1
COURS
E
MANUAL
Maste
r
o
f
Philosoph
y
in
Computer Science
(M
.
Phi
l
–
CS)
India
n
Institut
e
o
f
Informatio
n
Technolog
y
an
d
Managemen
t

Kerala (IIITM

K)
IIITMK Building,
Technopark
Trivandrum

695 081
www.iiitmk.ac.in
2
Preface
Thi
s
documen
t
de
scribe
s
th
e
cours
e
outlin
e
o
f
Maste
r
o
f
Philosoph
y
i
n
Computer
Science
a
t
the
Indian
Institute
of
Information
Technology
and
Management

Kerala
(IIIT
M

K).
IIIT
M

K is an autonomous institution establ
ished by the Government of Kerala in
2000
w
ith a mission to become a
centre
of excellence in education and research in
Information
Technology
,
Informatics
,
Managemen
t
an
d
allie
d
areas
.
IIIT
M

K
emphasizes
quality
educatio
n
t
o
it
s
student
s
throug
h
it
s
uniqu
e
pedagog
y
o
f a
combinatio
n
o
f
classroom
lec
tures
,
pee
r

interactions, exposure to real world
issues and application of what is
taught.
The need for the program is to produce skilled postgraduate research professionals
who are highly in dema
nd and at the forefront of information and emerging
technologies. We draw high quality staff and students from diverse disciplines and
backgrounds, and with our highly qualified and highly regarded academic, research
staff
and
general staff. The IIITM

K
provides a modern and enjoyable teaching
and research environment in areas of fundamentals of Computer Science,
Information Technology and domain specific application areas in which IT plays a
pivotal role in scientific, technology and commercial systems,
characterised by its
breadth, flexibility, and quality.
1.0
Introduction
Master of Philosophy in Computer Science will be a flagship programme offered
by the Indian Institute of Information Technology and Management

Kerala, aims
at high standards in basi
c and applied sciences, technology, management and
information system. The programme focuses on a broad grasp of philosophical
approach in Computer Science and IT, Research Methodology and Trends in
Computer Science and a deep understanding of the area
of specialisation, an
innovative ability to solve new problems, and a capacity to learn continually and
interact with trans

disciplinary groups.
The duration of the programme is 1 year and the courses are carefully designed to
attain theoretical, techni
cal and research aspects that enable them to grow into
competent, seasoned professionals.
1.1
Motivation
The skills of logical thinking, problem solving, abstraction and systematic analysis
acquired through a study of Computer Science are highly in demand a
nd also
transferable to other disciplines in today's need. As technology improves and
becomes an integral part of our society, it shapes and defines the way we live in.
The new amendments in Ph.D regulation of CUSAT stated that students with
3
M.Phil/Ph.D
degree will get exemption from NET qualification for taking up
teaching, jobs in Universities and Colleges. In Kerala, no university is offering an
M.Phil in Computer Science, so it's need of the society to provide an M.Phil
programme that allows us t
o cope with the rapid changes in technology that
are constantly occurring. M. Phil scholars in the Computing Science perform
individual research in Computer Science, Information Technology or related areas
under the direct supervision of a leading academic
expert in the field and also
making tie

up wth Scienfic R &D institutions and premier national and
international Universities.
1.2
Ai
m
and
Objective
Develop scholars into mature researchers, able to make original scientific
contributions that have both pr
actical significance and a rigorous, elegant
theoretical grounding
that underpins the various areas of Computer Science
and IT.
To impart sound knowledge in Computer Science and interdisciplinary areas
with Science, Technology and Management related to Inf
ormation Systems
and their applications in relevant fields with the latest technologies.
Build a pool of technically and scientifically qualified manpower to build
a
strong scientific community
Motivate and orient youngsters to do research with proper b
aseline.
Develop professionals and teachers with strong analytical and synthesizing
capability with innovative and creative thinking that can instill to student
community to develop a strong Scientific community
2.0
REGULATIONS
2.1
Course Description
Th
e
M
.
P
hi
l

CS
i
s
a
on
e

year
full
time
programme
and
adopts
a
credit
system.
Students
ear
n
thei
r
degre
e
b
y
completin
g
3
6
credi
t
points
,
spli
t
amon
g
cours
e
work
,
semina
r
an
d a
thesis
.
Th
e
programm
e
i
s
s
o
designe
d
tha
t
student
s
spen
t
thei
r
firs
t
si
x
month
s
earning
a
maximu
m
o
f
18
credit
s ,
before
proceeding to
carry
out
a
research
project
and a
thesis
,
whic
h
ca
n
ear
n
the
m
a
maximu
m
o
f
1
8
credi
t
points.
2.2
Salient
Features
Th
e
propose
d
programm
e
i
n
Computer Science
ha
s a
stron
g
emphasi
s
o
n
tran
s

disciplinary.
The
Mphil degree in Computer Science plan to the development of
computer science and engineering, and design skills through continual laboratory
4
access. Its focus on work integrated learning includes research oriented industry
placement in the final year, ens
uring post

graduates are fully prepared for a
challenging career in industry, and research and development enterprises.
The discipline major allows significant flexibility and allows you to choose courses
outside the main study area to develop a broader k
nowledge across a number of
areas. Students are provided an opportunity to embark on research projects under
the supervision of academic staff, while undertaking the Mphil program.
2.3
Eligibility
Qualifyin
g
examination/degre
e
fo
r
th
e
admissio
n
fo
r
th
e
M
Phi
l

CS
degree
course
is
M.
S
c/M C A/M.Tech
i
n
Computer Science/ Information
Technology/ Electronics/ Computational Sciences/Geoinformatics
or
equivalent having minimum of three papers in CS/IT in the
qualifying examination with at least 60% aggregate mar
ks , or
CGPA of above 6.5 in 10 points,
o
r
equivalen
t
o
n
th
e
abov
e
mentione
d
subjects.
2.4
Admissions
Admissio
n
t
o
th
e
Cours
e
i
s
base
d
o
n
th
e
Institute
Admissio
n
Tes
t
(IAT
)
conducted
b
y
India
n
Institut
e
o
f
Informatio
n
Technolog
y,
an
d
Management
supervised by
CUSAT
and/or
GATE/NET/JRF qualifying examination
score, foll owed by a techni cal interview
.
2.5
Assessment
and
Grading
System
Followin
g
Grad
e
Syste
m
o
n
Te
n
–
Poin
t
Scal
e
wil
l
b
e
adopted.
Rang
e
o
f
Mark
s
%
Grade
s
Weightage
(G)
9
0
an
d
above
S

