Abstract: Sinc-collocation scheme is one of the new techniques
used in solving numerical problems involving integral equations. This
method has been shown to be a powerful numerical tool for finding
fast and accurate solutions. So, in this paper, some properties of the
Sinc-collocation method required for our subsequent development
are given and are utilized to reduce integral equation of the first
kind to some algebraic equations. Then convergence with exponential
rate is proved by a theorem to guarantee applicability of numerical
technique. Finally, numerical examples are included to demonstrate
the validity and applicability of the technique.
Abstract: The problem of delay-range-dependent exponential synchronization is investigated for Lur-e master-slave systems with delay feedback control and Markovian switching. Using Lyapunov- Krasovskii functional and nonsingular M-matrix method, novel delayrange- dependent exponential synchronization in mean square criterions are established. The systems discussed in this paper is advanced system, and takes all the features of interval systems, Itˆo equations, Markovian switching, time-varying delay, as well as the environmental noise, into account. Finally, an example is given to show the validity of the main result.
Abstract: The Neuro-Fuzzy hybridization scheme has become
of research interest in pattern classification over the past decade. The
present paper proposes a novel Modified Adaptive Fuzzy Inference
Engine (MAFIE) for pattern classification. A modified Apriori
algorithm technique is utilized to reduce a minimal set of decision
rules based on input output data sets. A TSK type fuzzy inference
system is constructed by the automatic generation of membership
functions and rules by the fuzzy c-means clustering and Apriori
algorithm technique, respectively. The generated adaptive fuzzy
inference engine is adjusted by the least-squares fit and a conjugate
gradient descent algorithm towards better performance with a
minimal set of rules. The proposed MAFIE is able to reduce the
number of rules which increases exponentially when more input
variables are involved. The performance of the proposed MAFIE is
compared with other existing applications of pattern classification
schemes using Fisher-s Iris and Wisconsin breast cancer data sets and
shown to be very competitive.
Abstract: Staphylococcus aureus, one of the microflora in a
human external auditory canal (EAC) is frequently exposed to highfrequency
electromagnetic field (HF-EMF) generated by mobile
phones. It is normally non-pathogenic but in certain circumstances, it
can cause infections. This study investigates the changes in the
physiology of S. aureus when exposed to HF-EMF of a mobile
phone. Exponentially grown S. aureus were exposed to two
conditions of EMF irradiation (standby-mode and on-call mode) at
four durations; 15, 30, 45 and 60 min. Changes in the viability and
biofilm production of the S. aureus were compared between the two
conditions of exposure. EMF from the standby-mode has enhanced
the growth of S. aureus but during on-call, the growth was
suppressed. No significant difference in the amount of biofilm
produced in both modes of exposure was observed. Thus, HF-EMF
of mobile phone affects the viability of S. aureus but not its ability to
produce biofilm.
Abstract: A multi-rate discrete-time model, whose response
agrees exactly with that of a continuous-time original at all sampling
instants for any sampling periods, is developed for a linear system,
which is assumed to have multiple real eigenvalues. The sampling
rates can be chosen arbitrarily and individually, so that their ratios
can even be irrational. The state space model is obtained as a
combination of a linear diagonal state equation and a nonlinear output
equation. Unlike the usual lifted model, the order of the proposed
model is the same as the number of sampling rates, which is less than
or equal to the order of the original continuous-time system. The
method is based on a nonlinear variable transformation, which can be
considered as a generalization of linear similarity transformation,
which cannot be applied to systems with multiple eigenvalues in
general. An example and its simulation result show that the proposed
multi-rate model gives exact responses at all sampling instants.
Abstract: The householder RLS (HRLS) algorithm is an O(N2)
algorithm which recursively updates an arbitrary square-root of the
input data correlation matrix and naturally provides the LS weight
vector. A data dependent householder matrix is applied for such
an update. In this paper a recursive estimate of the eigenvalue
spread and misalignment of the algorithm is presented at a very low
computational cost. Misalignment is found to be highly sensitive to
the eigenvalue spread of input signals, output noise of the system and
exponential window. Simulation results show noticeable degradation
in the misalignment by increase in eigenvalue spread as well as
system-s output noise, while exponential window was kept constant.
