Abstract: In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
Abstract: To determine the presence and location of faults in a transmission by the adaptation of protective distance relay based on the measurement of fixed settings as line impedance is achieved by several different techniques. Moreover, a fast, accurate and robust technique for real-time purposes is required for the modern power systems. The appliance of radial basis function neural network in transmission line protection is demonstrated in this paper. The method applies the power system via voltage and current signals to learn the hidden relationship presented in the input patterns. It is experiential that the proposed technique is competent to identify the particular fault direction more speedily. System simulations studied show that the proposed approach is able to distinguish the direction of a fault on a transmission line swiftly and correctly, therefore suitable for the real-time purposes.
Abstract: In this paper we propose segmentation approach based
on Vector Quantization technique. Here we have used Kekre-s fast
codebook generation algorithm for segmenting low-altitude aerial
image. This is used as a preprocessing step to form segmented
homogeneous regions. Further to merge adjacent regions color
similarity and volume difference criteria is used. Experiments
performed with real aerial images of varied nature demonstrate that
this approach does not result in over segmentation or under
segmentation. The vector quantization seems to give far better results
as compared to conventional on-the-fly watershed algorithm.
Abstract: This paper is aimed at describing a delay-based endto-
end (e2e) congestion control algorithm, called Very FAST TCP
(VFAST), which is an enhanced version of FAST TCP. The main
idea behind this enhancement is to smoothly estimate the Round-Trip
Time (RTT) based on a nonlinear filter, which eliminates throughput
and queue oscillation when RTT fluctuates. In this context, an evaluation
of the suggested scheme through simulation is introduced, by
comparing our VFAST prototype with FAST in terms of throughput,
queue behavior, fairness, stability, RTT and adaptivity to changes in
network. The achieved simulation results indicate that the suggested
protocol offer better performance than FAST TCP in terms of RTT
estimation and throughput.
Abstract: Groundwater is one of the most important water resources in Fars province. Based on this study, 95 percent of the total annual water consumption in Fars is used for agriculture, whereas the percentages for domestic and industrial uses are 4 and 1 percent, respectively. Population growth, urban and industrial growth, and agricultural development in Fars have created a condition of water stress. In this province, farmers and other users are pumping groundwater faster than its natural replenishment rate, causing a continuous drop in groundwater tables and depletion of this resource. In this research variation of groundwater level , their effects and ways to help control groundwater levels in some plains of Fars were evaluated .Excessive exploitation of groundwater in Darab, Jahrom, Estahban, Arsanjan, Khir and Niriz plains of Fars caused the groundwater levels fall too fast or to unacceptable levels. The average drawdown of the water table in Arsanjan, Khir. Estahban and Niriz plain plains were 12,8, 9 and 6 meters during 16,11,11 and 13 years ago respectively. This not only reduces available water resources and well yields but also can saline water intrusion, reductions in river flow and in wetland areas , drying springs, and ground subsidence, considerable increase in pumping costs and a significant decline in crop yields as a result of the increasing salinity. Finally based on situation and condition of the aquifer some suggestions are recommended.
Abstract: A low bit rate still image compression scheme by
compressing the indices of Vector Quantization (VQ) and generating
residual codebook is proposed. The indices of VQ are compressed by
exploiting correlation among image blocks, which reduces the bit per
index. A residual codebook similar to VQ codebook is generated that
represents the distortion produced in VQ. Using this residual
codebook the distortion in the reconstructed image is removed,
thereby increasing the image quality. Our scheme combines these two
methods. Experimental results on standard image Lena show that our
scheme can give a reconstructed image with a PSNR value of 31.6 db
at 0.396 bits per pixel. Our scheme is also faster than the existing VQ
variants.
Abstract: Frequent pattern discovery over data stream is a hard
problem because a continuously generated nature of stream does not
allow a revisit on each data element. Furthermore, pattern discovery
process must be fast to produce timely results. Based on these
requirements, we propose an approximate approach to tackle the
problem of discovering frequent patterns over continuous stream.
Our approximation algorithm is intended to be applied to process a
stream prior to the pattern discovery process. The results of
approximate frequent pattern discovery have been reported in the
paper.
Abstract: Carrier mobility has become the most important
characteristic of high speed low dimensional devices. Due to
development of very fast switching semiconductor devices, speed of
computer and communication equipment has been increasing day by
day and will continue to do so in future. As the response of any
device depends on the carrier motion within the devices, extensive
studies of carrier mobility in the devices has been established
essential for the growth in the field of low dimensional devices.
Small-signal ac transport of degenerate two-dimensional hot
electrons in GaAs quantum wells is studied here incorporating
deformation potential acoustic, polar optic and ionized impurity
scattering in the framework of heated drifted Fermi-Dirac carrier
distribution. Delta doping is considered in the calculations to
investigate the effects of double delta doping on millimeter and submillimeter
wave response of two dimensional hot electrons in GaAs
nanostructures. The inclusion of delta doping is found to enhance
considerably the two dimensional electron density which in turn
improves the carrier mobility (both ac and dc) values in the GaAs
quantum wells thereby providing scope of getting higher speed
devices in future.
