Syllabus for Engineering Courses (UG and PG Level)

Syllabus at UG Level
 

  • Mechanical Engineering covers topics such as Chemistry, Mathematics, Electrical Sciences, Modern systems used for Biology, Engineering Mechanics, Thermodynamics, Solid Mechanics, Fluid Mechanics, and Applied Thermodynamics.
  • Electrical Or Electronic Engineering courses focus on topics like Digital Electronic, Electric Circuits, Signals, Power Electronics, Video Engineering, and Robotic and Embedded Systems Design.
  • With Civil Engineering courses, the focus shifts to stability and structure. Topics covered in-depth under this program include Concrete Structures, Soil Mechanics, Design, Hydraulic & Irrigation Structures, Calculus & Solid Geometry, and Foundation Engineering.
  • Students of Computer Science Engineering courses will study subjects, such as Data Structure & Algorithms, Computer Organization, Digital Electronics & Logic Design, Principles of Programming Language, and Database Management System.
  • Students pursuing B Tech in Aeronautical Engineering need to understand the details that go into Aerospace Engineering. The course focuses upon the essentials of Aero-Acoustics, Aero Elasticity, Fluid Mechanics, Arial Mathematics, Astro Dynamics, Component Material Science, Aircraft Design, Principles of Thrust, Aircraft Structures and Noise Control.
  • Chemical Engineering courses include the study of subjects like Process Design and Analysis, Chemical Reaction Engineering, and Plant Design and Construction.
  • Nuclear Engineering students study atomic physics, principles of nuclear physics, and nuclear fission. The course also includes topics such as nuclear safety, effects of radiation, and problems encountered while transporting or storing nuclear materials or waste.
  • Agricultural Engineering students study aspects related to industries like farming and dairy, as well as the forestry department. Students undertake subjects including aquaculture (farming of seafood), forestry, and food processing. Also, the curriculum focuses on how to develop better solutions for animal healthcare and waste disposal.


Syllabus at UG Level

 

CSE101: Algorithms and Algorithmic Complexity

  • Fundamentals of Mathematics: Linear Algebra, Combinatorics, Boolean Functions, Number Theory.
  • Fundamentals of Algorithms: Classification of Problems, Complexity, Asymptotic Notations.
  • Recurrences:  Master Theorem
  • Probabilistic Analysis: Sort, Search,  Random Binary Search trees, Red-black trees, Priority Queues, Bipartite Matching, Common Subsequence Problem, Flow Networks, Ford-Fulkerson Method, Fast Fourier Transforms, Knuth-Morris-Pratt Algorithm, Convex Hull, Point Location.
  • Combinatorial Algorithms: Generating Permutations, Generating Partitions.
  • Approximation Algorithms: Concept, Design, Applications. In approximability. Number -Theoretic Algorithms. Randomized Algorithms, Primality Testing, Constrained and Unconstrained Optimization, Evolutionary Algorithms.

CSE102: Cryptography and Network Security

  • Principles of Security, Basic Cryptographic techniques, Classification of attacks, Virus, Worm, Trojan Horse, Spam etc.
  • Symmetric Key Cryptography: Algorithm types and modes, Cryptographic Algorithms
  • Asymmetric Key Cryptographic Algorithms, Digital Signature
  • Digital Envelope, Message Authentication Code, Message Digest
  • Public-Key Infrastructure (PKI)
  • Authentication: Classifications, Mutual Authentication Algorithms, Kerberos
  • Security in layers and domains: IPsec, Secure Socket Layer (SSL), E-mail Security
  • Electronic transactions

CSE103: Advanced Database Management Systems

  • Distributed Database: Distributed database architecture, levels of distribution transparency, DDB design, Translation of global queries, Query optimisation for DDB, Concurrency control for DDB
  • Object Oriented Database: OO paradigm, OO data models: Object identifiers, Relationship and Integrity, ER Diagramming model for OO relationships, Object relational data models
  • Data Warehousing: Components, Building a data warehouse, Data extraction, cleanup and transformation, OLAP
  • Future Trends in data models: Semantic data models, DM for loosely structured data items, Multimedia database.

