Finite automaton discussed so far, is just associated with the RE or the language.
There is a question whether does there exist an FA which generates an output string corresponding to each input string ? The answer is yes. Such machines are called machines with output.
There are two types of machines with output.
Moore machine
Mealy machine
Equivalent machines Two machines are said to be equivalent if they print the same output string when the same input string is run on them.
Chromatic Number of a Graph (Graph Colouring)Adwait Hegde
A graph coloring is an assignment of labels, called colors, to the vertices of a graph such that no two adjacent vertices share the same color. The chromatic number χ(G) of a graph G is the minimal number of colors for which such an assignment is possible.
Finite automaton discussed so far, is just associated with the RE or the language.
There is a question whether does there exist an FA which generates an output string corresponding to each input string ? The answer is yes. Such machines are called machines with output.
There are two types of machines with output.
Moore machine
Mealy machine
Equivalent machines Two machines are said to be equivalent if they print the same output string when the same input string is run on them.
Chromatic Number of a Graph (Graph Colouring)Adwait Hegde
A graph coloring is an assignment of labels, called colors, to the vertices of a graph such that no two adjacent vertices share the same color. The chromatic number χ(G) of a graph G is the minimal number of colors for which such an assignment is possible.
In theoretical computer science and mathematics , the theory of computation is the branch that
deals with how efficiently problems can be solved on a model of computation, using an algorithm. The field is
divided into three major branches: automata theory, computability theory, and computational complexity
theory.
In order to perform a rigorous study of computation, computer scientists work with a mathematical
abstraction of computers called a model of computation. There are several models in use such as Lambda
calculus, Combinatory logic, mu-recursive functions , but the most commonly examined is the Turing machine.
Computer scientists study the Turing machine because it is simple to formulate, can be analyzed and used to
prove results, and because it represents what many consider the most powerful possible "reasonable" model of
computation . It might seem that the potentially infinite memory capacity is an unrealizable attribute, but
any decidable problem solved by a Turing machine will always require only a finite amount of memory. So in
principle, any problem that can be solved (decided) by a Turing machine can be solved by a computer that has a
bounded amount of memory.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This paper reviews algorithmic information theory, which is an attempt to apply information-theoretic and probabilistic
ideas to recursive function theory. Typical concerns in this approach are, for example, the number of bits of information required to
specify an algorithm, or the probability that a program whose bits are chosen by coin flipping produces a given output. During the past
few years the definitions of algorithmic information theory have been reformulated. The basic features of the new formalism are presented
here and certain results of R. M. Solovay are reported.
Simulating Turing Machines Using Colored Petri Nets with Priority Transitionsidescitation
In this paper, we present a new way to simulate
Turing machines using a specific form of Petri nets such that
the resulting nets are capable of thoroughly describing
behavior of the input Turing machines. We model every
element of a Turing machine’s tuple (i.e., Q, Γ, b, Σ, δ, q0, F) with
an equivalent translation in Colored Petri net’s set of elements
with priority transitions such that the resulting translation
(is a Petri net that) accepts the same language as the original
Turing machine. In the second part of the paper we analyze
time complexity of Turing machine’s input in the resulting
Petri net and show that it is a polynomial coefficient of time
complexity in the Turing machine.
Consistency in formatting and styling is crucial in a Table of Contents. Using consistent font styles, indentation, and numbering conventions enhances readability and makes it easier for readers to distinguish between different levels of headings and subheadings. Clear visual cues, such as bold or italicized text, can be employed to differentiate entries and aid navigation.
In addition to textual representation, a ToC can incorporate visual elements such as page numbers, hyperlinks, or bookmarks. Page numbers are particularly valuable in print documents, allowing readers to locate specific sections directly. Hyperlinks or bookmarks are beneficial in digital formats, enabling readers to click on entries in the ToC and instantly navigate to the corresponding section.
An often overlooked aspect of Table of Contents design is its role in improving document accessibility. For individuals with visual impairments or reading difficulties, a ToC serves as a valuable tool for accessing information. By providing a structured overview of the content, it enables users of assistive technologies to navigate and comprehend the document more effectively. Ensuring that the ToC is properly tagged and formatted in digital documents is essential for accessibility compliance.
Moreover, a Table of Contents can be enhanced with additional features to augment its functionality. For instance, some documents include supplementary lists, such as a List of Figures or List of Tables, which provide readers with a separate overview of visual or tabular content within the document. Cross-referencing between the ToC and these lists allows readers to easily locate specific figures or tables referenced in the text.
