Abstract: The conventional assessment of human semen is a
highly subjective assessment, with considerable intra- and interlaboratory
variability. Computer-Assisted Sperm Analysis (CASA)
systems provide a rapid and automated assessment of the sperm
characteristics, together with improved standardization and quality
control. However, the outcome of CASA systems is sensitive to the
method of experimentation. While conventional CASA systems use
digital microscopes with phase-contrast accessories, producing
higher contrast images, we have used raw semen samples (no
staining materials) and a regular light microscope, with a digital
camera directly attached to its eyepiece, to insure cost benefits and
simple assembling of the system. However, since the accurate finding
of sperms in the semen image is the first step in the examination and
analysis of the semen, any error in this step can affect the outcome of
the analysis. This article introduces and explains an algorithm for
finding sperms in low contrast images: First, an image enhancement
algorithm is applied to remove extra particles from the image. Then,
the foreground particles (including sperms and round cells) are
segmented form the background. Finally, based on certain features
and criteria, sperms are separated from other cells.
Abstract: Grasslands of Iran are encountered with a vast
desertification and destruction. Some legumes are plants of forage
importance with high palatability. Studied legumes in this project are
Onobrychis, Medicago sativa (alfalfa) and Trifolium repens. Seeds
were cultivated in research field of Kaboutarabad (33 km East of
Isfahan, Iran) with an average 80 mm. annual rainfall. Plants were
cultivated in a split plot design with 3 replicate and two water
treatments (weekly irrigation, and under stress with same amount per
15 days interval). Water entrance to each plots were measured by
Partial flow. This project lasted 20 weeks. Destructive samplings
(1m2 each time) were done weekly. At each sampling plants were
gathered and weighed separately for each vegetative parts. An Area
Meter (Vista) was used to measure root surface and leaf area. Total
shoot and root fresh and dry weight, leaf area index and soil coverage
were evaluated too. Dry weight was achieved in 750c oven after 24
hours. Statgraphic and Harvard Graphic software were used to
formulate and demonstrate the parameters curves due to time. Our
results show that Trifolium repens has affected 60 % and Medicago
sativa 18% by water stress. Onobrychis total fresh weight was
reduced 45%. Dry weight or Biomass in alfalfa is not so affected by
water shortage. This means that in alfalfa fields we can decrease the
irrigation amount and have some how same amount of Biomass.
Onobrychis show a drastic decrease in Biomass. The increases in
total dry matter due to time in studied plants are formulated. For
Trifolium repens if removal or cattle entrance to meadows do not
occurred at perfect time, it will decrease the palatability and water
content of the shoots. Water stress in a short period could develop the
root system in Trifolium repens, but if it last more than this other
ecological and soil factors will affect the growth of this plant. Low
level of soil water is not so important for studied legume forges. But
water shortage affect palatability and water content of aerial parts.
Leaf area due to time in studied legumes is formulated. In fact leaf
area is decreased by shortage in available water. Higher leaf area
means higher forage and biomass production. Medicago and
Onobrychis reach to the maximum leaf area sooner than Trifolium
and are able to produce an optimum soil cover and inhibit the
transpiration of soil water of meadows. Correlation of root surface to
Total biomass in studied plants is formulated. Medicago under water
stress show a 40% decrease in crown cover while at optimum
condition this amount reach to 100%. In order to produce forage in
areas without soil erosion Medicago is the best choice even with a
shortage in water resources. It is tried to represent the growth
simulation of three famous Forage Legumes. By growth simulation
farmers and range managers could better decide to choose best plant
adapted to water availability without designing different time and
labor consuming field experiments.
Abstract: Creative design requires new approaches to assessment
in vocational and technological education. To date, there has been little
discussion on instruments used to evaluate dies produced by students
in vocational and technological education. Developing a generic
instrument has been very difficult due to the diversity of creative
domains, the specificity of content, and the subjectivity involved in
judgment. This paper presents an instrument for measuring the
creativity in the design of products by expanding the Consensual
Assessment Technique (CAT). The content-based scale was evaluated
for content validity by 5 experts. The scale comprises 5 criteria:
originality; practicability; precision; aesthetics; and exchangeability.
Nine experts were invited to evaluate the dies produced by 38 college
students who enrolled in a Product Design and Development course.
To further explore the degree of rater agreement, inter-rater reliability
was calculated for each dimension using Kendall's coefficient of
concordance test. The inter-judge reliability scores achieved
significance, with coefficients ranging from 0.53 to 0.71.
Abstract: Biometrics, which refers to identifying an individual
based on his or her physiological or behavioral characteristics, has
the capability to reliably distinguish between an authorized person
and an imposter. Signature verification systems can be categorized as
offline (static) and online (dynamic). This paper presents a neural
network based recognition of offline handwritten signatures system
that is trained with low-resolution scanned signature images.
