Abstract: The advent of multi-million gate Field Programmable
Gate Arrays (FPGAs) with hardware support for multiplication opens
an opportunity to recreate a significant portion of the front end of a
human cochlea using this technology. In this paper we describe the
implementation of the cochlear filter and show that it is entirely
suited to a single device XC3S500 FPGA implementation .The filter
gave a good fit to real time data with efficiency of hardware usage.
Abstract: Due to the increasing and varying risks that economic units face with, derivative instruments gain substantial importance, and trading volumes of derivatives have reached very significant level. Parallel with these high trading volumes, researchers have developed many different models. Some are parametric, some are nonparametric. In this study, the aim is to analyse the success of artificial neural network in pricing of options with S&P 100 index options data. Generally, the previous studies cover the data of European type call options. This study includes not only European call option but also American call and put options and European put options. Three data sets are used to perform three different ANN models. One only includes data that are directly observed from the economic environment, i.e. strike price, spot price, interest rate, maturity, type of the contract. The others include an extra input that is not an observable data but a parameter, i.e. volatility. With these detail data, the performance of ANN in put/call dimension, American/European dimension, moneyness dimension is analyzed and whether the contribution of the volatility in neural network analysis make improvement in prediction performance or not is examined. The most striking results revealed by the study is that ANN shows better performance when pricing call options compared to put options; and the use of volatility parameter as an input does not improve the performance.
Abstract: Modeling of a heterogeneous industrial fixed bed
reactor for selective dehydrogenation of heavy paraffin with Pt-Sn-
Al2O3 catalyst has been the subject of current study. By applying
mass balance, momentum balance for appropriate element of reactor
and using pressure drop, rate and deactivation equations, a detailed
model of the reactor has been obtained. Mass balance equations have
been written for five different components. In order to estimate
reactor production by the passage of time, the reactor model which is
a set of partial differential equations, ordinary differential equations
and algebraic equations has been solved numerically.
Paraffins, olefins, dienes, aromatics and hydrogen mole percent as
a function of time and reactor radius have been found by numerical
solution of the model. Results of model have been compared with
industrial reactor data at different operation times. The comparison
successfully confirms validity of proposed model.
Abstract: This paper presents modeling and simulation of Grid Connected Photovoltaic (PV) system by using improved mathematical model. The model is used to study different parameter variations and effects on the PV array including operating temperature and solar irradiation level. In this paper stepped P&O algorithm is proposed for MPPT control. This algorithm will identify the suitable duty ratio in which the DC-DC converter should be operated to maximize the power output. Photo voltaic array with proposed stepped P&O-MPPT controller can operate in the maximum power point for the whole range of solar data (irradiance and temperature).
Abstract: As a result of urbanization, the unpredictable growth of industry and transport, production of chemicals, military activities, etc. the concentration of anthropogenic toxicants spread in nature exceeds all the permissible standards. Most dangerous among these contaminants are organic compounds having great persistence, bioaccumulation, and toxicity along with our awareness of their prominent occurrence in the environment and food chain. Among natural ecological tools, plants still occupying above 40% of the world land, until recently, were considered as organisms having only a limited ecological potential, accumulating in plant biomass and partially volatilizing contaminants of different structure. However, analysis of experimental data of the last two decades revealed the essential role of plants in environment remediation due to ability to carry out intracellular degradation processes leading to partial or complete decomposition of carbon skeleton of different structure contaminants. Though, phytoremediation technologies still are in research and development, their various applications have been successfully used. The paper aims to analyze mechanisms of organic contaminants uptake and detoxification in plants, being the less studied issue in evaluation and exploration of plants potential for environment remediation.
Abstract: Choosing the right metadata is a critical, as good
information (metadata) attached to an image will facilitate its
visibility from a pile of other images. The image-s value is enhanced
not only by the quality of attached metadata but also by the technique
of the search. This study proposes a technique that is simple but
efficient to predict a single human image from a website using the
basic image data and the embedded metadata of the image-s content
appearing on web pages. The result is very encouraging with the
prediction accuracy of 95%. This technique may become a great
assist to librarians, researchers and many others for automatically and
efficiently identifying a set of human images out of a greater set of
images.
Abstract: The dynamics of User Datagram Protocol (UDP) traffic
over Ethernet between two computers are analyzed using nonlinear
dynamics which shows that there are two clear regimes in the data
flow: free flow and saturated. The two most important variables
affecting this are the packet size and packet flow rate. However,
this transition is due to a transcritical bifurcation rather than phase
transition in models such as in vehicle traffic or theorized large-scale
computer network congestion. It is hoped this model will help lay
the groundwork for further research on the dynamics of networks,
especially computer networks.
