Abstract: Mammography is the most effective procedure for an
early diagnosis of the breast cancer. Nowadays, people are trying to
find a way or method to support as much as possible to the
radiologists in diagnosis process. The most popular way is now being
developed is using Computer-Aided Detection (CAD) system to
process the digital mammograms and prompt the suspicious region to
radiologist. In this paper, an automated CAD system for detection
and classification of massive lesions in mammographic images is
presented. The system consists of three processing steps: Regions-Of-
Interest detection, feature extraction and classification. Our CAD
system was evaluated on Mini-MIAS database consisting 322
digitalized mammograms. The CAD system-s performance is
evaluated using Receiver Operating Characteristics (ROC) and Freeresponse
ROC (FROC) curves. The archived results are 3.47 false
positives per image (FPpI) and sensitivity of 85%.
Abstract: Employee-s task performance has been recognized as a
core contributor to overall organizational effectiveness. Hence,
verifying the determinants of task performance is one of the most
important research issues. This study tests the influence of perceived
organizational support, abusive supervision, and exchange ideology
on employee-s task performance. We examined our hypotheses by
collecting self-reported data from 413 Korean employees in different
organizations. Our all hypotheses gained support from the results.
Implications for research and directions for future research are
discussed.
Abstract: Economic dispatch problem is an optimization problem where objective function is highly non linear, non-convex, non-differentiable and may have multiple local minima. Therefore, classical optimization methods may not converge or get trapped to any local minima. This paper presents a comparative study of four different evolutionary algorithms i.e. genetic algorithm, bacteria foraging optimization, ant colony optimization and particle swarm optimization for solving the economic dispatch problem. All the methods are tested on IEEE 30 bus test system. Simulation results are presented to show the comparative performance of these methods.
Abstract: Three-dimensional reconstruction of small objects has
been one of the most challenging problems over the last decade.
Computer graphics researchers and photography professionals have
been working on improving 3D reconstruction algorithms to fit the
high demands of various real life applications. Medical sciences,
animation industry, virtual reality, pattern recognition, tourism
industry, and reverse engineering are common fields where 3D
reconstruction of objects plays a vital role. Both lack of accuracy and
high computational cost are the major challenges facing successful
3D reconstruction. Fringe projection has emerged as a promising 3D
reconstruction direction that combines low computational cost to both
high precision and high resolution. It employs digital projection,
structured light systems and phase analysis on fringed pictures.
Research studies have shown that the system has acceptable
performance, and moreover it is insensitive to ambient light.
This paper presents an overview of fringe projection approaches. It
also presents an experimental study and implementation of a simple
fringe projection system. We tested our system using two objects
with different materials and levels of details. Experimental results
have shown that, while our system is simple, it produces acceptable
results.
Abstract: This paper presents a model for the evaluation of
energy performance and aerodynamic forces acting on a small
straight-bladed Darrieus-type vertical axis wind turbine depending on
blade geometrical section. It consists of an analytical code coupled to
a solid modeling software, capable of generating the desired blade
geometry based on the desired blade design geometric parameters.
Such module is then linked to a finite volume commercial CFD code
for the calculation of rotor performance by integration of the
aerodynamic forces along the perimeter of each blade for a full period
of revolution.After describing and validating the computational
model with experimental data, the results of numerical simulations
are proposed on the bases of two candidate airfoil sections, that is a
classical symmetrical NACA 0021 blade profile and the recently
developed DU 06-W-200 non-symmetric and laminar blade
profile.Through a full CFD campaign of analysis, the effects of blade
geometrical section on angle of attack are first investigated and then
the overall rotor torque and power are analyzed as a function of blade
azimuthal position, achieving a numerical quantification of the
influence of airfoil geometry on overall rotor performance.
Abstract: Linear approximation of point spread function (PSF) is a new method for determining subpixel translations between images. The problem with the actual algorithm is the inability of determining translations larger than 1 pixel. In this paper a multiresolution technique is proposed to deal with the problem. Its performance is evaluated by comparison with two other well known registration method. In the proposed technique the images are downsampled in order to have a wider view. Progressively decreasing the downsampling rate up to the initial resolution and using linear approximation technique at each step, the algorithm is able to determine translations of several pixels in subpixel levels.
Abstract: In this paper, the performance of three types of serial
concatenated convolutional codes (SCCC) is compared and analyzed
in additive white Gaussian noise (AWGN) channel. In Type I, only the
parity bits of outer encoder are passed to inner encoder. In Type II and
Type III, both the information bits and the parity bits of outer encoder
are transferred to inner encoder. As results of simulation, Type I shows
the best bit error rate (BER) performance at low signal-to-noise ratio
(SNR). On the other hand, Type III shows the best BER performance
at high SNR in AWGN channel. The simulation results are analyzed
using the distance spectrum.
