Abstract: Measurement of the quality of image compression is important for image processing application. In this paper, we propose an objective image quality assessment to measure the quality of gray scale compressed image, which is correlation well with subjective quality measurement (MOS) and least time taken. The new objective image quality measurement is developed from a few fundamental of objective measurements to evaluate the compressed image quality based on JPEG and JPEG2000. The reliability between each fundamental objective measurement and subjective measurement (MOS) is found. From the experimental results, we found that the Maximum Difference measurement (MD) and a new proposed measurement, Structural Content Laplacian Mean Square Error (SCLMSE), are the suitable measurements that can be used to evaluate the quality of JPEG200 and JPEG compressed image, respectively. In addition, MD and SCLMSE measurements are scaled to make them equivalent to MOS, given the rate of compressed image quality from 1 to 5 (unacceptable to excellent quality).
Abstract: During recent years, the traditional learning
approaches have undergone fundamental changes due to the
emergence of new technologies such as multimedia, hypermedia and
telecommunication. E-learning is a modern world phenomenon that
has come into existence in the information age and in a knowledgebased
society. E-learning has developed significantly within a short
period of time. Thus it is of a great significant to secure information,
allow a confident access and prevent unauthorized accesses. Making
use of individuals- physiologic or behavioral (biometric) properties is
a confident method to make the information secure. Among the
biometrics, fingerprint is more acceptable and most countries use it as
an efficient methods of identification. This article provides a new
method to compare the fingerprint comparison by pattern recognition
and image processing techniques. To verify fingerprint, the shortest
distance method is used together with perceptronic multilayer neural
network functioning based on minutiae. This method is highly
accurate in the extraction of minutiae and it accelerates comparisons
due to elimination of false minutiae and is more reliable compared
with methods that merely use directional images.
Abstract: Abdominal aortic aneurysms rupture (AAAs) is one of the main causes of death in the world. This is a very complex phenomenon that usually occurs “without previous warning". Currently, criteria to assess the aneurysm rupture risk (peak diameter and growth rate) can not be considered as reliable indicators. In a first approach, the main geometric parameters of aneurysms have been linked into five biomechanical factors. These are combined to obtain a dimensionless rupture risk index, RI(t), which has been validated preliminarily with a clinical case and others from literature. This quantitative indicator is easy to understand, it allows estimating the aneurysms rupture risks and it is expected to be able to identify the one in aneurysm whose peak diameter is less than the threshold value. Based on initial results, a broader study has begun with twelve patients from the Clinic Hospital of Valladolid-Spain, which are submitted to periodic follow-up examinations.
Abstract: The substrate heater designed for this investigation is a front side substrate heating system. It consists of 10 conventional tungsten halogen lamps and an aluminum reflector, total input electrical power of 5 kW. The substrate is heated by means of a radiation from conventional tungsten halogen lamps directed to the substrate through a glass window. This design allows easy replacement of the lamps and maintenance of the system. Within 2 to 6 minutes the substrate temperature reaches 500 to 830 C by varying the vertical distance between the glass window and the substrate holder. Moreover, the substrate temperature can be easily controlled by controlling the input power to the system. This design gives excellent opportunity to deposit many deferent films at deferent temperatures in the same deposition time. This substrate heater was successfully used for Chemical Vapor Deposition (CVD) of many thin films, such as Silicon, iron, etc.
Abstract: Mycotoxin (aflatoxins) contamination of peanuts is a
great concern for human health. A total of 72 samples of unripe,
roasted, and salty peanuts were collected randomly from Pothohar
plateau of Pakistan for the assessment of aflatoxin. Samples were
dried, ground and extracted by acetonitrile (84%). The filtered
extracts were cleaned up by MycoSep-226 and analyzed by high
performance liquid chromatography with flourescence detector.