Abstract: Security has been an important issue and concern in the
smart home systems. Smart home networks consist of a wide range of
wired or wireless devices, there is possibility that illegal access to
some restricted data or devices may happen. Password-based
authentication is widely used to identify authorize users, because this
method is cheap, easy and quite accurate. In this paper, a neural
network is trained to store the passwords instead of using verification
table. This method is useful in solving security problems that
happened in some authentication system. The conventional way to
train the network using Backpropagation (BPN) requires a long
training time. Hence, a faster training algorithm, Resilient
Backpropagation (RPROP) is embedded to the MLPs Neural
Network to accelerate the training process. For the Data Part, 200
sets of UserID and Passwords were created and encoded into binary
as the input. The simulation had been carried out to evaluate the
performance for different number of hidden neurons and combination
of transfer functions. Mean Square Error (MSE), training time and
number of epochs are used to determine the network performance.
From the results obtained, using Tansig and Purelin in hidden and
output layer and 250 hidden neurons gave the better performance. As
a result, a password-based user authentication system for smart home
by using neural network had been developed successfully.
Abstract: This paper focuses on cost and profit analysis of
single-server Markovian queuing system with two priority classes. In
this paper, functions of total expected cost, revenue and profit of the
system are constructed and subjected to optimization with respect to
its service rates of lower and higher priority classes. A computing
algorithm has been developed on the basis of fast converging
numerical method to solve the system of non linear equations formed
out of the mathematical analysis. A novel performance measure of
cost and profit analysis in view of its economic interpretation for the
system with priority classes is attempted to discuss in this paper. On
the basis of computed tables observations are also drawn to enlighten
the variational-effect of the model on the parameters involved
therein.
Abstract: A generalization of the concepts of Feistel Networks (FN), known as Extended Feistel Network (EFN) is examined. EFN splits the input blocks into n > 2 sub-blocks. Like conventional FN, EFN consists of a series of rounds whereby at least one sub-block is subjected to an F function. The function plays a key role in the diffusion process due to its completeness property. It is also important to note that in EFN the F-function is the most computationally expensive operation in a round. The aim of this paper is to determine a suitable type of EFN for a scalable cipher. This is done by analyzing the threshold number of rounds for different types of EFN to achieve the completeness property as well as the number of F-function required in the network. The work focuses on EFN-Type I, Type II and Type III only. In the analysis it is found that EFN-Type II and Type III diffuses at the same rate and both are faster than Type-I EFN. Since EFN-Type-II uses less F functions as compared to EFN-Type III, therefore Type II is the most suitable EFN for use in a scalable cipher.
Abstract: Hearing impairment is the number one chronic
disability affecting many people in the world. Background noise is
particularly damaging to speech intelligibility for people with
hearing loss especially for sensorineural loss patients. Several
investigations on speech intelligibility have demonstrated
sensorineural loss patients need 5-15 dB higher SNR than the normal
hearing subjects. This paper describes Discrete Hartley Transform
Power Normalized Least Mean Square algorithm (DHT-LMS) to
improve the SNR and to reduce the convergence rate of the Least
Means Square (LMS) for sensorineural loss patients. The DHT
transforms n real numbers to n real numbers, and has the convenient
property of being its own inverse. It can be effectively used for noise
cancellation with less convergence time. The simulated result shows
the superior characteristics by improving the SNR at least 9 dB for
input SNR with zero dB and faster convergence rate (eigenvalue ratio
12) compare to time domain method and DFT-LMS.
Abstract: Advances in technology (e.g. the internet,
telecommunication) and political changes (fewer trade barriers and an
enlarged European Union, ASEAN, NAFTA and other organizations)
have led to develop international competition and expand into new
markets. Companies in Thailand, Asia and around the globe are
increasingly being pressured on price and for faster time to enter the
market. At the same time, new markets are appearing and many
companies are looking for changes and shifts in their domestic
markets. These factors have enabled the rapid growth for companies
and globalizing many different business activities during the product
development process from research and development (R&D) to
production.
This research will show and clarify methods how to develop
global product. Also, it will show how important is a global product
impact into Thai Economy development.
Abstract: The purpose of this research is to develop and apply the
RSCMAC to enhance the dynamic accuracy of Global Positioning
System (GPS). GPS devices provide services of accurate positioning,
speed detection and highly precise time standard for over 98% area on
the earth. The overall operation of Global Positioning System includes
24 GPS satellites in space; signal transmission that includes 2
frequency carrier waves (Link 1 and Link 2) and 2 sets random
telegraphic codes (C/A code and P code), on-earth monitoring stations
or client GPS receivers. Only 4 satellites utilization, the client position
and its elevation can be detected rapidly. The more receivable
satellites, the more accurate position can be decoded. Currently, the
standard positioning accuracy of the simplified GPS receiver is greatly
increased, but due to affected by the error of satellite clock, the
troposphere delay and the ionosphere delay, current measurement
accuracy is in the level of 5~15m. In increasing the dynamic GPS
positioning accuracy, most researchers mainly use inertial navigation
system (INS) and installation of other sensors or maps for the
assistance. This research utilizes the RSCMAC advantages of fast
learning, learning convergence assurance, solving capability of
time-related dynamic system problems with the static positioning
calibration structure to improve and increase the GPS dynamic
accuracy. The increasing of GPS dynamic positioning accuracy can be
achieved by using RSCMAC system with GPS receivers collecting
dynamic error data for the error prediction and follows by using the
predicted error to correct the GPS dynamic positioning data. The
ultimate purpose of this research is to improve the dynamic positioning
error of cheap GPS receivers and the economic benefits will be
enhanced while the accuracy is increased.
