Abstract: In this work, a characterization and modeling of
packet loss of a Voice over Internet Protocol (VoIP) communication
is developed. The distributions of the number of consecutive received
and lost packets (namely gap and burst) are modeled from the
transition probabilities of two-state and four-state model.
Measurements show that both models describe adequately the burst
distribution, but the decay of gap distribution for non-homogeneous
losses is better fit by the four-state model. The respective
probabilities of transition between states for each model were
estimated with a proposed algorithm from a set of monitored VoIP
calls in order to obtain representative minimum, maximum and
average values for both models.
Abstract: In this paper, we consider the analysis of the
acquisition process for a hybrid double-dwell system with antenna
diversity for DS-CDMA (direct sequence-code division multiple
access) using an adaptive threshold. Acquisition systems with a fixed
threshold value are unable to adapt to fast varying mobile
communications environments and may result in a high false alarm
rate, and/or low detection probability. Therefore, we propose an
adaptively varying threshold scheme through the use of a cellaveraging
constant false alarm rate (CA-CFAR) algorithm, which is
well known in the field of radar detection. We derive exact
expressions for the probabilities of detection and false alarm in
Rayleigh fading channels. The mean acquisition time of the system
under consideration is also derived. The performance of the system is
analyzed and compared to that of a hybrid single dwell system.
Abstract: the paper presents the optimization results for several
electrical machines dedicated for powered electric wheel-chairs. The
optimization, using the Hook-Jeeves algorithm, was employed based
on a design approach which takes into consideration the road
conditions. Also, through numerical simulations (based on finite
element method), the analytical approach was validated. The
optimization approach gave satisfactory results and the best suited
variant was chosen for the motorization of the wheel-chair.
Abstract: In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called “modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.
Abstract: As the mobile Internet has become widespread in
recent years, communication based on mobile networks is increasing.
As a result, security threats have been posed with regard to the
abnormal traffic of mobile networks, but mobile security has been
handled with focus on threats posed by mobile malicious codes, and
researches on security threats to the mobile network itself have not
attracted much attention. In mobile networks, the IP address of the data
packet is a very important factor for billing purposes. If one mobile
terminal use an incorrect IP address that either does not exist or could
be assigned to another mobile terminal, billing policy will cause
problems. We monitor and analyze 3G mobile data networks traffics
for a period of time and finds some abnormal IP packets. In this paper,
we analyze the reason for abnormal IP packets on 3G Mobile Data
Networks. And we also propose an algorithm based on IP address table
that contains addresses currently in use within the mobile data network
to detect abnormal IP packets.
Abstract: The “PYRAMIDS" Block Cipher is a symmetric encryption algorithm of a 64, 128, 256-bit length, that accepts a variable key length of 128, 192, 256 bits. The algorithm is an iterated cipher consisting of repeated applications of a simple round transformation with different operations and different sequence in each round. The algorithm was previously software implemented in Cµ code. In this paper, a hardware implementation of the algorithm, using Field Programmable Gate Arrays (FPGA), is presented. In this work, we discuss the algorithm, the implemented micro-architecture, and the simulation and implementation results. Moreover, we present a detailed comparison with other implemented standard algorithms. In addition, we include the floor plan as well as the circuit diagrams of the various micro-architecture modules.
Abstract: This paper proposes a method which reduces power consumption in single-error correcting, double error-detecting checker circuits that perform memory error correction code. Power is minimized with little or no impact on area and delay, using the degrees of freedom in selecting the parity check matrix of the error correcting codes. The genetic algorithm is employed to solve the non linear power optimization problem. The method is applied to two commonly used SEC-DED codes: standard Hamming and odd column weight Hsiao codes. Experiments were performed to show the performance of the proposed method.
Abstract: A hybrid learning automata-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP-Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature.
Abstract: The main criteria of designing in the most hydraulic
constructions essentially are based on runoff or discharge of water. Two of those important criteria are runoff and return period. Mostly,
these measures are calculated or estimated by stochastic data.
Another feature in hydrological data is their impreciseness.
