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Editor–in-Chief
Dr.Narendra Kohli
Associate Professor,
Computer Science and Engineering Department,
Harcourt Butler Technological Institute,
Kanpur, UP, India.
Editorial Board
Ramoni Lasisi, PhD
Utah Water Research Lab.
Utah State University, USA.
Farzad Moradpouri
PhD Researcher
Faculty of Mining, Petroleum and
Geophysics,
Shahrood Univ. of Technology,
Shahrood, Iran.
Dr.Syed Fajal Rahiman Khadri
Professor and Head ,
P.G. Department of Geology,
Sant Gadge Baba Amravati University,
Amravati, Maharashtra, India.
Engr.Noman Naseer
Department of Cogno-Mechatronics
Engineering,
Pusan National University, South Korea.
Dr.Heru Susanto
University of Brunei, Information
System Group - FBEPS
& The Indonesian Institute of Sciences
Engr.Dr.Wan Khairunizam B. Wan
Ahmad
Senior Lecturer
School of Mechatronics, University
Malaysia Perlis, Malaysia
Dr.D.S.R.Murthy
Professor in Information Technology,
Sree Nidhi Institute of Science and
Technology,
Yamnampet, Hyderabad , India.
Ajay B.Gadicha
Department of Information Technology,
P.R.Pote(Patil) College of Engineering,
Amravati, Maharashtra, India.
Prof.(Er.) Anand Nayyar
Dept. of Computer Applications & IT
KCL Institute of Management and
Technology,
Jalandhar, Punjab, India.
Dr.Hamid Ali Abed Alasadi
Department of Computer Science,
Faculty of Education of Pure Science,
Basra University, Basra, Iraq.
Hassen Mohammed Abduallah Alsafi
Research Assistant,
IIUM, Malaysia.
Antoni Wibowo
Senior Lecturer,
Dept. Computer Science Faculty of
Computing,
Universiti Teknologi Malaysia, Johor,
Malaysia.
Contents
S.No. Title & Name of the Author(s)
Page
No.
1. Three Game Patterns
Takeo R. M. Nakagawa, Hiroyuki Iida
1-12
2. Effect of Microstructure of Different Treatments on the Electrical
Properties of Schottky Diodes Based on Silicon
I.G.Pashaev
13-20
3. Review of MRI Image Classification Techniques
Sivasundari .S, Dr.R. Siva Kumar, Dr.M.Karnan
21-28
4. Holistic Prediction of Student Attrition in Higher Learning Institutions in
Malaysia Using Support Vector Machine Model
AnbuselvanSangodiah, BalamuralitharaBalakrishnan
29-35
5. Comparative Appraise and Future Perspectives of Reactive and Proactive
Routing Protocols in Manets
Surinder singh, Dr. B S Dhaliwal, Dr. Rahul Malhotra
36-41
6. Detection of Design Patterns Using Design Pattern Nearness Marking
(DPNM) Algorithm
Shanker Rao A, M.A. Jabber, Mayank Sharma
42-51
7. Multi-Path Encrypted Data Security Architecture for Mobile Ad hoc
Networks
Suresha k, S.B.Mallikarjuna
52-56
8. Overcoming Ambiguity Concerns and Coarseness Evaluation with XML
Keyword Search
K.Sampth kumar, M.A. Jabber, Mayank Sharma
57-64
9. In Mobile Sensor Networks Localized Algorithms for Detection of Node
Replication Attacks
Sinthiya, S. Abirami
65-69
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE)
Volume 1, Issue 1, May 2014, PP 1-12
www.arcjournals.org
©ARC Page 1
Three Game Patterns
Takeo R. M. Nakagawa Pan-Asian Institute for the Liberal Study of
Science, Technology and the Humanities/
Jusup Balasaghyn Kyrgyz National
University, Bishkek,
Kyrgyz Republic
Hiroyuki Iida School of Information Science
JAIST, Nomi, Japan
Abstract: This paper is concerned with three elemental game progress patterns. It is found that each of the three games in 2010 FIFA World Cup, Group E is a combination of the elemental progress patterns. It
is inferred that this finding is universal and thus it is applicable to many other games. Time history of
information of game outcome obtained by the data analyses and existing models shows that for players
including winner-sided observers and loser-sided observers, “balanced game” is most exciting, “one-sided
game” is least exciting and “seesaw game is intermediate exciting. It is suggested that for neutral
observers “balanced game” is frustrating, “one-sided game” is boring, and “seesaw game” is exciting.
Keywords: Game Progress Patterns, Game Model, Soccer, Entertainment.
1. INTRODUCTION
While knowledge about game design patterns and game play patterns has grown fairly well, little
advancement has made to clarify game progress patterns, which show how information of game
outcome depends on game length of time. Making use of game design patterns, Kelle et al [1]
have implemented information channels to simulate ubiquitous learning support in an authentic
situation. Lindley & Sennersten [2]‟s schema theory provides a foundation for the analysis of
game play patterns created by players during their interaction with a game. Lindley & Sennersten[3] has proposed a framework which is developed not only to explain the structures of
game play, but also to provide schema models that may inform design processes and provide
detailed criteria for the design patterns of game features for entertainment, pedagogical and
therapeutic purposes.
Salen & Zimmerman [4] and Fullerton et al [5] argue in favor of iterative design method, which
relies on inviting feedback from players early on. „Iterative‟ refers to a process in which the game
is designed, tested, evaluated and redesigned throughout the project. As part of this approach
designers are encouraged to construct first playable version of the game immediately after
brainstorming and this way get immediate feed- back on their ideas (Fullerton et al [5]). Play-
testing, which lies in the heart of iterative approach, is probably most established method to
involve players in design. Play-testing is not primarily about identifying the target audience or
tweaking the interface, but it is performed to make sure that the game is balanced, fun to play, and
functioning as intended(Fullerton et al [5]).
Game Ontology Project (Zagal et al [6]) offers a framework for describing, analyzing, and
studying games by defining a hierarchy of concepts abstracted from an analysis of many specific
games. The project borrows concepts and methods from prototype theory and grounded theory to
achieve a framework that is continually evolving with each new game analysis or particular
research question. The term ontology is borrowed from computer science rather than used in the
philosophical sense. It refers to the identification and description of entities within a domain.
This project is distinct from design rules and design patterns approaches that offer imperative
advice to designers. It is intends not to describe rules for creating good games but rather to
identify the abstract commonalities and difference in design elements across a wide range of
concrete examples. The ontological approach is also distinct from genre analyses and related
attempts to answer the question “What is a game?”, which are indeed the same as the present
Takeo R. M. Nakagawa & Hiroyuki Iida
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 2
study. Rather than develop definitions to distinguish between games and non-games or among
their different types, it focuses on analyzing design elements that cut across a wide range of
games. Its goal is not to classify games according o their characteristics and/or
mechanics(Lundgren & Björk [7]) but to describe the design space of games. Another project
seeking the same goals using a different methodological approach can be seen in Björk &
Holopanionen[8].
Game information dynamic models (Iida et al[9.10]) make it possible to treat and identify game
progress patterns and thus enhance their detailed discussion . In these models, information of
game outcome is expressed as the analytical function of the game length or time, where
information of game outcome is the data that are the certainty of game outcome. The two models
are expressed, respectively, by
Model 1:ξ=ηn,
And
Model 2: ξ= [sin(π/2∙η)]n ,
Where ξ is the non-dimensional information, η the non-dimensional game length or time, and n
the positive real number parameter. The value of the parameter n depends on fairness of the
game, strength of the two teams, and strength difference between the two teams.
It is realized that there are various game progress patterns in Base Ball(Iida et al [9] )
,Soccer(Iida et al [10] ), Chess, Shogi and many others. In general, each the game proceeds with time in its characteristic manner. None the less, we sometimes encounter similar game
progress patterns in each the game, so that it is quite useful to understand the nature of game if we
can identify elemental game progress patterns, which are common in many games.
Main purpose of the present study is to confirm that game consists of the three elemental game
patterns based on the actual Soccer games and existing game models, and clarify how emotion of
players and observers varies with the elemental game progress patterns.
2. ELEMENTAL GAME PROGRESS PATTERNS
Three elemental game progress patterns, viz. “balanced game”, “seesaw game” and “one-sided
game” have been heuristically found by the present authors during the investigation of
information dynamics on Base Ball(Iida et al 2011a) and Soccer(Iida et al 2011b). It is realized
that each of real games is a combination of the three elemental game progress patterns, though
there are several supplementary game progress patterns such as “catchup game” and/or “against
all odds game”: In “catchup game”, one team always breaks a tie in their favor, but it goes back to
tied again, while in “against all odds game”, one team has a significant lead, but towards the end
of the game, the other team recovers and wins. And also that their detailed discussions are
essential for understanding emotion of players and observers during game. The elemental game
progress patterns have been introduced by using three artificial Soccer games as listed in Table 1:
Examples of the three artificial Soccer games, viz. “balanced game”, “seesaw game” and “one-
sided game”, have been proposed so as to satisfy conditions, to be defined for each the game
ideally.