Abstract: Rainfall data at fine resolution and knowledge of its
characteristics plays a major role in the efficient design and operation
of agricultural, telecommunication, runoff and erosion control as well
as water quality control systems. The paper is aimed to study the
statistical distribution of hourly rainfall depth for 12 representative
stations spread across Peninsular Malaysia. Hourly rainfall data of 10
to 22 years period were collected and its statistical characteristics
were estimated. Three probability distributions namely, Generalized
Pareto, Exponential and Gamma distributions were proposed to
model the hourly rainfall depth, and three goodness-of-fit tests,
namely, Kolmogorov-Sminov, Anderson-Darling and Chi-Squared
tests were used to evaluate their fitness. Result indicates that the east
cost of the Peninsular receives higher depth of rainfall as compared
to west coast. However, the rainfall frequency is found to be
irregular. Also result from the goodness-of-fit tests show that all the
three models fit the rainfall data at 1% level of significance.
However, Generalized Pareto fits better than Exponential and
Gamma distributions and is therefore recommended as the best fit.
Abstract: In this paper we propose new method for
simultaneous generating multiple quantiles corresponding to given
probability levels from data streams and massive data sets. This
method provides a basis for development of single-pass low-storage
quantile estimation algorithms, which differ in complexity, storage
requirement and accuracy. We demonstrate that such algorithms may
perform well even for heavy-tailed data.
Abstract: Measures of complexity and entropy have not converged to a single quantitative description of levels of organization of complex systems. The need for such a measure is increasingly necessary in all disciplines studying complex systems. To address this problem, starting from the most fundamental principle in Physics, here a new measure for quantity of organization and rate of self-organization in complex systems based on the principle of least (stationary) action is applied to a model system - the central processing unit (CPU) of computers. The quantity of organization for several generations of CPUs shows a double exponential rate of change of organization with time. The exact functional dependence has a fine, S-shaped structure, revealing some of the mechanisms of self-organization. The principle of least action helps to explain the mechanism of increase of organization through quantity accumulation and constraint and curvature minimization with an attractor, the least average sum of actions of all elements and for all motions. This approach can help describe, quantify, measure, manage, design and predict future behavior of complex systems to achieve the highest rates of self organization to improve their quality. It can be applied to other complex systems from Physics, Chemistry, Biology, Ecology, Economics, Cities, network theory and others where complex systems are present.
Abstract: The problem of robust fuzzy control for a class of
nonlinear fuzzy impulsive singular perturbed systems with
time-varying delay is investigated by employing Lyapunov functions.
The nonlinear delay system is built based on the well-known T–S
fuzzy model. The so-called parallel distributed compensation idea is
employed to design the state feedback controller. Sufficient conditions
for global exponential stability of the closed-loop system are derived
in terms of linear matrix inequalities (LMIs), which can be easily
solved by LMI technique. Some simulations illustrate the effectiveness
of the proposed method.
Abstract: Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.
Abstract: Exponentially weighted moving average control chart (EWMA) is a popular chart used for detecting shift in the mean of parameter of distributions in quality control. The objective of this paper is to compare the efficiency of control chart to detect an increases in the mean of a process. In particular, we compared the Maximum Exponentially Weighted Moving Average (MaxEWMA) and Maximum Generally Weighted Moving Average (MaxGWMA) control charts when the observations are Exponential distribution. The criteria for evaluate the performance of control chart is called, the Average Run Length (ARL). The result of comparison show that in the case of process is small sample size, the MaxEWMA control chart is more efficiency to detect shift in the process mean than MaxGWMA control chart. For the case of large sample size, the MaxEWMA control chart is more sensitive to detect small shift in the process mean than MaxGWMA control chart, and when the process is a large shift in mean, the MaxGWMA control chart is more sensitive to detect mean shift than MaxEWMA control chart.
Abstract: In this paper, based on almost periodic functional hull theory and M-matrix theory, some sufficient conditions are established for the existence and uniqueness of positive almost periodic solution for a class of BAM neural networks with time-varying delays. An example is given to illustrate the main results.