Quantification limit of Aflatoxin was 1 μg/kg and 70% Recovery was
observed in spiked samples in the range 1–10 μg/kg. The screening
of mycotoxins indicated that aflatoxins were present in most of the
samples being detected in 82%, in concentrations from 14.25 μg/kg
to 98.80 μg/kg. Optimal conditions for mycotoxin production and
fungal growth are frequently found in the crop fields as well as in
store houses. Human exposure of such toxin can be controlled by
pointed out such awareness and implemented the regulations.
Abstract: We prove detailed analysis of a waveguide-based Schottky barrier photodetector (SBPD) where a thin silicide film is put on the top of a silicon-on-insulator (SOI) channel waveguide to absorb light propagating along the waveguide. Taking both the confinement factor of light absorption and the wall scanning induced gain of the photoexcited carriers into account, an optimized silicide thickness is extracted to maximize the effective gain, thereby the responsivity. For typical lengths of the thin silicide film (10-20 Ðçm), the optimized thickness is estimated to be in the range of 1-2 nm, and only about 50-80% light power is absorbed to reach the maximum responsivity. Resonant waveguide-based SBPDs are proposed, which consist of a microloop, microdisc, or microring waveguide structure to allow light multiply propagating along the circular Si waveguide beneath the thin silicide film. Simulation results suggest that such resonant waveguide-based SBPDs have much higher repsonsivity at the resonant wavelengths as compared to the straight waveguidebased detectors. Some experimental results about Si waveguide-based SBPD are also reported.
Abstract: In the present study, the effect of ferrous sulfate concentration and total solids on bioleaching of heavy metals from sewage sludge has been examined using indigenous iron-oxidizing microorganisms. The experiments on effects of ferrous sulfate concentrations on bioleaching were carried out using ferrous sulfate of different concentrations (5-20 g L-1) to optimize the concentration of ferrous sulfate for maximum bioleaching. A rapid change in the pH and ORP took place in first 2 days followed by a slow change till 16th day in all the sludge samples. A 10 g L-1 ferrous sulfate concentration was found to be sufficient in metal bioleaching in the following order: Zn: 69%>Cu: 52%>Cr: 46%>Ni: 45. Further, bioleaching using 10 g/L ferrous sulfate was found to be efficient up to 20 g L-1 sludge solids concentration. The results of the present study strongly indicate that using 10 g L-1 ferrous sulfate indigenous iron-oxidizing microorganisms can bring down pH to a value needed for significant metal solubilization.
Abstract: The procurement and cost management approach adopted for mechanical and electrical (M&E) services in Malaysian construction industry have been criticized for its inefficiency. The study examined early cost estimating practices adopted for mechanical and electrical services (M&E) in Malaysia so as to understand the level of compliance of the current techniques with best practices. The methodology adopted for the study is a review of bidding documents used on both completed and on – going building projects awarded between 2008 – 2010 under 9th Malaysian Plan. The analysis revealed that, M&E services cost cannot be reliably estimated at pre-contract stage; the bidding techniques adopted for M&E services failed to provide uniform basis for contractors to submit tender; detailed measurement of items were not made which could complicate post contract cost control and financial management. The paper concluded that, there is need to follow a structured approach in determining the pre-contract cost estimate for M&E services which will serve as a virile tool for post contract cost control.
Abstract: Fundamental sensor-motor couplings form the backbone
of most mobile robot control tasks, and often need to be implemented
fast, efficiently and nevertheless reliably. Machine learning
techniques are therefore often used to obtain the desired sensor-motor
competences.
In this paper we present an alternative to established machine
learning methods such as artificial neural networks, that is very fast,
easy to implement, and has the distinct advantage that it generates
transparent, analysable sensor-motor couplings: system identification
through nonlinear polynomial mapping.
This work, which is part of the RobotMODIC project at the
universities of Essex and Sheffield, aims to develop a theoretical understanding
of the interaction between the robot and its environment.
One of the purposes of this research is to enable the principled design
of robot control programs.
