KUMARAGURU COLLEGE OF TECHNOLOGY
(Autonomous Institution Affiliated to Anna University, Chennai)
COIMBATORE – 641049
CURRICULUM & SYLLABUS CHOICE BASED CREDIT SYSTEM (REGULATIONS 2015)
I to IV Semester
M.E Applied Electronics Department of Electronics and Communication Engineering Department of Electronics and Communication Engineering
Vision To be a centre of repute for learning and research with internationally accredited curriculum, state-of-the-art infrastructure and laboratories to enable the students to succeed in globally competitive environments in academics and industry
Mission The Department is committed to set standards of excellence in its academic delivery aimed to imbibe right attitude and leadership quality in students to apply the acquired knowledge and skills to meet the challenges of evolving global and local needs adhering to professional ethics.
Kumaraguru College of Technology Coimbatore – 641 049 Regulation 2015
CBCS – PG Curriculum Name of the PG Programme: Applied Electronics
Foundation Courses (FC)
S. Course Course Title Periods/Wk Preferred No. Code & Credits Semester L T P C 1. P15MAT108 Applied Algebra 3 1 0 4 I 2. P15AET103 Advanced Signal Processing 3 1 0 4 I
Professional Core (PC)
S. Course Course Title Periods /Wk Preferred No. Code & Credits Semester L T P C Specilisation 1:
Advanced Digital System 1. P15AET101 3 1 0 4 I Design
2. P15AET102 VLSI Design Techniques 3 0 0 3 I
3. P15AET104 Embedded Systems 3 0 0 3 I Embedded Systems 4. P15AEP101 Laboratory 0 0 3 0 I Analog Integrated Circuit 5. P15AET201 3 1 0 4 II Design Advanced Control 6. P15AET202 3 0 0 3 II Engineering 7. P15AET203 ASIC Design 3 0 0 3 II
8. P15AEP201 VLSI Laboratory 0 0 0 3 II
Professional Electives (PE)
S. Course Course Title Periods /Wk Preferred No. Code & Credits Semester L T P C Advanced Digital Image 1. P15AETE01 3 0 0 3 II Processing
2. P15AETE02 Soft Computing 3 0 0 3 II
3. P15AETE03 Optimization Techniques 3 0 0 3 II
4. P15AETE04 Nonlinear Signal Processing 3 0 0 3 III Underwater Acoustic Signal 5. P15AETE05 3 0 0 3 I Processing Wavelets and Multiresolution 6. P15AETE06 3 0 0 3 II Processing
7. P15AETE07 Sparse Theory and Applications 3 0 0 3 III
8. P15AETE08 Advanced Computer Architecture 3 0 0 3 I
9. P15AETE09 Advanced Processors 3 0 0 3 I
10. P15AETE10 Virtual Instrumentation 3 0 0 3 I
11. P15AETE11 Power Electronics 3 0 0 3 I
12. P15AETE12 Principles of Remote Sensing 3 0 0 3 I
13. P15AETE13 Neural Networks and Applications 3 0 0 3 II Multimedia Compression 14. P15AETE14 3 0 0 3 II Techniques
15. P15AETE15 System Modeling and Simulation 3 0 0 3 II
Synthesis and Optimization of 16. P15AETE16 3 0 0 3 II Digital Circuits
17. P15AETE17 Hardware Software Co-Design 3 0 0 3 II
18. P15AETE18 Robotics 3 0 0 3 II 19. P15AETE19 Microelectromechanical Systems 3 0 0 3 II Embedded Systems in Automotive 20. P15AETE20 3 0 0 3 II Applications 21. P15AETE21 Advanced Embedded Development 3 0 0 3 II
22. P15AETE22 Stochastic Models and Simulation 3 0 0 3 II Cellular and Mobile 23. P15AETE23 3 0 0 3 II Communication 24. P15AETE24 Low Power VLSI Design 3 0 0 3 III
25. P15AETE25 VLSI Signal Processing 3 0 0 3 III
26. P15AETE26 Analog VLSI Design 3 0 0 3 III
27. P15AETE27 Mixed Signal VLSI Design 3 0 0 3 III
28. P15AETE28 VLSI Testing and Testability 3 0 0 3 III Computer Aided Design of VLSI 29. P15AETE29 3 0 0 3 III Circuits
30. P15AETE30 Design and Analysis of Algorithms 3 0 0 3 III DSP Processor Architecture and 31. P15AETE31 3 0 0 3 III Programming
32. P15AETE32 DSP Integrated Circuits 3 0 0 3 III
33. P15AETE33 Sensors and Signal Conditioning 3 0 0 3 III
34. P15AETE34 Machine Vision and Learning 3 0 0 3 III
35. P15AETE35 Pattern Recognition 3 0 0 3 III Solid State Device Modeling and 36. P15AETE36 3 0 0 3 III Simulation
37. P15AETE37 Nanoelectronics 3 0 0 3 III
Special Electives
38. P15AESE01 Research Methodology 3 0 0 3 IV 39 P15AESE02 Multirate Signal Processing 3 0 0 3 III
Multi-Sensor Data And Image 40. P15AESE03 3 0 0 3 III Fusion
41. P15AESE04 Hyperspectral Image Processing 3 0 0 3 III
42. P15AESE05 Missile Guidance and Control 3 0 0 3 III
Employability Enhancement Courses (EEC)
S. Course Course Title Periods /Wk Preferred No. Code & Credits Semester L T P C 1. P15AEP301 Project Work Phase I 0 0 12 6 III 2. P15AEP401 Project Work Phase II 0 0 24 12 IV One Credit Courses#
Advanced Embedded System STEPS Knowledge Services 1. P15AEIN01 Design using ARM P Ltd, Coimbatore Advanced Analog System STEPS Knowledge Services 2. P15AEIN02 Design P Ltd, Coimbatore Concepts in Modern Sensor Soliton Automation, 3. P15AEIN03 Technology Coimbatore
# 1. Overall CGPA will not include the credits scored in one credit courses. 2. One Credit Courses can be opted only during 1st to 3rd semester and is restricted to one course per semester. SEMESTER – I
Course Course Title Category Contact L T P C Code Hours Theory
1. P15MAT108 Applied Algebra FC 60 3 1 0 4 2. P15AET101 Advanced Digital System PC 60 3 1 0 4 Design 3. P15AET102 VLSI Design Techniques PC 45 3 0 0 3 4. P15AET103 Advanced Signal FC 60 3 1 0 4 Processing 5. P15AET104 Embedded Systems PC 45 3 0 0 3 6. E1 Elective I PE 45 3 0 0 3 Practicals 1. P15AEP101 Embedded Systems PC 45 0 0 3 1 Laboratory
Total credits 22 SEMESTER – II
Course Course Title Category Contact L T P C Code Hours Theory 1. Analog Integrated Circuit PC 60 3 1 0 4 P15AET201 Design 2. P15AET202 Advanced Control PC 45 3 0 0 3 Engineering 3. P15AET203 ASIC Design PC 45 3 0 0 3 4. E2 Elective II PE 45 3 0 0 3 5. E3 Elective III PE 45 3 0 0 3 6. E4 Elective IV PE 45 3 0 0 3 Practicals
1. P15AEP201 VLSI Laboratory PC 45 0 0 3 1
Total credits 20 SEMESTER – III
Course Course Title Category Contact L T P C Code Hours Theory 1. E5 Elective V PE 45 3 0 0 3 2. E6 Elective VI PE 45 3 0 0 3 3. E7 Elective VII PE 45 3 0 0 3 4. E8 Self Study Elective PE 45 0 0 0 3 Practicals 1. P15AEP301 Project Work (Phase I) EEC 45 0 0 12 6
Total credits 18 SEMESTER – IV
Course Course Title Category Contact L T P C Code Hours Practicals 1. P15AEP401 Project Work (Phase II) EEC 0 0 24 12
Total credits 12
Total Credits for 72 the Program
Department of Electronics and Communication Engineering
M.E APPLIED ELECTRONICS REGULATIONS 2015 SYLLABUS P15MAT108 - APPLIED ALGEBRA L T P C (Common to both Communication Systems and Applied Electronics) 3 1 0 4
Course Outcomes: Upon completion of the course the student should be able to: CO1: Solve linear equations and apply it to real-time problems. CO2: Apply matrix algebra and determinants to solve problems. CO3: Compute Eigen values, Eigen vectors and use linear transformations. CO4: Apply Gram-Schmidt orthogonalization procedure to compute orthogonal bases. CO5: Develop probabilistic models for observed phenomena
Pre-requisite: NIL Hrs LINEAR EQUATIONS 09+03 System of linear equations - Row reduction & Echelon forms -Vector equations - Matrix equation Ax=b - Solution sets of linear systems: Direct and Iterative methods - Application of linear systems – Linear Independence.
MATRIX ALGEBRA 09+03 Matrix operations - Inverse of a matrix - Characteristics of invertible matrices - Partitioned matrices -Matrix factorizations - Subspaces of Rn - Dimension & rank - Introduction to determinants - Properties of determinants - Cramer’s rule.
VECTOR SPACES 09+03 Vector spaces & subspaces - Null spaces, column spaces & linear transformations - Linearly independent sets; Bases - Coordinate systems - Dimension of a vector space – Rank - Change of basis- Eigen values & Eigen vectors - Characteristic equation –Diagonalization of symmetric matrices - Eigenvectors & linear transformations - Complex Eigen values - Applications to differential equations.
