Abstract: The concept of adaptive shape parameters (ASP) has been presented for solution of incompressible Navier Strokes equations using mesh-free local Radial Basis Functions (RBF). The aim is to avoid ill-conditioning of coefficient matrices of RBF weights and inaccuracies in RBF interpolation resulting from non-optimized shape of basis functions for the cases where data points (or nodes) are not distributed uniformly throughout the domain. Unlike conventional approaches which assume globally similar values of RBF shape parameters, the presented ASP technique suggests that shape parameter be calculated exclusively for each data point (or node) based on the distribution of data points within its own influence domain. This will ensure interpolation accuracy while still maintaining well conditioned system of equations for RBF weights. Performance and accuracy of ASP technique has been tested by evaluating derivatives and laplacian of a known function using RBF in Finite difference mode (RBFFD), with and without the use of adaptivity in shape parameters. Application of adaptive shape parameters (ASP) for solution of incompressible Navier Strokes equations has been presented by solving lid driven cavity flow problem on mesh-free domain using RBF-FD. The results have been compared for fixed and adaptive shape parameters. Improved accuracy has been achieved with the use of ASP in RBF-FD especially at regions where larger gradients of field variables exist.
Abstract: In this paper, a robust decentralized congestion control strategy is developed for a large scale network with Differentiated Services (Diff-Serv) traffic. The network is modeled by a nonlinear fluid flow model corresponding to two classes of traffic, namely the premium traffic and the ordinary traffic. The proposed congestion controller does take into account the associated physical network resource limitations and is shown to be robust to the unknown and time-varying delays. Our proposed decentralized congestion control strategy is developed on the basis of Diff-Serv architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the ultimate boundedness of the closed-loop system. Numerical simulation implementations are presented by utilizing the QualNet and Matlab software tools to illustrate the effectiveness and capabilities of our proposed decentralized congestion control strategy.
Abstract: Digital libraries become more and more necessary in
order to support users with powerful and easy-to-use tools for
searching, browsing and retrieving media information. The starting
point for these tasks is the segmentation of video content into shots.
To segment MPEG video streams into shots, a fully automatic
procedure to detect both abrupt and gradual transitions (dissolve and
fade-groups) with minimal decoding in real time is developed in this
study. Each was explored through two phases: macro-block type's
analysis in B-frames, and on-demand intensity information analysis.
The experimental results show remarkable performance in
detecting gradual transitions of some kinds of input data and
comparable results of the rest of the examined video streams. Almost
all abrupt transitions could be detected with very few false positive
alarms.
Abstract: It has proved that nonlinear diffusion and bilateral
filtering (BF) have a closed connection. Early effort and contribution
are to find a generalized representation to link them by using adaptive
filtering. In this paper a new further relationship between nonlinear
diffusion and bilateral filtering is explored which pays more attention
to numerical calculus. We give a fresh idea that bilateral filtering can
be accelerated by multigrid (MG) scheme which likes the nonlinear
diffusion, and show that a bilateral filtering process with large kernel
size can be approximated by a nonlinear diffusion process based on
full multigrid (FMG) scheme.
Abstract: Biometrics, which refers to identifying an individual
based on his or her physiological or behavioral characteristics, has
the capability to reliably distinguish between an authorized person
and an imposter. Signature verification systems can be categorized as
offline (static) and online (dynamic). This paper presents a neural
network based recognition of offline handwritten signatures system
that is trained with low-resolution scanned signature images.
Abstract: An adaptive Chinese hand-talking system is presented
in this paper. By analyzing the 3 data collecting strategies for new
users, the adaptation framework including supervised and unsupervised
adaptation methods is proposed. For supervised adaptation,
affinity propagation (AP) is used to extract exemplar subsets, and enhanced
maximum a posteriori / vector field smoothing (eMAP/VFS)
is proposed to pool the adaptation data among different models. For
unsupervised adaptation, polynomial segment models (PSMs) are
used to help hidden Markov models (HMMs) to accurately label
the unlabeled data, then the "labeled" data together with signerindependent
models are inputted to MAP algorithm to generate
signer-adapted models. Experimental results show that the proposed
framework can execute both supervised adaptation with small amount
of labeled data and unsupervised adaptation with large amount
of unlabeled data to tailor the original models, and both achieve
improvements on the performance of recognition rate.
Abstract: An adaptive dynamic cerebellar model articulation
controller (DCMAC) neural network used for solving the prediction
and identification problem is proposed in this paper. The proposed
DCMAC has superior capability to the conventional cerebellar model
articulation controller (CMAC) neural network in efficient learning
mechanism, guaranteed system stability and dynamic response. The
recurrent network is embedded in the DCMAC by adding feedback
connections in the association memory space so that the DCMAC
captures the dynamic response, where the feedback units act as
memory elements. The dynamic gradient descent method is adopted to
adjust DCMAC parameters on-line. Moreover, the analytical method
based on a Lyapunov function is proposed to determine the
learning-rates of DCMAC so that the variable optimal learning-rates
are derived to achieve most rapid convergence of identifying error.
