Abstract: In this paper, the difference between the Alternating
Direction Method (ADM) and the Non-Splitting Method (NSM) is
investigated, while both methods applied to the simulations for 2-D
multimaterial radiation diffusion issues. Although the ADM have the
same accuracy orders with the NSM on the uniform meshes, the
accuracy of ADM will decrease on the distorted meshes or the
boundary of domain. Numerical experiments are carried out to
confirm the theoretical predication.
Abstract: Linear cryptanalysis methods are rarely used to improve the security of chaotic stream ciphers. In this paper, we apply linear cryptanalysis to a chaotic stream cipher which was designed by strictly using the basic design criterion of cryptosystem – confusion and diffusion. We show that this well-designed chaos-based stream cipher is still insecure against distinguishing attack. This distinguishing attack promotes the further improvement of the cipher.
Abstract: Image fusion aims to enhance the perception
of a scene by combining important information captured by
different sensors. Dual-Tree Complex Wavelet (DT-CWT) has been
thouroughly investigated for image fusion, since it takes advantages
of approximate shift invariance and direction selectivity. But it can
only handle limited direction information. To allow a more flexible
directional expansion for images, we propose a novel fusion scheme,
referred to as complex contourlet transform (CCT). It successfully
incorporates directional filter banks (DFB) into DT-CWT. As a result
it efficiently deal with images containing contours and textures,
whereas it retains the property of shift invariance. Experimental
results demonstrated that the method features high quality fusion
performance and can facilitate many image processing applications.
Abstract: The first and basic cause of the failure of concrete is repeated freezing (thawing) of moisture contained in the pores, microcracks, and cavities of the concrete. On transition to ice, water existing in the free state in cracks increases in volume, expanding the recess in which freezing occurs. A reduction in strength below the initial value is to be expected and further cycle of freezing and thawing have a further marked effect. By using some experimental parameters like nuclear magnetic resonance variation (NMR), enthalpy-temperature (or heat capacity) variation, we can resolve between the various water states and their effect on concrete properties during cooling through the freezing transition temperature range. The main objective of this paper is to describe the principal type of water responsible for the reduction in strength and structural damage (frost damage) of concrete following repeated freeze –thaw cycles. Some experimental work was carried out at the institute of cryogenics to determine what happens to water in concrete during the freezing transition.
Abstract: This paper proposes new enhancement models to the
methods of nonlinear anisotropic diffusion to greatly reduce speckle
and preserve image features in medical ultrasound images. By
incorporating local physical characteristics of the image, in this case
scatterer density, in addition to the gradient, into existing tensorbased
image diffusion methods, we were able to greatly improve the
performance of the existing filtering methods, namely edge
enhancing (EE) and coherence enhancing (CE) diffusion. The new
enhancement methods were tested using various ultrasound images,
including phantom and some clinical images, to determine the
amount of speckle reduction, edge, and coherence enhancements.
Scatterer density weighted nonlinear anisotropic diffusion
(SDWNAD) for ultrasound images consistently outperformed its
traditional tensor-based counterparts that use gradient only to weight
the diffusivity function. SDWNAD is shown to greatly reduce
speckle noise while preserving image features as edges, orientation
coherence, and scatterer density. SDWNAD superior performances
over nonlinear coherent diffusion (NCD), speckle reducing
anisotropic diffusion (SRAD), adaptive weighted median filter
(AWMF), wavelet shrinkage (WS), and wavelet shrinkage with
contrast enhancement (WSCE), make these methods ideal
preprocessing steps for automatic segmentation in ultrasound
imaging.
Abstract: After the development of the Internet a suitable
discipline for trading goods electronically has been emerged.
However, this type of markets is not still mature enough in order to
become independent and get closer to seller/buyer-s needs.
Furthermore, the buyable and sellable goods in these markets still
don-t have essential standards for being well-defined. In this paper,
we will present a model for development of a market which can
contain goods with variable definitions and we will also investigate
its characteristics. Besides, by noticing the fact that people have
different discriminations, it-s figured out that the significance of each
attribute of a specific product may vary from different people-s view
points. Consequently we-ll present a model for weighting and
accordingly different people-s view points could be satisfied. These
two aspects will be discussed completely throughout this paper.
Abstract: The objective of this research was to find the diffusion properties of vehicles on the road by using the V-Sphere Code. The diffusion coefficient and the size of the height of the wake were estimated with the LES option and the third order MUSCL scheme. We evaluated the code with the changes in the moments of Reynolds Stress along the mean streamline. The results show that at the leading part of a bluff body the LES has some advantages over the RNS since the changes in the strain rates are larger for the leading part. We estimated that the diffusion coefficient with the computed Reynolds stress (non-dimensional) was about 0.96 times the mean velocity.
