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UWB numerical example
no vote
The memory will need write and read address generators, which will
generate different, read / write addresses. Further in UWB, these
addresses vary based on the data rate of transmission. Implementation of
the address generators will result in a huge amount of logic especially
in multiplexers based on data rate, and high fanout nets. Also to note
is that a similar logic needs to be replicated for the DeInterleaver.
Finally it results in a Transceiver that will have replicated logic
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exam conducted by UPSC in June / July India-wide
no vote
The exam is conducted by UPSC in June / July India-wide. There are
objective as well as conventional (descriptive) papers. One paper for GA
(General Ability) is part of the assessment system apart from the
engineering subject of the candidate. The entire technical subject is
divided into four papers (Papers II, III, IV & V). There is a
penalty (negative marking) for wrong answers marked by the candidate in
the objective type papers. There is a three-day schedule of the written
examination, comprising all five written examinations.
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Raw code Heterogenous network
no vote
Raw code heterogenous network
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hybrid median filter for impulse noise removing
4.0
This paper presents a simple and effective way to remove impulsive and random noises from digital images. The first step is to detect impulse and random noise in the image. At this stage, the only intensity value, based on pixels, is roughly divided into two categories, which are "noise free pixels" and "noise pixels". Then the second stage is to eliminate impulse and random noise from the image.
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MULTICARRIER MODULATION
no vote
The combination between multicarrier modulation and MIMO in one system
combines the advantages of these two technologies in broadband wireless
communications systems that use two
transmit
and one or two receive antennas with Discrete Wavelets Transform (DWT) and
conventional Fast Fourier transform (FFT) as two multicarrier modulation
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maximum-likelihood estimation (MLE) method In stat
4.0
In statistics, maximum-likelihood estimation ( MLE ) is a method of estimating the parameters of a statistical model . When applied to a data set and given a statistical model , maximum-likelihood estimation provides estimates for the model's parameters
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