Abstract: Dynamics of laser radiation – metal target interaction
in water at 1064 nm by applying Mach-Zehnder interference
technique was studied. The mechanism of generating the well
developed regime of evaporation of a metal surface and a spherical
shock wave in water is proposed. Critical intensities of the NIR for
the well developed evaporation of silver and gold targets were
determined. Dynamics of shock waves was investigated for earlier
(dozens) and later (hundreds) nanoseconds of time. Transparent
expanding plasma-vapor-compressed water object was visualized and
measured. The thickness of compressed layer of water and pressures
behind the front of a shock wave for later time delays were obtained
from the optical treatment of interferograms.
Abstract: This paper presents the application of a signal intensity
independent similarity criterion for rigid and non-rigid body
registration of binary objects. The criterion is defined as the
weighted ratio image of two images. The ratio is computed on a
voxel per voxel basis and weighting is performed by setting the raios
between signal and background voxels to a standard high value. The
mean squared value of the weighted ratio is computed over the union
of the signal areas of the two images and it is minimized using the
Chebyshev polynomial approximation.
Abstract: Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.
Abstract: Requirement engineering has been the subject of large
volume of researches due to the significant role it plays in the
software development life cycle. However, dynamicity of software
industry is much faster than advances in requirements engineering
approaches. Therefore, this paper aims to systematically review and
evaluate the current research in requirement engineering and identify
new research trends and direction in this field. In addition, various
research methods associated with the Evaluation-based techniques
and empirical study are highlighted for the requirements engineering
field. Finally, challenges and recommendations on future directions
research are presented based on the research team observations
during this study.
Abstract: The objective of this manuscript is to find area under the plasma concentration- time curve (AUC) for multiple doses of salbutamol sulphate sustained release tablets (Ventolin® oral tablets SR 8 mg, GSK, Pakistan) in the group of 18 healthy adults by using computational mathematics techniques. Following the administration of 4 doses of Ventolin® tablets 12 hourly to 24 healthy human subjects and bioanalysis of obtained plasma samples, plasma drug concentration-time profile was constructed. AUC, an important pharmacokinetic parameter, was measured using integrated equation of multiple oral dose regimens. The approximated AUC was also calculated by using computational mathematics techniques such as repeated rectangular, repeated trapezium and repeated Simpson's rule and compared with exact value of AUC calculated by using integrated equation of multiple oral dose regimens to find best computational mathematics method that gives AUC values closest to exact. The exact values of AUC for four consecutive doses of Ventolin® oral tablets were 150.5819473, 157.8131756, 164.4178231 and 162.78 ng.h/ml while the closest values approximated AUC values were 149.245962, 157.336171, 164.2585768 and 162.289224 ng.h/ml, respectively as found by repeated rectangular rule. The errors in the approximated values of AUC were negligible. It is concluded that all computational tools approximated values of AUC accurately but the repeated rectangular rule gives slightly better approximated values of AUC as compared to repeated trapezium and repeated Simpson's rules.
Abstract: Ultra-wide band (UWB) communication is one of
the most promising technologies for high data rate wireless networks
for short range applications. This paper proposes a blind channel
estimation method namely IMM (Interactive Multiple Model) Based
Kalman algorithm for UWB OFDM systems. IMM based Kalman
filter is proposed to estimate frequency selective time varying
channel. In the proposed method, two Kalman filters are concurrently
estimate the channel parameters. The first Kalman filter namely
Static Model Filter (SMF) gives accurate result when the user is static
while the second Kalman filter namely the Dynamic Model Filter
(DMF) gives accurate result when the receiver is in moving state. The
static transition matrix in SMF is assumed as an Identity matrix
where as in DMF, it is computed using Yule-Walker equations. The
resultant filter estimate is computed as a weighted sum of individual
filter estimates. The proposed method is compared with other existing
channel estimation methods.
Abstract: In this paper we consider a nonlinear feedback control
called augmented automatic choosing control (AACC) for nonlinear
systems with constrained input using weighted gradient optimization
automatic choosing functions. Constant term which arises from
linearization of a given nonlinear system is treated as a coefficient of
a stable zero dynamics. Parameters of the control are suboptimally
selected by maximizing the stable region in the sense of Lyapunov
with the aid of a genetic algorithm. This approach is applied to a
field excitation control problem of power system to demonstrate the
splendidness of the AACC. Simulation results show that the new
controller can improve performance remarkably well.
