I Semester Sl. No. Subject Code Subject Credits - … Semmtech.… · I Semester Sl. No. Subject...

I Semester

Sl. No. Subject Code Subject Credits

1 PEC121C Probability and Random Process 4.0

2 PEC122C Advanced Digital Communication 4.0

3 PEC123C Antenna Theory and Design 4.0

4 PEC124C Multimedia Communication 4.0

5 Elective – I 4.0

PEC125E DSP With FPGA

PEC126E Cloud Computing

PEC127E Advanced Computer Networks

6 Elective – II 4.0

PEC128E Optical Networks

PEC129E Image Processing

PEC130E Artificial Intelligence

7 PEC131L Communication Engineering Laboratory - I 2.0

Total 26.0

Course Title: Probability and Random Process Course Code: PEC121C

Credits: 4 Teaching Hours: 52 Hrs (13 Hrs/Unit)

Contact Hours: 4 Hrs/Week

CIE Marks: 50 SEE Marks: 50 Total Marks: 100

Department : Electronics and Communication Engg.

Designation : Core

Course Objectives:

1) Know probability and probabilistic models.

2) Equip the students with basic tools required to build and analyze models both in discrete and

continuous context.

3) Understand the concepts of random processes to apply for analog and digital communication

systems.

4) Understand the processes to apply for various applications such as transportation, traffic problems

and queuing.

Course Outcomes:

A student who successfully completes this course should be able to

1) Solve various probability and discrete distribution problems.

2) Understand the importance of special continuous distributions.

3) Explore the random experiments specified by multiple random variables and distribution of them.

4) Acquire knowledge and concepts of various random processes and their applications.

The topics that enable to meet the above objectives and course outcomes are given below:

Unit I (13 hours) Introduction to Probability Theory: Experiments, sample space, Events, Axioms, Assigning probabilities,

Joint and conditional probabilities, Baye’s Theorem, Independence, Discrete Random Variables, Engg

Example. Random Variables, Distributions, Density Functions: CDF, PDF, Gaussian random variable,

Uniform Exponential, Laplace, Gamma, Erlang, Chi-Square, Raleigh, Rician and Cauchy types of

random variables.

Unit II (13 hours) Operations on a Single R V: Expected value, EV of Random variables, EV of functions of Random

variables, Central Moments, Conditional expected values. Characteristic functions, Probability generating

functions, Moment generating functions, Engg applications, Scalar quantization, entropy and source

coding. Pairs of Random variables, Joint CDF, joint PDF, Joint probability mass functions, Conditional

Distribution, density and mass functions, EV involving pairs of Random variables, Independent Random

variables, Complex Random variables, Engg Application.

Unit III (13 hours)

Multiple Random Variables: Joint and conditional PMF, CDF, PDF,.EV involving multiple Random

variables, Gaussian Random variable in multiple dimension, Engg application, linear prediction.

Unit IV (13 hours)

Random Process: Definition and characterization, Mathematical tools for studying Random Processes,

Stationary and Ergodic Random processes, Properties of ACF. Example Processes: Markov processes,

Gaussian Processes, Poisson Processes, Engg application, Computer networks, Telephone networks.

Reference Books

1) S L Miller and D C Childers, Probability and random processes: application to Signal

processing and communication, Academic Press / Elsivier 2004.

2) Papoullis and S U Pillai, Probability, Random variables and stochastic processes, McGraw Hill

2002.

3) Peyton Z Peebles, Probability, Random variables and Random signal principles, TMH 4th

Edition 2007.

4) H Stark and Woods, Probability, random processes and applications, PHI 2001.

Course Title: Advanced Digital Communication Course Code: PEC122C





Designation : Core

Prerequisites : Digital Communication

Course Objectives:

1) Equip the students with basic mathematical tools required to analyze digital communication

systems.

2) Know different digital modulation techniques in-detail.

3) Know the problems and their solutions associated with digital data communication.

4) Understand the methods and techniques of secure data communication.

Course Outcomes:


1) Decide the required digital modulation / demodulation technique for a given communication

problem.

2) Understand the importance of sampling frequency, signal to noise ratio and channel bandwidth, in

improving performance of digital communication systems.

3) Appreciate the amazing power of mathematics (Especially groups, rings and fields) in secure

communication.

4) Understand the current trends in digital communication.


