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Lakshmanan, M.
- Design of Enhanced Cross-Layer Handoff Management Protocol for Next Generation Wireless Systems
Abstract Views :174 |
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Authors
Affiliations
1 School of Electrical Sciences of VIT University, Vellore–632014, Tamil Nadu, IN
2 School of Electrical Sciences of VIT University, Vellore – 632014, Tamil Nadu, IN
1 School of Electrical Sciences of VIT University, Vellore–632014, Tamil Nadu, IN
2 School of Electrical Sciences of VIT University, Vellore – 632014, Tamil Nadu, IN
Source
Networking and Communication Engineering, Vol 1, No 5 (2009), Pagination: 193-199Abstract
In Next-generation wireless systems (NGWS), different wireless network is integrated and each of the networks is optimized for some specific services to provide ubiquitous communications to the mobile users. It is an important and challenging issue to support seamless handoff management in this integrated architecture. This paper deals about design of enhanced cross-layer handoff management protocol HMP) and is developed to support seamless intra and intersystem handoff management in NGWS. Cross-layer handoff management protocol uses mobile’s speed and handoff signaling delay information to enhance the handoff performance of Mobile IP in terms of probability of false handoff initiation and probability of handoff failure. Firt, thehandoff performance of Mobile IP is analyzed with respect to its sensitivity to the link layer (Layer 2) and network layer (Layer 3)parameters. Then enhanced cross-layer handoff management architecture is developed using the insights learnt from the analysis.Finally, simulation results show that CHMP significantly enhances the performance of both intra and intersystem handoffs independent of mobile terminal velocity and network handoff latency.
Keywords
HMP, Link Layer, Mobile IP, Network Layer and NGWS.
- Frequency Domain Equalization for Single Carrier Wireless Systems
Abstract Views :143 |
PDF Views:3
Authors
Affiliations
1 School of Electrical Sciences of VIT University, Vellore–632014, TamilNadu, IN
1 School of Electrical Sciences of VIT University, Vellore–632014, TamilNadu, IN
Source
Networking and Communication Engineering, Vol 1, No 4 (2009), Pagination: 177-183Abstract
Computational complexity and error propagation phenomenon are important drawbacks of existing Decision Feedback Equalizers (DFE) for dispersive channels. A new Iterative Block DFE (IBDFE) is considered where the equalization is performed iteratively on blocks of received signal in the frequency domain i.e. both signal processing and filter design are in frequency domain. Thus computational complexity is reduced and error propagation is limited to one block. The feed forward and feedback filters of DFE are designed with the minimization of Mean Square Error (MSE) at detector input as the parameter for effective detection. Two design methods have been solved and simulated for a Rayleigh fading channel. Channel is assumed to be time in-variant during one block of data (128 symbols) transmission. In the first method, the hard detected data are used as the input to the feedback, and filters are designed according to the correlation between detected and transmitted data. In the second method, the feedback signal is directly designed from soft detection of the equalized signal at the previous iteration. Estimates of the parameters involved in the FF and FB filters are also solved and used to evaluate the filter coefficients. From simulation, it was found that the IBDFE as claimed in the research literature performs better than the time domain DFE.
