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 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: 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: 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: 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.
Abstract: Energy efficient protocol design is the aim of current
researches in the area of sensor networks where limited power
resources impose energy conservation considerations. In this paper
we care for Medium Access Control (MAC) protocols and after an
extensive literature review, two adaptive schemes are discussed. Of
them, adaptive-rate MACs which were introduced for throughput
enhancement show the potency to save energy, even more than
adaptive-power schemes. Then we propose an allocation algorithm
for getting accurate and reliable results. Through a simulation study
we validated our claim and showed the power saving of adaptive-rate
protocols.
Abstract: Speech corpus is one of the major components in a
Speech Processing System where one of the primary requirements
is to recognize an input sample. The quality and details captured
in speech corpus directly affects the precision of recognition. The
current work proposes a platform for speech corpus generation using
an adaptive LMS filter and LPC cepstrum, as a part of an ANN
based Speech Recognition System which is exclusively designed to
recognize isolated numerals of Assamese language- a major language
in the North Eastern part of India. The work focuses on designing an
optimal feature extraction block and a few ANN based cooperative
architectures so that the performance of the Speech Recognition
System can be improved.
Abstract: In this paper a neural adaptive control method has
been developed and applied to robot control. Simulation results are
presented to verify the effectiveness of the controller. These results
show that the performance by using this controller is better than
those which just use either direct inverse control or predictive
control. In addition, they show that the resulting is a useful method
which combines the advantages of both direct inverse control and
predictive control.
Abstract: The least mean square (LMS) algorithmis one of the
most well-known algorithms for mobile communication systems
due to its implementation simplicity. However, the main limitation
is its relatively slow convergence rate. In this paper, a booster
using the concept of Markov chains is proposed to speed up the
convergence rate of LMS algorithms. The nature of Markov
chains makes it possible to exploit the past information in the
updating process. Moreover, since the transition matrix has a
smaller variance than that of the weight itself by the central limit
theorem, the weight transition matrix converges faster than the
weight itself. Accordingly, the proposed Markov-chain based
booster thus has the ability to track variations in signal
characteristics, and meanwhile, it can accelerate the rate of
convergence for LMS algorithms. Simulation results show that the
LMS algorithm can effectively increase the convergence rate and
meantime further approach the Wiener solution, if the
Markov-chain based booster is applied. The mean square error is
also remarkably reduced, while the convergence rate is improved.
Abstract: Subdivision is a method to create a smooth surface from a coarse mesh by subdividing the entire mesh. The conventional ways to compute and render surfaces are inconvenient both in terms of memory and computational time as the number of meshes will increase exponentially. An adaptive subdivision is the way to reduce the computational time and memory by subdividing only certain selected areas. In this paper, a new adaptive subdivision method for triangle meshes is introduced. This method defines a new adaptive subdivision rules by considering the properties of each triangle's neighbors and is embedded in a traditional Loop's subdivision. It prevents some undesirable side effects that appear in the conventional adaptive ways. Models that were subdivided by our method are compared with other adaptive subdivision methods