Table 1. Time history of goals for three artificial Soccer games between team A and team B.
Game Result Goal time
balanced game 0 −0
seesaw game 5 −4 10(A), 20(B), 30(B), 40(A), 50(A),
60(B), 70(B), 80(A), 90(A)
one-sided game 9 −0 10(A), 20(A), 30(A), 40(A), 50(A),
60(A), 70(A), 80(A), 90(A)
Three Game Patterns
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 3
In the column “Result”, the left value is the goal sum for team A after the game, while the right value is the goal sum for team B.
In the column “Goal time”, characters A and B in the brackets denote team A and team B, respectively.
The non-dimensional information ξS in Soccer is here defined as follows: When the total goal(s)
of the two teams at the end of game GT≠0,
ξS=∣GA(η) − GB(η)∣/ GT for 0 ≤ η ≺ 1,
= 1 for η=1,
Where GA (η) is the current goal sum for the team A(winner), and GB(η) is the current goal sum
for the team B(loser). At η=1, ξS is assigned the value of 1, for at the end of game the
information must reach the total information of game outcome. On the other hand, when GT=0,
ξS=0 for 0 ≤ η ≺ 1,
=1 for η=1.
Note that in a draw case ξS may also take the value of 0 other than 1 atη=1, depending on the
game rules: In case of tournament match, ξS=1 at η=1, while in case of league match, ξS=0 at η=1.
The game length is defined as the current time (minutes), and it is normalized by the total time or
the total game length to obtain the non-dimensional value η. The total game length of Soccer is
normally 90 minutes, but in case of extended games it becomes 120 minutes.
Balanced game: Both of the teams have no goal through the game. Figure 1 shows the relation
between the non-dimensional informationξS and non-dimensional game length η for the artificial
balanced game. In this figure, the curve of Model 1 at n=50 is plotted for reference. In this case,
we consider a “balanced game”, in which winner and loser are determined by the penalty kick
match after the game. Note that there exist anther “balanced game”, in whichξS=0 at η=1 as being
stated already. It may be worth noting that the artificial balanced game, as shown in Figure 1 is
exactly the same as Japan vs. Paraguay, which is one of Round 16 in 2010 FIFA World Cup
South Africa. This is because ξS jumps to 1 at the end, so it is accounted for by the curve of
Model 1, having the large value of n=50.
Seesaw game: One team leads goal(s), then the other team leads goal(s), and this may be repeated
alternately. It is, however, necessary that the current goal difference between the two teams must
be smaller than the current safety lead, which is that once the goal difference exceeds to its value,
the leading team will win the game with 100 % certainty. Note that the safety lead decreases with
increasing the game length and depends on fairness of the game, strength of the two teams and
strength difference between the two teams. This suggests immediately existence of the safety
lead curve that once the game advantage goes above it, the advantageous team will win the game
with 100 % certainty. Figure 2 shows the relation between the non-dimensional informationξS
and non-dimensional game length η for the artificial seesaw game. In this figure, the curve of
Model 1 at n=4 is plotted for reference and roughly accounts for the seesaw game.
One-sided game: The current goal sum of one team (winner) is always greater than that of the
other team (loser), so that the goal difference between the two teams is kept to be positive.
However, “one-sided game” is further divided into “complete one-sided game or state” and
“incomplete one-sided game or state”.: When the goal difference is smaller than the current safety
lead, it is called “incomplete one-sided game or state”. On the other hand, when the goal
difference is greater than the current safety lead, it is called “complete one-sided game or state”.
However, when a game changes from incomplete one-sided state to complete one-sided state and
finishes, it is simply called “one-sided game”. Figure 3 shows the relation between the non-
dimensional informationξS and non-dimensional game length η for the artificial one-sided game.
In this figure, the curve of Model 1 at n=1 is plotted for reference and accounts for the one-sided
game.
Takeo R. M. Nakagawa & Hiroyuki Iida
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 4
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Non-dimensional Game Length
Non-di
mensi
onal
Info
rmat
ion
balanced game
Model 1 n=50
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Non-dimensional Game Length
Non-di
mensi
onal
Info
rmat
ion
seesaw game
Model 1 n=4
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Non-dimensional Game Length
Non-di
mensi
onal
Info
rmat
ion
one-sided game
Model 1 n=1
Figure 1. Non-dimensional informationξS against non-dimensional game length η for the artificial balanced game.
Figure 2. Non-dimensional information ξS against non-dimensional game length η for the artificial seesaw game.
Three Game Patterns
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 5
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0 0.2 0.4 0.6 0.8 1 1.2
Non-dimensional Game Length
Non-di
mensi
onal
Adv
anta
ge
seesaw game
Figure 3. Non-dimensional information ξS against non-dimensional game length η for the artificial one-
sided game.
The non-dimensional advantage α is here defined as follows: When the total goal(s) of the two
teams at the end of game GT≠0,
α=[GA(η) − G B(η)]/ GT for 0 ≤ η ≤ 1.
On the other hand, when GT=0,
α=0 for 0 ≤ η ≤ 1.
This means that when α ≻ 0, team A (winner) gets the advantage against team B(loser) in the
game, while whenα ≺ 0, team B (loser) gets the advantage against team A(winner). It is certain that when α=0 the game is balanced.
Figure 4 shows the relation between non-dimensional advantages α between non-dimensional
game length η for the artificial seesaw game. It is evident that in case of the seesaw game α
changes from positive value to negative value alternately with increasing η. In case of the
balanced game as shown in Figure 1, α takes the value of zero through the game, while in case of
the one-sided game, as shown in Figure 3, non-dimensional advantage α coincides with non-
dimensional information ξS , and takes the value , which is greater than or equal to zero through
all of η.
Figure 4. Non-dimensional advantages α against non-dimensional game length η for the artificial seesaw
game.
3. INFORMATION AND ADVANTAGE IN THREE SOCCER GAMES IN 2010 FIFA WORLD
In this section, some results of the data analyses on the three Soccer games in 2010 FIFA World
Cup, Group E will be presented at first and then the game progress patterns will be discussed with
reference to information dynamic models, Model 1 and Model 2. Some of the relevant
information on the three Soccer games in 2010 FIFA World Cup are summarized in Table 2.
Table 2. Three Soccer games in 2010 FIFA World Cup, Group E
Game Result Goal time (min) Total game
length (min)
Date Place
E1 Holland 2-0
Denmark
45(Holland)
85(Holland)
90 June 14 Yohannesburg
Takeo R. M. Nakagawa & Hiroyuki Iida
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 6
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Non-dimensional Game Length
Non-di
mensi
onal
Info
rmat
ion
Holland 2-0 Denmark Denmark 2-1 Cameroon Holland 2-1 Cameroon
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Non-dimensional Game Length
Non-di
mensi
onal
Adv
anta
ge
Holland 2-0 Denmark Denmark 2-1 Cameroon Holland 2-1 Cameroon
E2 Denmark 2-1
Cameroon
10(Cameroon)
33(Denmark)
61(Denmark)
90 June 19 Pretoria
E3 Holland 2-1
Cameroon
36(Holland)
65(Cameroon)
85(Holland)
90 June 24 Cape Town
Figure 5. Non-dimensional information ξS against non-dimensional game length η for three Soccer games.
Figure 5 shows the relation between the non-dimensional information ξS and non-dimensional
game length η for three Soccer games in 2010 FIFA World Cup, Group E. This figure clearly
indicates that non-dimensional information ξS for these three games varies with the non-
dimensional game length η in different manner each other. However, Denmark vs. Cameroon
and Holland vs. Cameroon have a common character that the information increases rapidly near
the end. It is realized that these games are accounted for by Model 1. This has been also suggested
by Iida et al [11]. On the other hand, Holland vs. Denmark has a distinctive feature that the
information gradually approaches to the total value of game outcome. It is realized that this
game can be accounted for by Model 2.
Figure 6. Non-dimensional advantage α against non-dimensional game length η for the three Soccer
games.