As a first step towards this aim we model the behaviour of the
robot, as this emerges from its interaction with the environment, with
the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving
Average models with eXogenous inputs). This method produces
explicit polynomial functions that can be subsequently analysed using
established mathematical methods.
In this paper we demonstrate the fidelity of the obtained NARMAX
models in the challenging task of robot route learning; we present a
set of experiments in which a Magellan Pro mobile robot was taught
to follow four different routes, always using the same mechanism to
obtain the required control law.
Abstract: Concerning the measurement of friction properties of
textiles and fabrics using Kawabata Evaluation System (KES), whose
output is constrained to the surface friction factor of fabric, and no
other data would be generated; this research has been conducted to
gain information about surface roughness regarding its surface
friction factor. To assess roughness properties of light nonwovens, a
3-dimensional model of a surface has been simulated with regular
sinuous waves through it as an ideal surface. A new factor was
defined, namely Surface Roughness Factor, through comparing
roughness properties of simulated surface and real specimens. The
relation between the proposed factor and friction factor of specimens
has been analyzed by regression, and results showed a meaningful
correlation between them. It can be inferred that the new presented
factor can be used as an acceptable criterion for evaluating the
roughness properties of light nonwoven fabrics.
Abstract: Oxidative stress is considered to be the cause for onset
and the progression of type 2 diabetes mellitus (T2DM) and
complications including neuropathy. It is a deleterious process that
can be an important mediator of damage to cell structures: protein,
lipids and DNA. Data suggest that in patients with diabetes and
diabetic neuropathy DNA repair is impaired, which prevents effective
removal of lesions. Objective: The aim of our study was to evaluate
the association of the hOGG1 (326 Ser/Cys) and XRCC1 (194
Arg/Trp, 399 Arg/Gln) gene polymorphisms whose protein is
involved in the BER pathway with DNA repair efficiency in patients
with diabetes type 2 and diabetic neuropathy compared to the healthy
subjects. Genotypes were determined by PCR-RFLP analysis in 385
subjects, including 117 with type 2 diabetes, 56 with diabetic
neuropathy and 212 with normal glucose metabolism. The
polymorphisms studied include codon 326 of hOGG1 and 194, 399
of XRCC1 in the base excision repair (BER) genes. Comet assay was
carried out using peripheral blood lymphocytes from the patients and
controls. This test enabled the evaluation of DNA damage in cells
exposed to hydrogen peroxide alone and in the combination with the
endonuclease III (Nth). The results of the analysis of polymorphism
were statistically examination by calculating the odds ratio (OR) and
their 95% confidence intervals (95% CI) using the ¤ç2-tests. Our data
indicate that patients with diabetes mellitus type 2 (including those
with neuropathy) had higher frequencies of the XRCC1 399Arg/Gln
polymorphism in homozygote (GG) (OR: 1.85 [95% CI: 1.07-3.22],
P=0.3) and also increased frequency of 399Gln (G) allele (OR: 1.38
[95% CI: 1.03-1.83], P=0.3). No relation to other polymorphisms
with increased risk of diabetes or diabetic neuropathy. In T2DM
patients complicated by neuropathy, there was less efficient repair of
oxidative DNA damage induced by hydrogen peroxide in both the
presence and absence of the Nth enzyme. The results of our study
suggest that the XRCC1 399 Arg/Gln polymorphism is a significant
risk factor of T2DM in Polish population. Obtained data suggest a
decreased efficiency of DNA repair in cells from patients with
diabetes and neuropathy may be associated with oxidative stress.
Additionally, patients with neuropathy are characterized by even
greater sensitivity to oxidative damage than patients with diabetes,
which suggests participation of free radicals in the pathogenesis of
neuropathy.