Abstract: Fault detection determines faultexistence and detecting
time. This paper discusses two layered fault detection methods to
enhance the reliability and safety. Two layered fault detection methods
consist of fault detection methods of component level controllers and
system level controllers. Component level controllers detect faults by
using limit checking, model-based detection, and data-driven
detection and system level controllers execute detection by stability
analysis which can detect unknown changes. System level controllers
compare detection results via stability with fault signals from lower
level controllers. This paper addresses fault detection methods via
stability and suggests fault detection criteria in nonlinear systems. The
fault detection method applies tothe hybrid control unit of a military
hybrid electric vehicleso that the hybrid control unit can detect faults
of the traction motor.
Abstract: A novel behavioral detection framework is proposed
to detect zero day buffer overflow vulnerabilities (based on network
behavioral signatures) using zero-day exploits, instead of the
signature-based or anomaly-based detection solutions currently
available for IDPS techniques. At first we present the detection
model that uses shadow honeypot. Our system is used for the online
processing of network attacks and generating a behavior detection
profile. The detection profile represents the dataset of 112 types of
metrics describing the exact behavior of malware in the network. In
this paper we present the examples of generating behavioral
signatures for two attacks – a buffer overflow exploit on FTP server
and well known Conficker worm. We demonstrated the visualization
of important aspects by showing the differences between valid
behavior and the attacks. Based on these metrics we can detect
attacks with a very high probability of success, the process of
detection is however very expensive.
Abstract: Preparation of hydrogel based on carrageenan
extracted from Kappaphycus alvarezii was conducted with film
immersion in glutaraldehyde solution (GA 4%w/w) for 2min and
then followed by thermal curing at 110°C for 25min. The method of
carrageenan recovery strongly determines the properties of
crosslinked carrageenan. Hydrogel obtained from alkali treated
carrageenan showed higher swelling ability compared to hydrogel
from nonalkali treated carrageenan. Hydrogel from alkali treated
showed the ability of sensitive to pH media.
Abstract: In the present study the efficiency of Big Bang-Big
Crunch (BB-BC) algorithm is investigated in discrete structural
design optimization. It is shown that a standard version of the BB-BC
algorithm is sometimes unable to produce reasonable solutions to
problems from discrete structural design optimization. Two
reformulations of the algorithm, which are referred to as modified
BB-BC (MBB-BC) and exponential BB-BC (EBB-BC), are
introduced to enhance the capability of the standard algorithm in
locating good solutions for steel truss and frame type structures,
respectively. The performances of the proposed algorithms are
experimented and compared to its standard version as well as some
other algorithms over several practical design examples. In these
examples, steel structures are sized for minimum weight subject to
stress, stability and displacement limitations according to the
provisions of AISC-ASD.
Abstract: An adaptive dynamic cerebellar model articulation
controller (DCMAC) neural network used for solving the prediction
and identification problem is proposed in this paper. The proposed
DCMAC has superior capability to the conventional cerebellar model
articulation controller (CMAC) neural network in efficient learning
mechanism, guaranteed system stability and dynamic response. The
recurrent network is embedded in the DCMAC by adding feedback
connections in the association memory space so that the DCMAC
captures the dynamic response, where the feedback units act as
memory elements. The dynamic gradient descent method is adopted to
adjust DCMAC parameters on-line. Moreover, the analytical method
based on a Lyapunov function is proposed to determine the
learning-rates of DCMAC so that the variable optimal learning-rates
are derived to achieve most rapid convergence of identifying error.
Finally, the adaptive DCMAC is applied in two computer simulations.
Simulation results show that accurate identifying response and
superior dynamic performance can be obtained because of the
powerful on-line learning capability of the proposed DCMAC.
Abstract: An electric utility-s main concern is to plan, design, operate and maintain its power supply to provide an acceptable level of reliability to its users. This clearly requires that standards of reliability be specified and used in all three sectors of the power system, i.e., generation, transmission and distribution. That is why reliability of a power system is always a major concern to power system planners. This paper presents the reliability analysis of Bangladesh Power System (BPS). Reliability index, loss of load probability (LOLP) of BPS is evaluated using recursive algorithm and considering no de-rated states of generators. BPS has sixty one generators and a total installed capacity of 5275 MW. The maximum demand of BPS is about 5000 MW. The relevant data of the generators and hourly load profiles are collected from the National Load Dispatch Center (NLDC) of Bangladesh and reliability index 'LOLP' is assessed for the period of last ten years.