Keywords
DFE, IBDFE, OFDM and Raleigh Fading Channel.- Waves and Oscillations in Nature:An Introduction
Abstract Views :212 |
PDF Views:68
Authors
Affiliations
1 Centre for Nonlinear Dynamics, Bharathidasan University, Tiruchirappalli 620 024, IN
1 Centre for Nonlinear Dynamics, Bharathidasan University, Tiruchirappalli 620 024, IN
Source
Current Science, Vol 110, No 12 (2016), Pagination: 2306-2307Abstract
Natural phenomena are dominated by the occurrence of oscillations and waves, whether it is light propagation, water wave disturbance, magneto hydrodynamics or plasma oscillations. There are many common features encompassing wave propagation and oscillatory behaviours in these diverse systems.Full Text
- Spectrum Sharing and Inter Cell Interference Mitigation in Wireless Cellular Networks
Abstract Views :161 |
PDF Views:0
Authors
Affiliations
1 School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
2 Department of ECE, Rajalakshmi Engineering College, Chennai - 602105, Tamil Nadu, IN
1 School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
2 Department of ECE, Rajalakshmi Engineering College, Chennai - 602105, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 35 (2016), Pagination:Abstract
Objective: Spectrum shortage and Inter Cell Interference (ICI) are two factors which limits the performance of wireless cellular networks. Methods/Analysis: One of the popular methods is Soft Frequency Reuse which effectively controls the spectrum management and reduces the ICI for wireless networks. In conventional static Soft Frequency Reuse (SFR) scheme, transmission power in cell and allocation of subcarriers are fixed during the system deployment, which affects the performance of system. Findings: This paper focuses Intercell Resource Allocation algorithm, which is dynamic in nature and optimizes both subcarrier and transmission power for cellular network. Novelty/Improvement: Intercell Resource Allocation algorithm result in greater improvement in data rate and system capacity.Keywords
Intercell Interference (ICI), Long-Term Evolution (LTE), LTE-Advanced (LTE-A), Soft Frequency Reuse (SFR), Spectrum Management.Full Text
- Energy Control in 4G Networks
Abstract Views :141 |
PDF Views:0
Authors
Affiliations
1 School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
2 Department of ECE, Rajalakshmi Engineering College, Chennai- 602105, Tamil Nadu, IN
1 School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
2 Department of ECE, Rajalakshmi Engineering College, Chennai- 602105, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 35 (2016), Pagination:Abstract
Objective: The design of wireless networks mainly focuses on resource allocation and energy efficient schemes to meet the increasing demands. Methods/Analysis: In this paper, we proposed an algorithm for Resource block (RB) allocation and Transmit power control in Long term evolution (LTE) downlink heterogeneous networks. Findings: Energy efficiency is increased by increasing the number of smaller cells. To satisfy the required user throughput energy efficiency is minimized and suitable resource block allocation is maximized. Novelty/Improvement: In the proposed power control algorithm, user is allowed to select minimum powers to reduce power of Evolved NodeB (eNB).Keywords
Energy efficiency (EE), LTE, Macro (MeNB), Resource Block (RB), Small eNodeB (SeNB).Full Text
- Anjan Kundu (1953–2016)
Abstract Views :251 |
PDF Views:79
Authors
Affiliations
1 Centre for Nonlinear Dynamics, Bharathidasan University, Tiruchirappalli 620 024, IN
2 Saha Institute of Nuclear Physics, Kolkata 700 064, IN
1 Centre for Nonlinear Dynamics, Bharathidasan University, Tiruchirappalli 620 024, IN
2 Saha Institute of Nuclear Physics, Kolkata 700 064, IN
Source
Current Science, Vol 112, No 04 (2017), Pagination: 865-866Abstract
On 31 December 2016, India lost one of its finest mathematical physicists, Professor Anjan Kundu, who breathed his last during a visit to Bengaluru.Full Text
- A Data Envelopment Analysis Approach to Performance Efficiency of Intellectual Capital – Case of Titan Company Limited#
Abstract Views :375 |
PDF Views:81
Authors
Affiliations
1 DJ Academy for Managerial Excellence, Coimbatore – 641032, Tamil Nadu, IN
2 DJ Academy for Managerial Excellence, Coimbatore - 641032, Tamil Nadu, IN
1 DJ Academy for Managerial Excellence, Coimbatore – 641032, Tamil Nadu, IN
2 DJ Academy for Managerial Excellence, Coimbatore - 641032, Tamil Nadu, IN
Source
SDMIMD Journal of Management, Vol 9, No 1 (2018), Pagination: 1-8Abstract
The purpose of this paper is to value the Performance efficiency of Intellectual Capital (IC) on Financial performance indicators of Titan Company Limited. Data required for analysis were collected from the Annual reports of the company for a period of twenty years. This study uses a DEA – CCR – Output Model which consist of intellectual capital indices as input and financial performance measures as output. Results of the efficiency analysis reveals that of the 20 years studied, only 6 years (2007, 2011, 2012, 2013, 2015, and 2016) were found to be the best performing years in terms of harnessing the goodness of intellectual capital. Some years were very close to perfect efficiency score of one, but the rest of the years showed very poor utilisation of intellectual capital to impact financial performance.
Keywords
Intellectual Capital Efficiency, VAICTM, Value Addition, DEA, Efficiency Scores, Efficiency Frontier.Full Text
References
- Bontis, N. (1999). Managing organizational knowledge by diagnosing intellectual capital: framing and advancing the state of the field. International Journal of Technology Management, 18(5-8), 433-62.
- Brooking, A. (1997). The Management of Intellectual Capital. Long Range Planning, 30(3), 364 – 355.