ORTHOGONALITY AND LEAST SQUARES 09+03 Inner product, Length and Orthogonality - Orthogonal sets - Orthogonal projections –Gram - Schmidt process - Inner product spaces - Applications of inner product spaces - Quadratic forms - Singular value decomposition - Applications to image processing.
RANDOM VARIABLES 09+03 One-dimensional Random Variables – Moments and MGF – Binomial, Poisson, Geometric, Exponential and Normal distributions – Two-dimensional Random Variables – Marginal and Conditional distribution – Covariance and Correlation coefficient.
Theory 45 Hrs Tutorial 15 Hrs Total 60 Hrs
References: 1. David C. Lay, Steven R Lay and Judy J McDonald “Linear Algebra and its Applications”,Global Edition Pearson Education Ltd , , 2015 2. Gilbert Strang, “Linear Algebra and its Applications”,Cencage Learning (RS), Fouth edition,2007 3. Seymour Lipschutz , Marc Lipson, “Schaum's Outline of Linear Algebra”, McGraw Hill , Fifth Edition, 2013 4. Howard A. Anton , “Elementary Linear Algebra”, John Wiley & Sons, Ninth Edition, 2008 5. Veerarajan. T., “ Probability and Random Process”, Tata McGraw Hill,2008
P15AET101 - ADVANCED DIGITAL SYSTEM DESIGN (Common to both Communication Systems and Applied Electronics) L T P C 3 1 0 4
Course Outcomes: Upon completion of the course the student should be able to: Design synchronous and asynchronous sequential circuits based on CO1: specifications CO2: Develop algorithm and VHDL code for design of digital circuits CO3: Illustrate digital design implementation on PLDs CO4: Identify the various faults that can occur in digital circuits CO5: Employ different methods for fault detection
Pre-requisite: 1. Digital Electronics
Hrs SYNCHRONOUS SEQUENTIAL CIRCUIT DESIGN 09+03 Analysis of Clocked Synchronous Sequential Circuits - Modeling, state table reduction, state assignment, Design of Synchronous Sequential Networks, Design of iterative circuits - ASM chart - ASM realization.
ASYNCHRONOUS SEQUENTIAL CIRCUIT DESIGN 09+03 Analysis of Asynchronous Sequential Circuits - Races in ASC – Primitive Flow Table - Flow Table Reduction Techniques, State Assignment Problem and the Transition Table – Design of ASC – Static and Dynamic Hazards – Essential Hazards – Data Synchronizers. Case study : Vending Machine Controller
SYSTEM DESIGN USING PLDS 09+03 Basic concepts – Programming Technologies - Programmable Logic Element (PLE) - Programmable Array Logic (PLA) - Programmable Array Logic (PAL) – Programmable Logic Architectures – 16L8 – 16R4 – 22V10 –- Design of combinational and sequential circuits using PLDs – Complex PLDs (CPLDs)
INTRODUCTION TO VHDL 09+03 Design flow - Software tools – VHDL : Data Objects - Data types - Operators – Entities and Architectures – Multivalued logic and Signal resolution – IEEE 1164 std logic - Components and Configurations – Signal Assignment –Concurrent and Sequential statements –– Behavioral, Data flow and Structural modeling – Transport and Inertial delays – Delta delays - Attributes – Generics – Packages and Libraries– Subprograms: Functions and Procedures – Operator overloading – Test Benches – Design examples.
LOGIC CIRCUIT TESTING AND TESTABLE DESIGN 09+03 Digital logic circuit testing - Fault models - Combinational logic circuit testing - Sequential logic circuit testing-Design for Testability - Built-in Self-test, Board and System Level Boundary Scan. Case Study: Traffic Light Controller
Theory 45 Hrs Tutorial 15 Hrs Total 60 Hrs
References: 1. Donald G. Givone, “Digital principles and Design”, Tata McGraw Hill, 2002. 2. Nelson, V.P., Nagale, H.T., Carroll, B.D., and Irwin, J.D., "Digital Logic Circuit Analysis and Design", Prentice Hall International, Inc., New Jersey, 1995 3. Volnei A. Pedroni, “Circuit Design with VHDL”, PHI Learning, 2011. 4. Parag K Lala, “Digital Circuit Testing and Testability”, Academic Press, 1997 5. Charles H Roth, “Digital Systems Design Using VHDL,” Cencage 2nd Edition 2012. 6. Nripendra N Biswas “Logic Design Theory” Prentice Hall of India,2001
P15AET102 - VLSI DESIGN TECHNIQUES L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Analyze the characteristics of MOS transistor. CO2: Understand various CMOS fabrication techniques. CO3: Identify latch up problems in CMOS circuits. CO4: Illustrate the concepts of CMOS inverters and their sizing methods. CO5: Estimate Power and delay in CMOS circuits
Pre-requisite: 1. Introduction to VLSI Design
Hrs MOS TRANSISTOR THEORY 09 NMOS and PMOS transistors, CMOS logic, MOS transistor theory – Introduction, Enhancement mode transistor action, Ideal I-V characteristics, DC transfer characteristics, Threshold voltage- Body effect- Design equations- Second order effects. MOS models and small signal AC characteristics, Simple MOS capacitance Models, Detailed MOS gate capacitance model, Detailed MOS Diffusion capacitance model.
CMOS TECHNOLOGY AND DESIGN RULE 09 CMOS fabrication and Layout, CMOS technologies, P -Well process, N -Well process, twin -tub process, MOS layers stick diagrams and Layout diagram, Layout design rules, Latch up in CMOS circuits, CMOS process enhancements, Technology – related CAD issues, Fabrication and packaging.
INVERTERS AND LOGIC GATES 09 NMOS and CMOS Inverters, Inverter ratio, DC and transient characteristics , switching times, Super buffers, Driving large capacitance loads, CMOS logic structures , Transmission gates, Static CMOS design, dynamic CMOS design.
CIRCUIT CHARACTERISATION AND PERFORMANCE ESTIMATION 09 Resistance estimation, Capacitance estimation, Inductance, switching characteristics, transistor sizing, power dissipation and design margining. Charge sharing .Scaling.
VLSI SYSTEM COMPONENTS CIRCUITS AND SYSTEM LEVEL 09 PHYSICAL DESIGN Multiplexers, Decoders, comparators, priority encoders, Shift registers. Arithmetic circuits – Ripple carry adders, Carry look ahead adders, High-speed adders, Multipliers. Physical design – Delay modelling, cross talk, floor planning, power distribution. Clock distribution. Basics of CMOS testing.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Neil H.E. Weste and Kamran Eshraghian, “Principles of CMOS VLSI Design”, Pearson Education ASIA, 2nd edition, 2000. 2. John P. Uyemura, “Introduction to VLSI Circuits and Systems”, John Wiley & Sons, Inc., 2002. 3. Eugene D. Fabricius, “Introduction to VLSI Design”, McGraw Hill International Editions, 1990. 4. Pucknell, “Basic VLSI Design”, Prentice Hall of India Publication, 1995. 5. Wayne Wolf, “Modern VLSI Design System on chip”, Pearson Education.2002.
P15AET103/ P15COT103 - ADVANCED SIGNAL PROCESSING L T P C 3 1 0 4 Course Outcomes: Upon completion of the course the student should be able to: CO1: Employ the concepts of discrete random processes and parameter estimation. Distinguish between parametric and nonparametric methods of power spectrum CO2: estimation. CO3: Relate concepts of linear prediction and Wiener filtering. CO4: Analyze concepts of adaptive filtering. CO5: Apply the concepts of multi-rate signal processing to real time problems
Pre-requisite: 1. Digital Signal Processing
Hrs DISCRETE RANDOM SIGNAL PROCESSING 09+03 Discrete Random Processes- Ensemble averages, stationary processes, Autocorrelation and Auto covariance matrices- Parameter estimation: Bias and consistency - Parseval's Theorem, White Noise, Power spectrum, Wiener-Khintchine Relation, Filtering random Processes, Spectral Factorization
SPECTRUM ESTIMATION 09+03 Non-Parametric Methods - Periodogram Estimator, Performance Analysis of Estimators - Unbiased, Consistent Estimators- Modified Periodogram, Bartlett and Welch methods, Blackman –Tukey method. AR, MA, ARMA processes - Yule-Walker equations - Parametric methods of Spectral Estimation
LINEAR ESTIMATION AND PREDICTION 09+03 Linear prediction- Forward and backward predictions, Lattice filter realization, Prony’s all pole modeling, Solutions of the Normal equations- Levinson-Durbin recursion - Levinson recursion. Optimum filters- FIR Wiener filter –Causal and Non-causal IIR Wiener filter-Discrete Kalman filter
ADAPTIVE FILTERS 09+03 FIR adaptive filters -adaptive filter based on steepest descent method- LMS adaptive algorithm, Normalized LMS. Adaptive channel equalization-Adaptive echo cancellation-Adaptive noise cancellation- Adaptive recursive filters (IIR). RLS adaptive filters-Exponentially weighted RLS- sliding window RLS.