Three Game Patterns
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 7
Figure 6 depicts the relation between non-dimensional advantage α and non-dimensional game
length η for the three Soccer games in 2010 FIFA World Cup, Group E. This figure, therefore,
illustrates how the non-dimensional advantage α of each the game changes with the non-
dimensional game length η: In case of Holland vs. Denmark, it is balanced until η≃ 0.49, but then
the advantage αincreases and takes the value of 0.5 at η≃0.49 and then becomes the value of 1 at
η≃0.93, keeping this value until η=1. In case of Denmark vs. Cameroon, it is balanced until
η≃0.10, but Cameroon gets the first goal and thus keeps the advantage fromη≃0.10 to 0.36.
However, the game becomes the second balanced state from η≃0.36 due to Denmark‟s goal and
this is kept until η≃0.67, but Denmark gets her second goal at η≃0.67 and keeps her advantage
and the game finishes at η=1. In case of Holland vs. Cameroon, it is balanced until η≃0.39, but
the balance breaks at η≃0.39 due to Holland‟s first goal and then Holland keeps the advantage
until η≃0.71. However, due to Cameroon‟s goal η≃0.71 the game becomes the second balanced
state and this continues until η≃0.93 at which Holland gets her second goal, and maintains the advantage until the end.
Figures 5 and 6 show that in Holland vs. Denmark, the game changes smoothly from “incomplete
one-sided state” to “complete one-sided state” with increasing η and finishes, though it is
balanced from η=0 to 0.49. Thus, we may state that this game is a combination of “one-sided
game” and “balanced game”. Denmark vs. Cameroon is a “seesaw game”, though it is balanced
during two intervals, viz. one is from η=0 to ≃0.10 and the other is from η≃0.36 to ≃0.67. Thus, we may state that this game is a combination of “seesaw game” and “balanced game”. Holland
vs. Cameroon is balanced during two intervals, viz. one is from η≃0 to ≃0.39 and the other is from η≃0.71 to ≃0.93. However, the goal difference between Holland(winner) and Cameroon(loser) during two intervals, viz. from η≃0.39 to ≃0.71 and from η≃0.93 to 1, is kept to be positive, but is only one. Thus, this game is considered as a combination of “incomplete one-
sided game“and “balanced game”.
4. CHESS DATA ANALYSES
In this section, it is inquired whether Chess can be expressed by a combination of the three
elemental game progress patterns or not.
A Chess match was played between, GreKo6.5 (White) and Boook4.15.1 (Black), both of which
are computer Chess Engines. In this game, Black mates White at the 25th move. Chess
evaluators count and sum up the relevant materials in principle (o David-Tabibi et al [12]). A
total of 25 evaluation function scores are collected from the computer Chess engine, GreKo6.5.
one for each of White‟s moves in that game. When the computer Chess engines make a decision
that the game is over, they may provide an extremely high value of evaluation function score. In
such a case, as the evaluation function score at the move, the maximum value within all of the
previous moves is substituted for it. This modified evaluation function score is used as current
advantage in our analysis. When the first engine (White) takes an advantage over the second
engine (Black), the sign of the current advantage is positive, while in the reverse case it is
negative. When both engines are even the current advantage becomes zero.
The non-dimensional information ξc in Chess is defined as follows:
ξc= ∣Ad(η)∣/ACT(1) for 0 ≤η ≺1,
1 for η=1,
where Ad(η) is the current advantage as described above. ACT (1) is the total advantage change at
the end of the match, such that
ACT (η) =ACT (m/N) = ∑∣Ad (i) ‒Ad (i ‒1) ∣,
1≤i≤m
where m is the current move count, N the total move count, and i a positive integer. η is the non-
dimensional game length, in which the current move count m is normalized by the total move
count N.
Takeo R. M. Nakagawa & Hiroyuki Iida
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 8
GreKo 6.5 --- Booot 4.15.1 (Black Mates)
0
1
0 0.2 0.4 0.6 0.8 1 1.2
η
ξC
GreKo 6.5 --- Booot 4.15.1 (Black Mates)
-1
0
1
0 0.2 0.4 0.6 0.8 1 1.2
η
αC
The non-dimensional advantage αc in Chess is defined as follows
αc = Ad(η)/ACT(1) for 0 ≤η ≤1,
Figure 7 shows the relation between the non-dimensional information ξc and the non-dimensional
game length η for the described Chess match. Figure 8 shows the relation between the non-
dimensional advantageαc and the non-dimensional game length η for the same match. Figures 7
and 8 indicate that from η=0 to ≃0.547, the match is “balanced”, from η≃0.547 to ≃0.767, it is “seesaw”, and from η≃0.779 to =1, it is “one-sided”. Hence, it is considered that the present Chess match is a combination of “balanced”, “seesaw” and “one-sided”.
Regarding entertainment, in this Chess match the neutral observer(s) feel three different emotions,
“frustrated”, “excited” and “bored” during the balanced state, seesaw state and onbe-sided state,
respectively, as to be discussed in the next section.
It is considered that the present results of the Chess match are supporting evidence to the
statement that each game is a combination of the three elemental game progress patterns. It may
be evident that this statement is applicable to many other games, such as Base Ball, Go, Shogi, or
Basket Ball.
Figure 7. Non-dimensional information ξc against non-dimensional game length η for Chess.
Figure 8. Non-dimensional information αc against non-dimensional game length η for Chess.
Three Game Patterns
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 9
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Non-dimensional Game Length
Non-di
mensi
onal
Info
rmat
ion
Holland 2-0 Denmark n=2 n=4 n=6
5. DISCUSSION
This section discusses the entertainment in game through a comparison between Model 1( or
Model 2) and data on three Soccer games in 2010 FIFA World Cup, Group E. Before the
discussion, it must be noted that winner(s), loser(s) and neutral observer(s) have different emotion
during the game from each other, where winner(s) is winning player(s) and winner-sided
observer(s) and loser (s) is losing player(s) and loser-sided observer(s). The present discussion on
entertainment in game only inquires how neutral observer(s) feels emotion during the game as the
first step to understand it. For neutral observer(s), “balanced game” is frustrating, for both of the
teams have no goal through the game even though the game may proceed experiencing alternate
changes from offense to defense by the two teams many times. “One-sided game” is boring, for
only one team scores goal(s) and the winning goal appears too early., and “seesaw game” is
exciting, for both of the teams score goal(s) and advantage changes its sign during the game.
However, it is important to note how one feels emotion during game essentially belongs to a
private affair. The present discussion is therefore based on the authors‟ subjective views of this
problem, and a more general discussion is beyond the scope of the present study.
Figure 9 shows the relation between the non-dimensional information ξ and the non-dimensional
game length η. In this figure, the non-dimensional information for Holland vs. Denmark has
been plotted and is compared with three curves for Model 2. It may be clear that although the
non-dimensional information for this game proceeds in zigzag line, the non-dimensional
information for Holland vs. Denmark roughly follows the model curve at n=4. As being already
stated, Holland vs. Denmark is a combination of “one-sided game” and “balanced game”, in
which Holland gets two consecutive goals, but Denmark gets no goal. While Holland leads only
one goal, the game is still a pending state or “incomplete one-sided game or state”, for if Denmark
gets one goal, the game reverts to a balanced state. One the other hand, once Holland leads two
goals near the end, the game becomes “complete one-sided state”, for the goal difference is
considered to be the current safety lead. This means that this game becomes less exciting or more
boring with increasing the game length for neutral observer(s).
Figure 10 shows the relation between the non-dimensional information ξ and the non-dimensional
game length η. In this figure, non-dimensional information for Denmark vs. Cameroon and
Holland vs. Cameroon, respectively, has been plotted and is compared with three curves for
Model 1. It is evident that none of the information for these games fits to any model curve
through the total non-dimensional
Figure 9. Non-dimensional informationξagainst non-dimensional game length η: A comparison between
Holland vs. Denmark and Model 2.
game length, but near the end the information for these games increases very rapidly with
increasing η. This figure shows that Holland vs. Cameroon roughly follows the curve of Model 1
Takeo R. M. Nakagawa & Hiroyuki Iida
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 10
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Non-dimensional Game Length
Non-di
mensi
onal
Info
rmat
ion
Denmark 2-1 Cameroon Holland 2-1 Cameroon n=2 n=10 n=50
at n=50 near the end, while Denmark vs. Cameroon roughly follows the curve of Model 1 at n=10
near the end. As being already stated, Denmark vs. Cameroon is a combination of “seesaw
game” and “balanced game”, in which Cameroon gets the first goal, but Cameroon is reversed by Denmark, and then Denmark gets her winning goal. This game is tough for the both players, for
the goal difference between the two teams is within 1 through the game. One the other hand,
Holland vs. Cameroon is a combination of “incomplete one-sided game” and balanced game”, in
which Holland gets the first goal, but Holland is reversed by Cameroon, and then Holland gets her winning goal. The goal difference between the two teams is within 1 through the game, so
that this game is also tough for the both players as Denmark vs. Cameroon.
