Abstract: In this paper, we argue the security protocols of
ZigBee wireless sensor network in MAC layer. AES 128-bit
encryption algorithm in CCM* mode is secure transferred data;
however, AES-s secret key will be break within nearest future.
Efficient public key algorithm, ECC has been mixed with AES to
rescue the ZigBee wireless sensor from cipher text and replay attack.
Also, the proposed protocol can parallelize the integrity function to
increase system performance.
Abstract: Vehicle which are turning or maneuvering at high speeds
are susceptible to sliding and subsequently deviate from desired path. In
this paper the dynamics governing the Yaw/Roll behavior of a vehicle
has been simulated. Two different simulations have been used one for
the real vehicle, for which a fuzzy controller is designed to increase its
directional stability property. The other simulation is for a hypothetical
vehicle with much higher tire cornering stiffness which is capable of
developing the required lateral forces at the tire-ground patch contact to
attain the desired lateral acceleration for the vehicle to follow the
desired path without slippage. This simulation model is our reference
model.
The logic for keeping the vehicle on the desired track in the cornering
or maneuvering state is to have some braking forces on the inner or
outer tires based on the direction of vehicle deviation from the desired
path. The inputs to our vehicle simulation model is steer angle δ and
vehicle velocity V , and the outputs can be any kinematical parameters
like yaw rate, yaw acceleration, side slip angle, rate of side slip angle
and so on. The proposed fuzzy controller is a feed forward controller.
This controller has two inputs which are steer angle δ and vehicle
velocity V, and the output of the controller is the correcting moment M,
which guides the vehicle back to the desired track. To develop the
membership functions for the controller inputs and output and the fuzzy
rules, the vehicle simulation has been run for 1000 times and the
correcting moment have been determined by trial and error. Results of
the vehicle simulation with fuzzy controller are very promising
and show the vehicle performance is enhanced greatly over the
vehicle without the controller. In fact the vehicle performance
with the controller is very near the performance of the reference
ideal model.
Abstract: In this paper, a novel method for recognition of musical
instruments in a polyphonic music is presented by using an
embedded hidden Markov model (EHMM). EHMM is a doubly
embedded HMM structure where each state of the external HMM
is an independent HMM. The classification is accomplished for
two different internal HMM structures where GMMs are used as
likelihood estimators for the internal HMMs. The results are compared
to those achieved by an artificial neural network with two
hidden layers. Appropriate classification accuracies were achieved
both for solo instrument performance and instrument combinations
which demonstrates that the new approach outperforms the similar
classification methods by means of the dynamic of the signal.
Abstract: In this paper, low end Digital Signal Processors (DSPs)
are applied to accelerate integer neural networks. The use of DSPs
to accelerate neural networks has been a topic of study for some
time, and has demonstrated significant performance improvements.
Recently, work has been done on integer only neural networks, which
greatly reduces hardware requirements, and thus allows for cheaper
hardware implementation. DSPs with Arithmetic Logic Units (ALUs)
that support floating or fixed point arithmetic are generally more
expensive than their integer only counterparts due to increased circuit
complexity. However if the need for floating or fixed point math
operation can be removed, then simpler, lower cost DSPs can be
used. To achieve this, an integer only neural network is created in
this paper, which is then accelerated by using DSP instructions to
improve performance.
Abstract: An Artificial Neural Network based modeling
technique has been used to study the influence of different
combinations of meteorological parameters on evaporation from a
reservoir. The data set used is taken from an earlier reported study.
Several input combination were tried so as to find out the importance
of different input parameters in predicting the evaporation. The
prediction accuracy of Artificial Neural Network has also been
compared with the accuracy of linear regression for predicting
evaporation. The comparison demonstrated superior performance of
Artificial Neural Network over linear regression approach. The
findings of the study also revealed the requirement of all input
parameters considered together, instead of individual parameters
taken one at a time as reported in earlier studies, in predicting the
evaporation. The highest correlation coefficient (0.960) along with
lowest root mean square error (0.865) was obtained with the input
combination of air temperature, wind speed, sunshine hours and
mean relative humidity. A graph between the actual and predicted
values of evaporation suggests that most of the values lie within a
scatter of ±15% with all input parameters. The findings of this study
suggest the usefulness of ANN technique in predicting the
evaporation losses from reservoirs.
