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    <title>NISCAIR Online Periodicals Repository Collection: IJRSP Vol.39(1) [February 2010]</title>
    <link>http://nopr.niscair.res.in/handle/123456789/7372</link>
    <description />
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      <title>A decision directed smart antenna system with neural estimation for M-quadrature amplitude modulated signals</title>
      <link>http://nopr.niscair.res.in/handle/123456789/7391</link>
      <description>Title: A decision directed smart antenna system with neural estimation for M-quadrature amplitude modulated signals
&lt;br/&gt;
&lt;br/&gt;Authors: Mathur, Sanjay; Gangwar, R P S
&lt;br/&gt;
&lt;br/&gt;Abstract: Adaptive beamforming and direction of arrival (DOA)&#xD;
estimation are among the prime areas of research which draw the attention of&#xD;
research community to address the ever growing demand for increased capacity in&#xD;
mobile communication systems. Smart antennas, which are essentially adaptive&#xD;
array antennas coupled with intelligent signal processing have emerged as an&#xD;
important category of systems capable of providing solution to this problem.&#xD;
Neural networks are also being widely&#xD;
used for developing intelligent systems and influencing signal processing in&#xD;
communication systems. A decision directed approach is proposed for blind&#xD;
adaptation of smart antenna system using a complex neural estimation of&#xD;
parameters for beamforming. The paper presents a simulation study of the&#xD;
decision directed smart antenna system with neural estimation (DDSA-NE) for&#xD;
multi-quadrature amplitude modulated (M-QAM) signal environment with 4, 16, and&#xD;
64 constellations. The results indicate&#xD;
the effectiveness of the proposed scheme.
&lt;br/&gt;
&lt;br/&gt;Page(s): 45-52</description>
      <pubDate>Fri, 29 Jan 2010 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Higher order Markov chain models for monsoon rainfall over West Bengal, India</title>
      <link>http://nopr.niscair.res.in/handle/123456789/7390</link>
      <description>Title: Higher order Markov chain models for monsoon rainfall over West Bengal, India
&lt;br/&gt;
&lt;br/&gt;Authors: Dastidar, Avik Ghosh; Ghosh, Deepanwita; Dasgupta, S; De, U K
&lt;br/&gt;
&lt;br/&gt;Abstract: &lt;smarttagtype namespaceuri="urn:schemas-microsoft-com:office:smarttags" name="place" downloadurl="http://www.5iantlavalamp.com/"&gt;&lt;smarttagtype namespaceuri="urn:schemas-microsoft-com:office:smarttags" name="country-region" downloadurl="http://www.5iantlavalamp.com/"&gt;&#xD;
&#xD;
&#xD;
&#xD;
Two-state Markov chain models of different&#xD;
orders have been used to simulate the pattern of rainfall during the monsoon&#xD;
season (June-September) over Gangetic West Bengal (India). The analysis is based on&#xD;
the relevant data for 31-year period (1970-2000) for four major meteorological&#xD;
stations in the region. The determination of the proper order of the Markov&#xD;
chain that best describes the rainfall pattern is an interesting problem and&#xD;
Bayesian information criterion has been used for the purpose. Bayesian&#xD;
information criterion (BIC) reveals that third order Markov chain model best&#xD;
describes the rainfall pattern in general except for one station. This is&#xD;
verified and it is found that third/fourth order chain simulates the observed&#xD;
data more closely than the chains of other orders using the classical goodness&#xD;
of fit test. The time independent behaviour of the chain has also been studied&#xD;
with the help of steady state probabilities. The theoretical and observed&#xD;
values of the mean recurrence time have been found to be in close agreement.&#xD;
&#xD;
&lt;/smarttagtype&gt;&lt;/smarttagtype&gt;
&lt;br/&gt;
&lt;br/&gt;Page(s): 39-44</description>
      <pubDate>Fri, 29 Jan 2010 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Extreme value analysis of summer monsoon rainfall over Andhra Pradesh</title>
      <link>http://nopr.niscair.res.in/handle/123456789/7389</link>
      <description>Title: Extreme value analysis of summer monsoon rainfall over Andhra Pradesh
&lt;br/&gt;
&lt;br/&gt;Authors: Sarma, A A L N; Kumar, T V Lakshmi; Rao, K Koteswara
&lt;br/&gt;
&lt;br/&gt;Abstract: The years of excess and deficit of all&#xD;
Andhra Pradesh summer monsoon rainfall (APSMR) are subjected to extreme&#xD;
statistic analysis in the context of one of the signals of the global&#xD;
teleconnections, namely El Niño Southern Oscillation (ENSO) and La Nina&#xD;
Southern Oscillation (LNSO) to understand its regional implications in terms of&#xD;
the associated size or level of a T-year event.
&lt;br/&gt;
&lt;br/&gt;Page(s): 32-38</description>
      <pubDate>Fri, 29 Jan 2010 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Estimation of surface vapour pressure deficits using satellite derived  land surface temperature data</title>
      <link>http://nopr.niscair.res.in/handle/123456789/7388</link>
      <description>Title: Estimation of surface vapour pressure deficits using satellite derived  land surface temperature data
&lt;br/&gt;
&lt;br/&gt;Authors: Singh, Devendra
&lt;br/&gt;
&lt;br/&gt;Abstract: Vapour pressure deficit (VPD), the&#xD;
difference between saturated vapour pressure at air temperature and actual&#xD;
vapour pressure, is one of the most important climatic variables used in&#xD;
ecosystem models to simulate fluxes and states of water and carbon. VPD at&#xD;
different spatial and temporal resolution over regional and local scale has&#xD;
been estimated. The methodology of estimating VPD is based on simple linear&#xD;
relationship between saturation vapour pressure and land surface temperature&#xD;
measurements captured by polar-orbiting satellites. The VPD is evaluated at&#xD;
regional and local scales after comparison with observed weather station data.&#xD;
The estimated VPD is found to be in good agreement with&lt;i style=""&gt; in-situ &lt;/i&gt;observations with rms error and bias as 0.79 kPa and 0.23&#xD;
kPa, respectively. Further, this study has brought out the impact of VPD on&#xD;
vegetation over a period of six years (2003-2008). The higher value of VPD for&#xD;
the year 2004 highlights drought condition while comparing amongst the six-years&#xD;
period (2003-2008).
&lt;br/&gt;
&lt;br/&gt;Page(s): 25-31</description>
      <pubDate>Fri, 29 Jan 2010 22:58:59 GMT</pubDate>
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