Abstract: The design problem of Infinite Impulse Response (IIR)
digital filters is usually expressed as the minimization problem of
the complex magnitude error that includes both the magnitude and
phase information. However, the group delay of the filter obtained
by solving such design problem may be far from the desired group
delay. In this paper, we propose a design method of stable IIR digital
filters with prespecified maximum group delay errors. In the proposed
method, the approximation problems of the magnitude-phase and
group delay are separately defined, and these two approximation
problems are alternately solved using successive projections. As a
result, the proposed method can design the IIR filters that satisfy the
prespecified allowable errors for not only the complex magnitude but
also the group delay by alternately executing the coefficient update
for the magnitude-phase and the group delay approximation. The
usefulness of the proposed method is verified through some examples.
Abstract: The prediction of financial time series is a very
complicated process. If the efficient market hypothesis holds, then the predictability of most financial time series would be a rather
controversial issue, due to the fact that the current price contains already all available information in the market. This paper extends
the Adaptive Neuro Fuzzy Inference System for High Frequency
Trading which is an expert system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in financial forecasting and trading in high
frequency. However, in order to eliminate unnecessary input in the
training phase a new event based volatility model was proposed.
Taking volatility and the scaling laws of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. This new model allows the observation of specific events and seasonalities in data and subsequently removes any unnecessary data. This new event based
volatility model provides the ANFIS system with more accurate input
and has increased the overall performance of the system.
Abstract: Obtaining labeled data in supervised learning is often
difficult and expensive, and thus the trained learning algorithm tends
to be overfitting due to small number of training data. As a result,
some researchers have focused on using unlabeled data which may
not necessary to follow the same generative distribution as the labeled
data to construct a high-level feature for improving performance on
supervised learning tasks. In this paper, we investigate the impact of
the relationship between unlabeled and labeled data for classification
performance. Specifically, we will apply difference unlabeled data
which have different degrees of relation to the labeled data for
handwritten digit classification task based on MNIST dataset. Our
experimental results show that the higher the degree of relation
between unlabeled and labeled data, the better the classification
performance. Although the unlabeled data that is completely from
different generative distribution to the labeled data provides the lowest
classification performance, we still achieve high classification performance.
This leads to expanding the applicability of the supervised
learning algorithms using unsupervised learning.
Abstract: Higher-order Statistics (HOS), also known as
cumulants, cross moments and their frequency domain counterparts,
known as poly spectra have emerged as a powerful signal processing
tool for the synthesis and analysis of signals and systems. Algorithms
used for the computation of cross moments are computationally
intensive and require high computational speed for real-time
applications. For efficiency and high speed, it is often advantageous
to realize computation intensive algorithms in hardware. A promising
solution that combines high flexibility together with the speed of a
traditional hardware is Field Programmable Gate Array (FPGA). In
this paper, we present FPGA-based parallel architecture for the
computation of third-order cross moments. The proposed design is
coded in Very High Speed Integrated Circuit (VHSIC) Hardware
Description Language (VHDL) and functionally verified by
implementing it on Xilinx Spartan-3 XC3S2000FG900-4 FPGA.
Implementation results are presented and it shows that the proposed
design can operate at a maximum frequency of 86.618 MHz.
Abstract: Banishing hunger from the face of earth has been
frequently expressed in various international, national and regional
level conferences since 1974. Providing food security has become
important issue across the world particularly in developing countries.
In a developing country like India, where growth rate of population is
more than that of the food grains production, food security is a
question of great concern. According to the International Food Policy
Research Institute's Global Hunger Index, 2011, India ranks 67 of the
81 countries of the world with the worst food security status. After
Green Revolution, India became a food surplus country. Its
production has increased from 74.23 million tonnes in 1966-67 to
257.44 million tonnes in 2011-12. But after achieving selfsufficiency
in food during last three decades, the country is now
facing new challenges due to increasing population, climate change,
stagnation in farm productivity. Therefore, the main objective of the
present paper is to examine the food security situation at national
level in the country and further to explain the paradox of food
insecurity in a food surplus state of India i.e in Punjab at micro level.
In order to achieve the said objectives, secondary data collected from
the Ministry of Agriculture and the Agriculture department of Punjab
State was analyzed. The result of the study showed that despite
having surplus food production the country is still facing food
insecurity problem at micro level. Within the Kandi belt of Punjab
state, the area adjacent to plains is food secure while the area along
the hills falls in food insecure zone.