Abstract: High speed networks provide realtime variable bit rate
service with diversified traffic flow characteristics and quality
requirements. The variable bit rate traffic has stringent delay and
packet loss requirements. The burstiness of the correlated traffic
makes dynamic buffer management highly desirable to satisfy the
Quality of Service (QoS) requirements. This paper presents an
algorithm for optimization of adaptive buffer allocation scheme for
traffic based on loss of consecutive packets in data-stream and buffer
occupancy level. Buffer is designed to allow the input traffic to be
partitioned into different priority classes and based on the input
traffic behavior it controls the threshold dynamically. This algorithm
allows input packets to enter into buffer if its occupancy level is less
than the threshold value for priority of that packet. The threshold is
dynamically varied in runtime based on packet loss behavior. The
simulation is run for two priority classes of the input traffic –
realtime and non-realtime classes. The simulation results show that
Adaptive Partial Buffer Sharing (ADPBS) has better performance
than Static Partial Buffer Sharing (SPBS) and First In First Out
(FIFO) queue under the same traffic conditions.
Abstract: The necessity of accurate and timely field data is
shared among organizations engaged in fundamentally different
activities, public services or commercial operations. Basically, there
are three major components in the process of the qualitative research:
data collection, interpretation and organization of data, and analytic
process. Representative technological advancements in terms of
innovation have been made in mobile devices (mobile phone, PDA-s,
tablets, laptops, etc). Resources that can be potentially applied on the
data collection activity for field researches in order to improve this
process.
This paper presents and discuss the main features of a mobile
phone based solution for field data collection, composed of basically
three modules: a survey editor, a server web application and a client
mobile application. The data gathering process begins with the
survey creation module, which enables the production of tailored
questionnaires. The field workforce receives the questionnaire(s) on
their mobile phones to collect the interviews responses and sending
them back to a server for immediate analysis.
Abstract: Network layer multicast, i.e. IP multicast, even after
many years of research, development and standardization, is not
deployed in large scale due to both technical (e.g. upgrading of
routers) and political (e.g. policy making and negotiation) issues.
Researchers looked for alternatives and proposed application/overlay
multicast where multicast functions are handled by end hosts, not
network layer routers. Member hosts wishing to receive multicast
data form a multicast delivery tree. The intermediate hosts in the tree
act as routers also, i.e. they forward data to the lower hosts in the
tree. Unlike IP multicast, where a router cannot leave the tree until all
members below it leave, in overlay multicast any member can leave
the tree at any time thus disjoining the tree and disrupting the data
dissemination. All the disrupted hosts have to rejoin the tree. This
characteristic of the overlay multicast causes multicast tree unstable,
data loss and rejoin overhead. In this paper, we propose that each node
sets its leaving time from the tree and sends join request to a number
of nodes in the tree. The nodes in the tree will reject the request if
their leaving time is earlier than the requesting node otherwise they
will accept the request. The node can join at one of the accepting
nodes. This makes the tree more stable as the nodes will join the tree
according to their leaving time, earliest leaving time node being at the
leaf of the tree. Some intermediate nodes may not follow their leaving
time and leave earlier than their leaving time thus disrupting the tree.
For this, we propose a proactive recovery mechanism so that disrupted
nodes can rejoin the tree at predetermined nodes immediately. We
have shown by simulation that there is less overhead when joining
the multicast tree and the recovery time of the disrupted nodes is
much less than the previous works. Keywords
Abstract: Due to the ever growing amount of publications about
protein-protein interactions, information extraction from text is
increasingly recognized as one of crucial technologies in
bioinformatics. This paper presents a Protein Interaction Extraction
System using a Link Grammar Parser from biomedical abstracts
(PIELG). PIELG uses linkage given by the Link Grammar Parser to
start a case based analysis of contents of various syntactic roles as
well as their linguistically significant and meaningful combinations.
The system uses phrasal-prepositional verbs patterns to overcome
preposition combinations problems. The recall and precision are
74.4% and 62.65%, respectively. Experimental evaluations with two
other state-of-the-art extraction systems indicate that PIELG system
achieves better performance. For further evaluation, the system is
augmented with a graphical package (Cytoscape) for extracting
protein interaction information from sequence databases. The result
shows that the performance is remarkably promising.
Abstract: In this paper a new approach to face recognition is
presented that achieves double dimension reduction, making the
system computationally efficient with better recognition results and
out perform common DCT technique of face recognition. In pattern
recognition techniques, discriminative information of image
increases with increase in resolution to a certain extent, consequently
face recognition results change with change in face image resolution
and provide optimal results when arriving at a certain resolution
level. In the proposed model of face recognition, initially image
decimation algorithm is applied on face image for dimension
reduction to a certain resolution level which provides best
recognition results. Due to increased computational speed and feature
extraction potential of Discrete Cosine Transform (DCT), it is
applied on face image. A subset of coefficients of DCT from low to
mid frequencies that represent the face adequately and provides best
recognition results is retained. A tradeoff between decimation factor,
number of DCT coefficients retained and recognition rate with
minimum computation is obtained. Preprocessing of the image is
carried out to increase its robustness against variations in poses and
illumination level. This new model has been tested on different
databases which include ORL , Yale and EME color database.
