Abstract: Fading noise degrades the performance of cellular
communication, most notably in femto- and pico-cells in 3G and 4G
systems. When the wireless channel consists of a small number of
scattering paths, the statistics of fading noise is not analytically
tractable and poses a serious challenge to developing closed
canonical forms that can be analysed and used in the design of
efficient and optimal receivers. In this context, noise is multiplicative
and is referred to as stochastically local fading. In many analytical
investigation of multiplicative noise, the exponential or Gamma
statistics are invoked. More recent advances by the author of this
paper utilized a Poisson modulated-weighted generalized Laguerre
polynomials with controlling parameters and uncorrelated noise
assumptions. In this paper, we investigate the statistics of multidiversity
stochastically local area fading channel when the channel
consists of randomly distributed Rayleigh and Rician scattering
centers with a coherent Nakagami-distributed line of sight component
and an underlying doubly stochastic Poisson process driven by a
lognormal intensity. These combined statistics form a unifying triply
stochastic filtered marked Poisson point process model.
Abstract: In wavelet regression, choosing threshold value is a crucial issue. A too large value cuts too many coefficients resulting in over smoothing. Conversely, a too small threshold value allows many coefficients to be included in reconstruction, giving a wiggly estimate which result in under smoothing. However, the proper choice of threshold can be considered as a careful balance of these principles. This paper gives a very brief introduction to some thresholding selection methods. These methods include: Universal, Sure, Ebays, Two fold cross validation and level dependent cross validation. A simulation study on a variety of sample sizes, test functions, signal-to-noise ratios is conducted to compare their numerical performances using three different noise structures. For Gaussian noise, EBayes outperforms in all cases for all used functions while Two fold cross validation provides the best results in the case of long tail noise. For large values of signal-to-noise ratios, level dependent cross validation works well under correlated noises case. As expected, increasing both sample size and level of signal to noise ratio, increases estimation efficiency.
Abstract: The (sub)-optimal soolution of linear filtering problem
with correlated noises is considered. The special recursive form of
the class of filters and criteria for selecting the best estimator are
the essential elements of the design method. The properties of the
proposed filter are studied. In particular, for Markovian observation
noise, the approximate filter becomes an optimal Gevers-Kailath filter
subject to a special choice of the parameter in the class of given linear
recursive filters.
Abstract: This paper presents a novel sinusoidal modulation
scheme that features least correlated noise and high linearity. The
modulation circuit, which is composed of a quantizer, a resonator, and
a comparator, is capable of eliminating correlated modulation noise
while doing modulation. The proposed modulation scheme combined
with the linear quadratic optimal control is applied to a single-phase
voltage source inverter and validated with the experiment results. The
experiments show that the inverter supplies stable 60Hz 110V AC
power with a total harmonic distortion of less than 1%, under the DC
input variation from 190 V to 300 V and the output power variation
from 0 to 600 W.
Abstract: Emerging Bio-engineering fields such as Brain
Computer Interfaces, neuroprothesis devices and modeling and
simulation of neural networks have led to increased research activity
in algorithms for the detection, isolation and classification of Action
Potentials (AP) from noisy data trains. Current techniques in the field
of 'unsupervised no-prior knowledge' biosignal processing include
energy operators, wavelet detection and adaptive thresholding. These
tend to bias towards larger AP waveforms, AP may be missed due to
deviations in spike shape and frequency and correlated noise
spectrums can cause false detection. Also, such algorithms tend to
suffer from large computational expense.
A new signal detection technique based upon the ideas of phasespace
diagrams and trajectories is proposed based upon the use of a
delayed copy of the AP to highlight discontinuities relative to
background noise. This idea has been used to create algorithms that
are computationally inexpensive and address the above problems.
Distinct AP have been picked out and manually classified from
real physiological data recorded from a cockroach. To facilitate
testing of the new technique, an Auto Regressive Moving Average
(ARMA) noise model has been constructed bases upon background
noise of the recordings. Along with the AP classification means this
model enables generation of realistic neuronal data sets at arbitrary
signal to noise ratio (SNR).