Outstanding
1
0
80

90
A

Excellent
9
70

80
B
–
ser
y
dood
8
㘰
J
㜰
C
–
dood
T
㔰
J
㘰
a
–
pati獦a捴ory
S
Belo
w
㔰
c
J
cailed
M
X

Y
mean
s
tha
t
X
i
s
include
d
an
d
Y
i
s
excluded.
Overall
Grade
Point
Average
(GPA)
calculated
as
follows
will
indicate
performance
after
eac
h
s
emester:
GP
A
=
(
C
1
G
1
+C
2
G
2
+
C
3
G
3
+…..
C
n
G
n
)
/
(
C
1
+
C
2
+…
C
n
)
Wher
e
C
refer
s
t
o
th
e
credi
t
valu
e
o
f
th
e
cours
e
an
d
G
i
s
th
e
Grad
e
weigh
t
age
.
CGP
A
wil
l
b
e
calculate
d
base
d
o
n
th
e
abov
e
formula.
5
2.6
Classification
for
the
Degree
will
be
given
as
follows:
Classifica
tio
n
CGPA
Firs
t
Clas
s
wit
h
Distinctio
n
8
an
d above
Firs
t
Clas
s
6.
5
an
d
a
bove
Secon
d
Clas
s
6
an
d
above
Fai
l
belo
w
6
2.7
Number of seats
I
t
i
s
propose
d
t
o
limi
t
th
e
numbe
r
o
f
seat
s
t
o
15.
2.8
Mode of Evaluation
A
studen
t
woul
d
b
e
considere
d
t
o
hav
e
progresse
d
s
atisfacto
r
il
y
a
t
th
e
en
d
o
f
a
semeste
r
i
f
he/sh
e
ha
s
a
minimum
of
75%
attendance.
There will be internal and external evaluation. In the internal evaluation, studen
t
shal
l
b
e
evaluated
continuously
throughou
t
th
e
semeste
r
an
d
marks
shall
be
awarded
o
n
the
basis
of
tests
and
assignments
as
detailed
below:
1
0
mark
s
ar
e
awarde
d
base
d
o
n
assignment
s
give
n
b
y
th
e
teacher.
10 marks are awarded for Seminars/Miniproject/Case
study/review of
paper/viva
Ther
e
shal
l
b
e
minimum of
tw
o
clas
s
test
s
an
d
on
e
en
d
sem
este
r
examination.
Th
e
clas
s
test
s
carr
y
a
maximu
m
o
f
2
0
mark
s
each.
Th
e
en
d
semeste
r
examinatio
n
i
s
fo
r
a
maximu
m
of
6
0
marks
and
carries
question
s
fro
m
entir
e
syllab
i
o
f
th
e
course.
The
question
papers
for
the
end
semester
examination
will
b
e
prepared
by
the
teacher
who
taught
the
course
. The teacher will prepare three sets of question
papers.
The
HOD/Director
will
s
elec
t
an
y
on
e
o
f
th
e
questio
n
pape
r
for
th
e
En
d
Semeste
r
Examination.
The
answer
papers
will
be
evaluated
by
the
teacher
who
t
aught
the
pape
r
.
Befor
e
finalising
th
e
resul
t
mark
s
wil
l
b
e
show
n
t
o
students
.
T h e s i s
wi l l
be
evaluate
d
b
y
interna
l
a
s
wel
l
a
s
externa
l
examiners.
The external evaluation will be done by CUSAT.
The evaluation of thesis/Project
report will be d
one externally by CUSAT and the viva will be done in IIITM

K with
the examiner nominated by CUSAT.
Ther
e
ca
n
b
e a
supplementar
y
examinatio
n
fo
r
eac
h
subject
,
conducte
d
withi
n a
wee
k
o
f
th
e
las
t
examinatio
n
o
f
th
e
en
d
semeste
r
examination
.
Thi
s
wil
l
b
e
ba
sed
o
n
th
e
recommendation
s
o
f
th
e Institute Academic
Council
,
o
n
receivin
g
specific
appl
i
catio
n
fro
m
student
s
an
d
base
d
o
n
th
e
meri
t
o
f
th
e
case
.
6
Th
e
pass
minimum
is
50%
mark
s of the total of 100 marks (40 marks
internal + 60 marks External
) ,
with a se
parate minimum of 45% for the
external.
I
f
th
e
candidat
e
fail
s
t
o
secur
e
50% h
e/sh
e
i
s
faile
d
i
n
th
e
subjec
t
an
d
ha
s
t
o
repea
t
th
e
subjec
t
i
n
th
e
nex
t
possible
chance
A
pas
s
i
n
th
e
cours
e
wil
l
entitl
e
th
e
studen
t
t
o
acquir
e
th
e
credi
t
value
allotte
d
fo
r
t
ha
t
particula
r
course
.
Detail
s
o
f
th
e
credi
t
value
s
ar
e
give
n
i
n
the
course structure. Student wi
l
l
be
promoted
to
the
second
semester
only
if
he/she
hav
e
complete
d
al
l
th
e
paper
s
i
n
th
e
firs
t
semester.
2.9
Review
of
Question
Papers
and
Valuation
of
Answer
Books
A
t
th
e
en
d
o
f
eac
h
semester
,
questio
n
paper
s
se
t
fo
r
class tests and end
semester
examinatio
n
an
d
th
e
schem
e
o
f
evaluatio
n
o
f
answe
r
book
s
b
e
reviewe
d
b
y
th
e
DC
.
The
revie
w
repor
t
ma
y
b
e
place
d
i
n
th
e
Boar
d
o
f
Studie
s
fo
r
scrutin
y
i
f
necessary.
2.10
Gri
evance Cell
The
DC
will
act
as
grievance
cell
where
co
m
plaints from students on the conduct
of class
tests
,
semeste
r
examinatio
n
an
d
valuatio
n
methodolog
y
ca
n
b
e
examined
.
Th
e
student shal
l
mak
e
suc
h
complaint
s
withi
n a
wee
k
afte
r
th
e
examinatio
n
t
o
th
e
H
OD/Directo
r
in writin
g
fo
r
scrutin
y
b
y
th
e
grievanc
e
ce
l
l.
2.11
Evaluation
of
the
Teachers
by
the
students
For
effectiveness
and
improvement
in
the
delivery
of
the
course,
there
should
be
student
evaluation
of
teacher
s
.
A
forma
t
fo
r
evaluatio
n
ma
y
b
e
prepare
d
b
y
th
e
DC
.
Format
give
n
i
n
th
e
NAA
C
Guid
e
line
s
ca
n
b
e
use
d
fo
r
thi
s
purpose
.
Th
e
fee
d
back
s
hav
e
t
o
be
confidentia
l
an
d
ma
y
b
e
discusse
d
wit
h
th
e
respectiv
e
teacher
s
b
y
th
e
HOD/Director
,
so
tha
t
he/sh
e
ca
n
modif
y
th
e
teachin
g
an
d
learnin
g
methodolog
y
fol
lowe
d
b
y
him/her.
2.12
E