Abstract: In power systems, protective relays must filter their
inputs to remove undesirable quantities and retain signal quantities of
interest. This job must be performed accurate and fast. A new
method for filtering the undesirable components such as DC and
harmonic components associated with the fundamental system
signals. The method is s based on a dynamic filtering algorithm. The
filtering algorithm has many advantages over some other classical
methods. It can be used as dynamic on-line filter without the need of
parameters readjusting as in the case of classic filters. The proposed
filter is tested using different signals. Effects of number of samples
and sampling window size are discussed. Results obtained are
presented and discussed to show the algorithm capabilities.
Abstract: Synthetic juice clarification was done through spiral
wound ultrafiltration (UF) membrane module. Synthetic juice was
clarified at two different operating conditions, such as, with and
without permeates recycle at turbulent flow regime. The performance
of spiral wound ultrafiltration membrane was analyzed during
clarification of synthetic juice. Synthetic juice was the mixture of
deionized water, sucrose and pectin molecule. The operating
conditions are: feed flowrate of 10 lpm, pressure drop of 413.7 kPa
and Reynolds no of 5000. Permeate sample was analyzed in terms of
volume reduction factor (VRF), viscosity (Pa.s), ⁰Brix, TDS (mg/l),
electrical conductivity (μS) and turbidity (NTU). It was observe that
the permeate flux declined with operating time for both conditions of
with and without permeate recycle due to increase of concentration
polarization and increase of gel layer on membrane surface. For
without permeate recycle, the membrane fouling rate was faster
compared to with permeate recycle. For without permeate recycle,
the VRF rose up to 5 and for with recycle permeate the VRF is 1.9.
The VRF is higher due to adsorption of solute (pectin) molecule on
membrane surface and resulting permeateflux declined with VRF.
With permeate recycle, quality was within acceptable limit. Fouled
membrane was cleaned by applying different processes (e.g.,
deionized water, SDS and EDTA solution). Membrane cleaning was
analyzed in terms of permeability recovery.
Abstract: This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. With the SA strategy, we proposed a multirate neural control of an AUV trajectory for a nontrivial mid-small size AUV “r2D4" stochastic model. This control system has been demonstrated and evaluated by simulation of diving maneuvers using software package Simulink. From the simulation results it can be seen that the chosen AUV model is stable in the presence of noises, and also can be concluded that the proposed research technique will be useful for fast SA of similar AUV systems in real-time search-and-rescue operations.
Abstract: Symbolic Circuit Analysis (SCA) is a technique used
to generate the symbolic expression of a network. It has become a
well-established technique in circuit analysis and design. The
symbolic expression of networks offers excellent way to perform
frequency response analysis, sensitivity computation, stability
measurements, performance optimization, and fault diagnosis. Many
approaches have been proposed in the area of SCA offering different
features and capabilities. Numerical Interpolation methods are very
common in this context, especially by using the Fast Fourier
Transform (FFT). The aim of this paper is to present a method for
SCA that depends on the use of Wavelet Transform (WT) as a
mathematical tool to generate the symbolic expression for large
circuits with minimizing the analysis time by reducing the number of
computations.
Abstract: This paper present an efficient and reliable technique of optimization which combined fuel cost economic optimization and emission dispatch using the Sigmoid Decreasing Inertia Weight Particle Swarm Optimization algorithm (PSO) to reduce the cost of fuel and pollutants resulting from fuel combustion by keeping the output of generators, bus voltages, shunt capacitors and transformer tap settings within the security boundary. The performance of the proposed algorithm has been demonstrated on IEEE 30-bus system with six generating units. The results clearly show that the proposed algorithm gives better and faster speed convergence then linearly decreasing inertia weight.
Abstract: As computer network technology becomes
increasingly complex, it becomes necessary to place greater
requirements on the validity of developing standards and the
resulting technology. Communication networks are based on large
amounts of protocols. The validity of these protocols have to be
proved either individually or in an integral fashion. One strategy for
achieving this is to apply the growing field of formal methods.
Formal methods research defines systems in high order logic so that
automated reasoning can be applied for verification. In this research
we represent and implement a formerly announced multicast protocol
in Prolog language so that certain properties of the protocol can be
verified. It is shown that by using this approach some minor faults in
the protocol were found and repaired. Describing the protocol as
facts and rules also have other benefits i.e. leads to a process-able
knowledge. This knowledge can be transferred as ontology between
systems in KQML format. Since the Prolog language can increase its
knowledge base every time, this method can also be used to learn an
intelligent network.