Abstract: Bangla Vowel characterization determines the spectral properties of Bangla vowels for efficient synthesis as well as recognition of Bangla vowels. In this paper, Bangla vowels in isolated word have been analyzed based on speech production model within the framework of Analysis-by-Synthesis. This has led to the extraction of spectral parameters for the production model in order to produce different Bangla vowel sounds. The real and synthetic spectra are compared and a weighted square error has been computed along with the error in the formant bandwidths for efficient representation of Bangla vowels. The extracted features produced good representation of targeted Bangla vowel. Such a representation also plays essential role in low bit rate speech coding and vocoders.
Abstract: Saturated hydraulic conductivity is one of the soil
hydraulic properties which is widely used in environmental studies
especially subsurface ground water. Since, its direct measurement is
time consuming and therefore costly, indirect methods such as
pedotransfer functions have been developed based on multiple linear
regression equations and neural networks model in order to estimate
saturated hydraulic conductivity from readily available soil
properties e.g. sand, silt, and clay contents, bulk density, and organic
matter. The objective of this study was to develop neural networks
(NNs) model to estimate saturated hydraulic conductivity from
available parameters such as sand and clay contents, bulk density,
van Genuchten retention model parameters (i.e. r
θ , α , and n) as well
as effective porosity. We used two methods to calculate effective
porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s
θ is
saturated water content, FC θ is water content retained at -33 kPa
matric potential, and inf θ is water content at the inflection point.
Total of 311 soil samples from the UNSODA database was divided
into three groups as 187 for the training, 62 for the validation (to
avoid over training), and 62 for the test of NNs model. A commercial
neural network toolbox of MATLAB software with a multi-layer
perceptron model and back propagation algorithm were used for the
training procedure. The statistical parameters such as correlation
coefficient (R2), and mean square error (MSE) were also used to
evaluate the developed NNs model. The best number of neurons in
the middle layer of NNs model for methods (1) and (2) were
calculated 44 and 6, respectively. The R2 and MSE values of the test
phase were determined for method (1), 0.94 and 0.0016, and for
method (2), 0.98 and 0.00065, respectively, which shows that method
(2) estimates saturated hydraulic conductivity better than method (1).
Abstract: Cross layer optimization based on utility functions has
been recently studied extensively, meanwhile, numerous types of
utility functions have been examined in the corresponding literature.
However, a major drawback is that most utility functions take a fixed
mathematical form or are based on simple combining, which can
not fully exploit available information. In this paper, we formulate a
framework of cross layer optimization based on Adaptively Weighted
Utility Functions (AWUF) for fairness balancing in OFDMA networks.
Under this framework, a two-step allocation algorithm is
provided as a sub-optimal solution, whose control parameters can be
updated in real-time to accommodate instantaneous QoS constrains.
The simulation results show that the proposed algorithm achieves
high throughput while balancing the fairness among multiple users.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: Mining frequent tree patterns have many useful
applications in XML mining, bioinformatics, network routing, etc.
Most of the frequent subtree mining algorithms (i.e. FREQT,
TreeMiner and CMTreeMiner) use anti-monotone property in the
phase of candidate subtree generation. However, none of these
algorithms have verified the correctness of this property in tree
structured data. In this research it is shown that anti-monotonicity
does not generally hold, when using weighed support in tree pattern
discovery. As a result, tree mining algorithms that are based on this
property would probably miss some of the valid frequent subtree
patterns in a collection of trees. In this paper, we investigate the
correctness of anti-monotone property for the problem of weighted
frequent subtree mining. In addition we propose W3-Miner, a new
algorithm for full extraction of frequent subtrees. The experimental
results confirm that W3-Miner finds some frequent subtrees that the
previously proposed algorithms are not able to discover.
Abstract: This paper study the high-level modelling and design
of delta-sigma (ΔΣ) noise shapers for audio Digital-to-Analog
Converter (DAC) so as to eliminate the in-band Signal-to-Noise-
Ratio (SNR) degradation that accompany one channel mismatch in
audio signal. The converter combines a cascaded digital signal
interpolation, a noise-shaping single loop delta-sigma modulator with
a 5-bit quantizer resolution in the final stage. To reduce sensitivity of
Digital-to-Analog Converter (DAC) nonlinearities of the last stage, a
high pass second order Data Weighted Averaging (R2DWA) is
introduced. This paper presents a MATLAB description modelling
approach of the proposed DAC architecture with low distortion and
swing suppression integrator designs. The ΔΣ Modulator design can
be configured as a 3rd-order and allows 24-bit PCM at sampling rate
of 64 kHz for Digital Video Disc (DVD) audio application. The
modeling approach provides 139.38 dB of dynamic range for a 32
kHz signal band at -1.6 dBFS input signal level.
