Abstract: Sputum smear conversion after one month of antituberculosis
therapy in new smear positive pulmonary tuberculosis
patients (PTB+) is a vital indicator towards treatment success. The
objective of this study is to determine the rate of sputum smear
conversion in new PTB+ patients after one month under treatment of
National Institute of Diseases of the Chest and Hospital (NIDCH).
Analysis of sputum smear conversion was done by re-clinical
examination with sputum smear microscopic test after one month.
Socio-demographic and hematological parameters were evaluated to
perceive the correlation with the disease status. Among all enrolled
patients only 33.33% were available for follow up diagnosis and of
them only 42.86% patients turned to smear negative. Probably this
consequence is due to non-coherence to the proper disease
management. 66.67% and 78.78% patients reported low haemoglobin
and packed cell volume level respectively whereas 80% and 93.33%
patients accounted accelerated platelet count and erythrocyte
sedimentation rate correspondingly.
Abstract: Camera calibration is an indispensable step for augmented
reality or image guided applications where quantitative information
should be derived from the images. Usually, a camera
calibration is obtained by taking images of a special calibration object
and extracting the image coordinates of projected calibration marks
enabling the calculation of the projection from the 3d world coordinates
to the 2d image coordinates. Thus such a procedure exhibits
typical steps, including feature point localization in the acquired
images, camera model fitting, correction of distortion introduced by
the optics and finally an optimization of the model-s parameters. In
this paper we propose to extend this list by further step concerning
the identification of the optimal subset of images yielding the smallest
overall calibration error. For this, we present a Monte Carlo based
algorithm along with a deterministic extension that automatically
determines the images yielding an optimal calibration. Finally, we
present results proving that the calibration can be significantly
improved by automated image selection.
Abstract: Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, many real life optimization problems often
require finding optimal solution to complex high dimensional,
multimodal problems involving computationally very expensive
fitness function evaluations. Use of evolutionary algorithms in such
problem domains is thus practically prohibitive. An attractive
alternative is to build meta models or use an approximation of the
actual fitness functions to be evaluated. These meta models are order
of magnitude cheaper to evaluate compared to the actual function
evaluation. Many regression and interpolation tools are available to
build such meta models. This paper briefly discusses the
architectures and use of such meta-modeling tools in an evolutionary
optimization context. We further present two evolutionary algorithm
frameworks which involve use of meta models for fitness function
evaluation. The first framework, namely the Dynamic Approximate
Fitness based Hybrid EA (DAFHEA) model [14] reduces
computation time by controlled use of meta-models (in this case
approximate model generated by Support Vector Machine
regression) to partially replace the actual function evaluation by
approximate function evaluation. However, the underlying
assumption in DAFHEA is that the training samples for the metamodel
are generated from a single uniform model. This does not take
into account uncertain scenarios involving noisy fitness functions.
The second model, DAFHEA-II, an enhanced version of the original
DAFHEA framework, incorporates a multiple-model based learning
approach for the support vector machine approximator to handle
noisy functions [15]. Empirical results obtained by evaluating the
frameworks using several benchmark functions demonstrate their
efficiency
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 investigated the organizational
innovativeness of public listed housing developers in Malaysia. We
conceptualized organizational innovativeness as a multi-dimensional
construct consisting of 5 dimensions: market innovativeness, product
innovativeness, process innovativeness, behavior innovativeness and
strategic innovativeness. We carried out questionnaire survey with all
accessible public listed developers in Malaysia and received a 56
percent response. We found that the innovativeness of public listed
housing developers is low. The paper ends by providing some
explanations for the results.
Abstract: This article discusses the customs and traditions in
Turkestan in the late XIXth and early XXth centuries. Having a long
history, Turkestan is well-known as the birthplace of many nations
and nationalities. The name of Turkestan is also given to it for a
reason - the land of the Turkic peoples who inhabited Central Asia
and united under together. Currently, nations and nationalities of the
Turkestan region formed their own sovereign states, and every year
they prove their country names in the world community. Political,
economic importance of Turkestan, which became the gold wire
between Asia and Europe was always very high. So systematically
various aggressive actions were made by several great powers. As a
result of expansionary policy of colonization of the Russian Empire -
the Turkestan has appeared.
