Abstract: Writer identification is one of the areas in pattern
recognition that attract many researchers to work in, particularly in
forensic and biometric application, where the writing style can be
used as biometric features for authenticating an identity. The
challenging task in writer identification is the extraction of unique
features, in which the individualistic of such handwriting styles
can be adopted into bio-inspired generalized global shape for
writer identification. In this paper, the feasibility of generalized
global shape concept of complimentary binding in Artificial
Immune System (AIS) for writer identification is explored. An
experiment based on the proposed framework has been conducted
to proof the validity and feasibility of the proposed approach for
off-line writer identification.
Abstract: Brain Computer Interface (BCI) has been recently
increased in research. Functional Near Infrared Spectroscope (fNIRs)
is one the latest technologies which utilize light in the near-infrared
range to determine brain activities. Because near infrared technology
allows design of safe, portable, wearable, non-invasive and wireless
qualities monitoring systems, fNIRs monitoring of brain
hemodynamics can be value in helping to understand brain tasks. In
this paper, we present results of fNIRs signal analysis indicating that
there exist distinct patterns of hemodynamic responses which
recognize brain tasks toward developing a BCI. We applied two
different mathematics tools separately, Wavelets analysis for
preprocessing as signal filters and feature extractions and Neural
networks for cognition brain tasks as a classification module. We
also discuss and compare with other methods while our proposals
perform better with an average accuracy of 99.9% for classification.
Abstract: ECG contains very important clinical information about the cardiac activities of the heart. Often the ECG signal needs to be captured for a long period of time in order to identify abnormalities in certain situations. Such signal apart of a large volume often is characterised by low quality due to the noise and other influences. In order to extract features in the ECG signal with time-varying characteristics at first need to be preprocessed with the best parameters. Also, it is useful to identify specific parts of the long lasting signal which have certain abnormalities and to direct the practitioner to those parts of the signal. In this work we present a method based on wavelet transform, standard deviation and variable threshold which achieves 100% accuracy in identifying the ECG signal peaks and heartbeat as well as identifying the standard deviation, providing a quick reference to abnormalities.
Abstract: This paper describes a new method for extracting the fetal heart rate (fHR) and the fetal heart rate variability (fHRV) signal non-invasively using abdominal maternal electrocardiogram (mECG) recordings. The extraction is based on the fundamental frequency (Fourier-s) theorem. The fundamental frequency of the mother-s electrocardiogram signal (fo-m) is calculated directly from the abdominal signal. The heart rate of the fetus is usually higher than that of the mother; as a result, the fundamental frequency of the fetal-s electrocardiogram signal (fo-f) is higher than that of the mother-s (fo-f > fo-m). Notch filters to suppress mother-s higher harmonics were designed; then a bandpass filter to target fo-f and reject fo-m is implemented. Although the bandpass filter will pass some other frequencies (harmonics), we have shown in this study that those harmonics are actually carried on fo-f, and thus have no impact on the evaluation of the beat-to-beat changes (RR intervals). The oscillations of the time-domain extracted signal represent the RR intervals. We have also shown in this study that zero-to-zero evaluation of the periods is more accurate than the peak-to-peak evaluation. This method is evaluated both on simulated signals and on different abdominal recordings obtained at different gestational ages.
Abstract: The effects of irrigation with dairy factory wastewater on soil properties were investigated at two sites that had received irrigation for > 60 years. Two adjoining paired sites that had never received DFE were also sampled as well as another seven fields from a wider area around the factory. In comparison with paired sites that had not received effluent, long-term wastewater irrigation resulted in an increase in pH, EC, extractable P, exchangeable Na and K and ESP. These changes were related to the use of phosphoric acid, NaOH and KOH as cleaning agents in the factory. Soil organic C content was unaffected by DFE irrigation but the size (microbial biomass C and N) and activity (basal respiration) of the soil microbial community were increased. These increases were attributed to regular inputs of soluble C (e.g. lactose) present as milk residues in the wastewater. Principal component analysis (PCA) of the soils data from all 11sites confirmed that the main effects of DFE irrigation were an increase in exchangeable Na, extractable P and microbial biomass C, an accumulation of soluble salts and a liming effect. PCA analysis of soil bacterial community structure, using PCR-DGGE of 16S rDNA fragments, generally separated individual sites from one another but did not group them according to irrigation history. Thus, whilst the size and activity of the soil microbial community were increased, the structure and diversity of the bacterial community remained unaffected.
