Abstract: Rotation or tilt present in an image capture by digital
means can be detected and corrected using Artificial Neural Network
(ANN) for application with a Face Recognition System (FRS). Principal
Component Analysis (PCA) features of faces at different angles
are used to train an ANN which detects the rotation for an input image
and corrected using a set of operations implemented using another
system based on ANN. The work also deals with the recognition
of human faces with features from the foreheads, eyes, nose and
mouths as decision support entities of the system configured using
a Generalized Feed Forward Artificial Neural Network (GFFANN).
These features are combined to provide a reinforced decision for
verification of a person-s identity despite illumination variations. The
complete system performing facial image rotation detection, correction
and recognition using re-enforced decision support provides a
success rate in the higher 90s.
Abstract: The objective this study was to characterize and
develop anthropomorphic liver phantoms in tomography hepatic
procedures for quality control and improvement professionals in
nuclear medicine. For the conformation of the anthropomorphic
phantom was used in plaster and acrylic. We constructed three
phantoms representing processes with liver cirrhosis. The phantoms
were filled with 99mTc diluted with water to obtain the scintigraphic
images. Tomography images were analyzed anterior and posterior
phantom representing a body with a greater degree cirrhotic. It was
noted that the phantoms allow the acquisition of images similar to
real liver with cirrhosis. Simulations of hemangiomas may contribute
to continued professional education of nuclear medicine, on the
question of image acquisition, allowing of the study parameters such
of the matrix, energy window and count statistics.
Abstract: Shot boundary detection is a fundamental step for the organization of large video data. In this paper, we propose a new method for video gradual shots detection and classification, using advantages of fractal analysis and AIS-based classifier. Proposed features are “vertical intercept" and “fractal dimension" of each frame of videos which are computed using Fourier transform coefficients. We also used a classifier based on Clonal Selection Algorithm. We have carried out our solution and assessed it according to the TRECVID2006 benchmark dataset.
Abstract: Classes on creativity, innovation, and entrepreneurship
are becoming quite popular at universities throughout the world.
However, it is not easy for business students to get involved to
innovative activities, especially patent application. The present study
investigated how to enhance business students- intention to participate
in innovative activities and which incentives universities should
consider. A 22-item research scale was used, and confirmatory factor
analysis was conducted to verify its reliability and validity. Multiple
regression and discriminant analyses were also conducted. The results
demonstrate the effect of growth-need strength on innovative behavior
and indicate that the theory of planned behavior can explain and
predict business students- intention to participate in innovative
activities. Additionally, the results suggest that applying our proposed
model in practice would effectively strengthen business students-
intentions to engage in innovative activities.
Abstract: In this paper, a design methodology to implement low-power and high-speed 2nd order recursive digital Infinite Impulse Response (IIR) filter has been proposed. Since IIR filters suffer from a large number of constant multiplications, the proposed method replaces the constant multiplications by using addition/subtraction and shift operations. The proposed new 6T adder cell is used as the Carry-Save Adder (CSA) to implement addition/subtraction operations in the design of recursive section IIR filter to reduce the propagation delay. Furthermore, high-level algorithms designed for the optimization of the number of CSA blocks are used to reduce the complexity of the IIR filter. The DSCH3 tool is used to generate the schematic of the proposed 6T CSA based shift-adds architecture design and it is analyzed by using Microwind CAD tool to synthesize low-complexity and high-speed IIR filters. The proposed design outperforms in terms of power, propagation delay, area and throughput when compared with MUX-12T, MCIT-7T based CSA adder filter design. It is observed from the experimental results that the proposed 6T based design method can find better IIR filter designs in terms of power and delay than those obtained by using efficient general multipliers.
Abstract: The key to the continued success of ANN depends, considerably,
on the use of hybrid structures implemented on cooperative
frame-works. Hybrid architectures provide the ability to the ANN
to validate heterogeneous learning paradigms. This work describes
the implementation of a set of Distributed and Hybrid ANN models
for Character Recognition applied to Anglo-Assamese scripts. The
objective is to describe the effectiveness of Hybrid ANN setups as
innovative means of neural learning for an application like multilingual
handwritten character and numeral recognition.
Abstract: In this paper, a new approach is introduced to solve
Blasius equation using parameter identification of a nonlinear
function which is used as approximation function. Bees Algorithm
(BA) is applied in order to find the adjustable parameters of
approximation function regarding minimizing a fitness function
including these parameters (i.e. adjustable parameters). These
parameters are determined how the approximation function has to
satisfy the boundary conditions. In order to demonstrate the
presented method, the obtained results are compared with another
numerical method. Present method can be easily extended to solve a
wide range of problems.
