B.Tech (Computer Science & Technology) Syllabus

Semester VII

  • COURSE CODE
    COURSE NAME
    CREDITS
  • JECS-701

    Distributed System:

    UNIT-I

    Characterization of Distributed Systems: Introduction, Examples of distributed Systems, Resource sharing and the Web Challenges. Architectural models, Fundamental Models. Theoretical Foundation for Distributed System: Limitation of Distributed system, absence of global clock, shared memory, Logical clocks, Lamport's& vectors logical clocks. Concepts in Message Passing Systems: causal order, total order, total causal order, Techniques for Message Ordering, Causal ordering of messages, global state, termination detection.

    UNIT-II

    Distributed Mutual Exclusion: Classification of distributed mutual exclusion, requirement of mutual exclusion theorem, Token based and non token based algorithms, performance metric for distributed mutual exclusion algorithms. Distributed Deadlock Detection: system model, resource Vs communication deadlocks, deadlock prevention, avoidance, detection & resolution, centralized dead lock detection, distributed dead lock detection, path pushing algorithms, edge chasing algorithms.

    UNIT-III

    Agreement Protocols: Introduction, System models, classification of Agreement Problem, Byzantine agreement problem, Consensus problem, Interactive consistency Problem, Solution to Byzantine Agreement problem, Application of Agreement problem, Atomic Commit in Distributed Database system. Distributed Resource Management: Issues in distributed File Systems, Mechanism for building distributed file systems, Design issues in Distributed Shared Memory, Algorithm for Implementation of Distributed Shared Memory.

    UNIT-IV

    Failure Recovery in Distributed Systems: Concepts in Backward and Forward recovery, Recovery in Concurrent systems, Obtaining consistent Checkpoints, Recovery in Distributed Database Systems. Fault Tolerance: Issues in Fault Tolerance, Commit Protocols, Voting protocols, Dynamic voting protocols.

    UNIT-V

    Transactions and Concurrency Control: Transactions, Nested transactions, Locks, Optimistic Concurrency control, Timestamp ordering, Comparison of methods for concurrency control. Distributed Transactions: Flat and nested distributed transactions, Atomic Commit protocols, Concurrency control in distributed transactions, Distributed deadlocks, Transaction recovery. Replication: System model and group communication, Fault - tolerant services, highly available services, Transactions with replicated data.

    04
  • JECS-702

    Digital Image Processing

    UNIT-I

    Introduction: Origin of Digital Image processing – fundamental steps – Components of Image processing system – Visual perception – Light and EM spectrum – Image sensing and acquisition – Image sampling and Quantization – relationship between pixels

    UNIT-II

    Image Enhancement: Spatial Domain: Gray level transformation – Histogram processing – Arithmetic / Logic operations- Spatial filtering – smoothing filters – sharpening filters Frequency Domain: Fourier transform – smoothing frequency domain filters – sharpening filters – Homographic filtering

    UNIT-III

    Image Restoration: Model of Image degradation/ restoration process – Noise models – mean filters – order statistics – adaptive filters – band reject – bandpass – notch – optimum notch filters – Linear, position invariant degradations – establishing degradation functions – Inverse filtering – Weiner – least square – Geometric mean filters

    UNIT-IV

    Image Compression: Fundamentals – Image compression models – Information theory – error free compression: variable length – LZW – Bitplane – Lossless predictive coding; Lossycompression :Lossy predictive – transform – wavelet coding; Image compression standards

    UNIT-V

    Image Segmentation, Representation & Description: Segmentation: Detection of discontinuities – Edge linking & Boundary detection – Thresholding – region based segmentation Representation & Description: Chain codes – Polygonal approximations – signatures – Boundary segments – Skeletons; Boundary Descriptors – Regional descriptors

    Books:

    • 1. Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing" , 2nd edition , Pearson Education.
    • 2. S. Annadurai, R. Shanmugalakshmi, "fundamentals of Digital Image Processing", Pearson Education.
    • 3. Rafael C. Gonzalez, Richard E. Woods, Eddins, "Digital Image Processing using MATLAB", Pearson Education.
    • 4. Anil Jain K. "Fundamentals of Digital Image Processing", PHI.
    • 5. William Pratt , "Digital Image Processing", Wiley Interscience.
    04
  • JECS-071-074

    Departmental Elective II (Data Mining and Data Warehousing/Advance Operating system/Neural Network/Bioinformatics)

    Data Mining and Data Warehousing:

    UNIT-I

    Overview, Motivation (for Data Mining), Data Mining-Definition & Functionalities, Data Processing, Form of Data Preprocessing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection),Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Clustering, Discretization and Concept hierarchy generation.

    UNIT-II

    Concept Description:- Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisions, Statistical measures in large Databases. Measuring Central Tendency, Measuring Dispersion of Data, Graph Displays of Basic Statistical class Description, Mining Association Rules in Large Databases, Association rule mining, mining Single-Dimensional Boolean Association rules from Transactional Databases– Apriori Algorithm, Mining Multilevel Association rules from Transaction Databases and Mining Multi-Dimensional Association rules from Relational Databases.

