Vol 2 No 1 - June 2013

ISSN: 1694-2507 (Print)
International Journal of Computer Science
and Business Informatics
(IJCSBI.ORG)
VOL 2, NO 1
JUNE 2013

Table of Contents VOL 2, NO 1 JUNE 2013
Analytical Hierarchy Process Based Framework for Modelling Preferences and Priorities in
Requirements....................................................................................................................................1
R.Subha and S.Palaniswami
Unstructured Data Integration through Automata-Driven Information Extraction ...............................1
Maroun Abi Assaf, Kablan Barbar, Youakim Badr and Mahmoud Rammal
Wireless Solution for Water Saving In Agriculture Using Embedded System.........................................1
Venkata Narayana Eluri, K. Madhusudhana Rao and A. Srinag
Upcoming Trends of Virtual Experiments for Laboratories...................................................................1
Bhaskar Y. Kathane, Pradeep B. Dahikar and Satish J. Sharma
Negotiation Based Resource Allocation for Distributed Environments .................................................1
Krishnamoorthy M. and Senthil Murugan B.
Wireless Solution for Water Saving In Agriculture Using Embedded System ........................................1
Venkata Narayana Eluri, K. Madhusudhana Rao and A. Srinag
Digital Image Tamper Detection Techniques - A Comprehensive Study ...............................................1
Minati Mishra and Flt. Lt. Dr. M. C. Adhikary
A Multi-parametric based W-PAC Mechanism in Ad Hoc Network using IPv6 and IPv4 Address............1
S. Thirumurugan and Dr. E. George Dharma Prakash Raj
A New Online XML Document Clustering Based on XCLS++..................................................................1
Ahmad Khodayar E Qaramaleki and Ahmad Khodayar E Qaramaleki
IJCSBI.ORG

Secured and Energy Based QoS Routing in MANETs ............................................................................1
S. Sridhar and R. Baskaran

International Journal of Computer Science and Business Informatics
IJCSBI.ORG
ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 1
Analytical Hierarchy Process Based
Framework for Modelling Preferences
and Priorities in Requirements
R.Subha
Assistant Professor,
Department of Computer Science and Engineering
Sri Krishna College of Technology, Kovaipudur, Coimbatore-641 042,
Tamil Nadu, India
S.Palaniswami
Principal,
Government College of Engineering,
Bodinayakanur, Tamil Nadu, India
ABSTRACT
Requirements engineering is a software engineering process which covers all the activities
involved in discovering, documenting and maintaining a set of requirements for a
computer-based system. The priorities that stakeholders associate with requirements may
vary from stakeholder to stakeholder. Different priorities imply different design decisions
for the system. So there must be a model to support the representation of preference in
requirements. In this paper we develop a framework to model alternative solutions for
mandatory goals and preferred goals based on priorities. A framework is created for
specifying preferences and priorities among requirements. The priorities among the
preferences are analysed by Analytical Hierarchy Process method (AHP). AHP‘s pair wise
comparison method is used to assess the relative value of the candidate requirements. The
preferences are analysed based on the prioritization of each task and a definite plan is
generated to view all those tasks according to priority.
Keywords
Requirements Engineering, Preference Goals, Priorities, Goal oriented frame work,
Analytic Hierarchy Process.
1. INTRODUCTION
Software engineering is a modelling activity in which, software engineers
deal with complexity through modelling by focusing at anytime, only the
relevant details and ignoring everything else. Software Engineering
encompasses all the life cycle activities of a project associated with
understanding a product‘s necessary capabilities and attributes.
Requirements Engineering (RE) is the process of establishing the services
that the customer requires from a system and the constraints under which it

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operates and is developed. It may range from a high-level abstract statement
of a service or a system constraint to a detailed mathematical functional
specification. Requirements engineering acts as a bridge between the real-
world needs of users, customers and other constituencies affected by the
software system and the capabilities and opportunities afforded by software-
intensive technologies.
Preference goals are the goals that are chosen by the stakeholder, but it will
not always be in the same degree of desirability [1]. Sometimes preference
goals are considered as those goals which help other goals benefit from it.
For instance, in the book shop details example, we may opt to accomplish
the book order through a payment approach which is undesirable for the
customer—e.g., money order—but still we favour the solution because, an
alternate goal may be suitable, say to maintain robust legal documents.
Such goals are represented as preferences or preference goals.
Prioritizing requirements is one of the tasks of the Requirements Phase. It
determines the implementation order of the requirements in an incremental
and iterative development cycle. Preferences are not always longed for or
essential [2]. For a distinct situation or context or for a distinct stakeholder,
a subset of preferences seems to be more applicable than any other
requirement. Stakeholder‘s satisfaction must be considered to be the
unblemished goal and a navigating theme by the prioritizing process.
Analytical Hierarchy Process (AHP) is used to estimate the priorities among
the requirements. AHP‘s pair wise comparison technique is used to assess
the relative value of the candidate requirements.AHP shows pair wise
comparisons and subsequent approximation of the Eigen vector of the
resulting comparison matrix in order to acquire greater accuracy of the
numeric prioritization result. This also instills confidence in us that the
model has been elicited and constructed appropriately, that the domain is
well established and that AHP yields an accurate prioritization profile (e.g.,
with a good consistency ratio).Requirement prioritization is utilized in the
software product management to investigate which candidate requirement in
a software product should be comprehended in a certain release.
Requirements are also prioritized to minimize risk during development so
that the most essential or the requirements that are extremely risky are
implemented first. It is always better to force the stakeholders to address all
the requirements and not just their own Prioritized requirements or the ones
with the determined order of importance to some stakeholder or class of
stakeholders pertaining to the requirements along one or more dimensions

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(e.g., personal preference, business value, cost of implementation, and risk).
Based on the priorities we develop a goal model to reveal better solutions of
goals that are mandatory and goals that are preferred. The priorities and
preferences among requirements are specified by creating a framework. A
Goal Model is a key aspect of Requirements Engineering that may be used
in Stakeholder analysis, Context analysis, and Scenario more evidently.
Through acute cooperation of actors in the predestined software a system
should acquire the targets that are goals. The tradeoffs between cost,
performance, flexibility, security and other goals can be located and quickly
resolved by utilizing goal modelling. Aberrant interests between
stakeholders are also revealed using goal modelling. Goal modelling can
spot conflicts when interference occurs among the goals. Goal modelling
empowers requirement completeness to be measured. If all the goals in the
goal model are attained the requirements can be considered to be complete.
A goal model is effective in concisely capturing large number of alternative
sets of low-level tasks, operations, and configurations that can fulfill high-
level stakeholder requirements. The high-level stakeholder‘s requirements
include specifying preferences and priorities among various requirements
and select specifications that fulfill mandatory requirements. Based on the
priorities among discrete tasks alternative solutions are detected. This
enhances the segregation between mandatory and preference goals, and
paves way to the destined goal. This also establishes the relative
significance of all the requirements.
The rest of the paper is organized as follows. In Sect. 2, we present the goal
and the preference goal and assign certain priorities to those goals by AHP
method. Then, in Sect. 3, we take a look at related literature. In Sect. 4, we
have various methodologies to support the goal oriented framework In Sect.
5, we discuss the performance of the proposed reasoning task and conclude
it in Sect. 6.
2. RELATED WORKS
Earlier, in modeling and analysis only the preferences of the goals were
considered for analysis and the priorities for the preferences were expelled.
Techne‘s perspective showcased the RE process which was involved in
describing requirements problems and comparing the candidate solutions at
an early stage. Object-orientation, predicates, temporal constraints, and task
sequencing are missing since they contribute to the detailed solution of the
chosen candidate solution [3]. Techne precedes and complements RMLs for
detailed specification which does include such features. In the course of

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these phases, the requirements problem for the system-to-be is being
structured and its candidate solutions are described and compared, based on
their desirability to the stakeholders.
In modeling mandatory requirements, only tasks and goals are considered,
whereas, the alternative solutions are not specified. A modelling and
reasoning toolset for exploring and evaluating the alternative solutions to
requirements problems, is adopted [4]. The temporal constraints are
expressed by extending the common goal modelling formalism and the
alternative plans for fulfilling stakeholder goals are represented using partial
goal satisfaction. The users can specify their priorities over optional, ―nice-
to-have‖ properties of potential solution by using a preference specification
language.
In customization technique the preferences are not taken into account and
the software is developed according to the customers individual
requirements. Software needs to support customization at a much finer-rate.
Software assists people to live their lives despite disabilities, constitute a
significant class of applications where greater degree of customization is
essential [3]. This paper proposes a framework that supports the design of
highly customizable software by adopting goal-oriented analysis techniques.
The working of this framework can be demonstrated with a simple example
involving the design of a customizable email system.
Different design decisions for the system crop up due to varying priorities.
So we need to introduce a framework to specify both the preference
requirements and priorities in order to fulfil the mandatory requirements.
This enhances the extension of a traditional goal modelling notation to
support the representation of optional and preference requirements [5]. In
our extension, optional goals are marked off from the mandatory ones.
Later, quantitative prioritizations of the former are constructed and used as a
criterion for evaluating alternative ways to achieve the latter. In
Requirements Engineering (RE), goal-oriented techniques assist as to what
the stakeholders wants (goals) and the means (actions/tasks/plans) by which
these goals can be achieved, thereby gaining noticeable attention.
The basic concerns that are involved in the stakeholders communication
does not come under requirement engineering. These include beliefs,
desires, intentions, and attitudes. To accelerate these, we propose a core
ontology that covers these concerns and is grounded in sound conceptual
foundations resting on a foundational ontology [6]. The new core ontology
for RE paves the way to a new formulation of the requirements problem.
New standards and criteria are inculcated to specify the minimum amount of

