Abstract: Mammals are known to use Interaural Intensity Difference (IID) to determine azimuthal position of high frequency sounds. In the Lateral Superior Olive (LSO) neurons have firing behaviours which vary systematicaly with IID. Those neurons receive excitatory inputs from the ipsilateral ear and inhibitory inputs from the contralateral one. The IID sensitivity of a LSO neuron is thought to be due to delay differences between both ears, delays due to different synaptic delays and to intensity-dependent delays. In this paper we model the auditory pathway until the LSO. Inputs to LSO neurons are at first numerous and differ in their relative delays. Spike Timing-Dependent Plasticity is then used to prune those connections. We compare the pruned neuron responses with physiological data and analyse the relationship between IID-s of teacher stimuli and IID sensitivities of trained LSO neurons.
Abstract: A new approach for the improvement of coding gain
in channel coding using Advanced Encryption Standard (AES) and
Maximum A Posteriori (MAP) algorithm is proposed. This new
approach uses the avalanche effect of block cipher algorithm AES
and soft output values of MAP decoding algorithm. The performance
of proposed approach is evaluated in the presence of Additive White
Gaussian Noise (AWGN). For the verification of proposed approach,
computer simulation results are included.
Abstract: Selective harmonic elimination-pulse width modulation techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. Traditional optimization methods suffer from various drawbacks, such as prolonged and tedious computational steps and convergence to local optima; thus, the more the number of harmonics to be eliminated, the larger the computational complexity and time. This paper presents a novel method for output voltage harmonic elimination and voltage control of PWM AC/AC voltage converters using the principle of hybrid Real-Coded Genetic Algorithm-Pattern Search (RGA-PS) method. RGA is the primary optimizer exploiting its global search capabilities, PS is then employed to fine tune the best solution provided by RGA in each evolution. The proposed method enables linear control of the fundamental component of the output voltage and complete elimination of its harmonic contents up to a specified order. Theoretical studies have been carried out to show the effectiveness and robustness of the proposed method of selective harmonic elimination. Theoretical results are validated through simulation studies using PSIM software package.
Abstract: The excellent suitability of the externally excited synchronous
machine (EESM) in automotive traction drive applications
is justified by its high efficiency over the whole operation range and
the high availability of materials. Usually, maximum efficiency is
obtained by modelling each single loss and minimizing the sum of all
losses. As a result, the quality of the optimization highly depends on
the precision of the model. Moreover, it requires accurate knowledge
of the saturation dependent machine inductances. Therefore, the
present contribution proposes a method to minimize the overall losses
of a salient pole EESM and its inverter in steady state operation based
on measurement data only. Since this method does not require any
manufacturer data, it is well suited for an automated measurement
data evaluation and inverter parametrization. The field oriented control
(FOC) of an EESM provides three current components resp. three
degrees of freedom (DOF). An analytic minimization of the copper
losses in the stator and the rotor (assuming constant inductances) is
performed and serves as a first approximation of how to choose the
optimal current reference values. After a numeric offline minimization
of the overall losses based on measurement data the results are
compared to a control strategy that satisfies cos (ϕ) = 1.
Abstract: Stocking density is considered one of the important
factors affecting fish growth. But, information related to impact of
stocking density on growth performance of monosex tilapia population
under the ecological conditions of Gangetic plains in West Bengal,
India is limited. The aim of our study was to compare the growth
potential of monosex tilapia at various stocking densities and to
determine an ideal stocking density for culture of all-male monosex
fish. The males were isolated by examination of genital papilla region
and were stocked separately in 0.01 ha earthen ponds at different
stocking densities (5000, 10000, 15000, 20000, 25000 and 30000
fingerlings/ha). It was found that the highest weight, length, daily
weight gain, growth rate and protein content were observed for the
20000 fish/ha density class. Thus, culture of monosex tilapia at a
density of 20000 fish/ha can be considered ideal for augmented
production of the fish under Indian context.
Abstract: Ad hoc networks are characterized by multi-hop
wireless connectivity and frequently changing network topology.
Forming security association among a group of nodes in ad-hoc
networks is more challenging than in conventional networks due to the
lack of central authority, i.e. fixed infrastructure. With that view in
mind, group key management plays an important building block of
any secure group communication. The main contribution of this paper
is a low complexity key management scheme that is suitable for fully
self-organized ad-hoc networks. The protocol is also password
authenticated, making it resilient against active attacks. Unlike other
existing key agreement protocols, ours make no assumption about the
structure of the underlying wireless network, making it suitable for
“truly ad-hoc" networks. Finally, we will analyze our protocol to show
the computation and communication burden on individual nodes for
key establishment.