Figure 10. Non-dimensional information ξ against non-dimensional game length η: A comparison between
Denmark vs. Cameroon or Holland vs. Cameroon and Model 1.
The main differences between Denmark vs. Cameroon and Holland vs. Cameroon are twofold:
Firstly, in Denmark vs. Cameroon, Cameroon (loser) gets the first goal, and then Denmark (winner) gets the second and winning goals. Whereas in Holland vs. Cameroon, Holland (winner) gets the first goal, then Cameroon(loser) gets the second goal. Finally, Holland
(winner) gets the winning goal. The advantage changes its sign in Denmark vs. Cameroon, but it
does not change in Holland vs. Cameroon. This means that in Denmark vs. Cameroon, Cameroon (loser) takes an advantage during one interval of the game, but in Holland vs.
Cameroon, Cameroon (loser) has no advantage through the game. Secondly, the winning goal
time in Holland vs. Cameroon is later than that in Denmark vs. Cameroon.
Thus, it may be evident that difference in excitement between Denmark vs. Cameroon and
Holland vs. Cameroon is quite small for neutral observers. However, Holland vs. Cameroon is
more exciting than Denmark vs. Cameroon for neutral observers at least near the end of game. It
must be noted that in case of “balanced game” the winning goal time corresponds to the end of
game (see Figure 1), so that “balanced game” may be more exciting than Holland vs. Cameroon
and Denmark vs. Cameroon for neutral observers, but they must be rather frustrating, for both of
the teams have no goal through the game.
The above results indicate that the greater the value of n in either Model 1 or Model 2 is, the more
the game is exciting for neutral observer(s), and vice versa (see Figures 7 and 8). However,
when the value of n in either Model 1 or Model 2 is too large, the game becomes frustrating for
neutral observer(s). This is because the balanced state is prolonged for almost entire game
length.
6. CONCLUSION
The new knowledge and insights obtained through the present investigation are summarized as
follows.
Three Game Patterns
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 11
Three elemental game progress patterns have been heuristically identified by observing the real
games, e.g. Base Ball, Soccer, Chess, Go and Shogi, and have been defined. It is found that each
of the real games is essentially a combination of the three elemental game progress patterns, viz.
“balanced game”, “seesaw game” or “one-sided game”, though there are several supplementary
game progress patterns such as “catchup game” and/or “against all adds game”.. This has been
confirmed by the three Soccer games in 2010 FIFA World Cup, Group E : Holland vs. Denmark
is a combination of “one-sided game” and “balanced game”, Denmark vs. Cameroon is a
combination of “seesaw game” and “balanced game” and Holland vs. Cameroon is a
combination of “ incomplete one-sided game” and “balanced game”. It is suggested that this
finding is universal, and thus it is applicable to Base Ball, Chess, Go, Shogi, Boxing, Rugby,
Hand Ball, Basket Ball and many others.
Time history of information of game outcome, which is obtained by the data analyses for the three
artificial Soccer games, as well as the three Soccer games in 2010 FIFA World Cup, Group E,
shows that for players including winner-sided observers and loser-sided observers, “balanced
game” is most exciting, “one-sided game” is least exciting, and “seesaw game” is intermediate
exciting. It is suggested that for neutral observers “balanced game” is frustrating, “one-sided
game” is boring, and “seesaw game” is exciting. This insight is quite useful for game design, for
one can design games in such a way that they are apt to become “seesaw game”, for example.
The information dynamic model ξ=ηn
, where ξ is the non-dimensional information, η the non-
dimensional game length, and n the real number positive parameter, has been used to assess the
degree of excitement of games: It is realized that in this model the “balanced game” takes the
maximum value of n, the “one-sided game” takes the minimum value of n. The “seesaw game”
takes the intermediate value of n. A comparison between the information obtained by the
information dynamic model and that of the real game provides us the degree of excitement in the
game: The greater the value of n is, the more the game is exciting for players, and vice versa In
another words, the later the winning goal is, the more the game is exciting for players, and vice
versa.
This work has clearly illustrated how to analize games interms of scoring outcomes (section 2)
together with in terms of evaluation function scores(section 4) or winning rate. The formaer
examples are Soccer, Base Ball, Rugby, Hockey, Basketball, Volleyball, Boxing, Judo, Kendo,
Karate and so forth, while the latter examples are Chess, Go, Shogi, Othello, Tic-Tac-Toe, Hex
and many others.
REFERENCES
[1] S. Kelle, D. Börner, M. Kalz, and M. Specht. Ambient displays and game design patterns. In WC-TEL710 Proc. of the 5th European Conference on Technology Enhanced Learning
Conference on Sustaining TEL from innovation to learning and practice, 512-517, Springer-
Verlag, Berlin, Heidelberg 2010.
[2] C.A. Lindley, and C.C. Sennersten. Game play schemes: from player analysis to adaptive game mechanics. International Journal of Computer Games Technology, 7 pages, Article
ID216784, 2008
[3] C.A. Lindley, and C.C. Sennersten. A cognitive framework for the analysis of game play: tasks, schemas and attention theory. In Proc. of the 28th Annual Conference of the
Cognitive Science Society, 13 pages, 26-29 July, Vancouver, Canada 2006.
[4] K. Salen, E. Zimmerman. Rules of Play: Game Design Fundamentals. MIT Press, Cambridge, MA, 2003.
[5] T. Fullerton, C. Swain, S. Hoffman. Game Design Workshop: Designing, Prototyping, and Play-testing Games. CMP Books, San Francisco, New York & Lawrence, 2004.
[6] Zagal, M.Mateas, C. Fernandez-Vara, B. Hochhalter, N. Lichi. Towards an ontological language for game analysis. In: S. de Castell & J.Jenson(eds.), Changing views: Worlds in
play: Selected papers of DIGRA 2005(pp.3-14), Vancouver, British Columbia, Canada:
Digital Games Research Association. 2005
[7] S. Lundgren, S. Björk. Describing computer-augmented games in terms of interaction. Paper presented at Technologies for Interactive Digital Storytelling and Entertainment,
Darmstadt, Germany, 2003.
Takeo R. M. Nakagawa & Hiroyuki Iida
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 12
[8] S. Björk, J. Holopanien. Patterns in game design. Hingham, MA, Charles River Media, 2005.
[9] H. Iida, T. Nakagawa, and K. Spoerer. A novel game information dynamic model based on fluid mechanics: case study using base ball data in World Series 2010. In Proc. of the 2nd
International Multi-Conference on Complexity Informatics and Cybernetics, pages 134-139,
2011a.
[10] H. Iida, T. Nakagawa, and K. Spoerer. On game information dynamics with reference to soccer. In 10th International Conference on Entertainment Computing ICEC 2011,
2011b(submitted for publication).
[11] H. Iida, K.Takehara, J.Nagashima, Y.Kajihara, and T. Hashimoto. An application of game refinement theory to moh-jong. In International Conference on Entertainment Computing,
pages 333-338, 2004.
[12] Davod-Tabibi, O., Koppe, M., Netanyahu, N.: Genetic algorithms for mentor-assisted evaluation function optimization. In:GECCO2008(2008)
AUTHORS’ BIOGRAPHY
Takeo R.M.Nakagawa
Born 1945 in Mikawa, Japan as a descendant of the Tokugawa family.
1965~1969.National Defense Academy. 1969 B. Sc. (Aeronautical
Engineering). 1977~1979, Monash University, Post-Graduate School.
1981 Ph. D(Fluid Mechanics). Fellow, Academy of Mechanics Japan.
President of Royal Society of Hakusan. Currently Director, Pan-Asian
Center for the Independent Liberal Study of Science Technology and the
Humanities, Jusup Balasagyn Kyrgyz National University. Major research
fields: Applied Mathematics, Mechanics, Natural Philosophy, History.
Dr. Hiroyuki Iida is Full Professor of the School of Information Science
and Director of Research Unit for Entertainment and Intelligence, JAIST.
He has served as the Secretary/Treasurer of International Computer Games
Association, while acting as important roles of international activities such
as conference chair and journal editor.