MULTIRATE SIGNAL PROCESSING 09+03 Mathematical description of change of sampling rate - Interpolation and Decimation, Decimation by an integer factor - Interpolation by an integer factor, Sampling rate conversion by a rational factor, Multistage implementation of multirate system, Direct form FIR filter structures, Polyphase filter structures, Subband Coding, Quadrature Mirror Filters – Condition for perfect reconstruction, Applications of Multirate systems
Theory 45 Hrs Tutorial 15 Hrs Total 60 Hrs
References: 1. Monson H. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley and Sons, Inc., 2008 2. John G. Proakis, Dimitris G.Manolakis, “ Digital Signal Processing’ Pearson Education, 2013. 3. John G. Proakis et. al., “Algorithms for Statistical Signal Processing’, Pearson Education”, 2002. 4. Dimitris G.Manolakis et.al., “Statistical and adaptive signal Processing”, McGraw Hill, New York, 2005.
P15AET104 - EMBEDDED SYSTEMS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Discuss the steps of embedded system design process. Explain the architecture, addressing modes and instruction set of Embedded CO2: Processors. CO3: Apply networking principles in embedded devices. CO4: Illustrate various scheduling algorithms. Interpret the concepts of a real time operating system CO5:
Pre-requisite: 1. Microprocessors and Microcontrollers
Hrs EMBEDDED ARCHITECTURE 09 Embedded Computers, Characteristics of Embedded Computing Applications, Embedded system design process- Requirements, Specification, Architectural Design, Designing Hardware and Software Components, System Integration, Unified modeling language (UML), Formalism for System Design- Structural Description, Behavioral Description, Design Example: Model Train Controller.
EMBEDDED PROCESSOR AND COMPUTING PLATFORM 09 ARM processor- processor and memory organization, Data operations, Flow of Control, TI C55X Digital Signal processor- Processor and Memory organization, Addressing modes, Data operations, Flow of Control, CPU Bus configuration, ARM Bus, SHARC Bus - Design Example : Alarm Clock.
NETWORKS 09 Distributed Embedded Architecture- Hardware and Software Architectures, Networks for embedded systems- I2C, CAN Bus, SHARC link ports, Ethernet, Myrinet, Internet, Network- Based design- Communication Analysis, system performance Analysis, Hardware platform design, Allocation and scheduling, Design Examples: Elevator Controller, Ink jet printer- Hardware Design and Software Design, Personal Digital Assistants, Set-top Boxes.
REAL-TIME CHARACTERISTICS 09 Clock driven Approach, weighted round robin Approach, Priority driven Approach, Dynamic Versus Static systems, effective release times and deadlines, Optimality of the Earliest deadline first (EDF) algorithm, challenges in validating timing constraints in priority driven systems, Off- line Versus On-line scheduling.
REAL TIME OPERATING SYSTEM 09 Operating system services-Process Management-Memory Management-Device and File Management- I/O sub systems- Interrupt routines in RTOS environment- RTOS –Services-Design using RTOS-Principles-Saving of memory and power, Functions and types of RTOS - RTOS cos-II
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Wayne Wolf, Computers as Components: “Principles of Embedded Computing System Design”, Morgan Kaufman Publishers, 2008. 2. Rajkamal, “Embedded System Architecture – Programming and Design” Tata McGraw- Hill, Fifth reprint, 2010 3. Jane.W.S. Liu , “Real-Time systems”, Pearson Education Asia, 2000. 4. C. M. Krishna and K. G. Shin, “Real-Time Systems”, McGraw-Hill, 2009. 5. Frank Vahid and Tony Givargi Embedded System Design: “A Unified Hardware/Software Introduction”, John Wiley & Sons, 2006.
P15AEP101 - EMBEDDED SYSTEMS LABORATORY L T P C 0 0 3 1 Course Outcomes: Upon completion of the course the student should be able to: Experiment algorithm and programming with ARM Processor for arithmetic CO1: operations and block transfers CO2: Demonstrate the use of various I/O interfaces with ARM Processor Develop algorithms and code for different applications for implementation on CO3: TMS320C54X processor
Pre-requisite: 1. Microprocessors and Microcontrollers 2. Digital Signal Processing and Processors
Hrs Experiments based on front-end and back-end tools of the following circuits: Experiments on ARM Processor 1. Data Operations: Arithmetic Operations Block Transfers 2. I/O Interface LCD Display Matrix Keyboard A/D Conversion D/A conversion 3. Timer Operation – Real Time Clock Experiments on TMS320C54X using CCS 1. Advanced Addressing Modes 2. Convolution/Correlation of Signals 3. Computation of FFT 4. Audio Capture and Processing 5. Implementation of LMS Algorithm
Theory -Hrs Tutorial - Hrs Total 45 Hrs P15AET201 - ANALOG INTEGRATED CIRCUIT DESIGN L T P C 3 1 0 4 Course Outcomes: Upon completion of the course the student should be able to: CO1: Analyze various voltage and current sources Compare different amplifier types in terms of their gain, frequency response CO2: and Noise Elimination Examine the functions of the internal circuits of single stage and two stage op CO3: amps Evaluate the stability and frequency compensation techniques for Op Amps CO4:
Pre-requisite: 1. Analog Electronics 2. Linear Integrated Circuits Hrs BIASING CIRCUITS OF CMOS 09+03 Basic current mirrors, cascode current mirrors, active current mirrors, voltage references, supply independent biasing, temperature independent references, PTAT current generation, Constant-Gm Biasing.
SINGLE STAGE AMPLIFIERS 09+03 Common source stage, Source follower, Common gate stage, Cascode stage, Single ended and differential operation, Basic differential pair, Differential pair with MOS loads
FREQUENCY RESPONSE AND NOISE ANALYSIS 09+03 Miller effect, Association of poles with nodes, frequency response of common source stage, Source followers, Common gate stage, Cascode stage, Differential pair, Statistical characteristics of noise, noise in single stage amplifiers, noise in differential amplifiers.
OPERATIONAL AMPLIFIERS 09+03 Concept of negative feedback, Effect of loading in feedback networks, operational amplifier performance parameters, One-stage Op Amps, Two-stage Op Amps, Input range limitations, Gain boosting, slew rate, power supply rejection, noise in Op Amps.
STABILITY AND FREQUENCY COMPENSATION 09+03 General considerations, Multipole systems, Phase Margin, Frequency Compensation, Compensation of two stage Op Amps, Slewing in two stage Op Amps, other compensation techniques.
Theory 45 Hrs Tutorial 15 Hrs Total 60 Hrs References: 1. Behzad Razavi, “Design of Analog CMOS Integrated Circuits”, Tata McGraw Hill, 2002 2. Grebene, “Bipolar and MOS Analog Integrated circuit design”, John Wiley & sons,Inc., 2003. 3. Kenneth R. Laker, Willy M.C. Sansen, “Design of Analog Integrated circuits and Systems”, 1994. 4. Phillip E.Allen, DouglasR.Holberg, “CMOS Analog Circuit Design”, Second edition, Oxford University Press, 2013
P15AET202 - ADVANCED CONTROL ENGINEERING L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Solve difference equations using z-transform. CO2: Review D/A and A/D conversion techniques. CO3: Apply state space techniques for analysis of systems. CO4: Design discrete time control systems using transform methods. Analyze the time and frequency response of discrete time systems CO5:
Pre-requisite: 1. Control Systems Engineering
Hrs INTRODUCTION TO DISCRETE TIME SYSTEMS 09 Introduction discrete systems, Transform methods, properties of Z transform, Solution of difference equation, Inverse Z transform, Simulation Diagram and Signal flow Graphs, Sampled Data control systems, Ideal Sampler, Evaluation of E*(S),Results from the Fourier Transform, Properties of E*(S), Data Reconstruction, Digital to Analog Conversion, Analog to Digital Conversion.
STATE SPACE ANALYSIS 09 State space representation of discrete time system, solving discrete time space equation, Pulse transfer function matrix, Continuous time state space equation, Discretization of continuous time state space equation, controllability, observability, useful transformation in state space analysis and design.
DESIGN OF DT CONTROL SYSTEM VIA TRANSFORM METHODS 09 Introduction, Obtaining discrete time equivalent of continuous time filter, Discretizing a simple continuous time filter, Backward difference method, Bilinear transformation method, Bilinear transformation method with frequency and prewarping, Impulse invariance method, Step invariance method, matched pole –Zero mapping method, Design principle based on a discrete time equivalent of an analog controller.
TIME RESPONSE AND STABILITY ANALYSIS OF DT SYSTEM 09 Transient analysis and steady state response analysis, transient response specification for second order continuous time system, relationship between Z plane pole and zero location and transient response, steady state error analysis based on root locus method& frequency response method , Bode Diagrams., Stability- Bilinear transformation , Routht Hurwitz criterian, Juris’s stability test.
