Atal Bihari Vajpayee Vishwavidyalaya M.Sc. III semester Syllabus
Adhoc Wireless Network
UNIT – 1
Introduction:
Introduction to wireless Networks. Characteristics of Wireless channel, Ad hoc Networks: Introduction, Issues in Ad hoc wireless networks, Adhoc mobility models, applications.
UNIT – 2
MAC:
MAC Protocols for Ad hoc wireless Networks: Introduction, Issues in designing a MAC protocol for Ad hoc wireless Networks, Design goals of a MAC protocol for Ad hoc wireless Networks, Classification of MAC protocols, Contention based protocols with reservation mechanisms.
UNIT –3
MAC:
Contention-based MAC protocols with scheduling mechanism, MAC protocols that use directional antennas, IEEE standards: 802.11a, 802.11b, 802.11g, 802.15 and 802.16.
UNIT – 4
Routing :
Routing protocols for Ad hoc wireless Networks: Introduction, Issues in designing a routing protocol for Ad hoc wireless Networks, Classification of routing protocols, Proactive routing protocol, Reactive Routing protocol, Hybrid routing protocol, Hierarchical routing protocols, Power aware routing protocols.
UNIT – 5
Transport Layer:
Transport layer protocols for Ad hoc wireless Networks: Introduction, Issues in designing a transport layer protocol for Ad hoc wireless Networks, Design goals of a transport layer protocol for Ad hoc wireless Networks, Classification of transport layer solutions, TCP over Ad hoc wireless Networks.
TEXT/REFERENCE BOOKS:
1. “Ad hoc Wireless Networks – Architectures and Protocols’, C. Siva Ram Murthy and B.S.Manoj, Pearson Education, 2004
2. “Wireless Sensor Networks”, Feng Zhao and Leonidas Guibas, , Morgan Kaufman Publishers, 2004.
3. “Adhoc Mobile Wireless Networks”, C.K.Toh, Pearson Education, 2002.
4. ‘Wireless Mesh Networking’Thomas Krag and Sebastin Buettrich, , O’Reilly Publishers, 2007
BIG DATA ANALYTICS
UNIT I –
INTRODUCTION TO BIG DATA
Big Data and its importance, Characteristics of Big Data, What Comes Under Big Data, Who’s Generating Big Data, Challenges in Handling Big Data, How Big Data Impact on IT, Big Data Analytics, Big data applications, Future of Big Data, Risks of Big Data.
UNIT II –
INTRODUCTION TO HADOOP
Introduction to Hadoop, Hadoop Architecture, Design Principles of Hadoop, Advantages of Hadoop, Hadoop Storage: Hadoop Distributed File System (HDFS), Properties of HDFS, NameNode, Secondary NameNode, DataNode, Goals of HDFS, Hadoop vs. Other Systems.
UNIT III -
HADOOP MapReduce Hadoop MapReduce, MapReduce paradigm, Resource manager, Node manager, Partitioner, combiner.
UNIT IV -
YARN Introduction to YARN, YARN Framework, Classic MapReduce Vs YARN, Schedulers: FIFO, Fair, Capacity.
UNIT V-
HADOOP Ecosystem Spark, Hive, HBase, Pig, Sqoop, Oozie.
TEXT/ REFERENCE BOOKS
1. “Professional Hadoop Solutions”, Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Wiley, 2015.
2. “Understanding Big data ”, Chris Eaton, Dirk deroos et al., McGraw Hill, 2012.
3. “HADOOP: The definitive Guide” , Tom White, O Reilly 2012.
4. “Big Data Analytics with R and Haoop”, Vignesh Prajapati, Packet Publishing 2013.
5. “Oracle Big Data Handbook”, Tom Plunkett, Brian Macdonald et al , Oracle Press, 2014.
6. http://www.bigdatauniversity.com/
7. “Big Data and Business analytics”, Jy Liebowitz, CRC press, 2013.
Data Mining and Data Warehousing
UNIT-I
Introduction
What is data mining, Why it is important ?, Mining on what kind of data, Data mining Functionalities, steps of data mining, Knowledge discovery.
UNIT-II
Data Data Warehouse Meaning, definition, OLTP Vs. OLAP, Data warehouse architecture, Three Tier Architecture Data warehouse architecture, Data cube and OLAP technology
UNIT-III
Association Rule Basic concept, Frequent item set mining: Apriori algorithm etc., Mining various kind of association rules: Mining Multilevel association rules, Mining multidimensional association rules
UNIT-IV
Classification and prediction What is classification and prediction, Decision tree algorithms: CART,ID3 C4.5, CHAID , Baysian classification, Rule based classification, Classification by backpropogation, Support vectio machine, Association classification and other classification methods. Prediction using Regression and Neural Network methods, Accuracy measures, Ensemble methods.
UNIT-V
Cluster analysis and Data mining Tools What is cluster analysis?, Partitioning method, Hierarchical methods, Experiments with WEKA data mining tools for model development, data preprocessing, feature selection for Financial data, health care data etc.
Text/Reference Books
1. Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishes (Elsevier, 2nd edition), 2006
2. Data Mining Methods for Knowledge Discovery , Cios, Pedrycz, Swiniarski,Kluwer Academic Publishers, London – 1998
3. Data mining techniques, Arun K Pujari, Universities Press (India) private limited, 2007.
4. Data Mining, Data Warehousing and OLAP, Gajendra Sharma, S.K. Kateria and Sons, 2010.
(A) Digital Image Processing
UNIT- I
Digital Image Fundamentals and Transforms:
Elements of visual perception, Image Sampling and quantization, Basic relationship between pixels, Basic geometric transformations, Introduction to Fourier Transform and DFT, Properties of 2D Fourier Transform,FFT Separable Image Transforms, Walsh, Hadamard, Discrete Cosine Traqnsform, Haar, Slant, Karhunen, Loeve transforms.
