Abstract: This paper proposes a novel approach that combines statistical models and support vector machines. A hybrid scheme which appropriately incorporates the advantages of both the generative and discriminant model paradigms is described and evaluated. Support vector machines (SVMs) are trained to divide the whole speakers' space into small subsets of speakers within a hierarchical tree structure. During testing a speech token is assigned to its corresponding group and evaluation using gaussian mixture models (GMMs) is then processed. Experimental results show that the proposed method can significantly improve the performance of text independent speaker identification task. We report improvements of up to 50% reduction in identification error rate compared to the baseline statistical model.
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: Anaerobic digestion process is one of the alternative
methods to convert organic waste into methane gas which is a fuel
and energy source. Activities of various kinds of microorganisms are
the main factor for anaerobic digestion which produces methane gas.
Therefore, in this study a modified Anaerobic Baffled Reactor (ABR)
with working volume of 50 liters was designed to identify the
microorganisms through biogas production. The mixture of 75%
kitchen waste and 25% sewage sludge was used as substrate.
Observations on microorganisms in the ABR showed that there exists
a small amount of protozoa (5%) and fungi (2%) in the system, but
almost 93% of the microorganism population consists of bacteria. It
is definitely clear that bacteria are responsible for anaerobic
biodegradation of kitchen waste. Results show that in the
acidification zone of the ABR (front compartments of reactor) fast
growing bacteria capable of growth at high substrate levels and
reduced pH was dominant. A shift to slower growing scavenging
bacteria that grow better at higher pH was occurring towards the end
of the reactor. Due to the ability of activity in acetate environment the
percentages of Methanococcus, Methanosarcina and Methanotrix
were higher than other kinds of methane former in the system.
Abstract: Competing risks survival data that comprises of more
than one type of event has been used in many applications, and one
of these is in clinical study (e.g. in breast cancer study). The
decision tree method can be extended to competing risks survival
data by modifying the split function so as to accommodate two or
more risks which might be dependent on each other. Recently,
researchers have constructed some decision trees for recurrent
survival time data using frailty and marginal modelling. We further
extended the method for the case of competing risks. In this paper,
we developed the decision tree method for competing risks survival
time data based on proportional hazards for subdistribution of
competing risks. In particular, we grow a tree by using deviance
statistic. The application of breast cancer data is presented. Finally,
to investigate the performance of the proposed method, simulation
studies on identification of true group of observations were executed.
Abstract: In the past 20 years the economy of the Czech
Republic has experienced substantial changes. In the 1990s the
development was affected by the transformation which sought to
establish the right conditions for privatization and creation of
elementary market relations. In the last decade the characteristic
elements such as private ownership and corresponding institutional
framework have been strengthened. This development was marked by
the accession of the Czech Republic to the EU. The Czech Republic
is striving to reduce the difference between its level of economic
development and the quality of institutional framework in
comparison with other developed countries. The process of finding
the adequate solutions has been hampered by the negative impact of
the world financial crisis on the Czech Republic and the standard of
living of its inhabitants. This contribution seeks to address the
question of whether and to which extent the economic development
of the transitive Czech economy is affected by the change in
behaviour of households and their tendency to consumption, i.e. in
the sense of reduction or increase in demand for goods and services.
It aims to verify whether the increasing trend of household
indebtedness and decreasing trend of saving pose a significant risk in
the Czech Republic. At a general level the analysis aims to contribute
to finding an answer to the question of whether the debt increase of
Czech households is connected to the risk of "eating through" the
borrowed money and whether Czech households risk falling into a
debt trap. In addition to household indebtedness risks in the Czech
Republic the analysis will focus on identification of specifics of the
transformation phase of the Czech economy in comparison with the
EU countries, or selected OECD countries.
Abstract: The fixed partial dentures are mainly used in the frontal
part of the dental arch because of their great esthetics. There are
several factors that are associated with the stress state created in
ceramic restorations, including: thickness of ceramic layers,
mechanical properties of the materials, elastic modulus of the
supporting substrate material, direction, magnitude and frequency of
applied load, size and location of occlusal contact areas, residual
stresses induced by processing or pores, restoration-cement
interfacial defects and environmental defects. The purpose of this
study is to evaluate the capability of Polarization Sensitive Optical
Coherence Tomography (PSOCT) in detection and analysis of
possible material defects in metal-ceramic and integral ceramic fixed
partial dentures. As a conclusion, it is important to have a non
invasive method to investigate fixed partial prostheses before their
insertion in the oral cavity in order to satisfy the high stress
requirements and the esthetic function.
Abstract: Atherosclerosis was identified as a chronic inflammatory process resulting from interactions between plasma lipoproteins, cellular components (monocyte, macrophages, T lymphocytes, endothelial cells and smooth muscle cells) and the extracellular matrix of the arterial wall. Several types of genes were known to express during formation of atherosclerosis. This study is carried out to identify unknown differentially expressed gene (DEG) in atherogenesis. Rabbit’s aorta tissues were stained by H&E for histomorphology. GeneFishing™ PCR analysis was performed from total RNA extracted from the aorta tissues. The DNA fragment from DEG was cloned, sequenced and validated by Real-time PCR. Histomorphology showed intimal thickening in the aorta. DEG detected from ACP-41 was identified as cathepsin B gene and showed upregulation at week-8 and week-12 of atherogenesis. Therefore, ACP-based GeneFishing™ PCR facilitated identification of cathepsin B gene which was differentially expressed during development of atherosclerosis.