DIGITAL CONTROLLER DESIGN 09 Control system specification, Compensation, Phase lag compensation, Phase lead compensation, Design procedure using Bode plot, Integration and Differentiation, Digital PID controllers.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Katsuhiko Ogata, “Discrete Time Control System”, Prentice Hall.inc, 2005. 2. Charles .L Phillips and H.Troy Nagle, “Digital control system analysis and design”,Fourth Edition , Prentice Hall International Edition, 2014. 3. M.Gopal “Digital control and state variable methods”, Tata McGraw publication company limited, 2012
P15AET203 - ASIC DESIGN L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Describe different types of ASICs CO2: Illustrate internal architecture of commercially available FPGAs. CO3: Develop algorithms to implement digital design on FPGAs CO4: Distinguish various steps in FPGA design implementation Evaluate case studies of FPGA architectures and logic synthesis CO5:
Pre-requisite: 1. Digital Electronics 2. VLSI Design
Hrs INTRODUCTION TO ASIC AND LIBRARY DESIGN 09
Types of ASICs - Design flow – Case Study: SPARC Station 1 – Economies of ASICs – ASIC Cell Libraries – CMOS Transistors - Combinational Logic Cell – Sequential logic cell - Data path logic cell – I/O Cells – Transistor Resistance and Parasitic Capacitance - Logical effort – Library cell design - Library architecture.
PROGRAMMABLE ASIC 09
Anti fuse -Static RAM - EPROM and EEPROM technology – Practical issues - PREP benchmarks - Actel ACT - Xilinx LCA –Altera FLEX - Altera MAX – Xilinx Spartan – Virtex FPGAs – Altera Cyclone FPGAs
PROGRAMMABLE ASIC I/O 09
DC inputs and Outputs – Totem Pole output –AC inputs and Outputs – Clock input – Power input – Supply bounce – Noise Margin – Meta-stability - Case Study: Xilinx XC4000 IOB
PROGRAMMABLE ASIC INTERCONNECT 09
Actel ACT 1/2/3 - Xilinx LCA - Xilinx EPLD - Altera MAX 5000/7000/9000 - Altera FLEX FPGAs – Case study: Spartan-3 Block RAM
LOGIC SIMULATION & SYNTHESIS 09
Types of simulation – Logic systems – How logic Simulation works – Delay models – Limitations of Logic simulation – Static Timing analysis - Design Systems - Logic synthesis – EDIF – Schematic Entry – Inside a logic Synthesizer – Case Study: Comparator/MUX
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. M.J.S .Smith, "Application Specific Integrated Circuits”, Addison -Wesley Longman Inc., 1997. 2. Wayne Wolf, “FPGA-Based System Design”, Prentice Hall PTR, 2004. 3. R. Rajsuman and Santa Clara, “System-on-a-Chip Design and Test”, CA: Artech House Publishers, 2000. 4. F. Nekoogar, “Timing Verification of Application-Specific Integrated Circuits (ASICs)”, Prentice Hall PTR, 1999.
P15AEP201 - VLSI LABORATORY L T P C 0 0 3 1 Course Outcomes: Upon completion of the course the student should be able to: CO1: Experiment with various front end and back end tools. CO2: Design and implement combinational and sequential logic circuits. Practice simulation and synthesis of combinational and sequential logic circuits using CO3: front end tools. CO4: Evaluate layout for simple designs using back end tools.
Pre-requisite: 1. Digital Electronics 2. VLSI Design Hrs Experiments based on front-end and back-end tools of the following circuits: Using Xilinx ISE front-end software (VHDL only) Combinational logic: 1. 4-bit parallel adder 2. 4-bit serial adder 3. Parallel multipliers 4. Multiply Accumulate unit Sequential logic: 1. Multi-bit pre-settable, up/down counters 2. FIFO buffer 3. Sequence detectors 4. Real-time Clock Using Microwind /Cadence back-end software: 1. 4-bit parallel adder 2. 4-bit serial adder 3. 4-bit pre-settable, up/down counters 4. 16 byte FIFO buffer 5. 3-bit Sequence detectors
Theory - Hrs Tutorial - Hrs Total 45 Hrs
ELECTIVES
P15AETE01/ P15COTE02 - ADVANCED DIGITAL IMAGE PROCESSING (Common to both Communication Systems and Applied Electronics) L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: Demonstrate knowledge of image acquisition, digitization and spatial filters for CO1: enhancement CO2: Employ color image processing techniques. CO3: Apply morphological image processing algorithms. CO4: Apply segmentation algorithms and descriptors for image processing. CO5: Use neural networks, fuzzy logic and genetic algorithms in object recognition. CO6: Apply compression, watermarking and steganography algorithms to images.
Pre-requisite: 1. Digital Image Processing
Hrs FUNDAMENTALS OF DIGITAL IMAGE PROCESSING 09 Elements of Visual Perception- Image acquisition, digitization- Histogram - Image enhancement – Spatial filters for smoothing and sharpening – Discrete 2D transforms - DFT, DCT, Walsh- Hadamard, Slant, KL, Wavelet Transform – Haar wavelet.
COLOR IMAGE PROCESSSING 09 Color Image Fundamentals-Color Models- RGB, CMY, CMYK and HSI Color Models- Pseudocolor Image Processing - Intensity Slicing- Intensity to Color transformations -Basics of Color Image Processing- Color Transformation - Color Image Smoothing and Sharpening- Color Segmentation - Noise in Color Images.
MORPHOLOGICAL IMAGE PROCESSING 09 Preliminaries- Basic Concepts from Set Theory-Logic Operations Involving Binary Images - Dilation and Erosion –Opening and Closing - Hit-or-Miss Transformation - Basic Morphological Algorithms -Boundary Extraction- Region Filling- Extraction of Connected Components- Convex Hull- Thinning-Thickening- Skeletons- Pruning- - Gray-Scale Morphology.
SEGMENTATION, REPRESENTATION AND DESCRIPTION 09 Edge Detection - Edge Linking and Boundary Detection -Thresholding- Segmentation by Morphological Watershed Segmentation Algorithm - Use of Markers- Representation and Boundary Descriptors.
OBJECT RECOGNITION AND IMAGE PROCESSING APPLICATIONS 09 Patterns and Pattern Classes -Recognition Based on Decision-Theoretic Methods -Matching - Optimum Statistical Classifiers- Neural Networks, Fuzzy Systems - GA. Image compression- JPEG, JPEG2000 JBIG standards - Watermarking - Steganography.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Rafael C. Gonzalez, “Digital Image Processing”, Pearson Education, Inc., 3rd Edition, 2008.
2. Milman Sonka, Vaclav Hlavac, Roger Boyle, “Image Processing, Analysis and Machine Vision”, Brooks/Cloe, Vikas Publishing House 2nd Edition, 1999. 3. Khalid Sayood, “Data Compression”, Morgan Kaufmann Publishers (Elsevier)., 3rd Edition, 2006. 4. Rafael C. Gonzalez, Richards E.Woods, Steven Eddins, “Digital Image Processing using MATLAB”, Pearson Education, Inc., 2004. 5. Willam K.Pratt, “Digital Image Processing”, John Wiley, New York, 2002.
P15AETE02/ P15COTE02 - SOFT COMPUTING (Common to both Communication Systems and Applied Electronics) L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Classify optimization algorithms. CO2: Explain the concepts of neural network theory. CO3: Discuss the principles of genetic algorithms. Apply neural networks, fuzzy logic and genetic algorithms for optimization CO4: problems CO5: Develop neuro fuzzy models for real-time applications.
Pre-requisite: NIL
Hrs ARTIFICIAL NEURAL NETWORKS 09 Supervised learning Neural networks-Introduction, Perception- Adaline, Back propagation- Multi layer perception- Unsupervised learning and other Neural networks-Introduction, Competitive learning networks, Kohenen self organizing networks, Learning vector quantization, Hebbian learning, Hopfield network , Content addressable nature, Binary Hopfield network, Continuous- valued Hopfield network , Travelling Salesperson problem.
FUZZY SET THEORY 09 Fuzzy sets, Basic definitions and terminology, Member function formulation & parameterization, Fuzzy rules, fuzzy reasoning - Extension principle, Fuzzy relation, Fuzzy inference systems: Mamdani model, Sugeno model. Tsukamoto model, Input space partitioning, Fuzzy modeling.
OPTIMIZATION 09 Derivative based optimization-Descent methods, Method of steepest descent, Classical Newtons method, Step-size determination; Derivative free optimization- Genetic algorithm, Simulated annealing, Random search, Downhill search.
ADVANCED NEURO-FUZZY MODELLING 09 Classification and regression trees, decision tress, Cart algorithm – Data clustering algorithms: K- means clustering, Fuzzy C-means clustering, Mountain clustering, Subtractive clustering – rule base structure, Input space partitioning, rule based organization, focus set based rule combination; Neuro-fuzzy control: Feedback Control Systems, Expert Control, Inverse Learning, Specialized Learning, Back propagation through real time Recurrent Learning.
GENETIC ALGORITHM 09 Fundamentals of genetic algorithm- Basic concepts - Encoding – Binary, Octal, Hex, Permutation, Value and tree, Reproduction- Roulette-wheel selection, Boltzman selection, Tournament selection, Rank selection, Steady state selection, Crossover single site, Two point, Multi point, Uniform and matrix, Crossover rate, Inversion, Deletion and duplication, Deletion and Regeneration, Segregation, Crossover, Mutation, Generational cycle.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Jang J.S.R., Sun C.T and Mizutani E, “Neuro Fuzzy and Soft computing”, Pearson education (Singapore) 2004. 2. S.Rajasekaran and G.A.Vijayalakshmi Pai, “Neural networks, Fuzzy logic, and Genetic Algorithms”, Prentice Hall of India, 2003. 3. David E.Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning”, Pearson Education, Asia,2002 4. Laurene Fauseett, “Fundamentals of Neural Networks”, Prentice Hall India, New Delhi, 2004. 5. Timothy J.Ross, “Fuzzy Logic Engineering Applications”, McGrawHill, NewYork, 2011.
