NOPR Collection:

A decision directed smart antenna system with neural estimation for M-quadrature amplitude modulated signals

2010-02-18T16:30:21Z

Title: A decision directed smart antenna system with neural estimation for M-quadrature amplitude modulated signals Authors: Mathur, Sanjay; Gangwar, R P S Abstract: Adaptive beamforming and direction of arrival (DOA) estimation are among the prime areas of research which draw the attention of research community to address the ever growing demand for increased capacity in mobile communication systems. Smart antennas, which are essentially adaptive array antennas coupled with intelligent signal processing have emerged as an important category of systems capable of providing solution to this problem. Neural networks are also being widely used for developing intelligent systems and influencing signal processing in communication systems. A decision directed approach is proposed for blind adaptation of smart antenna system using a complex neural estimation of parameters for beamforming. The paper presents a simulation study of the decision directed smart antenna system with neural estimation (DDSA-NE) for multi-quadrature amplitude modulated (M-QAM) signal environment with 4, 16, and 64 constellations. The results indicate the effectiveness of the proposed scheme. Page(s): 45-52

Higher order Markov chain models for monsoon rainfall over West Bengal, India

2010-02-16T16:30:24Z

Title: Higher order Markov chain models for monsoon rainfall over West Bengal, India Authors: Dastidar, Avik Ghosh; Ghosh, Deepanwita; Dasgupta, S; De, U K Abstract: Two-state Markov chain models of different orders have been used to simulate the pattern of rainfall during the monsoon season (June-September) over Gangetic West Bengal (India). The analysis is based on the relevant data for 31-year period (1970-2000) for four major meteorological stations in the region. The determination of the proper order of the Markov chain that best describes the rainfall pattern is an interesting problem and Bayesian information criterion has been used for the purpose. Bayesian information criterion (BIC) reveals that third order Markov chain model best describes the rainfall pattern in general except for one station. This is verified and it is found that third/fourth order chain simulates the observed data more closely than the chains of other orders using the classical goodness of fit test. The time independent behaviour of the chain has also been studied with the help of steady state probabilities. The theoretical and observed values of the mean recurrence time have been found to be in close agreement. Page(s): 39-44

Extreme value analysis of summer monsoon rainfall over Andhra Pradesh

2010-02-17T16:30:29Z

Title: Extreme value analysis of summer monsoon rainfall over Andhra Pradesh Authors: Sarma, A A L N; Kumar, T V Lakshmi; Rao, K Koteswara Abstract: The years of excess and deficit of all Andhra Pradesh summer monsoon rainfall (APSMR) are subjected to extreme statistic analysis in the context of one of the signals of the global teleconnections, namely El Niño Southern Oscillation (ENSO) and La Nina Southern Oscillation (LNSO) to understand its regional implications in terms of the associated size or level of a T-year event. Page(s): 32-38

Estimation of surface vapour pressure deficits using satellite derived land surface temperature data

2010-02-20T16:30:22Z

Title: Estimation of surface vapour pressure deficits using satellite derived land surface temperature data Authors: Singh, Devendra Abstract: Vapour pressure deficit (VPD), the difference between saturated vapour pressure at air temperature and actual vapour pressure, is one of the most important climatic variables used in ecosystem models to simulate fluxes and states of water and carbon. VPD at different spatial and temporal resolution over regional and local scale has been estimated. The methodology of estimating VPD is based on simple linear relationship between saturation vapour pressure and land surface temperature measurements captured by polar-orbiting satellites. The VPD is evaluated at regional and local scales after comparison with observed weather station data. The estimated VPD is found to be in good agreement with in-situ observations with rms error and bias as 0.79 kPa and 0.23 kPa, respectively. Further, this study has brought out the impact of VPD on vegetation over a period of six years (2003-2008). The higher value of VPD for the year 2004 highlights drought condition while comparing amongst the six-years period (2003-2008). Page(s): 25-31