Abhi PYQ

8th Semester Syllabus

Computer Science & Engineering (AI)

Credits Table

Course Title L T P Credits
Deep Learning [151801] 3 0 0 3
Program Elective -V [1518XX] 3 0 0 3
Open Elective II [1008XX] 3 0 0 3
Open Elective III [1008XX] 3 0 0 3
Project-II [100801P] - - - 6
TOTAL 18

Theory Subjects

Lab Subjects

Deep Learning [151801]

  • Unit 1.0: Introduction
    • Brief Introduction of Big Data Problem
    • Overview of Linear Algebra
    • Probability
    • Numerical Computation
    • Basics of Machine Learning/Feature Engineering
  • Unit 2.0: Basics of Neural Networks
    • Neural Networks
    • Tools for Deep Learning Network - Shallow vs Deep Network
  • Unit 3.0: Feed Forward Networks
    • Multilayer Perceptron
    • Gradient
    • Loss Function
    • Gradient Descent
    • Stochastic Gradient Descent (SDD)
    • Backpropagation Algorithm
    • Empirical Risk Minimization
    • Regularization
    • Gradient Based Learning - Cost Function
    • Learning Rate
    • Soft Max
    • Sigmoid Function
    • Hidden Unit - ReLU
    • Logistic Sigmoid
    • Hyperbolic Tangent Architecture Design
    • Heuristics for Faster Training
  • Unit 4.0: Unsupervised Learning
    • Deep Belief Network
    • Deep Boltzmann Machine (DBM)
    • Factor Analysis
    • Auto-Encoders (Standard, Sparse, Denoising, Contractive, Etc)
    • Variational Auto-Encoders
    • Adversarial Generative Networks
    • Auto-Encoder
    • Regularization Optimization for Training Deep Model
  • Unit 5.0: Advanced Topics
    • Convolutional Neural Network (CNN): Architectures
    • Convolution / Pooling Layers
    • Recurrent Neural Network (RNN)/ Sequence Modeling: Long Short Term Memory Networks (LSTM)
    • GRU
    • Encoder Decoder Architectures Reinforcement Learning
  • Unit 6.0: Practical Applications
    • Application of Deep Learning to Computer Vision
    • Speech Recognition
    • Natural Language Processing
    • Etc

Text/Reference Books:

  1. Ian Goodfellow, Yoshua Bengio and Aaron Courville, "Deep Learning", Book in preparation for MIT Press, 2016.
  2. Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, "The elements of statistical learning", Springer Series in Statistics, 2009.
  3. Charu C Aggarwal, "Neural Networks and Deep Learning", Springer.

Program Elective-V: Reinforcement Learning [151802]

  • Unit 1.0: Basics of Probability & Intelligent Agents
    • Random Variables
    • Probability Mass Function (PMF)
    • Probability Density Function (PDF)
    • Introduction to Agents
    • Intelligent Agents
    • Problem Solving: Searching
    • Logical Agents
    • Overview of Reinforcement Learning: Elements of RL
    • Scope and Limitations
  • Unit 2.0: The Multi-Arm Bandit Problem
    • Multi-Arm Bandit Fundamentals
    • The n-Armed Bandit Problem
    • Exploration vs. Exploitation
    • UCB (Upper Confidence Bound) Algorithm
    • Gradient Bandits
    • Median Elimination
    • Thomson (Thompson) Sampling
  • Unit 3.0: Markov Decision Processes (MDP)
    • Markov Property
    • Markov Chains
    • Markov Reward Process (MRP)
    • Bellman Optimality Equations for MRP
  • Unit 4.0: Dynamic Programming for RL
    • Policy Evaluation
    • Policy Improvement
    • Policy Iteration
    • Value Iteration
    • Asynchronous Dynamic Programming
  • Unit 5.0: Monte Carlo & Temporal Difference Methods
    • Monte Carlo Prediction
    • Estimation of Action Values
    • Control With and Without Exploring Starts
    • Off-Policy Control
    • Temporal Difference Prediction (TD(0))
    • SARSA (On-Policy TD Control)
    • Q-Learning (Off-Policy TD Control)
    • Special Cases: Games
    • Afterstates
  • Unit 6.0: Advanced Topics & Real-World RL
    • Multi-Agent Learning
    • Meta-Learning
    • Partially Observable Markov Decision Processes (POMDP)
    • Ethics in Reinforcement Learning
    • RL in Real-World Applications
    • Case Study: AlphaGo Zero