Abstract: This work deals with modeling and simulation of SO2 removal in a ceramic membrane by means of FEM. A mass transfer model was developed to predict the performance of SO2 absorption in a chemical solvent. The model was based on solving conservation equations for gas component in the membrane. Computational fluid dynamics (CFD) of mass and momentum were used to solve the model equations. The simulations aimed to obtain the distribution of gas concentration in the absorption process. The effect of the operating parameters on the efficiency of the ceramic membrane was evaluated. The modeling findings showed that the gas phase velocity has significant effect on the removal of gas whereas the liquid phase does not affect the SO2 removal significantly. It is also indicated that the main mass transfer resistance is placed in the membrane and gas phase because of high tortuosity of the ceramic membrane.
Abstract: An aqueous methanol sensor for use in direct
methanol fuel cells (DMFCs) applications is demonstrated; the
methanol sensor is built using dispersed single-walled carbon
nanotubes (SWCNTs) with Nafion117 solution to detect the methanol
concentration in water. The study is aimed at the potential use of the
carbon nanotubes array as a methanol sensor for direct methanol fuel
cells (DMFCs). The concentration of methanol in the fuel circulation
loop of a DMFC system is an important operating parameter, because
it determines the electrical performance and efficiency of the fuel cell
system. The sensor is also operative even at ambient temperatures
and responds quickly to changes in the concentration levels of the
methanol. Such a sensor can be easily incorporated into the methanol
fuel solution flow loop in the DMFC system.
Abstract: A data warehouse (DW) is a system which has value and role for decision-making by querying. Queries to DW are critical regarding to their complexity and length. They often access millions of tuples, and involve joins between relations and aggregations. Materialized views are able to provide the better performance for DW queries. However, these views have maintenance cost, so materialization of all views is not possible. An important challenge of DW environment is materialized view selection because we have to realize the trade-off between performance and view maintenance cost. Therefore, in this paper, we introduce a new approach aimed at solve this challenge based on Two-Phase Optimization (2PO), which is a combination of Simulated Annealing (SA) and Iterative Improvement (II), with the use of Multiple View Processing Plan (MVPP). Our experiments show that our method provides a further improvement in term of query processing cost and view maintenance cost.
Abstract: This paper solves the environmental/ economic dispatch
power system problem using the Non-dominated Sorting Genetic
Algorithm-II (NSGA-II) and its hybrid with a Convergence Accelerator
Operator (CAO), called the NSGA-II/CAO. These multiobjective
evolutionary algorithms were applied to the standard IEEE 30-bus
six-generator test system. Several optimization runs were carried out
on different cases of problem complexity. Different quality measure
which compare the performance of the two solution techniques were
considered. The results demonstrated that the inclusion of the CAO
in the original NSGA-II improves its convergence while preserving
the diversity properties of the solution set.
Abstract: In this paper we have proposed a methodology to
develop an amperometric biosensor for the analysis of glucose
concentration using a simple microcontroller based data acquisition
system. The work involves the development of Detachable
Membrane Unit (enzyme based biomembrane) with immobilized
glucose oxidase on the membrane and interfacing the same to the
signal conditioning system. The current generated by the biosensor
for different glucose concentrations was signal conditioned, then
acquired and computed by a simple AT89C51-microcontroller. The
optimum operating parameters for the better performance were found
and reported. The detailed performance evaluation of the biosensor
has been carried out. The proposed microcontroller based biosensor
system has the sensitivity of 0.04V/g/dl, with a resolution of
50mg/dl. It has exhibited very good inter day stability observed up to
30 days. Comparing to the reference method such as HPLC, the
accuracy of the proposed biosensor system is well within ± 1.5%.