Abstract: Although the level crossing concept has been the subject of intensive investigation over the last few years, certain problems of great interest remain unsolved. One of these concern is distribution of threshold levels. This paper presents a new threshold level allocation schemes for level crossing based on nonuniform sampling. Intuitively, it is more reasonable if the information rich regions of the signal are sampled finer and those with sparse information are sampled coarser. To achieve this objective, we propose non-linear quantization functions which dynamically assign the number of quantization levels depending on the importance of the given amplitude range. Two new approaches to determine the importance of the given amplitude segment are presented. The proposed methods are based on exponential and logarithmic functions. Various aspects of proposed techniques are discussed and experimentally validated. Its efficacy is investigated by comparison with uniform sampling.
Abstract: Hysteresis phenomenon has been observed in the
operations of both horizontal-axis and vertical-axis wind turbines
(HAWTs and VAWTs). In this study, wind tunnel experiments were
applied to investigate the characters of hysteresis phenomena between
the angular speed and the external resistance of electrical loading
during the operation of a Darrieus type VAWT. Data of output voltage,
output current, angular speed of wind turbine under different wind
speeds are measured and analyzed. Results show that the range of
external resistance changes with the wind speed. The range decreases
as the wind speed increases following an exponential decay form.
Experiments also indicate that the maximum output power of wind
turbines is always inside the range where hysteresis happened. These
results provide an important reference to the design of output control
system of wind turbines.
Abstract: In this paper, by using Mawhin-s continuation theorem of coincidence degree and a method based on delay differential inequality, some sufficient conditions are obtained for the existence and global exponential stability of periodic solutions of cellular neural networks with distributed delays and impulses on time scales. The results of this paper generalized previously known results.
Abstract: In this paper we present a statistical analysis of Voice
over IP (VoIP) packet streams produced by the G.711 voice coder
with voice activity detection (VAD). During telephone conversation,
depending whether the interlocutor speaks (ON) or remains silent
(OFF), packets are produced or not by a voice coder. As index of
dispersion for both ON and OFF times distribution was greater than
one, we used hyperexponential distribution for approximation of
streams duration. For each stage of the hyperexponential distribution,
we tested goodness of our fits using graphical methods, we calculated
estimation errors, and performed Kolmogorov-Smirnov test.
Obtained results showed that the precise VoIP source model can be
based on the five-state Markov process.
Abstract: New generalization of the new class matrix polynomial set have been obtained. An explicit representation and an expansion of the matrix exponential in a series of these matrix are given for these matrix polynomials.
Abstract: Decision Feedback equalizers (DFEs) usually outperform linear equalizers for channels with intersymbol interference. However, the DFE performance is highly dependent on the availability of reliable past decisions. Hence, in coded systems, where reliable decisions are only available after decoding the full block, the performance of the DFE will be affected. A symbol based DFE is a DFE that only uses the decision after the block is decoded. In this paper we derive the optimal settings of both the feedforward and feedback taps of the symbol based equalizer. We present a novel symbol based DFE filterbank, and derive its taps optimal settings. We also show that it outperforms the classic DFE in terms of complexity and/or performance.
Abstract: As the web continues to grow exponentially, the idea
of crawling the entire web on a regular basis becomes less and less
feasible, so the need to include information on specific domain,
domain-specific search engines was proposed. As more information
becomes available on the World Wide Web, it becomes more difficult
to provide effective search tools for information access. Today,
people access web information through two main kinds of search
interfaces: Browsers (clicking and following hyperlinks) and Query
Engines (queries in the form of a set of keywords showing the topic
of interest) [2]. Better support is needed for expressing one's
information need and returning high quality search results by web
search tools. There appears to be a need for systems that do reasoning
under uncertainty and are flexible enough to recover from the
contradictions, inconsistencies, and irregularities that such reasoning
involves. In a multi-view problem, the features of the domain can be
partitioned into disjoint subsets (views) that are sufficient to learn the
target concept. Semi-supervised, multi-view algorithms, which
reduce the amount of labeled data required for learning, rely on the
assumptions that the views are compatible and uncorrelated. This
paper describes the use of semi-structured machine learning approach
with Active learning for the “Domain Specific Search Engines". A
domain-specific search engine is “An information access system that
allows access to all the information on the web that is relevant to a
particular domain. The proposed work shows that with the help of
this approach relevant data can be extracted with the minimum
queries fired by the user. It requires small number of labeled data and
pool of unlabelled data on which the learning algorithm is applied to
extract the required data.