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information that has to be represented in RE languages and to determine if
the RE has gone into a favourable completion.
The existing system has goals that are modelled by intrinsic features such as
their type and attributes, and by their links to other goals and two other
elements of a requirements model. The modelling and reasoning about
preferences for stakeholders in the presence of mandatory goals remains
largely unexplored. Goal-oriented techniques in requirements engineering
have a unique way to keep one going between stakeholders wants (goals)
and the means (actions/tasks/plans) to achieve goals [1]. The current goal-
oriented modelling frameworks treat goals as mandatory requirements that
must be fulfilled by any proposed solution. In this respect, such frameworks
cannot accommodate reference (‗‗nice-to-have‘‘) requirements that might be
posed by stakeholders.
3. METHODOLOGY
3.1 Construction of a Goal Model
Goals are targets that are earned through acute cooperation of actors in the
predestined software. Initial phase of a project is best suited for goal
modeling. The relationships between a system and its environment are
expressed [7]. Goal models are effective in capturing large numbers of
alternative sets of low- level tasks, operations, and configurations that can
fulfil high-level stakeholder requirements.This module contains basic task
and the goals that are represented using GR Tool which is the goal
reasoning tool.
The GR-Tool is graphical tool that is employed to draw goal models and run
the algorithms and tools for forward and backward reasoning [8]. The
tradeoffs between cost, performance, flexibility, security and other goals can
be located and quickly resolved by utilizing goal modelling. It expresses the
relationships between a system and its environment. Goal models have been
found to be effective in concisely capturing large numbers of alternative sets
of low-level tasks, operations, and configurations that can fulfil high-level
stakeholder requirements. The algorithms for the forward reasoning have
been fully developed in java and are embedded in the GR-Tool. Tasks are
represented in hexagonal shapes and goals are in oval shapes. Goals and
tasks are connected with each other using AND- and OR-decompositions. A
goal model based on a book shop detail is shown in Figure 1.

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Figure 1 Goal model
3.2 Identification of Goal Preferences and Priorities
Initial phase of a project is best suited for goal modeling. [5]. A goal model
is effective in concisely capturing large numbers of alternative sets of low-
level tasks, operations, and configurations that can fulfill high-level
stakeholder requirements [9]. Goals and tasks are connected with each other
using AND- and OR-decompositions. AND-decomposition means
decomposing a goal into other goals or tasks, in which all children should
satisfy the fundamental goal or task. Some set of tasks are given below
Table 1. Set of tasks
Tasks
search book
user login
book database
storing book details
stock details
mention books
store details in database
book number
availability
integrity
manage categories
book name
change password
perform privacy control
provide weekly renewal of database
number of books

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If the goal is OR-decomposed into other goals or tasks, then one of these
goals or tasks should satisfy the parent goal. Preference goals are the goals
that are inclined by the stakeholder.The set of goals and its decomposition is
given below.
Table 2. Set of goals
Goals Decomposition
manage internet
searching
OR
internet querying OR
catalogue consulting OR
taking detail of
transaction
AND
date & time AND
member name AND
select item OR
pick available item OR
pre non ordered item OR
shopping cart AND
add item AND
check out AND
select item AND
get identification
details
AND
But the same degree of desirability will not be maintained. Sometimes
preference goals are considered as those goals which help other goals to
benefit from them. Prioritizing requirements is the requirements task of
determining the implementation order of the requirements in an incremental
and iterative development cycle.
3.3 AHP Method
The Analytic Hierarchy Process (AHP) is a theory of measurement through
pair wise comparisons and relies on the judgements of experts to derive
priority scales. It is these scales that measure intangibles in relative terms
[10]. To make decisions, we identify, analyse, and make tradeoffs between
different alternatives to achieve an objective.
The outcome is more likely to be satisfied when the means for analysing and
evaluating the alternatives are more efficient [3]. The Analytic Hierarchy
Process compares alternatives in a stepwise fashion and measures their
contribution systematically to take decisions.

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To what extent an aspect dominates another with respect to a given attribute
is featured by comparing a scale of absolute judgements. The judgements
may be inconsistent. A method to measure inconsistency and improve the
judgements and the possibility of obtaining a better consistency is a concern
of the AHP. In AHP method,
 Information is decomposed into a hierarchy of alternatives and
criteria.
 Information is then synthesized to determine relative ranking of
alternatives.
 Both qualitative and quantitative information can be compared using
informed judgments to derive weights and priorities.
3.4 Determination of Alternative Solution
The AND/OR goal decomposition model represents alternative solutions
for fulfilling the fundamental goal [7]. Each task consists of certain
priorities that may be calculated using AHP. In AHP information is
decomposed into a hierarchy of alternatives and criteria, then synthesized to
determine relative ranking of the alternatives. A set of preference goals is
given below.
Table 3. Set of Preference goals
In order to derive weights and priorities both qualitative and quantitative
information are compared using informed judgments. The alternative
solution can be found by taking the goals and task based on AND, OR
decomposition. Each task is prioritized and based on the priority‘s task the
goals may be compared .The goal which has the greatest priority may be
considered to be the first and the alternative solution is found out. The
Preference goals
Whole book details
Not take same book
Arrange book by
volume
Arrange books
Printed robust legal
document
Provides steps to user
Fear internet fraud
Secure form
Standard form

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priorities of goals are calculated by using values in the relative intensity
table.
Algorithm
Calculation of priority among goals
Input: Set of goals based on the preference
Method: Analytical Hierarchy Process
Step 1: Set up n goals in the rows and columns of an n * n matrix.
Step 2: Perform pair wise comparisons of the entire goal according
to the criterion.
Step 3: Use averaging over normalized columns to estimate the
Eigen values of the matrix.
Step 4: Assign to each goal, its relative value based on the estimated
Eigen values.
Output: Plan is generated based on the priorities calculated, and the
tasks connected to each goal are listed. The relative intensity table
is given below on which priorities between requirements is taken
on the basis of the relative intensity
Table 4. Relative intensity table
Relative
intensity
Definition Explanation
1 Of Equal Value Two goal are of equal value
3 Slightly more value Experience slightly favours one goal over another
5 Essential or strong value Experience strongly favours one goal over another
7 Very strong value A goal is strongly favoured and its dominance is
demonstrated in practice
9 Extreme value The evidence favouring one over another is of the
highest possible order of affirmation
2, 4, 6, 8 Intermediate values
between two adjacent
judgments
When compromise is needed
Reciprocals: If goal i has one of the above numbers assigned to it when compared with goal
j,then j has the reciprocal value when compared with i.

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3.5 Reasoning about preferences
Here, based on the priorities, a model is constructed and preferred plans are
organized. Ordered sequences known as plans are considered as alternative
solutions. The sequence of leaf-level tasks that altogether satisfy the
AND/OR structure of the fundamental goal is the plan for the fundamental
goal. The priorities may be listed as probabilistic values. To render solutions
to goal requirements problems a plan is essential [3]. Solutions in goal
models are specified as configurations of tasks with no dependencies.
Without specifying the system dynamics the stakeholders‘ requirements can
be modelled and solutions to requirements can be determined using
automated constraint solvers. Thus a set of tasks are contained in the plan
that is generated.
4. EXPERIMENT AND RESULTS
A comparison between the performance that is based on the AHP method
and the HTN based on the book shop detail is made [10]. It is evident from
the results that the priority specification is definite in our method. The set of
tasks of the book shop detail and the priority specifications among these
tasks are analysed. The AHP method and Hierarchical Task Plan are utilized
to prioritize the specifications for various tasks. By using AHP method the
percentage of the priority specification is most accurate than the HTN
method. HTN is an approach to automated planning in which the
dependency among actions can be given in the form of networks. The
domains where tasks are naturally organized in hierarchy are an area where
HTN is suitable .The priority specification is more precise while assigning
priorities to those tasks by using the AHP method because of its accuracy.
The task priority graph is shown in Figure 2.
Figure 2. Task priority graph

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5. CONCLUSION
The effort of reasoning, about preferences gives better understanding of the
connection between the stakeholder attitudes and is used to generate a plan
which consists of set of tasks based on preferences. The priorities among
preference goals are defined by the priority specification through the
assignment of numerical weights. A goal model is developed to recognize
alternative solutions of mandatory goals and preferred goals based on
priorities. The priority among the requirements is calculated using
Analytical Hierarchy Process. AHP‘s pair wise comparison method is used
to assess the relative value of the candidate requirements. The preferences
are analysed based on the prioritization of each task and a definite plan is
generated to view all those tasks according to priority.
6. REFERENCES
[1] Mussbacher G, Amyot D, Arau´jo Ja, Moreira A, ―Modelling software product lines
with AoURN”, Proc. EA ‘08, vol 2, pp 1–2 (8), 2008
[2] Jureta IJ, Borgida A, Ernst NA, Mylopoulos J, Techne, ―Towards a new generation of
requirements modelling languages with goals, preferences, and inconsistency
handling”, Proc of the 18th IEEE RE‘10, Sydney, Australia, pp 115–124, 2010
[3] Giorgini P, Mylopoulos J, Nicchiarelli E, Sebastiani R, ―Reasoning with goal models‖,
Proc. ER‘02, London, UK, pp 167–181,2002
[4] Sotirios Liaskos , Sheila A. McIlraith, Shirin Sohrabi, John Mylopoulos,“Representing
and Reasoning About Preferences,‖ Springer-Verlag., Vol 16, pp 227- 249, Aug 2011.
[5] Liaskos S, Lapouchnian A, Yu Y, Yu E, Mylopoulos J, ―On goal-based variability
acquisition and analysis‖, Proc.RE‘06, IEEE Computer Society, Minneapolis, pp 79–
88, 2006
[6] Azar J, Smith RK, Cords D ―Value-oriented requirements prioritization in a small
development organization‖. IEEE Software 24:32–37, (2007)
[7] Avesani P, Bazzanella C, Perini A, Susi A, ―Facing scalability issues in requirements
prioritization with machine learning techniques‖, Proc (RE‘05), pp 297–305, 2005
[8] Liaskos S, Litoiu M, Jungblut MD, Mylopoulos J ―Goal Based behavioral
customization of information system‖, Proc. CAiSE11, London, UK., 2011
[9] Van Lamsweerde A, ―Requirements Engineering in the year00: a research
perspective‖, Proc. ICSE‘00, pp 5-19, 2000
[10]Saaty RW, ―The analytic hierarchy process—what it is and how it is used‖ Math
Model 9(3-5): 161–17, 1987.