- Chames, A. Cooper, W. W., & Rhodes E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
- Chen, M. C., Cheng, S. J., & Hwang, Y. (2005). An empirical investigation of the relationship between intellectual capital and firms’ market value and financial performance. Journal of Intellectual Capital, 6(2), 159-76.
- Costa, R. (2012). Assessing Intellectual Capital Efficiency and Productivity: An application to the Italian Yacht manufacturing sector. Expert System with Applications, 39, 7255-7261.
- Edvinsson, L., & Sullivan, P. (1996). Developing a model for managing intellectual capital. European Management Journal, 14(4).
- Edvinsson, L. (1997). Developing intellectual capital at Skandia. Long Range Planning, 30(3), 266-373.
- Edvinsson, L. & Malone, M. (1997). Intellectual Capital: Realizing your Company’s True Value by Finding its Hidden Brainpower. Harper Collins, New York, NY.
- Feiwal, G. R. (1975). The Intellectual Capital of Michal Kalecki: A Study in Economic Theory and Policy. The University of Tennessee Press, Knoxville, TN.
- Firer, S., & Williams, S. M. (2003). Intellectual capital and traditional measures of corporate performance. Journal of Intellectual Capital, 4(3), 348-60.
- Goh, P. C. (2005). Intellectual capital performance of commercial banks in Malaysia. Journal of Intellectual Capital, 6(3). 385-96
- Itami, H. (1987). Mobilizing Invisible Assets. Harvard University Press, Cambridge
- Kujansivu, P., & Lonnqvist, A. (2007). Investigating the value and efficiency of intellectual capital. Journal of Intellectual Capital, 8(2). 272-87.
- Maaloul, A., & Zeghal, D. (2010). Analysing value added as an indicator of intellectual capital and its consequences on company performance. Journal of Intellectual Capital, 11(1), 39-60.
- Mavridis, D., & Kyrmizoglou, G. P. (2005). Intellectual capital performance drivers in the Greek banking sector. Management Research News, 28(5). 43-6.
- Mohiuddin, M., Najibullah, S., & Shahid, A. I. (2006). An exploratory study on intellectual capital performance of the commercial banks in Bangladesh. The Cost and Management, 34(6). 40-54.
- Prusak, L. (1998). Working Knowledge: How organizations Manage What They Know. Harvard Business School Press, Cambridge, MA.
- Pulic, A. (2000). An accounting tool for IC management. Available at: www.measuring-ip.at/Papers/ham99txt.htm.
- Sofian, S., Tyles, M., & Pike, R. (2005). The Implications of Intellectual Capital on Performance Measurement and Corporate Performance.
- Stewart, T. A. (1997). Intellectual Capital: The Wealth of New Organizations. Nicholas Brealey Publishing, London.
- Tseng, C., & Goo, Y. J. (2005). Intellectual capital and corporate value in an emerging economy: empirical studies of Taiwanese manufacturers. R&D Management, 35(2). 187-201.
- Venugopal, D. &, Subha, M. V. (2012). Does intellectual capital create value addition? An empirical investigation into the intellectual capital performance and financial performance of Indian software industry. European Journal of Social Sciences, 33(1), 119–132.
- Zeghal, D., (2000). New assets for the new economy. FMI Journal, 11(2). 35-40. (Financial Management Institute of Canada).
- K. Porsezian (1963–2018)
Abstract Views :291 |
PDF Views:73
Authors
Affiliations
1 Centre for Nonlinear Dynamics, Bharathidasan University, Tiruchirappalli 620024, IN
1 Centre for Nonlinear Dynamics, Bharathidasan University, Tiruchirappalli 620024, IN
Source
Current Science, Vol 115, No 5 (2018), Pagination: 992-993Abstract
On 11 August 2018, the country lost one of the most promising and dynamic upcoming researchers. Kuppuswamy Porsezian, Professor of Physics, Pondicherry Central University, breathed his last at Apollo Hospitals, Chennai due to an unexpected and sudden liver condition at the rather young age of 55. He was destined to achieve greater heights in science but nature snatched him quite unexpectedly leaving his loving family, colleagues, students and a large circle of friends in India and abroad in great despair.Full Text
- Dynamical Modelling and Analysis of COVID-19 in India
Abstract Views :174 |
PDF Views:70
Authors
Affiliations
1 Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, IN
2 Department of Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirappalli 620 014, IN
1 Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, IN
2 Department of Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirappalli 620 014, IN
Source
Current Science, Vol 120, No 8 (2021), Pagination: 1342-1349Abstract
We consider the pandemic spreading of COVID-19 in India after the outbreak of the coronavirus in Wuhan city, China. We estimate the transmission rate of the initial infecting individuals of COVID-19 in India using officially reported data at the early stage of the epidemic with the help of the susceptible (S), exposed (E), infected (I), and removed (R) population model, the so-called SEIR dynamical model. Numerical analysis and model verification are performed to calibrate the system parameters with official public information about the number of people infected, and then to evaluate several COVID-19 scenarios potentially applicable to India. Our findings provide an estimation of the number of infected individuals in the pandemic period of timeline, and also demonstrate the importance of governmental and individual efforts to control the effects and time of the pandemic-related critical situations. We also give special emphasis to individual reactions in the containment process.Keywords
Containment Process, COVID-19 Pandemic, Dynamical Modelling, Numerical Analysis.Full Text
References
- Cohen, J. and Normile, D., New SARS like virus in China triggers alarm, Science, 2020, 367, 6475.
