Abstract: A set of Artificial Neural Network (ANN) based methods
for the design of an effective system of speech recognition of
numerals of Assamese language captured under varied recording
conditions and moods is presented here. The work is related to
the formulation of several ANN models configured to use Linear
Predictive Code (LPC), Principal Component Analysis (PCA) and
other features to tackle mood and gender variations uttering numbers
as part of an Automatic Speech Recognition (ASR) system in
Assamese. The ANN models are designed using a combination of
Self Organizing Map (SOM) and Multi Layer Perceptron (MLP)
constituting a Learning Vector Quantization (LVQ) block trained in a
cooperative environment to handle male and female speech samples
of numerals of Assamese- a language spoken by a sizable population
in the North-Eastern part of India. The work provides a comparative
evaluation of several such combinations while subjected to handle
speech samples with gender based differences captured by a microphone
in four different conditions viz. noiseless, noise mixed, stressed
and stress-free.
Abstract: Process planning and production scheduling play
important roles in manufacturing systems. In this paper a multiobjective
mixed integer linear programming model is presented for
the integrated planning and scheduling of multi-product. The aim is
to find a set of high-quality trade-off solutions. This is a
combinatorial optimization problem with substantially large solution
space, suggesting that it is highly difficult to find the best solutions
with the exact search method. To account for it, a PSO-based
algorithm is proposed by fully utilizing the capability of the
exploration search and fast convergence. To fit the continuous PSO
in the discrete modeled problem, a solution representation is used in
the algorithm. The numerical experiments have been performed to
demonstrate the effectiveness of the proposed algorithm.
Abstract: This study was conducted to evaluate the effects of
gibberellic acid and 2,4- dichlorophenoxyacetic acid on flower
number, fruit growth and fruit quality of wax apple. GA3 and 2,4-D
were applied at small bud and petal fall stage. Number of flower,
fruit set, fruit drop, fruit crack, fruit growth and fruit quality were
recorded. Results indicated that spraying with 10 ppm GA3 had the
best results in number of flower. GA3 spray at 30 ppm gave the
faster rate of fruit growth than the other treatments. Fruit set, fruit
size as well as fruit weight markedly improved by spraying 30 ppm
GA3, followed by 10 ppm GA3 compared to untreated control.
Moreover, spray GA3 at 30 ppm was the most effective and
increased total soluble solids, reduced titratable acidity and fruit
drop. On the other hand, it was noticed that with 10 ppm 2,4-D
application also enhanced the fruit growth rate, improved
physiological and biochemical characters of fruit compared to
untreated control. It was concluded that both GA3 and 2,4-D spray
have positive effects on fruit development, reduced fruit drop, fruit
crack and improved fruit quality of wax apple under field conditions.
Abstract: Manufacturing Industries face a crucial change as products and processes are required to, easily and efficiently, be reconfigurable and reusable. In order to stay competitive and flexible, situations also demand distribution of enterprises globally, which requires implementation of efficient communication strategies. A prototype system called the “Broadcaster" has been developed with an assumption that the control environment description has been engineered using the Component-based system paradigm. This prototype distributes information to a number of globally distributed partners via an adoption of the circular-based data processing mechanism. The work highlighted in this paper includes the implementation of this mechanism in the domain of the manufacturing industry. The proposed solution enables real-time remote propagation of machine information to a number of distributed supply chain client resources such as a HMI, VRML-based 3D views and remote client instances regardless of their distribution nature and/ or their mechanisms. This approach is presented together with a set of evaluation results. Authors- main concentration surrounds the reliability and the performance metric of the adopted approach. Performance evaluation is carried out in terms of the response times taken to process the data in this domain and compared with an alternative data processing implementation such as the linear queue mechanism. Based on the evaluation results obtained, authors justify the benefits achieved from this proposed implementation and highlight any further research work that is to be carried out.
Abstract: Suppose KY and KX are large sets of observed and
reference signals, respectively, each containing N signals. Is it possible to construct a filter F : KY → KX that requires a priori
information only on few signals, p N, from KX but performs better than the known filters based on a priori information on every
reference signal from KX? It is shown that the positive answer is
achievable under quite unrestrictive assumptions. The device behind
the proposed method is based on a special extension of the piecewise
linear interpolation technique to the case of random signal sets. The proposed technique provides a single filter to process any signal from
the arbitrarily large signal set. The filter is determined in terms of pseudo-inverse matrices so that it always exists.
Abstract: The paper describes a self supervised parallel self organizing neural network (PSONN) architecture for true color image segmentation. The proposed architecture is a parallel extension of the standard single self organizing neural network architecture (SONN) and comprises an input (source) layer of image information, three single self organizing neural network architectures for segmentation of the different primary color components in a color image scene and one final output (sink) layer for fusion of the segmented color component images. Responses to the different shades of color components are induced in each of the three single network architectures (meant for component level processing) by applying a multilevel version of the characteristic activation function, which maps the input color information into different shades of color components, thereby yielding a processed component color image segmented on the basis of the different shades of component colors. The number of target classes in the segmented image corresponds to the number of levels in the multilevel activation function. Since the multilevel version of the activation function exhibits several subnormal responses to the input color image scene information, the system errors of the three component network architectures are computed from some subnormal linear index of fuzziness of the component color image scenes at the individual level. Several multilevel activation functions are employed for segmentation of the input color image scene using the proposed network architecture. Results of the application of the multilevel activation functions to the PSONN architecture are reported on three real life true color images. The results are substantiated empirically with the correlation coefficients between the segmented images and the original images.