Abstract: The objective of this research was to identify the
vegetation-soil relationships in Nodushan arid rangelands of Yazd. 5
sites were selected for measuring the cover of plant species and soil
attributes. Soil samples were taken in 0-10 and 10-80 cm layers. The
species studied were Salsola tomentosa, Salsola arbuscula, Peganum
harmala, Zygophylum eurypterum and Eurotia ceratoides. Canonical
correspondence analysis (CCA) was used to analyze the data. Based
on the CCA results, 74.9 % of vegetation-soil variation was explained
by axis 1-3. Axis 1, 2 and 3 accounted for 27.2%, 24.9 % and 22.8%
of variance respectively. Correlation between axis 1, 2, 3 and speciesedaphic
variables were 0.995, 0.989, 0.981 respectively. Soil texture,
lime, salinity and organic matter significantly influenced the
distribution of these plant species. Determination of soil-vegetation
relationships will be useful for managing and improving rangelands
in arid and semi arid environments.
Abstract: In this paper is described a new conception of the
Cartesian robot for automated assembly and also disassembly
process. The advantage of this conception is the utilization the
Cartesian assembly robot with its all peripheral automated devices for
assembly of the assembled product. The assembly product in the end
of the lifecycle can be disassembled with the same Cartesian
disassembly robot with the use of the same peripheral automated
devices and equipment. It is a new approach to problematic solving
and development of the automated assembly systems with respect to
lifecycle management of the assembly product and also assembly
system with Cartesian robot. It is also important to develop the
methodical process for design of automated assembly and
disassembly system with Cartesian robot. Assembly and disassembly
system use the same Cartesian robot input and output devices,
assembly and disassembly units in one workplace with different
application. Result of design methodology is the verification and
proposition of real automated assembly and disassembly workplace
with Cartesian robot for known verified model of assembled actuator.
Abstract: In this paper, image compression using hybrid vector
quantization scheme such as Multistage Vector Quantization
(MSVQ) and Pyramid Vector Quantization (PVQ) are introduced. A
combined MSVQ and PVQ are utilized to take advantages provided
by both of them. In the wavelet decomposition of the image, most of
the information often resides in the lowest frequency subband.
MSVQ is applied to significant low frequency coefficients. PVQ is
utilized to quantize the coefficients of other high frequency
subbands. The wavelet coefficients are derived using lifting scheme.
The main aim of the proposed scheme is to achieve high compression
ratio without much compromise in the image quality. The results are
compared with the existing image compression scheme using MSVQ.
Abstract: In this article, it is considered a class of optimal control
problems constrained by differential and integral constraints are
called canonical form. A modified measure theoretical approach is
introduced to solve this class of optimal control problems.
Abstract: The objective from this paper is to design a solar
thermal engine for space vehicles orbital control and electricity
generation. A computational model is developed for the prediction of
the solar thermal engine performance for different design parameters and conditions in order to enhance the engine efficiency. The engine is divided into two main subsystems. First, the concentrator dish
which receives solar energy from the sun and reflects them to the
cavity receiver. The second one is the cavity receiver which receives
the heat flux reflected from the concentrator and transfers heat to the
fluid passing over. Other subsystems depend on the application required from the engine. For thrust application, a nozzle is
introduced to the system for the fluid to expand and produce thrust.
Hydrogen is preferred as a working fluid in the thruster application.
Results model developed is used to determine the thrust for a
concentrator dish 4 meters in diameter (provides 10 kW of energy),
focusing solar energy to a 10 cm aperture diameter cavity receiver.
The cavity receiver outer length is 50 cm and the internal cavity is 47
cm in length. The suggested design material of the internal cavity is
tungsten to withstand high temperature. The thermal model and
analysis shows that the hydrogen temperature at the plenum reaches
2000oK after about 250 seconds for hot start operation for a flow rate
of 0.1 g/sec.Using solar thermal engine as an electricity generation
device on earth is also discussed. In this case a compressor and
turbine are used to convert the heat gained by the working fluid (air)
into mechanical power. This mechanical power can be converted into
electrical power by using a generator.