Abstract: This study was designed to investigate the role of serum nitric oxide and sialic acid in the development of diabetic nephropathy as disease marker. Total 210 diabetic patients (age and sex matched) were selected followed by informed consent and divided into four groups (70 each) as I: control; II: diabetic; III: diabetic hypertensive; IV: diabetic nephropathy. The blood samples of all subjects were collected and analyzed for serum nitric oxide, sialic acid, fasting blood glucose, serum urea, creatinine, HbA1c and GFR. The BMI, systolic and diastolic blood pressures, blood glucose, HbA1c and serum sialic acid levels were high (p
Abstract: This paper is focused on issues of process modeling
and two model based control strategies of a fed-batch sugar
crystallization process applying the concept of artificial neural
networks (ANNs). The control objective is to force the operation into
following optimal supersaturation trajectory. It is achieved by
manipulating the feed flow rate of sugar liquor/syrup, considered as
the control input. The control task is rather challenging due to the
strong nonlinearity of the process dynamics and variations in the
crystallization kinetics. Two control alternatives are considered –
model predictive control (MPC) and feedback linearizing control
(FLC). Adequate ANN process models are first built as part of the
controller structures. MPC algorithm outperforms the FLC approach
with respect to satisfactory reference tracking and smooth control
action. However, the MPC is computationally much more involved
since it requires an online numerical optimization, while for the FLC
an analytical control solution was determined.
Abstract: This paper presents a new approach for the protection
of Thyristor-Controlled Series Compensator (TCSC) line using
Support Vector Machine (SVM). One SVM is trained for fault
classification and another for section identification. This method use
three phase current measurement that results in better speed and
accuracy than other SVM based methods which used single phase
current measurement. This makes it suitable for real-time protection.
The method was tested on 10,000 data instances with a very wide
variation in system conditions such as compensation level, source
impedance, location of fault, fault inception angle, load angle at
source bus and fault resistance. The proposed method requires only
local current measurement.
Abstract: This paper describes a novel approach for deriving
modules from protein-protein interaction networks, which combines
functional information with topological properties of the network.
This approach is based on weighted clustering coefficient, which
uses weights representing the functional similarities between the
proteins. These weights are calculated according to the semantic
similarity between the proteins, which is based on their Gene
Ontology terms. We recently proposed an algorithm for identification
of functional modules, called SWEMODE (Semantic WEights for
MODule Elucidation), that identifies dense sub-graphs containing
functionally similar proteins. The rational underlying this approach is
that each module can be reduced to a set of triangles (protein triplets
connected to each other). Here, we propose considering semantic
similarity weights of all triangle-forming edges between proteins. We
also apply varying semantic similarity thresholds between
neighbours of each node that are not neighbours to each other (and
hereby do not form a triangle), to derive new potential triangles to
include in module-defining procedure. The results show an
improvement of pure topological approach, in terms of number of
predicted modules that match known complexes.
Abstract: Phytases (myo-inositol hexakisphosphate
phosphohydrolases; EC 3.1.3.8) catalyze the hydrolysis of phytic acid
(myoinositol hexakisphosphate) to the mono-, di-, tri-, tetra-, and
pentaphosphates of myo-inositol and inorganic phosphate.
Therrmophilic bacteria isolated from water sampled from hot spring.
About 120 isolates of bacteria were successfully isolated form hot
spring water sample and tested for extracellular phytase producing.
After 5 passages of the screening on the PSM media, 4 isolates were
found stable in producing phytase enzyme. The 16s RDNA
sequencing for identification of bacteria using molecular technique
revealed that all isolates those positive in phytase producing are
belong to Geobacillus spp. And Anoxybacillus spp. Anoxybacillus
rupiensis UniSZA-7 were identified for their carbon source utilization
using Phenotype Microarray Plate of Biolog and found they utilize
several kind of carbon source provided.
Abstract: This paper describes a blind algorithm for estimating a time varying and frequency selective fading channel. In order to identify blindly the impulse response of these channels, we have used Higher Order Statistics (HOS) to build our algorithm. In this paper, we have selected two theoretical frequency selective channels as the Proakis-s 'B' channel and the Macchi-s channel, and one practical frequency selective fading channel called Broadband Radio Access Network (BRAN A). The simulation results in noisy environment and for different data input channel, demonstrate that the proposed method could estimate the phase and magnitude of these channels blindly and without any information about the input, except that the input excitation is i.i.d (Identically and Independent Distributed) and non-Gaussian.
