Abstract: Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear techniques like PCA and LDA. In this work, we introduce a probabilistic Bayesian framework for face recognition in the infrared spectrum. In the infrared spectrum, variations can occur between face images of the same individual due to pose, metabolic, time changes, etc. Bayesian approaches permit to reduce intrapersonal variation, thus making them very interesting for infrared face recognition. This framework is compared with classical linear techniques. Non linear techniques we developed recently for infrared face recognition are also presented and compared to the Bayesian face recognition framework. A new approach for infrared face extraction based on SVM is introduced. Experimental results show that the Bayesian technique is promising and lead to interesting results in the infrared spectrum when a sufficient number of face images is used in an intrapersonal learning process.
Abstract: This paper shows possibility of extraction Social,
Group and Individual Mind from Multiple Agents Rule Bases. Types
those Rule bases are selected as two fuzzy systems, namely
Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are
describing (modeling) agent behavior. Modifying of agent behavior
in the time varying environment will be provided by learning fuzzyneural
networks and optimization of their parameters with using
genetic algorithms in development system FUZNET. Finally,
extraction Social, Group and Individual Mind from Multiple Agents
Rule Bases are provided by Cognitive analysis and Matching
criterion.
Abstract: In this study, the problem of discriminating between interictal epileptic and non- epileptic pathological EEG cases, which present episodic loss of consciousness, investigated. We verify the accuracy of the feature extraction method of autocross-correlated coefficients which extracted and studied in previous study. For this purpose we used in one hand a suitable constructed artificial supervised LVQ1 neural network and in other a cross-correlation technique. To enforce the above verification we used a statistical procedure which based on a chi- square control. The classification and the statistical results showed that the proposed feature extraction is a significant accurate method for diagnostic discrimination cases between interictal and non-interictal EEG events and specifically the classification procedure showed that the LVQ neural method is superior than the cross-correlation one.
Abstract: As a result of the daily workflow in the design
development departments of companies, databases containing huge
numbers of 3D geometric models are generated. According to the
given problem engineers create CAD drawings based on their design
ideas and evaluate the performance of the resulting design, e.g. by
computational simulations. Usually, new geometries are built either
by utilizing and modifying sets of existing components or by adding
single newly designed parts to a more complex design.
The present paper addresses the two facets of acquiring
components from large design databases automatically and providing
a reasonable overview of the parts to the engineer. A unified
framework based on the topographic non-negative matrix
factorization (TNMF) is proposed which solves both aspects
simultaneously. First, on a given database meaningful components
are extracted into a parts-based representation in an unsupervised
manner. Second, the extracted components are organized and
visualized on square-lattice 2D maps. It is shown on the example of
turbine-like geometries that these maps efficiently provide a wellstructured
overview on the database content and, at the same time,
define a measure for spatial similarity allowing an easy access and
reuse of components in the process of design development.
Abstract: Data mining, which is the exploration of
knowledge from the large set of data, generated as a result of
the various data processing activities. Frequent Pattern Mining
is a very important task in data mining. The previous
approaches applied to generate frequent set generally adopt
candidate generation and pruning techniques for the
satisfaction of the desired objective. This paper shows how
the different approaches achieve the objective of frequent
mining along with the complexities required to perform the
job. This paper will also look for hardware approach of cache
coherence to improve efficiency of the above process. The
process of data mining is helpful in generation of support
systems that can help in Management, Bioinformatics,
Biotechnology, Medical Science, Statistics, Mathematics,
Banking, Networking and other Computer related
applications. This paper proposes the use of both upward and
downward closure property for the extraction of frequent item
sets which reduces the total number of scans required for the
generation of Candidate Sets.
Abstract: Recognition of characters greatly depends upon the features used. Several features of the handwritten Arabic characters are selected and discussed. An off-line recognition system based on the selected features was built. The system was trained and tested with realistic samples of handwritten Arabic characters. Evaluation of the importance and accuracy of the selected features is made. The recognition based on the selected features give average accuracies of 88% and 70% for the numbers and letters, respectively. Further improvements are achieved by using feature weights based on insights gained from the accuracies of individual features.
Abstract: From the importance of the conference and its
constructive role in the studies discussion, there must be a strong
organization that allows the exploitation of the discussions in opening
new horizons. The vast amount of information scattered across the
web, make it difficult to find experts, who can play a prominent role
in organizing conferences. In this paper we proposed a new approach
of extracting researchers- information from various Web resources
and correlating them in order to confirm their correctness. As a
validator of this approach, we propose a service that will be useful to
set up a conference. Its main objective is to find appropriate experts,
as well as the social events for a conference. For this application we
us Semantic Web technologies like RDF and ontology to represent
the confirmed information, which are linked to another ontology
(skills ontology) that are used to present and compute the expertise.