Text/Reference Books:

  1. "Reinforcement learning: An Introduction", Second Edition, MIT Press, 2019." Richard S. Sutton and Andrew G. Barto.
  2. "An Introduction to Multi Agent Systems", Michael Wooldridge
  3. "Foundations of Deep Reinforcement Learning: Theory and Practice in Python", Keng, Wah Loon, Graesser, Laura, "Addison Wesley Data & Analytics Series"

Program Elective-V: Information Retrieval and Web Search [151803]

  • Unit 1.0: Introduction to Information Retrieval (IR)
    • Goals and History of IR
    • The Impact of the Web on IR
    • Overview of Search Engines and Retrieval Systems
    • Boolean Retrieval Model
  • Unit 2.0: Vector-Space Retrieval Model
    • Text-Similarity Metrics
    • TF-IDF (Term Frequency-Inverse Document Frequency) Weighting
    • Cosine Similarity
    • Ranked Retrieval Concepts
  • Unit 3.0: Tokenization and Indexing
    • Basic Tokenizing
    • Stop-Word Removal
    • Stemming
    • Inverted Index Structure
    • Efficient Processing of Sparse Vectors
    • Implementation Aspects (e.g., Java)
  • Unit 4.0: Evaluation of IR Systems
    • Recall
    • Precision
    • F-Measure
    • NDCG (Normalized Discounted Cumulative Gain)
    • Experimental Evaluation on Benchmark Text Collections
  • Unit 5.0: Text Representation, Classification & Clustering
    • Text Classification Problem
    • Naive Bayes Classification
    • K-Nearest Neighbours
    • Support Vector Machines (SVM)
    • Feature Selection
    • Text Clustering Principles
    • Word Statistics (Zipf's Law, Morphology)
    • Porter Stemmer
    • Thesauri and Index Term Selection
  • Unit 6.0: Query Operations
    • Relevance Feedback
    • Query Expansion Web Search Internals: Spidering and Metacrawlers
    • Directed Crawling
    • Link Analysis (HITS, PageRank)
    • Shopping Agents
    • Information Extraction and Integration: Semantic Web Basics
    • Ethical Issues in IR: Privacy
    • Fairness
    • Fake News & Disinformation
    • Filter Bubbles
    • Viewpoint Diversity
    • Internet Addiction
    • Extremism Concerns IR Applications: Information Extraction
    • Question Answering
    • Opinion Summarization
    • Social Network

Text/Reference Books:

  1. "Introduction to Information Retrieval"; Christopher D. Manning, "Cambridge University Press."
  2. "Search Engines: Information Retrieval"; W, Bruce Croft, D. Metzler, T. Strohman, "Pearson".
  3. "Introduction to Information Retrieval"; Christopher D. Manning Prabhakar Raghavan and Hinrich Schütze, "Cambridge University Press."
  4. "Statistical Language Models for Information Retrieval (Synthesis Lectures Series on Human Language Technologies)"; Cheng Xiang Zhai, "Morgan & Claypool Publishers".

Program Elective-V: Recommender System [151804]

  • Unit 1.0: Introduction to Recommender Systems
    • Overview of Recommendation Systems
    • Importance and Applications
    • Challenges and Considerations in Building Recommender Systems
    • Types of Recommendation Approaches: Collaborative Filtering
    • Content-Based Filtering
    • Hybrid Systems
  • Unit 2.0: User-Based Collaborative Filtering
    • Similarity Measures (Pearson Correlation, Cosine Similarity)
    • Neighborhood-Based Methods (k-Nearest Neighbors)
    • Rating Prediction (User-Item Rating Prediction)
  • Unit 3.0: Item-Based Collaborative Filtering & Matrix Factorization
    • Item-Based Collaborative Filtering
    • Item Similarity Computation
    • Matrix Factorization Methods
    • Singular Value Decomposition (SVD)
    • Alternating Least Squares (ALS)
  • Unit 4.0: Content Representation Techniques
    • Representation of Items and Users
    • Vector Space Model TF-IDF Representation
    • Similarity Computation (Cosine Similarity, Jaccard Similarity, Term Frequency and Inverse Document Frequency (TF-IDF)
  • Unit 5.0: Combining Collaborative and Content-Based Filtering
    • Weighted Approach
    • Cascade Approach
    • Advanced Hybrid Methods: Feature Combination
    • Meta-Level Approach
  • Unit 6.0: Evaluation Metrics for Recommender Systems
    • Accuracy Metrics (RMSE, MAE)
    • Ranking Metrics (Precision, Recall, F1-Score)
    • Optimization Techniques
    • Model Selection
    • Hyperparameter Tuning
    • Cross-Validation
    • Real-World Applications and Case Studies: Netflix Prize Case Study
    • Amazon Recommendation System
    • Spotify Discover Weekly
    • Ethical and Social Implications: Privacy and Security
    • Bias and Fairness
    • Transparency and Explainability