The present paper is divided into following three sections (i)
Introduction, (ii) Analysis of food security situation at national level
as well as micro level (Kandi belt of Punjab State) (iii) Concluding
Observations
Abstract: Gene expression profiling is rapidly evolving into a
powerful technique for investigating tumor malignancies. The
researchers are overwhelmed with the microarray-based platforms
and methods that confer them the freedom to conduct large-scale
gene expression profiling measurements. Simultaneously,
investigations into cross-platform integration methods have started
gaining momentum due to their underlying potential to help
comprehend a myriad of broad biological issues in tumor diagnosis,
prognosis, and therapy. However, comparing results from different
platforms remains to be a challenging task as various inherent
technical differences exist between the microarray platforms. In this
paper, we explain a simple ratio-transformation method, which can
provide some common ground for cDNA and Affymetrix platform
towards cross-platform integration. The method is based on the
characteristic data attributes of Affymetrix- and cDNA- platform. In
the work, we considered seven childhood leukemia patients and their
gene expression levels in either platform. With a dataset of 822
differentially expressed genes from both these platforms, we carried
out a specific ratio-treatment to Affymetrix data, which subsequently
showed an improvement in the relationship with the cDNA data.
Abstract: In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.
Abstract: In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method.
Abstract: Efficient preprocessing is very essential for automatic
recognition of handwritten documents. In this paper, techniques on
segmenting words in handwritten Arabic text are presented. Firstly,
connected components (ccs) are extracted, and distances among
different components are analyzed. The statistical distribution of this
distance is then obtained to determine an optimal threshold for words
segmentation. Meanwhile, an improved projection based method is
also employed for baseline detection. The proposed method has been
successfully tested on IFN/ENIT database consisting of 26459
Arabic words handwritten by 411 different writers, and the results
were promising and very encouraging in more accurate detection of
the baseline and segmentation of words for further recognition.
Abstract: Vibrations of circular cylindrical shells made of
layered composite materials are considered. The shells are weakened
by circumferential cracks. The influence of circumferential cracks
with constant depth on the vibration of the shell is prescribed with the
aid of a matrix of local flexibility coupled with the coefficient of the
stress intensity known in the linear elastic fracture mechanics.
Numerical results are presented for the case of the shell with one
circular crack.
Abstract: This paper presents key challenges reported by a
group of Australian undergraduate Physical Education students in
conducting a program for persons with an intellectual disability.
Strategies adopted to address these challenges are presented together
with representative feedback given by the Physical Education
students at the completion of the program. The significance of the
program’s findings is summarized.
Abstract: It is well known that a linear dynamic system including
a delay will exhibit limit cycle oscillations when a bang-bang sensor
is used in the feedback loop of a PID controller. A similar behaviour
occurs when a delayed feedback signal is used to train a neural
network. This paper develops a method of predicting this behaviour
by linearizing the system, which can be shown to behave in a manner
similar to an integral controller. Using this procedure, it is possible
to predict the characteristics of the neural network driven limit cycle
to varying degrees of accuracy, depending on the information known
about the system. An application is also presented: the intelligent
control of a spark ignition engine.
Abstract: In modern telecommunications industry, demand &
supply chain management (DSCM) needs reliable design and
versatile tools to control the material flow. The objective for efficient
DSCM is reducing inventory, lead times and related costs in order to
assure reliable and on-time deliveries from manufacturing units
towards customers. In this paper the multi-rate expert system based
methodology for developing simulation tools that would enable
optimal DSCM for multi region, high volume and high complexity
manufacturing environment was proposed.
Abstract: In recent years, IT convergence technology has been developed to get creative solution by combining robotics or sports science technology. Object detection and recognition have mainly applied to sports science field that has processed by recognizing face and by tracking human body. But object detection and recognition using vision sensor is challenge task in real world because of illumination. In this paper, object detection and recognition using vision sensor applied to sports simulator has been introduced. Face recognition has been processed to identify user and to update automatically a person athletic recording. Human body has tracked to offer a most accurate way of riding horse simulator. Combined image processing has been processed to reduce illumination adverse affect because illumination has caused low performance in detection and recognition in real world application filed. Face has recognized using standard face graph and human body has tracked using pose model, which has composed of feature nodes generated diverse face and pose images. Face recognition using Gabor wavelet and pose recognition using pose graph is robust to real application. We have simulated using ETRI database, which has constructed on horse riding simulator.
Abstract: A dent is a gross distortion of the pipe cross-section.
Dent depth is defined as the maximum reduction in the diameter of
the pipe compared to the original diameter. Pipeline dent finite
element (FE) simulation and theoretical analysis are conducted in this
paper to develop an understanding of the geometric characteristics
and strain distribution in the pressurized dented pipe. Based on the
results, the magnitude of the denting force increases significantly
with increasing the internal pressure, and the maximum
circumferential and longitudinal strains increase by increasing the
internal pressure and the dent depth. The results can be used for
characterizing dents and ranking their risks to the integrity of a
pipeline.
