Abstract: In this paper, we use Radial Basis Function Networks
(RBFN) for solving the problem of environmental interference
cancellation of speech signal. We show that the Second Order Thin-
Plate Spline (SOTPS) kernel cancels the interferences effectively.
For make comparison, we test our experiments on two conventional
most used RBFN kernels: the Gaussian and First order TPS (FOTPS)
basis functions. The speech signals used here were taken from the
OGI Multi-Language Telephone Speech Corpus database and were
corrupted with six type of environmental noise from NOISEX-92
database. Experimental results show that the SOTPS kernel can
considerably outperform the Gaussian and FOTPS functions on
speech interference cancellation problem.
Abstract: In recent years multi-agent systems have emerged as one of the interesting architectures facilitating distributed collaboration and distributed problem solving. Each node (agent) of the network might pursue its own agenda, exploit its environment, develop its own problem solving strategy and establish required communication strategies. Within each node of the network, one could encounter a diversity of problem-solving approaches. Quite commonly the agents can realize their processing at the level of information granules that is the most suitable from their local points of view. Information granules can come at various levels of granularity. Each agent could exploit a certain formalism of information granulation engaging a machinery of fuzzy sets, interval analysis, rough sets, just to name a few dominant technologies of granular computing. Having this in mind, arises a fundamental issue of forming effective interaction linkages between the agents so that they fully broadcast their findings and benefit from interacting with others.
Abstract: People detection from images has a variety of applications such as video surveillance and driver assistance system, but is still a challenging task and more difficult in crowded environments such as shopping malls in which occlusion of lower parts of human body often occurs. Lack of the full-body information requires more effective features than common features such as HOG. In this paper, new features are introduced that exploits global self-symmetry (GSS) characteristic in head-shoulder patterns. The features encode the similarity or difference of color histograms and oriented gradient histograms between two vertically symmetric blocks. The domain-specific features are rapid to compute from the integral images in Viola-Jones cascade-of-rejecters framework. The proposed features are evaluated with our own head-shoulder dataset that, in part, consists of a well-known INRIA pedestrian dataset. Experimental results show that the GSS features are effective in reduction of false alarmsmarginally and the gradient GSS features are preferred more often than the color GSS ones in the feature selection.
Abstract: Bloom filter is a probabilistic and memory efficient
data structure designed to answer rapidly whether an element is
present in a set. It tells that the element is definitely not in the set but
its presence is with certain probability. The trade-off to use Bloom
filter is a certain configurable risk of false positives. The odds of a
false positive can be made very low if the number of hash function is
sufficiently large. For spam detection, weight is attached to each set
of elements. The spam weight for a word is a measure used to rate the
e-mail. Each word is assigned to a Bloom filter based on its weight.
The proposed work introduces an enhanced concept in Bloom filter
called Bin Bloom Filter (BBF). The performance of BBF over
conventional Bloom filter is evaluated under various optimization
techniques. Real time data set and synthetic data sets are used for
experimental analysis and the results are demonstrated for bin sizes 4,
5, 6 and 7. Finally analyzing the results, it is found that the BBF
which uses heuristic techniques performs better than the traditional
Bloom filter in spam detection.
Abstract: Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertainty as well as the sampling design using neural networks will be provided. A simulation study was conducted to compare estimation results based on complete case analysis, multiple imputation using a Markov Chain Monte Carlo, and neural network imputation. Furthermore, a public-use dataset was used as an example to illustrate neural networks imputation under a complex survey design
Abstract: Group contribution methods such as the UNIFAC are
very useful to researchers and engineers involved in synthesis,
feasibility studies, design and optimization of separation processes.
They can be applied successfully to predict phase equilibrium and
excess properties in the development of chemical and separation
processes. The main focus of this work was to investigate the
possibility of absorbing selected volatile organic compounds (VOCs)
into polydimethylsiloxane (PDMS) using three selected UNIFAC
group contribution methods. Absorption followed by subsequent
stripping is the predominant available abatement technology of
VOCs from flue gases prior to their release into the atmosphere. The
original, modified and effective UNIFAC models were used in this
work. The thirteen selected VOCs that have been considered in this
research are: pentane, hexane, heptanes, trimethylamine, toluene,
xylene, cyclohexane, butyl acetate, diethyl acetate, chloroform,
acetone, ethyl methyl ketone and isobutyl methyl ketone. The
computation was done for solute VOC concentration of 8.55x10-8
which is well in the infinite dilution region. The results obtained in
this study compare very well with those published in literature
obtained through both measurements and predictions. The phase
equilibrium obtained in this study show that PDMS is a good
absorbent for the removal of VOCs from contaminated air streams
through physical absorption.