Abstract: This paper presents results of an experimental study performed to investigate effect of incorporating silica fume on physico-mechanical properties and durability of resulting fly ash geopolymers. Geopolymer specimens were prepared by activating fly ash incorporated with additional silica fume in the range of 2.5% to 5%, with a mixture of sodium hydroxide and sodium silicate solution having Na2O content of 8%. For studying durability, 10% magnesium sulphate solution was used to immerse the specimens up to a period of 15 weeks during which visual observation, weight changes and strength changes were monitored regularly. Addition of silica fume lowers performance of geopolymer pastes. However, in mortars, addition of silica fume significantly enhanced physico-mechanical properties and durability.
Abstract: This paper presents an effective technique for harmonic current mitigation using an adaptive notch filter (ANF) to estimate current harmonics. The proposed filter consists of multiple units of ANF connected in parallel structure; each unit is governed by two ordinary differential equations. The frequency estimation is carried out based on the output of these units. The simulation and experimental results show the ability of the proposed tracking scheme to accurately estimate harmonics. The proposed filter was implemented digitally in TMS320F2808 and used in the control of hybrid active power filter (HAPF). The theoretical expectations are verified and demonstrated experimentally.
Abstract: Spatial outliers in remotely sensed imageries represent
observed quantities showing unusual values compared to their
neighbor pixel values. There have been various methods to detect the
spatial outliers based on spatial autocorrelations in statistics and data
mining. These methods may be applied in detecting forest fire pixels
in the MODIS imageries from NASA-s AQUA satellite. This is
because the forest fire detection can be referred to as finding spatial
outliers using spatial variation of brightness temperature. This point is
what distinguishes our approach from the traditional fire detection
methods. In this paper, we propose a graph-based forest fire detection
algorithm which is based on spatial outlier detection methods, and test
the proposed algorithm to evaluate its applicability. For this the
ordinary scatter plot and Moran-s scatter plot were used. In order to
evaluate the proposed algorithm, the results were compared with the
MODIS fire product provided by the NASA MODIS Science Team,
which showed the possibility of the proposed algorithm in detecting
the fire pixels.
Abstract: Reliable water level forecasts are particularly
important for warning against dangerous flood and inundation. The
current study aims at investigating the suitability of the adaptive
network based fuzzy inference system for continuous water level
modeling. A hybrid learning algorithm, which combines the least
square method and the back propagation algorithm, is used to
identify the parameters of the network. For this study, water levels
data are available for a hydrological year of 2002 with a sampling
interval of 1-hour. The number of antecedent water level that should
be included in the input variables is determined by two statistical
methods, i.e. autocorrelation function and partial autocorrelation
function between the variables. Forecasting was done for 1-hour until
12-hour ahead in order to compare the models generalization at
higher horizons. The results demonstrate that the adaptive networkbased
fuzzy inference system model can be applied successfully and
provide high accuracy and reliability for river water level estimation.
In general, the adaptive network-based fuzzy inference system
provides accurate and reliable water level prediction for 1-hour ahead
where the MAPE=1.15% and correlation=0.98 was achieved. Up to
12-hour ahead prediction, the model still shows relatively good
performance where the error of prediction resulted was less than
9.65%. The information gathered from the preliminary results
provide a useful guidance or reference for flood early warning
system design in which the magnitude and the timing of a potential
extreme flood are indicated.
Abstract: To derive the fractional flow equation oil
displacement will be assumed to take place under the so-called
diffusive flow condition. The constraints are that fluid saturations at
any point in the linear displacement path are uniformly distributed
with respect to thickness; this allows the displacement to be described
mathematically in one dimension. The simultaneous flow of oil and
water can be modeled using thickness averaged relative permeability,
along the centerline of the reservoir. The condition for fluid potential
equilibrium is simply that of hydrostatic equilibrium for which the
saturation distribution can be determined as a function of capillary
pressure and therefore, height. That is the fluids are distributed in
accordance with capillary-gravity equilibrium.
This paper focused on the fraction flow of water versus
cumulative oil recoveries using Buckley Leverett method. Several
field cases have been developed to aid in analysis. Producing watercut
(at surface conditions) will be compared with the cumulative oil
recovery at breakthrough for the flowing fluid.
Abstract: The central recirculation zone (CRZ) in a swirl
stabilized gas turbine combustor has a dominant effect on the fuel air
mixing process and flame stability. Most of state of the art swirlers
share one disadvantage; the fixed swirl number for the same swirler
configuration. Thus, in a mathematical sense, Reynolds number
becomes the sole parameter for controlling the flow characteristics
inside the combustor. As a result, at low load operation, the
generated swirl is more likely to become feeble affecting the flame
stabilization and mixing process. This paper introduces a new swirler
concept which overcomes the mentioned weakness of the modern
configurations. The new swirler introduces air tangentially and
axially to the combustor through tangential vanes and an axial vanes
respectively. Therefore, it provides different swirl numbers for the
same configuration by regulating the ratio between the axial and
tangential flow momenta. The swirler aerodynamic performance was
investigated using four CFD simulations in order to demonstrate the
impact of tangential to axial flow rate ratio on the CRZ. It was found
that the length of the CRZ is directly proportional to the tangential to
axial air flow rate ratio.
