Abstract: Iran is one of the greatest producers of date in the
world. However due to lack of information about its viscoelastic
properties, much of the production downgraded during harvesting
and postharvesting processes. In this study the effect of temperature
and moisture content of product were investigated on stress
relaxation characteristics. Therefore, the freshly harvested date
(kabkab) at tamar stage were put in controlled environment chamber
to obtain different temperature levels (25, 35, 45, and 55 0C) and
moisture contents (8.5, 8.7, 9.2, 15.3, 20, 32.2 %d.b.). A texture
analyzer TAXT2 (Stable Microsystems, UK) was used to apply
uniaxial compression tests. A chamber capable to control temperature
was designed and fabricated around the plunger of texture analyzer to
control the temperature during the experiment. As a new approach a
CCD camera (A4tech, 30 fps) was mounted on a cylindrical glass
probe to scan and record contact area between date and disk.
Afterwards, pictures were analyzed using image processing toolbox
of Matlab software. Individual date fruit was uniaxially compressed
at speed of 1 mm/s. The constant strain of 30% of thickness of date
was applied to the horizontally oriented fruit. To select a suitable
model for describing stress relaxation of date, experimental data were
fitted with three famous stress relaxation models including the
generalized Maxwell, Nussinovitch, and Pelege. The constant in
mentioned model were determined and correlated with temperature
and moisture content of product using non-linear regression analysis.
It was found that Generalized Maxwell and Nussinovitch models
appropriately describe viscoelastic characteristics of date fruits as
compared to Peleg mode.
Abstract: Web sites are rapidly becoming the preferred media
choice for our daily works such as information search, company
presentation, shopping, and so on. At the same time, we live in a
period where visual appearances play an increasingly important
role in our daily life. In spite of designers- effort to develop a web
site which be both user-friendly and attractive, it would be difficult
to ensure the outcome-s aesthetic quality, since the visual
appearance is a matter of an individual self perception and opinion.
In this study, it is attempted to develop an automatic system for
web pages aesthetic evaluation which are the building blocks of
web sites. Based on the image processing techniques and artificial
neural networks, the proposed method would be able to categorize
the input web page according to its visual appearance and aesthetic
quality. The employed features are multiscale/multidirectional
textural and perceptual color properties of the web pages, fed to
perceptron ANN which has been trained as the evaluator. The
method is tested using university web sites and the results
suggested that it would perform well in the web page aesthetic
evaluation tasks with around 90% correct categorization.
Abstract: This paper presents a comparative analysis of a new
unsupervised PCA-based technique for steel plates texture segmentation
towards defect detection. The proposed scheme called Variance
Based Component Analysis or VBCA employs PCA for feature
extraction, applies a feature reduction algorithm based on variance of
eigenpictures and classifies the pixels as defective and normal. While
the classic PCA uses a clusterer like Kmeans for pixel clustering,
VBCA employs thresholding and some post processing operations to
label pixels as defective and normal. The experimental results show
that proposed algorithm called VBCA is 12.46% more accurate and
78.85% faster than the classic PCA.
Abstract: A new method of adaptation in a partially integrated learning environment that includes electronic textbook (ET) and integrated tutoring system (ITS) is described. The algorithm of adaptation is described in detail. It includes: establishment of Interconnections of operations and concepts; estimate of the concept mastering level (for all concepts); estimate of student-s non-mastering level on the current learning step of information on each page of ET; creation of a rank-order list of links to the e-manual pages containing information that require repeated work.
Abstract: Mostly the systems are dealing with time varying
signals. The Power efficiency can be achieved by adapting the system
activity according to the input signal variations. In this context
an adaptive rate filtering technique, based on the level crossing sampling
is devised. It adapts the sampling frequency and the filter order
by following the input signal local variations. Thus, it correlates the
processing activity with the signal variations. Interpolation is required
in the proposed technique. A drastic reduction in the interpolation
error is achieved by employing the symmetry during the interpolation
process. Processing error of the proposed technique is
calculated. The computational complexity of the proposed filtering
technique is deduced and compared to the classical one. Results
promise a significant gain of the computational efficiency and hence
of the power consumption.
Abstract: The values of managers and employees in organizations are phenomena that have captured the interest of researchers at large. Despite this attention, there continues to be a lack of agreement on what values are and how they influence individuals, or how they are constituted in individuals- mind. In this article content-based approach is presented as alternative reference frame for exploring values. In content-based approach human thinking in different contexts is set at the focal point. Differences in valuations can be explained through the information contents of mental representations. In addition to the information contents, attention is devoted to those cognitive processes through which mental representations of values are constructed. Such informational contents are in decisive role for understanding human behavior. By applying content-based analysis to an examination of values as mental representations, it is possible to reach a deeper to the motivational foundation of behaviors, such as decision making in organizational procedures, through understanding the structure and meanings of specific values at play.