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Unstructured Data Integration
through Automata-Driven
Information Extraction
Maroun Abi Assaf, Kablan Barbar
Dept of applied mathematics
Faculty of Sciences II – Lebanese University
Fanar, Lebanon
Youakim Badr
INSA – LYON, France
Mahmoud Rammal
Legal Law Center – Lebanese University
Sin-Fil, Lebanon
ABSTRACT
Extracting information from plain text and restructuring them into relational databases raise
a challenge as how to locate relevant information and update database records accordingly.
In this paper, we propose a wrapper to efficiently extract information from unstructured
documents, containing plain text expressed with natural-like language. Our extraction
approach is based on the automata formalism to describe the wrapping process running
from text documents to Databases. As usual, relevant information in the text document are
delimited by regular expressions, which define the extracting automaton. Each automaton is
enriched by an output function that automatically generates SQL queries synchronized with
the extracting process in order to insert extracted data into database records. We validate
our extraction approach with automaton-based prototype to extract legal information about
Lebanese official journal decrees and automatically insert them into a relational database.
Keywords
Wrappers, Regular Expressions, Automata, SQL Language, XML.
1. INTRODUCTION
Several approaches to generating wrappers have been introduced in the
literature to extract information from structured, semi-structured and
unstructured documents.
In [2], the authors create wrappers for XML Paragraph-Centric. The
extraction patterns are expressed with regular expressions and translated
into automata. The automata then carry out the information extraction and
produce tuples of values. In [1], the extraction system is based on two
automata. The first automaton decomposes grammatically the given text
into noun groups, verbs groups and others words groups. On the other hand,

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the extraction patterns are encoded into a recognizing patterns automaton
that takes as input all the preceding groups and produces the resulting
entries. In [4], the system runs in two phases, the learning and the testing
phases. The learning phase produces the lexical pattern rules based on a
training collection of reports. The testing phase has three components a
noun phrasing an automaton and a neutral network. The noun phrasing runs
as a syntactical analyzer and produces a list of relevant information. The
automaton makes a matching operation and the neutral network predicts the
matched phrases‟ most possible entity type. In [6] an approach to
automatically locate data-rich regions and to extract relevant attribute-value
pairs of database records from web pages across different sites is presented.
This approach relies on the fact that the attribute-value pairs of the records
usually occurs next to each other‟s in well-designed web pages.
We note that all these approaches do not deal with the context of relevant
information for plain text documents and do not integrate in a unique
formalism both the extracting and the database writing processes.
In contrast to these approaches, we propose an extraction approach, a.k.a.
wrapper, that deal with plain text without a predefined structure (i.e., XML
and HTML) and without a natural language pre-processing (i.e., lexical,
semantic analysis) to identify phase structures and relevant information. Our
extraction approach is based on a formal description of the wrapping
process from text document to databases by means of automata output
functions that generate automatically SQL queries synchronized with the
extracting process. Moreover, on the formal aspect, we express with the
same formalism both the extracting and the database mapping processes.
We particularly rely on the automata and regular expressions formalism to
express the structure of relevant data and locate them. Each relevant
information is described by a regular expression. The sequence separating
two relevant information is also formulated with regular expressions. After
recognizing relevant information, a SQL insert query is generated. For each
relevant information, we specify four elements; its regular expression, the
regular expressions for its preceding and proceeding sequence and the
corresponding SQL query. All, the regular expressions forms the extracting
automaton and the SQL queries are included in the output function of the
automaton. Then, the automata cover both the extracting and the database
writing processes. The research is a first step to build automata generic
system based on attributed grammars like in [3].
The remaining of the paper is organized as follows. Section 2 establishes a
panorama of related works. Section 3 recalls the formalism of automata with
output functions and shows how we translate the relevant information
descriptions into extracting automata. In section 4, we present our
experimental results to extract legal decrees from the text document of the

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Lebanese official journal and insert them into a database. In section 5, we
conclude our work and provide some research trends.
2. RELATED WORKS
Several approaches have been proposed to extract data from text document
using automata. In [1], the authors describe a finite-state processor for
information extraction from real-world text, named FASTUS. FASTUS uses
a pre-defined finite-state machine (Recognizing Phrases Automaton) to
decompose the sentences of the real-world text into noun groups, verb
groups, and several other critical word classes‟ phrases which will form the
candidate list of phrases. Next, the given patterns of interest (extraction
pattern) is encoded into a finite state machine (Recognizing Patterns
Automaton), and the resulted candidate list of phrases is passed as input to
this automaton that identifies phrasal matches with the given patterns of
interest and returns the necessary information. In [2], the authors deal with
the problem of creating a wrapper for XML paragraph-centric documents
named Xtractor. They propose a specification language to write the
extraction patterns, based on Regular Expressions, but is more simple and
easier to read than Regular Expressions themselves. This specification
language includes a set of meta-words that are referenced in a provided
dictionary for the domain of interest, and associates lexemes with these
meta-words. The extraction pattern, which is a regular language, is
translated into an equivalent finite-state automaton in the first phase. Then
in the second phase, the finite-state automaton will carry out the information
extraction, and the result is returned as data tuples of attributes.
In a similar approach, in [4] authors deals with the problem of extracting
meaningful entities, such as person names, addresses, narcotic drugs, or
vehicle names, from free texts of police narrative reports through a neural
network-based entity extractor. The system has three major components;
Noun phrasing, Finite state machine and lexical lookup, and Neural
network. The Noun phrasing component is a form of a syntactical analyzer
that extracts the noun phrases from the reports and will form the candidate
list of relevant information values. The Finite state machine and lexical
lookup component is a finite state machine that finds matches between the
words of the extracted noun phrases list and the items of a provided lexicon
that contains lists of possible values of the entities of interest. The Neural
network component, a feedforward/backpropagation neural network,
predicts the matched phrases‟ most possible entity type. The system also has
two states; a learning state where it identifies the lexical pattern rules
(extraction pattern rules) based on a training collection of reports, where the
entities are manually identified by a human, and saves these rules in the
neutral network as synaptic weight, and a testing state where the system

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extracts the phrases and find item matches from the reports and predicts
their entity type based on the obtained extraction pattern rules from the
learning state.
In [6], authors describe a web information extraction system that extracts
attribute-value pairs, like Product code, Manufacturer, Price, and
Description of products from web pages that present product descriptions.
The system introduces a notion called “Structural-Semantic Entropy” to
identify if a web page contains a data-rich region or not. The data-rich
region of a web page is the region that contains the actual relevant
information. “Structural-Semantic Entropy” means the measure of
randomness of the leaf nodes that contain a semantic role of a given non-
leaf node. A leaf node contains a semantic role if it contains a keyword label
associated with the attribute-value pairs. This keyword label list is known
beforehand and given as input to the wrapper. The more this randomness is
higher the more the non-leaf node that is inspected is likely to be a data-rich
node. For every non-leaf node in the DOM tree of the web page its
“Structural-Semantic Entropy” is calculated, and every node having a
“Structural-Semantic Entropy” higher than a given threshold, which is
learned by experimentation, is considered to be a data-rich node that
contains a record. Finally, for every discovered data-rich node, the contents
of the next text-nodes of its leaf nodes that are annotated with a semantic
role are usually extracted as the value of the semantic roles (or attributes).
We conclude that most of these representative approaches do not provide
formal description of the whole extracting and database writing processes
and they do not run on non-structured text documents.
3. METHODOLOGY
Automata are used to support syntactical analyzer in the compiling process
of programming languages. They act like a virtual machine to recognize
words belonging to rational languages by passing from one state to another
according to a symbol of the initial alphabet.
Formally, an automaton is a 5-tuple A=< ∑, S, s0, T, δ > where:
• ∑ is the alphabet
• S is the set of states
• s0 is the initial state and an element of S
• T is the set of terminal states and a subset of S
• δ is the transition function defined by:
δ : S x∑ S
The transition function is represented by a graph and leads the recognizing
process. As in [5], we can define a semantic for automata transition. A
semantic has the same domain as the transition function but it associates

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semantic values with each transition. This technique allows the enrichment
of the automaton and the possibility of executing of complementary
semantic operations in parallel with the automaton transition function. Then,
we formalize an extended automaton by a 7-tuple A=< ∑, S, s0, T, δ, D, λ
>where ∑, S, s0, T, δ remains the same as preceding and where D is a
semantic domain and λ is the semantic function defined by:
λ : S x∑ D
As application of the data integration process from text document to
database, we consider the data integration process applied to the Lebanese
Official Journal (LOJ) in the Center of Documentation and Research of the
Lebanese University. The main unit in LOJ is the decree that contains
general information like decree number, date,…, many 'based on' clauses,
many articles, a 'decrees' clause, a place, a date and many signatures (see
Figure 1). Each week, the center receives an electronic copy of LOJ as a text
document. The relevant information in the decrees are selected and
transferred manually to database by a copy-paste operation from text
document to database. This operation takes too much time and produces
errors. In the center, they want to develop a program that both selects
automatically the relevant information from the decrees of LOJ and insert
them in the database. That is why we are going now to use automata to
formalize the data integration process for LOJ decrees.
The relational schema of the database associated with the LOJ is described
as follows:
decree(decree_id_pk, type, number, title, location, date)
basedon(basedon _id_pk, decree_id_fk, value)
andafter(andafter _id_pk, decree_id_fk, value)
article(article_id_pk, decree_id_fk, value)
signature(signature_id_pk, decree_id_fk, value)
The list of relevant information with their values from the example decree of
Figure 1, and their corresponding regular expressions and database
operations are given in Table 1.
In addition, we give finite state automata where the transition function is
represented by a graph and the output function is formally identified. Each
state in the automata is implemented as a string to which we add the symbol
recognized at each transition. The domain D contains four operations:
1. insert: to insert a table record
2. update: to update a field of a table record
3. concatenate: to concatenate two strings
4. clear: to clear the string state.