Abstract: The performance of a sucrose-based H2 production in
a completely stirred tank reactor (CSTR) was modeled by neural
network back-propagation (BP) algorithm. The H2 production was
monitored over a period of 450 days at 35±1 ºC. The proposed model
predicts H2 production rates based on hydraulic retention time
(HRT), recycle ratio, sucrose concentration and degradation, biomass
concentrations, pH, alkalinity, oxidation-reduction potential (ORP),
acids and alcohols concentrations. Artificial neural networks (ANNs)
have an ability to capture non-linear information very efficiently. In
this study, a predictive controller was proposed for management and
operation of large scale H2-fermenting systems. The relevant control
strategies can be activated by this method. BP based ANNs modeling
results was very successful and an excellent match was obtained
between the measured and the predicted rates. The efficient H2
production and system control can be provided by predictive control
method combined with the robust BP based ANN modeling tool.
Abstract: Design and evaluation of reciprocating compressors
should include a pulsation study. The object is to ensure that
predicted pulsation levels meet guidelines to limit vibration, shaking
forces, noise, associated pressure drops, horsepower losses and
fabrication cost and time to acceptable levels. This paper explains
procedures and recommendations to select and size pulsation
suppression devices to obtain optimum arrangement in terms of
pulsation, vibration, shaking forces, performance, reliability, safety,
operation, maintenance and commercial conditions. Model and
advanced formulations for pulsation study are presented. The effect
of the full fluid dynamic model on the prediction of pulsation waves
and resulting frequency spectrum distributions are discussed.
Advanced and optimum methods of controlling pulsations are
highlighted. Useful recommendations and guidelines for pulsation
control, piping pulsation analysis, pulsation vessel design, shaking
forces, low pressure drop orifices, pulsation study report and devices
to mitigate pulsation and shaking problems are discussed.
Abstract: A laboratory set-up was designed to survey the
effectiveness of UV/O3 advanced oxidation process (AOP) for the
removal of Carbaryl from polluted water in batch reactor. The study
was carried out by UV/O3 process for water samples containing 1 to
20 mg/L of Carbaryl in distilled water. Also the range of drinking
water resources adjusted in synthetic water and effects of contact
time, pH and Carbaryl concentration were studied. The residual
pesticide concentration was determined by applying high
performance liquid chromatography (HPLC). The results indicated
that increasing of retention time and pH, enhances pesticide removal
efficiency. The removal efficiency has been affected by pesticide
initial concentration. Samples with low pesticide concentration
showed a remarkable removal efficiency compared to the samples
with high pesticide concentration. AOP method showed the removal
efficiencies of 80% to 100%. Although process showed high
performance for removal of pesticide from water samples, this
process has different disadvantages including complication,
intolerability, difficulty of maintenance and equipmental and
structural requirements.
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 article proposes a novel Pareto-based multiobjective
meta-heuristic algorithm named non-dominated ranking
genetic algorithm (NRGA) to solve multi-facility location-allocation
problem. In NRGA, a fitness value representing rank is assigned to
each individual of the population. Moreover, two features ranked
based roulette wheel selection including select the fronts and choose
solutions from the fronts, are utilized. The proposed solving
methodology is validated using several examples taken from the
specialized literature. The performance of our approach shows that
NRGA algorithm is able to generate true and well distributed Pareto
optimal solutions.
Abstract: In this paper we use data mining techniques to investigate factors that contribute significantly to enhancing the risk of acute coronary syndrome. We assume that the dependent variable is diagnosis – with dichotomous values showing presence or absence of disease. We have applied binary regression to the factors affecting the dependent variable. The data set has been taken from two different cardiac hospitals of Karachi, Pakistan. We have total sixteen variables out of which one is assumed dependent and other 15 are independent variables. For better performance of the regression model in predicting acute coronary syndrome, data reduction techniques like principle component analysis is applied. Based on results of data reduction, we have considered only 14 out of sixteen factors.
