PREAMBLE PREAMBLE OFOF

THEORY OF THEORY OF AUTOMATA AND AUTOMATA AND

FORMAL LANGUAGESFORMAL LANGUAGES

INDEXINDEX

• PREAMBLE STRUCTURE• HOLLISTIC FIX• KEY CONCEPT• KEY RESEARCH AREA• KEY APPLICATION• INDUSTRIAL APPLICATION• RESEARCH• HOW WE STUDY• KEY JOBS• PROJECTS ONE CAN DO• TRENDS

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PREMBLE STRUCTUREPREMBLE STRUCTURE

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1. NAME OF TEACHER: AWDHESH KUMAR

2. CABIN LOCATION: ROOM NO. 208

3. TELEPHONE NO.

4. EMAIL-ID: AWDHESH_82@REDIFFMAIL.COM

5. MEETING HOURS– 11:00 AM TO 12:00 PM.

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HOLLISTIC FIX OF THEORY OF HOLLISTIC FIX OF THEORY OF AUTOMATA & FORMAL LANGUAGESAUTOMATA & FORMAL LANGUAGES

PREREQUISITES Basic Knowledge of Set theory

(12th standard)

• Set Operations• Membership• Venn Diagram

HOLLISTIC FIX………CONTINUEDHOLLISTIC FIX………CONTINUED

PRE REQUISTES(3rd Semester)

Set theory

Basics of Tree• Graph (directed graph)

Basic knowledge of algorithms• Time complexity•Space complexity

Functions & Relations

Proof of Theorem

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HOLLISTIC FIX ………CONTINUEDHOLLISTIC FIX ………CONTINUED

ADVANCE COURSE (5th SEM)

• Compiler design• Principle of programming languages• Design analysis & algorithms• Artificial intelligence

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SCOPE IN RELATED FIELDS…

• Automatic machine Design• Robotics • Digital Circuits Design• Language Processing• Digital Image Processing

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MODEL OF DISCRETE AUTOMATA

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KEY CONCEPT OF TAFL

i1i2i3...in

o1o2o3...on

q1q2q3...qn

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Automaton

CPU

input memory

output memory

Program memory

temporary memory

Automaton

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Different Kinds of Automata

Automata are distinguished by the temporary memory

• Finite Automata: no temporary memory

• Pushdown Automata: stack

• Turing Machines: random access memory

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input memory

output memory

temporary memory

Finite

Automaton

Finite Automaton

Example: Vending Machines

(small computing power)

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input memory

output memory

Stack

Pushdown

Automaton

Pushdown Automaton

Example: Compilers for Programming Languages

(medium computing power)

Push, Pop

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input memory

output memory

Random Access Memory

Turing

Machine

Turing Machine

Examples: Any Algorithm

(highest computing power)

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Finite

Automata

Pushdown

Automata

Turing

Machine

Power of Automata

Less power More power

Solve more

computational problems

Chomsky Hierarchy

Language Class Complexity Machine

Recursively Enumerable

Undecidable Turing Machine

Context-Sensitive Languages

Exponential LBA

Context-Free Languages

Polynomial PDA

Regular Languages

Linear FSA

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KEY RESEARCH AREA OF TAFL

Theoretical Computer ScienceAutomata Theory, Formal Languages,

Computability, Complexity …

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Com

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Patte

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Sup

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Agile System Inc.http://www.agile-systems.com/ROBOSOFThttp://www.robosoft.fr/Atmel R&D Indiahttp://www.atmel.com/IC Softwarehttp://www.ic-software.co.uk/CRAY INC,http://www.cray.com/Impetus-People, Indiahttp://impetuspeople.com

PRIVATE SECTOR

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CSIR :: Council of Scientific and Industrial Researchhttp://www.csir.res.in/

Bharat Electronics Limited.www.bel-india.com  Defense Research & Development Organization.www.drdo.org

Bhabha Atomic Research Centre ( BARC ) http://www.barc.ernet.in/

Indian Space Research Organization http://www.isro.org/

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Tutorial Plan Handouts + LAN Server

Reference Sources Handouts + LAN Server

Lecture Plan Handouts + LAN Server

Individual Assignments LAN Server

Group Assignments Handouts + LAN Server

UPTU Paper Mapping LAN Server

I Google Will be shared by individual Gmail Ids

1. Simulation of Finite Automata2. Simulation of Push Down Automata3. Simulation of Turing Machine4. Speech Recognition5. Face Recognition6. Character Recognition7. Automation of Industry System8. Compiler Design9. Language Design10. Interpreter Design11. Debugger Design

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PROJECTS IN AUTOMATA

1937- Turing machine- Alan Turing -Turing" machine was described by Alan Turing in

1937,who called it an “(automatic)-machine".

