ME Avionics Curriculum & Syllabus_04 Jan 2018

Department of Aeronautical Engineering, REC


OUTCOME:

Upon completion of this course,


· Students will understand the advanced concepts of Spacecraft communication systems to the
engineers and to provide the necessary mathematical knowledge that are needed in understanding
the physical processes.


· The students will have an exposure on various topics such as Orbital mechanics, elements of
satellite communication system, links and multiplexing, multiple access, telemetry ,tracking and
telecommand and will be able to deploy these skills effectively in the solution of problems in
avionics engineering.



REFERENCES

1. Wilbur L. Pritchard and Joseph A.Sciulli, Satellite Communication Systems Engineering, Prentice
Hall, New Jersey, 1986.

2. Timothy Pratt and Charles W.Bostain, Satellite Communications, John Wiley and Sons, 1986.
3. Tri T Ha, Digital Satellite Communication, Macmillan Publishing Company, 1986.
4. Kadish, Jules E, Satellite Communications Fundamentals, Artech House, Boston 2000
5. Lida,Takashied.,Satellite communications:System and its design technology, Ohmsha Tokyo 2000
6. Maral, Gerard,Satellite communications systems: Systems, techniques and technology, John Wiley,
Newyork 2002.

7. Elbert, Bruce R, Satellite communication applications handbook, Artech house Boston 2004.




AV17E22 DIGITAL FLY-BY WIRE CONTROL L T P C

3 0 0 3

OBJECTIVE:

· To expose students to the basic concept of Digital Fly By Wire Control.



UNIT I INTRODUCTION TO FLY-BY-WIRE CONTROL 7

Need for FBW systems, Historical perspectives in design Programs-Douglas Long Beach Programs,
WPAFB B 47 In House Program, LTV IAP, Sperry Phoenix Programs, CAS and SAS, CCV and ACT
concepts.

UNIT II ELEMENTS OF DFBW CONTROL 9

Description of various elements of DFBW systems - Concept of redundancy and reliability, Fault
coverage and redundant architecture

UNIT III DFBW ARCHITECTURES 9

Need for redundant architecture, discussion on triplex vs. quadruplex architecture for DFBW system,
Concept of cross-strapping, Actuator command voting and servo force voting etc.



Curriculum and Syllabus | M.E. Avionics | R2017 Page 51

Department of Aeronautical Engineering, REC


UNIT IV SOME REQUIREMENTS FOR DFBW SYSTEM DESIGN 9

Survivable Flight control System programs, ADP Phases-Simplex package Evaluation -FBW

without Mechanical Backup-Survivable Stabilator Actuator package, Reliability requirements and their
relevance to DFBW system design, redundant power supply requirements, Environmental and weight,
volume constraints.

UNIT V DESIGN ISSUES IN DFBW SYSTEM DESIGN 11

Thermal consideration, Built-in-test features, reliable software development, Redundancy management
(voting, monitoring), Failure and maintenance philosophies, Implementation, Issues of digital control
laws, Generic failures in Hardware and software. Advanced concepts in DFBW System Design


TOTAL: 45 PERIODS
OUTCOME:


Upon completion of this course,
· Students will understand the advanced concepts of Fly-by-wire to the engineers and to provide
the necessary mathematical knowledge that are needed in understanding modern aircraft control
strategies.
· The students will have an exposure on various topics such as evolution of FBW, Elements,
architecture, design and design issues of DFBW and will be able to deploy these skills
effectively in the analyzing and understanding modern control methods.



REFERENCES
1. Vernon R Schmitt, James W Morris and Gavin D Jenny, “Fly By Wire-A Historical
Perspective”, SAE International, 1998.

2. AGARD-CP-137, “Advances in Control systems”, (Chap.10, 17,21, 22, 23, 24)
3. AGARD-CP-384, “Active Control Systems Review”, Evaluations and Projections.
4. AGARD-CP-260, “Stability and Control” (Chap.15)
5. ‘Modern Air Combat’, Salamander Books Ltd , 2001.




AV17E23 FAULT TOLERANT COMPUTING L T P C

3 0 0 3

OBJECTIVE:

· To expose students to the basic concepts of error detection and fault tolerance in systems.

· To introduce students to the concept of structure and reliability of systems.

· To expose students to the concept of software fault tolerance.







Curriculum and Syllabus | M.E. Avionics | R2017 Page 52

Department of Aeronautical Engineering, REC


UNIT II ERROR DETECTION 12

Measure for error detection – Mechanisms solution of problems in avionics engineering.

