Abstract: The modern telecommunication industry demands
higher capacity networks with high data rate. Orthogonal frequency
division multiplexing (OFDM) is a promising technique for high data
rate wireless communications at reasonable complexity in wireless
channels. OFDM has been adopted for many types of wireless
systems like wireless local area networks such as IEEE 802.11a, and
digital audio/video broadcasting (DAB/DVB). The proposed research
focuses on a concatenated coding scheme that improve the
performance of OFDM based wireless communications. It uses a
Redundant Residue Number System (RRNS) code as the outer code
and a convolutional code as the inner code. Here, a direct conversion
of analog signal to residue domain is done to reduce the conversion
complexity using sigma-delta based parallel analog-to-residue
converter. The bit error rate (BER) performances of the proposed
system under different channel conditions are investigated. These
include the effect of additive white Gaussian noise (AWGN),
multipath delay spread, peak power clipping and frame start
synchronization error. The simulation results show that the proposed
RRNS-Convolutional concatenated coding (RCCC) scheme provides
significant improvement in the system performance by exploiting the
inherent properties of RRNS.
Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: Recent years have seen a growing trend towards the
integration of multiple information sources to support large-scale
prediction of protein-protein interaction (PPI) networks in model
organisms. Despite advances in computational approaches, the
combination of multiple “omic" datasets representing the same type
of data, e.g. different gene expression datasets, has not been
rigorously studied. Furthermore, there is a need to further investigate
the inference capability of powerful approaches, such as fullyconnected
Bayesian networks, in the context of the prediction of PPI
networks. This paper addresses these limitations by proposing a
Bayesian approach to integrate multiple datasets, some of which
encode the same type of “omic" data to support the identification of
PPI networks. The case study reported involved the combination of
three gene expression datasets relevant to human heart failure (HF).
In comparison with two traditional methods, Naive Bayesian and
maximum likelihood ratio approaches, the proposed technique can
accurately identify known PPI and can be applied to infer potentially
novel interactions.
Abstract: Photovoltaic power generation forecasting is an
important task in renewable energy power system planning and
operating. This paper explores the application of neural networks
(NN) to study the design of photovoltaic power generation
forecasting systems for one week ahead using weather databases
include the global irradiance, and temperature of Ghardaia city
(south of Algeria) using a data acquisition system. Simulations were
run and the results are discussed showing that neural networks
Technique is capable to decrease the photovoltaic power generation
forecasting error.
Abstract: Many exist studies always use Markov decision
processes (MDPs) in modeling optimal route choice in
stochastic, time-varying networks. However, taking many
variable traffic data and transforming them into optimal route
decision is a computational challenge by employing MDPs in
real transportation networks. In this paper we model finite
horizon MDPs using directed hypergraphs. It is shown that the
problem of route choice in stochastic, time-varying networks
can be formulated as a minimum cost hyperpath problem, and
it also can be solved in linear time. We finally demonstrate the
significant computational advantages of the introduced
methods.
Abstract: This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.
Abstract: Mobile IP has been developed to provide the
continuous information network access to mobile users. In IP-based
mobile networks, location management is an important component of
mobility management. This management enables the system to track
the location of mobile node between consecutive communications. It
includes two important tasks- location update and call delivery.
Location update is associated with signaling load. Frequent updates
lead to degradation in the overall performance of the network and the
underutilization of the resources. It is, therefore, required to devise
the mechanism to minimize the update rate. Mobile IPv6 (MIPv6)
and Hierarchical MIPv6 (HMIPv6) have been the potential
candidates for deployments in mobile IP networks for mobility
management. HMIPv6 through studies has been shown with better
performance as compared to MIPv6. It reduces the signaling
overhead traffic by making registration process local. In this paper,
we present performance analysis of MIPv6 and HMIPv6 using an
analytical model. Location update cost function is formulated based
on fluid flow mobility model. The impact of cell residence time, cell
residence probability and user-s mobility is investigated. Numerical
results are obtained and presented in graphical form. It is shown that
HMIPv6 outperforms MIPv6 for high mobility users only and for low
mobility users; performance of both the schemes is almost equivalent
to each other.