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE)
Volume 1, Issue 1, May 2014, PP 13-20
www.arcjournals.org
©ARC Page 13
Effect of Microstructure of Different Treatments on the
Electrical Properties of Schottky Diodes Based on Silicon
I.G.Pashaev. The Baku State University, AZ1148 Baku, Azerbaijan
Abstract: In the given work are studied restoration degradatsionnye properties NiTi-nSi in diodes
Shottki (DSH) with thermoannealing and ultrasonic processing broken by an irradiation in-quanta of characteristics of solar elements (SE), the amorphous materials made with application. Restoration
degradatsionnye properties NiTi-nSi in diodes Shottki (DSH) are connected with change of structure amorphous Ni35Ti65 an alloy, time the basic stage of process annealing "cures" the damaged diodes. The
experimental results proving possibility restoration and managements in parametres silicon SE by means
of ultrasonic processing (UP) are considered. Restoration electrophysical and photo-electric properties SE
with UP broken an irradiation are connected from a regrouping and athermic annealing the radiating defects formed γ in-quanta.
The experimental results demonstrating the ability to influence and control ¬ leniya parameters of silicon
solar cells by sonication (RCD). The possibility of partial recovery of photovoltaic properties of solar cells
that disturbed - irradiation with ultrasonic treatment. C to investigate the impact of RCD on the change in the mechanism of charge transport, after each step of ultrasonic treatment, we measured the photovoltaic
characteristics and temperature dependence of current-voltage characteristics of silicon solar cells [SC] in
the forward and reverse current. The temperature was varied from 80K to 350K.
Keywords: diodes Schottky, annealing, degradations, ultrasonic influence, silicon solar element, ultrasonic waves, photo-electric properties, solar cells, ultrasonic processing, amorphous metals.
1. INTRODUCTION
It is known that the irradiation of semiconductor devices of high-energy charged particles
accumulate in the bulk of radiation defects, which leads to significant deterioration of the
electrophysical and photoelectric characteristics of devices [1,2,3]. Controlled impact on the
defect structure of a semiconductor device in the p-n junction and the base region can specifically
adjust its characteristics. Traditionally, to restore the damaged properties of irradiated materials
used heat treatment, utilization, which leads to some negative consequences . Therefore, as an
alternative, more and more attention is paid to thermal methods of processing, one of which is
ultrasonic machining (RCD).
The increase in reliability and improvement of quality of electronic devices, including devices on
the basis of a barrier of Shottki, remains a crying need of modern semiconductor engineering. A
metal role in most cases neglected. The role of metals and its crystal structure in processes or is
not considered or badly studied. To identify a metal role, recovery processes деградационных
properties depending on structure and area of contact piece of metals have been studied. [2-10.17]
As it is known, at an irradiation of semiconductor devices accumulation in volume of the
semiconductor of radiation defects that leads to essential deterioration of electro physical and
photo-electric characteristics of devices [1, 6.8.15.16]] occurs the charged particles high энергий.
Traditionally to recovery of the upset properties of the irradiated materials apply thermal
processing, use to which leads to some negative consequences [11]. Therefore, alternatively, the
attention атермическим to modes to the processing’s, one of which kinds is even more often
paid, UP is.
Therefore, as an alternative, more and more attention is paid to thermal methods of processing,
one of which is ultrasonic machining (RCD). In this paper we investigate the possibility of
I.G.Pashaev
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 14
recovery by means of ultrasonic treatment of the initial properties of the investigated silicon solar
cells, whose properties are worsened by exposure to radiation
In the given activity recovery деградационных properties αNiTi-nSi in DSH by means of a
thermoannealing and the ultrasonic processing, upset by an irradiation quanta of characteristics of the solar cells made with application of amorphous materials is studied. In the
given activity it is studied to influence of various processing’s: mechanical, термо and ultrasonic
(Ouse) on properties of DSH and upset by an irradiation - quanta of the characteristics made on technology DSH with application (αNiTi-nSi) of the sample of SE
2. EXPERIMENTAL PROCESS
For manufacturing DSH used a silicon plate п - type with orientation (III) and specific resistance
of 0,7 Om.sm. The matrix contained 14 diodes which areas changed in the range from 100 to
1400 mkm2
. The contact piece area was equal In our case 500 мкм2
. A metal alloy αNiTi put a
method of electron beam evaporation from two sources. Alloy Ni-Ti has been chosen from those
reasons that both components are widely applied in microelectronics, and the alloy is well
technological. For manufacturing αNiTi-nSi sample SE, it is applied on technology of DSH [2.17]
About a capability of obtaining of films of this alloy with amorphous structure it was informed in
activity [13]. Speeds of evaporation of components got out so that the film structure corresponded
to alloy Ni35Ti65 as in activity [13.9] were informed that such alloy is inclined to amorfez.
Fig. 1. Vakh for αNiTi-nSi DSH before and after an annealing at temperature 560Cº. S =500 mkm2.
SE were irradiated - quanta 60Со with a dose ~106 Rad at room temperature. Then these samples were consistently, in two stages, are subjected UP; the longitudinal wave was entered from the
back party of the sample, is perpendicular to its work face. At the first stage UP -1 (frequency F rcd ≈95Mqs, intensity W rcd
≈0,55Vt/sm2, duration t ≈120s); on the second, UP -2, ( F rcd ≈30Mqs, W rcd
≈15vt/sm2 and t ≈200s). After each stage UP electrophysical and photo-electric parametres SE were
measured. It is shown that - the irradiation negatively affects both return and to direct Vakh, worsening the last in comparison with initial (increase in return current Iобр).
Probe of degradation Vakh DSH consists that it in normal conditions meets infrequently, therefore
for detailed studying of the indicated questions investigated Vakh DSH degraded by an artificial
way, c the help микротвёрдомера PMT-3 created in the artificial image non-uniformity on
border section (BS) contact piece metal - the semiconductor (a Fig. 2). The structure of a film of
an alloy before and after an annealing was supervised by the radiographic analysis and
elektronno-microscopic probes of a surface of a film [1.].
Effect of Microstructure of Different Treatments on the Electrical Properties of Schottky Diodes
Based on Silicon
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 15
Ho
Ht
TII
II
The standard diffusive technology of obtaining was applied to manufacturing of silicon TiAu/Si-n +-p-p + SC on the basis of an amorphous metal alloy αTiAu p-n transition p-n transition in a
silicon plate [4.8] Capability of obtaining the films of this alloy with amorphous structure was
informed in paper [7]. Speed of evaporation of components got out so that the film structure
corresponded to alloy Ti60Au40 as in paper [7] was informed that such alloy is inclined to
amortization.
The investigated silicon solar cells were irradiated with - quanta 60Со with a dose of
~106 Rad at
room temperature. Then the samples were sequentially in two stages, subject to the RCD, the
longitudinal wave was introduced into the back of the sample perpendicular to its surface. At the
first stage RCD-1 (frequency Frcd ≈9MGts,intensity W rcd ≈0,5Vt/sm 2
, duration t ≈120min); on the
second, RCD-2, ( F rcd≈27МGts,W rcd ≈1W/sm2
and t ≈200min). After each stage of the RCD was
measured current-voltage characteristics of solar cells a wide temperature range (100 ÷ 350K). ).
It is shown that - the irradiation negatively affects both reverse and to direct current-voltage characteristics , worsening the last in comparison with initial (increase in reverse current Irev fig. 3,
a curve 2 and current reduction in forward direction. The subsequent RCD-1 and, especially,
RCD-2 restore dark current-voltage characteristics SE, approaching them to the initial.
3. RESULTS AND THEIR DISCUSSION
On fig. 1. Are presented Vakh for αNiTi-nSi DSH before and after an annealing at temperature
560Cº. Apparently from the schedule direct and return pressure there is a superfluous current. It is
known that amorphous films of metal at certain temperatures change structure and pass in a
polycrystalline condition [13]. Hence, it is possible to assume that occurrence of a superfluous
current to Vakh αNiTi-nSi DSH after an annealing at temperature 560ºC and is above connected
with change of structure of a metal film of an alloy [13]. The thermoannealing of diodes was
conducted at 100=600ºС temperatures during identical time on duration t =20 minutes
Table1. Results of recovery degraded properties αNiTi-nSi DSH in normal, it is artificial degraded and annealing (200 Cº - 400Cº) conditions, loading {F = 100)} and quantities of violations (N=1) during time:
(17s, 65s, 148s, 260s, 410s, and 580s.) (VoB=0,20V).
t-sek 17 65 148 260 410 580
T (200ºС) 0,260 0,160 0,110 0,089 0,082 0,06
T (300ºС) 0,060 0,038 0,031 0,022 0,021 0,018
T (400ºС) 0,031 0,020 0,015 0,012 0,009 0,007
On fig. 2. Are presented recovery Vakh for αNiTi-nSi DSH it is degraded it is artificial by means
of diamond идентера under loading F (100), quantities of violations (N=1) before and after an
annealing 400ºС during time: (1-17s, 2-65s, 3-148s, 4-260s, 5-410s, 6-580s.) (Voв=0,20V).