Finally, the adaptive DCMAC is applied in two computer simulations.
Simulation results show that accurate identifying response and
superior dynamic performance can be obtained because of the
powerful on-line learning capability of the proposed DCMAC.
Abstract: This paper presents an effective technique for harmonic current mitigation using an adaptive notch filter (ANF) to estimate current harmonics. The proposed filter consists of multiple units of ANF connected in parallel structure; each unit is governed by two ordinary differential equations. The frequency estimation is carried out based on the output of these units. The simulation and experimental results show the ability of the proposed tracking scheme to accurately estimate harmonics. The proposed filter was implemented digitally in TMS320F2808 and used in the control of hybrid active power filter (HAPF). The theoretical expectations are verified and demonstrated experimentally.
Abstract: Reliable water level forecasts are particularly
important for warning against dangerous flood and inundation. The
current study aims at investigating the suitability of the adaptive
network based fuzzy inference system for continuous water level
modeling. A hybrid learning algorithm, which combines the least
square method and the back propagation algorithm, is used to
identify the parameters of the network. For this study, water levels
data are available for a hydrological year of 2002 with a sampling
interval of 1-hour. The number of antecedent water level that should
be included in the input variables is determined by two statistical
methods, i.e. autocorrelation function and partial autocorrelation
function between the variables. Forecasting was done for 1-hour until
12-hour ahead in order to compare the models generalization at
higher horizons. The results demonstrate that the adaptive networkbased
fuzzy inference system model can be applied successfully and
provide high accuracy and reliability for river water level estimation.
In general, the adaptive network-based fuzzy inference system
provides accurate and reliable water level prediction for 1-hour ahead
where the MAPE=1.15% and correlation=0.98 was achieved. Up to
12-hour ahead prediction, the model still shows relatively good
performance where the error of prediction resulted was less than
9.65%. The information gathered from the preliminary results
provide a useful guidance or reference for flood early warning
system design in which the magnitude and the timing of a potential
extreme flood are indicated.
Abstract: Decentralized Tuple Space (DTS) implements tuple
space model among a series of decentralized hosts and provides the
logical global shared tuple repository. Replication has been introduced
to promote performance problem incurred by remote tuple access. In
this paper, we propose a replication approach of DTS allowing
replication policies self-adapting. The accesses from users or other
nodes are monitored and collected to contribute the decision making.
The replication policy may be changed if the better performance is
expected. The experiments show that this approach suitably adjusts the
replication policies, which brings negligible overhead.
Abstract: In this paper we propose a simple adaptive algorithm
iteratively solving the unit-norm constrained optimization problem.
Instead of conventional parameter norm based normalization,
the proposed algorithm incorporates scalar normalization which is
computationally much simpler. The analysis of stationary point is
presented to show that the proposed algorithm indeed solves the
constrained optimization problem. The simulation results illustrate
that the proposed algorithm performs as good as conventional ones
while being computationally simpler.
Abstract: This paper proposes an adaptive sliding mode
controller which combines adaptive control and sliding
mode control to control a nonlinear robotic manipulator
with uncertain parameters. We use an adaptive algorithm
based on the concept of sliding mode control to alleviate the
chattering phenomenon of control input. Adaptive laws are
developed to obtain the gain of switching input and the
boundary layer parameters. The stability and convergence
of the robotic manipulator control system are guaranteed
by applying the Lyapunov theorem. Simulation results
demonstrate that the chattering of control input can be
alleviated effectively. The proposed controller scheme can
assure robustness against a large class of uncertainties and
achieve good trajectory tracking performance.
Abstract: In this paper, an magnetorheological (MR) mount with
fuzzy sliding mode controller (FSMC) is studied for vibration
suppression when the system is subject to base excitations. In recent
years, magnetorheological fluids are becoming a popular material in
the field of the semi-active control. However, the dynamic equation of
an MR mount is highly nonlinear and it is difficult to identify. FSMC
provides a simple method to achieve vibration attenuation of the
nonlinear system with uncertain disturbances. This method is capable
of handling the chattering problem of sliding mode control effectively
and the fuzzy control rules are obtained by using the Lyapunov
stability theory. The numerical simulations using one-dimension and
two-dimension FSMC show effectiveness of the proposed controller
for vibration suppression. Further, the well-known skyhook control
scheme and an adaptive sliding mode controller are also included in
the simulation for comparison with the proposed FSMC.
