Abstract: Environmental factors affect agriculture production
productivity and efficiency resulted in changing of profit efficiency.
This paper attempts to estimate the impacts of environmental factors
to profitability of rice farmers in the Red River Delta of Vietnam. The
dataset was extracted from 349 rice farmers using personal
interviews. Both OLS and MLE trans-log profit functions were used
in this study. Five production inputs and four environmental factors
were included in these functions. The estimation of the stochastic
profit frontier with a two-stage approach was used to measure
profitability. The results showed that the profit efficiency was about
75% on the average and environmental factors change profit
efficiency significantly beside farm specific characteristics. Plant
disease, soil fertility, irrigation apply and water pollution were the
four environmental factors cause profit loss in rice production. The
result indicated that farmers should reduce household size, farm
plots, apply row seeding technique and improve environmental
factors to obtain high profit efficiency with special consideration is
given for irrigation water quality improvement.
Abstract: This paper evaluates performances of an adaptive noise
cancelling (ANC) based target detection algorithm on a set of real test
data supported by the Defense Evaluation Research Agency (DERA
UK) for multi-target wideband active sonar echolocation system. The
hybrid algorithm proposed is a combination of an adaptive ANC
neuro-fuzzy scheme in the first instance and followed by an iterative
optimum target motion estimation (TME) scheme. The neuro-fuzzy
scheme is based on the adaptive noise cancelling concept with the
core processor of ANFIS (adaptive neuro-fuzzy inference system) to
provide an effective fine tuned signal. The resultant output is then
sent as an input to the optimum TME scheme composed of twogauge
trimmed-mean (TM) levelization, discrete wavelet denoising
(WDeN), and optimal continuous wavelet transform (CWT) for
further denosing and targets identification. Its aim is to recover the
contact signals in an effective and efficient manner and then determine
the Doppler motion (radial range, velocity and acceleration) at very
low signal-to-noise ratio (SNR). Quantitative results have shown that
the hybrid algorithm have excellent performance in predicting targets-
Doppler motion within various target strength with the maximum
false detection of 1.5%.
Abstract: Phase-Contrast MR imaging methods are widely used
for measurement of blood flow velocity components. Also there are
some other tools such as CT and Ultrasound for velocity map
detection in intravascular studies. These data are used in deriving
flow characteristics. Some clinical applications are investigated
which use pressure distribution in diagnosis of intravascular disorders
such as vascular stenosis. In this paper an approach to the problem of
measurement of intravascular pressure field by using velocity field
obtained from flow images is proposed. The method presented in this
paper uses an algorithm to calculate nonlinear equations of Navier-
Stokes, assuming blood as an incompressible and Newtonian fluid.
Flow images usually suffer the lack of spatial resolution. Our
attempt is to consider the effect of spatial resolution on the pressure
distribution estimated from this method. In order to achieve this aim,
velocity map of a numerical phantom is derived at six different
spatial resolutions. To determine the effects of vascular stenoses on
pressure distribution, a stenotic phantom geometry is considered. A
comparison between the pressure distribution obtained from the
phantom and the pressure resulted from the algorithm is presented. In
this regard we also compared the effects of collocated and staggered
computational grids on the pressure distribution resulted from this
algorithm.
Abstract: Quantitative measurements of tumor in general and tumor volume in particular, become more realistic with the use of Magnetic Resonance imaging, especially when the tumor morphological changes become irregular and difficult to assess by clinical examination. However, tumor volume estimation strongly depends on the image segmentation, which is fuzzy by nature. In this paper a fuzzy approach is presented for tumor volume segmentation based on the fuzzy connectedness algorithm. The fuzzy affinity matrix resulting from segmentation is then used to estimate a fuzzy volume based on a certainty parameter, an Alpha Cut, defined by the user. The proposed method was shown to highly affect treatment decisions. A statistical analysis was performed in this study to validate the results based on a manual method for volume estimation and the importance of using the Alpha Cut is further explained.
Abstract: Maintenance is one of the most important activities in
the shipyard industry. However, sometimes it is not supported by
adequate services from the shipyard, where inaccuracy in estimating
the duration of the ship maintenance is still common. This makes
estimation of ship maintenance duration is crucial. This study uses
Data Mining approach, i.e., CART (Classification and Regression
Tree) to estimate the duration of ship maintenance that is limited to
dock works or which is known as dry docking. By using the volume
of dock works as an input to estimate the maintenance duration, 4
classes of dry docking duration were obtained with different linear
model and job criteria for each class. These linear models can then be
used to estimate the duration of dry docking based on job criteria.
