Abstract: This paper proposes an adaptive sliding mode
controller which combines adaptive control and sliding
mode control to control a nonlinear robotic manipulator
with uncertain parameters. We use an adaptive algorithm
based on the concept of sliding mode control to alleviate the
chattering phenomenon of control input. Adaptive laws are
developed to obtain the gain of switching input and the
boundary layer parameters. The stability and convergence
of the robotic manipulator control system are guaranteed
by applying the Lyapunov theorem. Simulation results
demonstrate that the chattering of control input can be
alleviated effectively. The proposed controller scheme can
assure robustness against a large class of uncertainties and
achieve good trajectory tracking performance.
Abstract: The paper presents an on-line recognition machine
(RM) for continuous/isolated, dynamic and static gestures that arise
in Flight Deck Officer (FDO) training. RM is based on generic pattern
recognition framework. Gestures are represented as templates using
summary statistics. The proposed recognition algorithm exploits temporal
and spatial characteristics of gestures via dynamic programming
and Markovian process. The algorithm predicts corresponding index
of incremental input data in the templates in an on-line mode.
Accumulated consistency in the sequence of prediction provides a
similarity measurement (Score) between input data and the templates.
The algorithm provides an intuitive mechanism for automatic detection
of start/end frames of continuous gestures. In the present paper,
we consider isolated gestures. The performance of RM is evaluated
using four datasets - artificial (W TTest), hand motion (Yang) and
FDO (tracker, vision-based ). RM achieves comparable results which
are in agreement with other on-line and off-line algorithms such as
hidden Markov model (HMM) and dynamic time warping (DTW).
The proposed algorithm has the additional advantage of providing
timely feedback for training purposes.
Abstract: A novel application of neural network approach to
fault classification and fault location of Medium voltage cables is
demonstrated in this paper. Different faults on a protected cable
should be classified and located correctly. This paper presents the use
of neural networks as a pattern classifier algorithm to perform these
tasks. The proposed scheme is insensitive to variation of different
parameters such as fault type, fault resistance, and fault inception
angle. Studies show that the proposed technique is able to offer high
accuracy in both of the fault classification and fault location tasks.
Abstract: The radius-of-curvature (ROC) defines the degree of
curvature along the centerline of a roadway whereby a travelling
vehicle must follow. Roadway designs must encompass ROC in
mitigating the cost of earthwork associated with construction while
also allowing vehicles to travel at maximum allowable design speeds.
Thus, a road will tend to follow natural topography where possible,
but curvature must also be optimized to permit fast, but safe vehicle
speeds. The more severe the curvature of the road, the slower the
permissible vehicle speed. For route planning, whether for urban
settings, emergency operations, or even parcel delivery, ROC is a
necessary attribute of road arcs for computing travel time.
It is extremely rare for a geo-spatial database to contain ROC. This
paper will present a procedure and mathematical algorithm to
calculate and assign ROC to a segment pair and/or polyline.
Abstract: In this paper we present a study of the impact of connection schemes on the performance of iterative decoding of Generalized Parallel Concatenated block (GPCB) constructed from one step majority logic decodable (OSMLD) codes and we propose a new connection scheme for decoding them. All iterative decoding connection schemes use a soft-input soft-output threshold decoding algorithm as a component decoder. Numerical result for GPCB codes transmitted over Additive White Gaussian Noise (AWGN) channel are provided. It will show that the proposed scheme is better than Hagenauer-s scheme and Lucas-s scheme [1] and slightly better than the Pyndiah-s scheme.
Abstract: Nano fibers produced by electrospinning are of industrial and scientific attention due to their special characteristics such as long length, small diameter and high surface area. Applications of electrospun structures in nanotechnology are included tissue scaffolds, fibers for drug delivery, composite reinforcement, chemical sensing, enzyme immobilization, membrane-based filtration, protective clothing, catalysis, solar cells, electronic devices and others. Many polymer and ceramic precursor nano fibers have been successfully electrospun with diameters in the range from 1 nm to several microns. The process is complex so that fiber diameter is influenced by various material, design and operating parameters. The objective of this work is to apply genetic algorithm on the parameters of electrospinning which have the most significant effect on the nano fiber diameter to determine the optimum parameter values before doing experimental set up. Effective factors including initial polymer concentration, initial jet radius, electrical potential, relaxation time, initial elongation, viscosity and distance between nozzle and collector are considered to determine finest diameter which is selected by user.