Unit I (13 hours) Digital band-pass modulation techniques, Phase Shift Keying, Frequency Shift Keying, Amplitude

Shift Keying, Detection of signals in Gaussian noise: Decision region, correlative receiver and binary

decision threshold, Coherent detection of Binary Phase Shift Keying, signal space representation and

computation of error probability, Coherent detection of Frequency Shift Keying, signal space diagram

and computation of error probability, Non-coherent detection of binary Differential Shift Keying

(DPSK), Non-coherent detection of Frequency Shift Keying, Quadrature Phase Shift Keying (QPSK).

Unit II (13 hours) Communication through band-limited linear filter channels, optimum receiver for channels with Inter

Symbol Interference (ISI) and white Gaussian Noise, optimum maximum- likelihood receiver, discrete-

time model for channel with ISI, Linear equalization: peak distortion criterion and Mean Square Error

criterion.

Unit III (13 hours)

Adaptive Equalization: Adaptive linear equalizer, adaptive decision feedback equalizer, adaptive

equalization of Trellis- coded signals, Recursive least squares algorithms for adaptive

equalization, self recovering (blind) equalization. Spread Spectrum Signals for Digital Communication:

Model of Spread Spectrum Digital Communication System, Direct Sequence Spread Spectrum Signals,

Frequency-Hopped Spread Spectrum Signals, CDMA, time-hopping SS, Synchronization of SS systems.

Unit IV (13 hours)

Digital Communication Through Fading Multi-Path Channels: Characterization of fading multi- path

channels, the effect of signal characteristics on the choice of a channel model, frequency- non-selective

slowly fading channel, diversity techniques for fading multi- path channels, Digital signal over a

frequency-selective slowly fading channel, coded waveforms for fading channels, multiple antenna

systems.

Reference Books

1) B. Sklar, Digital Communications, McGraw-Hill publications, II Edition, 1996.

2) Simon Haykin, Communication Systems, Wiley Eastern Publication, III Edition, 2001.

3) John G. Proakis, Digital Communications, McGraw-Hill publications, IV Edition, 2000.

Course Title: Antenna Theory and Design Course Code: PEC123C





Designation : Core

Course Objectives:

1) To introduce the fundamentals of antenna and array theory.

2) To understand standard antenna performance parameters such as gain, beamwidth, impedance,

bandwidth and polarization.

3) To provide the electromagnetic knowledge

4) To determine the radiation pattern of standard antenna and array structures.

Course Outcomes:


1) Distinguish the properties and parameters of antenna such as radiation pattern, radiation

impedance, directivity, antenna gain, effective area.

2) Design an antenna system, including the shape of the antenna, feed property, the requirement on

the arrangement of the radiating elements in an array, given the radiation parameters such as

radiation pattern, gain, operating frequency, transmit/receive power.

3) Identify the mechanism of the atmospheric effects on radio wave propagation.

4) Manage the fundamental properties of antennas (gain, input impedance, bandwidth, directivity,

mutual coupling etc) in order to construct a wireless communication link.


Unit I (13 hours) Antenna Fundamentals and Definitions: Radiation mechanism - overview, Electromagnetic Fundamentals,

Solution of Maxwell’s Equations for Radiation Problems, Ideal Dipole, Radiation Patterns, Directivity and

Gain, Antenna Impedance, Radiation Efficiency. Antenna Polarization. Resonant Antennas: Wires and

Patches, Dipole antennas, Yagi - Uda Antennas, Micro strip Antenna.

Unit II (13 hours) Arrays: Array factor for linear arrays, uniformly excited, equally spaced Linear arrays, pattern

multiplication, directivity of linear arrays, non- uniformly excited-equally spaced linear arrays, Mutual

coupling, multidimensional arrays, phased arrays, feeding techniques, perspective on arrays.

Unit III (13 hours)

Aperture Antennas: Techniques for evaluating Gain, reflector antennas - Parabolic reflector antenna

principles, Axi -symmetric parabolic reflector antenna, offset parabolic reflectors, dual reflector antennas,

Gain calculations for reflector antennas, feed antennas for reflectors, field representations, feed antennas

used in practice. Broad band Antennas: Traveling - wave antennas, Helical antennas, Biconical antennas,

sleave antennas, and Principles of frequency - independent Antennas, spiral antennas, and Log - Periodic

Antennas.