P15AETE03/ P15COTE03 – OPTIMIZATION TECHNIQUES (Common to both Communication Systems and Applied Electronics) L T P C 3 0 0 3
Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the theory of optimization methods and algorithms CO2: Formulate problems that arise in engineering in terms of optimization problems. CO3: Analyze and apply appropriate optimization algorithms for solving problems.
Pre-requisite: NIL
Hrs Classical Optimization Techniques 09 Single variable optimization, multivariable optimization with no constraints, multivariable optimization with equality constraints, multivariable optimization with inequality constraints, convex programming problem.
Linear Programming 09 Simplex method, Duality, Non-Simplex Method, Integer Linear Programming
Nonlinear Programming 09 Elimination methods, Interpolation methods, Unconstrained optimization techniques - Direct search methods - Indirect search methods, Constrained Optimization methods – Direct methods, Indirect methods.
Dynamic Programming 09 Multistage decision process, Concept of sub optimization and principle of optimality, computational procedure in dynamic programming
Modern Optimization Methods 09 Simulated annealing, Particle Swarm optimization, Ant colony optimization, Bee colony optimization, Cuckoo Search, Bat Algorithms, Firefly Algorithms.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Singiresu S Rao, “Engineering Optimization: Theory and Practice”, 4th Edition, John Wiley and Sons, 2009 2. Xin-Sie Yang, “Nature Inspired Optimization Techniques”, Elsevier, 2014. 3. Edwin K P Chong and Stanislaw S Zak, “An Introduction to Optimization”, Fourth Edition, John Wiley and Sons, 2013.
P15AETE04/ P15COTE04 - NONLINEAR SIGNAL PROCESSING (Common to both Communication Systems and Applied Electronics) L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the fundamentals of nonlinear filtering CO2: Design nonlinear filters. CO3: Analyze and use filters using order statistics CO4: Explain the basics of Adaptive nonlinear filters CO5: Understand the algorithms and architectures for nonlinear filtering CO6: Apply nonlinear filters to image processing problems
Pre-requisite: 1. Advanced Digital Signal Processing
Hrs INTRODUCTION TO NONLINEAR FILTERS AND STATISTICAL 08 PRELIMINARIES Nonlinear filters - measure of robustness - M estimators - L estimators - R estimators - order statistics - median filter and their characteristics - impulsive noise filtering by median filters - Recursive and weighted median filters - stock filters.
NONLINEAR DIGITAL SIGNAL PROCESSING BASED ON ORDER 07 STATISTICS Time ordered nonlinear filters - rank ordered nonlinear filters - max/median filtering - median hybrid filters - characteristics of ranked order filters - L filters - M filters - R filters - comparison.
ADAPTIVE NONLINEAR AND POLYNOMIAL FILTERS 10 Definition of polynomial filters - Wiener filters - robust estimation of scale - Adaptive filter based on local statistics - Decision directed filters - Adaptive L filters - Comparison of adaptive nonlinear filters - Neural networks for nonlinear filter.
ALGORITHMS AND ARCHITECTURES 10 Sorting and selection algorithm - running median algorithm - fast structures for median and order statistics filtering - systolic array implementation - Wavefront array implementation - quadratic digital filters implementation
APPLICATIONS OF NONLINEAR FILTERS 10 Power spectrum analysis - Morphological image processing - nonlinear edge detection impulse noise rejection in image and bio signals - two component image filtering - speech processing
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Loannis Pitas, Anastarios, N.Venetsanopoulos, “Nonlinear Digital filters - Principles and Applications”, Kluwer Academic Publishers, 1990 2. Jaakko T.Astola, Jaakko Astola Kuosmanen, “Fundamentals of Nonlinear Digital filtering”, CRC Press LLC, 1997 3. Wing Kuen Ling, “Nonlinear Digital filters: Analysis and Applications”, Elsevier Science & Tech. 2007 4. Gonzalo R. Arce, “Nonlinear Signal Processing - A Statistical Approach”, Wiley Publishers, 2005
P15AETE05/ P15COTE05 - UNDERWATER ACOUSTIC SIGNAL PROCESSING (Common to both Communication Systems and Applied Electronics) L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the procedure of sound propagation in ocean. CO2: Analyze various noise sources in the ocean. CO3: Understand the working of Sonar Systems. CO4: Explain the working of various imaging sonars
Pre-requisite: 1. Advanced Signal Processing Hrs FUNDAMENTALS OF UNDERWATER ACOUSTICS 09 The Ocean acoustic environment, measuring sound level, Sources and receivers, relevant units, sound velocity in sea water, typical vertical profiles of sound velocity, Sound propagation in the Ocean- characteristic sound propagation paths-deep water and shallow water, Range dependent environment. Sound attenuation in sea water, Bottom Loss, Surface bottom and volume scattering, Snell’s law for range dependent ocean.
AMBIENT NOISE IN THE SEA 09
Sources of ambient noise-introduction, different frequency bands of ambient noise, process of surface noise generation, shallow water, variability of ambient noise, spatial coherence of ambient noise, directional characteristics of ambient noise, intermittent sources of noise- biological & non biological (rain, earthquakes, explosions and valcanos).
SIGNALS, FILTERS AND RANDOM FUNCTIONS 09 Fourier representations, filters and noise, digital filter design techniques, temporal resolution and bandwidth of signals, signal to noise power ratio, Estimates of autocovariance, power spectrum, cross covariance and cross spectrum.
CHARACTERISTICS OF SONAR SYSTEMS 09 Sonar systems, active and passive sonar equations, transducers and their directivities, Sensor array characteristics-array gain, receiving directivity index, beam patterns, shading and super directivity, adaptive beamforming.
IMAGING SONARS 09 Sidescan Sonar, Synthetic Aperture Sonar, Multibeam Sonar – Principle of operation and Image formation.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs References: 1. Robert J Urick, “Principles of Underwater Sound”, Peninsula Publications, 3rd Edition, 2013. 2. Robert J Urick, “Ambient noise in the sea”, Peninsula Publications, 2nd Edition, 1986.
3. Clay & Medwin, “Fundamentals of Acoustical Oceanography”, Academic Press, 1998 4. L.M.Brekhovskikh & Yu.P.Lysanov, “Fundamentals of Ocean Acoustics”, Springer, 2002. 5. Richard O.Nielsen, “Sonar Signal Processing”, Artech House Acoustic Library, 1991. 6. A.D Waite, “Sonar for Practising Engineers”, Wiley, 3rd Edition, 2001.
P15AETE06/ P15COTE06 - WAVELETS AND MULTIRESOLUTION PROCESSING (Common to both Communication Systems and Applied Electronics) L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Illustrate the fundamentals of vectors, signals and their relationships. CO2: Examine the convergence of signals in Hilbert and Fourier signal spaces. CO3: Analyze signals using Multi Resolution Analysis CO4: Assess the different family of wavelets for real-time applications CO5: Apply wavelet transform for image processing CO6: Explain the principle of non-linear wavelets.
Pre-requisite: 1. Linear Algebra 2. Signal Processing
Hrs INTRODUCTION 09
Vector Spaces - properties - dot product - basis – dimension, orthogonality and orthonormality - relationship between vectors and signals - Signal spaces – concept of Convergence - Hilbert spaces for energy signals - Generalised Fourier Expansion.
MULTI RESOLUTION ANALYSIS 09
Definition of Multi Resolution Analysis (MRA) – Haar basis - Construction of general orthonormal MRA-Wavelet basis for MRA – Continuous time MRA interpretation for the DTWT – Discrete time MRA- Basis functions for the DTWT – PRQMF filter banks
CONTINUOUS WAVELET TRANSFORM 09
Wavelet Transform - definition and properties - concept of scale and its relation with frequency - Continuous Wavelet Transform (CWT) - Scaling function and wavelet functions (Daubechies, Coiflet, Mexican Hat, Sinc, Gaussian, Bi-Orthogonal) – Tiling of time -scale plane for CWT.
DISCRETE WAVELET TRANSFORM 09
Filter Bank and sub band coding principles - Wavelet Filters - Inverse DWT computation by Filter banks -Basic Properties of Filter coefficients - Choice of wavelet function coefficients - Mallat's algorithm for DWT - Lifting Scheme: Wavelet Transform using Polyphase matrix Factorization – Geometrical foundations of lifting scheme - Lifting scheme in Z –domain
APPLICATIONS 09
Image Compression using DWT – Sequential / Progressive - JPEG 2000 standard - Image denoising - Edge detection and object Isolation and Object Detection - Image Fusion -Wavelet Packets- Multiwavelets - Non linear wavelets – Ridgelets – Curvelets – Contourlets.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. C. Sidney Burvus, Ramesh A.Gopinath haito , “Introduction to wavelets and wavelet Transform”, Prentice Hall International, 1995.
