Abstract: Heavy metal pollution is an environmental concern.
Phytoremediation is a low-cost, environmental-friendly approach to
solve this problem. Mustard has the potential in reducing heavy metal
contents in soils. Among mustard (Brassica juncea (L.) Czern &
Coss) genotypes in Sri Lanka, accessions 7788, 8831 and 5088 give
significantly a high yield. Therefore, present study was conducted to
quantify the phytoextractive potential among these local mustard
accessions and to assess the interaction of heavy metals, Pb, Co, Mn
on phytoextraction. A pot experiment was designed with acid washed
sand (quartz) and a series of heavy metal solutions of 0, 25, 50, 75
and 100 μg/g. Experiment was carried out with factorial
experimental design. Mustard accessions were tolerant to heavy
metals and could be successfully used in removal of Pb, Co and Mn
and they are capable of accumulating significant quantities of heavy
metals in vegetative and reproductive organs. The order of the
accumulative potential of Pb, Co and Mn in mustard accessions is,
root > shoot >seed.
Abstract: Knowledge of an organization does not merely reside
in structured form of information and data; it is also embedded in
unstructured form. The discovery of such knowledge is particularly
difficult as the characteristic is dynamic, scattered, massive and
multiplying at high speed. Conventional methods of managing
unstructured information are considered too resource demanding and
time consuming to cope with the rapid information growth.
In this paper, a Multi-faceted and Automatic Knowledge
Elicitation System (MAKES) is introduced for the purpose of
discovery and capture of organizational knowledge. A trial
implementation has been conducted in a public organization to
achieve the objective of decision capture and navigation from a
number of meeting minutes which are autonomously organized,
classified and presented in a multi-faceted taxonomy map in both
document and content level. Key concepts such as critical decision
made, key knowledge workers, knowledge flow and the relationship
among them are elicited and displayed in predefined knowledge
model and maps. Hence, the structured knowledge can be retained,
shared and reused.
Conducting Knowledge Management with MAKES reduces work
in searching and retrieving the target decision, saves a great deal of
time and manpower, and also enables an organization to keep pace
with the knowledge life cycle. This is particularly important when
the amount of unstructured information and data grows extremely
quickly. This system approach of knowledge management can
accelerate value extraction and creation cycles of organizations.
Abstract: In this work, we present an automatic vehicle detection
system for airborne videos using combined features. We propose a
pixel-wise classification method for vehicle detection using Dynamic
Bayesian Networks. In spite of performing pixel-wise classification,
relations among neighboring pixels in a region are preserved in the
feature extraction process. The main novelty of the detection scheme is
that the extracted combined features comprise not only pixel-level
information but also region-level information. Afterwards, tracking is
performed on the detected vehicles. Tracking is performed using
efficient Kalman filter with dynamic particle sampling. Experiments
were conducted on a wide variety of airborne videos. We do not
assume prior information of camera heights, orientation, and target
object sizes in the proposed framework. The results demonstrate
flexibility and good generalization abilities of the proposed method on
a challenging dataset.
Abstract: Texture classification is an important image processing
task with a broad application range. Many different techniques for
texture classification have been explored. Using sparse approximation
as a feature extraction method for texture classification is a relatively
new approach, and Skretting et al. recently presented the Frame
Texture Classification Method (FTCM), showing very good results on
classical texture images. As an extension of that work the FTCM is
here tested on a real world application as detection of abnormalities
in mammograms. Some extensions to the original FTCM that are
useful in some applications are implemented; two different smoothing
techniques and a vector augmentation technique. Both detection of
microcalcifications (as a primary detection technique and as a last
stage of a detection scheme), and soft tissue lesions in mammograms
are explored. All the results are interesting, and especially the results
using FTCM on regions of interest as the last stage in a detection
scheme for microcalcifications are promising.
Abstract: Internet is largely composed of textual contents and a
huge volume of digital contents gets floated over the Internet daily.
The ease of information sharing and re-production has made it
difficult to preserve author-s copyright. Digital watermarking came
up as a solution for copyright protection of plain text problem after
1993. In this paper, we propose a zero text watermarking algorithm
based on occurrence frequency of non-vowel ASCII characters and
words for copyright protection of plain text. The embedding
algorithm makes use of frequency non-vowel ASCII characters and
words to generate a specialized author key. The extraction algorithm
uses this key to extract watermark, hence identify the original
copyright owner. Experimental results illustrate the effectiveness of
the proposed algorithm on text encountering meaning preserving
attacks performed by five independent attackers.