UNIT I FAULT TOLERANCE 10

Principles of fault tolerance – redundancy – quantitative reliability – evaluation – exception handling.
Application of fault tolerant systems in aircraft – reliability strategies – Fault Tolerant Processor –
Hardware and software

for error detection – Measures for damage confinement and damage assessment – Protection mechanisms
– Protection in multi-level systems

UNIT III ERROR RECOVERY 12
Measures for error recovery – mechanisms for error recovery – check points and audit trials – the
recovery cache – Concurrent processes – recovery for competing process – recovery for cooperating
process – distributed systems – fault treatment – location and repair.


UNIT IV SOFTWARE FAULT TOLERANCE 4
The recovery block scheme – Implementation of recovery block – Acceptance – tests – run-time
overheads

UNIT V SYSTEMS STRUCTURE AND RELIABILITY 7

System structure – systems model – Software / Hardware interaction and multi-level systems – atomic
actions – systems reliability – systems specification - Erroneous transitions and states – component /
design failure – errors and faults.


TOTAL: 45 PERIODS

OUTCOME

Upon completion of this course,

· Students will understand the advanced concepts of Fault Tolerance to the engineers and to
provide the necessary mathematical knowledge that are needed in understanding the necessary
procedures involved.

· The students will have an exposure on various topics such as Redundancy, Fault Tolerant
system architecture and design, error handling and recovery and will be able to deploy these
skills effectively in the analyzing and understanding fault tolerant methods.



REFERENCE

1. Anderson and Lee, Fault tolerant principles and practice, Prentice – Hall, 1981.
2. Siewiorek, C.P. and Swartz, R.S Theory and practice of reliable system design,
McGraw – Hill, 1983.
3. John D. Musa, Anthony Jannino, Kzuhira, Okunito, Software reliability measurement, prediction and
application, McGraw – Hill, 1989.








Curriculum and Syllabus | M.E. Avionics | R2017 Page 53

Department of Aeronautical Engineering, REC


SOFT COMPUTING FOR AVIONICS
AV17E24 ENGINEERS L T P C

3 0 0 3

OBJECTIVE:

· To expose students to the basic concept of fuzzy and neural networks.


UNIT I NEURAL NETWORKS 9
Supervised Learning Neural Networks – Perceptrons – Adaline – Back propagation Multilayer
Perceptron – Radial Basis Function Networks – Unsupervised Learning Neutral Networks – Competitive
Learning Networks – Kohonen Self-Organizing Networks – Counter Propagation Networks- Advances
In Neural Network

UNIT II FUZZY SET THEORY 9
Fuzzy Sets – Basic Definition and Terminology – Set Theoretic Operations – Member Function
Formulation and Parameterization – Fuzzy Rules And Reasoning – Extension Principle and Fuzzy
Relations – Fuzzy IF-THEN Rules – Fuzzy Reasoning – Fuzzy Inference Systems – Mamdani Fuzzy
Model – Sugeno Fuzzy Model – Tsukamoto Fuzzy Model – Input Space Partitioning and Fuzzy
Modeling.

UNIT III OPTIMIZATION METHODS 9
Derivative Based Optimization – Derivative free Optimization - Genetic Algorithm – Design Issues In
Genetic Algorithm , Genetic Modeling – Optimization of Membership Function and Rule Base using GA
– Fuzzy Logic Controlled GA.


UNITIV NEURAL AND FUZZY CONTROL SCHEMES 9

Direct and Indirect Neuro Control Schemes – Fuzzy Logic Controller – Familiarization of Neural

Network and Fuzzy Logic Toolbox - Case Studies.
UNIT V NEURO FUZZY MODELLING 9

Fuzzification and Rule Base using ANN – Fuzzy Neuron – Adaptive Neuro-fuzzy Inference
SystemArchitecture – Hybrid Learning Algorithm – Learning Methods that Cross fertilize ANFIS and
RBFN – Coactive Neuro Fuzzy Modeling.

TOTAL:45 PERIODS

OUTCOME:

Upon completion of this course,

· Students will understand the advanced concepts of Soft-computing to the engineers and to
provide the necessary mathematical knowledge that are needed in modelling the related
processes.

· The students will have an exposure on various topics such as Neural Networks, Fuzzy logic and
Neuro-fuzzy modeling and will be able to deploy these skills effectively in the solution of
problems in avionics engineering.






Curriculum and Syllabus | M.E. Avionics | R2017 Page 54

Department of Aeronautical Engineering, REC


REFERENCES

1.“Neural Networks: Algorithms, Applications and Programming Techniques”, Freeman J.A.

&D.M. Skapura, Addison Wesley,2000.

2. J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI, 2004,
Pearson Education 2004.

3. Anderson J.A “An Introduction to Neural Networks”,PHI, 2001.
4. Timothy J.Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997.
5. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”,
Addison Wesley, N.Y., 2000.
6. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI,
2003.







