Abstract: Sequences of execution of algorithms in an interactive
manner using multimedia tools are employed in this paper. It helps to
realize the concept of fundamentals of algorithms such as searching
and sorting method in a simple manner. Visualization gains more
attention than theoretical study and it is an easy way of learning
process. We propose methods for finding runtime sequence of each
algorithm in an interactive way and aims to overcome the drawbacks
of the existing character systems. System illustrates each and every
step clearly using text and animation. Comparisons of its time
complexity have been carried out and results show that our approach
provides better perceptive of algorithms.
Abstract: This paper proposes a new methodology for the
optimal allocation and sizing of Embedded Generation (EG)
employing Real Coded Genetic Algorithm (RCGA) to minimize the
total power losses and to improve voltage profiles in the radial
distribution networks. RCGA is a method that uses continuous
floating numbers as representation which is different from
conventional binary numbers. The RCGA is used as solution tool,
which can determine the optimal location and size of EG in radial
system simultaneously. This method is developed in MATLAB. The
effect of EG units- installation and their sizing to the distribution
networks are demonstrated using 24 bus system.
Abstract: This paper presents design trade-off and performance impacts of
the amount of pipeline phase of control path signals in a wormhole-switched
network-on-chip (NoC). The numbers of the pipeline phase of the control
path vary between two- and one-cycle pipeline phase. The control paths
consist of the routing request paths for output selection and the arbitration
paths for input selection. Data communications between on-chip routers are
implemented synchronously and for quality of service, the inter-router data
transports are controlled by using a link-level congestion control to avoid
lose of data because of an overflow. The trade-off between the area (logic
cell area) and the performance (bandwidth gain) of two proposed NoC router
microarchitectures are presented in this paper. The performance evaluation is
made by using a traffic scenario with different number of workloads under
2D mesh NoC topology using a static routing algorithm. By using a 130-nm
CMOS standard-cell technology, our NoC routers can be clocked at 1 GHz,
resulting in a high speed network link and high router bandwidth capacity
of about 320 Gbit/s. Based on our experiments, the amount of control path
pipeline stages gives more significant impact on the NoC performance than
the impact on the logic area of the NoC router.
Abstract: Traffic congestion has become a major problem in
many countries. One of the main causes of traffic congestion is due
to road merges. Vehicles tend to move slower when they reach the
merging point. In this paper, an enhanced algorithm for traffic
simulation based on the fluid-dynamic algorithm and kinematic wave
theory is proposed. The enhanced algorithm is used to study traffic
congestion at a road merge. This paper also describes the
development of a dynamic traffic simulation tool which is used as a
scenario planning and to forecast traffic congestion level in a certain
time based on defined parameter values. The tool incorporates the
enhanced algorithm as well as the two original algorithms. Output
from the three above mentioned algorithms are measured in terms of
traffic queue length, travel time and the total number of vehicles
passing through the merging point. This paper also suggests an
efficient way of reducing traffic congestion at a road merge by
analyzing the traffic queue length and travel time.
Abstract: In this work, we propose a hybrid heuristic in order to
solve the Team Orienteering Problem (TOP). Given a set of points (or
customers), each with associated score (profit or benefit), and a team
that has a fixed number of members, the problem to solve is to visit a
subset of points in order to maximize the total collected score. Each
member performs a tour starting at the start point, visiting distinct
customers and the tour terminates at the arrival point. In addition,
each point is visited at most once, and the total time in each tour
cannot be greater than a given value. The proposed heuristic combines
beam search and a local optimization strategy. The algorithm was
tested on several sets of instances and encouraging results were
obtained.
Abstract: This paper presents an adaptive technique for generation
of data required for construction of artificial neural network-based
performance model of nano-scale CMOS inverter circuit. The training
data are generated from the samples through SPICE simulation. The
proposed algorithm has been compared to standard progressive sampling
algorithms like arithmetic sampling and geometric sampling.
The advantages of the present approach over the others have been
demonstrated. The ANN predicted results have been compared with
actual SPICE results. A very good accuracy has been obtained.
Abstract: Graph has become increasingly important in modeling
complicated structures and schemaless data such as proteins, chemical
compounds, and XML documents. Given a graph query, it is desirable
to retrieve graphs quickly from a large database via graph-based
indices. Different from the existing methods, our approach, called
VFM (Vertex to Frequent Feature Mapping), makes use of vertices
and decision features as the basic indexing feature. VFM constructs
two mappings between vertices and frequent features to answer graph
queries. The VFM approach not only provides an elegant solution to
the graph indexing problem, but also demonstrates how database
indexing and query processing can benefit from data mining,
especially frequent pattern mining. The results show that the proposed
method not only avoids the enumeration method of getting subgraphs
of query graph, but also effectively reduces the subgraph isomorphism
tests between the query graph and graphs in candidate answer set in
verification stage.