Abstract: We present a BeeBot, Binus Multi-client Intelligent Telepresence Robot, a custom-build robot system specifically designed for teleconference with multiple person using omni directional actuator. The robot is controlled using a computer networks, so the manager/supervisor can direct the robot to the intended person to start a discussion/inspection. People tracking and autonomous navigation are intelligent features of this robot. We build a web application for controlling the multi-client telepresence robot and open-source teleconference system used. Experimental result presented and we evaluated its performance.
Abstract: This work presents the Risk Threshold RED (RTRED)
congestion control strategy for TCP networks. In addition to the
maximum and minimum thresholds in existing RED-based strategies,
we add a third dropping level. This new dropping level is the risk
threshold which works with the actual and average queue sizes to
detect the immediate congestion in gateways. Congestion reaction
by RTRED is on time. The reaction to congestion is neither too
early, to avoid unfair packet losses, nor too late to avoid packet
dropping from time-outs. We compared our novel strategy with RED
and ARED strategies for TCP congestion handling using a NS-2
simulation script. We found that the RTRED strategy outperformed
RED and ARED.
Abstract: It is well known that the channel capacity of Multiple-
Input-Multiple-Output (MIMO) system increases as the number of
antenna pairs between transmitter and receiver increases but it suffers
from multiple expensive RF chains. To reduce the cost of RF chains,
Antenna Selection (AS) method can offer a good tradeoff between
expense and performance. In a transmitting AS system, Channel
State Information (CSI) feedback is necessarily required to choose
the best subset of antennas in which the effects of delays and errors
occurred in feedback channels are the most dominant factors
degrading the performance of the AS method. This paper presents the
concept of AS method using CSI from channel reciprocity instead of
feedback method. Reciprocity technique can easily archive CSI by
utilizing a reverse channel where the forward and reverse channels
are symmetrically considered in time, frequency and location. In this
work, the capacity performance of MIMO system when using AS
method at transmitter with reciprocity channels is investigated by
own developing Testbed. The obtained results show that reciprocity
technique offers capacity close to a system with a perfect CSI and
gains a higher capacity than a system without AS method from 0.9 to
2.2 bps/Hz at SNR 10 dB.
Abstract: In this paper, we combine a probabilistic neural method with radial-bias functions in order to construct the lithofacies of the wells DF01, DF02 and DF03 situated in the Triassic province of Algeria (Sahara). Lithofacies is a crucial problem in reservoir characterization. Our objective is to facilitate the experts' work in geological domain and to allow them to obtain quickly the structure and the nature of lands around the drilling. This study intends to design a tool that helps automatic deduction from numerical data. We used a probabilistic formalism to enhance the classification process initiated by a Self-Organized Map procedure. Our system gives lithofacies, from well-log data, of the concerned reservoir wells in an aspect easy to read by a geology expert who identifies the potential for oil production at a given source and so forms the basis for estimating the financial returns and economic benefits.
Abstract: The purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learning vector quantization are used. Two methods are utilized for multilayer perceptron. First method has single hidden layer and between 3-15 nodes, second method has two hidden layer and each layer has between 3-15 nodes. Overall classification rate of 86.88% is achieved.
Abstract: Entrepreneurs are important for national labour markets and economies in that they contribute significantly to economic growth as well as provide the majority of jobs and create new ones. According to the Global Entrepreneurship Monitor’s “Report on Women and Entrepreneurship”, investment in women’s entrepreneurship is an important way to exponentially increase the impact of new venture creation finding ways to empower women’s participation and success in entrepreneurship are critical for more sustainable and successful economic development. Our results confirm that they are still differences between men and women entrepreneurs The reasons seems to be the lack of specific business skills, the less extensive social network, and the lack of identification patterns among women. Those differences can be explained by the fact that women still have fewer opportunities to make a career. If this is correct, we can predict an increasing proportion of women among entrepreneurs in the next years. Concerning the development of a favorable environment for developing and enhancing women entrepreneurship activities, our results show the insertion in a network and the role of a model doubtless represent elements determining in the choice to launch an entrepreneurship activity, as well as a precious resource for the success of her company.
Abstract: The current work focuses on rephrasing the harmful
effects of mercury that is being released from a number of sources.
Most of the sources are from the industrial waste water. Different
techniques of mercury removal have been discussed and a brief
comparison among these has been made. The experimental work has
been conducted for two most widely used methods of mercury
removal and comparison in terms of their efficiency has been made.
Abstract: It has been defined that the “network is the system".