Unit IV (13 hours)

Antenna Synthesis: Formulation of the synthesis problem, synthesis principles, line sources shaped beam

synthesis, linear array shaped beam synthesis — Fourier Series, Woodward — Lawson sampling method,

comparison of shaped beam synthesis methods, low side lobe narrow main beam synthesis methods Dolph

Chebyshev linear array, Taylor line source method. Method of Moments: Introduction to method of Moments,

Pocklington’s integral equation, integral equations and Kirchoff’s Networking Equations

Reference Books

1) Stutzman and Thiele, Antenna Theory and Design, 2nd Ed., John Wiley and Sons Inc.

2) C. A. Balanis, Antenna Theory Analysis and Design, John Wiley, 2nd Edition, 1997

3) Kraus, Antennas, McGraw Hill, TMH, 3rd Edition, 2003

4) Kraus and R.J. Marhefka, Antennas, McGraw Hil1, 2nd Edition, 1998

5) K. D. Prasad, Antenna & Wave Propogation, Satya Prakshan New Delhi, 1995.

Course Title: Multimedia Communication Course Code: PEC124C





Designation : Core

Course Objectives:

The objective of the course is to introduce the students

1) With basic concepts of multimedia data, its representation, storage and communication.

2) The needs of data compression and different data compression techniques.

3) With concepts of basic video and audio compression techniques and standards.

4) With how multimedia information can be communicated across the networks.

Course Outcomes:


1) Explain the main principles underlying the field of multimedia networking, and recognize the

wider context of networked multimedia.

2) Explain the different coding techniques and significance of compression in multimedia networks.

3) Realize the significance of motion estimation in video compression

4) Perform comparisons and evaluate performance of various issues in multimedia networking (like

multimedia networking applications and protocols), and conclude with and/or propose possible

solutions/enhancements.


Unit I (13 hours) Multimedia Communications: Introduction, multimedia information representation, multimedia networks,

multimedia applications, applications and networking terminology. Multimedia Information

Representation: Digitization principles, text, images, audio, and video.

Unit II (13 hours) Text and Image Compression techniques: Introduction, compression principles, text compression, image

compression.

Unit III (13 hours)

Audio and Video Compression: Introduction, audio compression, video compression. Standards of

Multimedia Communication: Introduction, reference models, standard relating to interpersonal

communications, standards relating to interactive applications over Internet.

Unit IV (13 hours)

Multimedia Network Communications and Applications: Quality of Multimedia data transmission,

Multimedia over IP, Multimedia over ATM networks. Information Network Design and Simulation:

Introduction, design procedure, business needs and requirements, design aids, modeling and simulating

networks, simulation, simulation languages and packages.

Reference Books

1) Fred Halsall, Multimedia Communications: Applications, Networks, Pearson Education Edition, 2001.

2) Ze-Nian Li, Mark S Drew, Fundamentals of Multimedia, Prentice Hall, 2004. 3) Nelin K Sharda, Multimedia Information Networking, Prentice Hall, 1999

,

Course Title: DSP with FPGA Course Code: PEC125E





Designation : Elective

Course Objectives:

1) To learn how to implement DSP algorithms using FPGA specifically by writing VHDL codes.

2) To implement the signal processing algorithms such as various forms of transforms, IIR and FIR

filters on FPGAs.

3) To predict the performance (speed, size, and power) of a implemented design.

4) To learn different Number systems/arithmetic concepts Suitable for implementation on FPGA

5) To learn and compare pipeline strategies for FIR and IIR filters.

Course Outcomes:


1) Understand the working of FPGA

2) Design and implement the various DSP algorithms on FPGA, such as DSP transforms, IIR and

FIR filters

3) Compare the DSP transforms, FIR and IIR filters on the basis of performance

4) Use different number system suitable for implementation on FPGA.


Unit I (13 hours)

Introduction: Overview of Digital Signal Processing (DSP), FPGA Technology, Classification by

Granularity, Classification by Technology, Benchmark for FPLs, DSP Technology Requirements, FPGA

and Programmable Signal Processors, Design Implementation, FPGA Structure, The Altera

EP2C35F672C6, Computer Arithmetic: Binary Adders: Pipelined Adders, Modulo Adders. Binary Multipliers:

Multiplier Blocks.

