Abstract: This paper presents the convergence analysis
of a prediction based blind equalizer for IIR channels.
Predictor parameters are estimated by using the recursive
least squares algorithm. It is shown that the prediction
error converges almost surely (a.s.) toward a scalar
multiple of the unknown input symbol sequence. It is
also proved that the convergence rate of the parameter
estimation error is of the same order as that in the iterated
logarithm law.
Abstract: This paper presents a dynamic adaptation scheme for
the frequency of inter-deme migration in distributed genetic algorithms
(GA), and its VLSI hardware design. Distributed GA,
or multi-deme-based GA, uses multiple populations which evolve
concurrently. The purpose of dynamic adaptation is to improve
convergence performance so as to obtain better solutions. Through
simulation experiments, we proved that our scheme achieves better
performance than fixed frequency migration schemes.
Abstract: The approach of subset selection in polynomial
regression model building assumes that the chosen fixed full set of
predefined basis functions contains a subset that is sufficient to
describe the target relation sufficiently well. However, in most cases
the necessary set of basis functions is not known and needs to be
guessed – a potentially non-trivial (and long) trial and error process.
In our research we consider a potentially more efficient approach –
Adaptive Basis Function Construction (ABFC). It lets the model
building method itself construct the basis functions necessary for
creating a model of arbitrary complexity with adequate predictive
performance. However, there are two issues that to some extent
plague the methods of both the subset selection and the ABFC,
especially when working with relatively small data samples: the
selection bias and the selection instability. We try to correct these
issues by model post-evaluation using Cross-Validation and model
ensembling. To evaluate the proposed method, we empirically
compare it to ABFC methods without ensembling, to a widely used
method of subset selection, as well as to some other well-known
regression modeling methods, using publicly available data sets.
Abstract: This frame work describes a computationally more
efficient and adaptive threshold estimation method for image
denoising in the wavelet domain based on Generalized Gaussian
Distribution (GGD) modeling of subband coefficients. In this
proposed method, the choice of the threshold estimation is carried out
by analysing the statistical parameters of the wavelet subband
coefficients like standard deviation, arithmetic mean and geometrical
mean. The noisy image is first decomposed into many levels to
obtain different frequency bands. Then soft thresholding method is
used to remove the noisy coefficients, by fixing the optimum
thresholding value by the proposed method. Experimental results on
several test images by using this method show that this method yields
significantly superior image quality and better Peak Signal to Noise
Ratio (PSNR). Here, to prove the efficiency of this method in image
denoising, we have compared this with various denoising methods
like wiener filter, Average filter, VisuShrink and BayesShrink.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.
Abstract: Differentiated impact of team sports (basketball, indoor soccer, handball) on general haemodynamics and aerobic potential of students who specialize in technical subjects is detected only on the fourth year of studies in the institute of higher education. Those who play basketball and indoor soccer have shown increase of stroke and minute volume of blood indices, pumping and contractile function of the heart, oxygenation of blood and oxygen delivery to tissues, aerobic energy supply and balance of sympathetic and parasympathetic activity of the nervous regulation mechanism of the circulatory system. Those who play handball have shown these indices statistically decreased. On the whole playing basketball and indoor soccer optimizes the strategy for adaptation of students to the studying process, but playing handball does the opposite thing. The leading factor for adaptation of students is: those who play basketball have increase of minute blood volume which stipulates velocity of the system blood circulation and well-timed oxygen delivery to tissues; those who play indoor soccer have increase of power and velocity of contractile function of the heart; those who play handball have increase of resistance of thorax to the system blood flow which minimizes contractile function of the heart, blood oxygen saturation and delivery of oxygen to tissues.
Abstract: The size, complexity and number of databases used
for protein information have caused bioinformatics to lag behind in
adapting to the need to handle this distributed information.
Integrating all the information from different databases into one
database is a challenging problem. Our main research is to develop a
tool which can be used to access and manipulate protein information
from difference databases. In our approach, we have integrated
difference databases such as Swiss-prot, PDB, Interpro, and EMBL
and transformed these databases in flat file format into relational
form using XML and Bioperl. As a result, we showed this tool can
search different sizes of protein information stored in relational
database and the result can be retrieved faster compared to flat file
database. A web based user interface is provided to allow user to
access or search for protein information in the local database.
Abstract: Particle Swarm Optimization (PSO) with elite PSO
parameters has been developed for power flow analysis under
practical constrained situations. Multiple solutions of the power flow
problem are useful in voltage stability assessment of power system.