    UNIT-III

    Classification and Predictions: What is Classification & Prediction, Issues regarding Classification and prediction, Decision tree, Bayesian Classification, Classification by Back propagation, Multilayer feed-forward Neural Network, Back propagation Algorithm, Classification methods K-nearest neighbor classifiers, Genetic Algorithm. Cluster Analysis: Data types in cluster analysis, Categories of clustering methods, Partitioning methods. Hierarchical Clustering- CURE and Chameleon, Density Based Methods-DBSCAN, OPTICS, Grid Based Methods- STING, CLIQUE, Model Based Method –Statistical Approach, Neural Network approach, Outlier Analysis.

    UNIT-IV

    Data Warehousing: Overview, Definition, Delivery Process, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept hierarchy, Process Architecture, 3 Tier Architecture, Data Marting.

    UNIT-V

    Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse.

    Books:

    • 1. M.H.Dunham,"DataMining:Introductory and Advanced Topics" Pearson Education
    • 2. Jiawei Han, MichelineKamber, "Data Mining Concepts & Techniques" Elsevier
    • 3. Sam Anahory, Dennis Murray, "Data Warehousing in the Real World : A Practical Guide for Building Decision Support Systems, Pearson Education
    • 4. Mallach,"Data Warehousing System",McGraw –Hill

    Advance Operating system:

    UNIT-I

    Multiprocessor Operating Systems: Threads – Process synchronization – Processor scheduling – Memory management – Reliability – Fault tolerance.

    UNIT-II

    Network Operating Systems (Nos): Types of NOS – NOS to LANs – Choosing and NOS – Multiple NOS on a single Network – NOS and Network management – Future Trends.

    UNIT-III

    Distributed Operating Systems: Issues - Communication Primitives – Remote procedure call – Logical clocks – Vector clocks – Distributed mutual exclusion – Non token based algorithms – Token based algorithms – Issues in deadlock detection and resolution – Centralized and distributed deadlock detection algorithms – Election algorithms, Issues in load distributing – Load distributing algorithms – Distributed File System design issues – Mechanisms for building DFS

    UNIT-IV

    Database Operating Systems: Requirements - Concurrency control model – Serializability theory – Distributed database systems – Synchronization primitives – Lock based and timestamp based algorithms – Fully replicated database systems.

    UNIT-V

    Real Time Operating Systems: Architecture of Real Time Systems – Operating Systems Issues – Performance Measures – Estimating Program runtimes – Uniprocessor Scheduling – IRIS Tasks – Task Assignment Mode changes – Fault – tolerant scheduling – Case Study: Design of a Protocol to access one OS to other

    Books:

    • 1. Mukesh Singhal, Niranjan G. Shivaratri, "Advanced Concepts in Operating systems", McGraw-Hill, New York, 1994.(UNIT 1, III & IV)
    • 2. C.M. Krishna, Kang G. Shin, "Real Time Systems", McGraw-Hill.(Unit – V)
    • 3. Philip Hunter, "Network Operating Systems – Making Right Choices", Addison Wesley, 1995. (Unit – II)
    • 4. Andrew S. Tanenbaum, "Modern Operating Systems", Prentice Hall, NJ (Section 9 – 13 only).
    • 5. Pradeep K. Sinha, "Distributed Operating Systems Concepts and Design", PHI, 1997.
    • 6. Gary Nutt, "Operating Systems – A Modern Perspective", Addison Wesley.

    Neural Network

    UNIT-I

    Introduction: Definition of ANN-Biological Neural Networks-Applications of ANN-Typical Architectures-Setting the weights-Common Activation functions-Development of Neural Networks-McCulloch-Pitts Neuron

    UNIT-II

    Simple Neural Nets For Pattern Classification: General discussion - Hebb net – Perceptron- Adaline - Backpropagation neural net- Architecture- Algorithm-Applications.

    UNIT-III

    Pattern Association: Training Algorithm for Pattern Association-Heteroassociative memory neural network-Autoassociative net-Iterative Autoassociative net-Bidirectional Associative Memory.

    UNIT-IV

    Neural Nets Based On Competition: Fixed Weights Competitve Nets- Kohonen's Self-Organizing Map – Learning Vector Quantization-Counter Propagation Network.

    UNIT-V

    Adaptive Resonance Theory And Neocognitron: Motivation – Basic Architecture- Basic Operation-ART1-ART2-Architecture-Algorithm-applications-Analysis- Probablistic Neural Net-Cascade Correlation-Neocognitron: Architecture—Algorithm.