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Figure 1. A decree of the Lebanese Official Journal
Table 1. Relevant Information
Relevant
Information
Value in
Example Decree
Preceding
Regular
Expression
Wanted Entity’
Regular
Expression
Following Regular
Expression
Database Operation
Type “Decree” ε [a-zA-Z ]+ No.
Insert a new record in
the table decree and
update the type field
in this record
Number 6873 No. [0-9]+ [a-zA-Z]
Update the number
field of the current
record in the decree
table
Title
“Giving the
Ministry of
Finance treasury
advance to pay
the last
installment of a
series of
[0-9]* [a-zA-Z ]+
The President of the
Republic,
Update the title field
of the current record
in the decree table
Decree No. 6873
Giving the Ministry of Finance treasury advance to pay the last installment of a
series of differences of ranks and salaries of civilian and military
The President of the Republic,
Based on the Constitution,
Based on the law of public accounting, as amended, particularly Article 203 and
subsequent of it,
Based on the suggestion of the Minister of Finance,
And after the approval of the Council of Ministers on 15/11/2011,
Decrees the following:
Article 1: To give the Ministry of Finance treasury advance worth / 500.000.000.000 /
LP (only five hundred billion pounds).
Article 2: It is not allowed for the party taking the advance, to use it for any other end
other than the one that is given for it.
Article 3: The Director of Treasury, director of the disbursements, director of budget
and expenditure control in the Ministry of Finance, and the director of Administrative
Affairs at the Directorate General of the Ministry of Finance, within their competences,
should pursue the repayment of the advance.
Article 4: This Decree shall be published and reported where needed.
Baabda, the 18th
of November 2011
Signature: Michel Sleiman
Issued by the President of the Republic
President of the Ministers‟ Council
Signature: Muhammad NajibMikati
Minister of Finance
Signature: Muhammad Al Safady

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Relevant
Information
Value in
Example Decree
Preceding
Regular
Expression
Wanted Entity’
Regular
Expression
Following Regular
Expression
Database Operation
differences of
ranks and salaries
of civilian and
military”
BasedOn
“the
Constitution”…
“the suggestion
of the Minister of
Finance”
Based on [a-zA-Z,.0-9 ]+ Based on | And after
the basedOn table
and update the value
field in this record
AndAfter
“the approval of
the Council of
Ministers on
15/11/2011”
And after [a-zA-Z,.0-9 ]+
And after | Decrees
the following:
the andAfter table
Article
“To give the
Ministry of
Finance treasury
advance worth /
500.000.000.000
/ LP (only five
hundred billion
pounds).”
…
Article [0-9]*: [a-zA-Z,.0-9 ]+
Article [0-9]*: | [a-zA-
Z]*, the [0-9]{2}th of
(January|…|December
) [0-9]{4}
the article table and
update the value field
in this record
Location “Baabda”
Article [0-9]*:
This Decree
shall be
published and
reported where
needed.
[a-zA-Z]+
, the [0-9]{2}th of
(January|…|December
) [0-9]{4}
Update the location
field in current record
of the decree table
Date
“the 18th
of
November 2011”
[a-zA-Z]+,
the [0-9]{2}th of
(January|…|Dece
mber) [0-9]{4}
Signature:
Update the date field
in current record of
the decree table
Signature
“Michel
Sleiman”,
“Muhammad
NajibMikati”,
“Muhammad Al
Safady”
Signature: [a-zA-Z ]+ Issued by | Minister
the signature table

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The automata associated with the decree relevant information are:
• FSA( Type ):
D = {Insert(qi, 'decree', 'type'), Concatenate(qi, c) }
λ : Q x ∑  D
λ(q0, c) = Concatenate(q0, c) where c a-z|A-Z| |
λ(q1, No.) = Insert(q0+q1, 'decree', 'type')
This automaton will extract all the alphabetical characters including a space
character between the start of the decree and a “No.” keyword and insert
them as the value of the „type‟ attribute of a new record in the „decree‟ table.
• FSA( Number ):
D= { Update(qi, 'decree', 'number'), Concatenate(qi, c) }
λ : Q x ∑  D
λ(q0, No.) = ε
λ(q1, c) = Concatenate(q1, c) where c  0-9
λ(q2, a-z|A-Z) = Update(q1+q2, 'decree', 'number')
This automaton will extract all the numerical characters between the “No.”
keyword and any alphabetical character and update the „number‟ attribute of
the last inserted record of the „decree‟ table with this value.
Figure 2. Type Finite State Automaton
Figure 3. Number Finite State Automaton
q0 q1
q2
a-z|A-Z| | No.
a-z|A-Z| |
q0 q1
q2
a-z|A-ZNo.
0-9
0-9 q3

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• FSA( Title ):
D = { Update(qi, 'decree', 'title'), Concatenate(qi, c) }
λ : Q x ∑  D
λ(q0, 0-9) = ε
λ(q1, 0-9) = ε
λ(q2, c ) = Concatenate(q2, c) where c a-z|A-Z| |
λ(q2, The President of the Republic,) = Update(q1+q2, 'decree', 'title')
character between the delimiter formed by any numerical characters and the
keywords “The President of the Republic,”, and updatethe „title‟ attribute of
the last inserted record of the „decree‟ table with this value.
• FSA( BasedOn ):
D = {Insert(qi, 'basedon', 'value'), Concatenate(qi, c),Clear(qi)}
λ : Q x ∑  D
λ(q0, Based on) = ε
λ(q1, c) = Concatenate(q1, c) where c a-z|A-Z| |0-9
λ(q2, c) = Concatenate(q2, c) where c a-z|A-Z| |0-9
λ(q2, Based on) = Insert (q1+q2, 'basedon', 'value'); Clear(q1); Clear(q2)
λ(q2, And after) = Insert(q1+q2, 'basedon', 'value')
Figure 4. Title Finite State Automaton
q0
q1 q2
a-z|A-Z| |
The President
of the
Republic,0-9
q3
0-9
a-z|A-Z| |
Figure 5. BasedOn Finite State Automaton
q1
q2
a-z|A-Z| |0-9
Based on a-z|A-Z| |0-9
Basedon
q0
And after q3

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This automaton will extract all the alphanumeric characters including a
space character between every “Based on” keyword and another “Based on”
or “And after” keywords, and insert them as the value of the „value‟
attribute of a new record in the „basedon‟ table.
• FSA( AndAfter ):
D = {Insert(qi, 'andafter', 'value'),Concatenate(qi, c), Clear(qi)}
λ : Q x ∑  D
λ(q0, And after) = ε
λ(q2, And after) = Insert (q1+q2, 'andafter', 'value'); Clear(q1); Clear(q2)
λ(q2, Decrees the following:) = Insert(q1+q2,'andafter', 'value'); Clear(q1);
Clear(q2)
space character between every “And after” keyword and another “And
after” or “Decrees the following:” keywords, and insert them as the value of
the „value‟ attribute of a new record in the „andafter‟ table.
• FSA( Article ):
Figure 6. AndAfter Finite State Automaton
q1
q2
a-z|A-Z| |0-9
Decrees the
following:
a-z|A-Z| |0-9q0
And after
And after
q3

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D = {Insert(qi, 'article', 'value'), Concatenate(qi, c), Clear(qi)}
λ : Q x ∑  D
λ(q0, Article) = ε
λ(q1, 0-9) = ε
λ(q1, : ) = ε
λ(q2, c) = Concatenate(q2, c) where c a-z|A-Z| |,|.
λ(q3, c) = Concatenate(q3, c) where c a-z|A-Z| |,|.
λ(q3, Article) = ε
λ(q4, 0-9) = ε
λ(q4, :) = Insert (q2+q3, 'article', 'value'); Clear(q2); Clear(q3)
λ(q3, a-z|A-Z) = ε
λ(q5, a-z|A-Z) = ε
λ(q5, , the) = ε
λ(q6, 0-9) = ε
λ(q6, th of) = Insert(q2+q3, 'article', 'value');
space, comma, and dot characters between every “Article n:” keyword
(where n is a number) and another “Article n:” keyword or any word
followed by a date delimiter, and insert them as the value of the „value‟
attribute of a new record in the „article‟ table.
• FSA( Location ):
D = { Update(qi, 'decree', 'location'), Concatenate(qi, c) }
λ : Q x ∑  D
λ(q0, This Decree shall be published and reported where needed.) = ε
λ(q1, c) = Concatenate(q1, c) where c a-z|A-Z
λ(q2, c) = Concatenate(q2, c) where c a-z|A-Z
λ(q2, , the) = Update(q1+q2, 'decree', 'location')
This automaton will extract the word between the two sequences “This
Decree shall be published and reported where needed.” and “, the”
keywords and update the „location‟ attribute of the last inserted record of the
„decree‟ table with this value.
Figure 8. Location Finite State Automaton
q0 q1
q2
a-z|A-Z
This Decree shall
be published and
reported where
needed. , the
q3
a-z|A-Z