Abstract: The objective of this work which is based on the
approach of simultaneous engineering is to contribute to the
development of a CIM tool for the synthesis of functional design
dimensions expressed by average values and tolerance intervals. In
this paper, the dispersions method known as the Δl method which
proved reliable in the simulation of manufacturing dimensions is
used to develop a methodology for the automation of the simulation.
This methodology is constructed around three procedures. The first
procedure executes the verification of the functional requirements by
automatically extracting the functional dimension chains in the
mechanical sub-assembly. Then a second procedure performs an
optimization of the dispersions on the basis of unknown variables.
The third procedure uses the optimized values of the dispersions to
compute the optimized average values and tolerances of the
functional dimensions in the chains. A statistical and cost based
approach is integrated in the methodology in order to take account of
the capabilities of the manufacturing processes and to distribute
optimal values among the individual components of the chains.
Abstract: A new blind gray-level watermarking scheme is described. In the proposed method, the host image is first divided into 4*4 non-overlapping blocks. For each block, two first AC coefficients of its Hadamard transform are then estimated using DC coefficients of its neighbor blocks. A gray-level watermark is then added into estimated values. Since embedding watermark does not change the DC coefficients, watermark extracting could be done by estimating AC coefficients and comparing them with their actual values. Several experiments are made and results suggest the robustness of the proposed algorithm.
Abstract: A number of competing methodologies have been developed
to identify genes and classify DNA sequences into coding
and non-coding sequences. This classification process is fundamental
in gene finding and gene annotation tools and is one of the most
challenging tasks in bioinformatics and computational biology. An
information theory measure based on mutual information has shown
good accuracy in classifying DNA sequences into coding and noncoding.
In this paper we describe a species independent iterative
approach that distinguishes coding from non-coding sequences using
the mutual information measure (MIM). A set of sixty prokaryotes is
used to extract universal training data. To facilitate comparisons with
the published results of other researchers, a test set of 51 bacterial
and archaeal genomes was used to evaluate MIM. These results
demonstrate that MIM produces superior results while remaining
species independent.
Abstract: Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.
Abstract: A new method identifies coupled fluid-structure system with a reduced set of state variables is presented. Assuming that the structural model is known a priori either from an analysis or a test and using linear transformations between structural and aeroelastic states, it is possible to deduce aerodynamic information from sampled time histories of the aeroelastic system. More specifically given a finite set of structural modes the method extracts generalized aerodynamic force matrix corresponding to these mode shapes. Once the aerodynamic forces are known, an aeroelastic reduced-order model can be constructed in discrete-time, state-space format by coupling the structural model and the aerodynamic system. The resulting reduced-order model is suitable for constant Mach, varying density analysis.
Abstract: Legionella pneumophila is involved in more than 95%
cases of severe atypical pneumonia. Infection is mainly by
inhalation the indoor aerosols through the water-coolant systems.
Because some Legionella strains may be viable but not culturable,
therefore, Taq polymerase, DNA amplification and semi-nested-PCR
were carried out to detect Legionella-specific 16S-rDNA sequence.
For this purpose, 1.5 litter of water samples from 77 water-coolant
system were collected from four different hospitals, two nursing
homes and one student hostel in Kerman city of Iran, each in a brand
new plastic bottle during summer season of 2006 (from April to
August). The samples were filtered in the sterile condition through
the Millipore Membrane Filter. DNA was extracted from membrane
and used for PCR to detect Legionella spp. The PCR product was
then subjected to semi-nested PCR for detection of L. pneumophila.
Out of 77 water samples that were tested by PCR, 30 (39%) were
positive for most species of Legionella. However, L. pneumophila
was detected from 14 (18.2%) water samples by semi-nested PCR.
From the above results it can be concluded that water coolant
systems of different hospitals and nursing homes in Kerman city of
Iran are highly contaminated with L. pneumophila spp. and pose
serious concern. So, we recommend avoiding such type of coolant
system in the hospitals and nursing homes.
Abstract: Data Warehouses (DWs) are repositories which contain the unified history of an enterprise for decision support. The data must be Extracted from information sources, Transformed and integrated to be Loaded (ETL) into the DW, using ETL tools. These tools focus on data movement, where the models are only used as a means to this aim. Under a conceptual viewpoint, the authors want to innovate the ETL process in two ways: 1) to make clear compatibility between models in a declarative fashion, using correspondence assertions and 2) to identify the instances of different sources that represent the same entity in the real-world. This paper presents the overview of the proposed framework to model the ETL process, which is based on the use of a reference model and perspective schemata. This approach provides the designer with a better understanding of the semantic associated with the ETL process.
