Abstract: In most of the popular implementation of Parallel GAs
the whole population is divided into a set of subpopulations, each
subpopulation executes GA independently and some individuals are
migrated at fixed intervals on a ring topology. In these studies,
the migrations usually occur 'synchronously' among subpopulations.
Therefore, CPUs are not used efficiently and the communication
do not occur efficiently either. A few studies tried asynchronous
migration but it is hard to implement and setting proper parameter
values is difficult.
The aim of our research is to develop a migration method which is
easy to implement, which is easy to set parameter values, and which
reduces communication traffic. In this paper, we propose a traffic
reduction method for the Asynchronous Parallel Distributed GA by
migration of elites only. This is a Server-Client model. Every client
executes GA on a subpopulation and sends an elite information to the
server. The server manages the elite information of each client and
the migrations occur according to the evolution of sub-population in
a client. This facilitates the reduction in communication traffic.
To evaluate our proposed model, we apply it to many function optimization
problems. We confirm that our proposed method performs
as well as current methods, the communication traffic is less, and
setting of the parameters are much easier.
Abstract: Many people regard food events as part of gastronomic tourism and important in enhancing visitors’ experiences. Realizing the importance and contribution of food events to a country’s economy, the Malaysia government is undertaking greater efforts to promote such tourism activities to international tourists. Among other food events, the Ramadan bazaar is a unique food culture event, which receives significant attention from the Malaysia Ministry of Tourism. This study reports the empirical investigation into the international tourists’ perceptions, attraction towards the Ramadan bazaar and willingness in disseminating the information. Using the Ramadan bazaar at Kampung Baru, Kuala Lumpur as the data collection setting, results revealed that the Ramadan bazaar attributes (food and beverages, events and culture) significantly influenced the international tourist attraction to such a bazaar. Their high level of experience and satisfaction positively influenced their willingness to disseminate information. The positive response among the international tourists indicates that the Ramadan bazaar as gastronomic tourism can be used in addition to other tourism products as a catalyst to generate and boost the local economy. The related authorities that are closely associated with the tourism industry therefore should not ignore this indicator but continue to take proactive action in promoting the gastronomic event as one of the major tourist attractions.
Abstract: Efficient handoff algorithms are a cost-effective way
of enhancing the capacity and QoS of cellular system. The higher
value of hysteresis effectively prevents unnecessary handoffs but
causes undesired cell dragging. This undesired cell dragging causes
interference or could lead to dropped calls in microcellular
environment. The problems are further exacerbated by the corner
effect phenomenon which causes the signal level to drop by 20-30 dB
in 10-20 meters. Thus, in order to maintain reliable communication
in a microcellular system new and better handoff algorithms must be
developed. A fuzzy based handoff algorithm is proposed in this paper
as a solution to this problem. Handoff on the basis of ratio of slopes
of normal signal loss to the actual signal loss is presented. The fuzzy
based solution is supported by comparing its results with the results
obtained in analytical solution.
Abstract: This paper presents the development of an MODAPTS based cost estimating system to help designers in estimating the manufacturing cost of a assembly products which is belonged from the workers in working fields. Competitiveness of manufacturing cost is getting harder because of the development of Information and telecommunication, but also globalization. Therefore, the accuracy of the assembly cost estimation is getting important. DFA and MODAPTS is useful method for measuring the working hour. But these two methods are used just as a timetable. Therefore, in this paper, we suggest the process of measuring the working hours by MODAPTS which includes the working field-s accurate information. In addition, we adduce the estimation method of accuracy assembly cost with the real information. This research could be useful for designers that can estimate the assembly cost more accurately, and also effective for the companies that which are concerned to reduce the product cost.
Abstract: At present, increased concerns about global
environmental problems have magnified the importance of
sustainability management. To move towards sustainability,
companies need to look at everything from a holistic perspective in
order to understand the interconnections between economic growth
and environmental and social sustainability. This paper aims to gain
an understanding of key determinants that drive sustainability
management and barriers that hinder its development. It employs
semi-structured interviews with key informants, site observation and
documentation. The informants are production, marketing and
environmental managers of the leading wine producer, which aims to
become an Asia-s leader in wine & wine based products. It is found
that corporate image and top management leadership are the primary
factors influencing the adoption of sustainability management. Lack
of environmental knowledge and inefficient communication are
identified as barriers.