AV17E25 DETECTION AND ESTIMATION THEORY L T P C

3 1 0 4

OBJECTIVES:

· To understand the concepts of detection and estimation.
· To learn the basics of multi-user detection theory
· To understand the theory behind various estimation techniques.
· To understand Wiener filter and Kalman filter in detail.


UNIT I REVEIW OF PROBABILITY AND STOCHASTIC PROCESS 9+3

ConditionalProbability, Bayes' Theorem , Random Variables, Conditional Distributions and Densities,
moments and distribution of random variables., Stationary ProcessesCyclostationary ProcessesAverages
and Ergodicity Autocorrelation Function Power Spectral Density Discrete Time Stochastic Processes,
Spatial Stochastic Processes Random Signals,Relationship of Power Spectral Density and
Autocorrelation Function.

UNIT II SINGLE AND MULTIPLE SAMPLE DETECTION 9+3

Hypothesis Testing and the MAP Criterion, Bayes Criterion, Minimax Criterion, Neyman-Pearson
Criterion, Sequential Detection, The Optimum Digital Detector in Additive Gaussian Noise ,
Performance of Binary Receivers in AWGN.

UNIT III FUNDAMENTALS OF ESTIMATION THEORY 9+3

Formulation of the General Parameter Estimation Problem, Relationship between Detection and
Estimation Theory, Types of Estimation Problems, Properties of Estimators, Bayes Estimation, Minimax
Estimation, Maximum-Likelihood Estimation, Comparison of Estimators of Parameters.






Curriculum and Syllabus | M.E. Avionics | R2017 Page 55

Department of Aeronautical Engineering, REC


UNIT IV WIENER AND KALMAN FILTERS 9+3

Orthogonality Principle, Autoregressive Techniques, Discrete Wiener Filter, Continuous Wiener Filter,
Generalization of Discrete and Continuous Filter Representations , Linear Least-Squares Methods,
Minimum-Variance Weighted Least-Squares Methods, Minimum-Variance, Least Squares, Kalman
Algorithm - Computational Considerations, Signal Estimation, Continuous Kalman Filter, Extended
Kalman Filter.

UNIT V APPLICATIONS 9+3

Detector Structures in Non-Gaussian Noise , Examples of Noise Models, Receiver Structures, and Error-
Rate Performance, Estimation of Non-Gaussian Noise Parameters Fading Multipath Channel Models,
Receiver Structures with Known Channel Parameters, Receiver Structures without Knowledge of Phase,
Receiver Structures without Knowledge of Amplitude or Phase, Receiver Structures and Performance
with No Channel Knowledge.

TOTAL: 45+15=60 PERIODS

OUTCOMES:

Upon completion of this course,

· To be able to apply detection and estimation theory to solve communication problems.

· To apply probability and stochastic process concepts in detection and estimation.

· To design Wiener and Kalman filters to solve linear estimation problems.
REFERENCES

1. Thomas Schonhoff, "Detection and Estimation Theory”, Prentice Hall, NewJersy,2007.

2. Steven M. Kay, "Fundamentals of Statistical Processing, Volume I: Estimation Theory”, Prentice
Hall Signal Processing Series, Prentice Hall, PTR, NewJersy, 1993.

3. Harry L. Van Trees, "Detection, Estimation and Modulation Theory", Part I

John Wiley and Sons, New York, 2001.





AV17E26 MICROWAVES AND RADAR L T P C

3 0 0 3

OBJECTIVE:

· To expose students to the concepts of microwaves.
· To introduce students to the basic concept of radar and radar signal processing.










Curriculum and Syllabus | M.E. Avionics | R2017 Page 56

Department of Aeronautical Engineering, REC


UNIT I MICROWAVE SOURCES 10

Passive waveguide components, Microstrip line structure and components, Simple theory and operating
characteristics of Reflex klystrons, Two cavity Klystrons, Magnetrons, and TWTS - solid state source -
TEDS, IMPATTS, TRAPATT, GaAs FETs and Tunnel diode.

UNIT II RADAR PRINCIPLES 8

Introduction to Radar – Radar range equation – Receiver noise and signal to noise ratio- Radar cross
section (RCS) – Radar system – Radar Antennas

UNIT III TYPES OF RADARS 10

CW and FMCW radars-Tracking radars-MTI radar -Principles of coherent MTI radars - Digital MTI,

Synthetic Aperture radar, Principles of Pulsed Doppler Radar, Low-, High-, and medium-PRF Mode.