Unit II (13 hours)

Multiply-Accumulator (MAC) and Sum of Product (SOP): Distributed Arithmetic Fundamentals, Signed

DA Systems, Modified DA Solutions. Fourier Transforms: The Discrete Fourier Transform Algorithms,

Fourier Transform Approximations Using the DFT, Properties of the DFT, The Goertzel Algorithm, The

Bluestein Chirp-z Transform, The Rader Algorithm, The Winograd DFT Algorithm. The Fast Fourier

Transform (FFT) Algorithms: The Cooley–Tukey FFT Algorithm, The Good–Thomas FFT Algorithm,

The Winograd FFT Algorithm, Comparison of DFT and FFT Algorithms

Unit III (13 hours)

Infinite Impulse Response (IIR) Digital Filters: IIR Theory, IIR Coefficient Computation, Summary of

Important IIR Design Attributes, IIR Filter Implementation, Finite Wordlength Effects. Optimization of

the Filter Gain Factor, Fast IIR Filter : Time-domain Interleaving, Clustered and Scattered Look-Ahead

Pipelining, IIR Decimator Design, Parallel Processing, IIR Design Using RNS.

Unit IV (13 hours)

Finite Impulse Response (FIR) Digital Filters: Digital Filters, FIR Theory3.2.1 FIR Filter with Transposed

Structure, Symmetry in FIR Filters, Linear-phase FIR Filters, Designing FIR Filters, Direct Window

Design Method, Equiripple Design Method.

Constant Coefficient FIR Design : Direct FIR Design, FIR Filter with Transposed Structure, FIR Filters

Using Distributed Arithmetic, Comparison of DA- and RAG-Based FIR Filters.

Reference Books

1) Uwe Meyer-Baese, “Digital Signal Processing with Field Programmable Gate Arrays”, 3rd

Edition, Springer Publications, 2007

2) Roger Woods, John McAllister, Gaye Lightbody, Ying Yi, “FPGA-based Implementation of

Signal Processing Systems”, A John Wiley and Sons, Ltd., Publication

3) Volnei A. Pedroni, “Circuit Design and Simulation with VHDL”, 2nd Edition, PHI publication.

4) Proakis & Monalakis, “Digital signal processing – Principles Algorithms & Applications”, PHI,

3rd Edition, New Delhi, 1997.

Course Title: Cloud Computing Course Code: PEC126E






Course Objectives:

1) To introduce the broad perceptive of cloud computing concepts, models and enabling technology.

2) To be familiar with cloud computing mechanisms with respect to infrastructure, management and

security.

3) To understand various cloud computing architectures, service quality metrics and SLAs .

4) To understand the basics of Internet of Things (IoT) and application areas where IoT can be

applied.

Course Outcomes:


1) Explain the strengths and limitations of cloud computing.

2) Identify the architecture, infrastructure and delivery models of cloud computing.

3) Understand the concept of network virtualization, cloud management and security.

4) Understand the basics of IoT and its applications.


Unit I (13 hours)

Fundamentals of Cloud Computing: Understanding cloud computing, fundamentals concepts and models,

cloud-enabling technology, fundamental cloud security.

Unit II (13 hours)

Cloud Computing Mechanisms: Cloud infrastructure mechanisms, specialized cloud mechanisms, cloud

management mechanisms, cloud security mechanisms.

Unit III (13 hours)

Cloud Computing Architectures: Fundamental cloud architectures, advanced cloud architectures,

specialized cloud architectures.

Unit IV (13 hours)

Working with Clouds: Cloud delivery model considerations, cost metrics and pricing models, service

quality metrics and SLAs. Internet of Things (IoT): Introduction, characteristics, architecture,

applications.

Reference Books

1) Thomas Erl, Zaigham Mahmood, Ricardo Puttini, “Cloud Computing: Concepts, Technology and

Architecture”, Pearson education limited, 2013.

2) Olivier Hersent, David Boswarthick, Omar Elloumi , “The Internet of Things – Key applications

and Protocols”, Wiley, 2012.

3) Kai Hwang, Jack Dongarra & Geoffrey C. Fox, “Distributed and Cloud Computing: Clusters,

Grids, Clouds, and the Future Internet”.

4) Rajkumar Buyya, Christian Vecchiola, S.Thamarai Selvi, “Mastering Cloud Computing”, TMGH,

2013.

5) Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From Parallel

Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.

6) Olivier Hersent, Omar Elloumi and David Boswarthick, “The Internet of Things: Applications to

the Smart Grid and Building Automation”, Wiley, 2012.

Course Title: Advanced Computer Networks Course Code: PEC127E






Prerequisites : Computer Networks

Course Objectives:

1) To become familiar with the Networking Protocols and LAN

2) To gain the knowledge of Transport and Congestion Control

3) To understand the knowledge of Wireless Networks and Mobile IP.

