Abstract: Extraction of Fe(III) from aqueous solution using Trin-
butyl Phosphate (TBP) as carrier needs a highly acidic medium
(>6N) as it favours formation of chelating complex FeCl3.TBP.
Similarly, stripping of Iron(III) from loaded organic solvents requires
neutral pH or alkaline medium to dissociate the same complex. It is
observed that TBP co-extracts acids along with metal, which causes
reversal of driving force of extraction and iron(III) is re-extracted
back from the strip phase into the feed phase during Liquid Emulsion
Membrane (LEM) pertraction. Therefore, rate of extraction of
different mineral acids (HCl, HNO3, H2SO4) using TBP with and
without presence of metal Fe(III) was examined. It is revealed that in
presence of metal acid extraction is enhanced. Determination of mass
transfer coefficient of both acid and metal extraction was performed
by using Bulk Liquid Membrane (BLM). The average mass transfer
coefficient was obtained by fitting the derived model equation with
experimentally obtained data. The mass transfer coefficient of the
mineral acid extraction is in the order of kHNO3 = 3.3x10-6m/s > kHCl =
6.05x10-7m/s > kH2SO4 = 1.85x10-7m/s. The distribution equilibria of
the above mentioned acids between aqueous feed solution and a
solution of tri-n-butyl-phosphate (TBP) in organic solvents have been
investigated. The stoichiometry of acid extraction reveals the
formation of TBP.2HCl, HNO3.2TBP, and TBP.H2SO4 complexes.
Moreover, extraction of Iron(III) by TBP in HCl aqueous solution
forms complex FeCl3.TBP.2HCl while in HNO3 medium forms
complex 3FeCl3.TBP.2HNO3
Abstract: In this paper three basic approaches and different
methods under each of them for extracting region of interest (ROI)
from stationary images are explored. The results obtained for each of
the proposed methods are shown, and it is demonstrated where each
method outperforms the other. Two main problems in ROI
extraction: the channel selection problem and the saliency reversal
problem are discussed and how best these two are addressed by
various methods is also seen. The basic approaches are 1) Saliency
based approach 2) Wavelet based approach 3) Clustering based
approach. The saliency approach performs well on images containing
objects of high saturation and brightness. The wavelet based
approach performs well on natural scene images that contain regions
of distinct textures. The mean shift clustering approach partitions the
image into regions according to the density distribution of pixel
intensities. The experimental results of various methodologies show
that each technique performs at different acceptable levels for
various types of images.
Abstract: Coal tar is a liquid by-product of the process of coal
gasification and carbonation. This liquid oil mixture contains various
kinds of useful compounds such as phenol, o-cresol, and p-cresol.
These compounds are widely used as raw material for insecticides,
dyes, medicines, perfumes, coloring matters, and many others.
This research needed to be done that given the optimum conditions
for the separation of phenol, o-cresol, and p-cresol from the coal tar
by solvent extraction process. The aim of the present work was to
study the effect of two kinds of aqueous were used as solvents:
methanol and acetone solutions, the effect of temperature (298, 306,
and 313K) and mixing (30, 35, and 40rpm) for the separation of
phenol, o-cresol, and p-cresol from coal tar by solvent extraction.
Results indicated that phenol, o-cresol, and p-cresol in coal tar
were selectivity extracted into the solvent phase and these
components could be separated by solvent extraction. The aqueous
solution of methanol, mass ratio of solvent to feed, Eo/Ro=1,
extraction temperature 306K and mixing 35 rpm were the most
efficient for extraction of phenol, o-cresol, and p-cresol from coal tar.
Abstract: In this paper we propose a method for recognition of
adult video based on support vector machine (SVM). Different kernel
features are proposed to classify adult videos. SVM has an advantage
that it is insensitive to the relative number of training example in
positive (adult video) and negative (non adult video) classes. This
advantage is illustrated by comparing performance between different
SVM kernels for the identification of adult video.
Abstract: Classification is an interesting problem in functional
data analysis (FDA), because many science and application problems
end up with classification problems, such as recognition, prediction,
control, decision making, management, etc. As the high dimension
and high correlation in functional data (FD), it is a key problem to
extract features from FD whereas keeping its global characters, which
relates to the classification efficiency and precision to heavens. In this
paper, a novel automatic method which combined Genetic Algorithm
(GA) and classification algorithm to extract classification features is
proposed. In this method, the optimal features and classification model
are approached via evolutional study step by step. It is proved by
theory analysis and experiment test that this method has advantages in
improving classification efficiency, precision and robustness whereas
using less features and the dimension of extracted classification
features can be controlled.