Abstract: Rule Discovery is an important technique for mining
knowledge from large databases. Use of objective measures for
discovering interesting rules leads to another data mining problem,
although of reduced complexity. Data mining researchers have
studied subjective measures of interestingness to reduce the volume
of discovered rules to ultimately improve the overall efficiency of
KDD process.
In this paper we study novelty of the discovered rules as a
subjective measure of interestingness. We propose a hybrid approach
based on both objective and subjective measures to quantify novelty
of the discovered rules in terms of their deviations from the known
rules (knowledge). We analyze the types of deviation that can arise
between two rules and categorize the discovered rules according to
the user specified threshold. We implement the proposed framework
and experiment with some public datasets. The experimental results
are promising.
Abstract: Fundamental motivation of this paper is how gaze estimation can be utilized effectively regarding an application to games. In games, precise estimation is not always important in aiming targets but an ability to move a cursor to an aiming target accurately is also significant. Incidentally, from a game producing point of view, a separate expression of a head movement and gaze movement sometimes becomes advantageous to expressing sense of presence. A case that panning a background image associated with a head movement and moving a cursor according to gaze movement can be a representative example. On the other hand, widely used technique of POG estimation is based on a relative position between a center of corneal reflection of infrared light sources and a center of pupil. However, a calculation of a center of pupil requires relatively complicated image processing, and therefore, a calculation delay is a concern, since to minimize a delay of inputting data is one of the most significant requirements in games. In this paper, a method to estimate a head movement by only using corneal reflections of two infrared light sources in different locations is proposed. Furthermore, a method to control a cursor using gaze movement as well as a head movement is proposed. By using game-like-applications, proposed methods are evaluated and, as a result, a similar performance to conventional methods is confirmed and an aiming control with lower computation power and stressless intuitive operation is obtained.
Abstract: Protein structure determination and prediction has
been a focal research subject in the field of bioinformatics due to the
importance of protein structure in understanding the biological and
chemical activities of organisms. The experimental methods used by
biotechnologists to determine the structures of proteins demand
sophisticated equipment and time. A host of computational methods
are developed to predict the location of secondary structure elements
in proteins for complementing or creating insights into experimental
results. However, prediction accuracies of these methods rarely
exceed 70%.
Abstract: This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.
Abstract: Web sites are rapidly becoming the preferred media
choice for our daily works such as information search, company
presentation, shopping, and so on. At the same time, we live in a
period where visual appearances play an increasingly important
role in our daily life. In spite of designers- effort to develop a web
site which be both user-friendly and attractive, it would be difficult
to ensure the outcome-s aesthetic quality, since the visual
appearance is a matter of an individual self perception and opinion.
In this study, it is attempted to develop an automatic system for
web pages aesthetic evaluation which are the building blocks of
web sites. Based on the image processing techniques and artificial
neural networks, the proposed method would be able to categorize
the input web page according to its visual appearance and aesthetic
quality. The employed features are multiscale/multidirectional
textural and perceptual color properties of the web pages, fed to
perceptron ANN which has been trained as the evaluator. The
method is tested using university web sites and the results
suggested that it would perform well in the web page aesthetic
evaluation tasks with around 90% correct categorization.
Abstract: This paper describes the smart energy monitoring system with a wireless sensor network for monitoring of electrical usage in smart house. Proposed system is composed of wireless plugs and energy control wallpad server. The wireless plug integrates an AC power socket, a relay to switch the socket ON/OFF, a Hall effect sensor to sense current of load appliance and a Kmote. The Kmote is a wireless communication interface based on TinyOS. We evaluated wireless plug in a laboratory, analyzed and presented energy consumption data from electrical appliances for 3 months in home.
Abstract: The Lattice Boltzmann Method (LBM) with double populations is applied to solve the steady-state laminar natural convective heat transfer in a triangular cavity filled with water. The bottom wall is heated, the vertical wall is cooled, and the inclined wall is kept adiabatic. The buoyancy effect was modeled by applying the Boussinesq approximation to the momentum equation. The fluid velocity is determined by D2Q9 LBM and the energy equation is discritized by D2Q4 LBM to compute the temperature field. Comparisons with previously published work are performed and found to be in excellent agreement. Numerical results are obtained for a wide range of parameters: the Rayleigh number from to and the inclination angle from 0° to 360°. Flow and thermal fields were exhibited by means of streamlines and isotherms. It is observed that inclination angle can be used as a relevant parameter to control heat transfer in right-angled triangular enclosures.
