Abstract: Partitions can play a significant role in minimising cochannel
interference of Wireless LANs by attenuating signals across
room boundaries. This could pave the way towards higher density
deployments in home and office environments through spatial
channel reuse. Yet, due to protocol limitations, the latest incantation
of IEEE 802.11 standard is still unable to take advantage of this fact:
Despite having clearly adequate Signal to Interference Ratio (SIR)
over co-channel neighbouring networks in other rooms, its goodput
falls significantly lower than its maximum in the absence of cochannel
interferers. In this paper, we describe how this situation can
be remedied via modest modifications to the standard.
Abstract: In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.
Abstract: Cellular communication is being widely used by all
over the world. The users of handsets are increasing due to the
request from marketing sector. The important aspect that has to be
touch in this paper is about the security system of cellular
communication. It is important to provide users with a secure channel
for communication. A brief description of the new GSM cellular
network architecture will be provided. Limitations of cellular
networks, their security issues and the different types of attacks will
be discussed. The paper will go over some new security mechanisms
that have been proposed by researchers. Overall, this paper clarifies
the security system or services of cellular communication using
GSM. Three Malaysian Communication Companies were taken as
Case study in this paper.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: A person-to-person information sharing is easily realized
by P2P networks in which servers are not essential. Leakage
of information, which are caused by malicious accesses for P2P
networks, has become a new social issues. To prevent information
leakage, it is necessary to detect and block traffics of P2P software.
Since some P2P softwares can spoof port numbers, it is difficult to
detect the traffics sent from P2P softwares by using port numbers.
It is more difficult to devise effective countermeasures for detecting
the software because their protocol are not public.
In this paper, a discriminating method of network applications
based on communication characteristics of application messages
without port numbers is proposed. The proposed method is based
on an assumption that there can be some rules about time intervals
to transmit messages in application layer and the number of necessary
packets to send one message. By extracting the rule from network
traffic, the proposed method can discriminate applications without
port numbers.
Abstract: This paper proposes the use of Bayesian belief
networks (BBN) as a higher level of health risk assessment for a
dumping site of lead battery smelter factory. On the basis of the
epidemiological studies, the actual hospital attendance records and
expert experiences, the BBN is capable of capturing the probabilistic
relationships between the hazardous substances and their adverse
health effects, and accordingly inferring the morbidity of the adverse
health effects. The provision of the morbidity rates of the related
diseases is more informative and can alleviate the drawbacks of
conventional methods.
Abstract: The competitive learning is an adaptive process in
which the neurons in a neural network gradually become sensitive to
different input pattern clusters. The basic idea behind the Kohonen-s
Self-Organizing Feature Maps (SOFM) is competitive learning.
SOFM can generate mappings from high-dimensional signal spaces
to lower dimensional topological structures. The main features of this
kind of mappings are topology preserving, feature mappings and
probability distribution approximation of input patterns. To overcome
some limitations of SOFM, e.g., a fixed number of neural units and a
topology of fixed dimensionality, Growing Self-Organizing Neural
Network (GSONN) can be used. GSONN can change its topological
structure during learning. It grows by learning and shrinks by
forgetting. To speed up the training and convergence, a new variant
of GSONN, twin growing cell structures (TGCS) is presented here.
This paper first gives an introduction to competitive learning, SOFM
and its variants. Then, we discuss some GSONN with fixed
dimensionality, which include growing cell structures, its variants
and the author-s model: TGCS. It is ended with some testing results
comparison and conclusions.
Abstract: Bridges are one of the main components of
transportation networks. They should be functional before and after
earthquake for emergency services. Therefore we need to assess
seismic performance of bridges under different seismic loadings.
Fragility curve is one of the popular tools in seismic evaluations. The
fragility curves are conditional probability statements, which give the
probability of a bridge reaching or exceeding a particular damage
level for a given intensity level. In this study, the seismic
performance of a two-span simply supported concrete bridge is
assessed. Due to usual lack of empirical data, the analytical fragility
curve was developed by results of the dynamic analysis of bridge
subjected to the different time histories in near-fault area.
Abstract: This paper presents the design and implements the prototype of an intelligent data processing framework in ubiquitous sensor networks. Much focus is put on how to handle the sensor data stream as well as the interoperability between the low-level sensor data and application clients. Our framework first addresses systematic middleware which mitigates the interaction between the application layer and low-level sensors, for the sake of analyzing a great volume of sensor data by filtering and integrating to create value-added context information. Then, an agent-based architecture is proposed for real-time data distribution to efficiently forward a specific event to the appropriate application registered in the directory service via the open interface. The prototype implementation demonstrates that our framework can host a sophisticated application on the ubiquitous sensor network and it can autonomously evolve to new middleware, taking advantages of promising technologies such as software agents, XML, cloud computing, and the like.
Abstract: In this paper, a reliable cooperative multipath routing
algorithm is proposed for data forwarding in wireless sensor networks
(WSNs). In this algorithm, data packets are forwarded towards the
base station (BS) through a number of paths, using a set of relay
nodes. In addition, the Rayleigh fading model is used to calculate
the evaluation metric of links. Here, the quality of reliability is
guaranteed by selecting optimal relay set with which the probability
of correct packet reception at the BS will exceed a predefined
threshold. Therefore, the proposed scheme ensures reliable packet
transmission to the BS. Furthermore, in the proposed algorithm,
energy efficiency is achieved by energy balancing (i.e. minimizing
the energy consumption of the bottleneck node of the routing path)
at the same time. This work also demonstrates that the proposed
algorithm outperforms existing algorithms in extending longevity of
the network, with respect to the quality of reliability. Given this, the
obtained results make possible reliable path selection with minimum
energy consumption in real time.
