Abstract: The methanolic extracts from seeds of tamarind
(Tamarindus indica) was prepared by Soxhlet apparatus extraction
and evaluated for total phenolic content by Folin-Ciocalteu method.
Then, methanolic extract was screened biological activities (In vitro)
for anti-melanogenic activity by tyrosinase inhibition test, antiinflammation
activity by cyclooxygenase 1 (COX-1) and
cyclooxygenase 2 (COX-2) inhibition test, and cytotoxic screening
test with Vero cells. The results showed that total phenolic content,
which contained in extract, was contained 27.72 mg of gallic acid
equivalent per g of dry weight. The ability to inhibit tyrosinase
enzyme, which exerted by Tamarind seed extracts (1 mg/ml) was
52.13 ± 0.42 %. The extract was not possessed inhibitory effect to
COX-1 and COX-2 enzymes and cytotoxic effect to Vero cells. The
finding is concludes that tested seed extract was possessed
antimelanogenic activity with non-toxic effects. However, there was
not exhibited anti-inflammatory activity. Further studies include the
use of advance biological models to confirm this biological activity,
as well as, the isolation and characterization of the purified
compounds that it was contained.
Abstract: Human identification at a distance has recently gained
growing interest from computer vision researchers. Gait recognition
aims essentially to address this problem by identifying people based
on the way they walk [1]. Gait recognition has 3 steps. The first step
is preprocessing, the second step is feature extraction and the third
one is classification. This paper focuses on the classification step that
is essential to increase the CCR (Correct Classification Rate).
Multilayer Perceptron (MLP) is used in this work. Neural Networks
imitate the human brain to perform intelligent tasks [3].They can
represent complicated relationships between input and output and
acquire knowledge about these relationships directly from the data
[2]. In this paper we apply MLP NN for 11 views in our database and
compare the CCR values for these views. Experiments are performed
with the NLPR databases, and the effectiveness of the proposed
method for gait recognition is demonstrated.
Abstract: The purpose of this paper is to solve the problem of protecting aerial lines from high impedance faults (HIFs) in distribution systems. This investigation successfully applies 3I0 zero sequence current to solve HIF problems. The feature extraction system based on discrete wavelet transform (DWT) and the feature identification technique found on statistical confidence are then applied to discriminate effectively between the HIFs and the switch operations. Based on continuous wavelet transform (CWT) pattern recognition of HIFs is proposed, also. Staged fault testing results demonstrate that the proposed wavelet based algorithm is feasible performance well.
Abstract: The extraction of meaningful information from image
could be an alternative method for time series analysis. In this paper,
we propose a graphical analysis of time series grouped into table
with adjusted colour scale for numerical values. The advantages of
this method are also discussed. The proposed method is easy to
understand and is flexible to implement the standard methods of
pattern recognition and verification, especially for noisy
environmental data.
Abstract: The proposed system identifies the species of the wood
using the textural features present in its barks. Each species of a wood
has its own unique patterns in its bark, which enabled the proposed
system to identify it accurately. Automatic wood recognition system
has not yet been well established mainly due to lack of research in this
area and the difficulty in obtaining the wood database. In our work, a
wood recognition system has been designed based on pre-processing
techniques, feature extraction and by correlating the features of those
wood species for their classification. Texture classification is a problem
that has been studied and tested using different methods due to its
valuable usage in various pattern recognition problems, such as wood
recognition, rock classification. The most popular technique used
for the textural classification is Gray-level Co-occurrence Matrices
(GLCM). The features from the enhanced images are thus extracted
using the GLCM is correlated, which determines the classification
between the various wood species. The result thus obtained shows a
high rate of recognition accuracy proving that the techniques used in
suitable to be implemented for commercial purposes.