Abstract: Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images, may cause misalignments. To handle with this limitation, we propose to extract the salient edges and extracted control points (CP) of medical images by using efficiency of multiresolution representation of data nonsubsampled contourlet transform (NSCT) that finds the best feature points. The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). The experimental results showed that the proposed method produces totally accurate performance for registration medical CT-MR images.
Abstract: A lot of matching algorithms with different characteristics have been introduced in recent years. For real time systems these algorithms are usually based on minutiae features. In this paper we introduce a novel approach for feature extraction in which the extracted features are independent of shift and rotation of the fingerprint and at the meantime the matching operation is performed much more easily and with higher speed and accuracy. In this new approach first for any fingerprint a reference point and a reference orientation is determined and then based on this information features are converted into polar coordinates. Due to high speed and accuracy of this approach and small volume of extracted features and easily execution of matching operation this approach is the most appropriate for real time applications.
Abstract: Gaharu that produced by Aquilaria spp. is classified as
one of the most valuable forest products traded internationally as it is
very resinous, fragrant and highly valuable heartwood. Gaharu has
been widely used in aromatheraphy, medicine, perfume and religious
practices. This work aimed to determine the factors affecting solid
liquid extraction of gaharu oil using hexane as solvent under
experimental condition. The kinetics of extraction was assumed and
verified based on a second-order mechanism. The effect of three
main factors, which were temperature, reaction time and solvent to
solid ratio were investigated to achieve maximum oil yield. The
optimum condition were found at temperature 65°C, 9 hours reaction
time and solvent to solid ratio of 12:1 with 14.5% oil yield. The
kinetics experimental data agrees and well fitted with the second
order extraction model. The initial extraction rate (h) was 0.0115
gmL-1min-1; the extraction capacity (Cs) was 1.282gmL-1; the second
order extraction constant (k) was 0.007 mLg-1min-1 and coefficient of
determination, R2 was 0.945.
Abstract: Regarding the multi-media property of internet and the facilities that can be provided for the users, the purpose of this paper is to investigate the users- behavioral patterns and the impact of internet on taboos of marriage. For this purpose a survey technique on the sample size amounted 403 students of governmental guidance schools of city of Mashhad in country of Iran were considered. The results showed, the process of using various internet environments depends on the degree of the users- familiarity with these sites. In order to clarify the effects of the Internet on the taboos of marriage, the non – internet parameters also considered to be controlled. The ttest held among the internet users and non-users, indicated that internet users possess lower taboos of marriage. Extraction of the effects of internet via considering the effects of non-internet parameters, indicate that addiction to the internet, creating a cordial atmosphere, emotional communication, and message attractive factors have significant effects on the family's traditional values.
Abstract: This paper presents the new results of energy plant –
rye and triticale at yellow ripeness and ripe, pre-treatment in high
pressure steam reactor and monosaccharide extraction. There were
investigated the influence of steam pressure (20 to 22 bar), retention
duration (180 to 240 s) and catalytic sulphuric acid concentration
strength (0 to 0.5 %) on the pre-treatment process, contents of
monosaccharides (glucose, arabinose, xylose, mannose) and
undesirable by-compounds (furfural and HMF) in the reactor. The
study has determined that the largest amount of monosaccharides
(37.2 % of glucose, 2.7 % of arabinose, 8.4 % of xylose, and 1.3 %
of mannose) was received in the rye at ripe, the samples of which
were mixed with 0.5 % concentration of catalytic sulphuric acid, and
hydrolysed in the reactor, where the pressure was 20 bar, whereas the
reaction time – 240 s.
Abstract: In this paper a one-dimension Self Organizing Map
algorithm (SOM) to perform feature selection is presented. The
algorithm is based on a first classification of the input dataset on a
similarity space. From this classification for each class a set of
positive and negative features is computed. This set of features is
selected as result of the procedure. The procedure is evaluated on an
in-house dataset from a Knowledge Discovery from Text (KDT)
application and on a set of publicly available datasets used in
international feature selection competitions. These datasets come
from KDT applications, drug discovery as well as other applications.
The knowledge of the correct classification available for the training
and validation datasets is used to optimize the parameters for positive
and negative feature extractions. The process becomes feasible for
large and sparse datasets, as the ones obtained in KDT applications,
by using both compression techniques to store the similarity matrix
and speed up techniques of the Kohonen algorithm that take
advantage of the sparsity of the input matrix. These improvements
make it feasible, by using the grid, the application of the
methodology to massive datasets.
Abstract: The copper flotation tailings from Konkola Copper
mine in Nchanga, Zambia were used in the study. The purpose of this
study was to determine the leaching characteristics of the tailings
material prior and after the physical beneficiation process is
employed. The Knelson gravity concentrator (KC-MD3) was used for
the beneficiation process. The copper leaching efficiencies and
impurity co-extraction percentages in both the upgraded and the raw
feed material were determined at different pH levels and temperature.