Text/Reference Books:

  1. "Recommender Systems: An Introduction", Dietmar Jannach, Markus, Zanker, Alexander Felfernig, and Gerhard Friedrich; Cambridge University Press.
  2. "Mining of Massive Datasets"; Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman; Cambridge University Press.
  3. "Practical Recommender Systems", Kim Falk; Manning Publications".

Program Elective-V: Generative AI [151805]

  • Unit 1.0: Introduction
    • Discriminative vs. Generative Models
    • Types of Generative Models: Explicit vs. Implicit
    • Likelihood-Based vs. Likelihood-Free
    • Maximum Likelihood Estimation and Variational Inference
    • Applications of Generative AI (Text, Image, Audio, Code)
    • Evaluation Metrics: Perplexity
    • BLEU
    • FID
    • Inception Score
    • Precision/Recall for Generative Models
  • Unit 2.0: Variational Autoencoders
    • Variational Autoencoders (VAE): Encoder-Decoder
    • ELBO
    • Latent Space Representations and Sampling
    • Conditional VAEs
    • Introduction to Flow-Based Models (e.g., RealNVP, Glow Overview Only)
  • Unit 3.0: GANs
    • Basic GAN Framework: Generator
    • Discriminator
    • Adversarial Loss
    • Deep Convolutional GAN (DCGAN)
    • Conditional GANs
    • Pix2Pix
    • CycleGAN
    • StyleGAN (Overview)
    • Challenges: Mode Collapse
    • Vanishing Gradients
    • Training Stability
    • Applications of GANs: Data Augmentation
    • Super-Resolution
    • Image-to-Image Translation
  • Unit 4.0: Language Models
    • Language Models as Generators: Autoregressive Models
    • Transformer Recap
    • GPT Architecture
    • Fine-Tuning
    • And Prompting
    • Sampling Strategies: Greedy
    • Beam Search
    • Top-K
    • Nucleus Sampling
    • Code Generation Models: Codex
    • CodeT5 (Overview and Demos)
  • Unit 5.0: Diffusion Models
    • Motivation: Overcoming GAN Limitations
    • Forward (Noising) and Reverse (De-Noising) Diffusion Processes
    • De-Noising Diffusion Probabilistic Models (DDPMs)
    • Architecture and Training of Diffusion Models
    • Text-to-Image Generation with Stable Diffusion
    • DALLE 2
    • Imagen
    • Tools and Applications: Hugging Face Diffusers
    • Colab Demos
  • Unit 6.0: Ethical Implications
    • Ethical Implications: Deepfakes
    • Fake News
    • Misinformation
    • Detecting AI-Generated Content
    • Bias
    • Fairness
    • And Adversarial Use of Generative Models
    • Policy
    • Regulation
    • And Responsible AI Guidelines (e.g., EU AI Act, UNESCO Principles)
    • Case Studies: ChatGPT
    • Midjourney
    • DALL E

Text/Reference Books:

  1. David Foster, Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play, O'Reilly
  2. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press. Philipp Singer, Diffusion Models from Scratch (online textbook).
  3. Numa Dhamani, Maggie Engler, Introduction to Generative ΑΙ Manning Publications Co., 2024
  4. Joseph Babcock, Raghav Bali, Generative Al with Python and TensorFlow, Packt, 2021.