Abstract: Facial expression analysis plays a significant role for
human computer interaction. Automatic analysis of human facial
expression is still a challenging problem with many applications. In
this paper, we propose neuro-fuzzy based automatic facial expression
recognition system to recognize the human facial expressions like
happy, fear, sad, angry, disgust and surprise. Initially facial image is
segmented into three regions from which the uniform Local Binary
Pattern (LBP) texture features distributions are extracted and
represented as a histogram descriptor. The facial expressions are
recognized using Multiple Adaptive Neuro Fuzzy Inference System
(MANFIS). The proposed system designed and tested with JAFFE
face database. The proposed model reports 94.29% of classification
accuracy.
Abstract: This paper proposes method of diagnosing ball screw
preload loss through the Hilbert-Huang Transform （HHT） and
Multiscale entropy （MSE） process. The proposed method can
diagnose ball screw preload loss through vibration signals when the
machine tool is in operation. Maximum dynamic preload of 2 %, 4 %,
and 6 % ball screws were predesigned, manufactured, and tested
experimentally. Signal patterns are discussed and revealed using
Empirical Mode Decomposition（EMD）with the Hilbert Spectrum.
Different preload features are extracted and discriminated using HHT.
The irregularity development of a ball screw with preload loss is
determined and abstracted using MSE based on complexity
perception. Experiment results show that the proposed method can
predict the status of ball screw preload loss. Smart sensing for the
health of the ball screw is also possible based on a comparative
evaluation of MSE by the signal processing and pattern matching of
EMD/HHT. This diagnosis method realizes the purposes of prognostic
effectiveness on knowing the preload loss and utilizing convenience.
Abstract: This paper presents the automated methods employed
for extracting craniofacial landmarks in white light images as part of
a registration framework designed to support three neurosurgical
procedures. The intraoperative space is characterised by white light
stereo imaging while the preoperative plan is performed on CT scans.
The registration aims at aligning these two modalities to provide a
calibrated environment to enable image-guided solutions. The
neurosurgical procedures can then be carried out by mapping the
entry and target points from CT space onto the patient-s space. The
registration basis adopted consists of natural landmarks (eye corner
and ear tragus). A 5mm accuracy is deemed sufficient for these three
procedures and the validity of the selected registration basis in
achieving this accuracy has been assessed by simulation studies. The
registration protocol is briefly described, followed by a presentation
of the automated techniques developed for the extraction of the
craniofacial features and results obtained from tests on the AR and
FERET databases. Since the three targeted neurosurgical procedures
are routinely used for head injury management, the effect of
bruised/swollen faces on the automated algorithms is assessed. A
user-interactive method is proposed to deal with such unpredictable
circumstances.
Abstract: In this paper, the absorption and fluorescence
emission spectra of Yb:Y3Al5O12 (YAG)(25 at%) crystal as a disk
laser medium are measured at high temperature (300-450K). The
absorption and emission cross sections of Yb:YAG crystal are
determined using Reciprocity method. Temperature dependence of
941nm absorption cross section and 1031nm emission cross section
is extracted in the range of 300-450K. According to our experimental
results, an exponential temperature dependence between 300K and
450K is acquired for the 1031nm peak emission cross section and
also for 941nm peak absorption cross section of Yb:YAG crystal.
These results could be used for simulation and design of high power
highly doped Yb:YAG thin disk lasers.
Abstract: This paper proposes a neural network weights and
topology optimization using genetic evolution and the
backpropagation training algorithm. The proposed crossover and
mutation operators aims to adapt the networks architectures and
weights during the evolution process. Through a specific inheritance
procedure, the weights are transmitted from the parents to their
offsprings, which allows re-exploitation of the already trained
networks and hence the acceleration of the global convergence of the
algorithm. In the preprocessing phase, a new feature extraction
method is proposed based on Legendre moments with the Maximum
entropy principle MEP as a selection criterion. This allows a global
search space reduction in the design of the networks. The proposed
method has been applied and tested on the well known MNIST
database of handwritten digits.