It was observed that the copper extraction increased with an increase
in temperature and a decrease in pH levels. In comparison to the raw
feed sample, the upgraded sample reported a maximum copper
extraction of 69% which was 9%, higher than raw feed % extractions.
The impurity carry over was reduced from 18% to 4 % on the
upgraded sample. The reduction in impurity co-extraction was as a
result of the removal of the reactive gangue elements during the
upgrading process, this minimized the number of side reaction
occurring during leaching.
Abstract: This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.
Abstract: Organochlorine pesticides (OCPs) are known to be
persistent and bioaccumulative toxicants that may cause reproductive
impairments in wildlife as well as human. The current study uses the
snail-eating turtle Malayemys macrocephala, a long-lived animal
commonly distribute in rice field habitat in central part of Thailand,
as a sentinel to monitor OCP contamination in environment. The
nest soil, complete clutch of eggs, and blood of the turtle were
collected from agricultural areas in the Chao Phraya River Basin,
Thailand during the nesting season of 2007-2008. The novel
methods for tissue extraction by an accelerated solvent extractor
(ASE, for egg) and liquid-liquid extraction (for blood) have been
developed. The nineteen OCP residues were analyzed by gas
chromatography with micro-electron captured detector (GC-μECD).
The validated methods have met requirements of the AOAC
standard. The results indicated that significant amounts of OCPs are
still contaminated in nest soil and eggs of the turtle even though the
OCPs had been banned in this area for many years. This suggested
the potential risk to health of wildlife as well as human in the area.
Abstract: Assessment for image quality traditionally needs its
original image as a reference. The conventional method for assessment
like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR)
is invalid when there is no reference. In this paper, we present a new
No-Reference (NR) assessment of image quality using blur and noise.
The recent camera applications provide high quality images by help of
digital Image Signal Processor (ISP). Since the images taken by the
high performance of digital camera have few blocking and ringing
artifacts, we only focus on the blur and noise for predicting the
objective image quality. The experimental results show that the
proposed assessment method gives high correlation with subjective
Difference Mean Opinion Score (DMOS). Furthermore, the proposed
method provides very low computational load in spatial domain and
similar extraction of characteristics to human perceptional assessment.
Abstract: Liquid-liquid extraction is a process using two immiscible
liquids to extract compounds from one phase without high
temperature requirement. Mostly, the technical implementation of
this process is carried out in mixer-settlers or extraction columns. In
real chemical processes, chemicals may have high viscosity and
contain impurities. These impurities may change the settling behavior
of the process without measurably changing the physical properties
of the phases. In the current study, the settling behavior and the affected
parameters in a high-viscosity system were observed. Batchsettling
experiments were performed to experimentally quantify the
settling behavior and the mixer-settler model of Henschke [1] was
used to evaluate the behavior of the toluene + water system. The
viscosity of the system was increased by adding polyethylene glycol
4000 to the aqueous phase. NaCl and Na2SO4 were used to study the
influence of electrolytes. The results from this study show that increasing
the viscosity of water has a higher influence on the settling
behavior in comparison to the effects of the electrolytes. It can be
seen from the experiments that at high salt concentrations, there was
no effect on the settling behavior.
Abstract: This research studied the hypoglycemic effect of
water soluble polysaccharide (WSP) extracted from yam (Dioscorea
hispida) tuber by three different methods: aqueous extraction, papain
assisted extraction, and tempeh inoculums assisted extraction. The
two later extraction methods were aimed to remove WSP binding
protein to have more pure WSP. The hypoglycemic activities were
evaluated by means in vivo test on alloxan induced hyperglycemic
rats, glucose response test (GRT), in situ glucose absorption test
using everted sac, and short chain fatty acids (SCFAs) analysis. All
yam WSP extracts exhibited ability to decrease blood glucose level in
hyperglycemia condition as well as inhibited glucose absorption and
SCFA formation. The order of hypoglycemic activity was tempeh
inoculums assisted- >papain assisted- >aqueous WSP extracts. GRT
and in situ glucose absorption test showed that order of inhibition
was papain assisted- >tempeh inoculums assisted- >aqueous WSP
extracts. Digesta of caecum of yam WSP extracts oral fed rats had
more SCFA than control. Tempeh inoculums assisted WSP extract
exhibited the most significant hypoglycemic activity.
Abstract: Content-Based Image Retrieval has been a major area
of research in recent years. Efficient image retrieval with high
precision would require an approach which combines usage of both
the color and texture features of the image. In this paper we propose
a method for enhancing the capabilities of texture based feature
extraction and further demonstrate the use of these enhanced texture
features in Texture-Based Color Image Retrieval.