Abstract: This study was designed to determine effect of
supplemented tomato pomace and fobrolytic enzyme on egg
production and egg quality. A total of 40 CP brown laying hens (95
week old) were used in completely randomized design in 2x2
factorial arrangement with or without enzyme supplementation. Four
dietary treatments included: Control (C), Fibrolytic enzyme (FE),
10% Tomato pomace (TP), and Fibrolytic enzyme + 10 % Tomato
pomace (FE+TP). Each of the four dietary treatments was fed up to
30 days (10 birds/treatment). Live performance, egg production, egg
weight and quality were determined for whole period. Dietary
treatments had no effect (P>0.05) on live performance, egg weight,
yolk color, and egg production. Therefore, laying hens fed diets with
fibrolytic enzyme were significantly (P
Abstract: Very Large and/or computationally complex optimization problems sometimes require parallel or highperformance computing for achieving a reasonable time for computation. One of the most popular and most complicate problems of this family is “Traveling Salesman Problem". In this paper we have introduced a Branch & Bound based algorithm for the solution of such complicated problems. The main focus of the algorithm is to solve the “symmetric traveling salesman problem". We reviewed some of already available algorithms and felt that there is need of new algorithm which should give optimal solution or near to the optimal solution. On the basis of the use of logarithmic sampling, it was found that the proposed algorithm produced a relatively optimal solution for the problem and results excellent performance as compared with the traditional algorithms of this series.
Abstract: In this paper, a post processing scheme is suggested
for improvement of Bit Error-Rate (BER) in optical fiber
transmission receivers. The developed scheme has been tested on
optical fiber systems operating with a non-return-to-zero (NRZ)
format at transmission rates of up to 10Gbps. The transmission
system considered is based on well known transmitters and receivers
blocks operating at wavelengths in the region of 1550 nm using a
standard single mode fiber. Performance of improved detected
signals has been evaluated via the analysis of quality factor and
computed bit error rates. Numerical simulations have shown a
noticeable improvement of the system BER after implementation of
the suggested post processing operation on the detected electrical
signals.
Abstract: Digital broadcasting has been an area of active
research, development, innovation and business models development
in recent years. This paper presents a survey on the characteristics of
the digital terrestrial television broadcasting (DTTB) standards, and
implementation status of DTTB worldwide showing the standards
adopted. It is clear that only the developed countries and some in the
developing ones shall be able to beat the ITU set analogue to digital
broadcasting migration deadline because of the challenges that these
countries faces in digitizing their terrestrial broadcasting. The
challenges to keep on track the DTTB migration plan are also
discussed in this paper. They include financial, technology gap,
policies alignment with DTTB technology, etc. The reported
performance comparisons for the different standards are also
presented. The interesting part is that the results for many
comparative studies depends to a large extent on the objective behind
such studies, hence counter claims are common.
Abstract: Because of the low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. Various speed control techniques like, Direct Torque Control, Sensorless Vector Control and Field Oriented Control are discussed in this paper. Soft computing technique – Fuzzy logic is applied in this paper for the speed control of induction motor to achieve maximum torque with minimum loss. The fuzzy logic controller is implemented using the Field Oriented Control technique as it provides better control of motor torque with high dynamic performance. The motor model is designed and membership functions are chosen according to the parameters of the motor model. The simulated design is tested using various tool boxes in MATLAB. The result concludes that the efficiency and reliability of the proposed speed controller is good.
Abstract: Time series forecasting is an important and widely
popular topic in the research of system modeling. This paper
describes how to use the hybrid PSO-RLSE neuro-fuzzy learning
approach to the problem of time series forecasting. The PSO
algorithm is used to update the premise parameters of the
proposed prediction system, and the RLSE is used to update the
consequence parameters. Thanks to the hybrid learning (HL)
approach for the neuro-fuzzy system, the prediction performance
is excellent and the speed of learning convergence is much faster
than other compared approaches. In the experiments, we use the
well-known Mackey-Glass chaos time series. According to the
experimental results, the prediction performance and accuracy in
time series forecasting by the proposed approach is much better
than other compared approaches, as shown in Table IV. Excellent
prediction performance by the proposed approach has been
observed.
Abstract: Safer driver behavior promoting is the main goal of this paper. It is a fact that drivers behavior is relatively safer when being monitored. Thus, in this paper, we propose a monitoring system to report specific driving event as well as the potentially aggressive events for estimation of the driving performance. Our driving monitoring system is composed of two parts. The first part is the in-vehicle embedded system which is composed of a GPS receiver, a two-axis accelerometer, radar sensor, OBD interface, and GPRS modem. The design considerations that led to this architecture is described in this paper. The second part is a web server where an adaptive hierarchical fuzzy system is proposed to classify the driving performance based on the data that is sent by the in-vehicle embedded system and the data that is provided by the geographical information system (GIS). Our system is robust, inexpensive and small enough to fit inside a vehicle without distracting the driver.