Abstract: In this paper, we present a novel objective nonreference performance assessment algorithm for image fusion. It takes into account local measurements to estimate how well the important information in the source images is represented by the fused image. The metric is based on the Universal Image Quality Index and uses the similarity between blocks of pixels in the input images and the fused image as the weighting factors for the metrics. Experimental results confirm that the values of the proposed metrics correlate well with the subjective quality of the fused images, giving a significant improvement over standard measures based on mean squared error and mutual information.
Abstract: In this paper we propose a new knowledge model using
the Dempster-Shafer-s evidence theory for image segmentation and
fusion. The proposed method is composed essentially of two steps.
First, mass distributions in Dempster-Shafer theory are obtained from
the membership degrees of each pixel covering the three image
components (R, G and B). Each membership-s degree is determined by
applying Fuzzy C-Means (FCM) clustering to the gray levels of the
three images. Second, the fusion process consists in defining three
discernment frames which are associated with the three images to be
fused, and then combining them to form a new frame of discernment.
The strategy used to define mass distributions in the combined
framework is discussed in detail. The proposed fusion method is
illustrated in the context of image segmentation. Experimental
investigations and comparative studies with the other previous methods
are carried out showing thus the robustness and superiority of the
proposed method in terms of image segmentation.
Abstract: The focal spot of a high intensity focused ultrasound
transducer is small. To heat a large target volume, multiple treatment spots are required. If the power of each treatment spot is fixed, it could
results in insufficient heating of initial spots and over-heating of later ones, which is caused by the thermal diffusion. Hence, to produce a
uniform heated volume, the delivered energy of each treatment spot
should be properly adjusted. In this study, we proposed an iterative, extrapolation technique to adjust the required ultrasound energy of
each treatment spot. Three different scanning pathways were used to evaluate the performance of this technique. Results indicate that by using the proposed technique, uniform heating volume could be obtained.
Abstract: Classifier fusion may generate more accurate
classification than each of the basic classifiers. Fusion is often based
on fixed combination rules like the product, average etc. This paper
presents decision templates as classifier fusion method for the
recognition of the handwritten English and Farsi numerals (1-9).
The process involves extracting a feature vector on well-known
image databases. The extracted feature vector is fed to multiple
classifier fusion. A set of experiments were conducted to compare
decision templates (DTs) with some combination rules. Results from
decision templates conclude 97.99% and 97.28% for Farsi and
English handwritten digits.
Abstract: To solve the problem of multisensor data fusion under
non-Gaussian channel noise. The advanced M-estimates are known
to be robust solution while trading off some accuracy. In order to
improve the estimation accuracy while still maintaining the equivalent
robustness, a two-stage robust fusion algorithm is proposed using
preliminary rejection of outliers then an optimal linear fusion. The
numerical experiments show that the proposed algorithm is equivalent
to the M-estimates in the case of uncorrelated local estimates and
significantly outperforms the M-estimates when local estimates are
correlated.
Abstract: In wireless sensor networks,the mobile agent technology is used in data fusion. According to the node residual energy and the results of partial integration,we design the node clustering algorithm. Optimization of mobile agent in the routing within the cluster strategy for wireless sensor networks to further reduce the amount of data transfer. Through the experiments, using mobile agents in the integration process within the cluster can be reduced the path loss in some extent.
Abstract: The present study was designed to investigate the
cardio protective role of chronic oral administration of alcoholic
extract of Terminalia arjuna in in-vivo ischemic reperfusion injury
and the induction of HSP72. Rabbits, divided into three groups, and
were administered with the alcoholic extract of the bark powder of
Terminalia arjuna (TAAE) by oral gavage [6.75mg/kg: (T1) and
9.75mg/kg: (T2), 6 days /week for 12 weeks]. In open-chest
Ketamine pentobarbitone anaesthetized rabbits, the left anterior
descending coronary artery was occluded for 15 min of ischemia
followed by 60 min of reperfusion. In the vehicle-treated group,
ischemic-reperfusion injury (IRI) was evidenced by depression of
global hemodynamic function (MAP, HR, LVEDP, peak LV (+) & (-
) (dP/dt) along with depletion of HEP compounds. Oxidative stress
in IRI was evidenced by, raised levels of myocardial TBARS and
depletion of endogenous myocardial antioxidants GSH, SOD and
catalase. Western blot analysis showed a single band corresponding
to 72 kDa in homogenates of hearts from rabbits treated with both the
doses. In the alcoholic extract of the bark powder of Terminalia
arjuna treatment groups, both the doses had better recovery of
myocardial hemodynamic function, with significant reduction in
TBARS, and rise in SOD, GSH, catalase were observed. The results
of the present study suggest that the alcoholic extract of the bark
powder of Terminalia arjuna in rabbit induces myocardial HSP 72
and augments myocardial endogenous antioxidants, without causing
any cellular injury and offered better cardioprotection against
oxidative stress associated with myocardial IR injury.