Abstract: The effect of wheat flour extraction rates on flour
composition, farinographic characteristics and the quality of
sourdough naans was investigated. The results indicated that by
increasing the extraction rate, the amount of protein, fiber, fat and
ash increased, whereas moisture content decreased. Farinographic
characteristic like water absorption and dough development time
increased with an increase in flour extraction rate but the dough
stabilities and tolerance indices were reduced with an increase in
flour extraction rates. Titratable acidity for both sourdough and
sourdough naans also increased along with flour extraction rate. The
study showed that overall quality of sourdough naans were affected
by both flour extraction rate and starter culture used. Sensory
analysis of sourdough naans revealed that desirable extraction rate
for sourdough naan was 76%.
Abstract: Groundwater has become the most dependable source
of fresh water for agriculture, domestic and industrial uses in the past
few decades. This wide use of groundwater if left uncontrolled and
unseen will lead to overexploitation causing sea water intrusion in the
coastal areas and illegal water marketing. Several Policies and Acts
have been enacted to regulate and manage the use of this valuable
resource. In spite of this the over extraction of groundwater beyond
the recharging capacity of aquifers and depletion in the quality of
groundwater is continuing. The current study aims at reviewing the
Acts and Policies existing in the State of Tamil Nadu and in the
National level regarding groundwater regulation and management.
Further an analysis is made on the rights associated with the usage of
groundwater resources and the gaps in these policies have been
analyzed. Some suggestions are made to reform the existing
groundwater policies for better management and regulation of the
resource.
Abstract: This paper describes an optimal approach for feature
subset selection to classify the leaves based on Genetic Algorithm
(GA) and Kernel Based Principle Component Analysis (KPCA). Due
to high complexity in the selection of the optimal features, the
classification has become a critical task to analyse the leaf image
data. Initially the shape, texture and colour features are extracted
from the leaf images. These extracted features are optimized through
the separate functioning of GA and KPCA. This approach performs
an intersection operation over the subsets obtained from the
optimization process. Finally, the most common matching subset is
forwarded to train the Support Vector Machine (SVM). Our
experimental results successfully prove that the application of GA
and KPCA for feature subset selection using SVM as a classifier is
computationally effective and improves the accuracy of the classifier.
Abstract: Electromyography (EMG) signal processing has been investigated remarkably regarding various applications such as in rehabilitation systems. Specifically, wavelet transform has served as a powerful technique to scrutinize EMG signals since wavelet transform is consistent with the nature of EMG as a non-stationary signal. In this paper, the efficiency of wavelet transform in surface EMG feature extraction is investigated from four levels of wavelet decomposition and a comparative study between different mother wavelets had been done. To recognize the best function and level of wavelet analysis, two evaluation criteria, scatter plot and RES index are recruited. Hereupon, four wavelet families, namely, Daubechies, Coiflets, Symlets and Biorthogonal are studied in wavelet decomposition stage. Consequently, the results show that only features from first and second level of wavelet decomposition yields good performance and some functions of various wavelet families can lead to an improvement in separability class of different hand movements.
Abstract: In this paper, a new approach for target recognition based on the Empirical mode decomposition (EMD) algorithm of Huang etal. [11] and the energy tracking operator of Teager [13]-[14] is introduced. The conjunction of these two methods is called Teager-Huang analysis. This approach is well suited for nonstationary signals analysis. The impulse response (IR) of target is first band pass filtered into subsignals (components) called Intrinsic mode functions (IMFs) with well defined Instantaneous frequency (IF) and Instantaneous amplitude (IA). Each IMF is a zero-mean AM-FM component. In second step, the energy of each IMF is tracked using the Teager energy operator (TEO). IF and IA, useful to describe the time-varying characteristics of the signal, are estimated using the Energy separation algorithm (ESA) algorithm of Maragos et al .[16]-[17]. In third step, a set of features such as skewness and kurtosis are extracted from the IF, IA and IMF energy functions. The Teager-Huang analysis is tested on set of synthetic IRs of Sonar targets with different physical characteristics (density, velocity, shape,? ). PCA is first applied to features to discriminate between manufactured and natural targets. The manufactured patterns are classified into spheres and cylinders. One hundred percent of correct recognition is achieved with twenty three echoes where sixteen IRs, used for training, are free noise and seven IRs, used for testing phase, are corrupted with white Gaussian noise.