Abstract: Realistic systems generally are systems with various
inputs and outputs also known as Multiple Input Multiple Output
(MIMO). Such systems usually prove to be complex and difficult to
model and control purposes. Therefore, decomposition was used to
separate individual inputs and outputs. A PID is assigned to each
individual pair to regulate desired settling time. Suitable parameters
of PIDs obtained from Genetic Algorithm (GA), using Mean of
Squared Error (MSE) objective function.
Abstract: Studies in economics domain tried to reveal the correlation between stock markets. Since the globalization era, interdependence between stock markets becomes more obvious. The Dynamic Interaction Network (DIN) algorithm, which was inspired by a Gene Regulatory Network (GRN) extraction method in the bioinformatics field, is applied to reveal important and complex dynamic relationship between stock markets. We use the data of the stock market indices from eight countries around the world in this study. Our results conclude that DIN is able to reveal and model patterns of dynamic interaction from the observed variables (i.e. stock market indices). Furthermore, it is also found that the extracted network models can be utilized to predict movement of the stock market indices with a considerably good accuracy.
Abstract: In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.
Abstract: This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.
Abstract: This paper summarizes and compares approaches to
solving the knapsack problem and its known application in capital
budgeting. The first approach uses deterministic methods and can be
applied to small-size tasks with a single constraint. We can also
apply commercial software systems such as the GAMS modelling
system. However, because of NP-completeness of the problem, more
complex problem instances must be solved by means of heuristic
techniques to achieve an approximation of the exact solution in a
reasonable amount of time. We show the problem representation and
parameter settings for a genetic algorithm framework.
Abstract: In this paper we introduced new wavelet based algorithm for speckle reduction of synthetic aperture radar images, which uses combination of undecimated wavelet transformation, wiener filter (which is an adaptive filter) and mean filter. Further more instead of using existing thresholding techniques such as sure shrinkage, Bayesian shrinkage, universal thresholding, normal thresholding, visu thresholding, soft and hard thresholding, we use brute force thresholding, which iteratively run the whole algorithm for each possible candidate value of threshold and saves each result in array and finally selects the value for threshold that gives best possible results. That is why it is slow as compared to existing thresholding techniques but gives best results under the given algorithm for speckle reduction.
Abstract: This paper discusses a new heavy tailed distribution based data hiding into discrete cosine transform (DCT) coefficients of image, which provides statistical security as well as robustness against steganalysis attacks. Unlike other data hiding algorithms, the proposed technique does not introduce much effect in the stegoimage-s DCT coefficient probability plots, thus making the presence of hidden data statistically undetectable. In addition the proposed method does not compromise on hiding capacity. When compared to the generic block DCT based data-hiding scheme, our method found more robust against a variety of image manipulating attacks such as filtering, blurring, JPEG compression etc.
Abstract: Ants are fascinating creatures that demonstrate the
ability to find food and bring it back to their nest. Their ability as a
colony, to find paths to food sources has inspired the development of
algorithms known as Ant Colony Systems (ACS). The principle of
cooperation forms the backbone of such algorithms, commonly used
to find solutions to problems such as the Traveling Salesman
Problem (TSP). Ants communicate to each other through chemical
substances called pheromones. Modeling individual ants- ability to
manipulate this substance can help an ACS find the best solution.
This paper introduces a Dynamic Ant Colony System with threelevel
updates (DACS3) that enhance an existing ACS. Experiments
were conducted to observe single ant behavior in a colony of
Malaysian House Red Ants. Such behavior was incorporated into the
DACS3 algorithm. We benchmark the performance of DACS3 versus
DACS on TSP instances ranging from 14 to 100 cities. The result
shows that the DACS3 algorithm can achieve shorter distance in
most cases and also performs considerably faster than DACS.