In conclusion, a Table of Contents is a vital component of lengthy documents, serving as a roadmap for readers and aiding in efficient information retrieval. By providing an organized overview of the document's structure, a well-designed ToC enhances document accessibility, readability, and comprehension. Adhering to best practices and incorporating visual cues, formatting consistency, and supplementary lists further optimizes the usefulness of a Table of Contents. Whether in print or digital form, a thoughtfully constructed ToC significantly improves the user experience and facilitates seamless navigation through complex documents
Turing Machine (TM) is a mathematical model which consists of an infinite length tape divided into cells on which input is given. It consists of a head which reads the input tape. A state register stores the state of the Turing machine. After reading an input symbol, it is replaced with another symbol, its internal state is changed, and it moves from one cell to the right or left. If the TM reaches the final state, the input string is accepted, otherwise rejected.
Turing Machine (TM) is a mathematical model which consists of an infinite length tape divided into cells on which input is given. It consists of a head which reads the input tape. A state register stores the state of the Turing machine. After reading an input symbol, it is replaced with another symbol, its internal state is changed, and it moves from one cell to the right or left. If the TM reaches the final state, the input string is accepted, otherwise rejected.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
1. 1
CHAPTER 1
INTRODUCTION
A Turing Machine is the mathematical tool equivalent to a digital computer. It was suggested
by the mathematician Turing in the 1930s, and has been since then the most widely used
model of computation in computability and complexity theory. According to Turing’s
hypothesis, a function is algorithmically computable if and only if it is computable by a
Turing machine .There are problems that cannot be solved by a Turing machine, thus, these
problems cannot be solved by a modern computer program[1]. The Turing machine is not
intended as practical computing technology, but rather as a hypothetical device representing a
computing machine. It helps computer scientists understand the limits of mechanical
computation.
1.1 HISTORICAL DEVELOPMENT
Robin Gandy, a student of Alan Turing and his lifelong traced the lineage of the notion of
"calculating machine"[2]. According to him the functions which can be calculated by the
arithmetic functions (+, - and x), sequence of operations and repetition of operations are
precisely those which are Turing computable.
With regards to Hilbert's problems posed by David Hilbert in 1900, an aspect of problem 10
had been floating about for almost 30 years before it was framed precisely. His tenth problem
was given a Diophantine equation with any number of unknown quantities and with rational
integral coefficients: To devise a process according to which it can be determined in a finite
number of operations whether the equation is solvable in rational integers. In 1937 Turing did
prove with his computational-machine model that the Hilbert’s problem can have no solution.
Therefore to show that there can be no general process for determining whether a given
formula U of the functional calculus K is provable, i.e. that there can be no machine which,
supplied with any one U of these formulae, will eventually say whether U is provable. later
he proved that a function is effectively calculable if its values can be found by some purely
mechanical process.
In the early to mid-1950s Hao Wang and Marvin Minsky reduced the Turing machine to a
simpler form; simultaneously European researchers were reducing the new-fangled electronic
computer to a computer-like theoretical object equivalent to what was now being called a
2. 2
"Turing machine". In the late 1950s and early 1960s, the coincidentally parallel developments
of Melzak and Lambek, and Shepherdson and Sturgis carried the European work further and
reduced the Turing machine to a more friendly, computer-like abstract model called the
counter machine; later between 1960s this work was carried even further with the register
machine and random access machine models—but basically all are just multi-tape Turing
machines with an arithmetic-like instruction set.
1.2 PRESENT SCENARIO
Today, the counter, register and random-access machines and their sire the Turing machine
continue to be the models of choice for theorists investigating questions in the theory of
computation. In particular, computational complexity theory makes use of the Turing
machine. Depending on the objects one likes to manipulate in the computations, two models
have obtained a dominant position in machine-based complexity theory: the off-line
multitape Turing machine which represents the standard model for string-oriented
computation, and the RAM model Only in the related area of analysis of algorithms this role
is taken over by the RAM model.
3. 3
CHAPTER 2
GENERAL MODEL
A Turing machine is a hypothetical device that manipulates symbols on a strip of tape
according to a table of rules. Despite its simplicity, a Turing machine can be adapted to
simulate the logic of any computer algorithm, and is particularly useful in explaining the
functions of a CPU inside a computer.
2.1 OVERVIEW
The Turing machine mathematically models a machine that mechanically operates on a tape
as shown in fig1. On this tape are symbols, which the machine can read and write, one at a
time, using a tape head. Operation is fully determined by a finite set of elementary
instructions such as "in state q0, if the symbol seen is 0, write a 1; if the symbol seen is 1,
change into state q2; in state q3, if the symbol seen is 0, write a 1 and change to state q5;" etc.
The machine consists of an infinite length tape consisting of cells. Each cell consists of
symbols for a finite alphabet. The empty cells are assumed to be filled with blank symbols .A
head that can read and write symbols on the tape and move the tape left and right one (and
only one) cell at a time .A state register that stores the state of the Turing machine, one of
finitely many. Among these is the special start state with which the state register is initialized.