Abstract: This Paper proposes a new facial feature extraction approach, Wash-Hadamard Transform (WHT). This approach is based on correlation between local pixels of the face image. Its primary advantage is the simplicity of its computation. The paper compares the proposed approach, WHT, which was traditionally used in data compression with two other known approaches: the Principal Component Analysis (PCA) and the Discrete Cosine Transform (DCT) using the face database of Olivetti Research Laboratory (ORL). In spite of its simple computation, the proposed algorithm (WHT) gave very close results to those obtained by the PCA and DCT. This paper initiates the research into WHT and the family of frequency transforms and examines their suitability for feature extraction in face recognition applications.
Abstract: The CMLP building was developed to be a model for
sustainability with strategies to reduce water, energy and pollution,
and to provide a healthy environment for the building occupants. The
aim of this paper is to investigate the environmental effects of energy
used by this building. A LCA (life cycle analysis) was led to measure
the real environmental effects produced by the use of energy. The
impact categories most affected by the energy use were found to be
the human health effects, as well as ecotoxicity. Natural gas
extraction, uranium milling for nuclear energy production, and the
blasting for mining and infrastructure construction are the processes
contributing the most to emissions in the human health effect. Data
comparing LCA results of CMLP building with a conventional
building results showed that energy used by the CMLP building has
less damage for the environment and human health than a
conventional building.
Abstract: A phorbol-12-myristate-13-acetate (TPA) is a synthetic analogue of phorbol ester (PE), a natural toxic compound of Euphorbiaceae plant. The oil extracted from plants of this family is useful source for primarily biofuel. However this oil might also be used as a foodstuff due to its significant nutrition content. The limitations for utilizing the oil as a foodstuff are mainly due to a toxicity of PE. Currently, a majority of PE detoxification processes are expensive as include multi steps alcohol extraction sequence.
Ozone is considered as a strong oxidative agent. It reacts with PE by attacking the carbon-carbon double bond of PE. This modification of PE molecular structure yields a non toxic ester with high lipid content.
This report presents data on development of simple and cheap PE detoxification process with water application as a buffer and ozone as reactive component. The core of this new technique is an application for a new microscale plasma unit to ozone production and the technology permits ozone injection to the water-TPA mixture in form of microbubbles.
The efficacy of a heterogeneous process depends on the diffusion coefficient which can be controlled by contact time and interfacial area. The low velocity of rising microbubbles and high surface to volume ratio allow efficient mass transfer to be achieved during the process. Direct injection of ozone is the most efficient way to process with such highly reactive and short lived chemical.
Data on the plasma unit behavior are presented and the influence of gas oscillation technology on the microbubble production mechanism has been discussed. Data on overall process efficacy for TPA degradation is shown.
Abstract: This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.
Abstract: Segmentation and quantification of stenosis is an
important task in assessing coronary artery disease. One of the main
challenges is measuring the real diameter of curved vessels.
Moreover, uncertainty in segmentation of different tissues in the
narrow vessel is an important issue that affects accuracy. This paper
proposes an algorithm to extract coronary arteries and measure the
degree of stenosis. Markovian fuzzy clustering method is applied to
model uncertainty arises from partial volume effect problem. The
algorithm employs: segmentation, centreline extraction, estimation of
orthogonal plane to centreline, measurement of the degree of
stenosis. To evaluate the accuracy and reproducibility, the approach
has been applied to a vascular phantom and the results are compared
with real diameter. The results of 10 patient datasets have been
visually judged by a qualified radiologist. The results reveal the
superiority of the proposed method compared to the Conventional
thresholding Method (CTM) on both datasets.