Abstract: Gaussian mixture background model is widely used in
moving target detection of the image sequences. However, traditional
Gaussian mixture background model usually considers the time
continuity of the pixels, and establishes background through statistical
distribution of pixels without taking into account the pixels- spatial
similarity, which will cause noise, imperfection and other problems.
This paper proposes a new Gaussian mixture modeling approach,
which combines the color and gradient of the spatial information, and
integrates the spatial information of the pixel sequences to establish
Gaussian mixture background. The experimental results show that the
movement background can be extracted accurately and efficiently, and
the algorithm is more robust, and can work in real time in tracking
applications.
Abstract: Many Wireless Sensor Network (WSN) applications necessitate secure multicast services for the purpose of broadcasting delay sensitive data like video files and live telecast at fixed time-slot. This work provides a novel method to deal with end-to-end delay and drop rate of packets. Opportunistic Routing chooses a link based on the maximum probability of packet delivery ratio. Null Key Generation helps in authenticating packets to the receiver. Markov Decision Process based Adaptive Scheduling algorithm determines the time slot for packet transmission. Both theoretical analysis and simulation results show that the proposed protocol ensures better performance in terms of packet delivery ratio, average end-to-end delay and normalized routing overhead.
Abstract: This paper reports on a receding horizon filtering for
mobile robot systems with cross-correlated sensor noises and
uncertainties. Also, the effect of uncertain parameters in the state of
the tracking error model performance is considered. A distributed
fusion receding horizon filter is proposed. The distributed fusion
filtering algorithm represents the optimal linear combination of the
local filters under the minimum mean square error criterion. The
derivation of the error cross-covariances between the local receding
horizon filters is the key of this paper. Simulation results of the
tracking mobile robot-s motion demonstrate high accuracy and
computational efficiency of the distributed fusion receding horizon
filter.
Abstract: This paper presents a systematic approach for the
design of power system stabilizer using genetic algorithm and
investigates the robustness of the GA based PSS. The proposed
approach employs GA search for optimal setting of PSS parameters.
The performance of the proposed GPSS under small and large
disturbances, loading conditions and system parameters is tested.
The eigenvalue analysis and nonlinear simulation results show the
effectiveness of the GPSS to damp out the system oscillations. It is
found tat the dynamic performance with the GPSS shows improved
results, over conventionally tuned PSS over a wide range of
operating conditions.
Abstract: This paper introduces a novel approach to estimate the
clique potentials of Gibbs Markov random field (GMRF) models
using the Support Vector Machines (SVM) algorithm and the Mean
Field (MF) theory. The proposed approach is based on modeling the
potential function associated with each clique shape of the GMRF
model as a Gaussian-shaped kernel. In turn, the energy function of
the GMRF will be in the form of a weighted sum of Gaussian
kernels. This formulation of the GMRF model urges the use of the
SVM with the Mean Field theory applied for its learning for
estimating the energy function. The approach has been tested on
synthetic texture images and is shown to provide satisfactory results
in retrieving the synthesizing parameters.
Abstract: Image coding based on clustering provides immediate
access to targeted features of interest in a high quality decoded
image. This approach is useful for intelligent devices, as well as for
multimedia content-based description standards. The result of image
clustering cannot be precise in some positions especially on pixels
with edge information which produce ambiguity among the clusters.
Even with a good enhancement operator based on PDE, the quality of
the decoded image will highly depend on the clustering process. In
this paper, we introduce an ambiguity cluster in image coding to
represent pixels with vagueness properties. The presence of such
cluster allows preserving some details inherent to edges as well for
uncertain pixels. It will also be very useful during the decoding phase
in which an anisotropic diffusion operator, such as Perona-Malik,
enhances the quality of the restored image. This work also offers a
comparative study to demonstrate the effectiveness of a fuzzy
clustering technique in detecting the ambiguity cluster without losing
lot of the essential image information. Several experiments have been
carried out to demonstrate the usefulness of ambiguity concept in
image compression. The coding results and the performance of the
proposed algorithms are discussed in terms of the peak signal-tonoise
ratio and the quantity of ambiguous pixels.
Abstract: This paper develops an unscented grid-based filter
and a smoother for accurate nonlinear modeling and analysis
of time series. The filter uses unscented deterministic sampling
during both the time and measurement updating phases, to approximate
directly the distributions of the latent state variable. A
complementary grid smoother is also made to enable computing
of the likelihood. This helps us to formulate an expectation
maximisation algorithm for maximum likelihood estimation of
the state noise and the observation noise. Empirical investigations
show that the proposed unscented grid filter/smoother compares
favourably to other similar filters on nonlinear estimation tasks.
Abstract: Scale Time Offset Robust Modulation (STORM) [1]–
[3] is a high bandwidth waveform design that adds time-scale
to embedded reference modulations using only time-delay [4]. In
an environment where each user has a specific delay and scale,
identification of the user with the highest signal power and that
user-s phase is facilitated by the STORM processor. Both of these
parameters are required in an efficient multiuser detection algorithm.
