Abstract: In this paper, we calculate the two-photon ionization
(TPI) cross-section for pump-probe scheme in Ag neutral cluster. The
pump photon energy is assumed to be close to the surface plasmon
(SP) energy of cluster in dielectric media. Due to this choice, the
pump wave excites collective oscillations of electrons-SP and the
probe wave causes ionization of the cluster. Since the interband
transition energy in Ag exceeds the SP resonance energy, the main
contribution into the TPI comes from the latter. The advantage of Ag
clusters as compared to the other noble metals is that the SP
resonance in silver cluster is much sharper because of peculiarities of
its dielectric function. The calculations are performed by separating
the coordinates of electrons corresponding to the collective
oscillations and the individual motion that allows taking into account
the resonance contribution of excited SP oscillations. It is shown that
the ionization cross section increases by two orders of magnitude if
the energy of the pump photon matches the surface plasmon energy
in the cluster.
Abstract: One of the tasks of optical surveillance is to detect
anomalies in large amounts of image data. However, if the size of the
anomaly is very small, limited information is available to distinguish
it from the surrounding environment. Spectral detection provides a
useful source of additional information and may help to detect
anomalies with a size of a few pixels or less. Unfortunately, spectral
cameras are expensive because of the difficulty of separating two
spatial in addition to one spectral dimension. We investigate the
possibility of modifying a simple spectral line detector for outdoor
detection. This may be especially useful if the area of interest forms a
line, such as the horizon. We use a monochrome CCD that also
enables detection into the near infrared. A simple camera is attached
to the setup to determine which part of the environment is spectrally
imaged. Our preliminary results indicate that sensitive detection of
very small targets is indeed possible. Spectra could be taken from the
various targets by averaging columns in the line image. By imaging a
set of lines of various widths we found narrow lines that could not be
seen in the color image but remained visible in the spectral line
image. A simultaneous analysis of the entire spectra can produce
better results than visual inspection of the line spectral image. We are
presently developing calibration targets for spatial and spectral
focusing and alignment with the spatial camera. This will present
improved results and more use in outdoor application.
Abstract: In this paper, a numerical simulation of a finned store
separating from a wing-pylon configuration has been studied and
validated. A dynamic unstructured tetrahedral mesh approach is
accomplished by using three grid sizes to numerically solving the
discretized three dimensional, inviscid and compressible Euler
equations. The method used for computations of separation of an
external store assuming quasi-steady flow condition. Computations of
quasi-steady flow have been directly coupled to a six degree-offreedom
(6DOF) rigid-body motion code to generate store
trajectories. The pressure coefficients at four different angular cuts
and time histories of various trajectory parameters and wing pressure
distribution during the store separation are compared for every grid
size with published experimental data.
Abstract: Content-based image retrieval (CBIR) uses the
contents of images to characterize and contact the images. This paper
focus on retrieving the image by separating images into its three color
mechanism R, G and B and for that Discrete Wavelet Transformation
is applied. Then Wavelet based Generalized Gaussian Density (GGD)
is practical which is used for modeling the coefficients from the
wavelet transforms. After that it is agreed to Histogram of Oriented
Gradient (HOG) for extracting its characteristic vectors with Relevant
Feedback technique is used. The performance of this approach is
calculated by exactness and it confirms that this method is wellorganized
for image retrieval.
Abstract: A polypropylene hollow fiber membrane module is used for separating dissolved gases which contain dissolved oxygen from water. These dissolved gases can be used for underwater breathing. To be used for a human, the minimum amount of oxygen is essential. To increase separation of dissolved gases, much water and high surface area of hollow fibers are requested. For efficient separation system, performance of single membrane module with high surface area needs to be investigated.
In this study, we set up experimental devices for analyzing separation characteristics of dissolved gases including oxygen from water using a polypropylene hollow fiber membrane module. Separation of dissolved gases from water is investigated with variations of water flow rates. Composition of dissolved gases is also measured using GC. These results expect to be used in developing the portable separation system.