Abstract: Frequency domain independent component analysis has
a scaling indeterminacy and a permutation problem. The scaling
indeterminacy can be solved by use of a decomposed spectrum. For
the permutation problem, we have proposed the rules in terms of gain
ratio and phase difference derived from the decomposed spectra and
the source-s coarse directions.
The present paper experimentally clarifies that the gain ratio and
the phase difference work effectively in a real environment but their
performance depends on frequency bands, a microphone-space and
a source-microphone distance. From these facts it is seen that it is
difficult to attain a perfect solution for the permutation problem in a
real environment only by either the gain ratio or the phase difference.
For the perfect solution, this paper gives a solution to the problems
in a real environment. The proposed method is simple, the amount of
calculation is small. And the method has high correction performance
without depending on the frequency bands and distances from source
signals to microphones. Furthermore, it can be applied under the real
environment. From several experiments in a real room, it clarifies
that the proposed method has been verified.
Abstract: In this paper we focus on event extraction from Tamil
news article. This system utilizes a scoring scheme for extracting and
grouping event-specific sentences. Using this scoring scheme eventspecific
clustering is performed for multiple documents. Events are
extracted from each document using a scoring scheme based on
feature score and condition score. Similarly event specific sentences
are clustered from multiple documents using this scoring scheme.
The proposed system builds the Event Template based on user
specified query. The templates are filled with event specific details
like person, location and timeline extracted from the formed clusters.
The proposed system applies these methodologies for Tamil news
articles that have been enconverted into UNL graphs using a Tamil to
UNL-enconverter. The main intention of this work is to generate an
event based template.
Abstract: DNA microarray technology is widely used by
geneticists to diagnose or treat diseases through gene expression.
This technology is based on the hybridization of a tissue-s DNA
sequence into a substrate and the further analysis of the image
formed by the thousands of genes in the DNA as green, red or yellow
spots. The process of DNA microarray image analysis involves
finding the location of the spots and the quantification of the
expression level of these. In this paper, a tool to perform DNA
microarray image analysis is presented, including a spot addressing
method based on the image projections, the spot segmentation
through contour based segmentation and the extraction of relevant
information due to gene expression.
Abstract: In this paper, we propose a new robust and secure
system that is based on the combination between two different
transforms Discrete wavelet Transform (DWT) and Contourlet
Transform (CT). The combined transforms will compensate the
drawback of using each transform separately. The proposed
algorithm has been designed, implemented and tested successfully.
The experimental results showed that selecting the best sub-band for
embedding from both transforms will improve the imperceptibility
and robustness of the new combined algorithm. The evaluated
imperceptibility of the combined DWT-CT algorithm which gave a
PSNR value 88.11 and the combination DWT-CT algorithm
improves robustness since it produced better robust against Gaussian
noise attack. In addition to that, the implemented system shored a
successful extraction method to extract watermark efficiently.
Abstract: This paper presents a comparison of metaheuristic
algorithms, Genetic Algorithm (GA) and Ant Colony Optimization
(ACO), in producing freeman chain code (FCC). The main problem
in representing characters using FCC is the length of the FCC
depends on the starting points. Isolated characters, especially the
upper-case characters, usually have branches that make the traversing
process difficult. The study in FCC construction using one
continuous route has not been widely explored. This is our
motivation to use the population-based metaheuristics. The
experimental result shows that the route length using GA is better
than ACO, however, ACO is better in computation time than GA.
Abstract: In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
Abstract: Thermodynamics characterization Sesame oil
extraction by Acetone, Hexane and Benzene has been evaluated. The
120 hours experimental Data were described by a simple
mathematical model. According to the simulation results and the
essential criteria, Acetone is superior to other solvents but under
certain conditions where oil extraction takes place Hexane is superior
catalyst.
Abstract: In this paper we present an off line system for the
recognition of the handwritten numeric chains. Our work is divided
in two big parts. The first part is the realization of a recognition
system of the isolated handwritten digits. In this case the study is
based mainly on the evaluation of neural network performances,
trained with the gradient back propagation algorithm. The used
parameters to form the input vector of the neural network are
extracted on the binary images of the digits by several methods: the
distribution sequence, the Barr features and the centred moments of
the different projections and profiles. The second part is the
extension of our system for the reading of the handwritten numeric
chains constituted of a variable number of digits. The vertical
projection is used to segment the numeric chain at isolated digits and
every digit (or segment) will be presented separately to the entry of
the system achieved in the first part (recognition system of the
isolated handwritten digits). The result of the recognition of the
numeric chain will be displayed at the exit of the global system.