Therefore, in order to deal with uncertainty and impreciseness, based
on Buckley-s estimation method, a new fuzzy method of evaluating hydrological measures are developed. The method introduces
triangular shape fuzzy numbers for different measures in which both
of the uncertainty and impreciseness concepts are considered. Besides, since another important consideration in most of the
hydrological studies is comparison of a measure during different
months or years, a new fuzzy method which is consistent with special form of proposed fuzzy numbers, is also developed. Finally, to
illustrate the methods more explicitly, the two algorithms are tested on one simple example and a real case study.
Abstract: One very interesting field of research in Pattern Recognition that has gained much attention in recent times is Gesture Recognition. In this paper, we consider a form of dynamic hand gestures that are characterized by total movement of the hand (arm) in space. For these types of gestures, the shape of the hand (palm) during gesturing does not bear any significance. In our work, we propose a model-based method for tracking hand motion in space, thereby estimating the hand motion trajectory. We employ the dynamic time warping (DTW) algorithm for time alignment and normalization of spatio-temporal variations that exist among samples belonging to the same gesture class. During training, one template trajectory and one prototype feature vector are generated for every gesture class. Features used in our work include some static and dynamic motion trajectory features. Recognition is accomplished in two stages. In the first stage, all unlikely gesture classes are eliminated by comparing the input gesture trajectory to all the template trajectories. In the next stage, feature vector extracted from the input gesture is compared to all the class prototype feature vectors using a distance classifier. Experimental results demonstrate that our proposed trajectory estimator and classifier is suitable for Human Computer Interaction (HCI) platform.
Abstract: In the self-stabilizing algorithmic paradigm, each node has a local view of the system, in a finite amount of time the system converges to a global state with desired property. In a graph G =
(V, E), a subset S C V is a 2-packing if Vi c V: IN[i] n SI
Abstract: The approach based on the wavelet transform has
been widely used for image denoising due to its multi-resolution
nature, its ability to produce high levels of noise reduction and the
low level of distortion introduced. However, by removing noise, high
frequency components belonging to edges are also removed, which
leads to blurring the signal features. This paper proposes a new
method of image noise reduction based on local variance and edge
analysis. The analysis is performed by dividing an image into 32 x 32
pixel blocks, and transforming the data into wavelet domain. Fast
lifting wavelet spatial-frequency decomposition and reconstruction is
developed with the advantages of being computationally efficient and
boundary effects minimized. The adaptive thresholding by local
variance estimation and edge strength measurement can effectively
reduce image noise while preserve the features of the original image
corresponding to the boundaries of the objects. Experimental results
demonstrate that the method performs well for images contaminated
by natural and artificial noise, and is suitable to be adapted for
different class of images and type of noises. The proposed algorithm
provides a potential solution with parallel computation for real time
or embedded system application.
Abstract: Classification is an interesting problem in functional
data analysis (FDA), because many science and application problems
end up with classification problems, such as recognition, prediction,
control, decision making, management, etc. As the high dimension
and high correlation in functional data (FD), it is a key problem to
extract features from FD whereas keeping its global characters, which
relates to the classification efficiency and precision to heavens. In this
paper, a novel automatic method which combined Genetic Algorithm
(GA) and classification algorithm to extract classification features is
proposed. In this method, the optimal features and classification model
are approached via evolutional study step by step. It is proved by
theory analysis and experiment test that this method has advantages in
improving classification efficiency, precision and robustness whereas
using less features and the dimension of extracted classification
features can be controlled.
Abstract: This paper proposes a Wavelength Division
Multiplexing (WDM) technology based Storage Area Network
(SAN) for all type of Disaster recovery operation. It considers
recovery when all paths failure in the network as well as the main
SAN site failure also the all backup sites failure by the effect of
natural disasters such as earthquakes, fires and floods, power outage,
and terrorist attacks, as initially SAN were designed to work within
distance limited environments[2]. Paper also presents a NEW PATH
algorithm when path failure occurs. The simulation result and
analysis is presented for the proposed architecture with performance
consideration.