Abstract: Rotor Flux based Model Reference Adaptive System
(RF-MRAS) is the most popularly used conventional speed
estimation scheme for sensor-less IM drives. In this scheme, the
voltage model equations are used for the reference model. This
encounters major drawbacks at low frequencies/speed which leads to
the poor performance of RF-MRAS. Replacing the reference model
using Neural Network (NN) based flux estimator provides an
alternate solution and addresses such drawbacks. This paper
identifies an NN based flux estimator using Single Neuron Cascaded
(SNC) Architecture. The proposed SNC-NN model replaces the
conventional voltage model in RF-MRAS to form a novel MRAS
scheme named as SNC-NN-MRAS. Through simulation the proposed
SNC-NN-MRAS is shown to be promising in terms of all major
issues and robustness to parameter variation. The suitability of the
proposed SNC-NN-MRAS based speed estimator and its advantages
over RF-MRAS for sensor-less induction motor drives is
comprehensively presented through extensive simulations.
Abstract: An effective approach for extracting document images from a noisy background is introduced. The entire scheme is divided into three sub- stechniques – the initial preprocessing operations for noise cluster tightening, introduction of a new thresholding method by maximizing the ratio of stan- dard deviations of the combined effect on the image to the sum of weighted classes and finally the image restoration phase by image binarization utiliz- ing the proposed optimum threshold level. The proposed method is found to be efficient compared to the existing schemes in terms of computational complexity as well as speed with better noise rejection.
Abstract: In this paper we introduced new wavelet based algorithm for speckle reduction of synthetic aperture radar images, which uses combination of undecimated wavelet transformation, wiener filter (which is an adaptive filter) and mean filter. Further more instead of using existing thresholding techniques such as sure shrinkage, Bayesian shrinkage, universal thresholding, normal thresholding, visu thresholding, soft and hard thresholding, we use brute force thresholding, which iteratively run the whole algorithm for each possible candidate value of threshold and saves each result in array and finally selects the value for threshold that gives best possible results. That is why it is slow as compared to existing thresholding techniques but gives best results under the given algorithm for speckle reduction.
Abstract: Video streaming over lossy IP networks is very
important issues, due to the heterogeneous structure of networks.
Infrastructure of the Internet exhibits variable bandwidths, delays,
congestions and time-varying packet losses. Because of variable
attributes of the Internet, video streaming applications should not
only have a good end-to-end transport performance but also have a
robust rate control, furthermore multipath rate allocation mechanism.
So for providing the video streaming service quality, some other
components such as Bandwidth Estimation and Adaptive Rate
Controller should be taken into consideration. This paper gives an
overview of video streaming concept and bandwidth estimation tools
and then introduces special architectures for bandwidth adaptive
video streaming. A bandwidth estimation algorithm – pathChirp,
Optimized Rate Controllers and Multipath Rate Allocation Algorithm
are considered as all-in-one solution for video streaming problem.
This solution is directed and optimized by a decision center which is
designed for obtaining the maximum quality at the receiving side.
Abstract: In this study, we developed an algorithm for detecting
seam cracks in a steel plate. Seam cracks are generated in the edge
region of a steel plate. We used the Gabor filter and an adaptive double
threshold method to detect them. To reduce the number of pseudo
defects, features based on the shape of seam cracks were used. To
evaluate the performance of the proposed algorithm, we tested 989
images with seam cracks and 9470 defect-free images. Experimental
results show that the proposed algorithm is suitable for detecting seam
cracks. However, it should be improved to increase the true positive
rate.
Abstract: This paper presents a new technique of compensation
of the effect of variation parameters in the direct field oriented
control of induction motor. The proposed method uses an adaptive
tuning of the value of synchronous speed to obtain the robustness for
the field oriented control. We show that this adaptive tuning allows
having robustness for direct field oriented control to changes in rotor
resistance, load torque and rotational speed. The effectiveness of the
proposed control scheme is verified by numerical simulations. The
numerical validation results of the proposed scheme have presented
good performances compared to the usual direct-field oriented
control.
Abstract: The drastic increase in the usage of SMS technology
has led service providers to seek for a solution that enable users of
mobile devices to access services through SMSs. This has resulted in
the proposal of solutions towards SMS-based service invocation in
service oriented environments. However, the dynamic nature of
service-oriented environments coupled with sudden load peaks
generated by service request, poses performance challenges to
infrastructures for supporting SMS-based service invocation. To
address this problem we adopt load balancing techniques. A load
balancing model with adaptive load balancing and load monitoring
mechanisms as its key constructs is proposed. The load balancing
model then led to realization of Least Loaded Load Balancing
Framework (LLLBF). Evaluation of LLLBF benchmarked with round
robin (RR) scheme on the queuing approach showed LLLBF
outperformed RR in terms of response time and throughput.
However, LLLBF achieved better result in the cost of high
processing power.