4) To get familiar with the new networking technologies of Ad hoc and Mesh Networks and also to

explore open research issues

Course Outcomes:


1) Classify network services, protocols and architectures, understand the concepts of link layer and

LANs

2) Understand logical addressing, Routing Algorithms, different addressing schemes, IPv4 and IPv6

3) Distinguish the services of TCP and UDP and also understand the mechanism to avoid congestion

4) Understand the infrastructure and infrastructure less wireless networks and Mobile IP


Unit I (13 hours)

Foundation of Networking Protocols: 5-layer TCP/IP Model, 7-Layer OSI Model, Internet Protocols and

Addressing, Equal-Sized Packets Model: ATM - Networking Devices: Multiplexers, Modems and Internet

Access Devices, Switching and Routing Devices, Router Structure. The Link Layer and Local Area

Networks: Link Layer: Introduction and Services, Error-Detection and Error-Correction techniques,

Multiple Access Protocols, Link Layer Addressing, Ethernet, Interconnections: Hubs and Switches, PPP:

The Point-to-Point Protocol, Link Virtualization.

Unit II (13 hours)

Routing and Internetworking: Network–Layer Routing, Least-Cost-Path algorithms, Non-Least-Cost-Path

algorithms, Intra-domain Routing Protocols, Inter-domain Routing Protocols, Congestion Control at

Network Layer Logical Addressing: IPv4 Addresses, IPv6 Addresses-Internet Protocol:

Internetworking,IPv4, IPv6, Transition from IPv4 to IPv6 – Multicasting Techniques and Protocols: Basic

Definitions and Techniques, Intra-domain Multicast Protocols, Inter-domain Multicast Protocols, Node-

Level Multicast algorithms.

Unit III (13 hours)

Transport and End-to-End Protocols: Transport Layer, Transmission Control Protocol (TCP), User

Datagram Protocol (UDP), Mobile Transport Protocols, TCP Congestion Control – Application Layer:

Principles of Network Applications, The Web and HTTP, File Transfer: FTP, Electronic Mail in the

Internet, Domain Name System(DNS), P2P File Sharing, Socket Programming with TCP and UDP.

Congestion Control and Resource Allocation: Congestion-Avoidance Mechanisms, DEC bit, Random

Early Detection (RED), Source-Based Congestion Avoidance.

Unit IV (13 hours)

Wireless Networks and Mobile IP: Infrastructure of Wireless Networks, Wireless LAN Technologies, IEEE

802.11 Wireless Standards, Cellular Networks, Mobile IP, Wireless Mesh Networks (WMNs), Wireless Mesh

Networks: Introduction- Network Architecture, Characteristics, Application Scenarios, Critical Design Factors.

Mobile Ad-Hoc Networks: Overview of Wireless Ad-Hoc Networks, Routing in Ad-Hoc Networks, Routing

Protocols for Ad-Hoc Networks

Reference Books

1) James F. Kurose, Keith W.Ross, Computer Networking: A Top-Down Approach Featuring the

Internet, Third Edition, Pearson Education, 2007

2) Larry Peterson and Bruce S Davis, Computer Networks :A System Approach, 5th Edition, Elsevier

-2014 3) C. Siva Ram Murthy & B. S. Manoj, Ad hoc Wireless Networks, 2nd Edition, Pearson Education, 2011

4) Ian F Akyildiz and Xudong Wang, Wireless Mesh Networks, first edition, Wiley Publications, 2009.

Course Title: Optical Networks Course Code: PEC128E






Course Objectives:

1) To know the basic fiber transmission, different transmission losses, first & second generation

optical networks and different optical network components.

2) To get familiar with the different client layers of optical layer and WDM network elements.

3) To understand the WDM network design and different network management functions.

4) To know the different protection schemes in SONET/SDH and optical layer.

Course Outcomes:


1) Understand the basic fiber optic communication Principles, different types of losses, optical

network components and first & second generation optical networks.

2) Understand the different client layers of optical layer such SONET/SDH, ATM, IP, storage area

networks and different WDM network elements.

3) Understand the LTD and RWA problems, design of WDM network and different network

management functions.

4) Understand the different protection schemes in SONET/SDH and optical layer.


