Abstract: In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
Abstract: A code has been developed in Mathematica using
Direct Simulation Monte Carlo (DSMC) technique. The code was
tested for 2-D air flow around a circular cylinder. Same geometry
and flow properties were used in FLUENT 6.2 for comparison. The
results obtained from Mathematica simulation indicated significant
agreement with FLUENT calculations, hence providing insight into
particle nature of fluid flows.
Abstract: Let T and S be a subspace of Cn and Cm, respectively.
Then for A ∈ Cm×n satisfied AT ⊕ S = Cm, the generalized
inverse A(2)
T,S is given by A(2)
T,S = (PS⊥APT )†. In this paper, a
finite formulae is presented to compute generalized inverse A(2)
T,S
under the concept of restricted inner product, which defined as <
A,B >T,S=< PS⊥APT,B > for the A,B ∈ Cm×n. By this
iterative method, when taken the initial matrix X0 = PTA∗PS⊥, the
generalized inverse A(2)
T,S can be obtained within at most mn iteration
steps in absence of roundoff errors. Finally given numerical example
is shown that the iterative formulae is quite efficient.
Abstract: A new approach to determine the machine layout in flexible manufacturing cell, and to find the feasible robot configuration of the robot to achieve minimum cycle time is presented in this paper. The location of the input/output location and the optimal robot configuration is obtained for all sequences of work tasks of the robot within a specified period of time. A more realistic approach has been presented to model the problem using the robot joint space. The problem is formulated as a nonlinear optimization problem and solved using Sequential Quadratic Programming algorithm.
Abstract: In order to accelerate the similarity search in highdimensional database, we propose a new hierarchical indexing method. It is composed of offline and online phases. Our contribution concerns both phases. In the offline phase, after gathering the whole of the data in clusters and constructing a hierarchical index, the main originality of our contribution consists to develop a method to construct bounding forms of clusters to avoid overlapping. For the online phase, our idea improves considerably performances of similarity search. However, for this second phase, we have also developed an adapted search algorithm. Our method baptized NOHIS (Non-Overlapping Hierarchical Index Structure) use the Principal Direction Divisive Partitioning (PDDP) as algorithm of clustering. The principle of the PDDP is to divide data recursively into two sub-clusters; division is done by using the hyper-plane orthogonal to the principal direction derived from the covariance matrix and passing through the centroid of the cluster to divide. Data of each two sub-clusters obtained are including by a minimum bounding rectangle (MBR). The two MBRs are directed according to the principal direction. Consequently, the nonoverlapping between the two forms is assured. Experiments use databases containing image descriptors. Results show that the proposed method outperforms sequential scan and SRtree in processing k-nearest neighbors.
Abstract: In this paper, Land Marks for Unique Addressing( LMUA) algorithm is develped to generate unique ID for each and every node which leads to the formation of overlapping/Non overlapping clusters based on unique ID. To overcome the draw back of the developed LMUA algorithm, the concept of clustering is introduced. Based on the clustering concept a Land Marks for Unique Addressing and Clustering(LMUAC) Algorithm is developed to construct strictly non-overlapping clusters and classify those nodes in to Cluster Heads, Member Nodes, Gate way nodes and generating the Hierarchical code for the cluster heads to operate in the level one hierarchy for wireless communication switching. The expansion of the existing network can be performed or not without modifying the cost of adding the clusterhead is shown. The developed algorithm shows one way of efficiently constructing the
Abstract: The identification and classification of weeds are of
major technical and economical importance in the agricultural
industry. To automate these activities, like in shape, color and
texture, weed control system is feasible. The goal of this paper is to
build a real-time, machine vision weed control system that can detect
weed locations. In order to accomplish this objective, a real-time
robotic system is developed to identify and locate outdoor plants
using machine vision technology and pattern recognition. The
algorithm is developed to classify images into broad and narrow class
for real-time selective herbicide application. The developed
algorithm has been tested on weeds at various locations, which have
shown that the algorithm to be very effectiveness in weed
identification. Further the results show a very reliable performance
on weeds under varying field conditions. The analysis of the results
shows over 90 percent classification accuracy over 140 sample
images (broad and narrow) with 70 samples from each category of
weeds.