Abstract: To produce sugar and ethanol, sugarcane processing
generates several agricultural residues, being straw and bagasse is
considered as the main among them. And what to do with this
residues has been subject of many studies and experiences in an
industry that, in recent years, highlighted by the ability to transform
waste into valuable products such as electric power. Cellulose is the
main component of these materials. It is the most common organic
polymer and represents about 1.5 x 1012 tons of total production of
biomass per year and is considered an almost inexhaustible source of
raw material. Pretreatment with mineral acids is one of the most
widely used as stage of cellulose extraction from lignocellulosic
materials for solubilizing most of the hemicellulose content. This
study had as goal to find the best reaction time of sugarcane bagasse
pretreatment with sulfuric acid in order to minimize the losses of
cellulose concomitantly with the highest possible removal of
hemicellulose and lignin. It was found that the best time for this
reaction was 40 minutes, in which it was reached a loss of
hemicelluloses around 70% and lignin and cellulose, around 15%.
Over this time, it was verified that the cellulose loss increased and
there was no loss of lignin and hemicellulose.
Abstract: Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.
Abstract: Based on the one-bit-matching principle and by turning the de-mixing matrix into an orthogonal matrix via certain normalization, Ma et al proposed a one-bit-matching learning algorithm on the Stiefel manifold for independent component analysis [8]. But this algorithm is not adaptive. In this paper, an algorithm which can extract kurtosis and its sign of each independent source component directly from observation data is firstly introduced.With the algorithm , the one-bit-matching learning algorithm is revised, so that it can make the blind separation on the Stiefel manifold implemented completely in the adaptive mode in the framework of natural gradient.
Abstract: In this paper, a new face recognition method based on
PCA (principal Component Analysis), LDA (Linear Discriminant
Analysis) and neural networks is proposed. This method consists of
four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii)
feature extraction using LDA and iv) classification using neural
network. Combination of PCA and LDA is used for improving the
capability of LDA when a few samples of images are available and
neural classifier is used to reduce number misclassification caused by
not-linearly separable classes. The proposed method was tested on
Yale face database. Experimental results on this database
demonstrated the effectiveness of the proposed method for face
recognition with less misclassification in comparison with previous
methods.
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: Extensive wind tunnel tests have been conducted to
investigate the unsteady flow field over and behind a 2D model of a
660 kW wind turbine blade section in pitching motion. The surface
pressure and wake dynamic pressure variation at a distance of 1.5
chord length from trailing edge were measured by pressure
transducers during several oscillating cycles at 3 reduced frequencies
and oscillating amplitudes. Moreover, form drag and linear
momentum deficit are extracted and compared at various conditions.
The results show that the wake velocity field and surface pressure of
the model have similar behavior before and after the airfoil beyond
the static stall angle of attack. In addition, the effects of reduced
frequency and oscillation amplitudes are discussed.
Abstract: The passive electrical properties of a tissue depends
on the intrinsic constituents and its structure, therefore by measuring
the complex electrical impedance of the tissue it might be possible to
obtain indicators of the tissue state or physiological activity [1].
Complete bio-impedance information relative to physiology and
pathology of a human body and functional states of the body tissue or
organs can be extracted by using a technique containing a fourelectrode
measurement setup. This work presents the estimation
measurement setup based on the four-electrode technique. First, the
complex impedance is estimated by three different estimation
techniques: Fourier, Sine Correlation and Digital De-convolution and
then estimation errors for the magnitude, phase, reactance and
resistance are calculated and analyzed for different levels of
disturbances in the observations. The absolute values of relative
errors are plotted and the graphical performance of each technique is
compared.
Abstract: There has been a growing interest in implementing humanoid avatars in networked virtual environment. However, most existing avatar communication systems do not take avatars- social backgrounds into consideration. This paper proposes a novel humanoid avatar animation system to represent personalities and facial emotions of avatars based on culture, profession, mood, age, taste, and so forth. We extract semantic keywords from the input text through natural language processing, and then the animations of personalized avatars are retrieved and displayed according to the order of the keywords. Our primary work is focused on giving avatars runtime instruction from multiple natural languages. Experiments with Chinese, Japanese and English input based on the prototype show that interactive avatar animations can be displayed in real time and be made available online. This system provides a more natural and interesting means of human communication, and therefore is expected to be used for cross-cultural communication, multiuser online games, and other entertainment applications.
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.