Abstract: The process of laser absorption in the skin during
laser irradiation was a critical point in medical application
treatments. Delivery the correct amount of laser light is a critical
element in photodynamic therapy (PDT). More amounts of laser
light able to affect tissues in the skin and small amount not able to
enhance PDT procedure in skin. The knowledge of the skin tone
laser dependent distribution of 635 nm radiation and its penetration
depth in skin is a very important precondition for the investigation of
advantage laser induced effect in (PDT) in epidermis diseases
(psoriasis). The aim of this work was to estimate an optimum effect
of diode laser (635 nm) on the treatment of epidermis diseases in
different color skin. Furthermore, it is to improve safety of laser in
PDT in epidermis diseases treatment. Advanced system analytical
program (ASAP) which is a new approach in investigating the PDT,
dependent on optical properties of different skin color was used in
present work. A two layered Realistic Skin Model (RSM); stratum
corneum and epidermal with red laser (635 nm, 10 mW) were used
for irradiative transfer to study fluence and absorbance in different
penetration for various human skin colors. Several skin tones very
fair, fair, light, medium and dark are used to irradiative transfer. This
investigation involved the principles of laser tissue interaction when
the skin optically injected by a red laser diode. The results
demonstrated that the power characteristic of a laser diode (635 nm)
can affect the treatment of epidermal disease in various color skins.
Power absorption of the various human skins were recorded and
analyzed in order to find the influence of the melanin in PDT
treatment in epidermal disease. A two layered RSM show that the
change in penetration depth in epidermal layer of the color skin has a
larger effect on the distribution of absorbed laser in the skin; this is
due to the variation of the melanin concentration for each color.
Abstract: To extract the important physiological factors related to
diabetes from an oral glucose tolerance test (OGTT) by mathematical
modeling, highly informative but convenient protocols are required.
Current models require a large number of samples and extended
period of testing, which is not practical for daily use. The purpose
of this study is to make model assessments possible even from a
reduced number of samples taken over a relatively short period.
For this purpose, test values were extrapolated using a support
vector machine. A good correlation was found between reference and
extrapolated values in evaluated 741 OGTTs. This result indicates
that a reduction in the number of clinical test is possible through a
computational approach.
Abstract: In the present study, 49 Hybrid (Catla catla ♂ x
Labeo rohita ♀) were sampled from Al-Raheem Fish Hatchery,
Village Ali Pure Shamali, Jhang Road, 18 Km from Muzaffar Garh
using a cast net and Live fishes were transported to research
laboratory. Mean percentage for water found 79.13 %, ash 6.58 %, fat
2.22 % and protein content 12.06 % in whole wet body weight. It was
observed that body constituents were found increasing in the same
proportion with an increase in body weight while significant
proportional increase was observed with total length. However,
condition factor remained insignificant (P>0.05) with body
constituents.
Abstract: The explosive growth of World Wide Web has posed
a challenging problem in extracting relevant data. Traditional web
crawlers focus only on the surface web while the deep web keeps
expanding behind the scene. Deep web pages are created
dynamically as a result of queries posed to specific web databases.
The structure of the deep web pages makes it impossible for
traditional web crawlers to access deep web contents. This paper,
Deep iCrawl, gives a novel and vision-based approach for extracting
data from the deep web. Deep iCrawl splits the process into two
phases. The first phase includes Query analysis and Query translation
and the second covers vision-based extraction of data from the
dynamically created deep web pages. There are several established
approaches for the extraction of deep web pages but the proposed
method aims at overcoming the inherent limitations of the former.
This paper also aims at comparing the data items and presenting them
in the required order.
Abstract: Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.
Abstract: Performance control law is studied for an
interconnected fractional nonlinear system. Applying a backstepping
algorithm, a backstepping sliding mode controller (BSMC) is
developed for fractional nonlinear system. To improve control law
performance, BSMC is coupled to an adaptive sliding mode observer
have a filtered error as a sliding surface. The both architecture
performance is studied throughout the inverted pendulum mounted on
a cart. Simulation result show that the BSMC coupled to an adaptive
sliding mode observer have stable control law and eligible control
amplitude than the BSMC.
Abstract: Autism spectrum disorder is characterized by
abnormalities in social communication, language abilities and
repetitive behaviors. The present study focused on some grammatical
deficits in autistic children. We evaluated the impairment of correct
use of different Persian verb tenses in autistic children-s speech. Two
standardized Language Test were administered then gathered data
were analyzed. The main result of this study was significant
difference between the mean scores of correct responses to present
tense in comparison with past tense in Persian language. This study
demonstrated that tense is severely impaired in autistic children-s
speech. Our findings indicated those autistic children-s production of
simple present/ past tense opposition to be better than production of
future and past periphrastic forms (past perfect, present perfect, past
progressive).