Recovery degradation properties αNiTi-nSi DSH was supervised by a method of removal Vakh
both in forward direction, and in the return.
The formula was applied to the quantitative characteristic of recovery of a superfluous current
under the influence of an annealing taking into account time:
Where IH normal (intact) diodes Shottki,
Io Diodes directly after effect identer (t=0),
It Damaged diodes, annealing during t sec,
I.G.Pashaev
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 16
T
Characterizes relative recovery of a superfluous current under the influence of a thermo
annealing in time t .
As shown in table 1. With change of parameters of an annealing its value changes in an
interval10
T
. From the received results it is visible that, first, the milestone of process of
an annealing occurs for short initial periods, secondly, annealing process "cures", restores the
damaged diodes Eventually, even at room temperature, level of a superfluous current decreases,
recovery process occurs that faster, than above temperature flow of time of an annealing.
Table2. Photo-electric parameters αNiTi/Si sample SE before and after - irradiations and after UP at Rizl
=120mvt/sm2 and Т=300К.
Parameters
Condition
A Uxx,V Iкз,mА Р,mvt
The sample
2,32 0,542 26,82 12,54 0,7232
To an irradiation
After -
irradiations
2,66 0,498 21,14 9,53 0,7214
After UP -1
2,56 0,528 22,61 10,52 0,7235
After UP-2
2,42 0,536 26,65 12,41 0,7263
Influence - an irradiation and UP is direct on fotoelektrik and electrophysical characteristics
investigated SE it is visible from table 2 and table 3 to which are presented fotoelektrik (where
Iк.з short circuits, Uх.х - open-circuit voltages, Iоб - a return current., А− factor, Pmax - the
maximum output power, and - space factor) and electrophysical (τn - factor of diffusion and time
of life of nonbasic carriers, Ln-diffuzionnaja length of nonbasic carriers, Io - a return current of
saturation, Nэф - effective concentration of the ionized centers, Ea - energy of activation)
parametres of sample SE that is shown in reduction of a current of short circuit Iкз and open-
circuit voltage Uхх and as consequence, in drop of maximum output power Pmax the Subsequent
UP -1 and, especially, UP -2 restore parameters SE, approaching them to the initial.
Fig.-2. Recovery Vakh of properties αNiTi-nSi DSH. In normal, it is artificial degraded and annealin
(400Cº) conditions loading F (100) and quantities of violations (N=1) during time: (1-17s, 2-65s, 3-148s,
4-260s, 5-410s, 6-580s), where HI
normal (intact) diodes Shottki, oI
diodes directly after effect identer
( 0t ), (Voв=0,20V)
Effect of Microstructure of Different Treatments on the Electrical Properties of Schottky Diodes
Based on Silicon
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 17
The irradiation - quanta 60Со with energy of an order ~1,35Mev, is equivalent to internal irradiation SE the fast electrons resulting dispersion and photoabsorption, leads basically to
formation of defects of dot type. Thus as a result of interaction of radiation defects with defects
already available in a crystal
Conducted in two stages, UP -1 and especially UP 2 investigated silicon SE, have led to kickdown
Nэф (table 3) that testifies about atermik an annealing of radiation defects. As it is known, to an
annealing of radiation defects there can correspond some gears: migration of defects on drains
[15], formation of more difficult defect, dissociation of a complex, etc.
Thus, effect UP is an effective mode of increase of internal energy of solids. Unlike thermal
energy absorbed in regular intervals in all volume of the semiconductor, attenuation UP of waves
occurs, basically, on defects of a crystal lattice, promoting their redistribution to an equilibrium
condition [1.6,10].
Tables 3. Electro physical parameters αNiTi/Si sample SE
Before and after - irradiations and after UP at =120mvt / sm2 and Т=300К.
Parametres
Condition
Nэф,sm-3
Ea Io, mkА Ln,mkm n, mks
The sample
2,34·1016
0,83 90,235 72,0 0,883
To an irradiation
After -
irradiations
3,25·1016
0,67 306,4 65,4 0,752
After UP -1 3,916·1016
0,73 286,9 69,7 0,801
After UP-2
2,621016
0,83 128,6 70,4 0,838
The structure of a film of an alloy was supervised by the radiographic analysis, as shown in
drawings-1. Alloy Ti60Au40 has amorphous structure. In amorphous film Ti60Au40 also, as well as in
crystals the first maximum is completely resolved, i.e. the first minimum concerns a shaft of
abscissas. It means that on certain distance firmness of absent-minded electrons is almost equal to
zero [3]. Effect of irradiations and RCD directly on the photoelectric characteristics of the
investigated solar cells can be seen from Figure 3, which shows the load current-voltage
characteristics of investigated solar cell. As might be expected, - irradiations leads to a
deterioration of the load VAC SC, resulting in a decrease in short-circuit current Isc and open-
circuit voltage Uhv and as consequence, in drop of maximum output power Pmax, and - space factor.
Follow the RCD-1, and particularly the RCD-2 reduced load VAC SC, bringing them closer to the
original figure 4 (curves 3 and 4).
Fig. 3. The X-ray analysis of amorphous metal films Ti60Au40
I.G.Pashaev
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 18
1L
LQ
n
n
1τDα
τDαqSNΙ
nn
nn
ΦΦ
o
кз
ххI
Iln
q
AkTU
Let us analyze the possible mechanisms for the observed changes. It is known that the magnitude
of the photocurrent is determined from the expression [5]:
If = qSNФQ, (1)
Here, q - electron charge, and SNf - total number of photogenerated electron-hole pairs at the site
S, Q - collection coefficient of charge carriers. Since the value of SNf remains practically constant
in this experiment, it is happening as a result of γ-irradiation drop in photocurrent SE is obviously
due to a decrease in Q. When the diffusion length of minority carriers in the base Ln
Effect of Microstructure of Different Treatments on the Electrical Properties of Schottky Diodes
Based on Silicon
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 19
4 - after RCD-2 (W rcd ≈1W/sm2
, t ≈200min. F rcd ≈27 MHz).
where k - Boltzmann constant, T - temperature, k - dimensionless coefficient characterizing the
rate of recombination in the space-charge layer, Io - the reverse saturation current flowing through
the p-n junction, Isc - short-circuit current
Tables 4. Photo-electric parameters of TiAu/Si-n +-p-p + sample SE before and after γ - irradiations and
after RCD at Rizl =120mVt/sm2
and Т=300К.
Parameteres
Condition of the sample
A Uxx,V Iкз,mA Р,mW
Before irradiation
2,32 0,542 26,82 12,54 0,7232
after -irradiation 2,66 0,498 21,14 9,53 0,7214
After RCD-1
2,56 0,528 22,61 10,52 0,7235
after RCD-2
2,42 0,536 26,65 12,41 0,7263
According to our estimates, the irradiation of γ-rays does not lead to significant change and the
effect of γ-irradiation and RCD directly on the photoelectric characteristics of the investigated
solar cells can be seen from Table 4, which represent the photovoltaic (where Isc-short-circuit
current, Uh.h - voltage idling, a dimensionless ratio, Pmax - the maximum output power, and -
fill factor), the parameters of the sample SE, resulting in a decrease in short-circuit current Isc and
open-circuit voltage Uhh, and as a consequence, to reduce the maximum power Pmax Follow the
RCD-1, and particularly the RCD-2 restore options SE, bringing them closer to the source. It is
known that exposure to γ-rays with energies of 60Co ~ 1.2 MeV, which is equivalent to the
external irradiation by fast electrons SE resulting from Compton scattering and photoabsorption,
which leads mainly to the formation of defects of the point type. In this case the interaction of
radiation defects with those already in the crystal defects in the p-n junction and the base are more
electrically and optically active centers, which play the role of recombination centers, resulting in
a decrease in the lifetime of minority carriers tn and parameters Q and IF-dependent tn. In the
initial state (Fig. 3, curve 1) the slope of the temperature dependence of Irev amounts 0,71 eV,
which indicates the presence of a diffusion mechanism of charge transport and generation. As the
irradiation γ - quanta creates radiation defects in SE which are more mobile at the subsequent UP
the acoustic wave co-operates mainly with the last, promoting their redistribution and atermik to
an annealing [11.1.15.14].
4. CONCLUSIONS
Thus, it is possible to conclude that recovery of a superfluous current is connected with change of
parametres of an annealing, in given to activity its value changes in an interval10
T
. From
the received results it is visible that, first, the milestone of process of an annealing occurs for short
initial periods, secondly, the milestone of process of an annealing "cures" the damaged diodes.