Abstract: Privacy issues commonly discussed among
researchers, practitioners, and end-users in pervasive healthcare.
Pervasive healthcare systems are applications that can support
patient-s need anytime and anywhere. However, pervasive healthcare
raises privacy concerns since it can lead to situations where patients
may not be aware that their private information is being shared and
becomes vulnerable to threat. We have systematically analyzed the
privacy issues and present a summary in tabular form to show the
relationship among the issues. The six issues identified are medical
information misuse, prescription leakage, medical information
eavesdropping, social implications for the patient, patient difficulties
in managing privacy settings, and lack of support in designing
privacy-sensitive applications. We narrow down the issues and chose
to focus on the issue of 'lack of support in designing privacysensitive
applications' by proposing a privacy-sensitive architecture
specifically designed for pervasive healthcare monitoring systems.
Abstract: Tacit knowledge has been one of the most discussed
and contradictory concepts in the field of knowledge management
since the mid 1990s. The concept is used relatively vaguely to refer
to any type of information that is difficult to articulate, which has led
to discussions about the original meaning of the concept (adopted
from Polanyi-s philosophy) and the nature of tacit knowing. It is
proposed that the subject should be approached from the perspective
of cognitive science in order to connect tacit knowledge to
empirically studied cognitive phenomena. Some of the most
important examples of tacit knowing presented by Polanyi are
analyzed in order to trace the cognitive mechanisms of tacit knowing
and to promote better understanding of the nature of tacit knowledge.
The cognitive approach to Polanyi-s theory reveals that the
tacit/explicit typology of knowledge often presented in the
knowledge management literature is not only artificial but totally
opposite approach compared to Polanyi-s thinking.
Abstract: In modern human computer interaction systems
(HCI), emotion recognition is becoming an imperative characteristic.
The quest for effective and reliable emotion recognition in HCI has
resulted in a need for better face detection, feature extraction and
classification. In this paper we present results of feature space analysis
after briefly explaining our fully automatic vision based emotion
recognition method. We demonstrate the compactness of the feature
space and show how the 2d/3d based method achieves superior features
for the purpose of emotion classification. Also it is exposed that
through feature normalization a widely person independent feature
space is created. As a consequence, the classifier architecture has
only a minor influence on the classification result. This is particularly
elucidated with the help of confusion matrices. For this purpose
advanced classification algorithms, such as Support Vector Machines
and Artificial Neural Networks are employed, as well as the simple k-
Nearest Neighbor classifier.
Abstract: Falling has been one of the major concerns and threats
to the independence of the elderly in their daily lives. With the
worldwide significant growth of the aging population, it is essential
to have a promising solution of fall detection which is able to operate
at high accuracy in real-time and supports large scale implementation
using multiple cameras. Field Programmable Gate Array (FPGA) is a
highly promising tool to be used as a hardware accelerator in many
emerging embedded vision based system. Thus, it is the main
objective of this paper to present an FPGA-based solution of visual
based fall detection to meet stringent real-time requirements with
high accuracy. The hardware architecture of visual based fall
detection which utilizes the pixel locality to reduce memory accesses
is proposed. By exploiting the parallel and pipeline architecture of
FPGA, our hardware implementation of visual based fall detection
using FGPA is able to achieve a performance of 60fps for a series of
video analytical functions at VGA resolutions (640x480). The results
of this work show that FPGA has great potentials and impacts in
enabling large scale vision system in the future healthcare industry
due to its flexibility and scalability.