UNIT IV RADAR SIGNAL PROCESSING 9

Radar requirements –Matched filters- Radar ambiguity function – Optimum waveforms for detection in
clutter – Classes of waveforms – Digital representation of signals -Pulse compression


UNIT V TRACKING RADAR 8
Tracking with radar – Monopulse Tracking – conical scan and sequential lobing – limitations to tracking
Accuracy- Kalman Tracker -Fundamentals of Airborne radar

TOTAL: 45 PERIODS

OUTCOME:

Upon completion of this course,
· Students will understand the advanced concepts of Microwave engineering and RADAR
concepts to the engineers and to provide the necessary mathematical knowledge that are needed
in modelling physical processes.
· The students will have an exposure on various topics such as Microwave sources, types,
principle and working of RADAR and relevant signal processing and will be able to deploy these
skills effectively in the solution of problems in avionics engineering.



REFERENCES
1. Fred E.Nathanson “ Radar design Principles “ Signal processing and the environment, Prentice Hall,
2004

2. Y. Liao, Microwave Devices and Circuits, Prentice Hall, 1980.
3. M.I. Skolnik, Introduction to Radar System (Second Edition) McGraw Hill, 1980.
4. M.I. Skolnik, Radar Handbook (Second Edition) McGraw Hill, 1990.
5. Guy V. Morris, Linda L. Harkness, Airborne Pulsed Doppler radar, Second Edition, Artech House
Publishers, 1996.
6. Blackman S.S., “Multiple target tracking with radar applications” Artech House 1986.








Curriculum and Syllabus | M.E. Avionics | R2017 Page 57

Department of Aeronautical Engineering, REC


AV17E27 REAL TIME EMBEDDED SYSTEMS L T P C

3 0 0 3

OBJECTIVE:

· To introduce students to the concept of embedded systems


· .To expose students to the basic concept of RTOS and Real Time UML.

UNIT I INTRODUCTION 12

Real Time System – Embedded Systems – Architecture of Embedded System - SimpleProgramming
for Embedded System – Process of Embedded System Development - Pervasive Computing – Information
Access Devices – Smart Cards – PIC Microcontroller – ARM Processor.

UNIT II EMBEDDED/REAL TIME OPERATING SYSTEM 9

Operating System Concepts: Processes, Threads, Interrupts, Events - Real Time Scheduling Algorithms -
Memory Management – Overview of Operating Systems for Embedded, Real Time, Handheld Devices –
Target Image Creation – Programming in Linux, RTLinux, VxWorks, uC/Os-overview.



UNIT III CONNECTIVITY 9

Wireless Connectivity - Bluetooth – Other short Range Protocols – Wireless Application Environment –
Service Discovery – Middleware

UNIT IV REAL TIME UML 6

Requirements Analysis – Object Identification Strategies – Object Behavior – Real Time Design Patterns

UNIT V SOFTWARE DEVELOPMENT AND CASE STUDY 9

Concurrency – Exceptions – Tools – Debugging Techniques – Optimization – Case Studies - Interfacing
Digital Camera with USB port and Data Compressor.

TOTAL: 45 PERIODS





OUTCOME:

Upon completion of this course,


· Students will understand the advanced concepts of Real-time embedded systems to the engineers
and to provide the necessary knowledge for their design and development.

· The students will have an exposure on various topics such as architecture of embedded systems,
connectivity, RTOS, Real time UML and will be able to deploy these skills effectively in the
solution of problems in avionics engineering.






Curriculum and Syllabus | M.E. Avionics | R2017 Page 58

Department of Aeronautical Engineering, REC


REFERENCES

1. R.J.A.Buhr, D.L.Bailey, “An Introduction to Real-Time Systems”, Prentice-Hall International,
1999.
2. David E-Simon, “An Embedded Software Primer”, Pearson Education, 2007.
(UNIT– II)

3. C.M.Krishna, Kang G.Shin, “Real Time Systems”, Mc-Graw Hill, 1997. (UNIT- II)
nd
4. B.P.Douglass, “Real Time UML 2 Edition”, Addison-Wesley 2000. ((UNIT – IV)
5. J.Schiller, “Mobile Communication”, Addison-Wesley, 1999. (UNIT – III)
6. Dr.K.V.K.K.Prasad, “Embedded/Real Time Systems: Concepts, Design and Programming”,
7. DreamTech press, Black Book, 2005. (UNIT – I)
8. R.Barnett, L.O.Cull, S.Cox, “Embedded C Programming and the Microchip PIC”, Thomason
Learning 2004. (UNIT – I)

9. Wayne Wolf, “Computers as Components - Principles of Embedded Computer System Design”,
Mergen Kaufman Publisher, 2006.
10. Sriram V Iyer, Pankaj Gupta, “Embedded Real Time Systems Programming”, Tata Mc-Graw
Hill, 2004.