A method of determination of multiple power flow solutions is
presented using a hybrid of Particle Swarm Optimization (PSO) and
local search technique. The unique and innovative learning factors of
the PSO algorithm are formulated depending upon the node power
mismatch values to be highly adaptive with the power flow problems.
The local search is applied on the pbest solution obtained by the PSO
algorithm in each iteration. The proposed algorithm performs reliably
and provides multiple solutions when applied on standard and illconditioned
systems. The test results show that the performances of
the proposed algorithm under critical conditions are better than the
conventional methods.
Abstract: This study is concerned with a new adaptive impedance control strategy to compensate for unknown time-varying environment stiffness and position. The uncertainties are expressed by Function Approximation Technique (FAT), which allows the update laws to be derived easily using Lyapunov stability theory. Computer simulation results are presented to validate the effectiveness of the proposed strategy.
Abstract: The use of renewable energy sources becomes more
necessary and interesting. As wider applications of renewable energy
devices at domestic, commercial and industrial levels has not only
resulted in greater awareness, but also significantly installed
capacities. In addition, biomass principally is in the form of woods,
which is a form of energy by humans for a long time. Gasification is
a process of conversion of solid carbonaceous fuel into combustible
gas by partial combustion. Many gasifier models have various
operating conditions; the parameters kept in each model are different.
This study applied experimental data, which has three inputs, which
are; biomass consumption, temperature at combustion zone and ash
discharge rate. One output is gas flow rate. For this paper, neural
network was used to identify the gasifier system suitable for the
experimental data. In the result,neural networkis usable to attain the
answer.
Abstract: The ever increasing use of World Wide Web in the
existing network, results in poor performance. Several techniques
have been developed for reducing web traffic by compressing the size
of the file, saving the web pages at the client side, changing the burst
nature of traffic into constant rate etc. No single method was
adequate enough to access the document instantly through the
Internet. In this paper, adaptive hybrid algorithms are developed for
reducing web traffic. Intelligent agents are used for monitoring the
web traffic. Depending upon the bandwidth usage, user-s preferences,
server and browser capabilities, intelligent agents use the best
techniques to achieve maximum traffic reduction. Web caching,
compression, filtering, optimization of HTML tags, and traffic
dispersion are incorporated into this adaptive selection. Using this
new hybrid technique, latency is reduced to 20 – 60 % and cache hit
ratio is increased 40 – 82 %.
Abstract: Recently, it has been suggested that thought control strategies aimed at controlling unwanted thoughts may be used to cope with paranoid thoughts in both clinical and nonclinical samples. The current study aims to examine the type of thought control strategies that were associated with the frequency of paranoid thoughts in nonclinical samples. A total of 159 Japanese undergraduate students completed the two scales–the Paranoia Checklist and the Thought Control Questionnaire. A hierarchical multiple regression analysis demonstrated that worry-based control strategies were associated with paranoid thoughts, whereas distraction- and social-based control strategies were inversely associated with paranoid thoughts. Our findings suggest that in a nonclinical population, worry-based strategies may be especially maladaptive, whereas distraction- and social-based strategies may be adaptive to paranoid thoughts.
Abstract: Goat milk has an hypoallergenic effects, and allergic
diseases related to abnormal of intestinal flora. Probiotic microorganisms
do exert an activity on the immune system in the skin of
the individual.The purpose of this study are to determine the number
of leukocyte and lymphocyte proliferation in rat supplemented with
fermented goat milk (acidophilus milk and kefir) and sensitized with
dinitrochlorobenzene (DNCB). Female Wistar rats 6-8 weeks olds
were divided into 3 treatment groups. The first group supplemented
goat milk kefir, second group acidophilus goat milk, and third group
as control. During 28-day experiment, on day 15 rat sensitized with
allergen DNCB on the dorsal of the body, and on day 24 was
challenged with DNCB on the ear. Sampling of blood and tissue of
intestinal Peyer'patch (PP) were performed on day 14 (before DNCB
sensitized) and on day 28 (after DNCB sensitized). The results
showed the number of neutrophils in rats supplemented with
acidophilus milk was higher (P
Abstract: This paper presents a technical speaker adaptation
method called WMLLR, which is based on maximum likelihood linear
regression (MLLR). In MLLR, a linear regression-based transform
which adapted the HMM mean vectors was calculated to maximize the
likelihood of adaptation data. In this paper, the prior knowledge of the
initial model is adequately incorporated into the adaptation. A series of
speaker adaptation experiments are carried out at a 30 famous city
names database to investigate the efficiency of the proposed method.
