Abstract: With the advent of inexpensive 32 bit floating point digital signal processor-s availability in market, many computationally intensive algorithms such as Kalman filter becomes feasible to implement in real time. Dynamic simulation of a self excited DC motor using second order state variable model and implementation of Kalman Filter in a floating point DSP TMS320C6713 is presented in this paper with an objective to introduce and implement such an algorithm, for beginners. A fractional hp DC motor is simulated in both Matlab® and DSP and the results are included. A step by step approach for simulation of DC motor in Matlab® and “C" routines in CC Studio® is also given. CC studio® project file details and environmental setting requirements are addressed. This tutorial can be used with 6713 DSK, which is based on floating point DSP and CC Studio either in hardware mode or in simulation mode.
Abstract: Every organization is continually subject to new damages and threats which can be resulted from their operations or their goal accomplishment. Methods of providing the security of space and applied tools have been widely changed with increasing application and development of information technology (IT). From this viewpoint, information security management systems were evolved to construct and prevent reiterating the experienced methods. In general, the correct response in information security management systems requires correct decision making, which in turn requires the comprehensive effort of managers and everyone involved in each plan or decision making. Obviously, all aspects of work or decision are not defined in all decision making conditions; therefore, the possible or certain risks should be considered when making decisions. This is the subject of risk management and it can influence the decisions. Investigation of different approaches in the field of risk management demonstrates their progress from quantitative to qualitative methods with a process approach.
Abstract: We present a system that finds road boundaries and
constructs the virtual lane based on fusion data from a laser and a
monocular sensor, and detects forward vehicle position even in no lane
markers or bad environmental conditions. When the road environment
is dark or a lot of vehicles are parked on the both sides of the road, it is
difficult to detect lane and road boundary. For this reason we use
fusion of laser and vision sensor to extract road boundary to acquire
three dimensional data. We use parabolic road model to calculate road
boundaries which is based on vehicle and sensors state parameters and
construct virtual lane. And then we distinguish vehicle position in each
lane.
Abstract: Knowledge sharing in general and the contextual
access to knowledge in particular, still represent a key challenge in
the knowledge management framework. Researchers on semantic
web and human machine interface study techniques to enhance this
access. For instance, in semantic web, the information retrieval is
based on domain ontology. In human machine interface, keeping
track of user's activity provides some elements of the context that can
guide the access to information. We suggest an approach based on
these two key guidelines, whilst avoiding some of their weaknesses.
The approach permits a representation of both the context and the
design rationale of a project for an efficient access to knowledge. In
fact, the method consists of an information retrieval environment
that, in the one hand, can infer knowledge, modeled as a semantic
network, and on the other hand, is based on the context and the
objectives of a specific activity (the design). The environment we
defined can also be used to gather similar project elements in order to
build classifications of tasks, problems, arguments, etc. produced in a
company. These classifications can show the evolution of design
strategies in the company.
Abstract: This purpose of this paper is to develop and validate a
model to accurately predict the cell temperature of a PV module that
adapts to various mounting configurations, mounting locations, and
climates while only requiring readily available data from the module
manufacturer. Results from this model are also compared to results
from published cell temperature models. The models were used to
predict real-time performance from a PV water pumping systems in
the desert of Medenine, south of Tunisia using 60-min intervals of
measured performance data during one complete year. Statistical
analysis of the predicted results and measured data highlight possible
sources of errors and the limitations and/or adequacy of existing
models, to describe the temperature and efficiency of PV-cells and
consequently, the accuracy of performance of PV water pumping
systems prediction models.
Abstract: This Classifying Bird Sounds (chip notes) project-s
purpose is to reduce the unwanted noise from recorded bird sound
chip notes, design a scheme to detect differences and similarities
between recorded chip notes, and classify bird sound chip notes. The
technologies of determining the similarities of sound waves have
been used in communication, sound engineering and wireless sound
applications for many years. Our research is focused on the similarity
of chip notes, which are the sounds from different birds. The program
we use is generated by Microsoft Cµ.
Abstract: Results of Chilean wine classification based on the
information provided by an electronic nose are reported in this paper.
The classification scheme consists of two parts; in the first stage,
Principal Component Analysis is used as feature extraction method to
reduce the dimensionality of the original information. Then, Radial
Basis Functions Neural Networks is used as pattern recognition
technique to perform the classification. The objective of this study is
to classify different Cabernet Sauvignon, Merlot and Carménère wine
samples from different years, valleys and vineyards of Chile.
Abstract: This study reports the implementation of Good
Manufacturing Practice (GMP) in a polycarbonate film processing
plant. The implementation of GMP took place with the creation of a
multidisciplinary team. It was carried out in four steps: conduct gap
assessment, create gap closure plan, close gaps, and follow up the
GMP implementation. The basis for the gap assessment is the
guideline for GMP for plastic materials and articles intended for Food
Contact Material (FCM), which was edited by Plastic Europe. The
effective results of the GMP implementation in this study showed
100% completion of gap assessment. The key success factors for
implementing GMP in production process are the commitment,
intention and support of top management.