Abstract: The basic aim of our study is to give a possible model for handling uncertain information. This model is worked out in the framework of DATALOG. The concept of multivalued knowledgebase will be defined as a quadruple of any background knowledge; a deduction mechanism; a connecting algorithm, and a function set of the program, which help us to determine the uncertainty levels of the results. At first the concept of fuzzy Datalog will be summarized, then its extensions for intuitionistic- and interval-valued fuzzy logic is given and the concept of bipolar fuzzy Datalog is introduced. Based on these extensions the concept of multivalued knowledge-base will be defined. This knowledge-base can be a possible background of a future agent-model.
Abstract: Finding suitable non-supersingular elliptic curves for
pairing-based cryptosystems becomes an important issue for the
modern public-key cryptography after the proposition of id-based
encryption scheme and short signature scheme. In previous work
different algorithms have been proposed for finding such elliptic
curves when embedding degree k ∈ {3, 4, 6} and cofactor h ∈ {1, 2, 3,
4, 5}. In this paper a new method is presented to find more
non-supersingular elliptic curves for pairing-based cryptosystems with
general embedding degree k and large values of cofactor h. In
addition, some effective parameters of these non-supersingular elliptic
curves are provided in this paper.
Abstract: This paper presents an algorithm based on the
wavelet decomposition, for feature extraction from the ECG signal
and recognition of three types of Ventricular Arrhythmias using
neural networks. A set of Discrete Wavelet Transform (DWT)
coefficients, which contain the maximum information about the
arrhythmias, is selected from the wavelet decomposition. After that a
novel clustering algorithm based on nature inspired algorithm (Ant
Colony Optimization) is developed for classifying arrhythmia types.
The algorithm is applied on the ECG registrations from the MIT-BIH
arrhythmia and malignant ventricular arrhythmia databases. We
applied Daubechies 4 wavelet in our algorithm. The wavelet
decomposition enabled us to perform the task efficiently and
produced reliable results.
Abstract: The increase popularity of multimedia application especially in image processing places a great demand on efficient data storage and transmission techniques. Network communication such as wireless network can easily be intercepted and cause of confidential information leaked. Unfortunately, conventional compression and encryption methods are too slow; it is impossible to carry out real time secure image processing. In this research, Embedded Zerotree Wavelet (EZW) encoder which specially designs for wavelet compression is examined. With this algorithm, three methods are proposed to reduce the processing time, space and security protection that will be secured enough to protect the data.
Abstract: The aim of this work is to present a multi-objective optimization method to find maximum efficiency kinematics for a flapping wing unmanned aerial vehicle. We restrained our study to rectangular wings with the same profile along the span and to harmonic dihedral motion. It is assumed that the birdlike aerial vehicle (whose span and surface area were fixed respectively to 1m and 0.15m2) is in horizontal mechanically balanced motion at fixed speed. We used two flight physics models to describe the vehicle aerodynamic performances, namely DeLaurier-s model, which has been used in many studies dealing with flapping wings, and the model proposed by Dae-Kwan et al. Then, a constrained multi-objective optimization of the propulsive efficiency is performed using a recent evolutionary multi-objective algorithm called є-MOEA. Firstly, we show that feasible solutions (i.e. solutions that fulfil the imposed constraints) can be obtained using Dae-Kwan et al.-s model. Secondly, we highlight that a single objective optimization approach (weighted sum method for example) can also give optimal solutions as good as the multi-objective one which nevertheless offers the advantage of directly generating the set of the best trade-offs. Finally, we show that the DeLaurier-s model does not yield feasible solutions.
Abstract: Video streaming over lossy IP networks is very
important issues, due to the heterogeneous structure of networks.
Infrastructure of the Internet exhibits variable bandwidths, delays,
congestions and time-varying packet losses. Because of variable
attributes of the Internet, video streaming applications should not
only have a good end-to-end transport performance but also have a
robust rate control, furthermore multipath rate allocation mechanism.
So for providing the video streaming service quality, some other
components such as Bandwidth Estimation and Adaptive Rate
Controller should be taken into consideration. This paper gives an
overview of video streaming concept and bandwidth estimation tools
and then introduces special architectures for bandwidth adaptive
video streaming. A bandwidth estimation algorithm – pathChirp,
Optimized Rate Controllers and Multipath Rate Allocation Algorithm
are considered as all-in-one solution for video streaming problem.
This solution is directed and optimized by a decision center which is
designed for obtaining the maximum quality at the receiving side.