Abstract: Segmentation in ultrasound images is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, a segmentation scheme using fuzzy c-means (FCM) clustering incorporating both intensity and texture information of images is proposed to extract breast lesions in ultrasound images. Firstly, the nonlinear structure tensor, which can facilitate to refine the edges detected by intensity, is used to extract speckle texture. And then, a spatial FCM clustering is applied on the image feature space for segmentation. In the experiments with simulated and clinical ultrasound images, the spatial FCM clustering with both intensity and texture information gets more accurate results than the conventional FCM or spatial FCM without texture information.
Abstract: The purpose of this paper is to investigate the
influence of a number of variables on the conditional mean and
conditional variance of credit spread changes. The empirical analysis
in this paper is conducted within the context of bivariate GARCH-in-
Mean models, using the so-called BEKK parameterization. We show
that credit spread changes are determined by interest-rate and equityreturn
variables, which is in line with theory as provided by the
structural models of default. We also identify the credit spread
change volatility as an important determinant of credit spread
changes, and provide evidence on the transmission of volatility
between the variables under study.
Abstract: This paper represents an investigation on how exploiting multiple transmit antennas by OFDM based wireless LAN subscribers can mitigate physical layer error rate. Then by comparing the Wireless LANs that utilize spatial diversity techniques with the conventional ones it will reveal how PHY and TCP throughputs behaviors are ameliorated. In the next step it will assess the same issues based on a cellular context operation which is mainly introduced as an innovated solution that beside a multi cell operation scenario benefits spatio-temporal signaling schemes as well. Presented simulations will shed light on the improved performance of the wide range and high quality wireless LAN services provided by the proposed approach.
Abstract: Speckled images arise when coherent microwave,
optical, and acoustic imaging techniques are used to image an object, surface or scene. Examples of coherent imaging systems include synthetic aperture radar, laser imaging systems, imaging sonar
systems, and medical ultrasound systems. Speckle noise is a form of object or target induced noise that results when the surface of the object is Rayleigh rough compared to the wavelength of the illuminating radiation. Detection and estimation in images corrupted
by speckle noise is complicated by the nature of the noise and is not
as straightforward as detection and estimation in additive noise. In
this work, we derive stochastic models for speckle noise, with an emphasis on speckle as it arises in medical ultrasound images. The
motivation for this work is the problem of segmentation and tissue classification using ultrasound imaging. Modeling of speckle in this
context involves partially developed speckle model where an underlying Poisson point process modulates a Gram-Charlier series
of Laguerre weighted exponential functions, resulting in a doubly
stochastic filtered Poisson point process. The statistical distribution of partially developed speckle is derived in a closed canonical form.
It is observed that as the mean number of scatterers in a resolution cell is increased, the probability density function approaches an
exponential distribution. This is consistent with fully developed speckle noise as demonstrated by the Central Limit theorem.
Abstract: Knowledge-based e-mail systems focus on
incorporating knowledge management approach in order to enhance
the traditional e-mail systems. In this paper, we present a knowledgebased
e-mail system called KS-Mail where people do not only send
and receive e-mail conventionally but are also able to create a sense
of knowledge flow. We introduce semantic processing on the e-mail
contents by automatically assigning categories and providing links to
semantically related e-mails. This is done to enrich the knowledge
value of each e-mail as well as to ease the organization of the e-mails
and their contents. At the application level, we have also built
components like the service manager, evaluation engine and search
engine to handle the e-mail processes efficiently by providing the
means to share and reuse knowledge. For this purpose, we present the
KS-Mail architecture, and elaborate on the details of the e-mail
server and the application server. We present the ontology mapping
technique used to achieve the e-mail content-s categorization as well
as the protocols that we have developed to handle the transactions in
the e-mail system. Finally, we discuss further on the implementation
of the modules presented in the KS-Mail architecture.
Abstract: For the past one decade, biclustering has become popular data mining technique not only in the field of biological data analysis but also in other applications like text mining, market data analysis with high-dimensional two-way datasets. Biclustering clusters both rows and columns of a dataset simultaneously, as opposed to traditional clustering which clusters either rows or columns of a dataset. It retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. Firefly Algorithm (FA) is a recently-proposed metaheuristic inspired by the collective behavior of fireflies. This paper provides a preliminary assessment of discrete version of FA (DFA) while coping with the task of mining coherent and large volume bicluster from web usage dataset. The experiments were conducted on two web usage datasets from public dataset repository whereby the performance of FA was compared with that exhibited by other population-based metaheuristic called binary Particle Swarm Optimization (PSO). The results achieved demonstrate the usefulness of DFA while tackling the biclustering problem.
Abstract: Facial expression analysis plays a significant role for
human computer interaction. Automatic analysis of human facial
expression is still a challenging problem with many applications. In
this paper, we propose neuro-fuzzy based automatic facial expression
recognition system to recognize the human facial expressions like
happy, fear, sad, angry, disgust and surprise. Initially facial image is
segmented into three regions from which the uniform Local Binary
Pattern (LBP) texture features distributions are extracted and
represented as a histogram descriptor. The facial expressions are
recognized using Multiple Adaptive Neuro Fuzzy Inference System
(MANFIS). The proposed system designed and tested with JAFFE
face database. The proposed model reports 94.29% of classification
accuracy.
