Abstract: This paper deals with the application for contentbased
image retrieval to extract color feature from natural images
stored in the image database by segmenting the image through
clustering. We employ a class of nonparametric techniques in which
the data points are regarded as samples from an unknown probability
density. Explicit computation of the density is avoided by using the
mean shift procedure, a robust clustering technique, which does not
require prior knowledge of the number of clusters, and does not
constrain the shape of the clusters. A non-parametric technique for
the recovery of significant image features is presented and
segmentation module is developed using the mean shift algorithm to
segment each image. In these algorithms, the only user set parameter
is the resolution of the analysis and either gray level or color images
are accepted as inputs. Extensive experimental results illustrate
excellent performance.
Abstract: This paper presents the methodology from machine
learning approaches for short-term rain forecasting system. Decision
Tree, Artificial Neural Network (ANN), and Support Vector Machine
(SVM) were applied to develop classification and prediction models
for rainfall forecasts. The goals of this presentation are to
demonstrate (1) how feature selection can be used to identify the
relationships between rainfall occurrences and other weather
conditions and (2) what models can be developed and deployed for
predicting the accurate rainfall estimates to support the decisions to
launch the cloud seeding operations in the northeastern part of
Thailand. Datasets collected during 2004-2006 from the
Chalermprakiat Royal Rain Making Research Center at Hua Hin,
Prachuap Khiri khan, the Chalermprakiat Royal Rain Making
Research Center at Pimai, Nakhon Ratchasima and Thai
Meteorological Department (TMD). A total of 179 records with 57
features was merged and matched by unique date. There are three
main parts in this work. Firstly, a decision tree induction algorithm
(C4.5) was used to classify the rain status into either rain or no-rain.
The overall accuracy of classification tree achieves 94.41% with the
five-fold cross validation. The C4.5 algorithm was also used to
classify the rain amount into three classes as no-rain (0-0.1 mm.),
few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall
accuracy of classification tree achieves 62.57%. Secondly, an ANN
was applied to predict the rainfall amount and the root mean square
error (RMSE) were used to measure the training and testing errors of
the ANN. It is found that the ANN yields a lower RMSE at 0.171 for
daily rainfall estimates, when compared to next-day and next-2-day
estimation. Thirdly, the ANN and SVM techniques were also used to
classify the rain amount into three classes as no-rain, few-rain, and
moderate-rain as above. The results achieved in 68.15% and 69.10%
of overall accuracy of same-day prediction for the ANN and SVM
models, respectively. The obtained results illustrated the comparison
of the predictive power of different methods for rainfall estimation.
Abstract: This work proposes an optical fiber system (OF) for
sensing various volatile organic compounds (VOCs) in human breath
for the diagnosis of some metabolic disorders as a non-invasive
methodology. The analyzed VOCs are alkanes (i.e., ethane, pentane,
heptane, octane, and decane), and aromatic compounds (i.e., benzene,
toluene, and styrene). The OF displays high analytical performance
since it provides near real-time responses, rapid analysis, and low
instrumentation costs, as well as it exhibits useful linear range and
detection limits; the developed OF sensor is also comparable to a
reference methodology (gas chromatography-mass spectrometry) for
the eight tested VOCs.
Abstract: Space Vector Pulse Width Modulation SVPWM is
one of the most used techniques to generate sinusoidal voltage and
current due to its facility and efficiency with low harmonics
distortion. This algorithm is specially used in power electronic
applications. This paper describes simulation algorithm of SVPWM
& SPWM using MatLab/simulink environment. It also implements a
closed loop three phases DC-AC converter controlling its outputs
voltages amplitude and frequency using MatLab. Also comparison
between SVPWM & SPWM results is given.
Abstract: WLAN Positioning has been presented by many
approaches in literatures using the characteristics of Received Signal
Strength (RSS), Time of Arrival (TOA) or Time Difference of
Arrival (TDOA), Angle of Arrival (AOA) and cell ID. Among these,
RSS approach is the simplest method to implement because there is
no need of modification on both access points and client devices
whereas its accuracy is terrible due to physical environments. For
TOA or TDOA approach, the accuracy is quite acceptable but most
researches have to modify either software or hardware on existing
WLAN infrastructure. The scales of modifications are made on only
access card up to the changes in protocol of WLAN. Hence, it is an
unattractive approach to use TOA or TDOA for positioning system.