Abstract: Perth will run out of available sustainable natural
water resources by 2015 if nothing is done to slow usage rates,
according to a Western Australian study [1]. Alternative water
technology options need to be considered for the long-term
guaranteed supply of water for agricultural, commercial, domestic
and industrial purposes. Seawater is an alternative source of water for
human consumption, because seawater can be desalinated and
supplied in large quantities to a very high quality.
While seawater desalination is a promising option, the technology
requires a large amount of energy which is typically generated from
fossil fuels. The combustion of fossil fuels emits greenhouse gases
(GHG) and, is implicated in climate change. In addition to
environmental emissions from electricity generation for desalination,
greenhouse gases are emitted in the production of chemicals and
membranes for water treatment. Since Australia is a signatory to the
Kyoto Protocol, it is important to quantify greenhouse gas emissions
from desalinated water production.
A life cycle assessment (LCA) has been carried out to determine
the greenhouse gas emissions from the production of 1 gigalitre (GL)
of water from the new plant. In this LCA analysis, a new desalination
plant that will be installed in Bunbury, Western Australia, and known
as Southern Seawater Desalinization Plant (SSDP), was taken as a
case study. The system boundary of the LCA mainly consists of three
stages: seawater extraction, treatment and delivery. The analysis
found that the equivalent of 3,890 tonnes of CO2 could be emitted
from the production of 1 GL of desalinated water. This LCA analysis
has also identified that the reverse osmosis process would cause the
most significant greenhouse emissions as a result of the electricity
used if this is generated from fossil fuels
Abstract: There are many kinds of metal borates found not only
in nature but also synthesized in the laboratory such as magnesium
borates. Due to its excellent properties, as remarkable ceramic
materials, they have also application areas in anti-wear and friction
reducing additives as well as electro-conductive treating agents. The
synthesis of magnesium borate powders can be fulfilled simply with
two different methods, hydrothermal and thermal synthesis.
Microwave assisted method, also another way of producing
magnesium borate, can be classified into thermal synthesis because of
using the principles of solid state synthesis. It also contributes
producing particles with small size and high purity in nano-size
material synthesize. In this study the production of magnesium
borates, are aimed using MgCl2.6H2O and H3BO3. The identification
of both starting materials and products were made by the equipments
of, X-Ray Diffraction (XRD) and Fourier Transform Infrared
Spectroscopy (FT-IR). After several synthesis steps magnesium
borates were synthesized and characterized by XRD and FT-IR, as
well.
Abstract: The aim of this paper is to investigate the influence of
market share and diversification on the nonlife insurers- performance.
The underlying relationships have been investigated in different
industries and different disciplines (economics, management...), still,
no consistency exists either in the magnitude or statistical
significance of the relationship between market share (and
diversification as well) on one side and companies- performance on
the other side. Moreover, the direction of the relationship is also
somewhat questionable. While some authors find this relationship to
be positive, the others reveal its negative association. In order to test
the influence of market share and diversification on companies-
performance in Croatian nonlife insurance industry for the period
from 1999 to 2009, we designed an empirical model in which we
included the following independent variables: firms- profitability
from previous years, market share, diversification and control
variables (i.e. ownership, industrial concentration, GDP per capita,
inflation). Using the two-step generalized method of moments
(GMM) estimator we found evidence of a positive and statistically
significant influence of both, market share and diversification, on
insurers- profitability.
Abstract: Term Extraction, a key data preparation step in Text
Mining, extracts the terms, i.e. relevant collocation of words,
attached to specific concepts (e.g. genetic-algorithms and decisiontrees
are terms associated to the concept “Machine Learning" ). In
this paper, the task of extracting interesting collocations is achieved
through a supervised learning algorithm, exploiting a few
collocations manually labelled as interesting/not interesting. From
these examples, the ROGER algorithm learns a numerical function,
inducing some ranking on the collocations. This ranking is optimized
using genetic algorithms, maximizing the trade-off between the false
positive and true positive rates (Area Under the ROC curve). This
approach uses a particular representation for the word collocations,
namely the vector of values corresponding to the standard statistical
interestingness measures attached to this collocation. As this
representation is general (over corpora and natural languages),
generality tests were performed by experimenting the ranking
function learned from an English corpus in Biology, onto a French
corpus of Curriculum Vitae, and vice versa, showing a good
robustness of the approaches compared to the state-of-the-art Support
Vector Machine (SVM).
Abstract: Prediction of viscosity of natural gas is an important parameter in the energy industries such as natural gas storage and transportation. In this study viscosity of different compositions of natural gas is modeled by using an artificial neural network (ANN) based on back-propagation method. A reliable database including more than 3841 experimental data of viscosity for testing and training of ANN is used. The designed neural network can predict the natural gas viscosity using pseudo-reduced pressure and pseudo-reduced temperature with AARD% of 0.221. The accuracy of designed ANN has been compared to other published empirical models. The comparison indicates that the proposed method can provide accurate results.