Abstract: The clinical usefulness of heart rate variability is
limited to the range of Holter monitoring software available. These
software algorithms require a normal sinus rhythm to accurately
acquire heart rate variability (HRV) measures in the frequency
domain. Premature ventricular contractions (PVC) or more
commonly referred to as ectopic beats, frequent in heart failure,
hinder this analysis and introduce ambiguity. This investigation
demonstrates an algorithm to automatically detect ectopic beats by
analyzing discrete wavelet transform coefficients. Two techniques
for filtering and replacing the ectopic beats from the RR signal are
compared. One technique applies wavelet hard thresholding
techniques and another applies linear interpolation to replace ectopic
cycles. The results demonstrate through simulation, and signals
acquired from a 24hr ambulatory recorder, that these techniques can
accurately detect PVC-s and remove the noise and leakage effects
produced by ectopic cycles retaining smooth spectra with the
minimum of error.
Abstract: The study investigated the effects of Teaching Games
for Understanding approach on students ‘cognitive learning outcome.
The study was a quasi-experimental non-equivalent pretest-posttest
control group design whereby 10 year old primary school students
(n=72) were randomly assigned to an experimental and a control
group. The experimental group students were exposed with TGfU
approach and the control group with the Traditional Skill approach of
handball game. Game Performance Assessment Instrument (GPAI)
was used to measure students' tactical understanding and decision
making in 3 versus 3 handball game situations. Analysis of
covariance (ANCOVA) was used to analyze the data. The results
reveal that there was a significant difference between the TGfU
approach group and the traditional skill approach group students on
post test score (F (1, 69) = 248.83, p < .05). The findings of this
study suggested the importance of TGfU approach to improve
primary students’ tactical understanding and decision making in
handball game.
Abstract: Yeast cells live in a constantly changing environment that requires the continuous adaptation of their genomic program in order to sustain their homeostasis, survive and proliferate. Due to the advancement of high throughput technologies, there is currently a large amount of data such as gene expression, gene deletion and protein-protein interactions for S. Cerevisiae under various environmental conditions. Mining these datasets requires efficient computational methods capable of integrating different types of data, identifying inter-relations between different components and inferring functional groups or 'modules' that shape intracellular processes. This study uses computational methods to delineate some of the mechanisms used by yeast cells to respond to environmental changes. The GRAM algorithm is first used to integrate gene expression data and ChIP-chip data in order to find modules of coexpressed and co-regulated genes as well as the transcription factors (TFs) that regulate these modules. Since transcription factors are themselves transcriptionally regulated, a three-layer regulatory cascade consisting of the TF-regulators, the TFs and the regulated modules is subsequently considered. This three-layer cascade is then modeled quantitatively using artificial neural networks (ANNs) where the input layer corresponds to the expression of the up-stream transcription factors (TF-regulators) and the output layer corresponds to the expression of genes within each module. This work shows that (a) the expression of at least 33 genes over time and for different stress conditions is well predicted by the expression of the top layer transcription factors, including cases in which the effect of up-stream regulators is shifted in time and (b) identifies at least 6 novel regulatory interactions that were not previously associated with stress-induced changes in gene expression. These findings suggest that the combination of gene expression and protein-DNA interaction data with artificial neural networks can successfully model biological pathways and capture quantitative dependencies between distant regulators and downstream genes.
Abstract: Cement, the most widely used construction material
is very brittle and characterized by low tensile strength and strain
capacity. Macro to nano fibers are added to cement to provide
tensile strength and ductility to it. Carbon Nanotube (CNT), one of
the nanofibers, has proven to be a promising reinforcing material in
the cement composites because of its outstanding mechanical
properties and its ability to close cracks at the nano level. The
experimental investigations for CNT reinforced cement is costly,
time consuming and involves huge number of trials. Mathematical
modeling of CNT reinforced cement can be done effectively and
efficiently to arrive at the mechanical properties and to reduce the
number of trials in the experiments. Hence, an attempt is made to
numerically study the effective mechanical properties of CNT
reinforced cement numerically using Representative Volume
Element (RVE) method. The enhancement in its mechanical
properties for different percentage of CNTs is studied in detail.