The system can be used for real time analysis of glucose
concentration in the field such as, food and fermentation and clinical
(In-Vitro) applications.
Abstract: We present our ongoing work on the development
of a new quadrotor aerial vehicle which has a tilt-wing
mechanism. The vehicle is capable of take-off/landing in vertical flight mode (VTOL) and flying over long distances in horizontal flight mode. Full dynamic model of the vehicle is derived using
Newton-Euler formulation. Linear and nonlinear controllers for
the stabilization of attitude of the vehicle and control of its
altitude have been designed and implemented via simulations. In particular, an LQR controller has been shown to be quite
effective in the vertical flight mode for all possible yaw angles. A sliding mode controller (SMC) with recursive nature has also
been proposed to stabilize the vehicle-s attitude and altitude. Simulation results show that proposed controllers provide
satisfactory performance in achieving desired maneuvers.
Abstract: When cars are released from the factory, strut noises are very small and therefore it is difficult to perceive them. As the use time and travel distance increase, however, strut noises get larger so as to cause users much uneasiness. The noises generated at the field include engine noises and flow noises and therefore it is difficult to clearly discern the noises generated from struts. This study developed a test method which can reproduce field strut noises in the lab. Using the newly developed noise evaluation test, this study analyzed the effects that insulator performance degradation and failure can have on car noises. The study also confirmed that the insulator durability test by the simple back-and-forth motion cannot completely reflect the state of the parts failure in the field. Based on this, the study also confirmed that field noises can be reproduced through a durability test that considers heat aging.
Abstract: A new digital watermarking technique for images that
are sensitive to blocking artifacts is presented. Experimental results
show that the proposed MDCT based approach produces highly
imperceptible watermarked images and is robust to attacks such as
compression, noise, filtering and geometric transformations. The
proposed MDCT watermarking technique is applied to fingerprints
for ensuring security. The face image and demographic text data of
an individual are used as multiple watermarks. An AFIS system was
used to quantitatively evaluate the matching performance of the
MDCT-based watermarked fingerprint. The high fingerprint
matching scores show that the MDCT approach is resilient to
blocking artifacts. The quality of the extracted face and extracted text
images was computed using two human visual system metrics and
the results show that the image quality was high.
Abstract: As the enormous amount of on-line text grows on the
World-Wide Web, the development of methods for automatically
summarizing this text becomes more important. The primary goal of
this research is to create an efficient tool that is able to summarize
large documents automatically. We propose an Evolving
connectionist System that is adaptive, incremental learning and
knowledge representation system that evolves its structure and
functionality. In this paper, we propose a novel approach for Part of
Speech disambiguation using a recurrent neural network, a paradigm
capable of dealing with sequential data. We observed that
connectionist approach to text summarization has a natural way of
learning grammatical structures through experience. Experimental
results show that our approach achieves acceptable performance.
Abstract: We present a simplified equalization technique for a
π/4 differential quadrature phase shift keying ( π/4 -DQPSK) modulated
signal in a multipath fading environment. The proposed equalizer is
realized as a fractionally spaced adaptive decision feedback equalizer
(FS-ADFE), employing exponential step-size least mean square
(LMS) algorithm as the adaptation technique. The main advantage of
the scheme stems from the usage of exponential step-size LMS algorithm
in the equalizer, which achieves similar convergence behavior
as that of a recursive least squares (RLS) algorithm with significantly
reduced computational complexity. To investigate the finite-precision
performance of the proposed equalizer along with the π/4 -DQPSK
modem, the entire system is evaluated on a 16-bit fixed point digital
signal processor (DSP) environment. The proposed scheme is found
to be attractive even for those cases where equalization is to be
performed within a restricted number of training samples.