    Books:

    • 1. LaureneFausett, "Fundamentals of Neural Networks-Architectures, Algorithms and Applications", Pearson
      • Education, 2004.
    • 2. James. A. Freeman and David. M. Skapura, "Neural Networks Algorithms, Applications and Programming Techniques " ,Pearson Education , 2002.
    • 3. B.Yegnanarayana, "Artificial Neural Networks",Prentice - Hall, of India, 2001.
    • 4. Simon Haykin, "Neural Networks - A Comprehensive Foundation', Pearson Education – 2001.
    • 5. L.O.Chua , T.Roska, "Cellular Neural Networks and Visual computing- Foundations andApplications", Cambridge University Press, 2002
    • 6. D.J.Mackay, "Information Theory, Inference and Learning Algorithms", Cambridge University Press, 2005.

    Bioinformatics:

    UNIT-I

    Introduction: Definition – Overview- Major databases in Bio Informatics- Molecular biology – Central Dogma- Data retrieval tools – Data mining of Databases – Gene Analysis – Prokaryotic and Eukaryotic Genomes – Sequence Assembly – Gene mapping – Physical maps – cloning – ORF – amino acids – DNA, RNA sequences – Genetic code.

    UNIT-II

    DNA and Protein Sequences: DNA: working with single DNA sequence : removing vector sequences- verifying restriction maps – PCR design – GC content – counting words – internal repeats – protein coding regions – ORFing – Genomescan Protein: predicting properties – primary structure analysis – transmembrane segments – PROSITE patterns – interpreting scanprosite results- finding domains – CD server results – pfscan results.

    UNIT-III

    Alignment of Pair of Sequences: Terminology – Global and Local alignment – Dot matrix – dynamic programming – using scoring matrices – PAM matrices – BLOSUM. Working with FASTA – Algorithm – output – E-values – Histogram. Working with BLAST – algorithm – output – services – gapped BLAST- PSIBLAST – comparison of FASTA and BLAST.

    UNIT-IV

    Multiple Sequence Alignment: Criteria for Multiple sequence alignment – applications – choosing the right sequences; FASTA, ClustalW, TCoffee methods – interpreting multiple sequence alignment – getting in right format – converting formats – using Jalview – preparing for publication.

    UNIT-V

    Protein Classification & Structure Prediction: Structure of amino acids – primary structure – secondary structure – folds and motifs – alpha and beta helix – structure based protein classification – protein structure Data bases – folding problem – PROPSEARCH – primary structure analysis and prediction – secondary structure analysis and prediction – motifs – profiles – patterns and fingerprints

    Books:

    • 1. S.C Rostogi, Mendiratta, P.Rasogi, " BioInformatics: methods and applications", second edition, PHI 2006.
    • 2. Jean Mickel Clavere & Cadrienotredom "Bio Informatics– A beginners guide" Wiley DreamTech, 2003.
    • 3. T.K. Attwood and D.J Perry Smith, "Introduction to Bio Informatics", Pearson Education, 1st Edition, 2001.
    • 4. Dan E.Krane, Michael L.Raymer, "fundamental concepts of BioInformatics ", Pearson Education, 2004.
    04
  • JECS-751

    Distributed Systems Lab:

    • 1. Simulate the functioning of Lamport's Logical Clock in 'C'.
    • 2. Simulate the Distributed Mutual Exclusion in 'C'.
    • 3. Implement a Distributed Chat Server using TCP Sockets in 'C'.
    • 4. Implement RPC mechanism for a file transfer across a network in 'C'
    • 5. Implement 'Java RMI' mechanism for accessing methods of remote systems.
    • 6. Simulate Balanced Sliding Window Protocol in 'C'.
    • 7. Implement CORBA mechanism by using 'C++' program at one end and 'Java' program on the other.
    02
  • JECS-752

    Digital Image Processing Lab:

    Books:

    • 1. Implement the spatial image enhancement functions on a bitmap image –
      • a) Mirroring (Inversion)
      • b) Rotation (Clockwise)
      • c) Enlargement (Double Size)
    • 2. Implement
      • a) Low Pass Filter
      • b) High Pass Filter
    • 3. Implement
      • a) Arithmetic Mean Filter
      • b) Geometric Mean Filter
    • 4. Implement Smoothing and Sharpening of an eight bit color image
    • 5. Implement
      • a) Boundary Extraction Algorithm
      • b) Graham's Scan Algorithm
    • 6. Implement
      • a) Edge Detection
      • b) Line Detection
    02
  • JECS-753

    Industrial Training :
    02
  • JECS-754

    Project Phase I:

    Each student is given a Project which will cover all the aspects ( to the extent possible) like investigation, planning, designing, detailing and estimating of a Computer Science and engineering structure in which the aspects like analysis, application of relevant codes, etc., will find a place. Alternately, a few research problems also may be identified for investigation and the use of laboratory facilities to the fullest extent may be taken as a project work. Alternately, a student is encouraged to take an industrial project with any Computer Science and engineering organization or firm. A project report is to be submitted on the topic which will be evaluated.
    02
  • JGP-701

    General Proficiency
    02
  • Total Credits
     
    30