On the basis of electro physical and photo-electric measurements of parameters it is proved that
recovery of electro physical and photo-electric properties silicon NiTi/Si sample SE by means of
the ultrasonic processing, upset γ - an irradiation, occurs at the expense of a regrouping and
atermiат an annealing of radiation defects formed gamma in quanta. The results resulted in
activity testify that UP partially restores perfection of crystal structure NiTi/Si sample SE, upset
I.G.Pashaev
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 20
in the course of an irradiation γ - quanta. The received results allow to make a conclusion that, at
structure Ti60Au40 the sample is amorphous. Laws of influence of ultrasonic processing on photo-
electric properties investigated silicon SE are revealed and it is established that interaction of
ultrasonic waves with heterogeneous semiconductor structure of silicon SE affects the generation-
recombination mechanism of conducting the current. The photo-electric measurement has proved
that recovery of photo-electric properties of silicon SE by means of the ultrasonic processing,
upset by γ - an irradiation, occurs at the expense of a regrouping and athermal annealing of
radiation defects formed by gamma in quanta.
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[2] S.M. Zi, “Physics of Semiconductor Devices”, R.A.Surusa Publication, Moscow, Russia, Vol. 1, p. 456,1984
[3] I.G. Pashaev //ElektronysikalL Properties of SCHOTTKYdiodes made on the basis of silikon wtth amorphous and polycrystaline metel alloy atlow direct International Journal on
//“Technical and Physical Problems of Engineering” (IJTPE), Iss. 10, Vol. 4, No. 1. 2012
pp. 41-44
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AUTHOR’S BIOGRAPHY
İSLAM GERAY OGLU PASHAYEV
Candidate of science in physics and mathematics, Associate professor of
the chair of Physical Electronics. Born on March 1st 1957 in Qubadli
region of Azerbaijan Republic, in a family of teachers, has higher
education. Married, has 3 children. Azerbaijani by nationality. Resides
in the city of Sumgait. Has been conducting the following subjects in
the chair of physical electronics of Baku State University since 2007:
Technology of Microcircuits, Semiconductor Electronics, Solid-state
Physics, Solid-state Electronics, Radiophysics, Optoelectronics. Is an
author of 95 scientific articles and 2 book. Is currenly conducting
scientific research in the field of metal-semiconductor contact physical
properties.
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE)
Volume 1, Issue 1, May 2014, PP 21-28
www.arcjournals.org
©ARC Page 21
Review of MRI Image Classification Techniques
Sivasundari .S Department of Computer and Communication,
Tamilnadu College of Engineering, Coimbatore-59
Dr.R. Siva Kumar Department of Information Technology,
Tamilnadu College of Engineering,Coimbatore
Dr.M.Karnan Department of Computer Science and
Engineering,
Tamilnadu College of Engineering, Coimbatore
Abstract: MRI is an important medical diagnosis tool for the detection of tumors in brain as it provides the detailed information associated to the anatomical structures of the brain.MR images helps the radiologist to
find the presence of abnormal cell growths or tissues (if any) which we call as tumors. The MRI image
analysis is performed under the sequence of operations such as Image Acquistion, Preprocessing, Feature
Extraction, Feature Reduction and Image Classification. In this paper, an effort was put to review the
existing MRI image processing techniques used in the brain tumor detection and their performances are
studied.
Keywords: Wavelet Transform, Support Vector Machine (SVM), Principle Component Analysis (PCA), Artificial Neural Network (ANN), Gray Level Co-occurrence Matrix (GLCM).
1. INTRODUCTION
A brain tumor is a very serious-type among all life threatening diseases which is increasing
drastically among the humans. A brain tumor is a mass of tissue formed by an unregulated growth
of the abnormal cells in the brain. A trigger in a single cell's genes causes a change and makes it to
divide out of control. Generally a primary brain tumor originates in the brain, the brain's coverings,
or its nerves. Most brain tumors identified in the children are primary tumors .In adults the brain
tumors are stated as metastatic or secondary tumors which means the cancer has spread to the brain
from the breast, lung, or other parts of the body. Nearly 1 in 4 people with cancer is affected by
secondary brain tumor. People with secondary brain tumors were expected to survive only several
weeks after diagnosis. Brain tumors are classified as benign or malignant. Benign tumors are
noncancerous cells and malignant tumors are cancerous cells. The first types do not invade brain or
other tissues. But they need to be treated because they might harm the neighboring tissues or other
vital organs. A malignant brain tumor invades normal tissue or contains cancerous cells either from
the brain or other parts of the body. These types of tumors are life-threatening, as they can spread
throughout the brain or to the spinal cord. So patients with either benign or malignant tumors,
needs immediate recovery treatment after the diagnosis. The choice of the recovery treatment
depends on the type of brain tumor and the patient's health state.
U.S News reports say that more than 180,000 brain tumors (malignant and benign) are diagnosed
each year. Of those, about 36,000 comprise primary brain tumors. Brain tumors can occur in adults
between the ages of 40 - 70 years and in children between 3-12 years. Primary brain tumors
account for only 2-3 percent of all new cancer cases in adults. In children, however, brain tumors
account for 25 percent of all cancers. About 2,900 children [below 20 years] diagnosed with brain
tumors each year in the United States. The Office for National statistics, UK reports that in the last
32 years, brain cancer occurrence rates have increased by 23% to 25%. In 2010, the rate was 8 new
cases per one lakh men and 5 new cases per one lakh women. This regards to nearly 2,300 newly
diagnosed cases in men and just fewer than 1,700 in women. The research people still investigates
basis for the increased occurrence of this rare cancer .The news report from the Indian Express
said that in India the Brain tumor comprises 1-2 per cent of all cancers. It is the second most
Sivasundari .S et al.
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 22
common cancer among children and is 70 per cent curable. In adults though, it is more challenging
considering diverse demographics, socio-economic system, delivery of care, etc.
2. MRI IMAGE ANALYSIS
The patients who suffer from the symptoms of brain tumor should start the earlier course of
diagnosis undergoing some physical tests, mental tests and the neurological examinations such as
brain scans. An analysis of the brain tissue gives the established manifest of the presence of brain
tumor. The analysis helps the doctors to classify the tumor from either least aggressive (benign) or
the most aggressive (malignant). In most cases, a brain tumor is named based on the cell type of
origin or its location in the brain.
A brain scan is a picture of the internal anatomy of the brain. Most commonly used scans are MRI
(Magnetic Resonance Imaging), CT or CAT scan (Computed Tomography) and PET scan
(Positron Emission Tomography) are used to discover the presence of brain tumor. The
information obtained from the above mentioned scans will exert significance on the treatment
given to a patient. The most extensively used clinical diagnostic and research technique is MRI. Its
working is based on the principal of nuclear magnetic resonance (NMR).
As the process of separation of cells and their nuclei separation is very important, much attention is
needed in the development of the expert diagnosis system for image segmentation & features
extraction. In studying human brain, magnetic resonance imaging (MRI) plays an important role in
progressive researches. Magnetic resonance (MR) imaging was introduced into clinical medicine
and has ever since assumed an unparalleled role of importance in brain imaging. Magnetic
resonance imaging is an advanced medical imaging technique that has proven to be an effective
tool in the study of the human brain. The rich information that MR images provide about the soft
tissue anatomy has dramatically improved the quality of brain pathology diagnosis and treatment.
Fig. 1. Normal MRI images
Fig. 2. Abnormal MRI Images
3. MRI ANALYSIS USING IMAGE PROCESSING
The Images obtained using MRI scanning is used in Machine intelligence for detection of
diseases like brain tumor using image processing techniques. For this algorithms are to be
developed so that the normal & abnormal MRI Images can be classified by machine or computer.
The MRI Image undergoes series of following steps for analysis using image processing
techniques.
3.1. Image Preprocessing and Segmentation techniques
Review of MRI Image Classification Techniques
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 23
Pre-processing of images includes two major steps a) Noise Removal and b) Image Enhancement.
Noise Removal can be done by using filters like Median filters, Sobel filters, Robert and Prewitt
filters, Laplacian filters etc., Image Enhancement improves the Image making it suitable for
further image processing by modifying the image attributes. The Median filters remove certain
types of noise (impulse noise) in which the individual pixel will have essential details [32].The
performance of Median filters are better analyzed by authors [55-60]. In some cases segmentation
is performed using Neural Network. The Feature vectors and selected regions are organized in the
pattern matrix. Input vectors fed into the NN layers, the output represents the number of
segmentation Classes [20]. [31] Introduces the threshold segmentation which provides an easiest
way based on intensities or colors. Black pixels indicating background and white pixels
representing foreground.
The author [47] uses weighted median filter (WMF) using Neural Network which reduces noise
but preserves the image edges. The Point Spread Function (PSF) is used to remove the
degradations like noise, blur and distortions during transmission of the image over the network.