A finite table of instructions that, given the state the machine is currently in and the symbol it
is reading on the tape tells the machine to do the following in sequence: Either erases or
writes a symbol, move the head, assume the same or a new state as prescribed[].
Fig1: Turing Machine
4. 4
The operation of a Turing machine proceeds as follows:
The Turing machine reads the tape symbol that is under the tape head. This symbol is
referred to as the current symbol. Then it uses its transition function to map the current state
and current symbol to the following: the next state, the next symbol and the movement for the
tape head. If the transition function is not defined for the current state and current symbol,
then the machine crashes otherwise the Turing machine changes its state to the next state,
which was returned by the transition function. Next the machine overwrites the current
symbol on the tape with the next symbol, which was returned by the transition function.
Then the Turing machine moves its tape head one symbol to the left or to the right, or does
not move the tape head, depending on the value of the 'movement' that is returned by the
transition function. If the Turing machine's state is a halt state, then the Turing machine halts.
Otherwise, repeat the operation again.
2.2 MATHEMETICAL REPRESENTATION
A Turing machine is a septuple
M= (Q, Γ, b, Σ, δ,q0 ,F) ……………………………………………………………....(1)
Where
Q is a finite & non empty set of states,
Γ is a finite, non-empty set of the tape alphabet/symbols,
b ∈ Γ is the blank symbol,
∑ ⊆ Γ is input alphabet,
δ :Q×T→Q×T×{L,R} is a transition function ,the value of δ(q, X) is either undefined
or a triple consisting of new state ,replacement symbol and direction of head motion,
q0 ∈ Q is start state and F ⊆ Q is set of accepting states[3][4].
Anything that operates according to these specifications is a Turing machine. One move in
Turing machine does the following:
δ (q, X)=(p, Y ,D)………………………………………………………………………..(2)
Where q is current state
X is current symbol on tape pointed by tape head.
After the move, X is replaced with symbol Y. If D=“L”, the tape head moves “left” by one
position. Alternatively, D=“R” the tape head moves “right” by one position.
2.3 STATE DIAGRAM
5. 5
Instruction set can also be expressed using a State Transition Diagram.
In a state transition diagram circle represents a state, arrows represent state transitions, Each
arrow also represents one instruction. Arrow is also labelled with: current symbol, new
symbol and direction.
EXAMPLE:
Here is a Turing machine that checks its third symbol is 0, accept is so and otherwise runs
forever.
M=({p ,q ,r ,s ,t},{0,1,},{0,1,B}, p, B,{s})
δ (p, X) = (q, X, R) for X=0,1
δ (q, X) = (r, X ,R) for X=0,1
δ (r,0) = (s,0,L)
δ (r,1) = (t,1,R)
δ (t ,X) = (t ,X,R) for X=0,1,B
State diagram of above example is shown in fig2.
Fig2: state diagram
6. 6
CHAPTER: 3
GENERAL TYPES
There are a number of other types of Turing machines in addition to the one we have seen
such as Turing machines with multiple tapes, ones having one tape but with multiple heads,
ones with two dimensional tapes, nondeterministic Turing machines etc. It turns out that
computationally all these Turing machines are equally powerful. That is, what one type can
compute any other can also compute[2]. However, the efficiency of computation, that is, how
fast they can compute, may vary.
3.1 MULTAPE TURING MACHINE
A Multi-tape Turing machine is like an ordinary Turing machine with several tapes. Each
tape has its own head for reading and writing. Initially the input appears on tape 1,and the
others start out blank .This model intuitively seems much more powerful than the single-tape
model, but any multi-tape machine, no matter how large the k, can be simulated by a single-
tape machine using only quadratically more computation time. Thus, multi-tape machines
cannot calculate any more functions than single-tape machines, and none of the robust
complexity classes (such as polynomial time) are affected by a change between single-tape
and multi-tape machines.
3.2 DETERMINISTIC & NON-DETERMINISTIC
In a deterministic Turing machine, the set of rules prescribes at most one action to be
performed for any given situation. A NTM, by contrast, may have a set of rules that
prescribes more than one action for a given situation. For example, a non-deterministic
Turing machine may have both "If you are in state 2 and you see an 'A', change it to a 'B' and
move left" and "If you are in state 2 and you see an 'A', change it to a 'C' and move right" in
its rule set.
An ordinary DTM has a transition function that, for a given state and symbol under the tape
head, specifies three things: the symbol to be written to the tape, the direction (left or right) in
which the head should move, and the subsequent state of the finite control. For example, an X
on the tape in state 3 might make the DTM write a Y on the tape, move the head one position
to the right, and switch to state 5.A NTM differs in that the state and tape symbol no longer
7. 7
uniquely specify these things; rather, many different actions may apply for the same
combination of state and symbol. For example, an X on the tape in state 3 might now allow
the NTM to write a Y, move right, and switch to state 5 or to write an X, move left, and stay
in state 3. How does the NTM "know" which of these actions it should take? There are two
ways of looking at it. One is to say that the machine is the "luckiest possible guesser"; it
always picks the transition that eventually leads to an accepting state, if there is such a
transition. The other is to imagine that the machine "branches" into many copies, each of
which follows one of the possible transitions. Whereas a DTM has a single "computation
path" that it follows, an NTM has a "computation tree". If at least one branch of the tree halts
with an "accept" condition, we say that the NTM accepts the input[5].
