Crisp relations are subsets of Cartesian products between sets. The Cartesian product of two sets A and B is the set of all ordered pairs where the first element is in A and the second is in B. Relations can be binary, ternary, quaternary, etc. depending on the number of sets involved. Operations like union, intersection, and complement can be performed on relations. Composition of relations R and S is defined as the set of all ordered pairs where the first element is related to the second by R and the second is related to the third by S. Max-min composition defines the composition relation matrix T as taking the max of the min of the relation matrices of R and S.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
Artificial Intelligence lecture notes. AI summarized notes on uncertainty and handling it through fuzzy logic, tipping problem scenarios are seen in it, for reading and may be for self-learning, I think.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
Artificial Intelligence lecture notes. AI summarized notes on uncertainty and handling it through fuzzy logic, tipping problem scenarios are seen in it, for reading and may be for self-learning, I think.
Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram processing
Using histogram statistics for image enhancement
Uses for Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram Processing
Basics of Spatial Filtering
Changing variable is something we come across very often in Integration. There are many
reasons for changing variables but the main reason for changing variables is to convert the
integrand into something simpler and also to transform the region into another region which is
easy to work with. When we convert into a new set of variables it is not always easy to find the
limits. So, before we move into changing variables with multiple integrals we first need to see
how the region may change with a change of variables. In order to change variables in an
integration we will need the Jacobian of the transformation.
It presents various approximation schemes including absolute approximation, epsilon approximation and also presents some polynomial time approximation schemes. It also presents some probabilistically good algorithms.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
Homogeneous function is one with multiplicative scaling behaviour - if all its arguments are multiplied by a factor, then its value is multiplied by some power of this factor.
Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram processing
Using histogram statistics for image enhancement
Uses for Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram Processing
Basics of Spatial Filtering
Changing variable is something we come across very often in Integration. There are many
reasons for changing variables but the main reason for changing variables is to convert the
integrand into something simpler and also to transform the region into another region which is
easy to work with. When we convert into a new set of variables it is not always easy to find the
limits. So, before we move into changing variables with multiple integrals we first need to see
how the region may change with a change of variables. In order to change variables in an
integration we will need the Jacobian of the transformation.
It presents various approximation schemes including absolute approximation, epsilon approximation and also presents some polynomial time approximation schemes. It also presents some probabilistically good algorithms.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
Homogeneous function is one with multiplicative scaling behaviour - if all its arguments are multiplied by a factor, then its value is multiplied by some power of this factor.
RELATIONS
A relation associates an element of one set with one or more elements of another set.
If ''a'' is an element from set A which associates another element ''b'' from set B, then the elements can be written in an ordered pairs as (a,b)Thus we can define a relation as a set of ordered pairs.Some relations are denoted by letter R; in set notation a relation can be written asR = {(a, b): a is an element of the first set, b is an element of the second set}
Example of a relation
1. 1. Mwajuma is a wife of Juma.
2. 2. Amina is a sister of Joyce.
3. 3. y = 2x + 3 4. Juma is tall, Anna is short. (Not a relation)
NOTE If the relation R defines the set of all ordered pairs (x,y) such that .
y = 2x + 3 this can be written symbolically as
R = {(x, y): y=2x +3}
PICTORIAL REPRESENTATION OF RELATIONS
Relation can be represented pictorially;
i) Arrow diagram.
ii) Cartesian graph.
In detail and In very simple method That can any one understand.
If you read this all you doubts about function will be clear.
because i have used very simple example and simple English words that you can pick quickly concept about functions.
#inshallah.
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Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
2. Crisp relations:
A subset of cartesian product A1*A2*…*Ar is called an r-r-any
relation over A1,A2,…Ar . Again the most common Case is for
r=2;inthis situation , the relation is a subset of cartesian product
A1*A2.
The cartesian product of two universes X and Y is determined as
X*Y={(X,Y)|xЄX , yЄY}
3. Cartesian product:
The Cartesian product of two sets A & B is denoted by A X B and
is the set of all ordered pairs such that the first element in the pair
belongs to set A and second element belong to set B.
A*B={(a , b) /aЄA , bЄB}
It can be observed that cardinality of A X B is the product of
cardinality of individual sets.
A = { 1, 2, 3 }
B = { a, b }
A X B = { (1, a), (1, b), (2, a), (2, b), (3, a), (3, b) }
4. Example
A1={a , b} , A2={1 , 2}, A3={α }
A1*A2={(a,1),(b,1),(a,2),(b,2)},|A1*A2|=4 and
|A1|=|a2|=2
|A1*A2|=|A1|.|A2|
A1*A2*A3={(a,1,α),(a,2,α ),(b,2,α)}
|A1*A2*A3|=4=|A1|.|A2|.|A3|
5. Other Crisp Relations :-
Any crisp relation R (x1, x2, x3………….xn) among crisp sets x1,
x2, x3,…………….,xn is a subset of the Cartesian product.
for n = 2 the relation R(x1, x2) is called binary relation.
for n = 3 the relation R(x1, x2, x3) is called ternary relation.
for n = 4 the relation R(x1, x2, x3, x4) is called quaternary relation.
for n = 5 the relation R(x1, x2, x3, x4, x5) is called quinary relation.
7. Operations on relations:
Two relations R and S defined on X*Y and represented by relation
matrices following operations are supported by R and S
Union
R ∪ S (x , y) = max [ R (x , y) , S (x , y) ]
Intersection
R ∩ S (x , y) = min [ R(x , y) , S (x , y) ]
Complement
Ṝ(x , y)=1-R(x , y)
8. Composition of relations:
Given R to be a relation on X,Y and S to be a relation on Y,Z then
R◌ S is a composition of elation on X,Z defined as
R ◌S={(x,z)/(x,z) ЄX*Z, y ЄY such that (x,y) ЄR and (y,z) ЄS
A common form of the composition relation is the max-min
composition
9. Max-Min Composition:
The relation matrices of the relation R and S , the max-min
composition is defined as
T=R◌S
T(x , y)=max(min(R(x , y) , S(y , z)))