Abstract: Web-based technologies have created numerous
opportunities for electronic word-of-mouth (eWOM) communication.
There are many factors that affect customer adoption and decisionmaking
process. However, only a few researches focus on some
factors such as the membership time of forum and propensity to trust.
Using a discrete-time event simulation to simulate a diffusion model
along with a consumer decision model, the study shows the effect of
each factor on adoption of opinions on on-line discussion forum. The
purpose of this study is to examine the effect of factor affecting
information adoption and decision making process. The model is
constructed to test quantitative aspects of each factor. The simulation
study shows the membership time and the propensity to trust has an
effect on information adoption and purchasing decision. The result of
simulation shows that the longer the membership time in the
communities and the higher propensity to trust could lead to the
higher demand rates because consumers find it easier and faster to
trust the person in the community and then adopt the eWOM. Other
implications for both researchers and practitioners are provided.
Abstract: This article provides empirical evidence on the effect
of domestic and international factors on the U.S. current account
deficit. Linear dynamic regression and vector autoregression models
are employed to estimate the relationships during the period from 1986
to 2011. The findings of this study suggest that the current and lagged
private saving rate and foreign current account for East Asian
economies have played a vital role in affecting the U.S. current
account. Additionally, using Granger causality tests and variance
decompositions, the change of the productivity growth and foreign
domestic demand are determined to influence significantly the change
of the U.S. current account. To summarize, the empirical relationship
between the U.S. current account deficit and its determinants is
sensitive to alternative regression models and specifications.
Abstract: Previous the 3D model texture generation from multi-view images and mapping algorithms has issues in the texture chart generation which are the self-intersection and the concentration of the texture in texture space. Also we may suffer from some problems due to the occluded areas, such as inside parts of thighs. In this paper we propose a texture mapping technique for 3D models using multi-view images on the GPU. We do texture mapping directly on the GPU fragment shader per pixel without generation of the texture map. And we solve for the occluded area using the 3D model depth information. Our method needs more calculation on the GPU than previous works, but it has shown real-time performance and previously mentioned problems do not occur.
Abstract: The present study was designed to investigate the
cardio protective role of chronic oral administration of alcoholic
extract of Terminalia arjuna in in-vivo ischemic reperfusion injury
and the induction of HSP72. Rabbits, divided into three groups, and
were administered with the alcoholic extract of the bark powder of
Terminalia arjuna (TAAE) by oral gavage [6.75mg/kg: (T1) and
9.75mg/kg: (T2), 6 days /week for 12 weeks]. In open-chest
Ketamine pentobarbitone anaesthetized rabbits, the left anterior
descending coronary artery was occluded for 15 min of ischemia
followed by 60 min of reperfusion. In the vehicle-treated group,
ischemic-reperfusion injury (IRI) was evidenced by depression of
global hemodynamic function (MAP, HR, LVEDP, peak LV (+) & (-
) (dP/dt) along with depletion of HEP compounds. Oxidative stress
in IRI was evidenced by, raised levels of myocardial TBARS and
depletion of endogenous myocardial antioxidants GSH, SOD and
catalase. Western blot analysis showed a single band corresponding
to 72 kDa in homogenates of hearts from rabbits treated with both the
doses. In the alcoholic extract of the bark powder of Terminalia
arjuna treatment groups, both the doses had better recovery of
myocardial hemodynamic function, with significant reduction in
TBARS, and rise in SOD, GSH, catalase were observed. The results
of the present study suggest that the alcoholic extract of the bark
powder of Terminalia arjuna in rabbit induces myocardial HSP 72
and augments myocardial endogenous antioxidants, without causing
any cellular injury and offered better cardioprotection against
oxidative stress associated with myocardial IR injury.
Abstract: A key to success of high quality software development
is to define valid and feasible requirements specification. We have
proposed a method of model-driven requirements analysis using
Unified Modeling Language (UML). The main feature of our method
is to automatically generate a Web user interface mock-up from UML
requirements analysis model so that we can confirm validity of
input/output data for each page and page transition on the system by
directly operating the mock-up. This paper proposes a support method
to check the validity of a data life cycle by using a model checking tool
“UPPAAL" focusing on CRUD (Create, Read, Update and Delete).
Exhaustive checking improves the quality of requirements analysis
model which are validated by the customers through automatically
generated mock-up. The effectiveness of our method is discussed by a
case study of requirements modeling of two small projects which are a
library management system and a supportive sales system for text
books in a university.
Abstract: This research work is aimed at speech recognition
using scaly neural networks. A small vocabulary of 11 words were
established first, these words are “word, file, open, print, exit, edit,
cut, copy, paste, doc1, doc2". These chosen words involved with
executing some computer functions such as opening a file, print
certain text document, cutting, copying, pasting, editing and exit.
It introduced to the computer then subjected to feature extraction
process using LPC (linear prediction coefficients). These features are
used as input to an artificial neural network in speaker dependent
mode. Half of the words are used for training the artificial neural
network and the other half are used for testing the system; those are
used for information retrieval.