Open Elective-II: Digital Image Processing [100815]

  • Unit 1.0: Introduction and Fundamentals
    • Introduction: Background
    • Digital Image Representation
    • Fundamental Steps in Image Processing
    • Elements of a Digital Image Processing System
    • Digital Image Fundamentals: Elements of Visual Perception
    • A Simple Image Model
    • Sampling and Quantization
    • Some Basic Relationships Between Pixels
    • Imagining Geometry
  • Unit 2.0: Image Transforms
    • Image Transforms: Introduction to the Fourier Transform
    • The Discrete Fourier Transform
    • Some Properties of the Two-Dimensional Fourier Transform
    • Other Separable Image Transforms
  • Unit 3.0: Image Enhancement (Part 1 - Spatial Domain)
    • Image Enhancement: Spatial Domain Methods
    • Some Simple Intensity Transformations
    • Histogram Processing
    • Image Subtraction
    • Image Averaging
    • Background
    • Smoothing Filters
    • Sharpening Filters
  • Unit 4.0: Image Enhancement (Part 2 - Frequency Domain)
    • Image Enhancement: Frequency Domain Methods
    • Lowpass Filtering
    • Highpass Filtering
    • Generation of Spatial Masks from Frequency Domain Specifications
  • Unit 5.0: Image Restoration
    • Image Restoring: Degradations Model Definitions
    • Degradation Model for Continuous Functions
    • Diagonalization of Circulant and Block-Circulant Matrices
    • Circulant Matrices
    • Block Circulant Matrices
    • Effects of Diagonalization on the Degradation Model
    • Algebraic Approach to Restoration
    • Unconstrained Restoration
    • Constrained Restoration
    • Inverse Filtering- Formulation
    • Removal of Blur Caused by Uniform Linear Motion
    • Restoration in the Spatial Domain
    • Geometric Transformation
  • Unit 6.0: Image Compression
    • Image Compression: Fundamentals Coding Redundancy
    • Interpixel Redundancy
    • Psychovisual Redundancy
    • Fidelity Criteria
    • Image Compression Models Encoder and Decoder
    • The Channel Encoder and Decoder
    • Elements of Information Theory - Measuring Information
    • The Information Channel
    • Fundamental Coding Theorems
    • Using Information Theory
    • Error-Free Compression- Variable-Length Coding
    • Bit-Plane Coding
    • Lossless Predictive Coding
    • Lossy Compression Lossy Predictive Coding
    • Transform Coding

Text/Reference Books:

  1. Rafael. C. Gonzalez & Richard E.Woods.- Digital Image Processing, 2/e Pearson Education, New Delhi - 2006.
  2. W.K.Pratt.-Digital Image Processing, 3/e Edn., John Wiley & sons, Inc. 2006.
  3. M. Sonka et.al Image Processing, Analysis and Machine Vision, 2/e Thomson, Learning, India Edition, 2007.

Open Elective-II: Control Systems [100821]

  • Unit 1.0: Introduction to Control Problem
    • Industrial Control Examples
    • Mathematical Models of Physical Systems
    • Control Hardware and Their Models
    • Transfer Function Models of Linear Time-Invariant Systems
    • Feedback Control: Open-Loop and Closed-Loop Systems
    • Benefits of Feedback
    • Block Diagram Algebra
    • Signal Flow Graph
  • Unit 2.0: Time Response Analysis
    • Standard Test Signals
    • Time Response of First and Second Order Systems for Standard Test Inputs
    • Application of Initial and Final Value Theorem
    • Design Specifications for Second-Order Systems Based on the Time-Response
    • Concept of Stability
    • Routh-Hurwitz Criteria
    • Relative Stability Analysis
    • Root-Locus Technique
    • Construction of Root-Loci
  • Unit 3.0: Frequency-Response Analysis
    • Relationship Between Time and Frequency Response
    • Polar Plots
    • Bode Plots
    • Nyquist Stability Criterion
    • Relative Stability Using Nyquist Criterion Gain and Phase Margin
    • Closed-Loop Frequency Response
  • Unit 4.0: Introduction to Controller Design
    • Stability
    • Steady-State Accuracy
    • Transient Accuracy
    • Disturbance Rejection
    • Insensitivity and Robustness of Control Systems
    • Root-Loci Method of Feedback Controller Design
    • Design Specifications in Frequency-Domain
    • Frequency-Domain Methods of Design
    • Application of Proportional
    • Integral and Derivative Controllers
    • Lead and Lag Compensation in Designs
    • Analog and Digital Implementation of Controllers
  • Unit 5.0: State Variable Analysis
    • Concepts of State Variables
    • State Space Model
    • Diagonalization of State Matrix
    • Solution of State Equations
    • Eigenvalues and Stability Analysis
    • Concept of Controllability and Observability
    • Pole-Placement by State Feedback
    • Discrete-Time Systems
    • Difference Equations
    • State-Space Models of Linear Discrete-Time Systems
    • Stability of Linear Discrete-Time Systems
  • Unit 6.0: Introduction to Optimal Control and Nonlinear Control
    • Performance Indices
    • Regulator Problem
    • Tracking Problem
    • Nonlinear System-Basic Concepts and Analysis