In this paper, the new concept of merging both RSS and TOA
positioning techniques is proposed. In addition, the method to
achieve TOA characteristic for positioning WLAN user without any
extra modification necessarily appended in the existing system is
presented. The measurement results confirm that the proposed
technique using both RSS and TOA characteristics provides better
accuracy than using only either RSS or TOA approach.
Abstract: Purpose: Planning and dosimetry of different VMAT algorithms (SmartArc, Ergo++, Autobeam) is compared with IMRT for Head and Neck Cancer patients. Modelling was performed to rule out the causes of discrepancies between planned and delivered dose. Methods: Five HNC patients previously treated with IMRT were re-planned with SmartArc (SA), Ergo++ and Autobeam. Plans were compared with each other and against IMRT and evaluated using DVHs for PTVs and OARs, delivery time, monitor units (MU) and dosimetric accuracy. Modelling of control point (CP) spacing, Leaf-end Separation and MLC/Aperture shape was performed to rule out causes of discrepancies between planned and delivered doses. Additionally estimated arc delivery times, overall plan generation times and effect of CP spacing and number of arcs on plan generation times were recorded. Results: Single arc SmartArc plans (SA4d) were generally better than IMRT and double arc plans (SA2Arcs) in terms of homogeneity and target coverage. Double arc plans seemed to have a positive role in achieving improved Conformity Index (CI) and better sparing of some Organs at Risk (OARs) compared to Step and Shoot IMRT (ss-IMRT) and SA4d. Overall Ergo++ plans achieved best CI for both PTVs. Dosimetric validation of all VMAT plans without modelling was found to be lower than ss-IMRT. Total MUs required for delivery were on average 19%, 30%, 10.6% and 6.5% lower than ss-IMRT for SA4d, SA2d (Single arc with 20 Gantry Spacing), SA2Arcs and Autobeam plans respectively. Autobeam was most efficient in terms of actual treatment delivery times whereas Ergo++ plans took longest to deliver. Conclusion: Overall SA single arc plans on average achieved best target coverage and homogeneity for both PTVs. SA2Arc plans showed improved CI and some OARs sparing. Very good dosimetric results were achieved with modelling. Ergo++ plans achieved best CI. Autobeam resulted in fastest treatment delivery times.
Abstract: In literatures, many researches proposed various
methods to reduce PAPR (Peak to Average Power Ratio). Among
those, DSI (Dummy Sequence Insertion) is one of the most attractive
methods for WiMAX systems because it does not require side
information transmitted along with user data. However, the
conventional DSI methods find dummy sequence by performing an
iterative procedure until achieving PAPR under a desired threshold.
This causes a significant delay on finding dummy sequence and also
effects to the overall performances in WiMAX systems. In this paper,
the new method based on DSI is proposed by finding dummy
sequence without the need of iterative procedure. The fast DSI
method can reduce PAPR without either delays or required side
information. The simulation results confirm that the proposed method
is able to carry out PAPR performances as similar to the other
methods without any delays. In addition, the simulations of WiMAX
system with adaptive modulations are also investigated to realize the
use of proposed methods on various fading schemes. The results
suggest the WiMAX designers to modify a new Signal to Noise Ratio
(SNR) criteria for adaptation.
Abstract: The speculative locking (SL) protocol extends the twophase locking (2PL) protocol to allow for parallelism among conflicting transactions. The adaptive speculative locking (ASL) protocol provided further enhancements and outperformed SL protocols under most conditions. Neither of these protocols consider the impact of network latency on the performance of the distributed database systems. We have studied the performance of ASL protocol taking into account the communication overhead. The results indicate that though system load can counter network latency, it can still become a bottleneck in many situations. The impact of latency on performance depends on many factors including the system resources. A flexible discrete event simulator was used as the testbed for this study.
Abstract: In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.
Abstract: A series of Ti based shape memory alloys with
composition of Ti50Ni49Cr1, Ti50Ni47Cr3 and Ti50Ni45Cr5 were
developed by vacuum arc-melting under a purified argon atmosphere.
The histometric and corrosion evaluation of Ti-Ni-Cr shape memory
alloys have been considered in this research work. The alloys were
developed by vacuum arc melting and implanted subcutaneously in
rabbits for 4, 8 and 12 weeks. Metallic implants were embedded in
order to determine the outcome of implantation on histometric and
corrosion evaluation of Ti-Ni-Cr metallic strips. Encapsulating
membrane formation around the alloys was minimal in the case of all
materials. After histomorphometric analyses it was possible to
demonstrate that there were no statistically significant differences
between the materials. Corrosion rate was also determined in this
study which is within acceptable range. The results showed the Ti-
Ni-Cr alloy was neither cytotoxic, nor have any systemic reaction on
living system in any of the test performed. Implantation shows good
compatibility and a potential of being used directly in vivo system.