[35] Uses two filtering algorithms viz Weiner Filter and Wavelet Filter. The author proposes the
Weiner Filter is optimal for Mean Square Errors and deblurring. The limitation of Weiner Filter is
that it gives poor performances for the large noise which is overcome by the Wavelet Filter.
Segmentation is important for healthy brain tissue differentiation [47].Pulse Coupling Neural
Network proposed by [45] which is capable of robustness over noise and considers even minor
intensity variations. Image Enhancement is followed by Image Restoration using Point Spread
Function (PSF) which characterizes the image degradation process. The misclassified errors in the
form of speckles can be removed using, a morphological filter which is proposed by an author
[16].Speckles can be removed by using Adaptive weighted median filter (AWMF) [26].
3.2. Features Reduction
After Features extraction the dominant features are selected using Principal component
analysis(PCA).The size of the dataset has been minimized from large to the most essential features
in order to reduce the computational cost and time. One of the widely used techniques is PCA.
Table1. Feature Reduction techniques
METHODS DESCRIPTION
Principal Component Analysis and kernel Support
Vector Machine [54].
PCA has reduced 65536 to 1024 feature vectors.
DWT+PCA+KSVM with GRB kernel achieved
the best accurate classification result 99.38% than
other HPOL and IPOL kernels.
Gray Level Co-occurrence Matrix, PCA and SVM
using RBF kernel function [9].
Features Extracted by using GLCM and
classified with RB-Kernel gives 100%
classification accuracy better than PCA.
Discrete wavelet Transform (DWT), Principal
component analysis (PCA), k-means clustering
and k-nearest neighbor classifier [50].
Seven Statistical measures including skewness,
Kurtosis, Specificity etc., are measured.
GLCM (Grey Level Co-occurrence Matrix) and
SVM [32].
Texture based feature selection using GLCM
and SVM classifier combination has proved to
get accurate results but only for smaller dataset.
Wavelet based Principal component analysis with
Fuzzy C-means Clustering [40].
PCA based Fuzzy C-means Clustering system
yields more and accurate information about the
abnormal tissues and WM through supportive
visuals than conventional PCA.
Linear Discriminant Analysis, PCA and SVM
[14].
LDA selects vital feature which are compared
with PCA and SVM accuracy of 98.87%.
PCA and Supervised Learning Techniques (BPN,
RBF and LVQ) [22].
PCA with BP has produced around 95- 96%
recognition rate for 4-5 error images.
GLCM, KNN, ANN, PCA+LDA [37].
GLCM, PCA + LDA combination best reduces
the dimensions reducing computational cost.
3.3. Image Classification
After dominant features vectors are selected, a classifier is to be selected for training
&classification. Various schemes of classifiers are available. A Study performed over the literature
Sivasundari .S et al.
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 24
works of different authors.
Table2. Image Classification techniques
METHODS DESCRIPTION
Multi-Classification Support Vector Machine
[23].
Multi- Classification SVM (MCSVM) extracted
the boundaries of 7 kinds of encephalic tissues
successfully and proved satisfactory
generalization accuracy.
PCA and PNN assisted automated brain tumor
classification [53].
Probabilistic Neural Network (PNN) with
mathematical technique called Principal
Component Analysis (PCA) is used to give more
accurate and fast solution than the Conventional
methods of brain tumor classification.
SVM–KNN: Discriminative Nearest Neighbor
Classification for Visual Category Recognition
[15].
A hybrid of these two methods which deals with
the multiclass setting that can be applied to large,
multiclass data’s and with less complexity in
computations both in training and at run time, and
yields outstanding results.
Classification of tumor type and grade using
SVM-RFE [11].
The binary SVM classification accuracy,
sensitivity, and specificity are proved to be high
for the discrimination of metastases from gliomas,
and for discrimination of high grade from low
grade neoplasm.
Texture features, Fuzzy weighting and SVM
[51].
Fuzzy logic is used to assign weights to different
feature values based on its discrimination
capability. The multi class SVM provides better
classification accuracy even if the features of
different classes have overlapping boundaries.
Wavelet Transformation (WT), Principal
Components Analysis (PCA), Feed forward -
Back propagation Neural Network (FP-ANN)
and k-Nearest Neighbors [10].
Sensitivity rate and Specificity rate for the
Classifiers FP-ANN is 95.9% and 96%and k-NN
obtained a success of 96% and 97% respectively.
Sphere-shaped support vector machine (SSVM)
and Immune algorithm [33].
Optimal parameters selection is done using
Immune Algorithm and SSVM classification is
very much successful in classifying data with high
irregularities.
Multiclass support vector machines (M-SVM)
followed by KNN (K-nearest neighbor) [15].
The multiple image queries are supported by using
M-SVM.
Least Squares Support Vector Machines (LS-
SVM) compared with k-Nearest Neighbor, Multi
layer Perceptron and Radial Basis Function
Networks [39].
Analysis of the statistical features like sensitivity,
specificity, and classification accuracy proved that
LS-SVM yields better.
Multiresolution Independent Component
Analysis (MICA) and SVM [41].
MICA based SVM classification accuracy has
increased 2.5 times than other ICA based
classifications
Spatial gray level dependence method
(SGLDM), Genetic Algorithm (GA) and SVM
[3].
A hybrid method using SGLDM for Feature
extraction, GA for Feature Reduction and SVM
classifier proves high statistical measures.
Texture feature coding method (TFCM) and
Support Vector Machine [34].
Along with Cascade-Sliding-Window technique
for automated target localization, this approach is
applicable to mammograms with 88% accuracy.
Connected component labeling (CCL), Discrete
Wavelet Transform (DWT) and SVM [36].
SVM works well with this combination proves to
be robust and produces high quality results.
Feature ranking based Ensemble SVM classifiers
[12].
Better results for nested feature set and thereby
suitable for detecting Alzheimer’s disease (AD)
and autism spectrum disease (ASD).
Discrete wavelet Transform (DWT), Principal
component analysis (PCA), k-means clustering
and k-nearest neighbor classifier [50].
Segmentation using k-means Clustering. Seven
Statistical measures including skewness, Kurtosis,
Specificity etc., are measured and compared.
Content Based Image Retrieval (C.B.I.R.) and
Support Vector Machine [1].
C.B.I.R based on texture retrieval along with
SVM classifier suitable for detecting Multiple
Review of MRI Image Classification Techniques
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Page 25
Sclerosis and tumors
Ripplet transforms Type-I (RT), PCA and Least
Square (LS-SVM) [48].
Overcomes the drawbacks of DWT and NN and
proves to be new successful combination as
RT+LS-SVM.
Grey Level Co-occurrence Matrix (GLCM),
Artificial Neural Network (ANN) and Back
Propagation Network [46].
Achieves a balance between the net’s
memorization and generalization. Detects
Astrocytoma type of tumors efficiently.
Artificial Neural Network (ANN), Grey Level
Co-occurrence Matrix (GLCM), and Neuro
Fuzzy Classifier [4].
Automated detection of Pathological tissue,
without any need for the Pathological testing.
Back Propagation Network [BPN], Probabilistic
Neural Network (PNN) and GLCM [22].
Histogram equalization is performed to avoid the
dark edges.BPN based classifier produces 77.56%
and PNN produces 98.07% of accuracy in tumor
detection.
Modified Probabilistic Neural Network (PNN)
model [30].
PNN Model based on Learning Vector
Quantization (LVQ) performance is measured
with 100% accuracy.
ANN,SVM, Fuzzy measures, Genetic Algorithms
(GA), Fuzzy support Vector Machines (FSVM)
and Genetic Algorithms with Neural
Networks[38].
FSVM resolves unclassifiable regions caused by
conventional SVM and genetic algorithm-based
neural network outperforms gradient descent-based
neural network.
PNN Classifier with Image Encryption [21]. Classification accuracy is about 100-85% and
original content has been encrypted to avoid
exploitation of the image.
Multimodal fuzzy image fusion [13]. Image quality is preserved even with blurs without
any limitations. Best suitable for blurry images.
CA(Cellular Automata) based segmentation and
ANN [27].
Seed based segmentation is reliable only for small
set of data. Seed is selected using co-occurrence and
Run-Length features.ANN provides high
classification accuracy.
In this paper various automated brain tumor detection methods through MRI has been surveyed
and compared. This is used to focus on the various combinations of techniques proposed by
different people in medical image processing and their performances. This paper deals with the
sequence of methods in image classification as i) Image Preprocessing and Segmentation ii)
Feature Reduction and iii) Classification. Many algorithms have been proposed in the literature for
each image processing stage. The results of various algorithms are discussed.
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