Text/Reference Books:

  1. M. Gopal, "Control Systems: Principles and Design", McGraw Hill Education, 1997.
  2. B. C. Kuo, "Automatic Control System", Prentice Hall, 1995.
  3. K.Ogata, "Modern Control Engineering", Prentice Hall, 1991.
  4. I. J. Nagrath and M. Gopal, "Control Systems Engineering", New Age International, 2009.

Open Elective-II: Simulation and Modelling [100856]

  • Unit 1.0: Introduction
    • System
    • Environment
    • Input and Output Variables
    • State Variables
    • Static and Dynamic Systems
    • Hierarchy of Knowledge About a System and Modelling Strategy
  • Unit 2.0: Physical Modelling
    • Dimensions Analysis
    • Dimensionless Grouping of Input and Output Variables of Find Empirical Relations
    • Similarity Criteria and Their Application to Physical Models
  • Unit 3.0: Modeling of System with Known Structure
    • Review of Conservation Laws and the Governing Equation for Heat
    • Mass and Momentum Transfer
    • Deterministic Model-(A) Distributed Parameter Models in Terms of Partial Identification and Their Solutions and (B) Lumped Parameter Models in Terms of Differential and Difference Equations
    • State Space Model
    • Transfer Functions Block Diagram and Sub Systems
    • Stability of Transfer Functions
    • Modelling for Control
  • Unit 4.0: Optimizations and Design of Systems
    • Summary of Gradient Based Techniques: Nontraditional Optimizations Techniques (1) Genetic Algorithm (GA)- Coding
    • GA Operations Elitism
    • Application Using MATLAB:(Ii) Simulated Annealing
  • Unit 5.0: Neural Network Modelling of Systems Only with Input-Output Database
    • Neurons
    • Architecture of Neural Networks
    • Knowledge Representation
    • Learning Algorithm
    • Multilayer Feed Forward Network and Its Back Propagation Learning Algorithm
    • Application to Complex Engineering Systems and Strategy for Optimum Output
  • Unit 6.0: Modelling Based on Expert Knowledge
    • Fuzzy Sets
    • Membership Functions
    • Fuzzy Inference Systems
    • Expert Knowledge and Fuzzy Models
    • Design of Fuzzy Controllers
    • Simulation of Engineering Systems
    • Monte-Carlo Simulation
    • Simulation of Continuous and Discrete Processes with Suitable Examples from Engineering Problems

Text/Reference Books:

  1. Theory of modeling and simulation, Zeigler B.P. Praehofer. H.and Kim I.G.
  2. System Simulation: the Art and Science, Shannon, R. E.
  3. Modern control Engineering, Ogata K Neuro-Fuzzy and soft Computing, Jang J.S.R. sun C.T and Mizutani E.