Abstract: Semantic Web services will enable the semiautomatic
and automatic annotation, advertisement, discovery,
selection, composition, and execution of inter-organization business
logic, making the Internet become a common global platform where
organizations and individuals communicate with each other to carry
out various commercial activities and to provide value-added
services. There is a growing consensus that Web services alone will
not be sufficient to develop valuable solutions due the degree of
heterogeneity, autonomy, and distribution of the Web. This paper
deals with two of the hottest R&D and technology areas currently
associated with the Web – Web services and the Semantic Web. It
presents the synergies that can be created between Web Services and
Semantic Web technologies to provide a new generation of eservices.
Abstract: Image data holds a large amount of different context
information. However, as of today, these resources remain largely
untouched. It is thus the aim of this paper to present a basic technical
framework which allows for a quick and easy exploitation of context
information from image data especially by non-expert users.
Furthermore, the proposed framework is discussed in detail
concerning important social and ethical issues which demand special
requirements in system design. Finally, a first sensor prototype is
presented which meets the identified requirements. Additionally,
necessary implications for the software and hardware design of the
system are discussed, rendering a sensor system which could be
regarded as a good, acceptable and justifiable technical and thereby
enabling the extraction of context information from image data.
Abstract: One of the main environmental problems which affect extensive areas in the world is soil salinity. Traditional data collection methods are neither enough for considering this important environmental problem nor accurate for soil studies. Remote sensing data could overcome most of these problems. Although satellite images are commonly used for these studies, however there are still needs to find the best calibration between the data and real situations in each specified area. Neyshaboor area, North East of Iran was selected as a field study of this research. Landsat satellite images for this area were used in order to prepare suitable learning samples for processing and classifying the images. 300 locations were selected randomly in the area to collect soil samples and finally 273 locations were reselected for further laboratory works and image processing analysis. Electrical conductivity of all samples was measured. Six reflective bands of ETM+ satellite images taken from the study area in 2002 were used for soil salinity classification. The classification was carried out using common algorithms based on the best composition bands. The results showed that the reflective bands 7, 3, 4 and 1 are the best band composition for preparing the color composite images. We also found out, that hybrid classification is a suitable method for identifying and delineation of different salinity classes in the area.
Abstract: This paper investigates the issue of building decision
trees from data with imprecise class values where imprecision is
encoded in the form of possibility distributions. The Information
Affinity similarity measure is introduced into the well-known gain
ratio criterion in order to assess the homogeneity of a set of
possibility distributions representing instances-s classes belonging to
a given training partition. For the experimental study, we proposed an
information affinity based performance criterion which we have used
in order to show the performance of the approach on well-known
benchmarks.
Abstract: Complexity, as a theoretical background has made it
easier to understand and explain the features and dynamic behavior
of various complex systems. As the common theoretical background
has confirmed, borrowing the terminology for design from the
natural sciences has helped to control and understand urban
complexity. Phenomena like self-organization, evolution and
adaptation are appropriate to describe the formerly inaccessible
characteristics of the complex environment in unpredictable bottomup
systems. Increased computing capacity has been a key element in
capturing the chaotic nature of these systems.
A paradigm shift in urban planning and architectural design has
forced us to give up the illusion of total control in urban
environment, and consequently to seek for novel methods for
steering the development. New methods using dynamic modeling
have offered a real option for more thorough understanding of
complexity and urban processes. At best new approaches may renew
the design processes so that we get a better grip on the complex
world via more flexible processes, support urban environmental
diversity and respond to our needs beyond basic welfare by liberating
ourselves from the standardized minimalism.
A complex system and its features are as such beyond human
ethics. Self-organization or evolution is either good or bad. Their
mechanisms are by nature devoid of reason. They are common in
urban dynamics in both natural processes and gas. They are features
of a complex system, and they cannot be prevented. Yet their
dynamics can be studied and supported.
The paradigm of complexity and new design approaches has been
criticized for a lack of humanity and morality, but the ethical
implications of scientific or computational design processes have not
been much discussed. It is important to distinguish the (unexciting)
ethics of the theory and tools from the ethics of computer aided
processes based on ethical decisions. Urban planning and architecture
cannot be based on the survival of the fittest; however, the natural
dynamics of the system cannot be impeded on grounds of being
“non-human".