Abstract: Omni directional mobile robots have been popularly
employed in several applications especially in soccer player robots
considered in Robocup competitions. However, Omni directional
navigation system, Omni-vision system and solenoid kicking
mechanism in such mobile robots have not ever been combined. This
situation brings the idea of a robot with no head direction into
existence, a comprehensive Omni directional mobile robot. Such a
robot can respond more quickly and it would be capable for more
sophisticated behaviors with multi-sensor data fusion algorithm for
global localization base on the data fusion. This paper has tried to
focus on the research improvements in the mechanical, electrical and
software design of the robots of team ADRO Iran. The main
improvements are the world model, the new strategy framework,
mechanical structure, Omni-vision sensor for object detection, robot
path planning, active ball handling mechanism and the new kicker
design, , and other subjects related to mobile robot
Abstract: Sensors have been used in various kinds of academic
fields and applications. In this article, we propose the idea of
modularized sensors that combine multiple sensor modules into a
unique sensor. We divide a sensor into several units according to
functionalities. Each unit has different sensor modules, which share
the same type of connectors and can be serially and arbitrarily
connected each other. A user can combine different sensor modules
into a sensor platform according to requirements. Compared with
current modularized sensors, the proposed sensor platform is highly
flexible and reusable. We have implemented the prototype of the
proposed sensor platform, and the experimental results show the
proposed platform can work correctly.
Abstract: In this paper we consider the issue of distributed adaptive estimation over sensor networks. To deal with more realistic scenario, different variance for observation noise is assumed for sensors in the network. To solve the problem of different variance of observation noise, the proposed method is divided into two phases: I) Estimating each sensor-s observation noise variance and II) using the estimated variances to obtain the desired parameter. Our proposed algorithm is based on a diffusion least mean square (LMS) implementation with linear combiner model. In the proposed algorithm, the step-size parameter the coefficients of linear combiner are adjusted according to estimated observation noise variances. As the simulation results show, the proposed algorithm considerably improves the diffusion LMS algorithm given in literature.
Abstract: The objective of the present study was to evaluate the
potential of hollow microneedles for enhancing the transdermal
delivery of Bovine Serum Albumin (MW~66,000 Da)-Fluorescein
Isothiocyanate (BSA-FITC) conjugate, a hydrophilic large molecular
compound. Moreover, the effect of different formulations was
evaluated. The series of binary mixtures composed of propylene
glycol (PG) and pH 7.4 phosphate buffer solution (PBS) was
prepared and used as a medium for BSA-FITC. The results showed
that there was no permeation of BSA-FITC solution across the
neonatal porcine skin without using hollow microneedles, whereas
the cumulative amount of BSA-FITC released at 8 h through the
neonatal porcine skin was about 60-70% when using hollow
microneedles. Furthermore, the results demonstrated that the higher
volume of PG in binary mixtures injected, the lower cumulative
amount of BSA-FITC released and release rate of BSA-FITC from
skin. These release profiles of BSA-FITC in binary mixtures were
expressed by Fick-s law of diffusion. These results suggest the
utilization of hollow microneedle to enhance transdermal delivery of
protein and provide useful information for designing an effective
hollow microneedle system.
Abstract: In this paper, a new method of information fusion – DSmT (Dezert and Smarandache Theory) is introduced to apply to managing and dealing with the uncertain information from robot map building. Here we build grid map form sonar sensors and laser range finder (LRF). The uncertainty mainly comes from sonar sensors and LRF. Aiming to the uncertainty in static environment, we propose Classic DSm (DSmC) model for sonar sensors and laser range finder, and construct the general basic belief assignment function (gbbaf) respectively. Generally speaking, the evidence sources are unreliable in physical system, so we must consider the discounting theory before we apply DSmT. At last, Pioneer II mobile robot serves as a simulation experimental platform. We build 3D grid map of belief layout, then mainly compare the effect of building map using DSmT and DST. Through this simulation experiment, it proves that DSmT is very successful and valid, especially in dealing with highly conflicting information. In short, this study not only finds a new method for building map under static environment, but also supplies with a theory foundation for us to further apply Hybrid DSmT (DSmH) to dynamic unknown environment and multi-robots- building map together.
Abstract: This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.