Learning
Format
in
Teaching
and
Learning
IIIT
M

K
campu
s
ha
s
1
G
B
connectivit
y
an
d
uses web based e

learning and
content management system.
.
Free software Moodle is used an e

learning
platform, teachers and students are encouraged to
use online teaching and
learning also.
2.13
Course Coordination Committee
Courses
in
each
semester
have
to
be
coordinated
by
a
Coordination
Committee
consisting
of
the
Director/
Head
of
Departments
/
School,
Course
coordinator
and all
the
teachers
handling
the
courses.
The
commi
ttee
should
meet
at
least
once
in
a
mont
h
t
o
monito
r
th
e
courses
.
A
student
representative
of
the
class
may
be
invited
as
an
d
whe
n
necessar
y
t
o
provid
e
feedbac
k
fro
m
th
e
7
sid
e
o
f
th
e
students.
2.14
Revision of Regulation and Cu
rriculum
The
University
ma
y
,
fro
m
tim
e
t
o
time
,
amen
d
o
r
chang
e
th
e
Regulations
,
Scheme
s
of
Examinations and Syllabus. In case of students already undergoing the course,
the change will
take
effective
from
the
beginning
of
the
following
academic
year
afte
r
the
changes
ar
e
introduce
d
and shall cover the part of the course that
remains to be completed.
3.0
M.
Phil
Course
Structure
and
Credits
Course
Subject
L
hr/wk
Credit
Points
Internal
Exam
External
Exam
Total
SEMESTER 1
CSMPh3101
Research
Me
thodolog
y
*
5
4
40
60
100
CSMPH3102
Paper 1
(Elective)
5
4
40
60
100
CSMPh3103
Paper 2
(Elective)
5
4
40
60
100
CSMPh3104
Mini Project

6
150

Tota
l
fo
r
I
semester
15
18
270
180
450
SEMESTER 2
CSMPh3201
Project
Dissertation/viva

18
150
150
3
00
Tota
l
fo
r
II
semester

18
150
150
300
Tota
l
for
the
course
36
420
330
750
ELECTIVES
CSMPh3101 Advanced Pattern Recognition
CSMPh3102 Networking and Information Security
CSMPh3103
Magnetic Resonance Imaging and Signal Proce
ssing
CSMPh3104
Circuits and Systems
CSMPh3105 Data Structures and Programming
CSMPh3106 Scientific Computing
8
CSMPh3107 High Performance Computing
CSMPh3108 Digital Signal Processing
CSMPh3109 Object Oriented Software Engineering
CSMPh3110 Soft Computing
CSMPh3111 Computational Linguistics
CSMPh3112 Embedded Systems
CSMPh3113 Data Analytics
CSMPh3114 Digital Image Processing
CSMPh3115 Internet of Things
CSMPh3116 e

Governance and IT Management
CSMPh311
7
Kernel Design
PROGRAMME DETAILS
CSMPh310
1
Advanced Pattern Recognition
Credits
:
4
Module

1 Mathematical Foundations
Significance testing, Paired t Tests, Wilcoxon signed ranks test, Friedman test,
Module

2 Nearest Neighbour
K

Nearest neighbour, metric learning techniques, onlin
e metric learning
Module

3 Support Vector Machines
One class SVM, Multi

class SVM, Cross

validation and Grid

search, linear versus
RBF Kernel, Implementation in C
Module

4 Neural Networks
Neural networks as classifiers, wavelet neural networks, neural netw
orks in image
compression
Module

5 HTM
Bayes predictive nets, HTM models, object recognition with HTM
Text Book
1.
V. Vapnik.
The Nature of Statistical Learning Theory
. Springer

Verlag, New
York, NY, 1995.
2.
Shakhnarovish, Darrell, and Indyk, ed (2005).
Nearest

Neighbor Methods in
Learning and Vision
.
MIT Press
.
ISBN
0

262

19547

X
.
9
3.
Jeff Hawkins and Dileep George,
Hierarchical Temporal Memory

Concepts,
Theory, and Terminology
,
Numenta Inc.
, 2006

05

17
4.
Bernhard Schlkopf
,
Alexander J. Smola
,
Learning with Kernels: Support
Vector Machines, Regularization, Optimization, and Beyond (Adaptive
Computation and Machine Learning)
ISBN

13:
978

0262194754
5.
Simon Haykin
,
Neural Networks: A Comprehensive Foundation
(2nd
Editio
n)
,
ISBN

10:
0132733501
 ISBN

13:
978

0132733502
References
1.
Randall Matignon,
Data Mining Using SAS Enterprise Miner
, isbn
0470149019,
Wiley

Interscience; 1 edition (August 3, 2007)
2.
Colleen Mc Cue,
Data Mining and Predictive Analysis: Intelligence Gathering
and Crime Analysis
, isbn 0750677961,
Butterworth

Heinemann; 1 edition
(May 1, 2007)
CSMPh3102 Cryptography And Network Security
Credits
:
4
MODULE 1
Classical Cryptography
,
Shift Ciphers, Substitution Ciphers, Affine Ciphers,
Vigenere Ciph
er, Hill Cipher, Permutation Ciphers, Vernam's One

time Pad,
Synchronous and Asynchronous Stream Ciphers, Linear Feedback Shift
Registers, Stream Ciphers Based on LFSR, RC4
MODULE 2
Block Ciphers
,
Modes of Operation,
Data Encryption Standard (DES), 3DES,
A
dvanced Encryption Standard (AES), Linear Cryptanalysis, Differential
Cryptanalysis
MODULE 3
RSA Encryption, Rabin Encryption, ElGamal Encryption, Diffie

Hellman Key
Exchange. RSA Signature, Rabin Signature, ElGamal Signature, DSA.
MODULE 4
Cryptographic
Hash Functions, Merkle
–
Damgård Construction, Message
Authentications Codes (MAC), Security of Hash Functions, MD5, SHA 1.
MODULE 5
10
IP Security Overview, Architecture, Authentication Header, Encapsulating Security
Payload, Key Management, Web Security Cons
iderations, Secure Socket Layer
and Transport Layer Security, Secure Electronic Transactions.
Text Books:
1.
William Stallings
,
Cryptography and Network Security Principles and
Practice
, Fourth Edition, Prentice

hall, India.
2.
Douglas R. Stinson,
Cryptography
Theory and Practice
,
Chapman &
Hall, 2
nd
Edition.
References:
1.
H. Deffs & H. Knebl
,
Introduction to Cryptography
, Springer
–
Verlag,
2002.
2.
Alfred J. Menezes, Paul C. van Oorschot and Scott A. Vanstone
,
Handbook of Applied Cryptography
, CRC Press, 1996.
3.
W
illiam Stallings
,
Cryptography and Network Security Principles and
Practice
, Third Edition, Prentice

hall India, 2003.
4.
Neal Koblitz
,
A Course in Number Theory and Cryptography
, Springer
International Students' Edition, 2nd edition, 1994.
CSMPh3103
Magneti
c Resonance Imaging and Signal Processing
Credits
:
4
Module