Abstract: Environmental decision making, particularly about
hazardous waste management, is inherently exposed to a high
potential conflict, principally because of the trade-off between sociopolitical,
environmental, health and economic factors. The need to
plan complex contexts has led to an increasing request for decision
analytic techniques as support for the decision process. In this work,
alternative systems of asbestos-containing waste management
(ACW) in Puglia (Southern Italy) were explored by a multi-criteria
decision analysis. In particular, through Analytic Hierarchy Process
five alternatives management have been compared and ranked
according to their performance and efficiency, taking into account
environmental, health and socio-economic aspects. A separated
valuation has been performed for different temporal scale. For short
period results showed a narrow deviation between the disposal
alternatives “mono-material landfill in public quarry" and “dedicate
cells in existing landfill", with the best performance of the first one.
While for long period “treatment plant to eliminate hazard from
asbestos-containing waste" was prevalent, although high energy
demand required to achieve the change of crystalline structure. A
comparison with results from a participative approach in valuation
process might be considered as future development of method
application to ACW management.
Abstract: With the proliferation of Weblogs (blogs) use in
educational contexts, gaining a better understanding of why
students are willing to utilize blog systems has become an
important topic for practitioners and academics. While perceived
enjoyment has been found to have a significant influence on
behavioral intentions to use blogs or hedonic systems, few studies
have investigated the antecedents of perceived enjoyment in the
acceptance of blogging. The main purpose of the present study is to
explore the individual difference antecedents of perceived
enjoyment and examine how they influence behavioral intention to
blog through the mediation of perceived enjoyment. Based on the
previous literature, the Big Five personality traits (i.e.,
extraversion, agreeableness, conscientiousness, neuroticism, and
openness to experience), as well as computer self-efficacy and
personal innovation in information technology (PIIT), are
hypothesized as potential antecedents of perceived enjoyment in
the acceptance of blogging. Data collected from 358 respondents in
Taiwan are tested against the research model using the structural
equation modeling approach. The results indicate that extraversion,
agreeableness, conscientiousness, and PIIT have a significant
influence on perceived enjoyment, which in turn significantly
influences the behavioral intention to blog. These findings lead to
several important implications for future research.
Abstract: The paper discuses the effect of initial stresses on the reflection coefficients of plane waves in a dissipative medium. Basic governing equations are formulated in context of Biot's incremental deformation theory. These governing equations are solved analytically to obtain the dimensional phase velocities of plane waves propagating in plane of symmetry. Closed-form expressions for the reflection coefficients of P and SV waves- incident at the free surface of an initially stressed dissipative medium are obtained. Numerical computations, using these expressions, are carried out for a particular model. Computations made with the results predicted in presence and absence of the initial stresses and the results have been shown graphically. The study shows that the presence of compressive initial stresses increases the velocity of longitudinal wave (P-wave) but diminishes that of transverse wave (SV-wave). Also the numerical results presented indicate that initial stresses and dissipation might affect the reflection coefficients significantly.
Abstract: The sonochemical decolorization and degradation of azo dye Methyl violet using Fenton-s reagent in the presence of a high-frequency acoustic field has been investigated. Dyeing and textile effluents are the major sources of azo dyes, and are most troublesome among industrial wastewaters, causing imbalance in the eco-system. The effect of various operating conditions (initial concentration of dye, liquid-phase temperature, ultrasonic power and frequency and process time) on sonochemical degradation was investigated. Conversion was found to increase with increase in initial concentration, temperature, power level and frequency. Both horntype and tank-type sonicators were used, at various power levels (250W, 400W and 500W) for frequencies ranging from 20 kHz - 1000 kHz. A 'Process Intensification' parameter PI, was defined to quantify the enhancement of the degradation reaction by ultrasound when compared to control (i.e., without ultrasound). The present work clearly demonstrates that a high-frequency ultrasonic bath can be used to achieve higher process throughput and energy efficiency at a larger scale of operation.
Abstract: Based on the combined shape feature and texture
feature, a fast object detection method with rotation invariant features
is proposed in this paper. A quick template matching scheme based
online learning designed for online applications is also introduced in
this paper. The experimental results have shown that the proposed
approach has the features of lower computation complexity and
higher detection rate, while keeping almost the same performance
compared to the HOG-based method, and can be more suitable for
run time applications.
Abstract: Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.
Abstract: The purpose of this study was to investigate the effects of computer–based instructional designs, namely modality and redundancy principles on the attitude and learning of music theory among primary pupils of different Music Intelligence levels. The lesson of music theory was developed in three different modes, audio and image (AI), text with image (TI) and audio with image and text (AIT). The independent variables were the three modes of courseware. The moderator variable was music intelligence. The dependent variables were the post test score. ANOVA was used to determine the significant differences of the pretest scores among the three groups. Analyses of covariance (ANCOVA) and Post hoc were carried out to examine the main effects as well as the interaction effects of the independent variables on the dependent variables. High music intelligence pupils performed significantly better than low music intelligence pupils in all the three treatment modes. The AI mode was found to help pupils with low music intelligence significantly more than the TI and AIT modes.