- WHO, 2020; https://www.who.int/emergencies/diseases/novelcoronavirus2019/technical-guid (accessed on 16 May and 5 November 2020).
- Lin, Q. et al., A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int. J. Infect. Dis., 2020, 93, 211– 216.
- Li, Q. et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New Engl. J. Med., 2020, 382, 1199; doi:10.1056/NEJMoa2001316.
- Ferguson, N. M. et al., Impact of non-pharmaceutical interventions (npis) to reduce COVID-19 mortality and healthcare demand. Imperial College COVID-19 Response Team, London, UK, 2020; doi:10.25561/77482.
- https://www.worldometers.info/coronavirus/country; https://www.covid19india.org (accessed on 16 May and 5 November 2020).
- Rothan, H. and Byrareddy, S., The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun., 2020, 109, 102433; doi:10.1016/j.jaut.2020.102433.
- Kermack, W. O. and McKendrick, A. G., Contributions to the mathematical theory of epidemics. Bull. Math. Biol., 1991, 53, 33.
- arXiv.org, https://arxiv.org; bioRxiv.org, https://www.biorxiv.org.
- Rihan, F. A., Al-Salti, N. S. and Anwar, M. N. Y., Dynamics of coronavirus infection in human, AIP Conf. Proc., 2018, 1982, 020009; doi:10.1063/1.5045415.
- Chen, T. M., Rui, J., Wang, W. P., Zhao, Z. Y., Cui, J. A. and Yin, L., A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infect. Dis. Poverty, 2020, 9, 24; doi:10.1186/s40249-020-00640-3.
- Savi, P. V., Savi, M. A. and Borges, B., A mathematical description of the dynamics of coronavirus disease (2019): (COVID-19): a case study of Brazil, 2020; arXiv:2004.03495v2.
- He, D., Dushoff, J., Day, T., Ma, J. and Earn, D. J. D., Inferring the causes of the three waves of the 1918 influenza pandemic in England and Wales. Proc. R. Soc. London Ser. B, 2013, 280, 20131345; doi:10.1098/rspb.2013.1345.
- He, D., Ionides E. L. and King, A. A., Plug-and-play inference for disease dynamics: measles in large and small populations as a case study. J. R. Soc. Interface, 2010, 7, 271–283; doi:10.1098/rsif.2009.0151.
- Das, S., Prediction of COVID-19 disease progression in India. 2020; arXiv:2004.031471v1.
- https://www.statisticstimes.com/demographics/population-of-india.php (accessed on 16 May 2020).
- https://www.mygov.in/covid-19 (accessed on 16 May and 5 November 2020).
- Chae, S. Y. et al., Estimation of infection rate and prediction of initial infected individuals of COVID-19. 2020; arxiv.org/pdf/2004.12665.
- Lakshmanan, M. and Rajasekar, S., Nonlinear dynamics: Integrability Chaos and Patterns, Springer-Verlag, Berlin, Germany, 2003.
- Liu, Y., Gayle, A. A., Smith, A. W. and Rocklow, J., The reproductive number of COVID-19 is higher compared to SARS coronovirus. J. Travel Med., 2020, 27(2), 1–4; doi:10.1093/jtm/taaa021.
- MoHFW, Corona virus disease 2019 (COVID-19). Ministry of Health and Family Welfare, Government of India, 2020; https://www.mohfw.gov.in