Abstract: In networks, mainly small and medium-sized businesses benefit from the knowledge, experiences and solutions offered by experts from industry and science or from the exchange with practitioners. Associations which focus, among other things, on networking, information and knowledge transfer and which are interested in supporting such cooperations are especially well suited to provide such networks and the appropriate web platforms. Using METORA as an example – a project developed and run by the Federal Association for Information Economy, Telecommunications and New Media e.V. (BITKOM) for the Federal Ministry of Economics and Technology (BMWi) – This paper will discuss how associations and other network organizations can achieve this task and what conditions they have to consider.
Abstract: Every day human life experiences new equipments
more automatic and with more abilities. So the need for faster
processors doesn-t seem to finish. Despite new architectures and
higher frequencies, a single processor is not adequate for many
applications. Parallel processing and networks are previous solutions
for this problem. The new solution to put a network of resources on a
chip is called NOC (network on a chip). The more usual topology for
NOC is mesh topology. There are several routing algorithms suitable
for this topology such as XY, fully adaptive, etc. In this paper we
have suggested a new algorithm named Intermittent X, Y (IX/Y). We
have developed the new algorithm in simulation environment to
compare delay and power consumption with elders' algorithms.
Abstract: this paper presents a novel neural network controller
with composite adaptation low to improve the trajectory tracking
problems of biped robots comparing with classical controller. The
biped model has 5_link and 6 degrees of freedom and actuated by
Plated Pneumatic Artificial Muscle, which have a very high power to
weight ratio and it has large stoke compared to similar actuators. The
proposed controller employ a stable neural network in to approximate
unknown nonlinear functions in the robot dynamics, thereby
overcoming some limitation of conventional controllers such as PD
or adaptive controllers and guarantee good performance. This NN
controller significantly improve the accuracy requirements by
retraining the basic PD/PID loop, but adding an inner adaptive loop
that allows the controller to learn unknown parameters such as
friction coefficient, therefore improving tracking accuracy.
Simulation results plus graphical simulation in virtual reality show
that NN controller tracking performance is considerably better than
PD controller tracking performance.
Abstract: The Mobile Ad-hoc Network (MANET) is a collection of self-configuring and rapidly deployed mobile nodes (routers) without any central infrastructure. Routing is one of the potential issues. Many routing protocols are reported but it is difficult to decide which one is best in all scenarios. In this paper on demand routing protocols DSR and DYMO based on IEEE 802.11 DCF MAC protocol are examined and characteristic summary of these routing protocols is presented. Their performance is analyzed and compared on performance measuring metrics throughput, dropped packets due to non availability of routes, duplicate RREQ generated for route discovery and normalized routing load by varying CBR data traffic load using QualNet 5.0.2 network simulator.
Abstract: This paper studies the mean square exponential synchronization problem of a class of stochastic neutral type chaotic neural networks with mixed delay. On the Basis of Lyapunov stability theory, some sufficient conditions ensuring the mean square exponential synchronization of two identical chaotic neural networks are obtained by using stochastic analysis and inequality technique. These conditions are expressed in the form of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. The feedback controller used in this paper is more general than those used in previous literatures. One simulation example is presented to demonstrate the effectiveness of the derived results.
Abstract: In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.
Abstract: Knowledge sharing in general and the contextual
access to knowledge in particular, still represent a key challenge in
the knowledge management framework. Researchers on semantic
web and human machine interface study techniques to enhance this
access. For instance, in semantic web, the information retrieval is
based on domain ontology. In human machine interface, keeping
track of user's activity provides some elements of the context that can
guide the access to information. We suggest an approach based on
these two key guidelines, whilst avoiding some of their weaknesses.
The approach permits a representation of both the context and the
design rationale of a project for an efficient access to knowledge. In
fact, the method consists of an information retrieval environment
that, in the one hand, can infer knowledge, modeled as a semantic
network, and on the other hand, is based on the context and the
objectives of a specific activity (the design). The environment we
defined can also be used to gather similar project elements in order to
build classifications of tasks, problems, arguments, etc. produced in a
company. These classifications can show the evolution of design
strategies in the company.
