Abstract: The motivation of image compression technique is to reduce the irrelevance and redundancy of the image data in order to store or pass data in an efficient way from one place to another place. There are several types of compression methods available. Without the help of compression technique, the file size is knowingly larger, usually several megabytes, but by doing the compression technique, it is possible to reduce file size up to 10% as of the original without noticeable loss in quality. Image compression can be lossless or lossy. The compression technique can be applied to images, audio, video and text data. This research work mainly concentrates on methods of encoding, DCT, compression methods, security, etc. Different methodologies and network simulations have been analyzed here. Various methods of compression methodologies and its performance metrics has been investigated and presented in a table manner.
Abstract: In wireless sensor networks, locality and positioning information can be captured using Global Positioning System (GPS). This message can be congregated initially from spot to identify the system. Users can retrieve information of interest from a wireless sensor network (WSN) by injecting queries and gathering results from the mobile sink nodes. Routing is the progression of choosing optimal path in a mobile network. Intermediate node employs permutation of device nodes into teams and generating cluster heads that gather the data from entity cluster’s node and encourage the collective data to base station. WSNs are widely used for gathering data. Since sensors are power-constrained devices, it is quite vital for them to reduce the power utilization. A tree-based data fusion clustering routing algorithm (TBDFC) is used to reduce energy consumption in wireless device networks. Here, the nodes in a tree use the cluster formation, whereas the elevation of the tree is decided based on the distance of the member nodes to the cluster-head. Network simulation shows that this scheme improves the power utilization by the nodes, and thus considerably improves the lifetime.
Abstract: Several parameters are established in order to measure
biodiesel quality. One of them is the iodine value, which is an
important parameter that measures the total unsaturation within a
mixture of fatty acids. Limitation of unsaturated fatty acids is
necessary since warming of higher quantity of these ones ends in
either formation of deposits inside the motor or damage of lubricant.
Determination of iodine value by official procedure tends to be very
laborious, with high costs and toxicity of the reagents, this study uses
artificial neural network (ANN) in order to predict the iodine value
property as an alternative to these problems. The methodology of
development of networks used 13 esters of fatty acids in the input
with convergence algorithms of back propagation of back
propagation type were optimized in order to get an architecture of
prediction of iodine value. This study allowed us to demonstrate the
neural networks’ ability to learn the correlation between biodiesel
quality properties, in this caseiodine value, and the molecular
structures that make it up. The model developed in the study reached
a correlation coefficient (R) of 0.99 for both network validation and
network simulation, with Levenberg-Maquardt algorithm.
Abstract: Distributed applications deployed on LEO satellites
and ground stations require substantial communication between
different members in a constellation to overcome the earth
coverage barriers imposed by GEOs. Applications running on LEO
constellations suffer the earth line-of-sight blockage effect. They
need adequate lab testing before launching to space. We propose
a scalable cloud-based network simulation framework to simulate
problems created by the earth line-of-sight blockage. The framework
utilized cloud IaaS virtual machines to simulate LEO satellites
and ground stations distributed software. A factorial ANOVA
statistical analysis is conducted to measure simulator overhead on
overall communication performance. The results showed a very low
simulator communication overhead. Consequently, the simulation
framework is proposed as a candidate for testing LEO constellations
with distributed software in the lab before space launch.
Abstract: In this paper is being described a possible use of
virtualization technology in teaching computer networks. The
virtualization can be used as a suitable tool for creating virtual
network laboratories, supplementing the real laboratories and
network simulation software in teaching networking concepts. It will
be given a short description of characteristic projects in the area of
virtualization technology usage in network simulation, network
experiments and engineering education. A method for implementing
laboratory has also been explained, together with possible laboratory
usage and design of laboratory exercises. At the end, the laboratory
test results of virtual laboratory are presented as well.
Abstract: CloudSim is a useful tool to simulate the cloud
environment. It shows the service availability, the power consumption,
and the network traffic of services on the cloud environment.
Moreover, it supports to calculate a network communication delay
through a network topology data easily. CloudSim allows inputting a
file of topology data, but it does not provide any generating process.
Thus, it needs the file of topology data generated from some other
tools. The BRITE is typical network topology generator. Also, it
supports various type of topology generating algorithms. If CloudSim
can include the BRITE, network simulation for clouds is easier than
existing version. This paper shows the potential of connection between
BRITE and CloudSim. Also, it proposes the direction to link between
them.
Abstract: This paper presents a critical study about the
application of Neural Networks to ion-exchange process. Ionexchange
is a complex non-linear process involving many factors
influencing the ions uptake mechanisms from the pregnant solution.
The following step includes the elution. Published data presents
empirical isotherm equations with definite shortcomings resulting in
unreliable predictions. Although Neural Network simulation
technique encounters a number of disadvantages including its “black
box", and a limited ability to explicitly identify possible causal
relationships, it has the advantage to implicitly handle complex
nonlinear relationships between dependent and independent
variables. In the present paper, the Neural Network model based on
the back-propagation algorithm Levenberg-Marquardt was developed
using a three layer approach with a tangent sigmoid transfer function
(tansig) at hidden layer with 11 neurons and linear transfer function
(purelin) at out layer. The above mentioned approach has been used
to test the effectiveness in simulating ion exchange processes. The
modeling results showed that there is an excellent agreement between
the experimental data and the predicted values of copper ions
removed from aqueous solutions.
Abstract: In this paper, we proposed a new routing protocol for
Unmanned Aerial Vehicles (UAVs) that equipped with directional
antenna. We named this protocol Directional Optimized Link State
Routing Protocol (DOLSR). This protocol is based on the well
known protocol that is called Optimized Link State Routing Protocol
(OLSR). We focused in our protocol on the multipoint relay (MPR)
concept which is the most important feature of this protocol. We
developed a heuristic that allows DOLSR protocol to minimize
the number of the multipoint relays. With this new protocol the
number of overhead packets will be reduced and the End-to-End
delay of the network will also be minimized. We showed through
simulation that our protocol outperformed Optimized Link State
Routing Protocol, Dynamic Source Routing (DSR) protocol and Ad-
Hoc On demand Distance Vector (AODV) routing protocol in
reducing the End-to-End delay and enhancing the overall
throughput. Our evaluation of the previous protocols was based
on the OPNET network simulation tool.