Abstract: Using mini modules of Tmotes, it is possible to automate a small personal area network. This idea can be extended to large networks too by implementing multi-hop routing. Linking the various Tmotes using Programming languages like Nesc, Java and having transmitter and receiver sections, a network can be monitored. It is foreseen that, depending on the application, a long range at a low data transfer rate or average throughput may be an acceptable trade-off. To reduce the overall costs involved, an optimum number of Tmotes to be used under various conditions (Indoor/Outdoor) is to be deduced. By analyzing the data rates or throughputs at various locations of Tmotes, it is possible to deduce an optimal number of Tmotes for a specific network. This paper deals with the determination of optimum distances to reduce the cost and increase the reliability of the entire sensor network with Wireless Local Loop (WLL) capability.
Abstract: Computations with higher than the IEEE 754 standard double-precision (about 16 significant digits) are required recently. Although there are available software routines in Fortran and C for high-precision computation, users are required to implement such routines in their own computers with detailed knowledges about them. We have constructed an user-friendly online system for octupleprecision computation. In our Web system users with no knowledges about high-precision computation can easily perform octupleprecision computations, by choosing mathematical functions with argument(s) inputted, by writing simple mathematical expression(s) or by uploading C program(s). In this paper we enhance the Web system above by adding the facility of uploading Fortran programs, which have been widely used in scientific computing. To this end we construct converter routines in two stages.
Abstract: In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.
Abstract: A mobile agent is a software which performs an
action autonomously and independently as a person or an
organizations assistance. Mobile agents are used for searching
information, retrieval information, filtering, intruder recognition in
networks, and so on. One of the important issues of mobile agent is
their security. It must consider different security issues in effective
and secured usage of mobile agent. One of those issues is the
integrity-s protection of mobile agents.
In this paper, the advantages and disadvantages of each method,
after reviewing the existing methods, is examined. Regarding to this
matter that each method has its own advantage or disadvantage, it
seems that by combining these methods, one can reach to a better
method for protecting the integrity of mobile agents. Therefore, this
method is provided in this paper and then is evaluated in terms of
existing method. Finally, this method is simulated and its results are
the sign of improving the possibility of integrity-s protection of
mobile agents.
Abstract: We present a new method to reconstruct a temporally
coherent 3D animation from single or multi-view RGB-D video data
using unbiased feature point sampling. Given RGB-D video data, in
form of a 3D point cloud sequence, our method first extracts feature
points using both color and depth information. In the subsequent
steps, these feature points are used to match two 3D point clouds in
consecutive frames independent of their resolution. Our new motion
vectors based dynamic alignement method then fully reconstruct
a spatio-temporally coherent 3D animation. We perform extensive
quantitative validation using novel error functions to analyze the
results. We show that despite the limiting factors of temporal and
spatial noise associated to RGB-D data, it is possible to extract
temporal coherence to faithfully reconstruct a temporally coherent
3D animation from RGB-D video data.