Abstract: In this manuscript, a wavelet-based blind
watermarking scheme has been proposed as a means to provide
security to authenticity of a fingerprint. The information used for
identification or verification of a fingerprint mainly lies in its
minutiae. By robust watermarking of the minutiae in the fingerprint
image itself, the useful information can be extracted accurately even
if the fingerprint is severely degraded. The minutiae are converted in
a binary watermark and embedding these watermarks in the detail
regions increases the robustness of watermarking, at little to no
additional impact on image quality. It has been experimentally shown
that when the minutiae is embedded into wavelet detail coefficients
of a fingerprint image in spread spectrum fashion using a
pseudorandom sequence, the robustness is observed to have a
proportional response while perceptual invisibility has an inversely
proportional response to amplification factor “K". The DWT-based
technique has been found to be very robust against noises,
geometrical distortions filtering and JPEG compression attacks and is
also found to give remarkably better performance than DCT-based
technique in terms of correlation coefficient and number of erroneous
minutiae.
Abstract: In this paper, we propose a modified version of the
Constant Modulus Algorithm (CMA) tailored for blind Decision
Feedback Equalizer (DFE) of first order Markovian time varying
channels. The proposed NonStationary CMA (NSCMA) is designed
so that it explicitly takes into account the Markovian structure of
the channel nonstationarity. Hence, unlike the classical CMA, the
NSCMA is not blind with respect to the channel time variations.
This greatly helps the equalizer in the case of realistic channels, and
avoids frequent transmissions of training sequences.
This paper develops a theoretical analysis of the steady state
performance of the CMA and the NSCMA for DFEs within a time
varying context. Therefore, approximate expressions of the mean
square errors are derived. We prove that in the steady state, the
NSCMA exhibits better performance than the classical CMA. These
new results are confirmed by simulation.
Through an experimental study, we demonstrate that the Bit Error
Rate (BER) is reduced by the NSCMA-DFE, and the improvement
of the BER achieved by the NSCMA-DFE is as significant as the
channel time variations are severe.
Abstract: The organic farmers use wider range of crop varieties than the conventional farming. Bread wheat is the most favorite and the most common food crop. The organic bread wheat is usually of worse technological quality. Therefore, it is supposed to be an attractive alternative to the hulled wheat species (einkorn, emmer wheat and spelt). Twenty-five hulled bread wheat varieties and control bread wheat ones were grown on the certified organic parcel in České Budějovice (the Czech Republic) between 2009 and 2012. Their baking quality was measured and evaluated with standard methods, and in accordance with ICC. The results have shown that the grain of hulled wheat varieties contain a lot of proteins in grains (up to 18 percent); even the organic hulled bread wheat varieties are characterized by such good baking quality. Einkorn and emmer wheat are of worse technological quality of proteins (low values of gluten index and Zeleny test), which is a disadvantage of these two wheat species. On the other hand, spelt wheat is of better technological quality and is similar to the control bread wheat varieties. Mixtures consisting of bread wheat, among others, are considered good alternatives; they may contribute to wider range of use of the hulled wheat species. It is one of the possibilities which may increase the proportion of proteins in bread wheat grains; the nutrition-rich hulled wheat grains may be also used in such way at the same time.
Abstract: Developing a stable early warning system (EWS)
model that is capable to give an accurate prediction is a challenging
task. This paper introduces k-nearest neighbour (k-NN) method
which never been applied in predicting currency crisis before with the
aim of increasing the prediction accuracy. The proposed k-NN
performance depends on the choice of a distance that is used where in
our analysis; we take the Euclidean distance and the Manhattan as a
consideration. For the comparison, we employ three other methods
which are logistic regression analysis (logit), back-propagation neural
network (NN) and sequential minimal optimization (SMO). The
analysis using datasets from 8 countries and 13 macro-economic
indicators for each country shows that the proposed k-NN method
with k = 4 and Manhattan distance performs better than the other
methods.
Abstract: This paper presents a boarding on biometric
authentication through the Keystrokes Dynamics that it intends to
identify a person from its habitual rhythm to type in conventional
keyboard. Seven done experiments: verifying amount of prototypes,
threshold, features and the variation of the choice of the times of the
features vector. The results show that the use of the Keystroke
Dynamics is simple and efficient for personal authentication, getting
optimum resulted using 90% of the features with 4.44% FRR and 0%
FAR.