In this paper, the STORM modulation approach is evaluated with
a direct sequence spread quadrature phase shift keying (DS-QPSK)
system. A misconception of the STORM time scale modulation is that
a fine temporal resolution is required at the receiver. STORM will
be applied to a QPSK code division multiaccess (CDMA) system
by modifying the spreading codes. Specifically, the in-phase code
will use a typical spreading code, and the quadrature code will
use a time-delayed and time-scaled version of the in-phase code.
Subsequently, the same temporal resolution in the receiver is required
before and after the application of STORM. In this paper, the bit error
performance of STORM in a synchronous CDMA system is evaluated
and compared to theory, and the bit error performance of STORM
incorporated in a single user WCDMA downlink is presented to
demonstrate the applicability of STORM in a modern communication
system.
Abstract: Throughout this paper, a relatively new technique, the Tabu search variable selection model, is elaborated showing how it can be efficiently applied within the financial world whenever researchers come across the selection of a subset of variables from a whole set of descriptive variables under analysis. In the field of financial prediction, researchers often have to select a subset of variables from a larger set to solve different type of problems such as corporate bankruptcy prediction, personal bankruptcy prediction, mortgage, credit scoring and the Arbitrage Pricing Model (APM). Consequently, to demonstrate how the method operates and to illustrate its usefulness as well as its superiority compared to other commonly used methods, the Tabu search algorithm for variable selection is compared to two main alternative search procedures namely, the stepwise regression and the maximum R 2 improvement method. The Tabu search is then implemented in finance; where it attempts to predict corporate bankruptcy by selecting the most appropriate financial ratios and thus creating its own prediction score equation. In comparison to other methods, mostly the Altman Z-Score model, the Tabu search model produces a higher success rate in predicting correctly the failure of firms or the continuous running of existing entities.
Abstract: We here propose improved version of elastic graph matching (EGM) as a face detector, called the multi-scale EGM (MS-EGM). In this improvement, Gabor wavelet-based pyramid reduces computational complexity for the feature representation often used in the conventional EGM, but preserving a critical amount of information about an image. The MS-EGM gives us higher detection performance than Viola-Jones object detection algorithm of the AdaBoost Haar-like feature cascade. We also show rapid detection speeds of the MS-EGM, comparable to the Viola-Jones method. We find fruitful benefits in the MS-EGM, in terms of topological feature representation for a face.
Abstract: The aeration process via injectors is used to combat
the lack of oxygen in lakes due to eutrophication. A 3D numerical
simulation of the resulting flow using a simplified model is presented.
In order to generate the best dynamic in the fluid with respect to
the aeration purpose, the optimization of the injectors location is
considered. We propose to adapt to this problem the topological
sensitivity analysis method which gives the variation of a criterion
with respect to the creation of a small hole in the domain. The main
idea is to derive the topological sensitivity analysis of the physical
model with respect to the insertion of an injector in the fluid flow
domain. We propose in this work a topological optimization algorithm
based on the studied asymptotic expansion. Finally we present some
numerical results, showing the efficiency of our approach
Abstract: In this paper, a pipelined version of genetic algorithm,
called PLGA, and a corresponding hardware platform are described.
The basic operations of conventional GA (CGA) are made pipelined
using an appropriate selection scheme. The selection operator, used
here, is stochastic in nature and is called SA-selection. This helps
maintaining the basic generational nature of the proposed pipelined
GA (PLGA). A number of benchmark problems are used to compare
the performances of conventional roulette-wheel selection and the
SA-selection. These include unimodal and multimodal functions with
dimensionality varying from very small to very large. It is seen that
the SA-selection scheme is giving comparable performances with
respect to the classical roulette-wheel selection scheme, for all the
instances, when quality of solutions and rate of convergence are considered.
The speedups obtained by PLGA for different benchmarks
are found to be significant. It is shown that a complete hardware
pipeline can be developed using the proposed scheme, if parallel
evaluation of the fitness expression is possible. In this connection
a low-cost but very fast hardware evaluation unit is described.
Results of simulation experiments show that in a pipelined hardware
environment, PLGA will be much faster than CGA. In terms of
efficiency, PLGA is found to outperform parallel GA (PGA) also.
Abstract: In this paper the neural network-based controller is
designed for motion control of a mobile robot. This paper treats the
problems of trajectory following and posture stabilization of the
mobile robot with nonholonomic constraints. For this purpose the
recurrent neural network with one hidden layer is used. It learns
relationship between linear velocities and error positions of the
mobile robot. This neural network is trained on-line using the
backpropagation optimization algorithm with an adaptive learning
rate. The optimization algorithm is performed at each sample time to
compute the optimal control inputs. The performance of the proposed
system is investigated using a kinematic model of the mobile robot.