Abstract: This paper propose the robust character segmentation method for license plate with topological transform such as twist,rotation. The first step of the proposed method is to find a candidate region for character and license plate. The character or license plate
must be appeared as closed loop in the edge image. In the case of
detecting candidate for character region, the evaluation of detected
region is using topological relationship between each character. When
this method decides license plate candidate region, character features
in the region with binarization are used. After binarization for the detected candidate region, each character region is decided again. In
this step, each character region is fitted more than previous step. In the
next step, the method checks other character regions with different
scale near the detected character regions, because most license plates
have license numbers with some meaningful characters around them.
The method uses perspective projection for geometrical normalization.
If there is topological distortion in the character region, the method
projects the region on a template which is defined as standard license
plate using perspective projection. In this step, the method is able to
separate each number region and small meaningful characters. The
evaluation results are tested with a number of test images.
Abstract: In this paper, a novel algorithm based on Ridgelet
Transform and support vector machine is proposed for human action
recognition. The Ridgelet transform is a directional multi-resolution
transform and it is more suitable for describing the human action by
performing its directional information to form spatial features
vectors. The dynamic transition between the spatial features is carried
out using both the Principal Component Analysis and clustering
algorithm K-means. First, the Principal Component Analysis is used
to reduce the dimensionality of the obtained vectors. Then, the kmeans
algorithm is then used to perform the obtained vectors to form
the spatio-temporal pattern, called set-of-labels, according to given
periodicity of human action. Finally, a Support Machine classifier is
used to discriminate between the different human actions. Different
tests are conducted on popular Datasets, such as Weizmann and
KTH. The obtained results show that the proposed method provides
more significant accuracy rate and it drives more robustness in very
challenging situations such as lighting changes, scaling and dynamic
environment
Abstract: This paper attempts to model and design a simple
fuzzy logic controller with Variable Reference. The Variable
Reference (VR) is featured as an adaptability element which is
obtained from two known variables – desired system-input and actual
system-output. A simple fuzzy rule-based technique is simulated to
show how the actual system-input is gradually tuned in to a value
that closely matches the desired input. The designed controller is
implemented and verified on a simple heater which is controlled by
PIC Microcontroller harnessed by a code developed in embedded C.
The output response of the PIC-controlled heater is analyzed and
compared to the performances by conventional fuzzy logic
controllers. The novelty of this work lies in the fact that it gives
better performance by using less number of rules compared to
conventional fuzzy logic controllers.
Abstract: An automatic method for the extraction of feature points for face based applications is proposed. The system is based upon volumetric feature descriptors, which in this paper has been extended to incorporate scale space. The method is robust to noise and has the ability to extract local and holistic features simultaneously from faces stored in a database. Extracted features are stable over a range of faces, with results indicating that in terms of intra-ID variability, the technique has the ability to outperform manual landmarking.
Abstract: Recently, the issue of machine condition monitoring
and fault diagnosis as a part of maintenance system became global
due to the potential advantages to be gained from reduced
maintenance costs, improved productivity and increased machine
availability. The aim of this work is to investigate the effectiveness
of a new fault diagnosis method based on power spectral density
(PSD) of vibration signals in combination with decision trees and
fuzzy inference system (FIS). To this end, a series of studies was
conducted on an external gear hydraulic pump. After a test under
normal condition, a number of different machine defect conditions
were introduced for three working levels of pump speed (1000, 1500,
and 2000 rpm), corresponding to (i) Journal-bearing with inner face
wear (BIFW), (ii) Gear with tooth face wear (GTFW), and (iii)
Journal-bearing with inner face wear plus Gear with tooth face wear
(B&GW). The features of PSD values of vibration signal were
extracted using descriptive statistical parameters. J48 algorithm is
used as a feature selection procedure to select pertinent features from
data set. The output of J48 algorithm was employed to produce the
crisp if-then rule and membership function sets. The structure of FIS
classifier was then defined based on the crisp sets. In order to
evaluate the proposed PSD-J48-FIS model, the data sets obtained
from vibration signals of the pump were used. Results showed that
the total classification accuracy for 1000, 1500, and 2000 rpm
conditions were 96.42%, 100%, and 96.42% respectively. The results
indicate that the combined PSD-J48-FIS model has the potential for
fault diagnosis of hydraulic pumps.
