B. Tech Fourth Year Computer Science Subjects and Syllabus
Syllabus of B Tech in Computer Science Fourth year contains mostly the elective subjects. Which is not much important for gate perspective as well as Interview Preparation. And Because of All Universities are following AICTE curriculum So each have similar syllabus.
Some of Fourth year Elective Subjects are Distributed System, Artificial Intelligence, Embedded Systems, Application Of Soft Computing, Cloud Computing etc.
List of Third Year Computer Science Subjects
|Application of Soft Computing||
|Agile Software Development||
Syllabus of Third Year Computer Science
Unit/Module 1 : Introduction and Characterization of Distributed Systems
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/Module 2 : Distributed Mutual Exclusion
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/Module 3 : Agreement Protocol
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/Module 4 : Failure Recovery in Distributed Systems
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/Module 5 : Transactions and Concurrency Control
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.
Unit/Module 1 : Introduction to Artificial Intelligence Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence, Applications of Artificial Intelligence, Intelligent Agents, Structure of Intelligent Agents. Computer vision, Natural Language Possessing.
Unit/Module 2 : Introduction to Search
Introduction to Search : Searching for solutions, Uniformed search strategies, Informed search strategies, Local search algorithms and optimistic problems, Adversarial Search, Search for games, Alpha – Beta pruning
Unit/Module 3 : Knowledge Representation & Reasoning
Knowledge Representation & Reasoning: Propositional logic, Theory of first order logic, Inference in First order logic, Forward & Backward chaining, Resolution, Probabilistic reasoning, Utility
theory, Hidden Markov Models (HMM), Bayesian Networks.
Unit/Module 4 : Machine Learning
Supervised and unsupervised learning, Decision trees, Statistical learning models, Learning with complete data – Naive Bayes models, Learning with hidden data – EM algorithm, Reinforcement learning
Unit/Module 5 : Pattern Recognition :Introduction, Design principles of pattern recognition system, Statistical Pattern recognition, Parameter estimation methods – Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA), Classification Techniques – Nearest Neighbor (NN) Rule, Bayes
Classifier, Support Vector Machine (SVM), K – means clustering.
Unit/Module 1 : Introduction to Embedded Systems
Introduction to Embedded Systems – The build process for
embedded systems- Structural units in Embedded processor , selection of processor & memory devices- DMA – Memory
management methods- Timer and Counting devices, Watchdog Timer, Real Time Clock, In circuit emulator, Target Hardware Debugging.
Unit/Module 2 : Embedded Networking
Embedded Networking: Introduction, I/O Device Ports & Buses– Serial Bus communication protocols – RS232 standard – RS422 – RS485 – CAN Bus -Serial Peripheral Interface (SPI) – Inter Integrated Circuits (I2C) –need for device drivers.
Unit/Module 3 : Embedded Firmware Development Environment
Embedded Product Development Life Cycleobjectives, different phases of EDLC, Modelling of EDLC; issues in Hardware-software Co-design, Data Flow Graph, state machine model, Sequential Program Model, concurrent Model, object oriented Model.
Unit/Module 4 : RTOS Based Embedded System Design Introduction to basic concepts of RTOS- Task, process
& threads, interrupt routines in RTOS, Multiprocessing and Multitasking, Preemptive and non preemptive scheduling, Task communication shared memory, message passing-, Inter process
Communication – synchronization between processes-semaphores, Mailbox, pipes, priority inversion, priority inheritance, comparison of Real time Operating systems: Vx Works, чC/OS-II, RT Linux.
Unit/Module 5 : Embedded System Application Development
Design issues and techniques Case Study of Washing Machine- Automotive Application- Smart card System Application.
APPLICATION OF SOFT COMPUTING
Unit/Module 1 : Neural Networks-I (Introduction & Architecture)
Neuron, Nerve structure and synapse, Artificial Neuron and its model, activation functions, Neural network architecture: single layer and multilayer feed forward networks, recurrent networks. Various learning techniques; perception and convergence rule, Auto-associative and hetro-associative memory.
Unit/Module 2 : Neural Networks-II (Back propogation networks)
Architecture: perceptron model, solution, single layer artificial neural network, multilayer perception model; back propogation learning methods, effect of learning rule co-efficient ;back propagation algorithm, factors affecting backpropagation training, applications.
Unit/Module 3 : Fuzzy Logic-I (Introduction)
Basic concepts of fuzzy logic, Fuzzy sets and Crisp sets, Fuzzy set
theory and operations, Properties of fuzzy sets, Fuzzy and Crisp relations, Fuzzy to Crisp
Unit/Module 4 : Fuzzy Logic –II (Fuzzy Membership, Rules)
Membership functions, interference in fuzzy logic,
fuzzy if-then rules, Fuzzy implications and Fuzzy algorithms, Fuzzyfications & Defuzzificataions,
Fuzzy Controller, Industrial applications
Unit/Module 5 : Genetic Algorithm(GA)
Basic concepts, working principle, procedures of GA, flow chart of GA, Genetic representations, (encoding) Initialization and selection, Genetic operators, Mutation, Generational Cycle, applications.
Unit/Module 1 : Introduction to Cloud Computing
Definition of Cloud – Evolution of Cloud Computing – Underlying Principles of Parallel and Distributed Computing – Cloud Characteristics – Elasticity in Cloud – On-demand Provisioning.
Unit/Module 2 : Cloud Enabling Technologies
Service Oriented Architecture – REST and Systems of Systems – Web Services – PublishSubscribe Model – Basics of Virtualization – Types of Virtualization – Implementation Levels of Virtualization – Virtualization Structures – Tools and Mechanisms – Virtualization of CPU – Memory – I/O Devices –Virtualization Support and Disaster Recovery.