CSE104(A): Distributed Systems

  • Introduction: definition, characteristics and challenges of distributed systems, Architectural models (client-server).
  • Time: Physical and logical time, Event ordering, Clock Synchronization, Message delivery order.
  • Inter-process communication (sockets, UDP/TCP), Overview of middleware, Web services, RPC.
  • Operating system support - Mutual exclusion, termination detection, deadlock, process migration, replication management, threads, multithreaded client/server.
  • Distributed file service (design options, file sharing, access control).
  • Distributed transactions (flat/nested, one/two phase commit).
  • Security - main threats and techniques for ensuring security (secure channels, firewalls).
  • Fault-tolerance and availability (passive/active replication, gossip architectures).
  • Applications. Pervasive computing environments: active office, home and city, Events, composite events, mobility and location-tracking, Electronic health, police and transport services.

CSE104(B): Bioinformatics

  • Basic Biology: What is life? The unity and the diversity of living things. Prokaryotes and Eukaryotes, Yeast and People, Evolutionary time and relatedness, Living parts: Tissues, cells, compartments and organelles, the Central dogma of molecular biology, Concept of DNA, RNA, Protein and metabolic pathway.  What is Bioinformatics? Recent challenges in Bioinformatics.
  • Biological databases: Their needs and challenges. The Example of different biological databases – sequence, structure, function, microarray, pathway, etc.
  • Sequence Analysis: Theory and Tools:  -Pairwise alignment – Different local and global search alignment, Heuristic searches (like BLAST) applicable to search against the database, Multiple alignment algorithms, Whole genome comparison.
  • Walk through the genome: Prediction of regulatory motifs, Operon, Gene, splices site, etc. 
  • Markov models: Hidden Markov models – The evaluation, decoding and estimation problem and the algorithms. Application in sequence analysis.
  • Molecular phylogeny: Maximum Parsimony, distance Matrix and maximum likelihood methods. Concepts of adaptive evolution.
  • Application of graph theory in Biology: Biochemical Pathway, Protein-protein interaction network, Regulatory network and their analysis.

CSE104(C): Soft Computing

  • Soft Computing: Introduction, requirement, different tools and techniques, usefulness and applications.
  • Fuzzy Sets and Fuzzy logic: Introduction, Fuzzy sets versus crisp sets, operations on fuzzy sets, Extension principle, Fuzzy relations and relation equations, Fuzzy numbers, Linguistic variables, Fuzzy logic, Linguistic hedges, Applications, fuzzy controllers, fuzzy pattern recognition, fuzzy image processing, fuzzy database.
  • Artificial Neural Network: Introduction, basic models, Hebb's learning, Adaline, Perceptron, Multilayer feed forward network, Back propagation, Different issues regarding the convergence of Multilayer Perceptron, Competitive learning, Self-Organizing Feature Maps, Adaptive Resonance Theory, Associative Memories, Applications.
  • Evolutionary and Stochastic techniques: Genetic Algorithm (GA), different operators of GA, analysis of selection operations, Hypothesis of building blocks, Schema theorem and convergence of Genetic Algorithm, Simulated annealing and Stochastic models, Boltzmann Machine, Applications.
  • Rough Set: Introduction, Imprecise Categories Approximations and Rough Sets, Reduction of Knowledge, Decision Tables, and Applications.
  • Hybrid Systems: Neural-Network-Based Fuzzy Systems, Fuzzy Logic-Based Neural Networks, Genetic Algorithm for Neural Network Design and Learning, Fuzzy Logic and Genetic Algorithm for Optimisation, Applications.

CSE104(D): Multimedia Systems

CSE 105(P) Systems Design Lab. - I:

  • Hardware-oriented Application Lab
  • Software-oriented Application Lab.

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