1 Mathematical Foundations
Commomnly used Functions

Convolution

The Fourier Transform

Radon
Transform

Signal Generation and Detection in MRI
–
MRI Signal characteristics
Module

2 Signal Localizat
ion
Slice

Selection

Spatial Information Encoding

Basic Imaging Methods

K

space
Sampling
Module

3 Image Reconstruction
General Issues

Fourier Reconstruction

Reconstruction using Radon Transform

Saturation Recovery Sequence

Inversion Recovery Sequence

Spin E
cho imaging

Gradient echo imaging
11
Module

4 Fast Scan Imaging
Fast

spin echo imaging

Fast Gradient echo imaging

Echo

Planar Imaging
Module

5 Constrained Reconstruction
Half Fourier Reconstruction

Extrapolation based reconstruction

Parametric
Reconstruction
Text Book
1. Z

P Liang, PC. Lauterbur
, Principles of MRI: A signal Processing Perspective
,
IEEE Press, NY 2000.
Reference Book
1. EM. Haacke, RW. Brown, MR. Thompson, R.Venkatesan,
Magnetic Resonance
Imaging: Physical Principles and Sequence Design
, Joh
n Wiley & Sons, NY 1999.
CSMPh3104
CIRCUITS AND SYSTEMS
Credits
:
4
Module

1 Basic Circuits
RLC filters, band pass filters, chaos generators
Module

2 MOSFET Process
MOSFET device structure, RCA Cleaning, Lithography, Oxidation, Metallisation
Module

3 MO
SFET Devices
Device modelling, PN Junctions, CV characterisations, Resistivity measurements,
Energy band diagrams
Module

4 Analog Circuits
OpAmp circuits, PLL circuits, Amplifiers, waveform generators, simulations in Spice
Module

5 Digital Circuits
Logic G
ates, CMOS, NMOS, Domino logic, ALU design
Text Book
1.
R. Jacob Baker
,
CMOS: Mixed

Signal Circuit Design
, 2nd Edition,
ISBN:
978

0

470

29026

2
12
2.
Paul Horowitz
and
Winfield Hill
(1989),
The Art of Electronics
(Second ed.),
Cambridge University Press,
ISBN
978
0521370950
References
1.
Thomas L. Floyd and David M. Buchla,
Electronics and Circuit Analysis
Study Guide: Signal Transforms, Fourier, Laplace & Z transform, Transfer
function, Electronic components, Analog & Digital Circuits
, Prentice Hall; 8
edition (Jul
y 3, 2009)
2.
Paul Scherz,
Practical Electronics for Inventors 2/E
, McGraw

Hill/TAB
Electronics; 2 edition (September 1, 2006)
CSMPh3105
Data Structures and Programming
Credits: 4
Module 1
Introduction to ADT and Algorithms: Principles of DSA, ADT, comp
utational
problem, algorithm notion, time complexity, space complexity, asymptotic analysis,
analysis of algorithms, design of algorithms, data, abstract data type, procedural
abstraction, worst case complexity, Big

Oh notation, incremental design.
Module
2
Stack and Queues: Introduction to stack, basic operations, implementation using
array and linked list, computational problems relating to stack, parenthesis
matching, expression representation using Polish and reverse Polish notations,
evaluation of expr
ession using stack, introduction to queues, basic operations,
implementation
Module 3
Lists and Linked List: Lists in ADT, List implementation in Stack and Queue, Linked
list, Insert, delete operations, doubly linked list, implementation, ADT and
applicati
ons, INFIX and POSTFIX evaluations.
Module 4
Recursion and Heap: Closed form, recursive form, problem solving, Fibonacci
series, Towers of Hanoi, celebrity problem (with and without recursion, Efficiency of
Recursion Algorithm, eight Queens, Heap: Introduc
tion, max heap, min heap,
representation, complexity.
Module 5
13
Trees, Graphs and Hashing: Binary tree, traversal in a tree, level order traversal,
ADT dictionary, dictionary implementation, balanced binary search tree, binary
search tree, extended binary
tree, insertion, deletion, AVL trees, Fibonacci tree, B

tree, red black tree.
Graph: Weighted graph, spanning tree, greedy method, Krushkals algorithm,
implementation, equivalence relation, parent chasing, traversal, DFS and BFS,
Hashing: open address hash
ing, double hashing, chaining, Different search and
sort algorithms: Bubble, quick sort, merge sort

divide and conquer method, Heap
sort.
Text Books
1.
A.D Aho, J. E. Hopcroft and J. D. Ullman,
Data Structures and Algorithms
,
Pearson education Asia, 1983.
2.
Y.
Langsam, M. J. Augenstein and A. M. Tenenbaum,
Data Structures using
C
, Pearson Education Asia, 2004
References
1.
T.H. Cormen, C.E.Leiserson, R.L.Riverst and C. Stien,
Introduction to
algorithms
, Second Edition. MIT Press and McGraw

Hill, 2001.
2.
Adam Drozdek
,
Data Structures and Algorithms in Java
, Published by
Brooks/Cole, 2000
CSMPh3106 Scientific Computing
Credits
:
4
Module 1
Introduction to scientific Computing, Approximations in Scientific Computing,
Computer Arithmetic, Linear Systems, Solvi
ng Linear systems, Special types of
linear systems, Linear Least Squares, Problem transformations, Orthogonalization
methods, Singular Value Decomposition, Comparison of methods
Module 2
Eiegen Value Problems, Computing Eiegen Values and Eiegen Vectors,
Ge
neralized Eigen Value Problem
Module 3
Non

linear Equations, Non

linear Equations in one dimension, Systems of Non

linear equations, Optimization problems, Unconstrained Optimizations, Non

linear
least squares, Interpolation, Polynomial interpolation
Modul
e 4
14
Numerical Integration and differentiation, Numerical quadrature, Ordinary
differential equations, Numerical Solutions to Ordinary Differential Equations,
Boundary problem for ODEs, Partial differential equations
Module 5
Fast Fourier Transform, Trigono
metric Interpolation, FFT Algorithm, Applications of
DFT, Wavelets, Random numbers and simulation, stochastic simulation,
randomness and random numbers, random number generators
Textbooks
1.
M. T. Heath,
Scientific Computing,
The McGraw

Hill Companies, Inc.
; 2nd
edition, 2002
2.
R. Hamming,
Numerical Methods for Scientists and Engineers
, Dover
Publications; 2 edition, 1987
References
1.
Gregoire Allaire and Alan Craig,
Numerical Analysis and Optimization: An
Introduction to Mathematical Modeling and Numerical Sim
ulation (Numerical
Mathematics and Scientific Computation)
, Oxford University Press, USA,
2007
CSMPh3107 High Performance Computing
Credits
:
4
Module 1
Parallel Processing and Supercomputing : Supercomputer Architecture, Vector
Machin
es, Parallel Processors, Data Parallel Processors, Single