Abstract: Electric impedance imaging is a method of
reconstructing spatial distribution of electrical conductivity inside a
subject. In this paper, a new method of electrical impedance imaging
using eddy current is proposed. The eddy current distribution in the
body depends on the conductivity distribution and the magnetic field
pattern. By changing the position of magnetic core, a set of voltage
differences is measured with a pair of electrodes. This set of voltage
differences is used in image reconstruction of conductivity
distribution. The least square error minimization method is used as a
reconstruction algorithm. The back projection algorithm is used to
get two dimensional images. Based on this principle, a measurement
system is developed and some model experiments were performed
with a saline filled phantom. The shape of each model in the
reconstructed image is similar to the corresponding model,
respectively. From the results of these experiments, it is confirmed
that the proposed method is applicable in the realization of electrical
imaging.
Abstract: As a result of the daily workflow in the design
development departments of companies, databases containing huge
numbers of 3D geometric models are generated. According to the
given problem engineers create CAD drawings based on their design
ideas and evaluate the performance of the resulting design, e.g. by
computational simulations. Usually, new geometries are built either
by utilizing and modifying sets of existing components or by adding
single newly designed parts to a more complex design.
The present paper addresses the two facets of acquiring
components from large design databases automatically and providing
a reasonable overview of the parts to the engineer. A unified
framework based on the topographic non-negative matrix
factorization (TNMF) is proposed which solves both aspects
simultaneously. First, on a given database meaningful components
are extracted into a parts-based representation in an unsupervised
manner. Second, the extracted components are organized and
visualized on square-lattice 2D maps. It is shown on the example of
turbine-like geometries that these maps efficiently provide a wellstructured
overview on the database content and, at the same time,
define a measure for spatial similarity allowing an easy access and
reuse of components in the process of design development.
Abstract: In this paper, zigbee communication based wireless energy surveillance system is presented. The proposed system consists of multiple energy surveillance devices and an energy surveillance monitor. Each different standby power-off value of electric device is set automatically by using learning function of energy surveillance device. Thus adaptive standby power-off function provides user convenience and it maximizes the energy savings. Also, power consumption monitoring function is helpful to reduce inefficient energy consumption in home. The zigbee throughput simulator is designed to evaluate minimum transmission power and maximum allowable information quantity in the proposed system. The test result of prototype has been satisfied all the requirements. The proposed system has confirmed that can be used as an intelligent energy surveillance system for energy savings in home or office.
Abstract: This paper presents the generation of bipolar square
wave pulses with characteristics that are suitable for liquid food
sterilization using a Cascaded H-bridge Multilevel Inverter (CHMI).
Bipolar square waves pulses have been reported as stable for a
longer time during the sterilization process with minimum heat
emission and increased efficiency. The CHMI allows the system to
produce bipolar square wave pulses and yielding high output voltage
without using a transformer while fulfilling the pulse requirements
for effective liquid food sterilization. This in turn can reduce power
consumption and cost of the overall liquid food sterilization system.
The simulation results have shown that pulses with peak output
voltage of 2.4 kV, pulse width of between 1 2s and 1 ms at
frequencies of 50 Hz and 100 Hz can be generated by a 7-level
CHMI. Results from the experimental set-up based on a 5-level
CHMI has indicated the potential of the proposed circuit in
producing bipolar square wave output pulses with peak values that
depends on the DC source level supplied to the CHMI modules,
pulse width of between 12.5 2s and 1 ms at frequencies of 50 Hz
and 100 Hz.
Abstract: Semantic query optimization consists in restricting the
search space in order to reduce the set of objects of interest for a
query. This paper presents an indexing method based on UB-trees
and a static analysis of the constraints associated to the views of the
database and to any constraint expressed on attributes. The result of
the static analysis is a partitioning of the object space into disjoint
blocks. Through Space Filling Curve (SFC) techniques, each
fragment (block) of the partition is assigned a unique identifier,
enabling the efficient indexing of fragments by UB-trees. The search
space corresponding to a range query is restricted to a subset of the
blocks of the partition. This approach has been developed in the
context of a KB-DBMS but it can be applied to any relational
system.
Abstract: Surface currents play a major role in the distribution
of contaminants, the connectivity of marine populations, and can
influence the vertical and horizontal distribution of nutrients within
the water column. This paper aims to determine the effects of sea
breeze-wind patterns on the climatology of the surface currents on the
continental shelf surrounding Rottnest Island, WA Australia. The
alternating wind patterns allow for full cyclic rotations of wind
direction, permitting the interpretation of the effect of the wind on the
surface currents. It was found that the surface currents only clearly
follow the northbound Capes Current in times when the Fremantle
Doctor sets in. Surface currents react within an hour to a change of
direction of the wind, allowing southerly currents to dominate during
strong northerly sea breezes, often followed by mixed currents
dominated by eddies in the inter-lying times.