Experimental results show that the WMLLR method outperforms the
conventional MLLR method, especially when only few utterances
from a new speaker are available for adaptation.
Abstract: The number of framework conceived for e-learning
constantly increase, unfortunately the creators of learning materials
and educational institutions engaged in e-formation adopt a
“proprietor" approach, where the developed products (courses,
activities, exercises, etc.) can be exploited only in the framework
where they were conceived, their uses in the other learning
environments requires a greedy adaptation in terms of time and
effort. Each one proposes courses whose organization, contents,
modes of interaction and presentations are unique for all learners,
unfortunately the latter are heterogeneous and are not interested by
the same information, but only by services or documents adapted to
their needs. Currently the new tendency for the framework
conceived for e-learning, is the interoperability of learning materials,
several standards exist (DCMI (Dublin Core Metadata Initiative)[2],
LOM (Learning Objects Meta data)[1], SCORM (Shareable Content
Object Reference Model)[6][7][8], ARIADNE (Alliance of Remote
Instructional Authoring and Distribution Networks for Europe)[9],
CANCORE (Canadian Core Learning Resource Metadata
Application Profiles)[3]), they converge all to the idea of learning
objects. They are also interested in the adaptation of the learning
materials according to the learners- profile. This article proposes an
approach for the composition of courses adapted to the various
profiles (knowledge, preferences, objectives) of learners, based on
two ontologies (domain to teach and educational) and the learning
objects.
Abstract: Chemically defined Schlegel-s medium was modified
to improve production of cell growth and other metabolites that are
produced by fluorescent pseudomonad R62 strain. The modified
medium does not require pH control as pH changes are kept within ±
0.2 units of the initial pH 7.1 during fermentation. The siderophore
production was optimized for the fluorescent pseudomonad strain in
the modified medium containing 1% glycerol as a major carbon
source supplemented with 0.05% succinic acid and 0.5% Ltryptophan.
Indole-3 acetic acid (IAA) production was higher when
L-tryptophan was used at 0.5%. The 2,4- diacetylphloroglucinol
(DAPG) was higher with amended three trace elements in medium.
The optimized medium produced 2.28 g/l of dry cell mass and 900
mg/l of siderophore at the end of 36 h cultivation, while the
production levels of IAA and DAPG were 65 mg/l and 81 mg/l
respectively at the end of 48 h cultivation.
Abstract: The conjugate gradient optimization algorithm
usually used for nonlinear least squares is presented and is
combined with the modified back propagation algorithm yielding
a new fast training multilayer perceptron (MLP) algorithm
(CGFR/AG). The approaches presented in the paper consist of
three steps: (1) Modification on standard back propagation
algorithm by introducing gain variation term of the activation
function, (2) Calculating the gradient descent on error with
respect to the weights and gains values and (3) the determination
of the new search direction by exploiting the information
calculated by gradient descent in step (2) as well as the previous
search direction. The proposed method improved the training
efficiency of back propagation algorithm by adaptively modifying
the initial search direction. Performance of the proposed method
is demonstrated by comparing to the conjugate gradient algorithm
from neural network toolbox for the chosen benchmark. The
results show that the number of iterations required by the
proposed method to converge is less than 20% of what is required
by the standard conjugate gradient and neural network toolbox
algorithm.
Abstract: The aim of this paper is to present a new method
which can be used for progressive transmission of electrocardiogram
(ECG). The idea consists in transforming any ECG signal to an
image, containing one beat in each row. In the first step, the beats are
synchronized in order to reduce the high frequencies due to inter-beat
transitions. The obtained image is then transformed using a discrete
version of Radon Transform (DRT). Hence, transmitting the ECG,
leads to transmit the most significant energy of the transformed
image in Radon domain. For decoding purpose, the receptor needs to
use the inverse Radon Transform as well as the two synchronization
frames.
The presented protocol can be adapted for lossy to lossless
compression systems. In lossy mode we show that the compression
ratio can be multiplied by an average factor of 2 for an acceptable
quality of reconstructed signal. These results have been obtained on
real signals from MIT database.
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: In this paper an effective approach for segmenting
human skin regions in images taken at different environment is
proposed. The proposed method uses a color distance map that is
flexible enough to reliably detect the skin regions even if the
illumination conditions of the image vary. Local image conditions is
also focused, which help the technique to adaptively detect differently
illuminated skin regions of an image. Moreover, usage of local
information also helps the skin detection process to get rid of picking
up much noisy pixels.