Instruction

Multiple

Data. Multiple

Instruction

Multiple

Data, Pipelining. Vectorization.
Module 2
Parallelization of Algorithms : Parallel linear algebra routines, Loop optimizations.
Implementati
on. Principal of Locality, Caches and Buffers. Massively Data Parallel
Algorithms, Array notation, Fortran90 and HPC Fortran, Parallel and Vector C
Code, Layout, Align, Replicate, Masking, Shifting, Spreading, Broadcasting, Forall
Loops, divide

and

Conquer
Algorithms, Adaptive Quadrature, Correct Termination.
Module 3
15
Algorithms and optimization
:
Graph algorithms, combinatorial
scientific
computing, Monte

Carlo simulations, linear, nonlinear and discrete optimization,
Module 4
Grid Computing: Types of
Computational Grids, Gid requirements of end users,
application, tool and grid developers, and system managers, Cloud Computing.
Module 5
Computing Platforms Operating Systems and Network Interfaces, Compilers,
Languages and Libraries for the Grid, Gri
d Scheduling, Resource Management,
Resource Brokers, Resource Reservations, Security, Accounting and Assurance
Text Books
1.
J. M. Ortega,
Introduction to Parallel and Vector Solution of Linear Systems
,
Springer; 1 edition (April 30, 1988)
2.
J. J. Dongarra,
I. B. Duff, D. C. Sorensen and H. A. van der Vorst,
Solving
Linear Systems on Vector and Shared Memory Computers
, SIAM, 1991.
Reference
1.
K. Hwang,
Advanced Computer Architecture: Parallelism, Scalability,
Programmability
, McGraw

Hill, 1993.
2.
Foster, I.,
D
esigning and Building Parallel Programs
. Addison

Wesley, 1995.
3.
Hennessy, J.L. and Patterson, D.A.,
Computer Architecture A Quantitative
Approach
. Morgan Kaufmann, 1996.
CSMPh3108 Digital Signal Processing
Credits
:
4
Module 1
Introdu
ction, simple manipulations of discrete

time signals, analog

to

digital
conversion of signals. Fourier Analysis of Periodic and Aperiodic Continuous

Time
Signals and Systems: trigonometric Fourier series, complex form of Fourier series
Parsevals identity f
or Fourier series, power spectrum of a periodic function, Fourier
transform, Fourier transform of some important signals, power and energy signals
Module 2
Applications of Laplace Transform to System Analysis: Introduction, definition of
Laplace transform,
region of convergence (ROC), initial and final value theorems,
convolution integral, table of Laplace transforms, partial fraction expansions,
16
network transfer function, s

plane poles and zeros, Laplace transform of periodic
functions, and application of
Laplace transformation in analyzing networks.
Module 3
z

transforms and Linear Time Invariant Systems: Introduction, definition of the z

transform, properties of the z

transform, evaluation of the inverse z

transform,
properties of a DSP system, difference
equation and its relationship with system
function, impulse response and frequency response Discrete and Fast Fourier
Transforms: Discrete convolution, discrete time Fourier transform (DTFT), fast
Fourier transform (FFT), computing an inverse DFT by doing
a direct DFT,
composite

radix FFT, fast convolution and correlation
Module 4
Finite Impulse Response (FIR) Filters: Introduction, magnitude response and
phase response of digital filters, frequency response of linear phase FIR filters,
design techniques f
or FIR filters and design of optimal linear phase FIR filters
Module 5
Infinite Impulse Response (IIR) Filters: Introduction, IIR filter design by
approximation of derivatives, IIR filter design by impulse invariant method, IIR filter
design by bilinear t
ransformation, butterworth filters, Chebyshev filters, inverse
Chebyshev filters, elliptic filters, frequency transformation, Realization of Digital
Linear Systems: Introduction, basic realization block diagram and the signal

flow
graph, basic structures f
or IIR systems, basic structures for FIR systems
Textbooks
1.
S. Salivahanan, A. Vallvaraj and C. Gnanapriya,
Digital Signal Processing,
Tata McGraw

Hill, New Delhi, 2000
2.
Sanjit K. Mitra,
Digital Signal Processing, 3/e
, Tata McGraw

Hill, New Delhi,
2006
Refer
ences
1.
A.V. Oppenheim and R.W. Schaffer,
Digital Signal Processing
, Prentice hall,
NJ, 1975
CSMPh3109 Object Oriented Software Engineering
Credits: 4
Module 1
Introduction to Software Engineering and Models: Different Software Life cycle
Models

Software Measurements: Software Metrics

Software costing and
estimation

SCM Processes

Version Control

Change Management

Risk
17
Managemen

Software Testing and Quality

Quality Assurance

Quality control
Quality
Module 2
Software Project Managem
ent and Process Frameworks: Project Management
Processes

Project Estimations

Project Planning and Tracking Scheduling

Scope Management

Cost Management, Integrated Change Management

Introduction to CMM

Five levels of CMM

Introduction to six si
gma
Module 3
Formal Methods in Software Engineering: Basic Concept, Mathematical
preliminaries, Applying Mathematical Notation for Formal specification Languages
Object constraint language (OCL),
Verification and Formal Methods, model
checking. Verificati
on and Validation
–
Planning verification and validation, software
Inspections, Automated static analysis.
Module 4
Object Orientation: Object Oriented Modeling, Introduction to UML, Features of
Object Orientation, Relationships, Best Practices in Softwa
re Engineering,
Iterative model, Unified Modelling Language, Use case Analysis, Interaction
Diagrams, Sequence and Collaboration Diagrams, Activity Diagrams, State
Chart Diagrams, Class Diagrams
Module 5
Architectural analysis: 4+1 view model,
Patterns and Design, Layered
Approach, Architectural Mechanism

Design elements

Runtime Architecture:
Concurrency Mechanism, Identify Process and Threads, Distribution of model
Elements, Distribution Patterns, Three Tyre Architecture, Web, Peer

to

Peer,
Network Configuration

Deployment: Modeling Client Server, Distributed Systems
Text Books
1.
Pressman R.S,
Software Engineering: A Practitioner’s Approach
(6
th
Edition), McGraw Hill, 2005
2.
Steve Schach ,
Classical and Object Oriented Software Engine
ering
(6
th
Edition), McGrawHill International, 2005
3.
G Booch, J Rumbaugh, I Jacobson
The Unified Modeling Language User
Guide
, Addison

