Editor in-Chief - Arc Journals · 2015. 1. 25. · Editor–in-Chief Dr.Narendra Kohli Associate...

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

[email protected]

Hiroyuki Iida School of Information Science

JAIST, Nomi, Japan

[email protected]

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 feedback 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


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.



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-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-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


-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-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



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-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-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


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.



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


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-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



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-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


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.



[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.



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

[email protected]

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


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


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


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)


Based on Silicon


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


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


Based on Silicon


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


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.

REFERENCES

[1] D.K. Wickenden, M.J. Sisson, “Amorphous Metal-Semiconductor Contacts for HighTemperature Electronics”, Solid State Electron, Vol. 27, No. 6. pр. 515-518, 1984.

[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

[4] Sh. G.Askerov I.G. Conference Proceeding Second International Conference on Technical and Physical Problems in Power Engineering. Tabriz –Iran 6-8 september 2004. pp/ 367 –

368.

[5] K.T.Y Kung, I.,Suni M.A Nikolet.//" Elektrikal charakterics of amorphorus molyubdenum-nickel contacts to Si"// J.Appl.Phus., 1984. Vol.55 No 10. pp3882-3884

[6] I.B.Yermolovich, V.V.Milenin, R.V.Konakova, etc., Letters in JTF, 1996, Vol. 22 No 6, pp.33-36.

[7] N.S.hare, V.G.Bojko, P.A.Gentsar, O.S.Litvin, V.P.Papusha, N.V.Sopinsky, FTP, 2008, Vol. 42 No 2, pp.207 - 211.

[8] I. G. Pashayev the Influence of Ultrasonic Treatment on the Properties of Schottky Diodes // Open Journal of Acoustics, 2013, 3, 9-12.

[9] Ш.Г Askerov., SH.G.Aslan, I.G.Pashaev, the Electronic engineering., Microelectronic devices, 1989, Vol.6, №78, pp.46-48.

[10] P.N.Krylov, the Bulletin of the Udmurt University. Physics. 2006, №4,p p. 125-136. [11] I.V.Ostrovsky, L.P.Steblenko, A.B.Nadtochy, FTP , 2000, Vol..34 № 3, pp. 257-260. [12] P.B.Parchinsky, S.I.Vlasov, R.A.Muminov,etc.,Letters in JTF,2000.Vol., 26 № 10, pp.40-45. [13] K.Sudzuki, F.Hasimota, «Amorphous metals». M. 1987. [14] P.B.Parchinsky, S.I.Vlasov, L.G.Ligaj, etc., letters in JTF, 2003, Vol.., 26 №10, pp.40-45. [15] I.G Pashaev.The study of the electrical properties of Schottky diodes based on silicon with

amorphous and polycrystalline material// Universal Journal of Electrical and Electronic

Engineering 2013, Vol. 1(4), pp. 118 - 121

[16] A.I.Vlasenko, J.M.Olih, R.K.Savkin.//ФТП, 1999, Vol 33, №4, pp.410-414. [17] Ю.К Kovneristyj, E.K.Osipov, E.A.Trofimova, the Physical and chemical bases of creation

of amorphous metal alloys. M.Nauka, 1983, pp.23-29.

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.



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

[email protected]

Dr.R. Siva Kumar Department of Information Technology,

Tamilnadu College of Engineering,Coimbatore

[email protected]

Dr.M.Karnan Department of Computer Science and

Engineering,

Tamilnadu College of Engineering, Coimbatore

[email protected]

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.


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



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.


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



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.

REFERENCES

[1] Aditi P. Killedar ,Veena P. Patil and Megha S. Borse, Content Based Image Retrieval Approach to Tumor Detection in Human Brain Using Magnetic Resonance Image, 1st

International Conference on Recent Trends in Engineering & Technology, 2012, ISSN:

2277-9477.

[2] Ahmad Mubashir, Mahmood ul-Hassan, Imran Shafi, and Abdelrahman Osman, Classifica

Editor in-Chief - Arc Journals · 2015. 1. 25. · Editor–in-Chief Dr.Narendra Kohli Associate...

Documents

Transcript of Editor in-Chief - Arc Journals · 2015. 1. 25. · Editor–in-Chief Dr.Narendra Kohli Associate...