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 Multi-scale signal analysis
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Dr. Manish M Kayasth
UCCC & SPBCBA & Udhna Academy College
of Computer Application and IT, India
Bikram Ballav
Assistant Professor, BPUT, Bhubaneswar,
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Ziyan Wu
Research Scientist, Vision Solutions and
Technologies, Siemens Corporate Research,
Princeton (NJ), U.S.
Dr. Sanjeev Kumar Singh
Associate Professor, Department of
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Mr. Bhaskar Mandal
Dept. of Computer Science and Engineering
National Institute of Technology, Jamshedpur
(JH), India
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Mr. S Sivakumar
Periyar University, Salem, India
Mr. Saptarshi Roy
Department of Electrical Engineering, NIT
Warangal, India, India
Kalyan Kumar Jena
Dept. of CSEA, IGIT, Sarang, Odisha, India,
Safar Irandoust
Assistant Professor at University of Tabriz

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Assistant Professor, Jaipur Engineering College,
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Mr. Kundan Lal Verma
DDU Gorakhpur University, Gorakhpur,
Uttar Pradesh, India

From the Editor's Desk
Dear Readers,
We would like to present, with great pleasure, the inaugural volume of a new scholarly
journal, International Journal of Image Processing and Pattern Recognition. This journal
is part of the Computer Science and Engineering, and is devoted to the scope of present
Engineering issues, from theoretical aspects to application-dependent studies and the
validationofemergingtechnologies.
This new journal was planned and established to represent the growing needs of Image Processing and
Pattern Recognition as an emerging and increasingly vital field, now widely recognized as an integral part
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International Journal of Image Processing and Pattern Recognition focuses on original high-quality
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Still image compression, Video image compression, Feature extraction, Multi-scale signal analysis Hidden
markov models,Anisotropicdiffusion,Quadraticdiscriminateanalysis.
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It is our hope that this fine collection of articles will be a valuable resource for Computer Science and
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Engineering.
PuneetMehrotra
ManagingDirector

1. Using Image and Pattern Recognition to Play the Game of Connect Four Using Computer
Punyajoy Saha 1
2. Brain Waves Can Be Used to Detect Potentially Harmful Personal Information
Aryan Mathur 6
3. Enhancement K-Mean Algorithm (EKM) for Image Processing in MATLAB
Himanshu Monga, Rubal Ahuja, Krishan Kumar 10
4. An Efficient Unconstrained Face Recognition System Based on Multimetric Learning
L. Monisha, A. Kolappan 14
5. Analysis of Virtual or Remote Laboratories of IITs
Ravi Kumar, Ankit Kumar1, Kapil Bhagat 19
Contents

IJIPPR (2016) 1–5 © JournalsPub 2016. All Rights Reserved Page 1
International Journal of Image Processing and Pattern Recognition
Vol. 2: Issue 2
www.journalspub.com
Using Image and Pattern Recognition to Play the Game of
Connect Four Using Computer
Punyajoy Saha*
Indian Institute of Engineering Science and Technology, Shibpur, India
ABSTRACT
Connect four is an advanced version of the commonly played game tic-tac-toe. Using an
image recognition system, the computer can be assigned the current state of the play and
using an algorithm the next move can be calculated. The extraction of image information and
using it to find the solution of the diff condition is a fundamental in this field which is
substantially extrapolated here. This paper is a result of our participation in a competition
where we applied this concept with our robot and found quite suprising result. To play a
game using a fully automated robot is a very interesting topic and this research is based on
that. This entire project is done using Matlab.
Keywords: algorithm, image recognition, robotics
INTRODUCTION
The game of connect four is a hard game
to solve even through many high ending
learning algorithms. With that using an
image recognition to identify the current
game of play makes this topic of research
even more interesting. Researches have
been done extensively in both these field
individually but a intermixing of two is a
rare occurrence.[1,2]
So this paper is a venture into the
combination of both thus completing the
totally automatic robot which can play the
game of connect four and beat human
players to an extent. This project is
entirely based on Matlab tools and
programming.
LITERATURE REVIEW
Connect Four as a Game
Connect four is solved many times by
many ways like in 1988, Vrije University,
Netherland, Victor Allis in his paper
‘Knowledge based approach of connect
four’ has showed a Shanon C-type
problem solving approach where he
included nine strategies to solve the game
of connect four. The paper clearly showed
that white has higher chances of winning
the game.
Another famous strategy of solving this is
by the minimax algorithm. In UC Berkley
2003 spring a paper was published by
Amir Kamil, which shows the algorithm
applied on the tic-tac-toe game which can
be extended in connect four.
Another paper named as ‘AI CONNECT
FOUR’ was published in 2010 by Abe
Karplus. He used the technique of ply
depth to search for the correct solution of
the game.
Another paper named as ‘Artificial player
for connect four’ by Allen Brubaker and
Jason Long in December 2010, applied
reinforcement training to create a viable
artificial agent to solve this game.