Abstract: Various fairness models and criteria proposed by academia and industries for wired networks can be applied for ad hoc wireless network. The end-to-end fairness in an ad hoc wireless network is a challenging task compared to wired networks, which has not been addressed effectively. Most of the traffic in an ad hoc network are transport layer flows and thus the fairness of transport layer flows has attracted the interest of the researchers. The factors such as MAC protocol, routing protocol, the length of a route, buffer size, active queue management algorithm and the congestion control algorithms affects the fairness of transport layer flows. In this paper, we have considered the rate of data transmission, the queue management and packet scheduling technique. The ad hoc network is dynamic in nature due to various parameters such as transmission of control packets, multihop nature of forwarding packets, changes in source and destination nodes, changes in the routing path influences determining throughput and fairness among the concurrent flows. In addition, the effect of interaction between the protocol in the data link and transport layers has also plays a role in determining the rate of the data transmission. We maintain queue for each flow and the delay information of each flow is maintained accordingly. The pre-processing of flow is done up to the network layer only. The source and destination address information is used for separating the flow and the transport layer information is not used. This minimizes the delay in the network. Each flow is attached to a timer and is updated dynamically. Finite State Machine (FSM) is proposed for queue and transmission control mechanism. The performance of the proposed approach is evaluated in ns-2 simulation environment. The throughput and fairness based on mobility for different flows used as performance metrics. We have compared the performance of the proposed approach with ATP and the transport layer information is used. This minimizes the delay in the network. Each flow is attached to a timer and is updated dynamically. Finite State Machine (FSM) is proposed for queue and transmission control mechanism. The performance of the proposed approach is evaluated in ns-2 simulation environment. The throughput and fairness based on not mobility for different flows used as performance metrics. We have compared the performance of the proposed approach with ATP and MC-MLAS and the performance of the proposed approach is encouraging.
Abstract: Breast region segmentation is an essential prerequisite in computerized analysis of mammograms. It aims at separating the breast tissue from the background of the mammogram and it includes two independent segmentations. The first segments the background region which usually contains annotations, labels and frames from the whole breast region, while the second removes the pectoral muscle portion (present in Medio Lateral Oblique (MLO) views) from the rest of the breast tissue. In this paper we propose hybridization of Connected Component Labeling (CCL), Fuzzy, and Straight line methods. Our proposed methods worked good for separating pectoral region. After removal pectoral muscle from the mammogram, further processing is confined to the breast region alone. To demonstrate the validity of our segmentation algorithm, it is extensively tested using over 322 mammographic images from the Mammographic Image Analysis Society (MIAS) database. The segmentation results were evaluated using a Mean Absolute Error (MAE), Hausdroff Distance (HD), Probabilistic Rand Index (PRI), Local Consistency Error (LCE) and Tanimoto Coefficient (TC). The hybridization of fuzzy with straight line method is given more than 96% of the curve segmentations to be adequate or better. In addition a comparison with similar approaches from the state of the art has been given, obtaining slightly improved results. Experimental results demonstrate the effectiveness of the proposed approach.
Abstract: This paper describes a method for preparing metallic Cu nanoparticles in aqueous solution, and a metal-metal bonding technique using the Cu particles.Preparation of the Cu particle colloid solution was performed in water at room temperature in air using a copper source (0.01 M Cu(NO3)2), a reducing reagent (0.2 - 1.0 M hydrazine), and stabilizers (0.5×10-3 M citric acid and 5.0×10-3 M cetyltrimethylammonium bromide). The metallic Cu nanoparticles with sizes of ca. 60nm were prepared at all the hydrazine concentrations examined. A stage and a plate of metallic Cu were successfully bonded under annealing at 400oC and pressurizing at 1.2 MPa for 5min in H2 gas with help of the metallic Cu particles. A shear strength required for separating the bonded Cu substrates reached the maximum value at a hydrazine concentration of 0.8M, and it decreased beyond the concentration. Consequently, the largest shear strength of 22.9 MPa was achieved at the 0.8 M hydrazine concentration.
Abstract: In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order to maximize the classification effect. A single electroencephalogram (EEG) signal is used for our analysis rather than depending on multiple biological signals, which makes greatly simplifies the data acquisition process. Experimental results demonstrate that the average epoch by epoch agreement between the visual and the proposed method in separating 30s wakefulness+S1, REM, S2 and SWS epochs was 88.83%. This study shows that the proposed method performed well in all the four stages, and can effectively limit error propagation at the same time. It could, therefore, be an efficient method for automatic sleep scoring. Additionally, since it requires only a small volume of data it could be suited to pervasive applications.
Abstract: In order to enhance the contrast in the regions where the pixels have similar intensities, this paper presents a new histogram equalization scheme. Conventional global equalization schemes over-equalizes these regions so that too bright or dark pixels are resulted and local equalization schemes produce unexpected discontinuities at the boundaries of the blocks. The proposed algorithm segments the original histogram into sub-histograms with reference to brightness level and equalizes each sub-histogram with the limited extents of equalization considering its mean and variance. The final image is determined as the weighted sum of the equalized images obtained by using the sub-histogram equalizations. By limiting the maximum and minimum ranges of equalization operations on individual sub-histograms, the over-equalization effect is eliminated. Also the result image does not miss feature information in low density histogram region since the remaining these area is applied separating equalization. This paper includes how to determine the segmentation points in the histogram. The proposed algorithm has been tested with more than 100 images having various contrasts in the images and the results are compared to the conventional approaches to show its superiority.