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• FSA( Date ):
D = { Update(qi, 'decree', 'date'), Concatenate(qi, c) }
λ : Q x ∑  D
λ(q0, a-z|A-Z) = ε
λ(q1, a-z|A-Z) = ε
λ(q1, , ) = ε
λ(q2, the) = Concatenate(q2, the)
λ(q3, th of) = Concatenate(q3, th of)
λ(q4, w) = Concatenate(q4, w) where w  January|…| December
λ(q5, Signature:) = Update(q2+q3+q4+q5, 'decree', 'date')
This automaton will extract the date according to the given format and that
is located between any word and a “Signature:” keyword, and update the
„date‟ attribute of the last inserted record of the „decree‟ table with this
value.
• FSA( Signature ):
D = { Insert(qi, 'signature', 'value'), Concatenate(qi, c) }
λ : Q x ∑  D
λ(q0, Signature:) = ε
λ(q2, Issued by|Minister ) = Insert(q1+q2, 'signature', 'value')
character between every “Signature:” and an “Issued by” or “Minister”
Figure 9. Date Finite State Automaton
q0 q1
q2
, th ofa-z|A-Z
0-9
the
q3
January|…|
December
a-z|A-Z
q4
Signature:
q5
0-9
q6
Figure 10. Signature Finite State Automaton
q0
q1
q2
a-z|A-Z| |Signature:
Issued
by|Minister q3
a-z|A-Z| |

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keywords, and insert them as the value of the „value‟ attribute of a new
record in the „signature‟ table.
4. RESULTS
In order to validate our extraction approach, we have developed a .NET-
based prototype, including two modules, the extracting module and the
database writing module. The extracting module is the implementation of
the regular expressions of all relevant information with their preceding and
following sequences and also includes the output function for the SQL
operations. It extracts relevant information values from decrees of the text
document of the Lebanese official journal, and inserts these values in the
SQL queries that are saved in an XML document. The database writing
module takes as input the XML document for the SQL operations, selects a
SQL query, makes a connection to the database and finally executes this
query.
We have tested our prototype application on a sum of 100 decrees from
several LOJs. Then we manually checked the database entries with the
initial 100 decrees‟ texts for consistency and errors. We found that out of the
100 decrees, 98 decrees where discovered and extracted, and out of the 98
extracted decrees, 5 errors were found in some of the attributes. To measure
the correctness of our prototype, we calculate two statistical classifications
used in pattern recognition and information retrieval, the Precision (also
called positive predictive value) which is the fraction of retrieved instances
that are relevant, and the Recall (also known as sensitivity) which is the
fraction of relevant instances that are retrieved. Based on the previously
found numbers, the number of relevant instances is equal to the 98 extracted
decrees minus the number of extracted decrees with errors which is 5,
giving us a 93 for the number of relevant instances. This will result in a
Precision that is equal to 93/98 which is approximately 95%, and a Recall
that is equal to 93/100 which is 93%.
5. CONCLUSIONS
In order to deal with information extraction from plain text without a
predefined structure (i.e., XML and HTML) and without a natural language
pre-processing, we have presented an automata-based model to describe a
wrapper that extracts meta-data from a text document containing the decrees
of the Lebanese Official Journals, generates the corresponding SQL writing
queries, and executes them in order to insert these extracted data into a
relational database. In this model, we specify with the automata formalism
both the extracting and the database writing processes. At present time, we
have developed a prototype extractor by directly coding the Finite State

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Automaton with the Output Function. The prototype extractor showed
prominent results in extracting correct meta-data about the articles and
successfully inserting them in their correct place in the relational database.
In our future works, we would like to target the extracting process by
considering conflicts when relevant information occurs in a wrong place.
Moreover, we are investigating a new specification language for automata
with output function in order to build a generic system that produces
automatically the automata program or the extraction and writing programs.
6. REFERENCES
[1] D. E. Appelt, J. R. Hobbs, J. Bear, D Israel and M. Tyson, FASTUS: A Finite-state
Processor for Information Extraction from Real-world Text. IJCAI 1993: Chambéry,
1993.
[2] Y. BADR, Xtractor: A Light Wrapper for XML Paragraph-Centric Documents.
Proceedings of the 1st International Conference on Signal-Image Technology and
Internet-Based Systems, SITIS 2005, 2005.
[3] K. Barbar, Automatic generation of XML-based editors for autonomic systems,
International Journal of Autonomic Computing, v.1 n.3, p.246-262, May 2010
[4] M. Chau, J. J. Xu, H. Chen, Extracting Meaningful Entities from Police Narrative
Reports. National Conference on Digital Government Research 2002, 2002.
[5] Knuth, D., "Semantics of context-free languages", Math. Systems Theory 5, 127-145,
1968.
[6] X. Zheng, Y. Gu, Y. Li, Data Extraction from Web Pages Based on Structural-
Semantic Entropy. Proceedings of the 21st international conference companion on
World Wide Web. Pages 93-102. ACM New York, NY, USA, 2012

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Wireless Solution for Water Saving In
Agriculture Using Embedded System
Venkata Narayana Eluri, K. Madhusudhana Rao, A. Srinag
Electronics and Communication Engineering,
KKR & KSR Institute of Technology and Sciences,
Guntur, Andhra Pradesh, India.
ABSTRACT
Agriculture is a source of livelihood of majority Indians and has great impact on the
economy of the country. In a country like India, where climatic conditions vary
substantially and irrigation facilities are poor. Agriculture is timely and sufficient supply of
water. Water pumps are crucial in agriculture where electricity is indeed. The frequent,
intermittent, low voltage supply of power to the agriculture sector has caused problems to
the farmers who are spending their time monitoring the supply of power without which no
progress of their work. The power supply with frequent power cuts have not only lowered
the efficiency of farmers but also have led to the frustration of the farmer to give up
agriculture and move to urban areas for better prospects in the globalized world. In this
paper we presented a system which shows, how mobile communication can benefit
millions of farmers in rural India by providing a solution for the irrigation problems caused
by intermittent electrical power supply. Information is exchanged in form of
messages/miscalls between the system and the user cell phones. The system is based on
ATMEGA32 micro-controller and includes protection against fluctuations in power supply.
For measuring time and temperature RTC DS1307 and DS18S20 are used. By using this
system the hardships of farmers relating water distribution can be relieved.
Keywords
AT command supporting GSM mobile phone, ATMEGA32 Micro controller, Relays,
Sensors, LCD for monitoring the current reading of all the parameters.
1. INTRODUCTION
Even in the modern era of industrialization, agriculture plays a very
significant role in the overall socio-economic development of India. India
has an agriculture based economy. 43% of India‟s territory comes under
agricultural lands. Agriculture along with other related fields like forestry
and logging provides employment to 52% of India‟s population. Agriculture
also accounts for 8.56% of the country‟s total exports. According to a
survey made in 2007, agriculture accounts for 16.6% of India‟s Gross
Domestic Product. Hence, it is agriculture that is the most influential field
as compared to others in India. This importance on agriculture leads to an
emphasis on better agricultural practices. The underground water level is
slowly falling down and forests are being cut which reduces the rainfall as
well. With increasing area available for cultivation and the need for
increasing the productivity from the farm land, there is a growing need for

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electrical energy for irrigation. The generation of electricity is not growing
proportionately to the demand.
The supply to agriculture is limited to few fixed hours throughout the day.
In a sample survey of village Komarolu, located at distance of 17kms from
Giddalur (Taluka in Prakasam District, Coastal Region, Andhra Pradesh
State, India), it was observed that 3-phase supply is normally available for 7
hours a day from 2.00 am to 9.00 am for a week and from 11.00 am to 6.00
pm for next week and changes alternately. Due to increase in demand of
electricity the schedule given was not followed and sometimes power given
at night times and fluctuations in voltage level leads to motor damage[1]. So
farmers, after carrying strenuous physical activities in farm during day, have
to return back to their farms in night to carry water distribution using 3-
phase motor pumps. Agriculture receives power mostly during mid night
(off-peak) as this reduces the cost of electricity supply for the transmission
and Distribution Company. The farmers have to be on their guard all the
time due to the unpredictable nature of supply of electrical energy. And the
farmers have to switch on their motor after electricity supply resumes. The
reduced amount of yielding, man power wastage, and idle state of
equipment can observed in its results.
Due to the fast development in tele-communication technologies, it is
believed that wireless solution for irrigation in agriculture. This system has
fully utilized wireless sensor network, Global System for Mobile
Communication (GSM) and short message service (SMS)/missed call to
carry out data from the sensors to computers or directly alert the farmers
through their mobile phone and to control the remote watering process [2-
4], also through the mobile phone. This practice eliminates the use of wired
technology, improves old method of collecting data in farming areas and
allows farmers to control their sprinklers remotely. Also, It has been
observed that source of electrical energy generation is slowly depleting
using solar energy. We are going to discuss an example of how the mobile
technology can benefit millions of farmers by providing a solution for the
irrigation problems. Simple cell phones having just voice call and
messaging facility or cell phones with non-working display can be easily
adapted for remote control applications.
2. SYSTEM DESCRIPTION
A system is developed for optimum water distribution in the fields through
motor pump. The crop yield is maximized to a great extent by providing
proper amount of water at suitable time intervals based on climatic
conditions. The system offers attractive features like automatic control
based on parameters specified through keyboard /SMS/ number of miscalls;
provides protection against single phasing, over-current, over-voltage, dry

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running and probable bearing faults; alerts users in case of abnormal
conditions like power failure, dry-running, etc. and provides audible
indication through buzzer/ miscall on completion of task.
The figure 1 shows the Block diagram of the scheme. Using keyboard the
parameters of the system can be set or received in form of SMS/number of
miscalls in specified duration from user mobile through serial cable
connected to control system cell phone (Model Nokia 6610). Based on
commands received microcontroller system sends signals to switch on / off
motor through Starter using relays controlled by its ports. Three phase 5
hp,7 hp,10.5 hp induction motor working on Direct-on-line Starter are
chosen for agriculture purpose.
Figure 1. System Block Diagram
2.1 Cell phone Interface:
Cell phone 6610 is connected to AVR Microcontroller board through
RS232C serial interface. AT commands are sent by sending text strings „A‟,
„T‟, along with specified command strings through serial port to cell phone
and are executed on receipt of carriage return [5-6]. The result codes are
sent by cell phone to system (TE) to indicate the status after execution of
command.
1) SMS Approach: SMS is store and forward way of transmitting messages
to and from cell phones. The major advantage of using SMS is provision of
intimation to the sender when SMS is delivered at the destination and ability
of SMSC to continue efforts for delivery of message for the specified
validity period if network is presently busy or called user is outside the
coverage area. Using CMGS command the text message is sent to cell
phone. CNMI command is used to indicate to TE about the receipt of
incoming SMS message from the network. On receipt of the SMS message,
unsolicited result code +CMT is obtained from which text message is
ATMEGA32
MICROCONTROLLER
BASED SYSTEM
USER
MOBILE
SYSTEM
MOBILE
CELLULAR
LINK STARTER
(MOTOR
CONTRACTER)
MOTORPUMP
LCD
DISPLAY
RTCSENS
ORS
KEY
BOARD
R Y B
3 PHASESUPPLY