Wesley object technology series, 2001
Reference
18
1.
W Boggs, M. Boggs
Mastering UML with Rational rose
, New York, Sybex
Inc.,
1999
2.
A Bahrami,
Object Oriented Software Development Using UML,
Mc GrawHill
International Edition, 1999
CSMPh3110 Soft Computing
Credits
:
4
Module 1
Introduction: Introduction to soft computing, introduction to biological and artificial
neur
al networks, introduction to fuzzy sets and fuzzy logic systems
Module 2
Artificial Neural Networks and Applications: Different artificial neural network
models, learning in artificial neural networks, neural network applications in control
systems
Module
3
Fuzzy Systems and Applications: Fuzzy sets, fuzzy reasoning, fuzzy inference
systems, fuzzy control, fuzzy clustering, applications of fuzzy systems
Module 4
Neuro

fuzzy systems: Neuro

fuzzy modeling, neuro

fuzzy control, Genetic
algorithms: Simple GA,
crossover and mutation, genetic algorithms in search and
optimization, Introduction to Ant Colony Optimization method and Swam
Intelligence
Module 5
Applications of soft computing: Pattern recognitions, image processing, biological
sequence alignment and d
rug design, robotics and sensors, information retrieval
systems, share market analysis, natural language processing
Text Books
1.
M. Friedman and A. Kandal,
Introduction to Pattern Recognition Statistical
,
Structural, Neural and Fuzzy Logic Approaches, Worl
d Scientific, 2005.
2.
Timothy J. Ross,
Fuzzy Logic with Engineering Applications
, McGraw Hill,
1997.
19
3.
J.S.R. Jang, C.T. Sun, E. Mizutani,
Neuro

Fuzzy and Soft Computing: A
Computational Approach to Learning and Machine Intelligence
, Prentice
Hall, 1996.
Re
ferences
1.
Melanie Mitchell,
An Introduction to Genetic Algorithms
, Prentice Hall of
India, 2004.
2.
David E. Goldberg,
Genetic Algorithms in Search, Optimization and Machine
Learning
, Addison

Wesley Professional, 1989.
CSMPh3111 Computational Linguistics
Credits: 4
Module 1
Introduction to Computational Linguistics and Grammar
: What is
Computational Linguistics? Interdisciplinary relevance: Formal Linguistics, Psycho

linguistics, Cognitive Science, Chomsky Hierarchy,
Initial Systems:
Turing Test,
Dialog systems: ELIZA. Lexical Functional Grammar (LFG), Head

Driven Phrase
Structure Grammar (HPSG). Context

Free grammars (CFGs), Descriptive
Grammar of Malayalam.
Module 2
Statistics:
Probability, Joint and Conditional Probability,
Bayes rule
, Regressi
on,
Graph theory.
Machine Learning
: Supervised, Unsupervised and Semi

supervised
learning. Decision trees (C4.5), Inductive logic programming, Naïve Bayesian
Classifier, Hidden Markov Model, Singular Value Decomposition (SVD), Support
Vector Machine(SVM)
, Conditional Random Fields(CRFs).
Module 3
Computational Corpus Linguistics
: Why corpus linguistics? What is a corpus?
Different Corpus types, Corpora Development, World Wide Web as a corpus,
British National Corpus, Speech Corpora, Multimedia corpora, P
arallel Corpus,
Corpus collection and design,
Font and Encoding:
Font design and development,
Encoding scheme , Character encoding and decoding, UNICODE (utf8) and ASCII,
ISCII
Corpus
Annotation:
Tagging, Parsing, Treebanks, Co
rpus Tools:
Dictionaries ,
Thesaurus creation,
Tokenization, Concordance, Stemmer.
Quantitative linguistics:
Quantitative data analysis, Collocations and idioms, Text
types and Genre.
Module 4
Language Modeling:
Language models and their role in Text and Speech
processing. Differ
ent types of Language modeling, Markov models, N

gram
20
models, Entropy, Relative entropy, Cross entropy, Mutual information, Statistical
estimation and smoothing for language models.
Module 5
Text Analytics:
Statistical Machine Translation (SMT), Alignment
Models and
Expectation Maximization (EM), EM and its use in statistical MT alignment models.
Statistical phrase based systems and syntax in SMT. Context

Free Grammars
(CFGs) Parsing, Top

down and bottom

up parsing, empty constituents, left
recursion, Prob
abilistic CFGs Parsing, Dependency Parsing, Modern Statistical
Parsers :
Charniak Parser, The Stanford Parser,
Malt Parser.
Document
Clustering, Text Similarity, Information Extraction (IE) and Named Entity
Recognition (NER), Coreference Resolution, Stati
stical and Rule

based methods.
Ranking Algorithms, Query Modification and Effectiveness, Representation of
Documents, IR Models

Boolean and Vector Space Models.
File Structures:
Inverted Files, Signature Files. Term and Query Operations: Lexical Analysis
and
Stop lists,
Precision, Recall and F

score: Different Evaluation metrics:
BLEU, B

CUBED, IR Evaluation: Relevance Judgment, Map Score. GATE , WEKA, CRF++,
Moses
Text Book
1.
Daniel Jurafsky and James H. Martin: 2000
,
Speech and Language
Processing: An Intr
oduction to Natural Language Processing, Computational
Linguistics, and Speech Recognition
. Prentice

Hall.
2.
Christopher Manning and Hinrich Schütze: 1999
,
Foundations of Statistical
Natural Language Processing
. MIT Press. Cambridge, MA.
3.
Charniak, Eugene.
1993.
Statistical Language Learning
. The MIT
Press.
References
1.
Jeffrey D Ullman, Rajeev Motwani and John E Hopcroft : 2000,
Introduction
to Automata Theory, Languages, and Computation
(2nd Edition)
,
Addison
Wesley.
2.
Carl Jesse Pollard
,
Ivan A. Sag
: 1994,
Head

Driven Phrase Structure
Grammar
, University of Chicago Press.
CSMPh3112 Embedded Systems
Credits
:
4
Module 1
21
Embedded System Architecture: Instruction set architecture, CISC and RISC
instruction set architecture, basic embedded processor,
microcontroller
architecture, CISC examples, 8051, RISC example, ARM, DSP processors,
Harvard architecture, PIC, memory system architecture, caches
Module 2
Memory Management: virtual memory, memory management, unit and address
translation, I/O sub

system
, busy

wait I/O, DMA, interrupt driven I/O, co

processors
and hardware accelerators, processor performance enhancement, pipelining,
super

scalar execution. Designing Embedded Computing Platform: Using CPU
bus, bus organization, memory devices and their cha
racteristics, RAM, ROM,
UVROM, EEPROM, ash memory, DRAM, I/O devices, timers and counters,
watchdog timers, interrupt controllers, A/D and D/A converters, displays,
keyboards, component interfacing, memory interfacing, I/O device interfacing
Module 3
Inte
l atom based embedded system,
Embedded platform architecture, Intel
embedded processor architecture, Embedded platform boot sequence, Operating
system overview, embedded linux
Module 4
Power optimisation, Embedded graphics and multimedia acceleration, Digi
tal signal
processing using general purpose processors, Network connectivity, Application
framework: Andriod and Qt, SMP, AMP and Virtualisation
Module 5
Developing an embedded system: Intel Atom E6XX, multi

radio communications
design, multimedia design,
Platform debug, performance tuning
Text Books
1.
Jonathan W. Volvano,
Embedded Microcomputer Systems: Real