Abstract: This paper aims to develop a NOx emission model of
an acid gas incinerator using Nelder-Mead least squares support
vector regression (LS-SVR). Malaysia DOE is actively imposing the
Clean Air Regulation to mandate the installation of analytical
instrumentation known as Continuous Emission Monitoring System
(CEMS) to report emission level online to DOE . As a hardware
based analyzer, CEMS is expensive, maintenance intensive and often
unreliable. Therefore, software predictive technique is often
preferred and considered as a feasible alternative to replace the
CEMS for regulatory compliance. The LS-SVR model is built based
on the emissions from an acid gas incinerator that operates in a LNG
Complex. Simulated Annealing (SA) is first used to determine the
initial hyperparameters which are then further optimized based on the
performance of the model using Nelder-Mead simplex algorithm.
The LS-SVR model is shown to outperform a benchmark model
based on backpropagation neural networks (BPNN) in both training
and testing data.
Abstract: Querying a data source and routing data towards sink
becomes a serious challenge in static wireless sensor networks if sink
and/or data source are mobile. Many a times the event to be observed
either moves or spreads across wide area making maintenance of
continuous path between source and sink a challenge. Also, sink can
move while query is being issued or data is on its way towards sink.
In this paper, we extend our already proposed Grid Based Data
Dissemination (GBDD) scheme which is a virtual grid based
topology management scheme restricting impact of movement of
sink(s) and event(s) to some specific cells of a grid. This obviates the
need for frequent path modifications and hence maintains continuous
flow of data while minimizing the network energy consumptions.
Simulation experiments show significant improvements in network
energy savings and average packet delay for a packet to reach at sink.
Abstract: We board the problem of creating a seismic alert
system, based upon artificial neural networks, trained by using the
well-known back-propagation and genetic algorithms, in order to emit
the alarm for the population located into a specific city, about an
eminent earthquake greater than 4.5 Richter degrees, and avoiding
disasters and human loses. In lieu of using the propagation wave, we
employed the magnitude of the earthquake, to establish a correlation
between the recorded magnitudes from a controlled area and the city,
where we want to emit the alarm. To measure the accuracy of the
posed method, we use a database provided by CIRES, which contains
the records of 2500 quakes incoming from the State of Guerrero
and Mexico City. Particularly, we performed the proposed method to
generate an issue warning in Mexico City, employing the magnitudes
recorded in the State of Guerrero.
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: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.
Abstract: Human activity is a major concern in a wide variety of
applications, such as video surveillance, human computer interface
and face image database management. Detecting and recognizing
faces is a crucial step in these applications. Furthermore, major
advancements and initiatives in security applications in the past years
have propelled face recognition technology into the spotlight. The
performance of existing face recognition systems declines significantly
if the resolution of the face image falls below a certain level.
This is especially critical in surveillance imagery where often, due to
many reasons, only low-resolution video of faces is available. If these
low-resolution images are passed to a face recognition system, the
performance is usually unacceptable. Hence, resolution plays a key
role in face recognition systems. In this paper we introduce a new
low resolution face recognition system based on mixture of expert
neural networks. In order to produce the low resolution input images
we down-sampled the 48 × 48 ORL images to 12 × 12 ones using
the nearest neighbor interpolation method and after that applying
the bicubic interpolation method yields enhanced images which is
given to the Principal Component Analysis feature extractor system.
Comparison with some of the most related methods indicates that
the proposed novel model yields excellent recognition rate in low
resolution face recognition that is the recognition rate of 100% for
the training set and 96.5% for the test set.
Abstract: In this paper, the discrete-time fuzzy BAM neural network with delays and impulses is studied. Sufficient conditions are obtained for the existence and global stability of a unique equilibrium of this class of fuzzy BAM neural networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. Some numerical examples are given to demonstrate the effectiveness of the obtained results.
Abstract: Long Term Evolution (LTE) is a 4G wireless
broadband technology developed by the Third Generation
Partnership Project (3GPP) release 8, and it's represent the
competitiveness of Universal Mobile Telecommunications System
(UMTS) for the next 10 years and beyond. The concepts for LTE
systems have been introduced in 3GPP release 8, with objective of
high-data-rate, low-latency and packet-optimized radio access
technology. In this paper, performance of different TCP variants
during LTE network investigated. The performance of TCP over
LTE is affected mostly by the links of the wired network and total
bandwidth available at the serving base station. This paper describes
an NS-2 based simulation analysis of TCP-Vegas, TCP-Tahoe, TCPReno,
TCP-Newreno, TCP-SACK, and TCP-FACK, with full
modeling of all traffics of LTE system. The Evaluation of the
network performance with all TCP variants is mainly based on
throughput, average delay and lost packet. The analysis of TCP
performance over LTE ensures that all TCP's have a similar
throughput and the best performance return to TCP-Vegas than other
variants.
Abstract: The prediction of financial time series is a very
complicated process. If the efficient market hypothesis holds, then the predictability of most financial time series would be a rather
controversial issue, due to the fact that the current price contains already all available information in the market. This paper extends
the Adaptive Neuro Fuzzy Inference System for High Frequency
Trading which is an expert system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in financial forecasting and trading in high
frequency. However, in order to eliminate unnecessary input in the
training phase a new event based volatility model was proposed.
Taking volatility and the scaling laws of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. This new model allows the observation of specific events and seasonalities in data and subsequently removes any unnecessary data. This new event based
volatility model provides the ANFIS system with more accurate input
and has increased the overall performance of the system.
Abstract: In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.