Abstract: The most common result of analysis of highthroughput
data in molecular biology represents a global list of
genes, ranked accordingly to a certain score. The score can be a
measure of differential expression. Recent work proposed a new
method for selecting a number of genes in a ranked gene list from
microarray gene expression data such that this set forms the
Optimally Functionally Enriched Network (OFTEN), formed by
known physical interactions between genes or their products. Here
we present calculation results of relative connectivity of genes from
META-OFTEN network and tentative biological interpretation of the
most reproducible signal. The relative connectivity and
inbetweenness values of genes from META-OFTEN network were
estimated.
Abstract: This paper presents a fast and efficient on-line technique for estimating impedance of unbalanced loads in power systems. The proposed technique is an application of a discrete timedynamic filter based on stochastic estimation theory which is suitable for estimating parameters in noisy environment. The algorithm uses sets of digital samples of the distorted voltage and current waveforms of the non-linear load to estimate the harmonic contents of these two signal. The non-linear load impedance is then calculated from these contents. The method is tested using practical data. Results are reported and compared with those obtained using the conventional least error squares technique. In addition to the very accurate results obtained, the method can detect and reject bad measurements. This can be considered as a very important advantage over the conventional static estimation methods such as the least error square method.
Abstract: Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE workand output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
Abstract: Dengue is a mosquito-borne infection that has peaked to an alarming rate in recent decades. It can be found in tropical and sub-tropical climate. In Malaysia, dengue has been declared as one of the national health threat to the public. This study aimed to map the spatial distributions of dengue cases in the district of Hulu Langat, Selangor via a combination of Geographic Information System (GIS) and spatial statistic tools. Data related to dengue was gathered from the various government health agencies. The location of dengue cases was geocoded using a handheld GPS Juno SB Trimble. A total of 197 dengue cases occurring in 2003 were used in this study. Those data then was aggregated into sub-district level and then converted into GIS format. The study also used population or demographic data as well as the boundary of Hulu Langat. To assess the spatial distribution of dengue cases three spatial statistics method (Moran-s I, average nearest neighborhood (ANN) and kernel density estimation) were applied together with spatial analysis in the GIS environment. Those three indices were used to analyze the spatial distribution and average distance of dengue incidence and to locate the hot spot of dengue cases. The results indicated that the dengue cases was clustered (p < 0.01) when analyze using Moran-s I with z scores 5.03. The results from ANN analysis showed that the average nearest neighbor ratio is less than 1 which is 0.518755 (p < 0.0001). From this result, we can expect the dengue cases pattern in Hulu Langat district is exhibiting a cluster pattern. The z-score for dengue incidence within the district is -13.0525 (p < 0.0001). It was also found that the significant spatial autocorrelation of dengue incidences occurs at an average distance of 380.81 meters (p < 0.0001). Several locations especially residential area also had been identified as the hot spots of dengue cases in the district.
Abstract: Motion estimation is the most computationally
intensive part in video processing. Many fast motion estimation
algorithms have been proposed to decrease the computational
complexity by reducing the number of candidate motion vectors.
However, these studies are for fast search algorithms themselves while
almost image and video compressions are operated with software
based. Therefore, the timing constraints for running these motion
estimation algorithms not only challenge for the video codec but also
overwhelm for some of processors. In this paper, the performance of
motion estimation is enhanced by using Intel's Streaming SIMD
Extension 2 (SSE2) technology with Intel Pentium 4 processor.
Abstract: A new technique based on Pattern search optimization is proposed for estimating different solar cell parameters in this paper. The estimated parameters are the generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor. The proposed approach is tested and validated using double diode model to show its potential. Performance of the developed approach is quite interesting which signifies its potential as a promising estimation tool.
Abstract: The paper presents the modeling of nonlinear
longitudinal aerodynamics using flight data of Hansa-3 aircraft at
high angles of attack near stall. The Kirchhoff-s quasi-steady stall
model has been used to incorporate nonlinear aerodynamic effects in
the aerodynamic model used to estimate the parameters, thereby,
making the aerodynamic model nonlinear. The Maximum Likelihood
method has been applied to the flight data (at high angles of attack)
for the estimation of parameters (aerodynamic and stall
characteristics) using the nonlinear aerodynamic model. To improve
the accuracy level of the estimates, an approach of fixing the strong
parameters has also been presented.
Abstract: This paper describes a system-level SoC energy
consumption estimation method based on a dynamic behavior of
embedded software in the early stages of the SoC development. A
major problem of SOC development is development rework caused by
unreliable energy consumption estimation at the early stages. The
energy consumption of an SoC used in embedded systems is strongly
affected by the dynamic behavior of the software. At the early stages
of SoC development, modeling with a high level of abstraction is
required for both the dynamic behavior of the software, and the
behavior of the SoC. We estimate the energy consumption by a UML
model-based simulation. The proposed method is applied for an actual
embedded system in an MFP. The energy consumption estimation of
the SoC is more accurate than conventional methods and this proposed
method is promising to reduce the chance of development rework in
the SoC development. ∈
Abstract: Color constancy algorithms are generally based on the
simplified assumption about the spectral distribution or the reflection
attributes of the scene surface. However, in reality, these assumptions
are too restrictive. The methodology is proposed to extend existing
algorithm to applying color constancy locally to image patches rather
than globally to the entire images.