Abstract: In this paper a new Joint Adaptive Block Matching
Search (JABMS) algorithm is proposed to generate motion vector
and search a best match macro block by classifying the motion vector
movement based on prediction error. Diamond Search (DS)
algorithm generates high estimation accuracy when motion vector is
small and Adaptive Rood Pattern Search (ARPS) algorithm can
handle large motion vector but is not very accurate. The proposed
JABMS algorithm which is capable of considering both small and
large motions gives improved estimation accuracy and the
computational cost is reduced by 15.2 times compared with
Exhaustive Search (ES) algorithm and is 1.3 times less compared
with Diamond search algorithm.
Abstract: One of the main limitations for the resolution of
optical instruments is the size of the sensor-s pixels. In this paper we
introduce a new sub pixel resolution algorithm to enhance the
resolution of images. This method is based on the analysis of multiimages
which are fast recorded during the fine relative motion of
image and pixel arrays of CCDs. It is shown that by applying this
method for a sample noise free image one will enhance the resolution
with 10-14 order of error.
Abstract: In this paper, a near lossless image coding scheme
based on Orthogonal Polynomials Transform (OPT) has been
presented. The polynomial operators and polynomials basis operators
are obtained from set of orthogonal polynomials functions for the
proposed transform coding. The image is partitioned into a number of
distinct square blocks and the proposed transform coding is applied to
each of these individually. After applying the proposed transform
coding, the transformed coefficients are rearranged into a sub-band
structure. The Embedded Zerotree (EZ) coding algorithm is then
employed to quantize the coefficients. The proposed transform is
implemented for various block sizes and the performance is
compared with existing Discrete Cosine Transform (DCT) transform
coding scheme.
Abstract: This paper presents the development of an active
vibration control using direct adaptive controller to suppress the
vibration of a flexible beam system. The controller is realized based
on linear parametric form. Differential evolution optimisation
algorithm is used to optimize the controller using single objective
function by minimizing the mean square error of the observed
vibration signal. Furthermore, an alternative approach is developed to
systematically search for the best controller model structure together
with it parameter values. The performance of the control scheme is
presented and analysed in both time and frequency domain.
Simulation results demonstrate that the proposed scheme is able to
suppress the unwanted vibration effectively.
Abstract: In order to consider the effects of the higher modes in
the pushover analysis, during the recent years several multi-modal
pushover procedures have been presented. In these methods the
response of the considered modes are combined by the square-rootof-
sum-of-squares (SRSS) rule while application of the elastic modal
combination rules in the inelastic phases is no longer valid. In this
research the feasibility of defining an efficient alternative
combination method is investigated. Two steel moment-frame
buildings denoted SAC-9 and SAC-20 under ten earthquake records
are considered. The nonlinear responses of the structures are
estimated by the directed algebraic combination of the weighted
responses of the separate modes. The weight of the each mode is
defined so that the resulted response of the combination has a
minimum error to the nonlinear time history analysis. The genetic
algorithm (GA) is used to minimize the error and optimize the weight
factors. The obtained optimal factors for each mode in different cases
are compared together to find unique appropriate weight factors for
each mode in all cases.
Abstract: The purpose of this paper is to present a Dynamic
Time Warping technique which reduces significantly the data
processing time and memory size of multi-dimensional time series
sampled by the biometric smart pen device BiSP. The acquisition
device is a novel ballpoint pen equipped with a diversity of sensors
for monitoring the kinematics and dynamics of handwriting
movement. The DTW algorithm has been applied for time series
analysis of five different sensor channels providing pressure,
acceleration and tilt data of the pen generated during handwriting on
a paper pad. But the standard DTW has processing time and memory
space problems which limit its practical use for online handwriting
recognition. To face with this problem the DTW has been applied to
the sum of the five sensor signals after an adequate down-sampling
of the data. Preliminary results have shown that processing time and
memory size could significantly be reduced without deterioration of
performance in single character and word recognition. Further
excellent accuracy in recognition was achieved which is mainly due
to the reduced dynamic time warping RDTW technique and a novel
pen device BiSP.