Open Elective-II: Microprocessor [100842]

  • Unit 1.0: Fundamentals of Microprocessors
    • Fundamentals of Microprocessor Architecture
    • 8-BitMicroprocessor and Microcontroller Architecture
    • Comparison of 8-Bit Microcontrollers
    • 16-Bit and 32-Bit Microcontrollers
    • Definition of Embedded System and Its Characteristics
    • Role of Microcontroller sin Embedded Systems
    • Overview of the 8051 Family
  • Unit 2.0: The 8051 Architecture
    • Internal BlockDiagram
    • CPU
    • ALU
    • Address
    • Data and Control Bus
    • Working Registers
    • SFRs
    • Clock and RESET Circuits
    • Stack and Stack Pointer
    • Program Counter
    • I/O Ports
    • Memory Structures
    • Data and Program Memory
    • Timing Diagrams and Execution Cycles
  • Unit 3.0: Instruction Set and Programming
    • Addressing Modes: Introduction
    • Instruction Syntax
    • Data Types
    • Subroutines Immediate Addressing
    • Register Addressing
    • Direct Addressing
    • Indirect Addressing
    • Relative Addressing
    • Indexed Addressing
    • Bit Inherent Addressing
    • Bit Direct Addressing
    • 8051 Instruction Set
    • Instruction Timings
    • Data Transfer Instructions
    • Arithmetic Instructions
    • Logical Instructions
    • Branch Instructions
    • Subroutine Instructions
    • Bit Manipulation Instruction
    • Assembly Language Programs
    • C Language Programs
    • Assemblers and Compilers
    • Programming and Debugging Tools
  • Unit 4.0: Memory and I/O Interfacing
    • Memory and I/O Expansion Buses
    • Control Signals
    • Memory Wait States
    • Interfacing of Peripheral Devices Such as General Purpose I/O
    • ADC
    • DAC
    • Timers
    • Counters
    • Memory Devices
  • Unit 5.0: External Communication Interface
    • Synchronous and Asynchronous Communication
    • RS232
    • SPI
    • I2C
    • Introduction and Interfacing to Protocols Like Blue-Tooth and Zig-Bee
  • Unit 6.0: Applications
    • LED
    • LCD and Keyboard Interfacing
    • Stepper Motor Interfacing
    • DC Motor Interfacing
    • Sensor Interfacing

Text/Reference Books:

  1. M. A.Mazidi, J. G. Mazidi and R. D. McKinlay, "The8051 Microcontroller and Embedded Systems: Using Assembly and C", Pearson Education, 2007.
  2. K.J. Ayala, "8051 Microcontroller", Delmar Cengage Learning, 2004.
  3. I R. Kamal, "Embedded System", McGraw HillEducation, 2009.
  4. R.S. Gaonkar,", Microprocessor Architecture: Programming and Applications with the 8085", Penram International Publishing, 1996.
  5. I D.A. Patterson and J.H. Hennessy, "Computer Organization and Design: The Hardware/ D. V. Hall, "Microprocessors & Interfacing", McGraw Hill Higher Education, 1991.

Open Elective-III: Operation Research [100857]

  • Unit 1.0: Introduction
    • Features of Operations Research (OR)
    • Methodology of OR
    • Scopes and Objectives of OR
    • Models in OR
  • Unit 2.0: Inventory and LPP
    • Inventory Classification
    • Different Cost Associated to Inventory
    • Economic Order Quantity
    • Inventory Models with Deterministic Demands
    • ABC Analysis Introduction and Assumptions of LPP
    • Mathematical Formulation of LPP
    • Graphical Method
    • Simplex Method
  • Unit 3.0: Transportation and Assignment Problems
    • Transportation Problems: Introduction
    • North West Corner Method
    • Least Cost Method
    • Vogels Approximation Method
    • Test for Optimality
    • Assignment Problems: Introduction
    • Hungarian Assignment Method
    • Unbalanced Assignment Problems
  • Unit 4.0: Sequencing and Network Analysis
    • Sequencing: Introduction
    • Formulation of Sequencing Problem
    • Johnsons Rule
    • Network Analysis: Introduction
    • PERT and CPM
    • Time-Cost Trade-Off (Project Crashing)
    • Resource Leveling
  • Unit 5.0: Dynamic Programming and Simulation
    • Dynamic Programming: Introduction
    • Deterministic Dynamic Programming
    • Probabilistic Dynamic Programming
    • Simulation: Introduction
    • Monte Carlo Simulation
    • Simulation of Inventory and Queuing System
  • Unit 6.0: Queuing Theory and Replacement Theory
    • Queuing Theory: Introduction
    • General Structure of Queuing System
    • Operating Characteristics of Queuing System
    • Queuing Models
    • Replacement Theory: Introduction
    • Replacement Policies: Gradually Deteriorating Equipments
    • Items That Fail Suddenly

Text/Reference Books:

  1. Operations research- An Introduction, Hamdy A Taha, 8th Edition, Pearson Education.
  2. Introduction to Operations Research, Hillier and Lieberman, 8th Edition, TMH.
  3. Operations Research, R Panneerselvan, 2nd Edition, PHI.
  4. Quantitative Techniques in Management, N D Vohra, 4th Edition, McGraw Hill.