AV17E28 AERODYNAMICS FOR UAV L T P C
3 0 0 3


OBJECTIVE:

· To introduce students to the basic concept of aerodynamics for UAV.

· To expose students to the basic concepts of turbulence modelling.



UNIT I INTRODUCTION 9

Introduction to aerodynamics for UAV application, Numerical Solutions of Equations- Direct iteration
methods, Bisection method, the Secant Method, under relaxation, convergence criteria and the Newton
Raphsons method

UNIT II FINITE DIFFERENCE METHOD 9

Introduction to finite difference scheme, approximating derivatives, Taylor series approximation, order of
approximation, polynomial approximation, approximating higher-order derivatives, non-uniform grids, the
convection diffusion problem, upwinding, numerical diffusion, boundary conditions, extension to multiple
dimensions.

UNIT III FINITE VOLUME METHOD 9


Introduction, basic finite volume method, finite volume method for a rectangular 2D grid, First order
upwind scheme, central difference scheme, QUICK scheme, convection scheme performance, boundary
condition treatment, numerical flow solver strategy

UNIT IV TURBULENCE MODELING 9

Transition to turbulence, turbulence in engineering, characteristics of turbulence, eddy and energy
transfer, turbulent kinetic energy spectrum, Kolmogorov hypothesis, Turbulent kinetic energy and rate of



Curriculum and Syllabus | M.E. Avionics | R2017 Page 59

Department of Aeronautical Engineering, REC


dissipation, integral length scale, Kolmogorov length scale, inertial sub range and Taylor length scale,
local equilibrium of turbulent motion, range of length scales - turbulence bandwidth, numerical
approaches to resolving and solving turbulence- DNS, LES, RANS, Hybrid Methods.


UNIT V SIMPLE & INDUSTRIAL TURBULENCE MODELS 9
Analysis of 2D boundary layers, Y-momentum equation in a boundary layer, total shear across a boundary
layer, mean flow kinetic energy balance, turbulent kinetic energy balance, energy transfer process near a
wall, Linear Eddy viscosity models, general eddy viscosity models, mixing length hypothesis, the Log
layer, near wall damping in viscous layer, one-equation models, length scales in non-trivial flows, two
equation models, log law region of an equilibrium boundary layer, decaying grid turbulence, the Shear
Stress Transport(SST) model, near wall modeling, high Reynolds number modeling and wall functions,
low Reynolds number models, channel flow example.

TOTAL: 45 PERIODS

OUTCOME:

Upon completion of this course,

· Students will understand the concepts of aerodynamics for UAV to the engineers and to provide
the necessary knowledge for their design and development.

· The students will have an exposure on various topics such as finite difference method, finite
volume method and turbulence modelling and will be able to deploy these skills effectively in
the solution of problems in avionics engineering.



REFERENCES
1. J. Ferziger, M. Peric, Computational Methods for Fluid Dynamics, Springer; 3rd edition (November 6,
2001)

2. H.K. Versteeg, W. Malalasekara, An Introduction to Computational Fluid Dynamics: The Finite
Volume Method, Prentice Hall; 2 edition (February 26, 2007)

3. S.V. Patankar, Numerical Heat Transfer and Fluid Flow, Taylor & Francis Group (1980)
4. Brian Launder, Modelling Turbulence in Engineering and the Environment: Second-Moment Routes
to Closure, Cambridge University Press (2011)
5. Progress in Wall Turbulence: Understanding and Modeling: Proceedings of the WALLTURB
International Workshop Held in Lille, France, April 21-23, 2009, Springer (2010)
6. Van de Kamp, “Elements of astromechanics”, Pitman Publishing Co., Ltd., London, 1980.
7. E.R. Parker, “Materials for Missiles and Spacecraft”, McGraw-Hill Book Co., Inc., 1982.
















Curriculum and Syllabus | M.E. Avionics | R2017 Page 60

Department of Aeronautical Engineering, REC


AV17E29 PAYLOAD AND SENSORS FOR UAV L T P C


3 0 0 3
OBJECTIVE:

· To introduce students to the basic concepts of different types of sensors used in UAV.
· To expose students to the concepts of artificial neural networks and fuzzy logic.



UNIT I PAYLOAD FOR UAV 9

Introduction – Types – Non-dispensable Payloads - Electro-optic Payload Systems - Electro-optic
Systems Integration - Radar Imaging Payloads - Other Non-dispensable Payloads - Dispensable Payloads
- Payload Development.