Abstract: Polyphenolics and sugar are the components of many
fruit juices. In this work, the performance of ultra-filtration (UF) for
separating phenolic compounds from apple juice was studied by
performing batch experiments in a membrane module with an area of
0.1 m2 and fitted with a regenerated cellulose membrane of 1 kDa
MWCO. The effects of various operating conditions: transmembrane
pressure (3, 4, 5 bar), temperature (30, 35, 40 ºC), pH (2, 3, 4, 5),
feed concentration (3, 5, 7, 10, 15 ºBrix for apple juice) and feed flow
rate (1, 1.5, 1.8 L/min) on the performance were determined.
The optimum operating conditions were: transmembrane pressure
4 bar, temperature 30 ºC, feed flow rate 1 – 1.8 L/min, pH 3 and 10
Brix (apple juice). After performing ultrafiltration under these
conditions, the concentration of polyphenolics in retentate was
increased by a factor of up to 2.7 with up to 70% recovered in the
permeate and with approx. 20% of the sugar in that stream..
Application of diafiltration (addition of water to the concentrate) can
regain the flux by a factor of 1.5, which has been decreased due to
fouling. The material balance performed on the process has shown
the amount of deposits on the membrane and the extent of fouling in
the system. In conclusion, ultrafiltration has been demonstrated as a
potential technology to separate the polyphenolics and sugars from
their mixtures and can be applied to remove sugars from fruit juice.
Abstract: One of the biggest drawbacks of the wireless
environment is the limited bandwidth. However, the users sharing
this limited bandwidth have been increasing considerably. SDMA
technique which entails using directional antennas allows to increase
the capacity of a wireless network by separating users in the medium.
In this paper, it has been presented how the capacity can be enhanced
while the mean delay is reduced by using directional antennas in
wireless networks employing TDMA/FDD MAC. Computer
modeling and simulation of the wireless system studied are realized
using OPNET Modeler. Preliminary simulation results are presented
and the performance of the model using directional antennas is
evaluated and compared consistently with the one using
omnidirectional antennas.
Abstract: In this paper, we proposed the robust mobile object
detection method for light effect in the night street image block based
updating reference background model using block state analysis.
Experiment image is acquired sequence color video from steady
camera. When suddenly appeared artificial illumination, reference
background model update this information such as street light, sign
light. Generally natural illumination is change by temporal, but
artificial illumination is suddenly appearance. So in this paper for
exactly detect artificial illumination have 2 state process. First process
is compare difference between current image and reference
background by block based, it can know changed blocks. Second
process is difference between current image-s edge map and reference
background image-s edge map, it possible to estimate illumination at
any block. This information is possible to exactly detect object,
artificial illumination and it was generating reference background
more clearly. Block is classified by block-state analysis. Block-state
has a 4 state (i.e. transient, stationary, background, artificial
illumination). Fig. 1 is show characteristic of block-state respectively
[1]. Experimental results show that the presented approach works well
in the presence of illumination variance.
Abstract: Automatic currency note recognition invariably
depends on the currency note characteristics of a particular country
and the extraction of features directly affects the recognition ability.
Sri Lanka has not been involved in any kind of research or
implementation of this kind. The proposed system “SLCRec" comes
up with a solution focusing on minimizing false rejection of notes.
Sri Lankan currency notes undergo severe changes in image quality
in usage. Hence a special linear transformation function is adapted to
wipe out noise patterns from backgrounds without affecting the
notes- characteristic images and re-appear images of interest. The
transformation maps the original gray scale range into a smaller
range of 0 to 125. Applying Edge detection after the transformation
provided better robustness for noise and fair representation of edges
for new and old damaged notes. A three layer back propagation
neural network is presented with the number of edges detected in row
order of the notes and classification is accepted in four classes of
interest which are 100, 500, 1000 and 2000 rupee notes. The
experiments showed good classification results and proved that the
proposed methodology has the capability of separating classes
properly in varying image conditions.
Abstract: The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.
Abstract: This paper presents an online method that learns the
corresponding points of an object from un-annotated grayscale images
containing instances of the object. In the first image being
processed, an ensemble of node points is automatically selected
which is matched in the subsequent images. A Bayesian posterior
distribution for the locations of the nodes in the images is formed.