Abstract: The explosive growth of World Wide Web has posed
a challenging problem in extracting relevant data. Traditional web
crawlers focus only on the surface web while the deep web keeps
expanding behind the scene. Deep web pages are created
dynamically as a result of queries posed to specific web databases.
The structure of the deep web pages makes it impossible for
traditional web crawlers to access deep web contents. This paper,
Deep iCrawl, gives a novel and vision-based approach for extracting
data from the deep web. Deep iCrawl splits the process into two
phases. The first phase includes Query analysis and Query translation
and the second covers vision-based extraction of data from the
dynamically created deep web pages. There are several established
approaches for the extraction of deep web pages but the proposed
method aims at overcoming the inherent limitations of the former.
This paper also aims at comparing the data items and presenting them
in the required order.
Abstract: A research project dealing with the phytoremediation
of a soil polluted by some heavy metals is currently running. The
case study is represented by a mining area in Hamedan province in
the central west part of Iran. The potential of phytoextraction and
phytostabilization of plants was evaluated considering the
concentration of heavy metals in the plant tissues and also the
bioconcentration factor (BCF) and the translocation factor (TF). Also
the several established criteria were applied to define
hyperaccumulator plants in the studied area. Results showed that
none of the collected plant species were suitable for phytoextraction
of Cu, Zn, Fe and Mn, but among the plants, Euphorbia macroclada
was the most efficient in phytostabilization of Cu and Fe, while,
Ziziphora clinopodioides, Cousinia sp. and Chenopodium botrys
were the most suitable for phytostabilization of Zn and Chondrila
juncea and Stipa barbata had the potential for phytostabilization of
Mn. Using the most common criterion, Euphorbia macroclada and
Verbascum speciosum were Fe hyperaccumulator plants. Present
study showed that native plant species growing on contaminated sites
may have the potential for phytoremediation.
Abstract: Optimization of extraction of phenolic compounds
from Avicennia marina using response surface methodology was
carried out during the present study. Five levels, three factors
rotatable design (CCRD) was utilized to examine the optimum
combination of extraction variables based on the TPC of Avicennia
marina leaves. The best combination of response function was 78.41
°C, drying temperature; 26.18°C; extraction temperature and 36.53
minutes of extraction time. However, the procedure can be promptly
extended to the study of several others pharmaceutical processes like
purification of bioactive substances, drying of extracts and
development of the pharmaceutical dosage forms for the benefit of
consumers.
Abstract: Partial discharge (PD) detection is an important
method to evaluate the insulation condition of metal-clad apparatus.
Non-intrusive sensors which are easy to install and have no
interruptions on operation are preferred in onsite PD detection.
However, it often lacks of accuracy due to the interferences in PD
signals. In this paper a novel PD extraction method that uses frequency
analysis and entropy based time-frequency (TF) analysis is introduced.
The repetitive pulses from convertor are first removed via frequency
analysis. Then, the relative entropy and relative peak-frequency of
each pulse (i.e. time-indexed vector TF spectrum) are calculated and
all pulses with similar parameters are grouped. According to the
characteristics of non-intrusive sensor and the frequency distribution
of PDs, the pulses of PD and interferences are separated. Finally the
PD signal and interferences are recovered via inverse TF transform.
The de-noised result of noisy PD data demonstrates that the
combination of frequency and time-frequency techniques can
discriminate PDs from interferences with various frequency
distributions.
Abstract: The third phase of web means semantic web requires many web pages which are annotated with metadata. Thus, a crucial question is where to acquire these metadata. In this paper we propose our approach, a semi-automatic method to annotate the texts of documents and web pages and employs with a quite comprehensive knowledge base to categorize instances with regard to ontology. The approach is evaluated against the manual annotations and one of the most popular annotation tools which works the same as our tool. The approach is implemented in .net framework and uses the WordNet for knowledge base, an annotation tool for the Semantic Web.