UNIT- II
Image Enhancement Techniques:
Spatial Domain Methods: Basic grey level transformation, Histogram equalization, Image Subtraction, Image Averaging, Spatial filtering: Smoothing, Sharpenning filters: Laplacian filters, Frequency domain filters: Smoothing, Sharpening filters, Homomorphic filtering.
UNIT- III
Image Restoration:
Model of Image Degradation/Restoration process, Noise models, inverse filtering, least mean square filtering, Constrained least mean square filtering, Blind image restoration, Pseudo inverse, Singular value decomposition.
UNIT- IV
Image Compression:
Lossless compression: Variable length coding, LZW coding, Bit plane coding, predictive coding, DPCM. Lossy Compression: Transform coding, Wavelet coding, Basic of Image compression standards: JPEG, MPEG, Basic of Vector quantization.
UNIT- V
Image Segmentation and Representation;
Edge detection, Thresholding, Region Based segmentation, Boundary Representation: Chair codes: Polygonal approximation, Boundary segments, boundary descriptors: Simple descriptors, Fourier descriptors, Regional descriptors, Simple descriptors, Texture
TEXT/REFERENCE BOOKS
1. “Digital Image processing”, R.C. Gonzalez & R.E. Woods, Addison Wesley/ Pearson education, 2nd Education, 2003.
2. “Fundamentals of Digital Image processing”, A.K.Jain , PHI.
3. “ Digital Image processing using MAT LAB”, Rafael C. Gonzalez, Richard E Woods and Steven L. Edition, PEA, 2004.
4. “Digital Image Processing”, William K. Pratt, John Wilely, 3rd Edition, 2004.
5. “Fundamentals of Electronic Image Processing”, Weeks Jr., SPIC/IEEE Series, PHI.
6. “Image Processing Analysis and Machine Vision”, Millman Sonka, Vaclav hlavac, Roger Boyle, Broos/colic, Thompson Learniy (1999)
7. “Digital Image Processing and Applications”, Chanda Dutta Magundar, PHI, 2000
(B) Graph Theory
UNIT -I
Graphs, Sub graphs, some basic properties, various example of graphs & their sub graphs, walks, path & circuits, connected graphs, disconnected graphs and component, Euler graphs, various operation on graphs, Hamiltonian paths and circuits, the travelling sales man problem.
UNIT- II
Trees and fundamental circuits, distance diameters, radius and pendent vertices, rooted and binary trees, counting trees, spanning trees, fundamental circuits, finding all spanning trees of a graph and a weighted graph, prim’s algorithm, Kruskal algorithm and Dijkstra Algorithm.
UNIT -III
Cuts sets and cut vertices, some properties, all cut sets in a graph, fundamental circuits and cut sets , connectivity and separability, network flows Planer graphs, combinatorial and geometric dual: Kuratowski graphs, detection of planarity, geometric dual, Discussion on criterion of planarity, thickness and crossings.
UNIT -IV
Vector space of a graph and vectors, basis vector, cut set vector, circuit vector, circuit and cut set subspaces, Matrix representation of graph – Basic concepts; Incidence matrix, Circuit matrix, Path matrix, Cut-set matrix and Adjacency matrix.
UNIT -V
Coloring, covering and partitioning of a graph, chromatic number, chromatic partitioning, chromatic polynomials, matching, covering, four color problem Discussion.
REFERENCE/TEXT BOOKS
1. Graph theory with applications to Engineering and Computer Science, Deo, N, PHI
2. Introduction to Graph Theory, Gary Chartrand and Ping Zhang, TMH
3. Introduction to Graph Theory, Robin J. Wilson, Pearson Education
5. Graph theory and application., Bondy and Murthy, Addison Wesley.
6. Schaum's Outline of Graph Theory, V. Balakrishnan, TMH
7. Graph Theory: Modelling, Applications and Algorithms, Geir Agnarsson, Pearson Education
(C) Pattern Recognition
Unit-I
Introduction:
Basics of pattern recognition, Design principles of pattern recognition system, Learning and adaptation, Pattern recognition approaches, Mathematical foundations – Linear algebra, Probability Theory, Expectation, mean and covariance, Normal distribution, multivariate normal densities, Chi squared test.
Unit-II
Statistical Patten Recognition:
Bayesian Decision Theory, Classifiers, Normal density and discriminate functions,
Unit – III
Parameter estimation methods:
Maximum-Likelihood estimation, Bayesian Parameter estimation, Dimension reduction methods - Principal Component Analysis (PCA), Fisher Linear discriminate analysis, Expectation-maximization (EM), Hidden Markov Models (HMM), Gaussian mixture models.
Unit - IV
Nonparametric Techniques:
Density Estimation, Parzen Windows, K-Nearest Neighbour Estimation, Nearest Neighbour Rule, Fuzzy classification.
Unit - V
Unsupervised Learning & Clustering:
Criterion functions for clustering, Clustering Techniques: Iterative square - error partitional clustering – K means, agglomerative hierarchical clustering, Cluster validation.
Reference/Text Books
1. “Pattern Classification”, Richard O. Duda, Peter E. Hart and David G. Stork, , 2nd Edition, John Wiley, 2006.
2. “Pattern Recognition and Machine Learning”, C. M. Bishop, Springer, 2009.
3. “Pattern Recognition”.S. Theodoridis and K. Koutroumbas, , 4th Edition, Academic Press, 200