Abstract: This research sought to discover the forms of
promotion and dissemination of traditional local wisdom that are
used to create occupations among the elderly at Noanmueng
Community, Muang Sub-District, Baan Doong District, Udornthani
Province. The criteria used to select the research sample group were:
having a role involved in the promotion and dissemination of
traditional local wisdom to create occupations among the elderly;
being an experienced person who the residents of Noanmueng
Community find trustworthy; and having lived in Noanmueng
Community for a long time so as to be able to see the development
and change that occurs. A total of 16 persons were thus selected. Data
was gathered through a qualitative study, using semi-structured indepth
interviews. The collected data was then summarized and
discussed according to the research objectives. Finally, the data was
presented in narrative format. Results found that the identifying
traditional local wisdom of the community (which grew from the
residents’ experience and beneficial usage in daily life, passed down
from generation to generation) was the weaving of cloth and
basketry. As for the manner of promotion and dissemination of
traditional local wisdom, these skills were passed down through
teaching by example to family members, relatives and others in the
community. This was largely the initiative of the elders or elderly
members of the community. In order for the promotion and
dissemination of traditional local wisdom to create occupations
among the elderly, the traditional local wisdom should be supported
in every way through participation of the community members. For
example, establish a museum of traditional local wisdom for the
collection of traditional local wisdom in various fields, both from the
past and present innovations. This would be a source of pride for the
community, simultaneously helping traditional local wisdom to
become widely known and to create income for the community’s
elderly. Additional ways include organizing exhibitions of products
made by traditional local wisdom, finding both domestic and
international markets, as well as building both domestic and
international networks aiming to find opportunities to market
products made by traditional local wisdom.
Abstract: Studies in neuroscience suggest that both global and
local feature information are crucial for perception and recognition of
faces. It is widely believed that local feature is less sensitive to
variations caused by illumination, expression and illumination. In
this paper, we target at designing and learning local features for face
recognition. We designed three types of local features. They are
semi-global feature, local patch feature and tangent shape feature.
The designing of semi-global feature aims at taking advantage of
global-like feature and meanwhile avoiding suppressing AdaBoost
algorithm in boosting weak classifies established from small local
patches. The designing of local patch feature targets at automatically
selecting discriminative features, and is thus different with traditional
ways, in which local patches are usually selected manually to cover
the salient facial components. Also, shape feature is considered in
this paper for frontal view face recognition. These features are
selected and combined under the framework of boosting algorithm
and cascade structure. The experimental results demonstrate that the
proposed approach outperforms the standard eigenface method and
Bayesian method. Moreover, the selected local features and
observations in the experiments are enlightening to researches in
local feature design in face recognition.
Abstract: Based on the feature of model disturbances and uncertainty being compensated dynamically in auto – disturbances-rejection-controller (ADRC), a new method using ADRC is proposed for the decoupling control of dispenser longitudinal movement in big flight envelope. Developed from nonlinear model directly, ADRC is especially suitable for dynamic model that has big disturbances. Furthermore, without changing the structure and parameters of the controller in big flight envelope, this scheme can simplify the design of flight control system. The simulation results in big flight envelope show that the system achieves high dynamic performance, steady state performance and the controller has strong robustness.
Abstract: All practical real-time scheduling algorithms in multiprocessor systems present a trade-off between their computational complexity and performance. In real-time systems, tasks have to be performed correctly and timely. Finding minimal schedule in multiprocessor systems with real-time constraints is shown to be NP-hard. Although some optimal algorithms have been employed in uni-processor systems, they fail when they are applied in multiprocessor systems. The practical scheduling algorithms in real-time systems have not deterministic response time. Deterministic timing behavior is an important parameter for system robustness analysis. The intrinsic uncertainty in dynamic real-time systems increases the difficulties of scheduling problem. To alleviate these difficulties, we have proposed a fuzzy scheduling approach to arrange real-time periodic and non-periodic tasks in multiprocessor systems. Static and dynamic optimal scheduling algorithms fail with non-critical overload. In contrast, our approach balances task loads of the processors successfully while consider starvation prevention and fairness which cause higher priority tasks have higher running probability. A simulation is conducted to evaluate the performance of the proposed approach. Experimental results have shown that the proposed fuzzy scheduler creates feasible schedules for homogeneous and heterogeneous tasks. It also and considers tasks priorities which cause higher system utilization and lowers deadline miss time. According to the results, it performs very close to optimal schedule of uni-processor systems.
Abstract: Today we tend to go back to the past to our root
relation to nature. Therefore in search of friendly spaces there are
elements of natural environment introduced as elements of spatial
composition. Though reinvented through the use of the new
substance such as greenery, water etc. made possible by state of the
art technologies, still, in principal, they remain the same. As a result,
sustainable design, based upon the recognized means of composition
in addition to the relation of architecture and urbanism vs. nature
introduces a new aesthetical values into architectural and urban
space.