Abstract: Environmental accounting is a recent phenomenon in the modern jurisprudence. It may reflect the corporate governance mechanisms in line with the natural resources and environmental sound management and administration systems in any country of the world. It may be a corporate focused on the improving of the environmental quality. But it is often identified that it is ignored due to some reasons such as unconsciousness, lack of ethical education etc. At present, the world community is very much concerned about the state of the environmental accounting and auditing systems as it bears sustainability on the mother earth for our generations. It is one of the important tools for understanding on the role played by the natural environment in the economy. It provides adequate data which is highlighted both in the contribution of natural resources to economic well-being as well as the costs imposed by pollution or resource degradation. It can play a critical role as on be a part of the many international environmental organizations such as IUCN, WWF, PADELIA, WRI etc.; as they have been taking many initiatives for ensuring the environmental accouting for our competent survivals. The global state actors have already taken some greening accounting initiatives under the forum of the United Nations Division for Sustainable Dedevolpment, the United Nations Statistical Division, the United Nations Conference on Environment and development known as Earth Summit in Rio de Janeiro, Johannesburg Conference 2002 etc. This study will provide an overview of the environmental accounting education consisting of 25 respondents based on the primary and secondary sources.
Abstract: Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
output.
Abstract: We study the possibility of using geometric operators
in the selection of human resources. We develop three new methods
that use the ordered weighted geometric (OWG) operator in different
indexes used for the selection of human resources. The objective of
these models is to manipulate the neutrality of the old methods so the
decision maker is able to select human resources according to his
particular attitude. In order to develop these models, first a short
revision of the OWG operator is developed. Second, we briefly
explain the general process for the selection of human resources.
Then, we develop the three new indexes. They will use the OWG
operator in the Hamming distance, in the adequacy coefficient and in
the index of maximum and minimum level. Finally, an illustrative
example about the new approach is given.
Abstract: Manufacturing Industries face a crucial change as products and processes are required to, easily and efficiently, be reconfigurable and reusable. In order to stay competitive and flexible, situations also demand distribution of enterprises globally, which requires implementation of efficient communication strategies. A prototype system called the “Broadcaster" has been developed with an assumption that the control environment description has been engineered using the Component-based system paradigm. This prototype distributes information to a number of globally distributed partners via an adoption of the circular-based data processing mechanism. The work highlighted in this paper includes the implementation of this mechanism in the domain of the manufacturing industry. The proposed solution enables real-time remote propagation of machine information to a number of distributed supply chain client resources such as a HMI, VRML-based 3D views and remote client instances regardless of their distribution nature and/ or their mechanisms. This approach is presented together with a set of evaluation results. Authors- main concentration surrounds the reliability and the performance metric of the adopted approach. Performance evaluation is carried out in terms of the response times taken to process the data in this domain and compared with an alternative data processing implementation such as the linear queue mechanism. Based on the evaluation results obtained, authors justify the benefits achieved from this proposed implementation and highlight any further research work that is to be carried out.
Abstract: An optimal mean-square fusion formulas with scalar
and matrix weights are presented. The relationship between them is
established. The fusion formulas are compared on the continuous-time
filtering problem. The basic differential equation for cross-covariance
of the local errors being the key quantity for distributed fusion is
derived. It is shown that the fusion filters are effective for multi-sensor
systems containing different types of sensors. An example
demonstrating the reasonable good accuracy of the proposed filters is
given.
Abstract: The Minimum Vertex Cover (MVC) problem is a classic
graph optimization NP - complete problem. In this paper a competent
algorithm, called Vertex Support Algorithm (VSA), is designed to
find the smallest vertex cover of a graph. The VSA is tested on a
large number of random graphs and DIMACS benchmark graphs.
Comparative study of this algorithm with the other existing methods
has been carried out. Extensive simulation results show that the VSA
can yield better solutions than other existing algorithms found in the
literature for solving the minimum vertex cover problem.
Abstract: We study the problem of decision making with Dempster-Shafer belief structure. We analyze the previous work developed by Yager about using the ordered weighted averaging (OWA) operator in the aggregation of the Dempster-Shafer decision process. We discuss the possibility of aggregating with an ascending order in the OWA operator for the cases where the smallest value is the best result. We suggest the introduction of the ordered weighted geometric (OWG) operator in the Dempster-Shafer framework. In this case, we also discuss the possibility of aggregating with an ascending order and we find that it is completely necessary as the OWG operator cannot aggregate negative numbers. Finally, we give an illustrative example where we can see the different results obtained by using the OWA, the Ascending OWA (AOWA), the OWG and the Ascending OWG (AOWG) operator.