Abstract: This paper deals with automatic sentence modality
recognition in French. In this work, only prosodic features are
considered. The sentences are recognized according to the three
following modalities: declarative, interrogative and exclamatory
sentences. This information will be used to animate a talking head for
deaf and hearing-impaired children. We first statistically study a real
radio corpus in order to assess the feasibility of the automatic
modeling of sentence types. Then, we test two sets of prosodic
features as well as two different classifiers and their combination. We
further focus our attention on questions recognition, as this modality
is certainly the most important one for the target application.
Abstract: In the paper the research of flat textile products for use
as electrodes was presented. Material-s resistance measurements were
carried out to determine the suitability of the textiles. Based on the received results of studies different types of textile electrodes were
designed. Textile electrodes tests were carried out on human
phantoms. The electro-conductive properties of human forearm
phantom were also described. Based on this results special electroconductive
hydrogels with electro-conductive particles were feasible. The hydrogel is an important element of the forearm-s phantom
model of a survey of electrodes for muscle electrostimulation. The
hydrogel is an equivalent human skin and tissue. The hydrogel should
have a permanence and recurrence of the electro-conductive properties.
Abstract: An attempt has been made several times to identify
and discuss the U.S. experience on the formation of political nation in
political science. The purpose of this research paper is to identify the
main aspects of the formation of civic identity in the United States
and Kazakhstan, through the identification of similarities and
differences that can get practical application in making decisions of
national policy issues in the context of globalization, as well as to
answer the questions “What should unite the citizens of Kazakhstan
to the nation?" and “What should be the dominant identity: civil or
ethnic (national) one?"
Can Kazakhstan being multiethnic country like America, adopt its
experience in the formation of a civic nation? Since it is believed that
the “multi-ethnic state of the population is a characteristic feature of
most modern countries in the world," it states that “inter-ethnic
integration is one of the most important aspects of the problem of
forming a new social community (metaetnic - Kazakh people,
Kazakh nation" [1].
Abstract: Today modern simulations solutions in the wind turbine industry have achieved a high degree of complexity and detail in result. Limitations exist when it is time to validate model results against measurements. Regarding Model validation it is of special interest to identify mode frequencies and to differentiate them from the different excitations. A wind turbine is a complex device and measurements regarding any part of the assembly show a lot of noise. Input excitations are difficult or even impossible to measure due to the stochastic nature of the environment. Traditional techniques for frequency analysis or features extraction are widely used to analyze wind turbine sensor signals, but have several limitations specially attending to non stationary signals (Events). A new technique based on autoregresive analysis techniques is introduced here for a specific application, a comparison and examples related to different events in the wind turbine operations are presented.
Abstract: The Learning Management Systems present learning
environment which offers a collection of e-learning tools in a
package that allows a common interface and information sharing
among the tools. South East European University initial experience
in LMS was with the usage of the commercial LMS-ANGEL. After a
three year experience on ANGEL usage because of expenses that
were very high it was decided to develop our own software. As part
of the research project team for the in-house design and development
of the new LMS, we primarily had to select the features that would
cover our needs and also comply with the actual trends in the area of
software development, and then design and develop the system. In
this paper we present the process of LMS in-house development for
South East European University, its architecture, conception and
strengths with a special accent on the process of migration and
integration with other enterprise applications.
Abstract: Electronic seal is an electronic device to check the
authenticity and integrity of freight containers at the point of arrival.
While RFID-based eSeals are gaining more acceptances and there are
also some standardization processes for these devices, a recent
research revealed that the current RFID-based eSeals are vulnerable to
various attacks. In this paper, we provide a feasible solution to
enhance the security of active RFID-based eSeals. Our approach is to
use an authentication and key agreement protocol between eSeal and
reader device, enabling data encryption and integrity check. Our
protocol is based on the use of block cipher AES, which is reasonable
since a block cipher can also be used for many other security purposes
including data encryption and pseudo-random number generation. Our
protocol is very simple, and it is applicable to low-end active RFID
eSeals.