Unit I (13 hours)

Introduction to optical networks: Telecommunications network architecture, Services, Circuit switching

and Packet Switching, Optical Networks, The optical Layer, Optical Packet switching. Propagation of

Signals in optical fiber: Light propagation in optical fiber, Loss and Bandwidth, Chromatic Dispersion,

Nonlinear effects, Solitons. Components: Couplers, Isolators and Circulators, Multiplexers and Filters,

Optical amplifiers, Switches, Wavelength converters.

Unit II (13 hours)

Client layers of the optical layer: SONET/SDH, ATM, IP, Storage area networks, Gigabit and 10-Gigabit

Ethernet. WDM Network Elements: Optical line terminals, Optical line amplifiers, optical add/drop

multiplexers, optical cross connects.

Unit III (13hours)

WDM Network Design: Cost tradeoffs, LTD and RWA problems, Dimensioning wavelength-routing

networks, Statistical dimensioning models, Maximum load dimensioning models. Control and

Management: Network management functions, optical layer services and interfacing, Layers within the

optical layer, Multivendor interoperability, Performance and fault management, Configuration

management, Optical safety.

Unit IV (13 hours)

Network survivability: Basic concepts, Protection in SONET/SDH, Protection in IP networks, Why

optical layer Protection, Optical layer protection schemes, Interworking between Layers. Access Networks

Reference Books

1) Rajiv Ramaswami, N Sivarajan, Optical Networks, M. Kauffman Publishers, 2nd edition, 2002.

2) John M. Senior, Optical fiber Communications, Pearson, 2nd edition 2006.

3) Gerd Keiser, Optical Fiber Communications, MGH, 4th edition, 2008.

Course Title: Image Processing Course Code: PEC129E






Prerequisites : Signals and Systems

Course Objectives:

To understand and gain complete knowledge about

1) The fundamentals of digital image processing

2) Image transform used in digital image processing

3) Image enhancement techniques used in digital image processing

4) Image restoration techniques and methods used in digital image processing

5) Image compression and Segmentation used in digital image processing

Course Outcomes:


1) Describe principles of different digital imaging systems.

2) Analyze and design digital filters for two-dimensional (2-D) signals.

3) Apply the 2-D discrete Fourier transform (DFT).

4) Implement image processing algorithms on computers.

5) Devise algorithms to solve image processing problems.


Unit I (13 hours)

Digital Image Fundamentals Image perception - light, luminance, brightness, and contrast; color

representation, matching and reproduction; elements of a digital image processing system; two

dimensional signal and systems- image sampling and quantization; some basic relationships between

pixels; point spread function; imaging geometry - camera model, calibration and perspective

transformation.

Unit II (13 hours)

Histogram equalization and specification techniques, Noise distributions, Spatial averaging, Directional

Smoothing, Median, Geometric mean, Harmonic mean, Homomorphic filtering, Color image

enhancement, Image restoration; degradation model; inverse filtering; the Wiener filter.

Unit III (13hours)

Color Image Processing: Color fundamentals, color models; pseudo-color and full-color image processing;

color transformations; color image smoothing and sharpening; color image segmentation and matching;

noise reduction in color images.

Unit IV (13 hours)

Need for data compression, Huffman, Run Length Encoding, Shift codes, Arithmetic coding, Vector

Quantization, Transform coding, JPEG standard, MPEG.

Reference Books

1) Gonzalez R. C. and Woods R.E., Digital Image Processing, 3rd Edition, Prentice Hall, 2008

2) Anil K. Jain, Fundamentals of Digital Image Processing, Pearson 2002.

Course Title: Artificial Intelligence Course Code: PEC130E






Course Objectives:

The course is intended to provide the knowledge about

1) Various symbolic knowledge representation to specify domains and reasoning tasks of a situated

software agent.

2) Different logical systems for inference over formal domain representations, and trace how a

particular inference algorithm works on a given problem specification.

3) The conceptual and computational trade-offs between the expressiveness of different formal

representations.

4) Key logic-based techniques in a variety of research settings.

5) Communicating scientific knowledge at different levels of abstraction.

Course Outcomes:


1) Use various symbolic knowledge representation to specify domains and reasoning tasks of a

situated software agent.

2) Use different logical systems for inference over formal domain representations, and trace how a

particular inference algorithm works on a given problem specification.

3) Understand the conceptual and computational trade-offs between the expressiveness of different

formal representations.

4) Use key logic-based techniques in a variety of research settings;

5) Communicate scientific knowledge at different levels of abstraction.