Abstract: A retrospective study was undertaken to record the
occurrence and pattern of fractures in small animals (dogs and cats)
from year 2005 to 2010. A total of 650 cases were presented in small
animal surgery unit out of which of 116 (dogs and cats) were
presented with history of fractures of different bones. A total of
17.8% (116/650) cases were of fractures which constituted dogs 67%
while cats were 23%. The majority of animals were intact. Trauma in
the form of road side accident was the principal cause of fractures in
dogs whereas as in cats it was fall from height. The ages of the
fractured dog ranged from 4 months to 12 years whereas in cat it was
from 4 weeks to 10 years. The femoral fractures represented 37.5%
and 25% respectively in dogs and cats. Diaphysis, distal metaphyseal
and supracondylar fractures were the most affected sites in dog and
cats. Tibial fracture in dogs and cats represented 21.5% and 10%
while humoral fractures were 7.9% and 14% in dogs and cats
respectively. Humoral condyler fractures were most commonly seen
in puppies aged 4 to 6 months. Fractured radius-ulna incidence was
19% and 14% in dogs and cats respectively. Other fractures recorded
were of lumbar vertebrae, mandible and metacarpals etc. The
management comprised of external and internal fixation in both the
species. The most common internal fixation technique employed was
Intramedullary fixation in long followed by other methods like stack
or cross pinning, wiring etc as per findings in the cases. The cast
bandage was used majorly as mean for external coaptation. The
paper discusses the outcome of the case as per the technique
employed.
Abstract: A 1.2 V, 0.61 mA bias current, low noise amplifier
(LNA) suitable for low-power applications in the 2.4 GHz band is
presented. Circuit has been implemented, laid out and simulated using
a UMC 130 nm RF-CMOS process. The amplifier provides a 13.3 dB
power gain a noise figure NF< 2.28 dB and a 1-dB compression point
of -15.69 dBm, while dissipating 0.74 mW. Such performance make
this design suitable for wireless sensor networks applications such as
ZigBee.
Abstract: A large number of semantic web service composition
approaches are developed by the research community and one is
more efficient than the other one depending on the particular
situation of use. So a close look at the requirements of ones particular
situation is necessary to find a suitable approach to use. In this paper,
we present a Technique Recommendation System (TRS) which using
a classification of state-of-art semantic web service composition
approaches, can provide the user of the system with the
recommendations regarding the use of service composition approach
based on some parameters regarding situation of use. TRS has
modular architecture and uses the production-rules for knowledge
representation.
Abstract: Knowledge about the magnetic quantities in a magnetic circuit is always of great interest. On the one hand, this information is needed for the simulation of a transformer. On the other hand, parameter studies are more reliable, if the magnetic quantities are derived from a well established model. One possibility to model the 3-phase transformer is by using a magnetic equivalent circuit (MEC). Though this is a well known system, it is often not an easy task to set up such a model for a large number of lumped elements which additionally includes the nonlinear characteristic of the magnetic material. Here we show the setup of a solver for a MEC and the results of the calculation in comparison to measurements taken. The equations of the MEC are based on a rearranged system of the nodal analysis. Thus it is possible to achieve a minimum number of equations, and a clear and simple structure. Hence, it is uncomplicated in its handling and it supports the iteration process. Additional helpful tasks are implemented within the solver to enhance the performance. The electric circuit is described by an electric equivalent circuit (EEC). Our results for the 3-phase transformer demonstrate the computational efficiency of the solver, and show the benefit of the application of a MEC.
Abstract: Switched-mode converters play now a significant role in
modern society. Their operation are often crucial in various electrical
applications affecting the every day life. Therefore, the quality of
the converters needs to be reliably verified. Recent studies have
shown that the converters can be fully characterized by a set of
frequency responses which can be efficiently used to validate the
proper operation of the converters. Consequently, several methods
have been proposed to measure the frequency responses fast and
accurately. Most often correlation-based techniques have been applied.
The presented measurement methods are highly sensitive to
external errors and system nonlinearities. This fact has been often
forgotten and the necessary uncertainty analysis of the measured
responses has been neglected. This paper presents a simple approach
to analyze the noise and nonlinearities in the frequency-response
measurements of switched-mode converters. Coherence analysis is
applied to form a confidence interval characterizing the noise and
nonlinearities involved in the measurements. The presented method is
verified by practical measurements from a high-frequency switchedmode
converter.
Abstract: Granular computing deals with representation of information in the form of some aggregates and related methods for transformation and analysis for problem solving. A granulation scheme based on clustering and Rough Set Theory is presented with focus on structured conceptualization of information has been presented in this paper. Experiments for the proposed method on four labeled data exhibit good result with reference to classification problem. The proposed granulation technique is semi-supervised imbibing global as well as local information granulation. To represent the results of the attribute oriented granulation a tree structure is proposed in this paper.