This implies providing levels of service, reliability, predictability and
availability that are commensurate with or better than those that
individual computers provide today. To provide this requires
integrated network management for interconnected networks of
heterogeneous devices covering both the local campus. In this paper
we are addressing a framework to effectively deal with this issue. It
consists of components and interactions between them which are
required to perform the service fault management. A real-world
scenario is used to derive the requirements which have been applied
to the component identification. An analysis of existing frameworks
and approaches with respect to their applicability to the framework is
also carried out.
Abstract: This paper aimed to study the factors that relate to
working behavior of employees at Pakkred Municipality, Nonthaburi
Province. A questionnaire was utilized as the tool in collecting
information. Descriptive statistics included frequency, percentage,
mean and standard deviation. Independent- sample t- test, analysis of
variance and Pearson Correlation were also used. The findings of this
research revealed that the majority of the respondents were female,
between 25- 35 years old, married, with a Bachelor degree. The
average monthly salary of respondents was between 8,001- 12,000
Baht, and having about 4-7 years of working experience. Regarding
the overall working motivation factors, the findings showed that
interrelationship, respect, and acceptance were ranked as highly
important factors, whereas motivation, remunerations & welfare,
career growth, and working conditions were ranked as moderately
important factors. Also, overall working behavior was ranked as high.
The hypotheses testing revealed that different genders had a
different working behavior and had a different way of working as a
team, which was significant at the 0.05 confidence level, Moreover,
there was a difference among employees with different monthly
salary in working behavior, problem- solving and decision making,
which all were significant at the 0.05 confidence level. Employees
with different years of working experience were found to have work
working behavior both individual and as a team at the statistical
significance level of 0.01 and 0.05. The result of testing the
relationship between motivation in overall working revealed that
interrelationship, respect and acceptance from others, career growth,
and working conditions related to working behavior at a moderate
level, while motivation in performing duties and remunerations and
welfares related to working behavior towards the same direction at a
low level, with a statistical significance of 0.01.
Abstract: Face and facial expressions play essential roles in
interpersonal communication. Most of the current works on the facial
expression recognition attempt to recognize a small set of the
prototypic expressions such as happy, surprise, anger, sad, disgust
and fear. However the most of the human emotions are
communicated by changes in one or two of discrete features. In this
paper, we develop a facial expressions synthesis system, based on the
facial characteristic points (FCP's) tracking in the frontal image
sequences. Selected FCP's are automatically tracked using a crosscorrelation
based optical flow. The proposed synthesis system uses a
simple deformable facial features model with a few set of control
points that can be tracked in original facial image sequences.
Abstract: In this work, a special case of the image superresolution
problem where the only type of motion is global
translational motion and the blurs are shift-invariant is investigated.
The necessary conditions for exact reconstruction of the original
image by using finite impulse-response reconstruction filters are
developed. Given that the conditions are satisfied, a method for exact
super-resolution is presented and some simulation results are shown.
Abstract: Ever increasing capacities of contemporary storage devices
inspire the vision to accumulate (personal) information without
the need of deleting old data over a long time-span. Hence the target
of SemanticLIFE project is to create a Personal Information Management
system for a human lifetime data. One of the most important
characteristics of the system is its dedication to retrieve information
in a very efficient way. By adopting user demands regarding the
reduction of ambiguities, our approach aims at a user-oriented and
yet powerful enough system with a satisfactory query performance.
We introduce the query system of SemanticLIFE, the Virtual Query
System, which uses emerging Semantic Web technologies to fulfill
users- requirements.
Abstract: In IETF RFC 2002, Mobile-IP was developed to
enable Laptobs to maintain Internet connectivity while moving
between subnets. However, the packet loss that comes from
switching subnets arises because network connectivity is lost while
the mobile host registers with the foreign agent and this encounters
large end-to-end packet delays. The criterion to initiate a simple and
fast full-duplex connection between the home agent and foreign
agent, to reduce the roaming duration, is a very important issue to be
considered by a work in this paper. State-transition Petri-Nets of the
modeling scenario-based CIA: communication inter-agents procedure
as an extension to the basic Mobile-IP registration process was
designed and manipulated to describe the system in discrete events.
The heuristic of configuration file during practical Setup session for
registration parameters, on Cisco platform Router-1760 using IOS
12.3 (15)T and TFTP server S/W is created. Finally, stand-alone
performance simulations from Simulink Matlab, within each subnet
and also between subnets, are illustrated for reporting better end-toend
packet delays. Results verified the effectiveness of our Mathcad
analytical manipulation and experimental implementation. It showed
lower values of end-to-end packet delay for Mobile-IP using CIA
procedure-based early registration. Furthermore, it reported packets
flow between subnets to improve losses between subnets.
Abstract: Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.