UNIT II SENSOR 9

Data fusion applications to multiple sensor systems - Selection of sensors - Benefits of multiple sensor
systems - Influence of wavelength on atmospheric attenuation - Fog characterization - Effects of operating
frequency on MMW sensor performance - Absorption of MMW energy in rain and fog - Backscatter of
MMW energy from rain - Effects of operating wavelength on IR sensor performance - Visibility metrics -
Visibility - Meteorological range - Attenuation of IR energy by rain - Extinction coefficient values -
Summary of attributes of electromagnetic sensors - Atmospheric and sensor system computer simulation
models

UNIT III DATA FUSION ALGORITHMS AND ARCHITECTURES 9

Definition of data fusion - Level 1 processing - Detection, classification, and identification algorithms
for data fusion - State estimation and tracking algorithms for data fusion - Level 2, 3, and 4 processing -
Data fusion processor functions - Definition of an architecture - Data fusion architectures - Sensor-level
fusion - Central-level fusion - Hybrid fusion - Pixel-level fusion - level fusion-Decision-level fusion -
Sensor footprint registration and size considerations - Dempster-Shafer Evidential Theory- Summary

UNIT IV ARTIFICIAL NEURAL NETWORKS 9

Applications of artificial neural networks - Adaptive linear combiner - Linear classifiers - Capacity of
linear classifiers - Nonlinear classifiers - Madaline - Feedforward network - Capacity of nonlinear
classifiers - Supervised and unsupervised learning - Supervised learning rules - Voting Logic Fusion


UNIT V FUZZY LOGIC AND FUZZY NEURAL NETWORKS 9

Conditions under which fuzzy logic provides an appropriate solution - Illustration of fuzzy logic in an
automobile antilock braking system - Basic elements of a fuzzy system - Fuzzy logic processing - Fuzzy
centroid calculation

TOTAL: 45 PERIODS












Curriculum and Syllabus | M.E. Avionics | R2017 Page 61

Department of Aeronautical Engineering, REC


OUTCOME:


Upon completion of this course,

· Students will understand the advanced concepts of payloads and sensors used in UAV and
provide the necessary knowledge for their design and development.

· The students will have an exposure on various topics such as data fusion algorithms and
architecture, artificial and fuzzy neural network and fuzzy logic and will be able to deploy these
skills effectively in the solution of problems in avionics engineering.


REFERENCES:

1. UNMANNED AIRCRAFT SYSTEMS UAVS DESIGN, DEVELOPMENT AND
DEPLOYMENT Reg Austin Aeronautical Consultant, A John Wiley and Sons, Ltd.,
Publication

2. Mathematical Techniques in Multisensor Data Fusion (Artech House Information Warfare
Library) [Hardcover] David L. Hall, Sonya A. H. McMullen ,


3. Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition (Electrical
Engineering & Applied Signal Processing Series) Martin Liggins II David Hall, James

4. Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, Second
Edition (SPIE Press Monograph PM222) Lawrence A. Klein .


5. Multi-Sensor Data Fusion with MATLAB by Jitendra R. Raol






AV17E30 BIG DATA ANALYTIC USING IOT L T P C

3 0 0 3

OBJECTIVES:

· To learn the concepts of big data analytics
· To learn the concepts about Internet of things
· To understand and implement smart systems


UNIT I - BIG DATA PLATFORMS FOR THE INTERNET OF THINGS
9

Big Data Platforms for the Internet of Things: network protocol- data dissemination–current
state of art Improving Data and Service Interoperability with Structure, Compliance, Conformance
and Context Awareness: interoperability problem in the IoT context- Big Data Management Systems for the
Exploitation of Pervasive Environments – Big Data challenges and requirements coming from different
Smart City applications





Curriculum and Syllabus | M.E. Avionics | R2017 Page 62

Department of Aeronautical Engineering, REC


UNIT II - RFID FALSE AUTHENTICATIONS 9

On RFID False Authentications: YA TRAP – Necessary and sufficient condition for false authentication
prevention - Adaptive Pipelined Neural Network Structure in Self-aware Internet of Things: self-healing
systems-Role of adaptive neural network- Spatial Dimensions of Big Data: Application of Geographical
Concepts and Spatial Technology to the Internet of Things- Applying spatial relationships, functions, and
models

UNIT III - FOG COMPUTING 9


Fog Computing: A Platform for Internet of Things and Analytics: a massively distributed number of sources
- Big Data Metadata Management in Smart Grids: semantic inconsistencies – role of metadata

UNIT IV - WEB ENHANCED BUILDING 9

Toward Web Enhanced Building Automation Systems: heterogeneity between existing installations and
native IP devices - loosely-coupled Web protocol stack –energy saving in smart building- Intelligent
Transportation Systems and Wireless Access in Vehicular Environment Technology for Developing Smart
Cities: advantages and achievements- Emerging Technologies in Health Information Systems: Genomics
Driven Wellness Tracking and Management System (GO-WELL) – predictive care – personalized medicine