Abstract: Biometric techniques are gaining importance for
personal authentication and identification as compared to the
traditional authentication methods. Biometric templates are
vulnerable to variety of attacks due to their inherent nature. When a
person-s biometric is compromised his identity is lost. In contrast to
password, biometric is not revocable. Therefore, providing security
to the stored biometric template is very crucial. Crypto biometric
systems are authentication systems, which blends the idea of
cryptography and biometrics. Fuzzy vault is a proven crypto
biometric construct which is used to secure the biometric templates.
However fuzzy vault suffer from certain limitations like nonrevocability,
cross matching. Security of the fuzzy vault is affected
by the non-uniform nature of the biometric data. Fuzzy vault when
hardened with password overcomes these limitations. Password
provides an additional layer of security and enhances user privacy.
Retina has certain advantages over other biometric traits. Retinal
scans are used in high-end security applications like access control to
areas or rooms in military installations, power plants, and other high
risk security areas. This work applies the idea of fuzzy vault for
retinal biometric template. Multimodal biometric system
performance is well compared to single modal biometric systems.
The proposed multi modal biometric fuzzy vault includes combined
feature points from retina and fingerprint. The combined vault is
hardened with user password for achieving high level of security.
The security of the combined vault is measured using min-entropy.
The proposed password hardened multi biometric fuzzy vault is
robust towards stored biometric template attacks.
Abstract: This paper proposed a stiffness analysis method for a
3-PRS mechanism for welding thick aluminum plate using FSW
technology. In the molding process, elastic deformation of lead-screws
and links are taken into account. This method is based on the virtual
work principle. Through a survey of the commonly used stiffness
performance indices, the minimum and maximum eigenvalues of the
stiffness matrix are used to evaluate the stiffness of the 3-PRS
mechanism. Furthermore, A FEA model has been constructed to verify
the method. Finally, we redefined the workspace using the stiffness
analysis method.
Abstract: Centrally controlled authentication and authorization services can provide enterprise with an increase in security, more flexible access control solutions and an increased users' trust. By using redirections, users of all Web-based applications within an organization are authenticated at a single well known and secure Web site and using secure communication protocol. Users are first authenticated at the central server using their domain wide credentials before being redirected to a particular Web-based application. The central authentication server will then provide others with pertinence authorization related particulars and credentials of the authenticated user to the specific application. The trust between the clients and the server hosts is established by secure session keys exchange. Case- studies are provided to demonstrate the usefulness and flexibility of the proposed solution.
Abstract: This paper introduces new algorithms (Fuzzy relative
of the CLARANS algorithm FCLARANS and Fuzzy c Medoids
based on randomized search FCMRANS) for fuzzy clustering of
relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd)
in which the within cluster dissimilarity of each cluster is minimized
in each iteration by recomputing new medoids given current
memberships, FCLARANS minimizes the same objective function
minimized by FCMdd by changing current medoids in such away
that that the sum of the within cluster dissimilarities is minimized.
Computing new medoids may be effected by noise because outliers
may join the computation of medoids while the choice of medoids in
FCLARANS is dictated by the location of a predominant fraction of
points inside a cluster and, therefore, it is less sensitive to the
presence of outliers. In FCMRANS the step of computing new
medoids in FCMdd is modified to be based on randomized search.
Furthermore, a new initialization procedure is developed that add
randomness to the initialization procedure used with FCMdd. Both
FCLARANS and FCMRANS are compared with the robust and
linearized version of fuzzy c-medoids (RFCMdd). Experimental
results with different samples of the Reuter-21578, Newsgroups
(20NG) and generated datasets with noise show that FCLARANS is
more robust than both RFCMdd and FCMRANS. Finally, both
FCMRANS and FCLARANS are more efficient and their outputs
are almost the same as that of RFCMdd in terms of classification
rate.