Abstract: Results of Chilean wine classification based on the
information provided by an electronic nose are reported in this paper.
The classification scheme consists of two parts; in the first stage,
Principal Component Analysis is used as feature extraction method to
reduce the dimensionality of the original information. Then, Radial
Basis Functions Neural Networks is used as pattern recognition
technique to perform the classification. The objective of this study is
to classify different Cabernet Sauvignon, Merlot and Carménère wine
samples from different years, valleys and vineyards of Chile.
Abstract: This research work is aimed at speech recognition
using scaly neural networks. A small vocabulary of 11 words were
established first, these words are “word, file, open, print, exit, edit,
cut, copy, paste, doc1, doc2". These chosen words involved with
executing some computer functions such as opening a file, print
certain text document, cutting, copying, pasting, editing and exit.
It introduced to the computer then subjected to feature extraction
process using LPC (linear prediction coefficients). These features are
used as input to an artificial neural network in speaker dependent
mode. Half of the words are used for training the artificial neural
network and the other half are used for testing the system; those are
used for information retrieval.
The system components are consist of three parts, speech
processing and feature extraction, training and testing by using neural
networks and information retrieval.
The retrieve process proved to be 79.5-88% successful, which is
quite acceptable, considering the variation to surrounding, state of
the person, and the microphone type.
Abstract: In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.
Abstract: Image Compression using Artificial Neural Networks
is a topic where research is being carried out in various directions
towards achieving a generalized and economical network.
Feedforward Networks using Back propagation Algorithm adopting
the method of steepest descent for error minimization is popular and
widely adopted and is directly applied to image compression.
Various research works are directed towards achieving quick
convergence of the network without loss of quality of the restored
image. In general the images used for compression are of different
types like dark image, high intensity image etc. When these images
are compressed using Back-propagation Network, it takes longer
time to converge. The reason for this is, the given image may
contain a number of distinct gray levels with narrow difference with
their neighborhood pixels. If the gray levels of the pixels in an image
and their neighbors are mapped in such a way that the difference in
the gray levels of the neighbors with the pixel is minimum, then
compression ratio as well as the convergence of the network can be
improved. To achieve this, a Cumulative distribution function is
estimated for the image and it is used to map the image pixels. When
the mapped image pixels are used, the Back-propagation Neural
Network yields high compression ratio as well as it converges
quickly.
Abstract: Transport and land use are two systems that are
mutually influenced. Their interaction is a complex process
associated with continuous feedback. The paper examines the
existing land use around an under construction metro station of the
new metro network of Thessaloniki, Greece, through the use of field
investigations, around the station-s predefined location. Moreover,
except from the analytical land use recording, a sampling
questionnaire survey is addressed to several selected enterprises of
the study area. The survey aims to specify the characteristics of the
enterprises, the trip patterns of their employees and clients, as well as
the stated preferences towards the changes the new metro station is
considered to bring to the area. The interpretation of the interrelationships
among selected data from the questionnaire survey takes
place using the method of Principal Components Analysis for
Categorical Data. The followed methodology and the survey-s results
contribute to the enrichment of the relevant bibliography concerning
the way the creation of a new metro station can have an impact on the
land use pattern of an area, by examining the situation before the
operation of the station.
Abstract: Despite the recent surge of research in control of
worm propagation, currently, there is no effective defense system
against such cyber attacks. We first design a distributed detection
architecture called Detection via Distributed Blackholes (DDBH).
Our novel detection mechanism could be implemented via virtual
honeypots or honeynets. Simulation results show that a worm can be
detected with virtual honeypots on only 3% of the nodes. Moreover,
the worm is detected when less than 1.5% of the nodes are infected.
We then develop two control strategies: (1) optimal dynamic trafficblocking,
for which we determine the condition that guarantees
minimum number of removed nodes when the worm is contained and
(2) predictive dynamic traffic-blocking–a realistic deployment of
the optimal strategy on scale-free graphs. The predictive dynamic
traffic-blocking, coupled with the DDBH, ensures that more than
40% of the network is unaffected by the propagation at the time
when the worm is contained.