Time
Interfacing
, 2nd edition, CENGAGE

Engineering, 2006.
2.
Muhammed Ali Mazidi, Janice Mazidi and Rolin McKinlay,
8051
Microcontroller and Embedded Syst
ems
, 2nd edition, Prentice Hall, 2005.
3.
Peter Barry, Patrick Crowley,
Modern Embedded Computing: Designing
Connected, Pervasive, Media

Rich Systems
, Morgan Kaufmann; 1 edition
(February 10, 2012)
References
22
1.
Kenneth J. Ayala,
8051 Microcontroller
, 3rd edi
tion, Thomson, 2005.
2.
Lori Matassa, Max Domeika,
Break Away with Intel Atom Processors: A
Guide to Architecture Migration
, Intel Press (December 16, 2010)
CSMPh3113 Data Analytics ‘
Credits
:
4
Module 1: Data Exploration
Process flow, Explori
ng the problem space and solution space, mining data, types
of data models, active and passive models, explanatory and predictive models,
static and continuously learning models
Module 2:Data Preparation
Prepare, Survey and model the data, modelling with
decision trees, neural network
and evolution programs, missing data, stages of data preparation, data
characterization, set assembly
Module 3:Sampling Study
Sampling, confidence, and variability. Variability of numerical and alpha variables,
measuring c
onfidence, confidence of capturing variability, problems of taking
samples using variability.
Module 4:Nonnumerical Variables
Alphas and remapping, state space, joint distribution tables,dimensionality,
practical problem simulations in R or weka or scila
b.
Module 5:Normalization Techniques and Variable Processing
Normalizing variable ranges, redistribution of values, retaining and replacing
missing value information, series data modelling and repairing, sparse variables,
issues with high dimensionality,
neural net simulations in scilab or R
Text Book
1. Dorian Pyle, Data Preparation for Data Mining (The Morgan Kaufmann Series in
Data Management Systems), 1999, Morgan Kaufmann; 1 edition
Reference
1. Ian H. Witten, Eibe Frank, Mark A. Hall , Dat
a Mining: Practical Machine
Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in
Data Management Systems), Morgan Kaufmann; 3 edition (January 20, 2011)
CSMPh3114 Digital Image Processing
Credits
:
4
23
Module 1
Fundam
entals of Image Processing, Elements of visual perception, Steps in Image
Processing Systems, Image Acquisition, Sampling and Quantization, Pixel
Relationships, Color Fundamentals and Modules, File Formats
Module 2
Image Enhancement and Restoration, Spatia
l Domain Gray Level Transformations,
Histogram Processing, Spatial Filtering, Smoothing and Sharpening, Frequency
Domain, Filtering in Frequency Domain, DFT, FFT, DCT, Smoothing and
Sharpening Filters, Homomorphic Filtering, Noise Models, Constrained and
U
nconstrained Restoration Models
Module 3
Image Segmentation and Feature Analysis, Detection of Discontinuities, Edge
Operators, Edge Linking and Boundary Detection, Thresholding, Region based
Segmentation, Motion Segmentation, Feature Analysis and Extrac
tion
Module 4
Overview of Pattern Recognition, Discriminant Functions, Supervised Learning,
Parametric Estimation, Maximum Likelihood Estimation, Perception Algorithm,
LMSE Algorithm, Problems with Bayes Approach, Pattern Classification by
Distance Functio
ns, Minimum Distance Pattern Classifier
Module 5
Unsupervised Classification, Clustering for Unsupervised Learning and
Classification, Clustering Concept, C

Means Algorithm, Hierarchical Clustering
Procedures, Graph theoretic Approach to Pattern Clustering
, Validity of Clustering
Solutions
Text Books
1.
Refael C Gonzalez and Richard E Woods, Digital Image Processing, Third
Edition, Pearson Education, , 2008
2.
Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis
and Machine Vision, Third Edition
, Brroks Col, 2008
3.
Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall India,
2008
References
24
1.
Madhuri A Joshi, Digital Image Processing: An Algorithmic Approach,
Prentice Hall India, 2006
2.
Rafael C. Gonzalez, Richard E woods, Steven L Edd
ins, Digital Image
Processing Using MATLAB, First Edition, Pearson Education, 2004
3.
Robert J. Schalkoff, Pattern Recognition: Statistical Structural and Neural
approaches, John Wiley & Sons Inc., New York, 1992.
CSMPh3115 Internet of Things
Credit: 4
Module 1
RFID Tags, RFID Automatic Identification and Data Capture,
RFID Data
Warehousing and Analysis,
RFID Data Management Issues, Solutions, and
Directions,
Module 2
RFID Secur
ity: Threats and Solutions,
RFIG Geometric Context of Wireless Tags
Module 3
Structured Web Documents in XML, Describing Web Resources in RDF, Web
Ontology Language: OWL, Logic and Inference: Rules, Applications
Module 4
Introducing Android, Stacking up
Android, Booting Android development, An
Android application, The Android SDK, Fitting the pieces together, Building an
Android application in Eclipse, User interfaces, Creating the Activity, Working with
views. Using resources, Understanding the Android
Manifest file, Working with
Intent classes, Listening in with broadcast receivers, Building a Service, Performing
Inter

Process Communication, Storing and retrieving data, using preferences,
Using the filesystem, Persisting data to a database, Working with
Content Provider
classes
Module 5
Networking and web services, An overview of networking, Checking the network
status, Communicating with a server socket, Working with HTTP,web services,
Telephony background and terms, Accessing telephony information, In
teracting
with the phone, Working with messaging: SMS, Notifications and alarms, Graphics
and animation, Multimedia
Textbooks
1.
The Internet of Things: From RFID to the Next

Generation Pervasive
Networked Systems (Wireless Networks and Mobile Communications
) , Lu
Yan (Editor), Yan Zhang (Editor), Laurence T. Yang (Editor), Huansheng
Ning
2.
Unlocking Android A Developer's Guide Covers Android SDK 1.x, W. Frank
Ableson, Charlie Collins, and Robi Sen
3.
A Semantic Web Primer, Grigoris Antoniou and Frank van Harmel
en
25
Reference
1.
Hakima Chaouchi, The Internet of Things: Connecting Objects (ISTE),
Wiley

ISTE; 1 edition (May 24, 2010)
CSMPh3116 e

Governance and IT Management Credit: 4
Module 1
Philosophy of e

Governance

Govern
ance and Good Governance
–
indicators of
good governance
–
structure of governance
–
good governance and e

governance.
Need of e

Goverance

NeGP

Vision, Objective, Strategy; Status. Central, State
and Integrated Mission Mode projects.
Module 2
Governm
ent Process Reengineering

