Analyzing human aura using fuzzy matrix Essay


1A. Venkatesan , 2K. Kiruthika

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1Assistant professor, 2PG Student, PG and Research Department of Mathematics,

St. Joseph’s college of arts and science(Autonomous), Cuddalore, Tamil Nadu.

[email protected], [email protected]

Abstract: The possibilities of what we might see by looking at our own or someone’s aura are endless. We don’t have to be mystic to read an aura. In fact, it’s widely believed that we all have auric sights that is.

, the ability to read auras. And learning to read and protect our own aura is important to our physical, emotional and spiritual health. Also, an aura imaging technique is used to capture the energy field by an instrument / device called kirlian camera. In this paper, we are using Fuzzy matrix to find which age people having which type of auras also analyzing those people who are possibly going to affect by which kind of diseases.

Keywords: Aura, Kirlian photography, Fuzzy matrix, Relativity function, Comparison matrix.

1. Introduction

All matter in the universe is made up of energy. An Aura or human energy field is a subtle or colored emation simply, an electromagnetic energy field that surrounds each and every person like an egg-shaped ball of energy [1]. We can even see those energy in the form of colors. Every person has a unique aura like fingerprints. This aura is said to extend above the head and below the feet into the ground about 2-3 feet (0.6-0.9m) on all sides. In many pictures we might have seen white or yellow light behind God which indicates their aura. Human aura colors show personality type and characteristics of a person. Each of them is associated with one of the glands, nerve centers and major organs of the body. In 1939, Russian inventor Semyon Davidavich Kirlian invented a method of capturing human aura by an instrument and named it as Kirlian Photography [10]. Kirlian Photography shows information on the subject’s phychological, emotional and physical stipulation [11]. This photography reveals life force and even predict illness of the visible aura around the objects photographed. It was possible to see full body health problem at present as well as 4-6 months in future. Recently, aura images are used to find out the energy levels of the human body which is used to diagnose diseases [12]. Illness in a person may be represented by characteristic defects in the finger images which corresponds to the main organs of the body [5]. Using aura imaging technique, the proper diagnosis of health problems at present which is connected with our body and mind is possible [6]. Since our future can’t be predicted. It is impossible to be clear about what exactly is going to happen day to day. But though this aura reading [9].we can be certain about our upcoming health problems.

In this paper, we have used fuzzy matrix to analyses which age people having what type of auras also calculated the best thought of human being, further analysed the disease they were going to be affected using kirlian photography.

2. Preliminaries

Definition 2.1[7]

A Fuzzy matrix ‘A’ of order m?n is define as

Where is the membership value of the elements in A that is, A= []

A Special type of fuzzy matrix is called as Boolean fuzzy matrix, whose definition…

A Fuzzy matrix A= []m?n is said to be Boolean fuzzy matrix of order m?n if all the elements of A are either 0 or 1.

Definition 2.2[2]

Let x and y be the variables on a set X. We denote the relativity function f() as, f( where, (x) is the membership function of x with respect to y and (y) is the membership function of y with respect to x. The relativity function is a measurement of the membership value of choosing x over y. This function f() can be regarded as the membership of the preferring variable x over the variable y.

Definition 2.3

Let A = {} be the set of n variables defined on universe X. From a matrix of relativity values f ( where ‘s for i=1 to n, are n variables defined on a universe X. The matrix C=Cij a square matrix of order n with =f () is called the comparison matrix or C-matrix. The smallest value in the row of the C-matrix that is, is the membership value of the variable. The minimum of , that is the smallest value in each of the rows of the C-matrix will have the lowest weights for ranking purpose. Thus ranking the variables are determined by ordering the membership values

3. Example for Analysing human Aura using Fuzzy Matrix.

Here, different thoughts of person are evaluated using their aura so that to analyses the best thought that suits a person. Different person may have different thoughts when compared to each other and to the lifestyle of them[3]. Here, I assumed different thoughts and according to the people answers, the most answered five questions were calculated and entered in the column matrix. Those are and . Where, represents that how much a person is physically comfort, The same way indicates a person’s divine love and spiritual ecstasy, represents anxiety and depression of a person, denotes how much a person accept themselves andsays about their migraine.The same way, the row matrix represents the age wise calculation. Those are Child(), Young(), Adult(), Middle age(), and Old(). According to the analysis, the pairwise functions are as follows,

=0.95, =0.9, =0.5, =0.4 and =0.3

=0.8, =0.5, =0.4, =0.8 and =0.8

=0.1, =0.3, =0.6, =0.8 and =0.9

=1, =0.95, =1, =0.9 and =0.95

=0.2, =0.5, =0.8, =0.95 and =0.9

Developing a comparison matrix based on this information and determining the overall ranking.