Abstract: Trace element speciation of an integrated soil
amendment matrix was studied with a modified BCR sequential
extraction procedure. The analysis included pseudo-total
concentration determinations according to USEPA 3051A and
relevant physicochemical properties by standardized methods. Based
on the results, the soil amendment matrix possessed neutralization
capacity comparable to commercial fertilizers. Additionally, the
pseudo-total concentrations of all trace elements included in the
Finnish regulation for agricultural fertilizers were lower than the
respective statutory limit values. According to chemical speciation,
the lability of trace elements increased in the following order: Hg <
Cr < Co < Cu < As < Zn < Ni < Pb < Cd < V < Mo < Ba. The
validity of the BCR approach as a tool for chemical speciation was
confirmed by the additional acid digestion phase. Recovery of trace
elements during the procedure assured the validity of the approach
and indicated good quality of the analytical work.
Abstract: In this study, a new criterion for determining the number of classes an image should be segmented is proposed. This criterion is based on discriminant analysis for measuring the separability among the segmented classes of pixels. Based on the new discriminant criterion, two algorithms for recursively segmenting the image into determined number of classes are proposed. The proposed methods can automatically and correctly segment objects with various illuminations into separated images for further processing. Experiments on the extraction of text strings from complex document images demonstrate the effectiveness of the proposed methods.1
Abstract: Automatic currency note recognition invariably
depends on the currency note characteristics of a particular country
and the extraction of features directly affects the recognition ability.
Sri Lanka has not been involved in any kind of research or
implementation of this kind. The proposed system “SLCRec" comes
up with a solution focusing on minimizing false rejection of notes.
Sri Lankan currency notes undergo severe changes in image quality
in usage. Hence a special linear transformation function is adapted to
wipe out noise patterns from backgrounds without affecting the
notes- characteristic images and re-appear images of interest. The
transformation maps the original gray scale range into a smaller
range of 0 to 125. Applying Edge detection after the transformation
provided better robustness for noise and fair representation of edges
for new and old damaged notes. A three layer back propagation
neural network is presented with the number of edges detected in row
order of the notes and classification is accepted in four classes of
interest which are 100, 500, 1000 and 2000 rupee notes. The
experiments showed good classification results and proved that the
proposed methodology has the capability of separating classes
properly in varying image conditions.
Abstract: A feasibility study for the design and construction of a
pilot plant for the extraction of castor oil in South Africa was
conducted. The study emphasized the four critical aspects of project
feasibility analysis, namely technical, financial, market and
managerial aspects. The technical aspect involved research on
existing oil extraction technologies, namely: mechanical pressing and
solvent extraction, as well as assessment of the proposed production
site for both short and long term viability of the project. The site is
on the outskirts of Nkomazi village in the Mpumalanga province,
where connections for water and electricity are currently underway,
potential raw material supply proves to be reliable since the province
is known for its commercial farming. The managerial aspect was
evaluated based on the fact that the current producer of castor oil will
be fully involved in the project while receiving training and technical
assistance from Sasol Technology, the TSC and SEDA. Market and
financial aspects were evaluated and the project was considered
financially viable with a Net Present Value (NPV) of R2 731 687 and
an Internal Rate of Return (IRR) of 18% at an annual interest rate of
10.5%. The payback time is 6years for analysis over the first 10
years with a net income of R1 971 000 in the first year. The project
was thus found to be feasible with high chance of success while
contributing to socio-economic development. It was recommended
for lab tests to be conducted to establish process kinetics that would
be used in the initial design of the plant.
Abstract: The morphology, mineralogical and chemical
composition of a low-grade nickel ore from Mpumalanga, South
Africa, were studied by scanning electron microscope (SEM), X-ray
diffraction (XRD) and X-ray fluorescence (XRF), respectively. The
ore was subjected to atmospheric agitation leaching using sulphuric
acid to investigate the effects of acid concentration, leaching
temperature, leaching time and particle size on extraction of nickel
and cobalt. Analyses results indicated the ore to be a saprolitic nickel
laterite belonging to the serpentine group of minerals. Sulphuric acid
was found to be able to extract nickel from the ore. Increased acid
concentration and temperature only produced low amounts of nickel
but improved cobalt extraction. As high as 77.44% Ni was achieved
when leaching a -106+75μm fraction with 4.0M acid concentration at
25oC. The kinetics of nickel leaching from the saprolitic ore were
studied and the activation energy was determined to be 18.16kJ/mol.
This indicated that nickel leaching reaction was diffusion controlled.
Abstract: Some fast exact algorithms for the maximum weight clique problem have been proposed. Östergard’s algorithm is one of them. Kumlander says his algorithm is faster than it. But we confirmed that the straightforwardly implemented Kumlander’s algorithm is slower than O¨ sterga˚rd’s algorithm. We propose some improvements on Kumlander’s algorithm.
Abstract: This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.