Unit/Module 3 : Cloud Architecture, Services AND Storage
CLOUD ARCHITECTURE, SERVICES AND STORAGE
Layered Cloud Architecture Design – NIST Cloud Computing Reference Architecture – Public, Private and Hybrid Clouds – laaS – PaaS – SaaS – Architectural Design Challenges – Cloud Storage – Storage-as-a-Service – Advantages of Cloud Storage – Cloud Storage Providers – S3.
Unit/Module 4 : Resource Management And Security In Cloud
Inter Cloud Resource Management – Resource Provisioning and Resource Provisioning Methods – Global Exchange of Cloud Resources – Security Overview – Cloud Security Challenges –
Software-as-a-Service Security – Security Governance – Virtual Machine Security – IAM – Security Standards.
Unit/Module 5 : Cloud Technologies And Advancement
Hadoop – MapReduce – Virtual Box — Google App Engine – Programming Environment for Google App Engine –– Open Stack – Federation in the Cloud – Four Levels of Federation – Federated Services and Applications – Future of Federation
AGILE SOFTWARE DEVELOPMENT
Unit/Module 1 : Agile Methodology
Theories for Agile Management – Agile Software Development – Traditional Model vs. Agile Model – Classification of Agile Methods – Agile Manifesto and Principles – Agile Project Management – Agile Team Interactions – Ethics in Agile Teams – Agility in Design, Testing – Agile Documentations – Agile Drivers, Capabilities and Values
Unit/Module 2 : Agile Processes
Lean Production – SCRUM, Crystal, Feature Driven Development- Adaptive Software Development – Extreme Programming: Method Overview – Lifecycle – Work Products, Roles and Practices.
Unit/Module 3 : Agility And Knowledge Management
Agile Information Systems – Agile Decision Making – Earl‗S Schools of KM – Institutional Knowledge Evolution Cycle – Development, Acquisition, Refinement, Distribution, Deployment ,
Leveraging – KM in Software Engineering – Managing Software Knowledge – Challenges of Migrating to Agile Methodologies – Agile Knowledge Sharing – Role of Story-Cards – Story-Card Maturity Model (SMM).
Unit/Module 4 : Agility And Requirements Engineering
Impact of Agile Processes in RE–Current Agile Practices – Variance – Overview of RE Using Agile – Managing Unstable Requirements – Requirements Elicitation – Agile Requirements Abstraction Model – Requirements Management in Agile Environment, Agile Requirements Prioritization – Agile Requirements Modeling and Generation – Concurrency in Agile Requirements Generation.
Unit/Module 5 : Agility And Quality Assurance
Agile Product Development – Agile Metrics – Feature Driven Development (FDD) – Financial and Production Metrics in FDD – Agile Approach to Quality Assurance – Test Driven Development –
Agile Approach in Global Software Development.
Unit/Module 1 : Introduction to Machine Learning
Well defined learning problems, Designing a Learning System, Issues in Machine Learning; THE CONCEPT LEARNING TASK – General-to-specific ordering of hypotheses, Find-S, List then eliminate algorithm, Candidate elimination algorithm, Inductive bias
Unit/Module 2 : Decision Tree Learning and Artificial Neural Networks
Decision tree learning algorithm-Inductive bias- Issues in
Decision tree learning;
ARTIFICIAL NEURAL NETWORKS – Perceptrons, Gradient descent and the Delta rule, Adaline, Multilayer networks, Derivation of backpropagation rule Backpropagation AlgorithmConvergence,
Unit/module 3 : Evaluating Hypotheses And Bayesian Learning
Evaluating Hypotheses: Estimating Hypotheses Accuracy, Basics of sampling Theory, Comparing Learning Algorithms; Bayesian Learning: Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm
Unit/Module4 : Computational Learning Theory
Sample Complexity for Finite Hypothesis spaces, Sample
Complexity for Infinite Hypothesis spaces, The Mistake Bound Model of Learning; INSTANCE-BASED LEARNING – k-Nearest Neighbour Learning, Locally Weighted Regression, Radial basis function networks, Case-based learning
Unit/Module 5 : Genetic Algorithms
An illustrative example, Hypothesis space search, Genetic Programming, Models of Evolution and Learning; Learning first order rules-sequential covering algorithmsGeneral to specific beam search-FOIL; REINFORCEMENT LEARNING – The Learning Task, Q
Unit/Module 1 : Digital Image Fundamentals
Steps in Digital Image Processing – Components –
Elements of Visual Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships between pixels – Color image fundamentals – RGB, HSI models, Two-dimensional mathematical preliminaries, 2D transforms – DFT, DCT.
Unit/Module 2 : Image Enhancement
Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering–Smoothing and Sharpening Spatial Filtering, Frequency Domain: Introduction to Fourier Transform– Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian filters, Homomorphic filtering, Color image enhancement.
Unit/Module 3 : Image Restoration
Image Restoration – degradation model, Properties, Noise models – Mean Filters – Order Statistics– Adaptive filters – Band reject Filters – Band pass Filters – Notch Filters – Optimum Notch
Filtering – Inverse Filtering – Wiener filtering.
Unit/Module 4 : Image Segmentation
Edge detection, Edge linking via Hough transform – Thresholding – Region based segmentation – Region growing – Region splitting and merging – Morphological processing- erosion and dilation,
Segmentation by morphological watersheds – basic concepts – Dam construction – Watershed segmentation algorithm.
Unit/Module 5 : Image Compression And Recognition
Need for data compression, Huffman, Run Length Encoding, Shift codes, Arithmetic coding, JPEG standard, MPEG. Boundary representation, Boundary description, Fourier Descriptor, Regional
Descriptors – Topological feature, Texture – Patterns and Pattern classes – Recognition based on
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