2. Gilbert Strang, “Linear Algebra and its Applications”, 3rd edition.
3. J.C. Goswami, A.K. Chan, “Fundamentels of wavelets”, John wiley and sons, 1999.
4. Strang G, Nguyen T, "Wavelets and Filter Banks," Wellesley Cambridge Press,1996.
5. Vetterli M, Kovacevic J, "Wavelets and Sub-band Coding," Prentice Hall, 1995.
6. Mallat S., "Wavelet Signal Processing”, Academic Press, 1996.
P15AETE07/ P15COTE07 - SPARSE THEORY AND APPLICATIONS (Common to both Communication Systems and Applied Electronics) L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Analyze nonlinear multiscale transforms CO2: Apply compressive sensing algorithms in space science CO3: Identify relevant research problems CO4: Contribute to the frontier research in the area
Pre-requisite: 1. Multiresolution Signal Processing 2. Wavelets and Multiresolution Processing
Hrs SPARSITY AND WAVELET 09 Sparse Representation: Sparsity, Sparsity Terminologies, Underdetermined Linear Systems - Regularization - Convexity - l1Minimization - Moving to Sparse Solutions - The l0 Norm and Implications - The P0 Problem in sparse signal processing. Fourier to STFT, From STFT to Wavelets, From Wavelets to Over complete Representations.
DISCRETE WAVELET TRANSFORMS 09 Continuous Wavelet transform (CWT), Definition of CWT and Inverse CWT, Discretization of CWT, Approximation of Vectors in nested vector spaces, Multiresolution analysis in L2(R), Haar scaling function, Haar wavelet, Haar wavelet decomposition, Wavelet Packets, Haar Wavelet Packets.
NONLINEAR MULTISCALE TRANSFORMS 10 Decimated Nonlinear Transform – Multiresolution Based on the Median Transform - Guided Numerical Experiments - The Ridgelet and Curvelet Transforms: The Continuous Ridgelet Transform - The Rectopolar Ridgelet Transform - The Orthonormal Finite Ridgelet Transform - Sparse Representation by Ridgelets - The First-Generation Curvelet Transform - Sparse Representation by First-Generation Curvelets.
LINEAR INVERSE PROBLEMS 08 Sparsity-Regularized Linear Inverse Problems - Monotone Operator Splitting Framework - Selected Problems and Algorithms - Sparsity Penalty with Analysis Prior - Other Sparsity Regularized Inverse Problems General Discussion: Sparsity, Inverse Problems and Iterative Thresholding.
COMPRESSIVE SENSING 09 Incoherence and Sparsity, Sensing Protocol Stable Compressed Sensing Designing Good Matrices: Random Sensing Sensing with Redundant Dictionaries Compressed Sensing in Space Science Guided Numerical Experiments.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Jean-Luc Starck, Fionn Murtagh and Jalal M. Fadili, "Sparse image and signal processing Wavelets, Curvelets, Morphological Diversity", Cambridge University press, First Edition, 2010. 2. Michael Elad,"Sparse and Redundant Representations: From Theory to Applications in Signal and image processing", Spinger Science, Kindle Edition, 2010. 3. Do, M. N., and Vetterli, M and G. V. Welland (eds.), "Contourlets Beyond Wavelets", in J. Stoeckler, Academic, San Diego, CA, First Edition, 2003. 4. Mallat, S, A Wavelet Tour of Signal Processing, The Sparse Way, Academic, San Diego, CA, Third Edition, 2008. 5. http://ese.wustl.edu/~nehorai/research/sparse/sparse_ref.html 6. http://en.wikipedia.org/wiki/Sparse approximation
P15AETE08 - ADVANCED COMPUTER ARCHITECTURE L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Describe advanced processor architecture. CO2: Illustrate special architectural features of processors. Demonstrate advanced programming models such as pipelining, superscalar, CO3: multithreading and vector processing. CO4: Explain the need and use of real time OS for embedded application. CO5: Formulate algorithms for solving complex computations
Pre-requisite: 1. Computer Architecture
Hrs PRINCIPLES OF PARALLEL PROCESSING 09 Multiprocessors and Multicomputers – Multivector and SIMD Computers- PRAM and VLSI Models- Conditions of Parallelism- Program Partitioning and scheduling-program flow mechanisms- speed up performance law.
PROCESSOR AND MEMORY ORGANIZATION 09 Advanced processor technology – Superscalar and vector processors- Memory hierarchy technology- Virtual memory technology- Eleven advanced optimization of cache performance.
PIPELINE AND PARALLEL ARCHITECTURE 09 Linear pipeline processors- Non linear pipeline processors- Instruction pipeline design- Arithmetic design- Superscalar and super pipeline design-Message passing mechanisms
VECTOR, MULTITHREAD AND DATAFLOW ARCHITECTURE 09 Vector processing principle- Compound Vector processing-Principles of multithreading- scalable and multithread architectures – Dataflow and hybrid architectures.
SOFTWARE AND PARALLEL PROGRAMMING 09 Parallel programming models – Parallel Languages and compilers - parallel programming environments- message passing program development- multiprocessor UNIX design goals- MACH/OS kernel architecture- OSF/1 architecture and applications.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Kai Hwang, “Advanced Computer Architecture”, Tata McGraw Hill 2011 2. John L. Hennessey and David A. Patterson," Computer Architecture: A Quantitative Approach", Fourth Edition, Morgan Kaufmann, 2012. 3. William Stallings, “ Computer Organization and Architecture”, , Pearson 2008 4. H.S. Stone, “High-performance Computer Architecture”, 3rd edition, Addison-Wesley, 1993.
P15AETE09 - ADVANCED PROCESSORS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Distinguish between RISC and CISC generic architectures. CO2: Describe the architectural features of Pentium processors. CO3: Outline the architecture of ARM processors. CO4: Develop simple applications using ARM processors. CO5: Explain the applications of special purpose processors
Pre-requisite: 1. Microprocessors and Microcontrollers
Hrs MICROPROCESSOR ARCHITECTURE 09 Instruction set – Data formats – Instruction formats – Addressing modes – Memory hierarchy – register file – Cache – Virtual memory and paging – Segmentation – Pipelining – The instruction pipeline – pipeline hazards – Instruction level parallelism – reduced instruction set – Computer principles – RISC versus CISC – RISC properties – RISC evaluation.
HIGH PERFORMANCE CISC ARCHITECTURE – PENTIUM 09 The software model – functional description – CPU pin descriptions – Addressing modes – Processor flags – Instruction set – Bus operations – Super scalar architecture – Pipe lining – Branch prediction – The instruction and caches – Floating point unit– Programming the Pentium processor.
HIGH PERFORMANCE CISC ARCHITECTURE – PENTIUM INTERFACE 09 Protected mode operation – Segmentation – paging – Protection – multitasking – Exception and interrupts - Input /Output – Virtual 8086 model – Interrupt processing.
HIGH PERFORMANCE RISC ARCHITECTURE: ARM 09 ARM architecture – ARM assembly language program – ARM organization and implementation – ARM instruction set - Thumb instruction set .
SPECIAL PURPOSE PROCESSORS 09 Altera Cyclone Processor – Audio codec – Video codec design – Platforms – General purpose processor – Digital signal processor – Embedded processor – Media Processor – Video signal Processor – Custom Hardware – Co-Processor.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Daniel Tabak, “Advanced Microprocessors”, McGraw Hill.Inc., 2011. 2. James L. Antonakos, “The Pentium Microprocessor”, Pearson Education, 1997. 3. Steve Furber, “ARM System –On –Chip architecture”,Addison Wesley, 2009. 4. Gene .H.Miller, “Micro Computer Engineering”, Pearson Education, 2003. 5. Barry.B.Brey, “The Intel Microprocessors Architecture, Programming and Interfacing”, PHI, 2008. 6. Valvano, "Embedded Microcomputer Systems" Cencage Learing India Pvt Ltd, 2011. 7. Iain E.G.Richardson, “Video codec design”, John Wiley & sons Ltd, U.K, 2002.
P15AETE10 - VIRTUAL INSTRUMENTATION L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Identify the features and characteristics of a virtual instrument. CO2: Employ LabVIEW to develop applications. CO3: Use hardware with LabVIEW to develop systems. CO4: Illustrate the various applications of LabVIEW
Pre-requisite: 1. Measurements and Instrumentation Hrs INTRODUCTION 09 General Functional description of a digital instrument - Block diagram of a Virtual Instrument - Physical quantities and Analog interfaces - Hardware and Software - User interfaces - Advantages of Virtual instruments over conventional instruments - Architecture of a Virtual instrument and its relation to the operating system.
SOFTWARE OVERVIEW 09 LabVIEW - Graphical user interfaces - Controls and Indicators - 'G' programming - Data types - Data flow programming - Editing - Debugging and Running a Virtual instrument - Graphical programming pallets - Front panel objects - Controls, Indicators, Object properties and their configuration – Typical examples.
PROGRAMMING STRUCTURE 09 FOR loops, WHILE loop, CASE structure, formula node, Sequence structures - Arrays and Clusters - Array operations - Bundle - Bundle/Unbundle by name, graphs and charts - String and file I/O - High level and Low level file I/O - Attribute modes Local and Global variables.
HARDWARE ASPECTS 09 Installing hardware, installing drivers - Configuring the hardware - Addressing the hardware in LabVIEW - Digital and Analog I/O function - Data Acquisition - Buffered I/O - Real time Data Acquisition.