Abstract: The paper presents a one-dimensional transient
mathematical model of compressible thermal multi-component gas
mixture flows in pipes. The set of the mass, momentum and enthalpy
conservation equations for gas phase is solved. Thermo-physical
properties of multi-component gas mixture are calculated by solving
the Equation of State (EOS) model. The Soave-Redlich-Kwong
(SRK-EOS) model is chosen. Gas mixture viscosity is calculated on
the basis of the Lee-Gonzales-Eakin (LGE) correlation. Numerical
analysis on rapid decompression in conventional dry gases is
performed by using the proposed mathematical model. The model is
validated on measured values of the decompression wave speed in
dry natural gas mixtures. All predictions show excellent agreement
with the experimental data at high and low pressure. The presented
model predicts the decompression in dry natural gas mixtures much
better than GASDECOM and OLGA codes, which are the most
frequently-used codes in oil and gas pipeline transport service.
Abstract: Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.
Abstract: This research aims to create a model for analysis of student motivation behavior on e-Learning based on association rule mining techniques in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The model was created under association rules, one of the data mining techniques with minimum confidence. The results showed that the student motivation behavior model by using association rule technique can indicate the important variables that influence the student motivation behavior on e-Learning.
Abstract: Sports Sciences has been historically supported by the positivism idea of science, especially by the mechanistic/reductionist and becomes a field that views experimentation and measurement as the mayor research domains. The disposition to simplify nature and the world by parts has fragmented and reduced the idea of bodyathletes as machine. In this paper we intent to re-think this perception lined by Complexity Theory. We come with the idea of athletes as a reflexive and active being (corporeity-body). Therefore, the construction of a training that considers the cultural, biological, psychological elements regarding the experience of the human corporal movements in a circumspect and responsible way could bring better chances of accomplishment. In the end, we hope to help coaches understand the intrinsic complexity of the body they are training, how better deal with it, and, in the field of a deep globalization among the different types of knowledge, to respect and accepted the peculiarities of knowledge that comprise this area.
Abstract: Wireless Sensor Networks (WSNs) are used to monitor/observe vast inaccessible regions through deployment of large number of sensor nodes in the sensing area. For majority of WSN applications, the collected data needs to be combined with geographic information of its origin to make it useful for the user; information received from remote Sensor Nodes (SNs) that are several hops away from base station/sink is meaningless without knowledge of its source. In addition to this, location information of SNs can also be used to propose/develop new network protocols for WSNs to improve their energy efficiency and lifetime. In this paper, range free localization protocols for WSNs have been proposed. The proposed protocols are based on weighted centroid localization technique, where the edge weights of SNs are decided by utilizing fuzzy logic inference for received signal strength and link quality between the nodes. The fuzzification is carried out using (i) Mamdani, (ii) Sugeno, and (iii) Combined Mamdani Sugeno fuzzy logic inference. Simulation results demonstrate that proposed protocols provide better accuracy in node localization compared to conventional centroid based localization protocols despite presence of unintentional radio frequency interference from radio frequency (RF) sources operating in same frequency band.
Abstract: This paper presents an approach for an unequal error
protection of facial features of personal ID images coding. We
consider unequal error protection (UEP) strategies for the efficient
progressive transmission of embedded image codes over noisy
channels. This new method is based on the progressive image
compression embedded zerotree wavelet (EZW) algorithm and UEP
technique with defined region of interest (ROI). In this case is ROI
equal facial features within personal ID image. ROI technique is
important in applications with different parts of importance. In ROI
coding, a chosen ROI is encoded with higher quality than the
background (BG). Unequal error protection of image is provided by
different coding techniques and encoding LL band separately. In our
proposed method, image is divided into two parts (ROI, BG) that
consist of more important bytes (MIB) and less important bytes
(LIB). The proposed unequal error protection of image transmission
has shown to be more appropriate to low bit rate applications,
producing better quality output for ROI of the compresses image.
The experimental results verify effectiveness of the design. The
results of our method demonstrate the comparison of the UEP of
image transmission with defined ROI with facial features and the
equal error protection (EEP) over additive white gaussian noise
(AWGN) channel.