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extracted and checked with predetermined format, which includes
password, desired time or ON/OFF commands or status query.
Microcontroller carries out for the valid control message. In this application,
any incoming SMS message is directly routed to micro-controller (TE) and
any outgoing text message is directly sent by micro-controller to designated
cell phone number without being stored in control system cell phone
memory. As a result, phone memory is not inundated with messages in spite
of many messages being transferred.
2) Miscall Approach: The operational cost of communication between user
and control system cell phones is further reduced by using concept of
miscall where in no charges are incurred by using only ring signal for
information transfer. Miscalls are treated in two situations one is calling
party disconnects after receiving ring tones and second one when called
party does not respond to call within mentioned 5 minutes. The system cell
phone is designed to send specified number of miscall(s) within five
minutes duration to user cell phone to report various conditions as shown in
Table 1. Similarly, user cell phone sends commands to system cell phone by
making specified number of miscalls as shown in Table 2.
Table 1. Messages based on missed calls from system cellphone
No. of missed calls in 5 min. Message indication
01
02
03
04
05
No Power
Power failure
Resumption after normal conditions
Task completion
Probable motor faults/dry running
Table 2. Command based on missed calls from user cell phone
No. of missed calls in 5min. Message indication
01
02
03
04
05
Switch on
Switch off
Increase on time by 0ne hour
Decrease on time by one hour
Present status of the motor
This novel concept of miscalls results in substantial savings without
comprising the utility of system. Another advantage of miscall over SMS is
that during night time, ringing tone can easily wake-up farmer to carry out
necessary arrangement like shifting pipes to new locations, etc..CLIP

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command is used in recognizing particular saved recipient. Micro-controller
checks incoming number with user cell phone when reception of unsolicited
code RING along with CLIP occurs. Micro-controller waits for five minutes
duration to check total number of calls and carries out specified task if
match founds. Similarly, microcontroller carries out number of voice calls
from control system cell phone to user cell phone within five minutes time
duration using ATD command. And SMS mode activated when the BUSY
or ERROR occurred while making voice call.
3. MICROCONTROLLER SYSTEM
AT Mega32 microcontroller has RISC architecture with 32 KB of in-system
programmable Flash, 1k E2PROM, 2k SRAM, 32-bit General purpose I/O,
8channel 10-bit ADC, TWI, USART, SPI, JTAG interface support etc [7-8].
Ponyprog software was used for flash programming [9]. The software was
developed in C language using GCC compiler.
3.1 Interfacing
Interfacing diagram of micro-controller system is shown in Fig. 2. 8-bits of
Port A are configured as analog inputs ports. PA0-PA2 bits monitor the
present values of supply phase voltages. Water level of well indicated by
port A 3 and 4 pins while PA5 to PA7 are used to sense whether water has
reached the desired final destination of the regions. PB0-PB5 pins are
connected to 2 × 16 characters LCD display in 4-bit data length mode. Two
relays are controlled by upper 2 bits of Port B. Start (green) pushbutton of
starter for automated starting of the pump from micro-controller board is
connected to one relay while other relay is connected in series with stop
(red) pushbutton for stopping the motor pump from micro-controller. Upper
4 bits of Port C and upper 4 bits of Port D are used to interface 4x4
keyboard matrixes. DS1307 (RTC) is chosen for implementation of timing
applications. It is connected through TWI interface (I2C) i.e. PC0 (SCL)
and PC1 (SDA) pins. Two temperature sensors (DS18S20) are used having
single wire interface for connectivity. PC2 bit is used for single-wire
interface. Internally RxD (PD0) and TxD (PD1) are connected to 9-pin
RS232 female connector through MAX 232 IC for TTL-
RS232Csignaltranslation.
3.2 Real Time Clock
The DS1307 serial real-time clock (RTC) is a low power, full binary-coded
decimal (BCD) clock/calendar [10]. Address and data are transferred
serially through an I2C™, bi-directional bus (TWI). The clock/calendar
provides all timing information from seconds to years. With the help of
keyboard and LCD display, present date and time are written into
corresponding internal memory locations of this IC using I2C protocol.

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Whenever on-time duration for pump is specified, the two registers of AT
Mega32 are used to store time duration in minutes and hours.
Figure 2. Microcontroller system interfacing
The pump is switched on through relays using ports of micro-controller.
After passage of every minute (known through RTC) relevant registers of
microcontroller are updated if normal conditions exists and when their
values reach null, by using PB7 bit the pump is switched off. The
occurrence of abnormal conditions like unbalanced phase voltages, dry-
running, etc. causes pump to be switched OFF and the counting is
temporary stopped and is resumed on restoration of normal conditions. Thus
it is ensured that proper quantity of water is distributed by keeping pump
ON for specified time duration under normal conditions and user is
informed about restoration of normal conditions through miscalls.
3.3 Phase Voltage Measurement
In order to measure phase voltages, three transformers of equal ratings (6-0-
6) are used to step down voltage. These voltages are converted into
appropriate dc levels at analog inputs of micro-controller. Phase voltages
C1
C2
R C
3
+5V
XTA
L1
XTA
L2
GND
RES
ET
TXD(P
D1)
RXD
(PD0)
REL1
REL2
MOTOR
STARTER
LCD
DISPLAY
D4
D5
D6
D7
ENIN
RS
MAX232
4X4
KEY BOARD
ATMEGA32
ADC0 (PA0)
ADC2 (PA2)
ADC4 (PA4)
ADC3 (PA3)
ADC (PA5)
ADC (PA7)
SCL (PC0)
SDA (PC1)
PC2
PD7
PD4
PC4
PC7
PB5
PB4
PB3
PB2
PB1
PB0
PB7
PB6
VOLTAGE
SENSING
BLOCK
WATER
LEVEL
SENSOR
WATER
REACH
SENSOR
DS
18S20
DS 1307
R
Y
B
LL
HL
R1
R2
R3
XTAL1
XTAL2
GND
RESET
TXD (PD1)
RXD (PD0)

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are read at regular intervals by micro-controller. The values of phase
voltages are compared with one another. If the result of any comparison
exceeds specified value, signal is sent to switch off the pump along with
error message indicating unbalanced phase voltage condition and conveying
this information to user cell phone through miscalls. Internal 10-bit ADC of
micro-controller is configured to work in left justified format and only most
significant 8-bit values are used for comparison.
3.4 Temperature measurement
There has been tremendous research in fault diagnosis and protection
schemes for induction motor [11]. The basic over-current protection for
motor is provided by bimetallic strip of starter. Maximum reliability ensured
by mounting temperature sensor on body of motor and another temperature
sensor is mounted at a suitable location to measure ambient temperature.
Whenever temperature difference between the two sensors exceeds
specified safety limit (250C), signal is sent to switch off pump along with
error indication to LCD display and conveying miscalls to user cell phone to
indicate probable fault occurrence. This arrangement ensures that
catastrophic event like burning of motor due to any fault like over-current,
bearing blockage and insulation failure are avoided. And preventive
maintenance is carried out at substantially lower cost. The chosen
temperature sensor is DS 18S20 manufactured by Dallas Semiconductor
(Maxim) [12-13].It has operating temperature range of –55o
C to +125o
C. A
major advantage associated with this sensor is the availability of output
directly in digital form obviating the need of analog to digital conversion.
Moreover, this sensor provides inherent error-detection capability through
CRC technique.
3.5 Water Level Sensor
In order to prevent dry running of motor and allow automatic restart of
motor when sufficient water level is regained, three wires are inserted into
the well. One wire (GND) is inserted at bottom of well while second wire is
adjusted just above foot-valve of the suction pipe of motor pump (LL) and
third one at suitable level above second wire (HL). Microcontroller switches
OFF the motor and sends miscalls indicating empty well (dry running)
conditions whenever water falls below LL level. Whenever water rises
beyond HL level and sends miscalls indicating resumption of task, the
micro-controller switches ON the motor again. The flexibility of system can
be increased by using water level sensor and prevents the damage of motor.