Abstract: Business process management (BPM) has become
widely accepted within business community as a means for
improving business performance. However, it is of the highest
importance to incorporate BPM as part of the curriculum at the
university level education in order to achieve the appropriate
acceptance of the method. Goal of the paper is to determine the
current state of education in business process management (BPM) at
the Croatian universities and abroad. It investigates the applied forms
of instruction and teaching methods and gives several proposals for
BPM courses improvement. Since majority of undergraduate and
postgraduate students have limited understanding of business
processes and lack of any practical experience, there is a need for
introducing new teaching approaches. Therefore, we offer some
suggestions for further improvement, among which the introduction
of simulation games environment in BPM education is strongly
recommended.
Abstract: One major source of performance decline in speaker
recognition system is channel mismatch between training and testing.
This paper focuses on improving channel robustness of speaker
recognition system in two aspects of channel compensation technique
and channel robust features. The system is text-independent speaker
identification system based on two-stage recognition. In the aspect of
channel compensation technique, this paper applies MAP (Maximum
A Posterior Probability) channel compensation technique, which was
used in speech recognition, to speaker recognition system. In the
aspect of channel robust features, this paper introduces
pitch-dependent features and pitch-dependent speaker model for the
second stage recognition. Based on the first stage recognition to
testing speech using GMM (Gaussian Mixture Model), the system
uses GMM scores to decide if it needs to be recognized again. If it
needs to, the system selects a few speakers from all of the speakers
who participate in the first stage recognition for the second stage
recognition. For each selected speaker, the system obtains 3
pitch-dependent results from his pitch-dependent speaker model, and
then uses ANN (Artificial Neural Network) to unite the 3
pitch-dependent results and 1 GMM score for getting a fused result.
The system makes the second stage recognition based on these fused
results. The experiments show that the correct rate of two-stage
recognition system based on MAP channel compensation technique
and pitch-dependent features is 41.7% better than the baseline system
for closed-set test.
Abstract: Nowadays, the plant location selection has a critical
impact on the performance of numerous companies. In this paper, a
methodology is presented to solve this problem. The three decision
making methods, namely Delphi, AHP and improved VIKOR, are
hybridized in order to make the best use of information available
based on the decision makers or experts. In this respect, the aim of
using Delphi is to select the most influential criteria by a few decision
makers. The AHP is utilized to give weights of the selected criteria.
Finally, the improved VIKOR method is applied to rank alternatives.
At the end of paper, an application example demonstrates the
applicability of the proposed methodology.
Abstract: Transmission control protocol (TCP) Vegas detects
network congestion in the early stage and successfully prevents
periodic packet loss that usually occurs in TCP Reno. It has been
demonstrated that TCP Vegas outperforms TCP Reno in many
aspects. However, TCP Vegas suffers several problems that affect its
congestion avoidance mechanism. One of the most important
weaknesses in TCP Vegas is that alpha and beta depend on a good
expected throughput estimate, which as we have seen, depends on a
good minimum RTT estimate. In order to make the system more
robust alpha and beta must be made responsive to network conditions
(they are currently chosen statically). This paper proposes a modified
Vegas algorithm, which can be adjusted to present good performance
compared to other transmission control protocols (TCPs). In order to
do this, we use PSO algorithm to tune alpha and beta. The simulation
results validate the advantages of the proposed algorithm in term of
performance.
Abstract: This paper presents a new classification algorithm using colour and texture for obstacle detection. Colour information is computationally cheap to learn and process. However in many cases, colour alone does not provide enough information for classification. Texture information can improve classification performance but usually comes at an expensive cost. Our algorithm uses both colour and texture features but texture is only needed when colour is unreliable. During the training stage, texture features are learned specifically to improve the performance of a colour classifier. The algorithm learns a set of simple texture features and only the most effective features are used in the classification stage. Therefore our algorithm has a very good classification rate while is still fast enough to run on a limited computer platform. The proposed algorithm was tested with a challenging outdoor image set. Test result shows the algorithm achieves a much better trade-off between classification performance and efficiency than a typical colour classifier.