Vol. 2: Issue 2
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Brain Waves Can Be Used to Detect Potentially Harmful Personal
Information
Aryan Mathur*
Department of Science and Technology, IIMT College, Greater Noida, India
ABSTRACT
A researcher is working to advance research to develop secure user authentication methods,
by looking at using brain waves as individual identifiers. However, those brain waves can tell
more about a person than just his or her identity, warns this expert.
Keywords: brain, framework, information
INTRODUCTION
Securing systems has gone past
fundamentally thinking about a shrewd
mystery key that could keep detestable PC
authorities from hacking into your
Facebook account. The more perplexing
the system, or the more fundamental,
private information that structure holds,
the more moved the recognizing evidence
system guaranteeing it gets the opportunity
to be.
Interesting finger impression compasses
and iris recognizing evidence are just two
sorts of acceptance methods, once
considered as science fiction, that are in
wide use by the most secure systems.
Nevertheless, fingerprints can be stolen
and iris scopes can be copied. Nothing has
exhibited bonehead confirmation from
being at risk to PC software engineers.[1]
“The fundamental dispute for behavioral,
biometric approval is that standard
techniques for affirmation, like a mystery
word, confirm you once before you get to
the organization,” said Abdul Serwadda a
cybersecurity ace and accomplice educator
in the Department of Computer Science at
Texas Tech University.
“In no time, once you have gotten to the
organization, there is no other course for
the structure to regardless know it is you.
The structure is outwardly debilitated
concerning who is using the organization.
So the scope of behavioral acceptance
looks customer perceiving plans that can
keep the system aware of the person who
is using it. Through such cases, the system
can screen some conviction metric about
who may use it and rapidly incite for
reentry of the mystery key at whatever
point the sureness metric falls underneath
a particular edge.”
One of those examples, that is, developing
in ubiquity inside the examination group is
the utilization of cerebrum waves acquired
from an electroencephalogram, or EEG. A
few research gathers around the nation
have as of late showcased frameworks
which utilize EEG to verify clients with
high exactness.
Be that as it may, those mind waves can
educate more regarding a man than simply
his or her personality. It could uncover
therapeutic, behavioral or passionate parts
of a man that, if conveyed to light, could
humiliate or harming to that individual.

Vol. 2: Issue 2
www.journalspub.com
Enhancement K-Mean Algorithm (EKM) for Image Processing in
MATLAB
Himanshu Monga*, Rubal Ahuja, Krishan Kumar
Department of CSE, JCDM College of Engineering, Sirsa, India
ABSTRACT
In this paper, a new segmentation scheme for the white blood cells from microscopic images
is proposed. This is based on the K-means clustering technique. The RGB test images are
converted to the L*a*b color space, and then the two color machinery (a and b) are used as
features to the K-means clustering algorithm. The success of image analysis depends on
segmentation reliability. The accurate partition of the image into regions is a challenging
task. K-Means Clustering algorithm is the popular unsupervised clustering for dividing the
images into multiple regions based on image color property. The major feature of the
algorithm is that the user can specify the number of clusters- K, which is used to split the
image into K regions.
Keywords: image processing, K-mean clustering, segmentation
INTRODUCTION
Clustering is a multivariate technique
widely adopted in medical diagnostic
studies, pattern analysis, image processing
etc.[1]
We divide the components of a
dataset in a way that the ones having
similar properties are allotted to cluster
group.[2]
Clustering can be seemed as
optimization where the objective is to find
the cluster solution according to some
cluster validity measure [MSE, PSNR].[3]
Clustering is one of the simplest
techniques, and has been widely used in
segmentation. And K means is one of the
most popular and simplest methods for
clustering schemes.[4]
K-means clustering
technique the RGB test images are
converted to the L*a*b color space, and
then the two color components (a and b)
are used as features to the K-
means clustering algorithm.[5]
In this
paper, the extended version of KM is
proposed, called the Enhanced K-
Means (EKM) algorithm, which
overcomes the KM weakness without
limiting their preexisting capability (i.e.
avoiding trapped center at local optimum
location).[6,7]
We divided the cluster
images through the optimization images
and comparisons among them with image
matrices is represented by certain image-
related quantities.[8]
PROPOSED WORK
The proposed EKM algorithm employs
hard membership function as its clustering
concept, where the data are clustered
into non-overlapping regions. Consider an
image, X with N data to be clustered
into K regions. Initially, all centers values
are randomly assigned. The i-th data, i v is
assigned to the nearest data cluster, j
c based on the minimum Euclidean