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Figure 3. Implementation
3.6 Water Reach Sensor
There are two approaches for water distribution in this system. One method
is to specify the ON time duration of pump and second approach is to
specify the area of water distribution before stopping motor. First approach
is more suitable for sprinkle-based irrigation system while second approach
is chosen for ground-level water irrigation. For ground level water
irrigation, two wires are extended to extreme end of region where water is
to be reached. In this scheme, three such regions are presently supported
and one end of wire of these regions (R1, R2, R3) are connected to PA5,
PA6 and PA7 port bits of microcontroller using pull-up resistors of 100k
while other ends are connected to GND.
4. CONCLUSION AND RECOMMENDATIONS
By using this system optimum water distribution can be achieved. All the
electric defects are vanished and the maximum utilization of power supply
achievement is possible with the proposed system and during normal
conditions the motor will be restarted automatically. The system result in
uniform distribution of water at given time periods, minimizing man power
effort, water wastage reduction, minimization of occurrences of motor faults
and intimation to user about the completion of task through miscalls/SMS
system proves to be great boon to farmers whose pump sets are located far
away from their homes and intimation about any abnormal conditions.
With introduction of MMS message support, it is possible to capture images
from field using higher end cell phone and disease-pest control management
can be carried out by analysis of these images by agriculturists. Various
parameters such are warmness, moisture in the weather, etc., can be noted at
regular intervals on daily basis and time duration of motor, amount and type
of fertilizers and pesticides can be decided based on analysis of acquired
data. The technological assistance to farmers can tremendously boost the

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productivity of food grains and bring prosperity to this hardworking
population ensured with the usage of proposed system.
5. REFERENCES
[1] Ilonen J. Kamarainen J. K, Lindh T, Ahola J, Kalviainen H andPartanen,
“Diagnosis tool for motor condition monitoring.” IEEE Trans. Ind. Application,
Vol 41, No. 4, 2005 pp. 963-971.
[2] KuniakiUmino, Yasuhiro Ohyama, Jin-Hua She and Hiroyuki Kobayashi,
“Remote Controlled Embedded System”, 4th China-Japan International
Workshop on Internet Technology and Control Applications, Hunan, China, 21-
26 October, 2005.
[3] A Weaver J Luo, and S Zhang, “Monitoring and Control using Internet and
Java”, Proc. 25th Annual Conference on IEEE Industrial Electronics IECON‟99,
Vol. 3 , pp 1152-1158, 1999.
[4] Trohandl C, Proske M &Elmeureich W, “Remote Target Monitoring in
Embedded System Lab courses using a Sensor Network”, Proc. 32nd Annual
Conference on IEEE Industrial Electronics IECON 2006, 6-10 Nov 2006, pp
5433-5438.
[5] AT Commands Set for Nokia GSM and WCDMA products,Version 1.2, July
2005, available online: http://forum.nokia.com.
[6] ETSI TS 127 007 V5.3.0 (2003-03) Digital cellular telecommunications system
(Phase2+); Universal Mobile Telecommunications System (UMTS); AT
command set for 3G User Equipment (UE) (3GPP TS 27.007 version 5.3.0
Release 5, available online: http://www.etsi.org.
[7] ATMELAVRATMega32 Datasheet, available online:
http://www.atmel.com/dyn/resources/prod_documents/2503S.pdf.
[8] Richard Barnett, Larry O‟Cull and Sarah Cox, “Embedded C Programming and
Atmel AVR”, Delmar-Cengage Learning India Pvt. Ltd., 2007 Edition (India).
[9] Ponyprog software, available online: http://www.lancos.com/
[10] DS1307 64 x 8, Serial, I2C Real-Time Clock Datasheet, availableonline:
http://pdfserv.maxim-ic.com/en/ds/DS1307.
[11] Benbouzid, M.E.H, “A review of induction motors signature analysis as a
medium for faults detection”, IEEE Trans. Ind.Electronics, Vol 47, No.5, 2000
pp. 984-993.
[12] DS18S20 High-Precision 1-Wire Digital Thermometer, available online:
http://pdfserv.maxim-ic.com/en/ds/DS18S20.
[13] Overview of 1-Wire® Technology and Its Use, available
online:http://www.maxim-ic.com/an1796.

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Upcoming Trends of Virtual
Experiments for Laboratories
Bhaskar Y. Kathane
PCD ICSR, VMV College Campus, Wardhaman Nagar, Nagpur (MS), India
Pradeep B. Dahikar
Kamla Nehru Mahavidyalaya Sakkardara, Nagpur (MS), India
Satish J. Sharma
Department of Electronics and Computer Science, R.T.M. Nagpur University, Nagpur
ABSTRACT
The scope of this paper includes development and implementation of virtual laboratories.
Virtual Experiments can be performed using the concept of virtual Intelligent SoftLab
(VIS). The virtual experiments described here will help students to perform it at anytime
and anywhere. The model accepts inputs using virtual instruments and observed virtual
results on the screen. There is a facility for constructing an experimental circuit, change the
input values and observed outputs respectively. The graphical and simulated effects of
virtual experiments are visible on the screen. SoftLab will help Electronics, Computer
Science and Engineering students perform and practice experiments to improve their
understanding of the subject.
Keywords
SoftLab, Virtual, VIS Model, Virtualization, Virtual Lab.
1. INTRODUCTION
For the last two decades, Electronics and Computer Science technology
have been among the fastest growing areas in the research field. Due to this,
there is a traditional laboratory in keeping up with some of the modern
industry's requirements. The concept of traditional real laboratories in
educational institutions has many limitations. The availability of resources
may be too limited to provide basic as well as advanced laboratory
equipments, and there may be a paucity of qualified faculty members. Even
if the facilities are there, in many cases a student may end up as a spectator
in the conduct of experiments. Students are not free to do experiments
according to their own schedules, as the time slot for an experiment may be
limited to the usual working hours. Numbers of subjects like Physics and
Electronics students have to perform a large number of experiments in an
academic year. Many times students do not get time to repeat experiments
which they have performed during the session. Also many of the
laboratories lack of resources to perform experiments in which sophisticated

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instruments are required. The new era of education demands a revolution
and modernization in techniques for different subjects. One gap in subjects
having component of practice is the lack of a complete set of e-experiments
that is a full scale virtual laboratory, instead of little virtual experiments.
Science subjects always have a component of practical. In subjects like
Physics and Electronics students have to perform a large number of
experiments in an academic year. Many times students do not get time to
repeat experiments which they have performed during the session. Also
many of the laboratories lack of resources to perform experiments in which
sophisticated instruments are required. Therefore to provide access to
laboratory experiments, anytime anywhere, concept of virtual laboratory is
developed. This virtual laboratory can cater to students at undergraduate
(UG) and post graduate (PG) levels. Some software‘s like Mat Lab and Lab
VIEW are available for simulation of experiments and for other purposes.
However, these software‘s are generally available only in big
Institutes/Laboratories and the student can use them only during college
hours. It is therefore decided to develop software for performing individual
experiments virtually on a computer screen. An attempt is made to develop
software for electronics experiments from basic to advance level. The
facilities of the laboratories in educational institutions are generally
insufficient when the number of students is considered. Implementation of a
laboratory to meet the requirements has a very high price.
The Virtualization of experiments is one of the most efficient ways for the
modernization of traditional laboratories. Virtualization is basically the
conversion of real experiments into virtual experiments with the help of
information and communication technologies (ICT), which provide a real
laboratory environment and 'feel' to perform the experiment. The basic goal
for Virtualization is to provide a facility to perform the experiment by using
either the Internet or s specific computer program. This can provide a highly
interactive and powerful learning environment for the engineering and
science disciplines and enables a learner to select and control all the related
parameters of the experiments. A complete graphical interface with the
adequate learning components and a scientific approach can provide the
'feel' of performing experiments efficiently. The Virtualization of an
experiment and its application by Internet based remote techniques can
provide a relevant and meaningful practical learning experience. This
relatively new concept of Virtualization is cost effective and there is no time
bound for the users as experiments could be accessed "24X7" anywhere any
time. Virtual experiments can be used extensively for teaching, e-learning,
and other computer-based education [1].

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2. LITERATURE REVIEW
In 1990, Mercer, Lynn from Canada University of Regina proposed the
concept of the Virtual Lab and built a simple one. During the next 20 years,
a lot of researchers further built and enriched the concept of Virtual Lab.
Experimental simulations have appeared with discipline specific texts, such
as Jones and Childers (2001), and Christian and Belloni (2001), or on the
World Wide Web at sites such as the Physical Sciences Resource Center
[2]. Many of these simulations are implemented using Java applets, and are
well designed and executed. Other online experimentation includes the
Stevens Institute of Technology in their Remote Dynamical Systems
Laboratory [3], which incorporated several online experiments but is
subject to limited use and limited data. Mercer University has implemented
the Online Interactive Chaotic Pendulum [4]. The site has an exemplary
interface and data presentation, but doesn't provide easy access to data.
Several mechanical engineering courses have online experimentation,
including Curtin University [5]. These sites tend to be highly technical and
their online availability appears to be limited. Other organizations that have
executed online experimentation include the Advanced Liquid Crystalline
Optical Materials (ALCOM) Science and Technology Center [6] of the Kent
State University. Tan, Tang and Paterson at Queensland University of
Technology had a project on developing a web based remote controlled
mechanical vibrations laboratory via the Internet.
The system allowed flexibility for students to access a range of laboratory
experiments at any time and any place where there is an Internet connection.
Coito et al. (2007), Bauer and Fedák (2007) and Bachnak et al. (2003)
discussed the ability to control and monitor processes from remote locations
through a PC-based data acquisition for online and off line analyses.
Advances in networking technologies and development of measurement
hardware and software have turned PCs into platforms capable of
continuous remote monitoring using the Internet (Fountain & Wright, 2000).
Software programs that facilitate the developers of such applications are
available. A user can publish data on the web with little or no programming
experience. The software creates user interfaces on a Tiwari and Singh 673
web page to give the user access to the system (Travis, 2000). Antunes,
Paulino & Piteau (1998) developed predictive methods to remotely analyze
heat exchanger tube responses and wear for realistic multi-supported tubes
and flow configurations. Buckman (2000) presented an introduction to the
virtual instrumentation to increase the ease of use for students, and the
capability to add new measurements that were otherwise unaffordable.
Overstreet and Tzes (1998) describes the design and development of generic
virtual instrument used for real time experimentation in the control
engineering laboratory of their institute in a remote-access environment.