3.3 REGISTER MACHINE MODELS
The register machine gets its name from its use of one or more registers. In contrast to the
tape and head used by a Turing machine, the model uses multiple, uniquely addressed
registers, each of which holds a single positive integer[6].
3.3.1 COUNTER MACHINE
A counter machine is an abstract machine used in formal logic and theoretical computer
science to model computation. It is the most primitive of the four types of register machines.
A counter machine comprises a set of one or more unbounded registers, each of which can
hold a single non-negative integer, and a list of (usually sequential) arithmetic and control
instructions for the machine to follow.
3.3.2 RANDAM ACCESS MACHINE
There are a couple serious defects in his register machine model: (i) Without a form of
indirect addressing it is not possible to easily show the model is Turing equivalent, (ii) The
program and registers were in different spaces, so self-modifying programs would not be
easy. When indirect addressing was added to his model a random access machine model was
created. Unlike the RASP model, the RAM model does not allow the machine's actions to
modify its instructions. Sometimes the model works only register-to-register with no
accumulator, but most models seem to include an accumulator.
8. 8
3.3.3 RANDAM ACCESS STORED PROGRAM MODEL
The RASP is a RAM with the instructions stored together with their data in the same space
i.e. sequence of registers. It had a "mill" - an accumulator, but now the instructions were in
the registers with the data so-called von Neumann architecture. When the RASP has
alternating even and odd registers the even holding the "operation code" (instruction) and the
odd holding its "operand" (parameter), then indirect addressing is achieved by simply
modifying an instruction's operand. The RASP models allow indirect as well as direct-
addressing; some allow "immediate" instructions too, e.g. "Load accumulator with the
constant 3".
3.3.4 POINTER MACHINE
It is a blend of counter machine and RAM models. Less common and more abstract than
either model. Instructions are in the finite state machine in the manner of the Harvard
architecture.
3.4 UNIVERSAL TURING MACHINE
UTM is a Turing machine that can simulate an arbitrary Turing machine on arbitrary input. It
achieves this by reading both the description of the machine to be simulated as well as the
input thereof from its own tape. it is considered to be the origin of stored program computer.
Every Turing machine computes a certain fixed partial computable function from the input
strings over its alphabet. In that sense it behaves like a computer with a fixed program.
However, we can encode the action table of any Turing machine in a string. Thus we can
construct a Turing machine that expects on its tape a string describing an action table
followed by a string describing the input tape, and computes the tape that the encoded Turing
machine would have computed[7].
9. 9
CHAPTER 4
CONCLUSION
The Turing Machine is the most comprehensive, deep, and accessible model of computation
extant, and its associated theories allow many ideas involving "complexity" to be profitably
discussed. In providing a sort of atomic structure for the concept of computation, it has led to
new mathematical investigations. One development of the last 30 years , is that of classifying
different problems in terms of their complexity. It gives a platform-independent way of
measuring this complexity. Nowadays computer can be used to simulate the working of a
Turing machine, and so see on the screen. It can have various applications such as
enumerator, function computer. Universal Turing Machine can be used to simulate all type of
other turing machines.
10. 10
REFERENCE
1. Introduction to Turing Machines, B2B integration solutions from unidex,
https://www.unidex.com/turing/tm_intro.htm (as on august-19-2013)
2. Wikipedia, turing machine, ,
https://en.wikipedia.org/wiki/Turing_machine (as on august-19-2013)
3.Ananth Kalyanaraman, turing machine, lecture notes, cpt s 317, school of eecs Washington,
state university, http://www.eecs.wsu.edu/~ananth/CptS317/Lectures/TuringMachines.pdf(as
on august-19-2013)
4. Tim Sheard, CS581 Theory of Computation, Portland state university,
,http://web.cecs.pdx.edu/~sheard/course/CS311/Fall2012/ppt/TuringMachines.pdf(as on
august-19-2013)
5. http://en.wikipedia.org/wiki/Non-deterministic_Turing_machine (as on august-19-2013))
6. internet,http://www.cs.odu.edu/~toida/nerzic/390teched/tm/othertms.html(as on august-20-
2013)
7. Manolis Kamvysselis, universal turing machine,,
https://web.mit.edu/manoli/turing/www/turing.html (as on august-19-2013)
8. John C. Martin, Introduction to languages and theory of computation, 2nd
edition, TMH.