Abstract: Supply chain networks are frequently hit by
unplanned events which lead to disruptions and cause operational and
financial consequences. It is neither possible to avoid disruption risk
entirely, nor are network members able to prepare for every possible
disruptive event. Therefore a continuity planning should be set up
which supports effective operational responses in supply chain
networks in times of emergencies. In this research network related
degrees of freedom which determine the options for responsive
actions are derived from interview data. The findings are further
embedded into a common risk management process. The paper
provides support for researchers and practitioners to identify the
network related options for responsive actions and to determine the
need for improving the reaction capabilities.
Abstract: In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively
Abstract: This study investigates the relationship between 10
year bond value, Yen/U.S dollar exchange rate, non-farm payrolls (all
employs) and crude oil to U.S. Dow Jones Sustainability Index. A
GARCH model is used to test these relationships for the period
January 1st 1999 to January 31st 2008 using monthly data. Results
show that an increase of the 10 year bond and non farm payrolls (all
employs) lead to an increase of the D.J.S.I returns. On the contrary
the volatility of the Yen/U.S dollar exchange rates as well as the
increase of crude oil returns has negative effects on the U.S D.J.S.I
returns. This study aims at assisting investors to understand the
influences certain macroeconomic indicators have on the companies-
stock returns as reported by the D.J.S.I.
Abstract: The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.
Abstract: Influence of octane and benzene on plant cell
ultrastructure and enzymes of basic metabolism, such as nitrogen
assimilation and energy generation have been studied. Different
plants: perennial ryegrass (Lolium perenne) and alfalfa (Medicago
sativa); crops- maize (Zea mays L.) and bean (Phaseolus vulgaris);
shrubs – privet (Ligustrum sempervirens) and trifoliate orange
(Poncirus trifoliate); trees - poplar (Populus deltoides) and white
mulberry (Morus alba L.) were exposed to hydrocarbons of different
concentrations (1, 10 and 100 mM). Destructive changes in bean and
maize leaves cells ultrastructure under the influence of benzene
vapour were revealed at the level of photosynthetic and energy
generation subcellular organells. Different deviations at the level of
subcellular organelles structure and distribution were observed in
alfalfa and ryegrass root cells under the influence of benzene and
octane, absorbed through roots. The level of destructive changes is
concentration dependent. Benzene at low 1 and 10 mM concentration
caused the increase in glutamate dehydrogenase (GDH) activity in
maize roots and leaves and in poplar and mulberry shoots, though to
higher extent in case of lower, 1mM concentration. The induction
was more intensive in plant roots. The highest tested 100mM
concentration of benzene was inhibitory to the enzyme in all plants.
Octane caused induction of GDH in all grassy plants at all tested
concentrations; however the rate of induction decreased parallel to
increase of the hydrocarbon concentration. Octane at concentration 1
mM caused induction of GDH in privet, trifoliate and white mulberry
shoots. The highest, 100mM octane was characterized by inhibitory
effect to GDH activity in all plants. Octane had inductive effect on
malate dehydrogenase in almost all plants and tested concentrations,
indicating the intensification of Trycarboxylic Acid Cycle.
The data could be suggested for elaboration of criteria for plant
selection for phytoremediation of oil hydrocarbons contaminated
soils.
Abstract: If price and quantity are the fundamental building
blocks of any theory of market interactions, the importance of trading
volume in understanding the behavior of financial markets is clear.
However, while many economic models of financial markets have
been developed to explain the behavior of prices -predictability,
variability, and information content- far less attention has been
devoted to explaining the behavior of trading volume. In this article,
we hope to expand our understanding of trading volume by
developing a new measure of herding behavior based on a cross
sectional dispersion of volumes betas. We apply our measure to the
Toronto stock exchange using monthly data from January 2000 to
December 2002. Our findings show that the herd phenomenon
consists of three essential components: stationary herding, intentional
herding and the feedback herding.
Abstract: In this paper, we propose an algorithm to compute
initial cluster centers for K-means clustering. Data in a cell is
partitioned using a cutting plane that divides cell in two smaller cells.
The plane is perpendicular to the data axis with the highest variance
and is designed to reduce the sum squared errors of the two cells as
much as possible, while at the same time keep the two cells far apart
as possible. Cells are partitioned one at a time until the number of
cells equals to the predefined number of clusters, K. The centers of
the K cells become the initial cluster centers for K-means. The
experimental results suggest that the proposed algorithm is effective,
converge to better clustering results than those of the random
initialization method. The research also indicated the proposed
algorithm would greatly improve the likelihood of every cluster
containing some data in it.
Abstract: Given the motivation of maps impact in enhancing the
perception of the quality of life in a region, this work examines the
use of spatial analytical techniques in exploring the role of space in
shaping human development patterns in Assiut governorate.
Variations of human development index (HDI) of the governorate-s
villages, districts and cities are mapped using geographic information
systems (GIS). Global and local spatial autocorrelation measures are
employed to assess the levels of spatial dependency in the data and to
map clusters of human development. Results show prominent
disparities in HDI between regions of Assiut. Strong patterns of
spatial association were found proving the presence of clusters on the
distribution of HDI. Finally, the study indicates several "hot-spots" in
the governorate to be area of more investigations to explore the
attributes of such levels of human development. This is very
important for accomplishing the development plan of poorest regions
currently adopted in Egypt.