Open Elective-III: Analog Communication [100858]

  • Unit 1.0: Introduction to the Communication System
    • Block Diagram of Communication System and Comparative Study of Analog and Digital Communication
  • Unit 2.0: Modulation
    • Modulation (Upward Frequency Translation) and Demodulation (Downward Frequency Translation) and the Need for Modulation
    • Broad Classification of Modulation [Linear (Amplitude-AM) and Exponential (Frequency-FM and Phase-PM)
  • Unit 3.0: Generation and Demodulation
    • Generation of Double Side Band (DSB) with Carrier
    • Double Side Band with Suppressed Carrier (DSB-SC) and Single Side Band with Suppressed Carrier
    • De-Modulation of Double Side Band with Carrier -Incoherent Detector or Envelope Detector
    • Peak Diode Detector
    • Coherent or Synchronous Detection of DSBSC and Single Side Band with Suppressed Carrier
  • Unit 4.0: Superhetrodyne Receivers
    • Characteristics
    • Intermediate Frequency and Its Advantages
    • Image Rejection of the Receiver
  • Unit 5.0: FM Signals
    • Generation of FM Signals (Direct and Indirect Methods) and Demodulation
  • Unit 6.0: Noise and Analog Pulse Modulation
    • Noise: Different Types of Noise
    • SNR in AM
    • FM and PM System and Use of Emphasis Circuit in FM for SNR Optimization
    • Analog Pulse Modulation: PAM
    • PWM
    • PPM and Demodulation
    • Comparative Study of Various Analog Pulse Modulation

Text/Reference Books:

  1. "Electronic Communication system", by Kennedy. TMH.
  2. "Communication system", by Haykin, Wiley.
  3. "Communication system", by Bruce carison. TMH.
  4. "Modern Digital And Analog Communication", B.P.LATHI Oxford.

Open Elective-III: Satellite Communication [100819]

  • Unit 1.0: Introduction to Satellite Communication
    • Principles and Architecture of Satellite Communication
    • Brief History of Satellite Systems
    • Advantages
    • Disadvantages
    • Applications and Frequency Bands Used for Satellite Communication
  • Unit 2.0: Orbital Mechanics
    • Orbital Equations
    • Kepler's Laws
    • Apogee and Perigee for an Elliptical Orbit
    • Evaluation of Velocity
    • Orbital Period
    • Angular Velocity Etc. of a Satellite
    • Concepts of Solar Day and Sidereal Day
  • Unit 3.0: Satellite Sub-Systems
    • Study of Architecture and Roles of Various Sub Systems of a Satellite System Such as Telemetry
    • Tracking
    • Command and Monitoring (TTC and M)
    • Attitude and Orbit Control System (AOCS)
    • Communication Sub-System
  • Unit 4.0: Typical Phenomena in Satellite Communication
    • Solar Eclipse on Satellite
    • Its Effects
    • Remedies for Eclipse
    • Sun Transit Outage Phenomena
    • Its Effects and Remedies
    • Doppler Frequency Shift Phenomena and Expression for Doppler Shift
  • Unit 5.0: Received Signal Power
    • Flux Density and Received Signal Power Equations
    • Calculation of System Noise Temperature for Satellite Receiver
  • Unit 6.0: Noise Power Calculation
    • Noise Power Calculation
    • C/N Ratio Calculations in Clear Air and Rainy Conditions

Text/Reference Books:

  1. Timothy Pratt Charles Bostian, Jeremy E. Allnutt: Satellite Communications; Wiley.
  2. Tri T. Ha: Digital Satellite Communications: Tata McGraw Hill.
  3. Dennis Roddy: Satellite Communication: 4th Edition, McGraw Hill.

Project-II [100801P]

Details for Project-II will be provided by the department

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