Vol. 2: Issue 2
www.journalspub.com
An Efficient Unconstrained Face Recognition System Based on
Multimetric Learning
L. Monisha*, A. Kolappan
Department of Computer Science and Engineering, KNSK College of Engineering, Nagercoil, India
ABSTRACT
This paper is basically proposed for reconstruction-based metric learning method to learn
distance metric for unconstrained face verification. As compare to other metric (only helps
for label information and do not applicable for reconstruction based metric) this metric id
advanced. This metric is used for multiple metric learning to remove irrelavent data for
recognition.
Keywords: metric learning, RMML, unconstrained face recognition
INTRODUCTION
For face recognition this metric technique
is implicated for unconstrained images
with various algorithm perceptions. For
this technique almost 20 years of research
was going and now a new technique came
into know for the face recognition metric.
Likewise biometric technique this is also a
driving one to switch over on it. The
problem to search a face among number of
images is must.
Over the past three decades, a large
number of face recognition methods have
been proposed in computer vision and
most of them have achieved encouraging
performance when face images were
captured under controlled conditions.
In many real world applications, human
faces are usually captured in unconstrained
environments and the performance of most
existing face recognition methods drops
heavily in this scenario because large
intra-class variations caused by varying
poses, illuminations, expressions,
resolutions, expressions and backgrounds
usually occur, which makes unconstrained
face recognition more difficult and
challenging than controlled face
recognition.
Generally, face recognition can be
categorized into two classes: face
verification and face identification.
The first aims to verify whether a given
pair of face images/video is from the same
person or not, and the second aims to
recognize the given face image/video from
a gallery set and find the most matched
one.
In this work, we focus on face verification,
which aims to determine whether a given
pair of face images/videos captured in
unconstrained environments is from the
same person or not.
From recent years lots of techniques can
be came for the same but this metric
technique is one of the successful
technique till now (Figure 1).

Vol. 2: Issue 2
www.journalspub.com
Analysis of Virtual or Remote Laboratories of IITs
Ravi Kumar1*
, Ankit Kumar1
, Kapil Bhagat2
1
Department of Electronics and Communication Engineering, Ganga Institute of Technology and Management,
Kablana, Jhajjar, Haryana, India
2
Senior Software Architect, IBM, Auckland, New Zealand
ABSTRACT
This paper is about virtual labs challenge of Ministry of Human resource development,
authorities of India. The idea of virtual laboratory is to provide a danger to perform
experiments the usage of the internet and visual aids without having the equipments at their
quit. The digital lab software gives a completely unique opportunity to enhance the first-rate
of engineering schooling, deepen information, and provide the necessary practical
competencies to young minds through price effective outreach and distance getting to know
sports. digital Labs is a undertaking initiated through the Ministry of Human resource
development, government of India, below the national project on training via statistics and
communique generation. The undertaking ambitions to provide remote-get entry to to
Laboratories in diverse disciplines of science and engineering for students at all degrees
from beneath-graduate to investigate. It additionally intends to broaden a whole getting to
know management machine in which you can avail the various tools for studying, consisting
of extra web-sources, video-lectures, animated demonstrations and self-assessment. there's
also a issue in which expensive gadget and assets are shared, which are in any other case to
be had to only a limited range of customers due to constraints on time and geographical
distances. The challenge intends to cowl bodily sciences, chemical technological know-how
and various branches of engineering like electronics and communications, computer
technological know-how and engineering, electrical engineering, mechanical engineering,
chemical engineering, biotechnology engineering and civil engineering.
Keywords: digital, electronics, remote laboratories
INTRODUCTION
It is miles quite hard to carry out actual
time experiments in a few specialised
regions of engineering, which require
sophisticated equipments and skilled
manpower. The unavailability of proper
teachers with required information is any
other bottleneck in this respect. The
concept of digital laboratory is to provide
college students a danger to carry out
experiments using the internet and visual
aids while not having the equipments at
their cease. The RF and Microwave lab is
a great candidate for digital laboratory
because it requires high priced equipments
including Ratiometres, community
Analyzers, Waveguides, Resonators and
many others as well as professional and
specialised employees, which most of the
institutes cannot afford. The laboratory
experience is manifestly an important a
part of Engineering training, which goes a
protracted way in constructing up desirable
satisfactory engineers. The virtual lab
application offers a completely unique
possibility to enhance the great of
engineering training, deepen
understanding, and provide the necessary

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