Introduction to Government Process
Reengineering
–
Reengineering and the organisations of tomorrow

government
process
–
effectiveness, automation, quality and GPR
–
Need and goal for GPR
–
attributes of customer friendly servic
es
–
implementing GPR
–
Challenges, success
and failures in GPR

Change Management
Module 3
Management of Citizen Services

State as service provider
–
Role of Government
and Citizen
–
Citizen requirements
–
Life Cycle Needs
–
Design and Delivery

Promoti
on

Pricing

Framework For Design & Delivery of Services

Modes of
Service Delivery

Commercialisation

Public

Private Partnerships

Co

production

Decentralization

Enabling Public Service Delivery

The Environment for Effective
Service Delivery
Modu
le 4
Introduction to Transactional Services
–
Types of transactional services
–
G2G
–
G2C
–
G2E
–
B2G

Payment gateways

Introduction to B2C

Challenges and
Issues in Legacy to Electronic Models
–
Designing work flow
–
infrastructure
–
people
–
revenue coll
ection

Building Blocks of Transactional Services
–
components
–
Concept of Three Tier Architecture
–
Web Technologies

e

Governance standards
–
interoperability
–
seamless integration across departments
–
Data standards
Module 5
Basic concepts of Informat
ion Systems and management

types of IS: functional
and Enterprise; IT Infrastructure, Web 2.0, CRM, SCM, ERP, Data mining,
Business Intelligence, Ethical issues relating to IS

Agency Theory and IS;
26
Transaction Cost Theory; Impact of IS on organizations a
nd markets; Effects of IT
on organizational design: organizational memory;organizational intelligence and
decision

making
Text Books
1.
Prabhu CSR , E

Governance : Concepts and Case Studies,, Prentice Hall
of India
Pvt Ltd., 2004
2.
Deva, Vasu , E

Governance i
n India A Reality, , Publisher: Commonwealth
Publisher, 2005
3.
James a O’Brien ,
Managing Information Technology in the E

Business
Enterprise,
TMG, 2002
References
1.
Robert A. Schultheis, Mary Summer,
Richard d Irwin
,
Management
Information Systems: The M
anager’s View,
1999
2.
Vikram Sethi, William King , Organizational transformation through business
process reengineering: Applying lessons learned., Pearson, 1998
CSMPh3117 Kernel Design
Credits: 4
Module 1:
Operating systems, Preparatio
n: Read The Evolution of the Unix Time

Sharing
System, C, Assembly, Tools, and Bootstrapping, PC hardware and x86
programming, OS Organization, Exokernel
Module 2:
Processes and page tables (registers, page table translation), Fork/exec, JOS
memory lay
out and System call, Interrupt, and Exception Handling (JOS memory
layout, IDT)
Module 3:
Multiprocessors and locking, Process scheduling, Processes and coordination,
Files and disk I/O, Naming, File system performance and crash recovery
Module 4:
27
Perform
ance and durability, Scheduling, Microkernels and capabilities,
Language/OS co

design, Multi

processor coordination: scalable locks,Multi

processor coordination: lock free
Module 5:
Multikernel operating systems (AMD slides, source, IPC latency and
L2 misses),
Deterministic Parallelism, Virtual Machines, Software vs Hardware Virtualization,
Execution Replay for Multiprocessor Virtual Machines
Textbooks
1.
The UNIX Time

Sharing System, Dennis M. Ritchie and Ken
L.Thompson,. Bell System Technical Journal
57, number 6, part 2 (July

August 1978) pages 1905

1930.
2.
The Evolution of the Unix Time

sharing System, Dennis M. Ritchie, 1979.
References
1.
Abraham Silberschatz, Peter B. Galvin and Greg Gagne, Operating
System Concepts.Wiley; 8 edition (July 29, 2008)
2.
T
he C programming language (second edition) by Kernighan and Ritchie.
Prentice Hall, Inc., 1988. ISBN 0

13

110362

8, 1998.
CSMPh3101
Research Methodologies In Computer Science
Credits
:
4
Module 1
Principles of Scientific Research: Introducti
on to problem
–
Determining the mode
of attack
–
Literature Survey
–
Reference
–
Awareness of current status
–
Abstraction of a research paper
–
possible ways of getting oneself abreast of
current literature
–
Assessing the status of the problem

Document a
nd thesis
preparation using Latex

Guidance from the Supervisor
–
Actual Investigation
preparation of Manuscript
–
Thesis Writing.
Module 2
Linear Programming, Simplex method, Meaning Basic Concepts and Notations
General form of Linear programming model

Simplex Minimisation and
Maximisation Procedure

Technical issues in Linear Programming

Linear
programming applications. Discrete approach to problem solving Spanning Tree,
Shortest Path, Assignment Problem

Traveling salesman; Knapsack problem

Statistica
l Research Methods

Tools for Scientific problem solving
Module 3
Programming Concepts

Structured

Functional

Object oriented

New trends in
Programming
–

Introduction to coding Theory, Shannon's theory of Information,
28
Entropy, Mathematical Theory of C
laude Shannon

Hamming vs Shannon

Computational Theories

Evolutionary Algorithms

Dynamic Computing
Module 4
Introduction to learning theories, learning paradigms, history of instructional
technology

social, cognitive, developmental theories

David Merr
ill's model

Technology Enhanced Learning and Teaching

communication and collaboration
process

Educational multimedia, print, audio and video media, Computer Assisted
Instruction, web based instructional models, interactive video, teleconferencing,
mobile
based delivery
Module 5
e

Learning

advantages and characteristics, components of e

learning: CBT, WBT
and Virtual Classroom, e

Learning tools, e

Learning tools

moodle, Learning
Management System, Content development system
–
Jumla/ Drupal
–
standards of
content developmemt, Malcom Bridge Content Management model

Open
Research Methodologies

FOSS
–
Wiki
Textbooks:
1.
Rajammal P Devadas, A Hand Book of Methodology of Research, S.R.K.
Vidyalaya Press (1976).
2.
Anderson J. Durstan B H and Poole M, Thesis an
d Assignment Writing,
Wiley Eastern (1997).
3.
Taha Hamdy A, Operations Research, Macmillan, New York, 1987.
4.
Biggerstaff J, System Software Tools, Prentice Hall.
5.
Kanti Swarup, Gupta P K, and Man Mohan, Operation Research, Sulthan
Chand and sons Pub, New Delhi
.
6.
David E. Goldberg, ‘Genetic Algorithms in Search, Optimization and
Machine Learning, ADDISON

WESLEY, 1989.
7.
Melonie Mitchell, ‘An Introduction to Genetic Algorithms’ PHI, 1996.
References
1.
Koza, John. R, ‘Genetic Programming, on the programming of computer
s by
means of natural selection’, CAMBRIDGE, MA: THE MIT PRESS, 1992.
2.
C. R. Kothari
–
Research Methodology Methods and Techniques

Wishwa
Prakashan Publishers
–
Second Edition.
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