In this paper the ethical challenges of using the dynamic models
are contemplated in light of a few examples of new architecture and
dynamic urban models and literature. It is suggested that ethical
challenges in computational design processes could be reframed
under the concepts of responsibility and transparency.
Abstract: The growing influence of service industries has
prompted greater attention being paid to service operations
management. However, service managers often have difficulty
articulating the veritable effects of their service innovation. Especially,
the performance evaluation process of service innovation problems
generally involves uncertain and imprecise data. This paper presents a
2-tuple fuzzy linguistic computing approach to dealing with
heterogeneous information and information loss problems while the
processes of subjective evaluation integration. The proposed method
based on group decision-making scenario to assist business managers
in measuring performance of service innovation manipulates the
heterogeneity integration processes and avoids the information loss
effectively.
Abstract: Supply chain networks are frequently hit by
unplanned events which lead to disruptions and cause operational and
financial consequences. It is neither possible to avoid disruption risk
entirely, nor are network members able to prepare for every possible
disruptive event. Therefore a continuity planning should be set up
which supports effective operational responses in supply chain
networks in times of emergencies. In this research network related
degrees of freedom which determine the options for responsive
actions are derived from interview data. The findings are further
embedded into a common risk management process. The paper
provides support for researchers and practitioners to identify the
network related options for responsive actions and to determine the
need for improving the reaction capabilities.
Abstract: The transformation of vocal characteristics aims at
modifying voice such that the intelligibility of aphonic voice is
increased or the voice characteristics of a speaker (source speaker) to
be perceived as if another speaker (target speaker) had uttered it. In
this paper, the current state-of-the-art voice characteristics
transformation methodology is reviewed. Special emphasis is placed
on voice transformation methodology and issues for improving the
transformed speech quality in intelligibility and naturalness are
discussed. In particular, it is suggested to use the modulation theory
of speech as a base for research on high quality voice transformation.
This approach allows one to separate linguistic, expressive, organic
and perspective information of speech, based on an analysis of how
they are fused when speech is produced. Therefore, this theory
provides the fundamentals not only for manipulating non-linguistic,
extra-/paralinguistic and intra-linguistic variables for voice
transformation, but also for paving the way for easily transposing the
existing voice transformation methods to emotion-related voice
quality transformation and speaking style transformation. From the
perspectives of human speech production and perception, the popular
voice transformation techniques are described and classified them
based on the underlying principles either from the speech production
or perception mechanisms or from both. In addition, the advantages
and limitations of voice transformation techniques and the
experimental manipulation of vocal cues are discussed through
examples from past and present research. Finally, a conclusion and
road map are pointed out for more natural voice transformation
algorithms in the future.
Abstract: This paper aims to present the main instruments used
in the economic literature for measuring the price risk, pointing out
on the advantages brought by the conditional variance in this respect.
The theoretical approach will be exemplified by elaborating an
EGARCH model for the price returns of wheat, both on Romanian
and on international market. To our knowledge, no previous
empirical research, either on price risk measurement for the
Romanian markets or studies that use the ARIMA-EGARCH
methodology, have been conducted. After estimating the
corresponding models, the paper will compare the estimated
conditional variance on the two markets.
Abstract: Decrease in hardware costs and advances in computer
networking technologies have led to increased interest in the use of
large-scale parallel and distributed computing systems. One of the
biggest issues in such systems is the development of effective
techniques/algorithms for the distribution of the processes/load of a
parallel program on multiple hosts to achieve goal(s) such as
minimizing execution time, minimizing communication delays,
maximizing resource utilization and maximizing throughput.
Substantive research using queuing analysis and assuming job
arrivals following a Poisson pattern, have shown that in a multi-host
system the probability of one of the hosts being idle while other host
has multiple jobs queued up can be very high. Such imbalances in
system load suggest that performance can be improved by either
transferring jobs from the currently heavily loaded hosts to the lightly
loaded ones or distributing load evenly/fairly among the hosts .The
algorithms known as load balancing algorithms, helps to achieve the
above said goal(s). These algorithms come into two basic categories -
static and dynamic. Whereas static load balancing algorithms (SLB)
take decisions regarding assignment of tasks to processors based on
the average estimated values of process execution times and
communication delays at compile time, Dynamic load balancing
algorithms (DLB) are adaptive to changing situations and take
decisions at run time.
The objective of this paper work is to identify qualitative
parameters for the comparison of above said algorithms. In future this
work can be extended to develop an experimental environment to
study these Load balancing algorithms based on comparative
parameters quantitatively.