UNIT V - SUSTAINABILITY DATA AND ANALYTICS 9

Sustainability Data and Analytics in Cloud-Based M2M Systems - potential stakeholders and their complex
relationships to data and analytics applications - Social Networking Analysis - Building a useful
understanding of a social network - Leveraging Social Media and IoT to Bootstrap Smart Environments :
lightweight Cyber Physical Social Systems - citizen actuation

TOTAL: 45 PERIODS
OUTCOME:


Upon completion of this course,

· Students will understand the advanced concepts of Big data analysis using IOT and provide the
necessary knowledge for their design and development.


· The students will have an exposure on various topics such as false authentication ,fog computing
,web enhanced building ,sustainability data and analytics and will be able to deploy these skills
effectively in the solution of problems in avionics engineering.


REFERENCES

1. Stackowiak, R., Licht, A., Mantha, V., Nagode, L.,” Big Data and The Internet of Things Enterprise
Information Architecture for A New
Age”, Apress, 2015.

2. Dr. John Bates , “Thingalytics - Smart Big Data Analytics for the Internet of Things”, john Bates, 2015.









Curriculum and Syllabus | M.E. Avionics | R2017 Page 63

Department of Aeronautical Engineering, REC


AV17E31 FLIGHT DATA MONITORING L T P C

3 0 0 3

OBJECTIVE:

· To introduce students to the concept of flight data management and flight data recording.



UNIT I INTRODUCTION 5

Definitions-Fundamental concepts and definitions-FDM and SMS, Objectives of an operator’s FDM
system, Description of a typical FDB system-system outline-information flow, Aircraft operations, Data
Acquisitions, Ground based data replay and Analysis programs, Information and Information data base,
continued monitoring.

UNIT II FLIGHT DATA MONITORING
10

FDM within Safety Management System-Safety culture, risk identification, How sms can benefit from
FDM and vice-versa,FDM Technology-Data Recording Technology, Interpretation and use of FDM
information

UNIT III AIRCRAFT FDM SYSTEMS 10

Introduction . Equipment Specification, Maintaining equipment performance,QAR serviceability and
MEL’S.,Safety Reports and Mandotory occurrence reporting,FDM in small fleets and Business
Aviation,Helicopter Flight Data Monitoring

UNIT IV FLIGHT DATA RECORDER 10

Applicable Recorded Flight Data, FDR Data: Disclosure and Access , FDR Recovery: From On Scene
to the FDR Laboratory , FDR Data: Non-Safety Board FDR Download, FDR Arrival at the Safety
Board Recorder Laboratory, Initial FDR Readout , FDR Preliminary Data: Release to the Parties , FDR
Preliminary Data: Safety Board Staff and Official Use, Planning the FDR Group Meeting, The FDR
Group Meeting ,FDR Animations ,The FDR Factual Report , Release of the Recorder and Original Data
Media,Military Investigations or Other Federal Agencies , NTSB Investigation with Foreign
Representatives, Foreign Investigations with NTSB Participation or Assistance.


UNIT V FLIGHT DATA ANALYSIS CASE STUDIES 10
Go-around procedure,Low-speed after take off,Fuel conservation of short- haul operators,Air-craft
deicing,FDM for the business jet user

TOTAL: 45 PERIODS




OUTCOME

Upon completion of the course, students will understand the concept of Flight Data management,the
necessary knowledge needed in modelling the Data Management process and methods. The students will
have an exposure on various Flight Data Management process such as flight data recording, flight data
analysis and airline safety management. The students will be able to use these skills effectively in
providing solution to problems in aircraft safety and management.




Curriculum and Syllabus | M.E. Avionics | R2017 Page 64

Department of Aeronautical Engineering, REC






REFERENCES



1.Flight Data Recorder Handbook for Aviation Accident Investigations Office of Research and
Engineering Office of Aviation Safety Washington, DC 20594

2.CAP 739-Flight Data Monitoring

3.Flight data monitoring on ATR aircraft 2016



L T P C
AV17E32 AIR TRAFFIC CONTROL
3 0 0 3

OBJECTIVE:

· To study the procedure of the formation of aerodrome and its design and air traffic control.

UNIT I BASIC CONCEPTS 9

Objectives of air traffic control systems - Parts of ATC services – Scope and Provision of ATCs – VFR&
IFR operations – Classification of ATS air spaces – Various kinds of separation – Altimeter
settingprocedures – Establishment, designation and identification of units providing ATS – Division
ofresponsibility of control.