- A formal version of Electromechanical machine appeared in 1943 in McCulloch-Pitts neural network models. (An earlier analog had appeared in Markov chains.) - Intensive work on them in the 1950s (sometimes under the name sequential machines) established many basic properties, including interpretation as regular languages and equivalence to regular expressions.

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TRENDS IN TAFL

- Connections to formal power series and to substitution systems were studied in the 1960s

- With the development of the Unix operating system in the 1970s regular expressions began to be widely used in practical computing in lexical analysis (lex) and text searching. Regular languages also arose in dynamical systems theory in the early 1970s under the name of sofic systems.

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Cellular Automata A cellular automaton is a collection of "colored" cells on a grid of

specified shape that evolves through a number of discrete time steps according to a set of rules based on the states of neighboring cells. The rules are then applied iteratively for as many time steps as desired. von Neumann was one of the first people to consider such a model, and incorporated a cellular model into his "universal constructor." Cellular automata were studied in the early 1950s as a possible model for biological systems (Wolfram 2002, p. 48). Comprehensive studies of cellular automata have been performed by S. Wolfram starting in the 1980s, and Wolfram's fundamental research in the field culminated in the publication of his book A New Kind of Science (Wolfram 2002) in which Wolfram presents a gigantic collection of results concerning automata, among which are a number of groundbreaking new discoveries.

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Hybrid Automaton A hybrid automaton is a formal model for a mixed discrete-continuous system.

We classify hybrid automata according to what questions about their behavior can be answered algorithmically. The classification reveals structure on mixed discrete-continuous state spaces that was previously studied on purely discrete state spaces only. In particular, various classes of hybrid automata induce finitary trace equivalence (or similarity, or bisimilarity) relations on an uncountable state space, thus permitting the application of various model-checking techniques that were originally developed for finite-state systems.

This research was supported in part by the Office of Naval Research Young Investigator award N00014-95-1-0520, by the National Science Foundation CAREER award CCR-9501708, by the National Science Foundation grant CCR-9504469, by the Air Force Office of Scientific Research contract F49620-93-1-0056, by the Army Research Office MURI grant DAAH-04-96-1-0341, by the Advanced Research Projects Agency grant NAG2-892, and by the Semiconductor Research Corporation contract 96-DC-324.036. A preliminary version of this paper appeared in the Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science (LICS 96), pp. 278{292.

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DNA computing DNA computing is a form of computing which uses DNA, biochemistry and

molecular biology, instead of the traditional silicon-based computer technologies. DNA computing, or, more generally, molecular computing, is a fast developing interdisciplinary area. Research and development in this area concerns theory, experiments and applications of DNA computing.

This field was initially developed by Leonard Adleman of the University of Southern California, in 1994. Adleman demonstrated a proof-of-concept use of DNA as a form of computation which solved the seven-point Hamiltonian path problem. Since the initial Adleman experiments, advances have been made and various Turing machines have been proven to be constructible.

In 2002, researchers from the Weizmann Institute of Science in Rehovot, Israel, unveiled a programmable molecular computing machine composed of enzymes and DNA molecules instead of silicon microchips. On April 28, 2004, Ehud Shapiro, Yaakov Benenson, Binyamin Gil, Uri Ben-Dor, and Rivka Adar at the Weizmann Institute announced in the journal Nature that they had constructed a DNA computer coupled with an input and output module which would theoretically be capable of diagnosing cancerous activity within a cell, and releasing an anti-cancer drug upon diagnosis.

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Membrane computing is an area within computer science that seeks to discover new computational models from the study of biological cells, particular of the cellular membranes. It is a sub-task of creating a cellular model.

Membrane computing or MC deals with distributed and parallel computing models, processing multisets of symbol objects in a localized manner. Thus, evolution rules and evolving objects are encapsulated into compartments defined by membranes. The communications between compartments and with the environment play an essential role in the processes. The various types of membrane systems are known as P systems after Gheorghe Pӑun who first conceived the model in 1998.

An essential ingredient of a P system is its membrane structure, which can be a hierarchical arrangement of membranes, as in a cell, or a net of membranes (placed in the nodes of a graph), as in a tissue or a neural net. P systems are often depicted graphically with drawings.

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THANK YOU

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