Abstract: In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.
Abstract: The incorporation of computational fluid dynamics in the design of modern hydraulic turbines appears to be necessary in order to improve their efficiency and cost-effectiveness beyond the traditional design practices. A numerical optimization methodology is developed and applied in the present work to a Turgo water turbine. The fluid is simulated by a Lagrangian mesh-free approach that can provide detailed information on the energy transfer and enhance the understanding of the complex, unsteady flow field, at very small computing cost. The runner blades are initially shaped according to hydrodynamics theory, and parameterized using Bezier polynomials and interpolation techniques. The use of a limited number of free design variables allows for various modifications of the standard blade shape, while stochastic optimization using evolutionary algorithms is implemented to find the best blade that maximizes the attainable hydraulic efficiency of the runner. The obtained optimal runner design achieves considerably higher efficiency than the standard one, and its numerically predicted performance is comparable to a real Turgo turbine, verifying the reliability and the prospects of the new methodology.
Abstract: In this paper, we propose the robust water level detection method based on the accumulated histogram under small changed image which is acquired from water level surveillance camera. In general surveillance system, this is detecting and recognizing invasion from searching area which is in big change on the sequential images. However, in case of a water level detection system, these general surveillance techniques are not suitable due to small change on the water surface. Therefore the algorithm introduces the accumulated histogram which is emphasizing change of water surface in sequential images. Accumulated histogram is based on the current image frame. The histogram is cumulating differences between previous images and current image. But, these differences are also appeared in the land region. The band pass filter is able to remove noises in the accumulated histogram Finally, this algorithm clearly separates water and land regions. After these works, the algorithm converts from the water level value on the image space to the real water level on the real space using calibration table. The detected water level is sent to the host computer with current image. To evaluate the proposed algorithm, we use test images from various situations.
Abstract: In this paper we propose segmentation approach based
on Vector Quantization technique. Here we have used Kekre-s fast
codebook generation algorithm for segmenting low-altitude aerial
image. This is used as a preprocessing step to form segmented
homogeneous regions. Further to merge adjacent regions color
similarity and volume difference criteria is used. Experiments
performed with real aerial images of varied nature demonstrate that
this approach does not result in over segmentation or under
segmentation. The vector quantization seems to give far better results
as compared to conventional on-the-fly watershed algorithm.
Abstract: In this paper we present simulation results for the
application of a bandwidth efficient algorithm (mapping algorithm)
to an image transmission system. This system considers three
different real valued transforms to generate energy compact
coefficients. First results are presented for gray scale and color image
transmission in the absence of noise. It is seen that the system
performs its best when discrete cosine transform is used. Also the
performance of the system is dominated more by the size of the
transform block rather than the number of coefficients transmitted or
the number of bits used to represent each coefficient. Similar results
are obtained in the presence of additive white Gaussian noise. The
varying values of the bit error rate have very little or no impact on
the performance of the algorithm. Optimum results are obtained for
the system considering 8x8 transform block and by transmitting 15
coefficients from each block using 8 bits.
Abstract: This paper is aimed at describing a delay-based endto-
end (e2e) congestion control algorithm, called Very FAST TCP
(VFAST), which is an enhanced version of FAST TCP. The main
idea behind this enhancement is to smoothly estimate the Round-Trip
Time (RTT) based on a nonlinear filter, which eliminates throughput
and queue oscillation when RTT fluctuates. In this context, an evaluation
of the suggested scheme through simulation is introduced, by
comparing our VFAST prototype with FAST in terms of throughput,
queue behavior, fairness, stability, RTT and adaptivity to changes in
network. The achieved simulation results indicate that the suggested
protocol offer better performance than FAST TCP in terms of RTT
estimation and throughput.