LABVIEW APPLICATIONS 09 Motion Control: General Applications - Feedback devices, Motor Drives – Machine vision – LabVIEW IMAQ vision – Machine vision Techniques – Configuration of IMAQ DAQ Card - Instrument Connectivity - GPIB, Serial Communication - General, GPIB Hardware & Software specifications - PXI / PCI: Controller and Chassis Configuration and Installation.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Garry W Johnson, "LabView Graphical Programming", Tata McGraw Hill, 3rd Edition, 2001. 2. Sanjay Gupta and Joseph John, “Virtual Instrumentation Using LabVIEW”, Tata McGraw-Hill, Ist Edition, 2008. 3 LabView: Basics I & II Manual, National Instruments, 2006 4 Barry Paron, "Sensors, Transducers and LabVIEW", Prentice Hall , 2000. 5. William Buchanan and Bill Buchanan, “Computer Basics”, CRC Press, 2000.
P15AETE11 - POWER ELECTRONICS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the working principles of modern rectifiers and resonant converters CO2: Evaluate and design a converter. CO3: Design a transformer CO4: Design electronic devices for efficient power control.
Pre-requisite: NIL
Hrs CONVERTERS IN EQUILIBRIUM 09 Principles of Steady State Converter Analysis –Boost and Buck Converter Examples Steady-State Equivalent Circuit Modeling, Losses, and Efficiency –Equivalent circuit model –complete circuit model --Switch Realization-Switching loss -Converter Circuits –Circuit manipulation – Transformer isolation – Converter evaluation and design
CONVERTER DYNAMICS AND CONTROL 09 The Basic AC Modeling Approach -Averaging the Inductor and capacitor Waveforms -A Nonideal Flyback Converter -State-Space Averaging -Circuit Averaging and Averaged Switch Modeling-The Canonical Circuit Model -Converter Transfer Functions -Analysis of Converter Transfer Functions -Graphical Construction of Impedances and Transfer Functions -Controller Design –Input Filter Design-Current Programmed Control
MAGNETICS 09 Basic Magnetics Theory -Transformer Modeling -Loss Mechanisms in Magnetic Devices -Eddy Currents in Winding Conductors -Inductor Design -Filter Inductor Design Constraints -A Step-by- Step Transformer Design Procedure
MODERN RECTIFIERS AND POWER SYSTEM HARMONICS 09 Power Phasors in Sinusoidal Systems -Harmonic Currents in Three-Phase Systems -AC Line Current Harmonic Standards -Line-Commutated Rectifiers -The Single-Phase Full-Wave Rectifier -The Three-Phase Bridge Rectifier -Phase Control -Pulse-Width Modulated Rectifiers -Realization of a Near-Ideal Rectifier -Control of the Current Waveform -Ideal Three-Phase Rectifiers
RESONANT CONVERTERS 09 Resonant Conversion -Sinusoidal Analysis of Resonant Converters –Examples –Soft Switching - Soft-Switching Mechanisms of Semiconductor Devices -The Zero-Current-Switching Quasi- Resonant Switch Cell Resonant Switch Topologies -Soft Switching in PWM Converters
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Robert W. Erickson, Dragan Maksimovic,” Fundamentals of Power Electronics”, Kluwer Academic Publishers, Second Edition, New York, Boston, Dordrecht, London, Moscow. 2. Muhammad H Rashid, “Power Electronics Circuits, Devices and Applications”, Third Edition, Prentice Hall of India, 2004. 3. M.D. Singh, K.B.Khanchandani, “Power Electronics”, Tata McGraw Hill, 1998.
4. Ned Mohan, Tore M Undeland, William P. Robbins, Power Electronics, Converters, Applications and Design”, John Wiley & Sons, 1994
P15AETE12 - PRINCIPLES OF REMOTE SENSING L T P C 3 0 0 3
Course Outcomes: Upon completion of the course the student should be able to: Explain the physics behind remote sensing and understand the different remote CO1: sensing platforms used. CO2: Explain the concepts of microwave remote sensing CO3: Describe the sensors used in thermal sensing and its principle CO4: Outline remote sensing data analysis methods.
Pre-requisite: NIL Hrs PHYSICS OF REMOTE SENSING 09 Introduction of Remote Sensing - Electro Magnetic Spectrum, Physics of Remote Sensing- Effects of Atmosphere- Scattering – Different types –Absorption-Atmospheric window- Energy interaction with surface features – Spectral reflectance of vegetation, soil ,and water –atmospheric influence on spectral response patterns- multi concept in Remote sensing.
DATA ACQUISITION 09 Types of Platforms – different types of aircrafts-Manned and Unmanned spacecrafts – sun synchronous and geo synchronous satellites – Types and characteristics of different platforms – LANDSAT,SPOT,IRS,INSAT,IKONOS,QUICKBIRD etc - Photographic products, B/W, colour, colour IR film and their characteristics – resolving power of lens and film - Opto mechanical electro optical sensors – across track and along track scanners – multi spectral scanners and thermal scanners – geometric characteristics of scanner imagery - calibration of thermal scanners.
SCATTERING SYSTEM 09 Microwave scatterometry – types of RADAR – SLAR – resolution - range and azimuth – real aperture and synthetic aperture RADAR. Characteristics of Microwave imagestopographic effect - different types of Remote Sensing platforms –airborne and space borne sensors – ERS, JERS, RADARSAT, RISAT - Scatterometer, Altimeter- LiDAR remote sensing, principles, applications.
THERMAL SENSING 09 Sensors characteristics - principle of spectroscopy - imaging spectroscopy - field conditions, compound spectral curve, Spectral library, radiative models, processing procedures, derivative spectrometry, thermal remote sensing – thermal sensors, principles, thermal data processing, applications.
DATA ANALYSIS 09 Resolution – Spatial, Spectral, Radiometric and temporal resolution- signal to noise ratio- data products and their characteristics - visual and digital interpretation –Basic principles of data processing –Radiometric correction –Image enhancement – Image classification – Principles of LiDAR, Aerial Laser Terrain Mapping.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. 1. Lillesand T.M., and Kiefer,R.W. Remote Sensing and Image interpretation, VI edition of John Wiley & Sons-2000.
2. John R. Jensen , Introductory Digital Image Processing: A Remote Sensing Perspective , 2nd Edition, 1995.
3. John A.Richards, Springer –Verlag, Remate Sensing Digital Image Analysis 1999.
4. Paul Curran P.J. Principles of Remote Sensing, ELBS; 1995.
5. Charles Elachi and Jakob J. van Zyl , Introduction To The Physics and Techniques of Remote Sensing , Wiley Series in Remote Sensing and Image Processing, 2006.
6. Sabins, F.F.Jr, Remote Sensing Principles and Image interpretation, W.H.Freeman & Co, 1978.
P15AETE13 - NEURAL NETWORKS AND APPLICATIONS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: Explain the concepts of neural networks and different training / learning CO1: algorithms. CO2: Design BPNN to solve real time problems. CO3: Apply the concept of counter propagation network for various applications. Illustrate problem-solving based on pattern matching with specified Self CO4: Organizing Map algorithm CO5: Apply spatial - temporal networks for speech recognition
Pre-requisite: NIL Hrs INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS 09 Neuro-physiology - General Processing Element - ADALINE - LMS learning rule - MADALINE – MR2 training algorithm.
BPN AND BAM 09 Back Propagation Network - updating of output and hidden layer weights -application of BPN – associative memory - Bi-directional Associative Memory - Hopfield memory - traveling sales man problem.
SIMULATED ANNEALING AND CPN 09 Annealing, Boltzmann machine - learning - application - Counter Propagation network - architecture -training - Applications.
SOM AND ART 09 Self organizing map - learning algorithm - feature map classifier - applications - architecture of Adaptive Resonance Theory - pattern matching in ART network.
NEOCOGNITRON 09 Architecture of Neocognitron - Data processing and performance of architecture of spacio – temporal networks for speech recognition.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. J.A. Freeman and B.M.Skapura , "Neural Networks, Algorithms Applications and Programming Techniques", Addison-Wesely,2003. 2. Laurene Fausett, "Fundamentals of Neural Networks: Architecture, Algorithms and Applications", Prentice Hall, 2004. 3. Simon Haykin, “Neural Networks & Learning Machines” third edition Pearson Education 2011. 4. Martin T. Hagan, Howard B. Demuth, Mark Beale, “ Neural Network Design ”, Thomson and Learning, Third Reprint 2008.
P15AETE14 - MULTIMEDIA COMPRESSION TECHNIQUES L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Discuss data formats for different multimedia data. CO2: Illustrate compression/decompression of text, image, audio and video. CO3: Compare data compression algorithms. CO4: Apply different compression techniques for image and video. Design multimedia systems according to the requirements of applications. CO5:
Pre-requisite: 1. Information Theory and Coding Hrs INTRODUCTION 09 Multimedia data - features –– Storage requirements for multimedia - Need for Compression - Taxonomy of compression – Metrics – Quantitative and Qualitative techniques - Overview of source coding – Scalar quantization - Adaptive - Vector quantization.
TEXT COMPRESSION 09 Characteristics of text data – RLE, Huffmann coding – Adaptive Huffmann Coding – Arithmetic coding –– Dictionary techniques – static and adaptive- digram coding – LZW algorithm - GIF, TIF, Digitized documents, JBIG, JBIG2.