Abstract: The quantum mechanics simulation was applied for
calculating the interaction force between 2 molecules based on atomic level. For the simple extractive distillation system, it is ternary
components consisting of 2 closed boiling point components (A,lower boiling point and B, higher boiling point) and solvent (S). The
quantum mechanics simulation was used to calculate the intermolecular force (interaction force) between the closed boiling
point components and solvents consisting of intermolecular between
A-S and B-S.
The requirement of the promising solvent for extractive distillation
is that solvent (S) has to form stronger intermolecular force with only
one component than the other component (A or B). In this study, the
systems of aromatic-aromatic, aromatic-cycloparaffin, and paraffindiolefin
systems were selected as the demonstration for solvent
selection. This study defined new term using for screening the solvents called relative interaction force which is calculated from the
quantum mechanics simulation. The results showed that relative
interaction force gave the good agreement with the literature data
(relative volatilities from the experiment). The reasons are discussed. Finally, this study suggests that quantum mechanics results can improve the relative volatility estimation for screening the solvents leading to reduce time and money consuming
Abstract: The aim of this study was to extract sugar from
sugarcane using high electric field pulse (HELP) as a non-thermal cell permeabilization method. The result of this study showed that it
is possible to permeablize sugar cane cells using HELP at very short times (less than 10 sec.) and at room temperature. Increasing the field strength (from 0.5kV/cm to 2kV/cm) and pulse number (1 to 12) led to increasing the permeabilization of sugar cane cells. The energy
consumption during HELP treatment of sugar cane (2.4 kJ/kg) was about 100 times less compared to thermal cell disintegration at 85
Abstract: This paper presents an effective traffic lights detection
method at the night-time. First, candidate blobs of traffic lights are
extracted from RGB color image. Input image is represented on the
dominant color domain by using color transform proposed by Ruta,
then red and green color dominant regions are selected as candidates.
After candidate blob selection, we carry out shape filter for noise
reduction using information of blobs such as length, area, area of
boundary box, etc. A multi-class classifier based on SVM (Support
Vector Machine) applies into the candidates. Three kinds of features
are used. We use basic features such as blob width, height, center
coordinate, area, area of blob. Bright based stochastic features are also
used. In particular, geometric based moment-s values between
candidate region and adjacent region are proposed and used to improve
the detection performance. The proposed system is implemented on
Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the
urban and rural road videos. Through the test, we show that the
proposed method using PF, BMF, and GMF reaches up to 93 % of
detection rate with computation time of in average 15 ms/frame.
Abstract: Utilization of various sensors has made it possible to
extend capabilities of industrial robots. Among these are vision
sensors that are used for providing visual information to assist robot
controllers. This paper presents a method of integrating a vision
system and a simulation program with an industrial robot. The vision
system is employed to detect a target object and compute its location
in the robot environment. Then, the target object-s information is sent
to the robot controller via parallel communication port. The robot
controller uses the extracted object information and the simulation
program to control the robot arm for approaching, grasping and
relocating the object. This paper presents technical details of system
components and describes the methodology used for this integration.
It also provides a case study to prove the validity of the methodology
developed.
Abstract: Heart sound is an acoustic signal and many techniques
used nowadays for human recognition tasks borrow speech recognition
techniques. One popular choice for feature extraction of accoustic
signals is the Mel Frequency Cepstral Coefficients (MFCC) which
maps the signal onto a non-linear Mel-Scale that mimics the human
hearing. However the Mel-Scale is almost linear in the frequency
region of heart sounds and thus should produce similar results with
the standard cepstral coefficients (CC). In this paper, MFCC is
investigated to see if it produces superior results for PCG based
human identification system compared to CC. Results show that the
MFCC system is still superior to CC despite linear filter-banks in
the lower frequency range, giving up to 95% correct recognition rate
for MFCC and 90% for CC. Further experiments show that the high
recognition rate is due to the implementation of filter-banks and not
from Mel-Scaling.
Abstract: Relational databases are often used as a basis for persistent storage of ontologies to facilitate rapid operations such as search and retrieval, and to utilize the benefits of relational databases management systems such as transaction management, security and integrity control. On the other hand, there appear more and more OWL files that contain ontologies. Therefore, this paper proposes to extract ontologies from OWL files and then store them in relational databases. A prerequisite for this storing is transformation of ontologies to relational databases, which is the purpose of this paper.
Abstract: Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in "real-life" operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases.
Abstract: In this paper, we proposed a method to classify each
type of natural rock texture. Our goal is to classify 26 classes of rock
textures. First, we extract five features of each class by using
principle component analysis combining with the use of applied
spatial frequency measurement. Next, the effective node number of
neural network was tested. We used the most effective neural
network in classification process. The results from this system yield
quite high in recognition rate. It is shown that high recognition rate
can be achieved in separation of 26 stone classes.