In this paper, a method based on low-level image features using
superpixels is proposed. Superpixel segmentation partition an image
into regions that are approximately uniform in size and shape. Instead
of using entire pixel set for estimating the illuminant, only superpixels
with the most valuable information are used. Based on large scale
experiments on real-world scenes, it can be derived that the estimation
is more accurate using superpixels than when using the entire image.
Abstract: In gas lifted oil fields, the lift gas should be distributed optimally among the wells which share gas from a common source to maximize total oil production. One of the objectives of the paper is to show that a linear MPC consisting of a control objective and an economic objective can be used both as an optimizer and a controller for gas lifted systems. The MPC is based on linearized model of the oil field developed from first principles modeling. Simulation results show that the total oil production is increased by 3.4%. Difficulties in accurately measuring the bottom hole pressure using sensors in harsh operating conditions can be resolved by using an Unscented Kalman Filter (UKF) for estimation. In oil fields where input disturbance (total supply of gas) is not measured, UKF can also be used for disturbance estimation. Increased total oil production due to optimization leads to increased profit.
Abstract: In this paper we present a generic approach for the problem of the blind estimation of the parameters of linear and convolutional error correcting codes. In a non-cooperative context, an adversary has only access to the noised transmission he has intercepted. The intercepter has no knowledge about the parameters used by the legal users. So, before having acess to the information he has first to blindly estimate the parameters of the error correcting code of the communication. The presented approach has the main advantage that the problem of reconstruction of such codes can be expressed in a very simple way. This allows us to evaluate theorical bounds on the complexity of the reconstruction process but also bounds on the estimation rate. We show that some classical reconstruction techniques are optimal and also explain why some of them have theorical complexities greater than these experimentally observed.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.
Abstract: The paper makes part from a complex research project
on Romanian Grey Steppe, a unique breed in terms of biological and
cultural-historical importance, on the verge of extinction and which
has been included in a preservation programme of genetic resources
from Romania. The study of genetic polymorphism of protean
fractions, especially kappa-casein, and the genotype relations of
these lactoproteins with some quantitative and qualitative features of
milk yield represents a current theme and a novelty for this breed. In
the estimation of the genetic parameters we used R.E.M.L.
(Restricted Maximum Likelihood) method.
The main lactoprotein from milk, kappa - casein (K-cz),
characterized in the specialized literature as a feature having a high
degree of hereditary transmission, behaves as such in the nucleus under
study, a value also confirmed by the heritability coefficient (h2 = 0.57
%). We must mention the medium values for milk and fat quantity
(h2=0.26, 0.29 %) and the fat and protein percentage from milk
having a high hereditary influence h2 = 0.71 - 0.63 %.
Correlations between kappa-casein and the milk quantity are
negative and strong. Between kappa-casein and other qualitative
features of milk (fat content 0.58-0.67 % and protein content 0.77-
0.87%), there are positive and very strong correlations. At the same
time, between kappa-casein and β casein (β-cz), β lactoglobulin (β-
lg) respectively, correlations are positive having high values (0.37 –
0.45 %), indicating the same causes and determining factors for the
two groups of features.
Abstract: Fast delay estimation methods, as opposed to
simulation techniques, are needed for incremental performance
driven layout synthesis. On-chip inductive effects are becoming
predominant in deep submicron interconnects due to increasing clock
speed and circuit complexity. Inductance causes noise in signal
waveforms, which can adversely affect the performance of the circuit
and signal integrity. Several approaches have been put forward which
consider the inductance for on-chip interconnect modelling. But for
even much higher frequency, of the order of few GHz, the shunt
dielectric lossy component has become comparable to that of other
electrical parameters for high speed VLSI design. In order to cope up
with this effect, on-chip interconnect has to be modelled as
distributed RLCG line. Elmore delay based methods, although
efficient, cannot accurately estimate the delay for RLCG interconnect
line. In this paper, an accurate analytical delay model has been
derived, based on first and second moments of RLCG
interconnection lines. The proposed model considers both the effect
of inductance and conductance matrices. We have performed the
simulation in 0.18μm technology node and an error of as low as less
as 5% has been achieved with the proposed model when compared to
SPICE. The importance of the conductance matrices in interconnect
modelling has also been discussed and it is shown that if G is
neglected for interconnect line modelling, then it will result an delay
error of as high as 6% when compared to SPICE.