Abstract: Developing techniques for mobile robot navigation constitutes one of the major trends in the current
research on mobile robotics. This paper develops a local
model network (LMN) for mobile robot navigation. The
LMN represents the mobile robot by a set of locally valid
submodels that are Multi-Layer Perceptrons (MLPs).
Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular
region. The submodels then are combined in a unified
structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This
proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the
proposed LMN reflect the soundness of the proposed
scheme.
Abstract: Censored Production Rule is an extension of standard
production rule, which is concerned with problems of reasoning with
incomplete information, subject to resource constraints and problem
of reasoning efficiently with exceptions. A CPR has a form: IF A
(Condition) THEN B (Action) UNLESS C (Censor), Where C is the
exception condition. Fuzzy CPR are obtained by augmenting
ordinary fuzzy production rule “If X is A then Y is B with an
exception condition and are written in the form “If X is A then Y is B
Unless Z is C. Such rules are employed in situation in which the
fuzzy conditional statement “If X is A then Y is B" holds frequently
and the exception condition “Z is C" holds rarely. Thus “If X is A
then Y is B" part of the fuzzy CPR express important information
while the unless part acts only as a switch that changes the polarity of
“Y is B" to “Y is not B" when the assertion “Z is C" holds. The
proposed approach is an attempt to discover fuzzy censored
production rules from set of discovered fuzzy if then rules in the
form:
A(X)  B(Y) || C(Z).
Abstract: Biclustering aims at identifying several biclusters that
reveal potential local patterns from a microarray matrix. A bicluster is
a sub-matrix of the microarray consisting of only a subset of genes
co-regulates in a subset of conditions. In this study, we extend the
motif of subspace clustering to present a K-biclusters clustering (KBC)
algorithm for the microarray biclustering issue. Besides minimizing
the dissimilarities between genes and bicluster centers within all
biclusters, the objective function of the KBC algorithm additionally
takes into account how to minimize the residues within all biclusters
based on the mean square residue model. In addition, the objective
function also maximizes the entropy of conditions to stimulate more
conditions to contribute the identification of biclusters. The KBC
algorithm adopts the K-means type clustering process to efficiently
make the partition of K biclusters be optimized. A set of experiments
on a practical microarray dataset are demonstrated to show the
performance of the proposed KBC algorithm.
Abstract: In this paper, we propose a dual version of the first
threshold ring signature scheme based on error-correcting code proposed
by Aguilar et. al in [1]. Our scheme uses an improvement of
Véron zero-knowledge identification scheme, which provide smaller
public and private key sizes and better computation complexity than
the Stern one. This scheme is secure in the random oracle model.
Abstract: In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot-s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervised learning. A modified A* algorithm is used to find the best path using this internal representation between starting and goal points. This method can be used with good performance for both known and unknown environments.
Abstract: Interaction Model plays an important role in Modelbased
Intelligent Interface Agent Architecture for developing
Intelligent User Interface. In this paper we are presenting some
improvements in the algorithms for development interaction model of
interface agent including: the action segmentation algorithm, the
action pair selection algorithm, the final action pair selection
algorithm, the interaction graph construction algorithm and the
probability calculation algorithm. The analysis of the algorithms also
presented. At the end of this paper, we introduce an experimental
program called “Personal Transfer System".
Abstract: We propose an enhanced collaborative filtering
method using Hofstede-s cultural dimensions, calculated for 111
countries. We employ 4 of these dimensions, which are correlated to
the costumers- buying behavior, in order to detect users- preferences
for items. In addition, several advantages of this method
demonstrated for data sparseness and cold-start users, which are
important challenges in collaborative filtering. We present
experiments using a real dataset, Book Crossing Dataset.
Experimental results shows that the proposed algorithm provide
significant advantages in terms of improving recommendation
quality.