UNIT II AIR TRAFFIC SYSTEMS 9

Area control service, assignment of cruising levels - minimum flight altitude - ATS routes and

significant points – RNAV and RNP – Vertical, lateral and longitudinal separations based on time /distance –
ATC clearances – Flight plans – position report

UNIT III FLIGHT INFORMATION SYSTEMS 10

Radar service, Basic radar terminology – Identification procedures using primary / secondary radar –
performance checks – use of radar in area and approach control services – assurance control and
coordination between radar / non

radar control – emergencies – Flight information and advisory service – Alerting service – Co-ordination and
emergency procedures – Rules of the air.


UNIT IV AERODROME DATA 9

Aerodrome data - Basic terminology – Aerodrome reference code – Aerodrome reference point –Aerodrome
elevation – Aerodrome reference temperature – Instrument runway, physical Characteristics; length of
primary / secondary runway – Width of runways – Minimum distance between parallel runways etc. –
obstacles restriction.






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Department of Aeronautical Engineering, REC


UNIT V NAVIGATION AND OTHER SERVICES 8

Visual aids for navigation Wind direction indicator – Landing direction indicator – Location and
characteristics of signal area – Markings, general requirements – Various markings – Lights, general
requirements – Aerodrome beacon, identification beacon – Simple approach lighting system and various
lighting systems – VASI & PAPI - Visual aids for denoting obstacles; object to be marked and lighter –
Emergency and other services.



TOTAL : 45 PERIODS

OUTCOME:

· Understanding the requirement of air traffic control systems and types of air traffic control
system.
· Knowledge in flight information systems and rules of air traffic systems.
· Knowledge indirection indicator systems for air navigation.


TEXT BOOK
1. AIP (India) Vol. I & II, “The English Book Store”, 17-1, Connaught Circus, New Delhi.



AV17E33 ARTIFICIAL INTELLIGENCE L T P C

3 0 03

OBJECTIVE:

· To introduce students to the basic concepts of machine learning and artificial intelligence.

UNIT I INTRODUCTION TO Al AND PRODUCTION SYSTEMS 9
Introduction to AI-Problem formulation, Problem Definition -Production systems, Control strategies, Search
strategies. Problem characteristics, Production system characteristics -Specialized production system-
Problem solving methods - Problem graphs, Matching, Indexing and Heuristic functions -Hill Climbing-
Depth first and Breath first, Constraints satisfaction - Related algorithms, Measure of performance and
analysis of search algorithms.

UNIT II REPRESENTATION OF KNOWLEDGE 9
Game playing - Knowledge representation, Knowledge representation using Predicate logic, Introduction to
predicate calculus, Resolution, Use of predicate calculus, Knowledge representation using other logic-
Structured representation of knowledge.

UNIT III KNOWLEDGE INFERENCE 9
Knowledge representation -Production based system, Frame based system. Inference - Backward chaining,
Forward chaining, Rule value approach, Fuzzy reasoning - Certainty factors, Bayesian Theory-Bayesian
Network-Dempster - Shafer theory.

UNIT IV PLANNING AND MACHINE LEARNING 9
Basic plan generation systems - Strips -Advanced plan generation systems – K strips -Strategic explanations
-Why, Why not and how explanations. Learning- Machine learning, adaptive Learning.







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Department of Aeronautical Engineering, REC


UNIT V EXPERT SYSTEMS 9
Expert systems - Architecture of expert systems, Roles of expert systems - Knowledge Acquisition –Meta
knowledge, Heuristics. Typical expert systems - MYCIN, DART, XOON, Expert systems shells.



TOTAL: 45 PERIODS

OUTCOME:

Upon completion of the course,

· Students will be able to identify the problems that are responsive to provide solution by AI
methods and identify appropriate AI methods to solve a given problem.


· The student will be able to give a definite structure to a given problem in the language or
framework of different AI methods and implement basic AI algorithms.

· The student will be able to design and evaluate different algorithms based on observation and
experience.



TEXT BOOKS:
1. Kevin Night and Elaine Rich, Nair B., “Artificial Intelligence (SIE)”, Mc Graw Hill- 2008. (Units-I,II,VI
& V)
2. Dan W. Patterson, “Introduction to AI and ES”, Pearson Education, 2007. (Unit-III).
REFERENCES:
1. Peter Jackson, “Introduction to Expert Systems”, 3 rd Edition, Pearson Education, 2007.
2. Stuart Russel and Peter Norvig “AI – A Modern Approach”, 2 nd Edition, Pearson Education 2007.
3. Deepak Khemani “Artificial Intelligence”, Tata Mc Graw Hill Education 2013.
4. http://nptel.ac.in






































Curriculum and Syllabus | M.E. Avionics | R2017 Page 67