The likelihood is formed from Gabor responses and the prior assumes
the mean shape of the node ensemble to be similar in a translation
and scale free space. An association model is applied for separating
the object nodes and background nodes. The posterior distribution is
sampled with Sequential Monte Carlo method. The matched object
nodes are inferred to be the corresponding points of the object
instances. The results show that our system matches the object nodes
as accurately as other methods that train the model with annotated
training images.
Abstract: The paper shows some ability to manage two-phase
flows arising from the use of unsteady effects. In one case, we
consider the condition of fragmentation of the interface between the
two components leads to the intensification of mixing. The problem
is solved when the temporal and linear scale are small for the
appearance of the developed mixing layer. Showing that exist such
conditions for unsteady flow velocity at the surface of the channel,
which will lead to the creation and fragmentation of vortices at Re
numbers of order unity. Also showing that the Re is not a criterion of
similarity for this type of flows, but we can introduce a criterion that
depends on both the Re, and the frequency splitting of the vortices. It
turned out that feature of this situation is that streamlines behave
stable, and if we analyze the behavior of the interface between the
components it satisfies all the properties of unstable flows. The other
problem we consider the behavior of solid impurities in the extensive
system of channels. Simulated unsteady periodic flow modeled
breaths. Consider the behavior of the particles along the trajectories.
It is shown that, depending on the mass and diameter of the particles,
they can be collected in a caustic on the channel walls, stop in a
certain place or fly back. Of interest is the distribution of particle
velocity in frequency. It turned out that by choosing a behavior of the
velocity field of the carrier gas can affect the trajectory of individual
particles including force them to fly back.
Abstract: MRAM technology provides a combination of fast
access time, non-volatility, data retention and endurance. While a
growing interest is given to two-terminal Magnetic Tunnel Junctions
(MTJ) based on Spin-Transfer Torque (STT) switching as the
potential candidate for a universal memory, its reliability is
dramatically decreased because of the common writing/reading path.
Three-terminal MTJ based on Spin-Orbit Torque (SOT) approach
revitalizes the hope of an ideal MRAM. It can overcome the
reliability barrier encountered in current two-terminal MTJs by
separating the reading and the writing path. In this paper, we study
two possible writing schemes for the SOT-MTJ device based on
recently fabricated samples. While the first is based on precessional
switching, the second requires the presence of permanent magnetic
field. Based on an accurate Verilog-A model, we simulate the two
writing techniques and we highlight advantages and drawbacks of
each one. Using the second technique, pioneering logic circuits based
on the three-terminal architecture of the SOT-MTJ described in this
work are under development with preliminary attractive results.
Abstract: Digital news with a variety topics is abundant on the
internet. The problem is to classify news based on its appropriate
category to facilitate user to find relevant news rapidly. Classifier
engine is used to split any news automatically into the respective
category. This research employs Support Vector Machine (SVM) to
classify Indonesian news. SVM is a robust method to classify
binary classes. The core processing of SVM is in the formation of an
optimum separating plane to separate the different classes. For
multiclass problem, a mechanism called one against one is used to
combine the binary classification result. Documents were taken
from the Indonesian digital news site, www.kompas.com. The
experiment showed a promising result with the accuracy rate of 85%.
This system is feasible to be implemented on Indonesian news
classification.
Abstract: There has been a growing interest in utilizing surfactants in remediation processes to separate the hydrophobic volatile organic compounds (HVOCs) from aqueous solution. One attractive process is cloud point extraction (CPE), which utilizes nonionic surfactants as a separating agent. Since the surfactant cost is a key determination of the economic viability of the process, it is important that the surfactants are recycled and reused. This work aims to study the performance of the co-current vacuum stripping using a packed column for HVOCs removal from contaminated surfactant solution. Six types HVOCs are selected as contaminants. The studied surfactant is the branched secondary alcohol ethoxylates (AEs), Tergitol TMN-6 (C14H30O2). The volatility and the solubility of HVOCs in surfactant system are determined in terms of an apparent Henry’s law constant and a solubilization constant, respectively. Moreover, the HVOCs removal efficiency of vacuum stripping column is assessed in terms of percentage of HVOCs removal and the overall liquid phase volumetric mass transfer coefficient. The apparent Henry’s law constant of benzenz , toluene, and ethyl benzene were 7.00×10-5, 5.38×10-5, 3.35× 10-5 respectively. The solubilization constant of benzene, toluene, and ethyl benzene were 1.71, 2.68, 7.54 respectively. The HVOCs removal for all solute were around 90 percent.