate appear to be quite significant. Annual or decadal variations in solar activity are correlated with sunspot activity. Sunspot numbers have been observed and recorded over hundreds of years. From a global perspective the processes through which changes in incident solar radiation affect the temperature of the Earth’s atmosphere, and the climate at the surface, are fairly understood. The response of climate on regional scales to changes in the composition and intensity of incident solar radiation is more complex. Understanding the role of variability in solar activity is essential for the interpretation of past climate and prediction of the future. An effort is made to understand the change in atmospheric conditions through boundary studies of all the meteorological parameters with incoming solar radiation and outgoing long wave radiation.
- Page(s): 22-27
- Date of Publication: 30 April 2016
- Kamsali Nagaraja
ASSR Lab, Department of Physics, Bangalore University, Bangalore 560 056, India - Manikiam B
Sir M Visvesvaraya – ISRO Chair, Bangalore University, Bangalore 560 056, India - S. Ganapathy Venkatasubramanian
Centre for Environmental Studies, Anna University, Chennai, India
[1]. Archibald, D. “Solar Cycles 24 and 25 and Predicted Climate Response”, Energy and Environment, 17, 29-38, 2006.
[2]. Brunetti, M. “Solar signals in instrumental historical series of meteorological parameters”, Memorie della Societa Astronomica Italiana, 74 (3), 778–785, 2003.
[3]. Friis-Christensen, E. and K. Lassen. “Length of the solar cycle: an indicator of solar activity closely associated with climate”, Science, 254, 698–700, 1991.
[4]. Lassen, K. and Friis-Christensen, E. “Variability of the solar cycle length during past five centuries and apparent association with terrestrial climate”, J. Atmos. Terr. Phys., 57, 835-845, 1995.
[5]. Rajesh Agnihotri and Koushik Dutta. “Centennial scale variations in monsoonal rainfall (Indian, east equatorial and Chinese monsoons): Manifestations of solar variability”, Curr. Sci., 85, 459-463, 2003.
[6]. Reichel, R., P. A.Thejll and K. Lassen. “The cause-and-effect relationship of solar cycle length and the Northern hemisphere air surface temperature”, J Geophy. Res. 106, 15635–15641, 2001.
[7]. Reid, G. C. “Solar total irradiance variations and the global sea surface temperature record”, J. Geophy. Res., 96, 2835–2844, 1991.
[8]. Verschuren, D., Laird, K. R. and Cumming, B. F. “Rainfall and drought in equatorial east Africa during the past 1100 years”, Nature, 403, 410–414, 2000.
[9]. Willson, R.C. “Total solar irradiance trend during solar cycles 21 and 22”, Science, 277, 1963-1965, 1997.
Kamsali Nagaraja, Manikiam B and S. Ganapathy Venkatasubramanian “Is the Sun affects our climate?” International Journal of Research and Innovation in Applied Science -IJRIAS vol.1 issue 1, pp.22-27 2016
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