Abstract: This study introduces a new method for detecting,
sorting, and localizing spikes from multiunit EEG recordings. The
method combines the wavelet transform, which localizes distinctive
spike features, with Super-Paramagnetic Clustering (SPC) algorithm,
which allows automatic classification of the data without assumptions
such as low variance or Gaussian distributions. Moreover, the method
is capable of setting amplitude thresholds for spike detection. The
method makes use of several real EEG data sets, and accordingly the
spikes are detected, clustered and their times were detected.
Abstract: This paper studies dynamic stability of homogeneous
beams with piezoelectric layers subjected to periodic axial
compressive load that is simply supported at both ends lies on a
continuous elastic foundation. The displacement field of beam is
assumed based on Bernoulli-Euler beam theory. Applying the
Hamilton's principle, the governing dynamic equation is established.
The influences of applied voltage, foundation coefficient and
piezoelectric thickness on the unstable regions are presented. To
investigate the accuracy of the present analysis, a compression study
is carried out with a known data.
Abstract: Recently, many web services to provide information for public transport are developed and released. They are optimized for mobile devices such a smartphone. We are also developing better path planning system for route buses and trains called “Bus-Net"[1]. However these systems only provide paths and related information before the user start moving. So we propose a context aware navigation to change the way to support public transport users. If we go to somewhere using many kinds of public transport, we have to know how to use them. In addition, public transport is dynamic system, and these have different characteristic by type. So we need information at real-time. Therefore we suggest the system that can support on user-s state. It has a variety of ways to help public transport users by each state, like turn-by-turn navigation. Context aware navigation will be able to reduce anxiety for using public transport.
Abstract: Yield and Crop Water Productivity are crucial issues
in sustainable agriculture, especially in high-demand resource crops such as sweet corn. This study was conducted to investigate
agronomic responses such as plant growth, yield and soil parameters (EC and Nitrate accumulation) to several deficit irrigation treatments
(100, 75, 50, 25 and 0% of ETm) applied during vegetative growth
stage, rainfed treatment was also tested.
The finding of this research indicates that under deficit irrigation
during vegetative growth stage applying 75% of ETm lead to increasing of 19.4% in terms of fresh ear yield, 9.4% in terms of dry grain yield, 10.5% in terms of number of ears per plant, 11.5% for
the 1000 grains weight and 19% in terms of crop water productivity compared with fully irrigated treatment. While those parameters in
addition to root, shoot and plant height has been affected by deficit
irrigation during vegetative growth stage when increasing water stress degree more than 50% of ETm.
Abstract: The goal of data mining algorithms is to discover
useful information embedded in large databases. One of the most
important data mining problems is discovery of frequently occurring
patterns in sequential data. In a multidimensional sequence each
event depends on more than one dimension. The search space is quite
large and the serial algorithms are not scalable for very large
datasets. To address this, it is necessary to study scalable parallel
implementations of sequence mining algorithms.
In this paper, we present a model for multidimensional sequence
and describe a parallel algorithm based on data parallelism.
Simulation experiments show good load balancing and scalable and
acceptable speedup over different processors and problem sizes and
demonstrate that our approach can works efficiently in a real parallel
computing environment.
Abstract: Lectins have a good scope in current clinical
microbiology research. In the present study evaluated the
antimicrobial activities of a D-galactose binding lectin (PnL) was
purified from the annelid, Perinereis nuntia (polychaeta) by affinity
chromatography. The molecular mass of the lectin was determined to
be 32 kDa as a single polypeptide by SDS-PAGE under both reducing
and non-reducing conditions. The hemagglutinating activity of the
PnL showed against trypsinized and glutaraldehyde-fixed human
erythrocytes was specifically inhibited by D-Gal, GalNAc,
Galβ1-4Glc and Galα1-6Glc. PnL was evaluated for in vitro
antibacterial screening studies against 11 gram-positive and
gram-negative microorganisms. From the screening results, it was
revealed that PnL exhibited significant antibacterial activity against
gram-positive bacteria. Bacillus megaterium showed the highest
growth inhibition by the lectin (250 μg/disc). However, PnL did not
inhibit the growth of gram-negative bacteria such as Vibrio cholerae
and Pseudomonas sp. PnL was also examined for in vitro antifungal
activity against six fungal phytopathogens. PnL (100 μg/mL) inhibited
the mycelial growth of Alternaria alternata (24.4%). These results
indicate that future findings of lectin applications obtained from
annelids may be of importance to life sciences.
Abstract: This paper presents the research agenda that has been proposed to develop an integrated model to explain technology adoption of SMEs in Malaysia. SMEs form over 90% of all business entities in Malaysia and they have been contributing to the development of the nation. Technology adoption has been a thorn issue among SMEs as they require big outlay which might not be available to the SMEs. Although resource has been an issue among SMEs they cannot lie low and ignore the technological advancements that are taking place at a rapid pace. With that in mind this paper proposes a model to explain the technology adoption issue among SMEs.