The Relativity function,


To find the comparison matrix and ranking[8],

= 1, f (=1, f (.

f() = = = =0.9

f() = = = =0.2

f() = = = =1

f() = = = =0.7

f() = = = =1

f() = = = =0.8

f() = = = =1

f() = = = =0.6

f() = = = =1

f() = = = =1

f() = = = =1

f() = = = =1

f() = = = =0.9

f() = = = =0.4

f() = = = =0.84

f() = = = =0.8

f() = = = =1

f() = = = =1

f() = = = =1

f() = = = =1

f() = = = =12

The comparison matrix c= = f() is given by,

C =

Matrix A

The extra row below the comparison matrix Ci is the minimum value for each of the columns. The ranking is P1 ,P2, P3, P4 and P5 .

The best thought that suits a person is P4 that is Accepting Ourselves.

Further, we are going to identify the abnormal energy levels of three random persons using Kirlian Photography in the body which will/may lead to diseases[4].

The Kirlian Photography of different persons are as analysed and according to the defects the values in the matrix are taken in the form (left, right). Here left indicates the left side organs and right indicates the right side organs of a human.

In the below matrix, represents respiratory system of the human body, , the lumbar region, indicates immune system, shows nervous system and, the abdominal zone.

Matrix B

From the above matrix, we can say that person A and person C are not physically comfort.

4. Conclusion

This paper has shown best thought of Human being using Aura and capabilities of detecting diseases using Kirlian photography. From this paper we can conclude that, from the matrix A, we can say that (Accepting Ourself) is the best thought for all age people.

Also, from the matrix B, we can say that,

Person A is emotionally week

Person B is normal

Person C is physically not healthy.


B. Shanmuga Priya, R. Rajesh- Understanding Abnormal Energy Levels in Aura Images, ICGST AIML-11 Conference, Dubai, UAE, 12-14 April 2011.

C. Venkatesan, P, Balaganesan, J. Vimala- Fuzzzy Matrix With Application in Decision Making, Volume 116 No. 23, 2017, 551-554.

Harry Olfield, “From Kirlian Photography to Polycontrast Interfernce Photography – PIP”, 2007.

J. Serra, Image Analysis and Mathematical Morphology”, Academic Press, Ny,1982.S. Devita, “Kinesiology-Energy Medicine and the GDV Kirlian camera”, Journal of the ASK-US/CAN-ASK,2003.

K.G. Korotkov, “Aura and Consciousness-Book”,2nd Ed, saint Petersburg,1999.

K.G. Korotkov, ”Measuring Energy Fields”, Saint Petersburg, 1999

Meenakshmi.A.R. and Sriram.S (2003)- “Some Remarks on Regular Fuzzy Matrices”, Annamalai University, Science Journal.

M. Shimura, Fuzzy sets concept in rank ordering objects, J. Math. Anal. 1 Appl., 43(1973).

R. Rajesh, M.R. Kaimal, K. Srinivasan, “A note on Medical Image Analysis and visualization using Matlab”, CGST International journal on Graphics Vision and image processing, special issue on Medical Image processing, pp. 352-355, 2006.

S. Devita, “Kinesiology-Energy Medicine and the GDV Kirlian camera”, Journal of the ASK-US/CAN-ASK,2003.

Victor Adamenko, “Computerised Bioelectrography: Kirlian Photography”,Caduceus, Issue 12,pp.18,1990.

Xandu C. Halkias, Petros Maragos, “Analysis of Kirlian Images: Feature Extraction and Segmentation”, Proc. of ICSP,2004.

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