Abstract: This paper presents the optimal design and development
of an axial flux motor for blood pump application. With the design
objective of maximizing the motor efficiency and torque, different
topologies of AFPM machine has been examined. Selection of
optimal magnet fraction, Halbach arrangement of rotor magnets and
the use of Soft Magnetic Composite (SMC) material for the stator
core results in a novel motor with improved efficiency and torque
profile. The results of the 3D Finite element analysis for the novel
motor have been shown.
Abstract: In this paper we illuminate a frequency domain based
classification method for video scenes. Videos from certain topical
areas often contain activities with repeating movements. Sports
videos, home improvement videos, or videos showing mechanical
motion are some example areas. Assessing main and side frequencies
of each repeating movement gives rise to the motion type. We
obtain the frequency domain by transforming spatio-temporal motion
trajectories. Further on we explain how to compute frequency features
for video clips and how to use them for classifying. The focus of
the experimental phase is on transforms utilized for our system.
By comparing various transforms, experiments show the optimal
transform for a motion frequency based approach.
Abstract: In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefaces
Abstract: Automatic detection of bleeding is of practical
importance since capsule endoscopy produces an extremely large
number of images. Algorithm development of bleeding detection in
the digestive tract is difficult due to different contrasts among the
images, food dregs, secretion and others. In this study, were assigned
weighting factors derived from the independent features of the
contrast and brightness between bleeding and normality. Spectral
analysis based on weighting factors was fast and accurate. Results
were a sensitivity of 87% and a specificity of 90% when the accuracy
was determined for each pixel out of 42 endoscope images.
Abstract: Feature and model selection are in the center of
attention of many researches because of their impact on classifiers-
performance. Both selections are usually performed separately but
recent developments suggest using a combined GA-SVM approach to
perform them simultaneously. This approach improves the
performance of the classifier identifying the best subset of variables
and the optimal parameters- values. Although GA-SVM is an
effective method it is computationally expensive, thus a rough
method can be considered. The paper investigates a joined approach
of Genetic Algorithm and kernel matrix criteria to perform
simultaneously feature and model selection for SVM classification
problem. The purpose of this research is to improve the classification
performance of SVM through an efficient approach, the Kernel
Matrix Genetic Algorithm method (KMGA).
Abstract: Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron.
Abstract: The last years have seen an increasing use of image analysis techniques in the field of biomedical imaging, in particular in microscopic imaging. The basic step for most of the image analysis techniques relies on a background image free of objects of interest, whether they are cells or histological samples, to perform further analysis, such as segmentation or mosaicing. Commonly, this image consists of an empty field acquired in advance. However, many times achieving an empty field could not be feasible. Or else, this could be different from the background region of the sample really being studied, because of the interaction with the organic matter. At last, it could be expensive, for instance in case of live cell analyses. We propose a non parametric and general purpose approach where the background is built automatically stemming from a sequence of images containing even objects of interest. The amount of area, in each image, free of objects just affects the overall speed to obtain the background. Experiments with different kinds of microscopic images prove the effectiveness of our approach.
Abstract: One of the disadvantages of using OFDM is the larger
peak to averaged power ratio (PAPR) in its time domain signal. The
larger PAPR signal would course the fatal degradation of bit error
rate performance (BER) due to the inter-modulation noise in the nonlinear
channel. This paper proposes an improved DSI (Dummy
Sequence Insertion) method, which can achieve the better PAPR and
BER performances. The feature of proposed method is to optimize
the phase of each dummy sub-carrier so as to reduce the PAPR
performance by changing all predetermined phase coefficients in the
time domain signal, which is calculated for data sub-carriers and
dummy sub-carriers separately. To achieve the better PAPR
performance, this paper also proposes to employ the time-frequency
domain swapping algorithm for fine adjustment of phase coefficient
of the dummy subcarriers, which can achieve the less complexity of
processing and achieves the better PAPR and BER performances
than those for the conventional DSI method. This paper presents
various computer simulation results to verify the effectiveness of
proposed method as comparing with the conventional methods in the
non-linear channel.
Abstract: Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.