Abstract: Formulation of biological profile is one of the modern roles of forensic anthropologist. The present study was conducted to estimate height using foot and shoeprint length of Malaysian population. The present work can be very useful information in the process of identification of individual in forensic cases based on shoeprint evidence. It can help to narrow down suspects and ease the police investigation. Besides, stature is important parameters in determining the partial identify of unidentified and mutilated bodies. Thus, this study can help the problem encountered in cases of mass disaster, massacre, explosions and assault cases. This is because it is very hard to identify parts of bodies in these cases where people are dismembered and become unrecognizable. Samples in this research were collected from 200 Malaysian adults (100 males and 100 females) with age ranging from 20 to 45 years old. In this research, shoeprint length were measured based on the print of the shoes made from the flat shoes. Other information like gender, foot length and height of subject were also recorded. The data was analyzed using IBM® SPSS Statistics 19 software. Results indicated that, foot length has a strong correlation with stature than shoeprint length for both sides of the feet. However, in the unknown, where the gender was undetermined have shown a better correlation in foot length and shoeprint length parameter compared to males and females analyzed separately. In addition, prediction equations are developed to estimate the stature using linear regression analysis of foot length and shoeprint length. However, foot lengths give better prediction than shoeprint length.
Abstract: Crypto System Identification is one of the challenging tasks in Crypt analysis. The paper discusses the possibility of employing Neural Networks for identification of Cipher Systems from cipher texts. Cascade Correlation Neural Network and Back Propagation Network have been employed for identification of Cipher Systems. Very large collection of cipher texts were generated using a Block Cipher (Enhanced RC6) and a Stream Cipher (SEAL). Promising results were obtained in terms of accuracy using both the Neural Network models but it was observed that the Cascade Correlation Neural Network Model performed better compared to Back Propagation Network.
Abstract: Complex assemblies of interacting proteins carry out
most of the interesting jobs in a cell, such as metabolism, DNA
synthesis, mitosis and cell division. These physiological properties
play out as a subtle molecular dance, choreographed by underlying
regulatory networks that control the activities of cyclin-dependent
kinases (CDK). The network can be modeled by a set of nonlinear
differential equations and its behavior predicted by numerical
simulation. In this paper, an innovative approach has been proposed
that uses genetic algorithms to mine a set of behavior data output by
a biological system in order to determine the kinetic parameters of
the system. In our approach, the machine learning method is
integrated with the framework of existent biological information in a
wiring diagram so that its findings are expressed in a form of system
dynamic behavior. By numerical simulations it has been illustrated
that the model is consistent with experiments and successfully shown
that such application of genetic algorithms will highly improve the
performance of mathematical model of the cell division cycle to
simulate such a complicated bio-system.
Abstract: Real world Speaker Identification (SI) application
differs from ideal or laboratory conditions causing perturbations that
leads to a mismatch between the training and testing environment
and degrade the performance drastically. Many strategies have been
adopted to cope with acoustical degradation; wavelet based Bayesian
marginal model is one of them. But Bayesian marginal models
cannot model the inter-scale statistical dependencies of different
wavelet scales. Simple nonlinear estimators for wavelet based
denoising assume that the wavelet coefficients in different scales are
independent in nature. However wavelet coefficients have significant
inter-scale dependency. This paper enhances this inter-scale
dependency property by a Circularly Symmetric Probability Density
Function (CS-PDF) related to the family of Spherically Invariant
Random Processes (SIRPs) in Log Gabor Wavelet (LGW) domain
and corresponding joint shrinkage estimator is derived by Maximum
a Posteriori (MAP) estimator. A framework is proposed based on
these to denoise speech signal for automatic speaker identification
problems. The robustness of the proposed framework is tested for
Text Independent Speaker Identification application on 100 speakers
of POLYCOST and 100 speakers of YOHO speech database in three
different noise environments. Experimental results show that the
proposed estimator yields a higher improvement in identification
accuracy compared to other estimators on popular Gaussian Mixture
Model (GMM) based speaker model and Mel-Frequency Cepstral
Coefficient (MFCC) features.
Abstract: In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..
Abstract: Despite extensive study on wireless sensor network
security, defending internal attacks and finding abnormal behaviour
of the sensor are still difficult and unsolved task. The conventional
cryptographic technique does not give the robust security or detection
process to save the network from internal attacker that cause by
abnormal behavior. The insider attacker or abnormally behaved
sensor identificationand location detection framework using false
massage detection and Time difference of Arrival (TDoA) is
presented in this paper. It has been shown that the new framework
can efficiently identify and detect the insider attacker location so that
the attacker can be reprogrammed or subside from the network to
save from internal attack.
Abstract: Recently, the RFID (Radio Frequency
Identification) technology attracts the world market attention as
essential technology for ubiquitous environment. The RFID
market has focused on transponders and reader development.
But that concern has shifted to RFID software like as
high-valued e-business applications, RFID middleware and
related development tools. However, due to the high sensitivity
of data and service transaction within the RFID network,
security consideration must be addressed. In order to guarantee
trusted e-business based on RFID technology, we propose a
security enhanced RFID middleware system. Our proposal is
compliant with EPCglobal ALE (Application Level Events),
which is standard interface for middleware and its clients. We
show how to provide strengthened security and trust by
protecting transported data between middleware and its client,
and stored data in middleware. Moreover, we achieve the
identification and service access control against illegal service
abuse. Our system enables secure RFID middleware service
and trusted e-business service.