Abstract: Microarrays have become the effective, broadly used tools in biological and medical research to address a wide range of problems, including classification of disease subtypes and tumors. Many statistical methods are available for analyzing and systematizing these complex data into meaningful information, and one of the main goals in analyzing gene expression data is the detection of samples or genes with similar expression patterns. In this paper, we express and compare the performance of several clustering methods based on data preprocessing including strategies of normalization or noise clearness. We also evaluate each of these clustering methods with validation measures for both simulated data and real gene expression data. Consequently, clustering methods which are common used in microarray data analysis are affected by normalization and degree of noise and clearness for datasets.
Abstract: This paper deals with heterogeneous autoregressive
models of realized volatility (HAR-RV models) on high-frequency
data of stock indices in the USA. Its aim is to capture the behavior of
three groups of market participants trading on a daily, weekly and
monthly basis and assess their role in predicting the daily realized
volatility. The benefits of this work lies mainly in the application of
heterogeneous autoregressive models of realized volatility on stock
indices in the USA with a special aim to analyze an impact of the
global financial crisis on applied models forecasting performance.
We use three data sets, the first one from the period before the global
financial crisis occurred in the years 2006-2007, the second one from
the period when the global financial crisis fully hit the U.S. financial
market in 2008-2009 years, and the last period was defined over
2010-2011 years. The model output indicates that estimated realized
volatility in the market is very much determined by daily traders and
in some cases excludes the impact of those market participants who
trade on monthly basis.
Abstract: Data mining, which is the exploration of
knowledge from the large set of data, generated as a result of
the various data processing activities. Frequent Pattern Mining
is a very important task in data mining. The previous
approaches applied to generate frequent set generally adopt
candidate generation and pruning techniques for the
satisfaction of the desired objective. This paper shows how
the different approaches achieve the objective of frequent
mining along with the complexities required to perform the
job. This paper will also look for hardware approach of cache
coherence to improve efficiency of the above process. The
process of data mining is helpful in generation of support
systems that can help in Management, Bioinformatics,
Biotechnology, Medical Science, Statistics, Mathematics,
Banking, Networking and other Computer related
applications. This paper proposes the use of both upward and
downward closure property for the extraction of frequent item
sets which reduces the total number of scans required for the
generation of Candidate Sets.
Abstract: We propose a method for discrimination and
classification of ovarian with benign, malignant and normal tissue
using independent component analysis and neural networks. The
method was tested for a proteomic patters set from A database, and
radial basis functions neural networks. The best performance was
obtained with probabilistic neural networks, resulting I 99% success
rate, with 98% of specificity e 100% of sensitivity.
Abstract: Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.
Abstract: One of the mayor problems of programming a cruise
circuit is to decide which destinations to include and which don-t.
Thus a decision problem emerges, that might be solved using a linear
and goal programming approach. The problem becomes more
complex if several boats in the fleet must be programmed in a limited
schedule, trying their capacity matches best a seasonal demand and
also attempting to minimize the operation costs. Moreover, the
programmer of the company should consider the time of the
passenger as a limited asset, and would like to maximize its usage.
The aim of this work is to design a method in which, using linear and
goal programming techniques, a model to design circuits for the
cruise company decision maker can achieve an optimal solution
within the fleet schedule.
Abstract: A simple approach is demonstrated for growing large
scale, nearly vertically aligned ZnO nanowire arrays by thermal
oxidation method. To reveal effect of temperature on growth and
physical properties of the ZnO nanowires, gold coated zinc substrates
were annealed at 300 °C and 400 °C for 4 hours duration in air. Xray
diffraction patterns of annealed samples indicated a set of well
defined diffraction peaks, indexed to the wurtzite hexagonal phase of
ZnO. The scanning electron microscopy studies show formation of
ZnO nanowires having length of several microns and average of
diameter less than 500 nm. It is found that the areal density of wires
is relatively higher, when the annealing is carried out at higher
temperature i.e. at 400°C. From the field emission studies, the values
of the turn-on and threshold field, required to draw emission current
density of 10 μA/cm2 and 100 μA/cm2 are observed to be 1.2 V/μm
and 1.7 V/μm for the samples annealed at 300 °C and 2.9 V/μm and
3.7 V/μm for that annealed at 400 °C, respectively. The field
emission current stability, investigated over duration of more than 2
hours at the preset value of 1 μA, is found to be fairly good in both
cases. The simplicity of the synthesis route coupled with the
promising field emission properties offer unprecedented advantage
for the use of ZnO field emitters for high current density
applications.
Abstract: In this paper, we define permutable and mutually permutable fuzzy subgroups of a group. Then we study their relation with permutable and mutually permutable subgroups of a group. Also we study some properties of fuzzy quasinormal subgroup. We define superfuzzy subset of a fuzzy subset and we study some properties of superfuzzy subset of a fuzzy subset.