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Salzmann (1999) proposed the steps of remote, real time control over the
Internet, and demonstrated the feasibility of using a distributed online
laboratory to complement and enhance the traditional laboratory. Ertugrul
(2000) reported such software tool applications, and his paper aimed to
provide some background knowledge about the tools and to address
common problems encountered by users. Resendez and Bachnak (2003) and
Bachnak et al. (2003) showed that such software tools could be used to
perform data acquisition and remotely control hardware devices through the
Internet. Trevelyan (2002) aimed to provide a cost effective online
laboratory to staff and students. Their first project was a tele-robot, which
was written in special purpose software written in C, C++ and Java.
Trevelyan (2004) reviewed the entire principal lessons their group learned
since 1994 and briefly described Telelabs, a cost-effective framework to
provide an extendable series of online labs that could be sustained from
normal operating budgets. Hofmann and Bubb (2003) explained about the
virtual environment for the typical industrial application. Almgren and
Cahow (2005) explained the way to make engineering education more
innovative by using the Internet.
Pheatt and Ballester (2003) discussed the design and implementation of web
courses and remote experiments, and the incorporation of these techniques
into the curriculum without acquiring equipment, setting up equipment or
creating a laboratory environment. Che (2005) presented the development of
biological engineering education along with a discussion on the
development of e-learning with a time sharing mainframe model and
providing a centralized, remote controllable biology lab. Feisel and Rosa
(2005) explored the major factors influencing conventional laboratories.
They described the various limitations which affect the effectiveness of
laboratory work and also its importance in science and engineering
education. Ozeki et al. (2006) produced remote experiments with a digital
certificate and encryption of communication data to protect a supervisory
control system against illegal access. They did the development of the
remote experiment system with testing and demonstration. Jeschke et al.
(2008) discussed the integration of experimental setups into a virtual
cooperative knowledge space, so that availability and accessibility can be
enhanced for a wide range of users. They worked out the architecture and
implementation of the remote experiment. Gröber, Vetter, Eckert & Jodl
(2007) described a remote laboratory and its helpfulness to provide a tool to
sustain this shift towards a student-centered engineering teaching approach.
In papers by Bauer and Fedák (2007) and Bauer et al. (2008) a distance
measurement application for educational purposes was described, and the
monitoring of industrial applications was studied through web based
applications. Macías and Méndez (2008) describes the automation of their

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laboratory and rapid integration of automation systems into most of the
engineering processes. Cui, Wang, Zhang and Akujuobi, 2011, 27 (4)
(2008) focused on the reasons for traditional laboratories lagging behind and
emphasize the basic demands behind the modern trend to increased
virtualisation. Kolias et al. (2007) provided a categorization of remote
experimentation in education according to the platform used for conducting
the experiments and the scientific field represented. Gadzhanov and
Nafalski (2010) reviewed the pedagogical effectiveness of distance
education, with a special focus on remote laboratories for measurement and
control. Machotka et al. (2010) developed the remote laboratory NetLab at
the University of South Australia. NetLab was developed from the
beginning as a collaborative learning environment that enables students to
cooperate while conducting remote experiments via the Internet on both the
domestic and international levels. Herrera and Fuller (2011) proposed a
model for the implementation of remote experimentation laboratories in a
distributed collaborative scenario, focusing on two crucial key elements,
namely the shared knowledge and the interaction for the collaboration. They
contributed towards the implementation of remote experimentation using
collaborative scenarios. R Tiwari and K Singh summarized their experiences
during the design, evaluation and implementation of Virtualization for a
number of engineering experiments, enabling these to be accessed through
the Internet as virtual experiments. They also covered the features required
to give the 'feel' of performing experiments, inclusion of learning
components, incorporation of easy navigation, testing of the effectiveness of
learning, and development of useful feedback mechanisms.
From the geographical point of view, the educational environment can be
divided into two categories called classroom environment and lab
environment. The most common modern classroom environment is the
multimedia classroom which includes: projectors and large screens, object
display platform, DVD player and computers. Among them, the computer
is increasingly becoming the center of multimedia classrooms. The virtual
classroom is also called live classroom, which can provide real-time
teaching function through web technology. Teachers can use text, images,
handwritings, voices, videos and other forms of media to teach students with
theoretical knowledge. Students can use text, voices and other forms of
media to ask questions at any time. The learning process is similar to the
classroom environment.
3. VIRTUALIZATION OF LABORATORIES
Virtualization is the creation of a virtual rather than actual version of
something, such as a hardware platform, an operating system, a storage

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device or a network resource. Virtualization is a technique that allows you
to run more than one server (or other infrastructure component) on the same
hardware [7]. The virtual machine concept was in existence since 1960s
when it was first developed by IBM to provide concurrent, interactive
access to a mainframe computer. Each virtual machine (VM) used to be an
instance of the physical machine that gave users an illusion of accessing the
physical machine directly. It was an elegant and transparent way to enable
time-sharing and resource-sharing on the highly expensive hardware. Each
VM was a fully protected and isolated copy of the underlying system. Users
could execute, develop, and test applications without ever having to fear
causing a crash to systems used by other users on the same computer.
Virtualization was thus used to reduce the hardware acquisition cost and
improving the productivity by letting number of users work on it
simultaneously. As hardware got cheaper and multiprocessing operating
systems emerged, VMs were almost extinct in the 1970s and 1980s. With
the emergence of wide varieties of PC based hardware and operating
systems in 1990s, the Virtualization ideas were in demand again [8]. There
can be innumerous reasons how Virtualization can be useful in practical
scenarios, a few of which are the following:
 Operating System (OS) Level Virtualization: An OS-level VM is a
virtual execution environment that can be forked instantly from the base
operating environment. OS-level Virtualization has been widely used to
improve security, manageability and availability of today‘s complex
software environment, with small runtime and resource overhead, and
with minimal changes to the existing computing infrastructure.
 Hardware Level Virtualization: Hardware-level Virtualization
technologies simulate the complete or a subset of hardware to run
unmodiﬁed guest OS, or modify the guest OS to utilize the underlying
physical hardware. Examples of the hardware-level virtualization
technologies are Bochs, VMware, Virtual PC, Xen.
 Application Virtualization: Application Virtualization creates isolated
virtual environments on a host OS to sandbox the deployment and
execution of selected application programs. Such virtual environments
are mainly used for four purposes: resolving application conflicts,
migrating running processes, isolating untrusted programs, and
supporting portable application environments.
 Cloud Computing: Cloud Computing is the delivery of computing as a
service rather than a product, whereby shared resources, software, and
information are provided with computers and other devices as a metered
service over a network (typically the Internet). Cloud Computing may
look like Virtualization because it appears that your application is
running on a virtual server detached from any reliance or connection to a

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single physical host. However, Cloud Computing can be better described
as a service where Virtualization is part of a physical infrastructure.
Cloud Computing builds on top of a virtualized infrastructure (compute,
storage, network) by using standardization and automated delivery to
provide service management. This makes monitoring the virtualized
resources and the responsible deployment of these resources possible.
3.1 Need of Virtual Lab
Physical distances and the lack of resources make us unable to perform
experiments, especially when they involve sophisticated instruments. Also,
good teachers are always a scarce resource. Web-based and video-based
courses address the issue of teaching to some extent. Conducting joint
experiments by two participating institutions and also sharing costly
resources has always been a challenge. With the present day internet and
computer technologies the above limitations can no more hamper students
and researchers in enhancing their skills and knowledge. In India costly
instruments and equipment need to be shared with fellow researchers to the
extent possible. Web enabled experiments can be designed for remote
operation and viewing so as to enthuse the curiosity and innovation into
students. This would help in learning basic and advanced concepts through
remote experimentation. Today most equipment has a computer interface
for control and data storage. It is possible to design good experiments
around some of this equipment which would enhance the learning of a
student. Internet-based experimentation further permits use of resources –
knowledge, software, and data available on the web, apart from encouraging
skillful experiments being simultaneously performed at points separated in
space.
3.2 Objectives of the Virtual Labs
 To provide remote-access to Labs in various disciplines of Science and
Engineering. These Virtual Labs would cater to students at the
undergraduate level, postgraduate level as well as to research scholars.
 To enthuse students to conduct experiments by arousing their curiosity.
This would help them in learning basic and advanced concepts through
remote experimentation.
 To provide a complete Learning Management System around the Virtual
Labs, where the students can avail the various tools for learning,
including additional web-resources, video-lectures, animated
demonstrations and self evaluation.
 To share costly equipment and resources, which are otherwise available
to a limited number of users due to constraints on time and geographical
distances?

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3.3 Salient Features of the Virtual Lab
 Virtual Labs will provide to the students the result of an experiment by
one of the following methods (or possibly a combination)
 Modeling the physical phenomenon by a set of equations and carrying
out simulations to yield the result of the particular experiment. This can,
at-the-best, provide an approximate version of the ‗real-world‘
experiment.
 Providing measured data for virtual lab experiments corresponding to
the data previously obtained by measurements on an actual system.
 Remotely triggering an experiment in an actual lab and providing the
student the result of the experiment through the computer interface. This
would entail carrying out the actual lab experiment remotely.
 Virtual Labs will be made more effective and realistic by providing
additional inputs to the students like to accompany audio and video
streaming of an actual lab experiment and equipment.
 For the ‗touch and feel‘ part, the students can possibly visit an actual
laboratory for a short duration.
Laboratories play an important role in engineering education. The laboratory
work provides an opportunity to witness classroom-learned theoretical
concepts at work; operate instruments used in the experimental set-up;
measure different quantities and thereafter analyze the experimental data;
and work collaboratively. However, physical distances and the lack of
resources make us unable to perform experiments, especially when they
involve sophisticated instruments. Also, trained teachers are always a scarce
resource. Rapid development and ever increasing use of Information and
Communication Technology (ICT) has revolutionized the education system
by eliminating limitations in terms of physical distance, time and access, by
providing equal opportunity to everyone, irrespective of place and time.
Virtual laboratories have been developed in different areas, to reproduce
experiments that were made in physical laboratories. Virtual labs are useful
for pre-practice and post-analysis of experiments developed in physics labs,
and in some cases they can replace th
e physical lab itself. Although virtual labs may have many limitations, they
have many advantages over physical labs. For example, some physical labs
have a scarcity of resources (in equipment and staff), limiting the
researcher's performance. Virtual labs have relatively low costs,
experiments can easily be repeated, and there are no inconveniences in
failing experiments, because the virtual environment is controlled, and there
are no risks for natural systems. It is desirable that virtual labs exploit the
advantages of virtual reality, multimedia, and the Internet. Virtual labs have

Vol 2 No 1 - June 2013

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Vol 2 No 1 - June 2013