The system components are consist of three parts, speech
processing and feature extraction, training and testing by using neural
networks and information retrieval.
The retrieve process proved to be 79.5-88% successful, which is
quite acceptable, considering the variation to surrounding, state of
the person, and the microphone type.
Abstract: In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.
Abstract: We investigated oxidative DNA damage caused by
radio frequency radiation using 8-oxo-7, 8-dihydro-2'-
deoxyguanosine (8-oxodG) generated in mice tissues after exposure
to 900 MHz mobile phone radio frequency in three independent
experiments. The RF was generated by a Global System for Mobile
Communication (GSM) signal generator. The radio frequency field
was adjusted to 25 V/m. The whole body specific absorption rate
(SAR) was 1.0 W/kg. Animals were exposed to this field for 30 min
daily for 30 days. 24 h post-exposure, blood serum, brain and spleen
were removed and DNA was isolated. Enzyme-linked
immunosorbent assay (ELISA) was used to measure 8-oxodG
concentration. All animals survived the whole experimental period.
The body weight of animals did not change significantly at the end of
the experiment. No statistically significant differences observed in
the levels of oxidative stress. Our results are not in favor of the
hypothesis that 900 MHz RF induces oxidative damage.
Abstract: A ten-year grazing study was conducted at the
Agriculture and Agri-Food Canada Brandon Research Centre in
Manitoba to study the effect of alfalfa inclusion and fertilizer (N, P,
K, and S) addition on economics and efficiency of non-renewable
energy use in meadow brome grass-based pasture systems for beef
production. Fertilizing grass-only or alfalfa-grass pastures to full soil
test recommendations improved pasture productivity, but did not
improve profitability compared to unfertilized pastures. Fertilizing
grass-only pastures resulted in the highest net loss of any pasture
management strategy in this study. Adding alfalfa at the time of
seeding, with no added fertilizer, was economically the best pasture
improvement strategy in this study. Because of moisture limitations,
adding commercial fertilizer to full soil test recommendations is
probably not economically justifiable in most years, especially with
the rising cost of fertilizer. Improving grass-only pastures by adding
fertilizer and/or alfalfa required additional non-renewable energy
inputs; however, the additional energy required for unfertilized
alfalfa-grass pastures was minimal compared to the fertilized
pastures. Of the four pasture management strategies, adding alfalfa
to grass pastures without adding fertilizer had the highest efficiency
of energy use. Based on energy use and economic performance, the
unfertilized alfalfa-grass pasture was the most efficient and
sustainable pasture system.
Abstract: Nevertheless the widespread application of finite
mixture models in segmentation, finite mixture model selection is
still an important issue. In fact, the selection of an adequate number
of segments is a key issue in deriving latent segments structures and
it is desirable that the selection criteria used for this end are effective.
In order to select among several information criteria, which may
support the selection of the correct number of segments we conduct a
simulation study. In particular, this study is intended to determine
which information criteria are more appropriate for mixture model
selection when considering data sets with only categorical
segmentation base variables. The generation of mixtures of
multinomial data supports the proposed analysis. As a result, we
establish a relationship between the level of measurement of
segmentation variables and some (eleven) information criteria-s
performance. The criterion AIC3 shows better performance (it
indicates the correct number of the simulated segments- structure
more often) when referring to mixtures of multinomial segmentation
base variables.
Abstract: Image Compression using Artificial Neural Networks
is a topic where research is being carried out in various directions
towards achieving a generalized and economical network.
Feedforward Networks using Back propagation Algorithm adopting
the method of steepest descent for error minimization is popular and
widely adopted and is directly applied to image compression.
Various research works are directed towards achieving quick
convergence of the network without loss of quality of the restored
image. In general the images used for compression are of different
types like dark image, high intensity image etc. When these images
are compressed using Back-propagation Network, it takes longer
time to converge. The reason for this is, the given image may
contain a number of distinct gray levels with narrow difference with
their neighborhood pixels. If the gray levels of the pixels in an image
and their neighbors are mapped in such a way that the difference in
the gray levels of the neighbors with the pixel is minimum, then
compression ratio as well as the convergence of the network can be
improved. To achieve this, a Cumulative distribution function is
estimated for the image and it is used to map the image pixels. When
the mapped image pixels are used, the Back-propagation Neural
Network yields high compression ratio as well as it converges
quickly.
Abstract: This paper presents the region based segmentation method for ultrasound images using local statistics. In this segmentation approach the homogeneous regions depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are to be extracted. This method uses a look up table comprising of the local statistics of every pixel, which are consisting of the homogeneity and similarity bounds according to the kernel size. The shape and size of the growing regions depend on this look up table entries. The algorithms are implemented by using connected seeded region growing procedure where each pixel is taken as seed point. The region merging after the region growing also suppresses the high frequency artifacts. The updated merged regions produce the output in formed of segmented image. This algorithm produces the results that are less sensitive to the pixel location and it also allows a segmentation of the accurate homogeneous regions.