Abstract: Natural fibres have emerged as the potential reinforcement material for composites and thus gain attraction by many researchers. This is mainly due to their applicable benefits as they offer low density, low cost, renewable, biodegradability and environmentally harmless and also comparable mechanical properties with synthetic fibre composites. The properties of hybrid composites highly depends on several factors, including the interaction of fillers with the polymeric matrix, shape and size (aspect ratio), and orientation of fillers [1]. In this study, natural fibre kenaf composites and kenaf/fibreglass hybrid composites were fabricated by a combination of hand lay-up method and cold-press method. The effect of different fibre types (powder, short and long) on the tensile properties of composites is investigated. The kenaf composites with and without the addition of fibreglass were then characterized by tensile testing and scanning electron microscopy. A significant improvement in tensile strength and modulus were indicated by the introduction of long kenaf/woven fibreglass hybrid composite. However, the opposite trends are observed in kenaf powder composite. Fractographic observation shows that fibre/matrix debonding causes the fibres pull out. This phenomenon results in the fibre and matrix fracture.
Abstract: One of the major pollutants in the environment is arsenic (As). Due to the toxic effects of As to all organisms, its remediation is necessary. Conventional technologies used in the remediation of As contaminated soils are expensive and may even compromise the structure of the soil. An attractive alternative is phytoremediation, which is the use of plants which can take up the contaminant in their tissues. Plant growth promoting bacteria (PGPB) has been known to enhance growth of plants through several mechanisms such as phytohormone production, phosphate solubilization, siderophore production and 1-aminocyclopropane-1- carboxylate (ACC) deaminase production, which is an essential trait that aids plants especially under stress conditions such as As stress. Twenty one bacteria were isolated from As-contaminated soils in the vicinity of the Janghang Smelter in Chungnam Province, South Korea. These exhibited high tolerance to either arsenite (As III) or arsenate (As V) or both. Most of these isolates possess several plant growth promoting traits which can be potentially exploited to increase phytoremediation efficiency. Among the identified isolates is Pseudomonas sp. JS1215, which produces ACC deaminase, indole acetic acid (IAA), and siderophore. It also has the ability to solubilize phosphate. Inoculation of JS1215 significantly enhanced root and shoot length and biomass accumulation of maize under normal conditions. In the presence of As, particularly in lower As level, inoculation of JS1215 slightly increased root length and biomass. Ethylene increased with increasing As concentration, but was reduced by JS1215 inoculation. JS1215 can be a potential bioinoculant for increasing phytoremediation efficiency.
Abstract: Software Reusability is primary attribute of software
quality. There are metrics for identifying the quality of reusable
components but the function that makes use of these metrics to find
reusability of software components is still not clear. These metrics if
identified in the design phase or even in the coding phase can help us
to reduce the rework by improving quality of reuse of the component
and hence improve the productivity due to probabilistic increase in
the reuse level. In this paper, we have devised the framework of
metrics that uses McCabe-s Cyclometric Complexity Measure for
Complexity measurement, Regularity Metric, Halstead Software
Science Indicator for Volume indication, Reuse Frequency metric
and Coupling Metric values of the software component as input
attributes and calculated reusability of the software component. Here,
comparative analysis of the fuzzy, Neuro-fuzzy and Fuzzy-GA
approaches is performed to evaluate the reusability of software
components and Fuzzy-GA results outperform the other used
approaches. The developed reusability model has produced high
precision results as expected by the human experts.
Abstract: The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values according to the input pattern variations. The new approach is based on splitting the whole network into two subnets; main traditional net and a supportive net. The first deals with the required output of trained patterns with predefined settings, while the second tolerates output generation dynamically with tuning capability for any newly applied input. This tuning comes in the form of an adjustment to the threshold values. Two levels of supportive net were studied; one implements an extended additional layer with adjustable neuronal threshold setting mechanism, while the second implements an auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net that is constructed in dual-layer architecture. Experiment results and analysis of the proposed designs have given quite satisfactory conducts. The supportive layer approach achieved over 90% recognition rate, while the multiple network technique shows more effective and acceptable level of recognition. However, this is achieved at the price of network complexity and computation time. Recognition generalization may be also improved by accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities with the needs of further advanced learning phases.