Unit I (13 hours)

Introduction-Concepts and definition of AI, AI Problems, The Underlying assumption, What is an AI

technique? AI characteristics, AI versus Natural Intelligence, Applications of AI, etc. Problems, Problem

Spaces, and Search- Defining the Problem as State Space Search, Production Systems, Problem

Characteristics, Production Systems Characteristics, Issues in the Design of Search Programs, Advantages

and Disadvantages of DFS & BFS Technique. Heuristic Search Techniques- What is heuristic?, Heuristic

Function, Importance of Heuristic Function, Examples, Search Techniques: Generate – and – Test, Hill

Climbing, Best-First Search, Problem reduction, Constraint – Satisfaction, Means-Ends Analysis.

Unit II (13 hours)

Knowledge Representation- Representations and Mappings, Approaches to Knowledge Representation,

Issues in Knowledge Representation. Procedural versus Declarative Knowledge, Logic Programming,

Forward versus Backward reasoning, Matching. Approaches: Propositional Logic, Predicate Logic,

Representing Simple Facts in Logic, Representing Instance and Isa Relationships, Computable Functions

and Predicates, Resolution, Natural Deduction. Structured Representation Approaches: Semantic

Networks, Frames, Conceptual Dependency, Scripts, Etc.

Unit III (13 hours)

Reasoning under Uncertainty- Introduction to Nonmonotonic Reasoning, Logics for Nonmonotonic

Reasoning, Implementation Issues, Augmenting a Problem Solver, Statistical Reasoning, Probability and

Bay’s Theorem, Certainty Factors and Rule-based Systems, Bayesian Networks, Dempster-Shafer Theory.

Game Playing- Overview, The Minima Search Procedure, Adding Alpha-Beta Cutoffs, Additional

Refinements, Examples.

Unit IV (13 hours)

Learning- What is Learning?, Rote Learning, Learning by taking Advice, Learning in Problem Solving,

Learning from Examples: Induction, Explanation-based Learning, Discovery Analogy, Formal Learning

Theory, Neural Net Learning and Genetic Learning. Planning- Overview, An Example Domain: The

Blocks world, Components of a Planning System, Goal Stack Planning, Nonlinear Planning using

Constraint Posting, Hierarchical Planning, Other Planning Techniques. Natural Languages Processing &

Understanding- What is Understanding?, What makes Understanding Hard?, Understanding as Constraint

satisfaction, Introduction to NLP, Syntactic Processing, Semantic Analysis, Discourse and Pragmatic

Processing.

Reference Books

1) Elaine Rich, Kevin Knight, “Artificial Intelligence”, Second Edition, Tata McGraw Hill.

2) Efraim Turban and Jay E. Aronson, “Decision Support Systems and Intelligent Systems”, Sixth

Edition 2002, Pearson Education Asia

Course Title: Communication Engineering Laboratory - I Course Code: PEC131L

Credits: 2.0 Teaching Hours: Contact Hours: 3Hrs/Week



Designation : Laboratory

Course Objectives:

1) To know the implementation of different digital modulation and demodulation techniques.

2) Obtain the radiation pattern of different types of antenna.

3) Know the basics of optical fiber communication.

4) Compute characteristics of random processes.

Course Outcomes:

After completion of Laboratory the students are able to

1) Understand the design principles of different digital modulation and demodulation techniques.

2) Characterize different types of antenna using their radiation pattern.

3) Appreciate the merits of optical fiber communication.

4) Handle random signals generally encountered in communication.

The Experiments that enable to meet the above objectives and course outcomes are given below:

Sl.

No

LIST OF THE EXPERIMENTS

1 Matlab / Simulink model for digital modulation techniques (ASK, FSK, PSK and QPSK).

2 Matlab / Simulink model for band limited channel and its characterization.

3 Matlab implementation of decimation and interpolation operations by an integer and a fraction,

along with process visualization in spectral domain.

4 Matlab implementation of DFT as a filter bank.

5 Radiation pattern of dipole antenna, horn antenna and Yagi-Uda antenna.

6 Radiation pattern of different micro-strip antenna.

7 Simulation of different probability distributions using MATLAB.

8 Computation of numerical aperture and bending loss of a given optical fiber.

9 Generation of PN sequence and verification of its properties using Matlab.

10 Matlab / Simulink model for direct sequence spread spectrum modulation.

11 Matlab / Simulink model for frequency hopping spread spectrum modulation.

12 Computation of gain and directivity of horn antenna.

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