AUDIO COMPRESSION 09 Fundamental concepts of digital audio - Audio compression techniques –μ Law and A- Law companding - PCM, DPCM, DM, ADM - sub-band coding – Application to speech coding – G.722 – MPEG audio – MP3 - Dolby audio - Model based coding – Channel Vocoders – LPC - Formant and CELP coders.
IMAGE COMPRESSION 09 Image data representation - Predictive techniques – DPCM: Optimal Predictors and Optimal Quantizers –Transform Coding – JPEG Standard – Sub-band coding – QMF Filters - Wavelet based compression – EZW, SPIHT coders – JPEG 2000 standard – File formats.
VIDEO COMPRESSION 09 Fundamental concepts of video – digital video signal - video formats – video compression techniques and standards - AVI, FLV, MP4, Real media - Motion estimation and compensation Techniques, Block matching- Full search motion estimation methods – MPEG Video Coding : MPEG – 1 and 2, MPEG – 4, 7 and 21 –– H.26X Standard - Packet Video.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs References: 1. Khalid Sayood, “Introduction to Data Compression”, Morgan Kauffman Harcourt India, 2nd Edition, 2000. 2. David Salomon, “Data Compression – The Complete Reference”, Springer Verlag New York Inc., 2nd Edition, 2001. 3. I.E.G. Richardson, “Video codec design”, John Wiley & Sons Ltd, 2002 Edition. 4. Yun Q.Shi, Huifang Sun, “Image and Video Compression for Multimedia Engineering - Fundamentals, Algorithms & Standards”, CRC press, 2003. 5. Peter Symes , “Digital Video Compression”, McGraw Hill Pub., 2004. 6. Mark Nelson , “Data compression”, BPB Publishers, New Delhi,1998. 7. Mark S.Drew, Ze-Nian Li, “ Fundamentals of Multimedia”, PHI, 1st Edition, 2003. 8. Watkinson,J, “ Compression in Video and Audio”, Focal press,London.1995. 9. Jan Vozer , “Video Compression for Multimedia”, AP Profes, NewYork, 1995
P15AETE15 - SYSTEM MODELLING AND SIMULATION L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the basics of various simulation methodologies. CO2: Develop mathematical models for electronic systems. CO3: Analyze the data used for simulation by means of different tests. CO4: Evaluate the system using mathematical model. CO5: Categorize simulation software for various applications.
Pre-requisite: 1. Probability Theory and Random Process
Hrs SIMULATION 09 Introduction – Systems, Models and Simulation- Application areas – Model Classification- Types of Simulation- Discrete Event Simulation – Distributed Simulation --Monte Carlo Simulation- Steps in a Simulation Study - Review of Probability and Statistics.
MATHEMATICAL MODELS 09 Statistical Models - Concepts – Discrete Distributions- Continuous Distributions – Poisson Process- Empirical Distributions - Queuing Models– Characteristic Notation - Queuing Systems – Markovian Models- Properties of Random Numbers-Generation of Pseudo Random Numbers- Techniques for generating random numbers-Testing random number generators- Generating Random Variates - Inverse Transform Technique - Acceptance- Rejection Technique – Composition and Convolution Method.
ANALYSIS OF SIMULATION DATA 09 Input Modeling - Data collection - Assessing sample independence – Hypothesizing distribution family with data - Parameter Estimation- Goodness-of-fit tests – Selecting input models in absence of data- Output analysis for a Single system –Terminating Simulations – Steady state simulations.
VERIFICATION AND VALIDATION 09 Building – Verification of Simulation Models – Calibration and Validation of Models – Validation of Model Assumptions – Validating Input – Output Transformations.
SIMULATION SOFTWARE AND CASE STUDIES 09 Simulation Tools –Simulation Languages–Classification of Simulation Software –Desirable Software Features– GPSS/H– SIMAN– SIMSCRIPT II.5– SLAM II and Related Software – Comparison of Simulation Languages and General Purpose Languages – Case studies.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Jerry Banks and John Carson, “Discrete Event System Simulation”, Fourth Edition, PHI, 2005.
2. Geoffrey Gordon, “System Simulation”, Second Edition, PHI, 2006.
3. Frank L. Severance, “System Modeling and Simulation”, Wiley, 2001.
4 Averill M. Law and W.David Kelton, “Simulation Modeling and Analysis”, Third Edition, McGraw Hill, 2006.
5. Jerry Banks, “Handbook of Simulation: Principles, Methodology, Advances, Applications and Practice”, Wiley-Interscience, 1 edition, 1998.
P15AETE16 - SYNTHESIS AND OPTIMIZATION OF DIGITAL CIRCUITS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Infer the basic modeling concepts and logic optimization. CO2: Analyze the architectural synthesis problems for various units. CO3: Relate the different types of scheduling algorithms CO4: Employ appropriate algorithms for optimization of digital circuits.
Pre-requisite: 1. Digital Electronics 2. VLSI Design
Hrs CIRCUITS AND HARDWARE MODELING 09 Design of Microelectronic Circuits - Computer Aided Synthesis and optimization-Combinatorial optimization-Boolean Algebra and Application-Hardware Modeling Languages –Compilation and Behavioral optimization.
ARCHITECTURAL LEVEL SYNTHESIS AND OPTIMIZATION 09 Fundamental Architectural synthesis Problems- Area and performance Estimation-Control unit synthesis-Synthesis of pipelined circuits.
SCHEDULING ALGORITHMS AND RESOURCE SHARING 09 Unconstrained Scheduling-ASAP Algorithm-ALAP Scheduling Algorithm- Scheduling with Resource Constraints- Scheduling pipelined circuits-Sharing and binding for Dominated circuits- Area Binding-Concurrent Binding –Module selection problems-Structural testability.
LOGIC-LEVEL SYNTHESIS AND OPTIMIZATION 09 Logic optimization Principles-Algorithms and Logic Minimization –Encoding problems- Multiple-level optimization of logic networks-Algebraic and Boolean model-Algorithm for delay Evaluation-Rule based logic optimization
SEQUENTIAL LOGIC OPTIMIZATION 09 Sequential circuit -State Encoding-Minimization methods-Retiming- Finite state machine- testability for synchronous circuits-Algorithm for library binding- Look-Up table - FPGA- Rule- based library binding.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Giovanni De Micheli, “Synthesis and optimization of Digital Circuits”, Tata McGraw-Hill, 2003. 2. John Paul Shen, Mikko H. Lipasti, “Modern processor Design”, Tata McGraw Hill, 2003 3. Gary D. Hachtel and Fabio Somenzi, “Logic Synthesis and Verification Algorithms”,Springer 4. Frank Vahid, “Digital Design”, John Wiley & Sons
P15AETE17 - HARDWARE SOFTWARE CO-DESIGN L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Identify the hardware and software co-design models. CO2: Appraise hardware and software partitioning approaches. CO3: Relate the role of scheduling to partitioning for hardware / software co-design. CO4: Evaluate case studies related to hardware and software prototypes. Compare and contrast hardware and software models for meeting design CO5: specifications.
Pre-requisite: 1. ASIC Design Hrs SYSTEM SPECIFICATION AND MODELLING 09
Embedded Systems, Hardware/Software Co-Design, Co-Design for System Specification and Modelling, Co-Design for Heterogeneous Implementation – Processor Synthesis, Single-Processor Architectures with one ASIC, Single-Processor Architectures with many ASICs, Multi-Processor Architectures Comparison of Co-Design Approaches, Models of Computation, Requirements for Embedded System Specification .
HARDWARE/SOFTWARE PARTITIONING 09
Hardware/Software Partitioning Problem, Hardware-Software Cost Estimation, Generation of the Partitioning Graph, Formulation of the HW/SW Partitioning Problem, Optimization HW/SW Partitioning based on Heuristic Scheduling, HW/SW Partitioning based on Genetic Algorithms.
HARDWARE/SOFTWARE CO-SYNTHESIS 09
Co-Synthesis Problem, State-Transition Graph, Refinement and Controller Generation, Distributed System Co-Synthesis.
PROTOTYPING AND EMULATION 09
Introduction, Prototyping and Emulation Techniques, Prototyping and Emulation Environments, Future Developments in Emulation and Prototyping, Target Architecture- Architecture Specialization Techniques, System Communication Infrastructure, Target Architectures and Application System Classes, Architectures for Control-Dominated Systems, Architectures for Data-Dominated Systems, Mixed Systems and Less Specialized Systems
DESIGN SPECIFICATION AND VERIFICATION 09
Concurrency, Coordinating Concurrent Computations, Interfacing Components, Verification, Languages for System Level Specification and Design System - Level Specification, Design Representation for System Level Synthesis, System Level Specification Languages, Heterogeneous Specification and Multi-Language Co-simulation
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Ralf Niemann , “Hardware/Software Co-Design for Data Flow Dominated Embedded Systems”, Kluwer Academic Pub, 1998
2. Jorgen Staunstrup , Wayne Wolf , “Hardware/Software Co-Design: Principles and Practice”, Kluwer Academic Pub,1997.
3. Giovanni De Micheli , Rolf Ernst Morgon, “Reading in Hardware/Software Co-Design” Kaufmann Publishers,2001
4. Patrick Schaumont, “A Practical Introduction to Hardware/Software Codesign", Springer, 2009.
P15AETE18 - ROBOTICS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Discuss the fundamentals of robotics. CO2: Relate image processing to robotic vision. CO3: Choose sensors and sensing devices for robotic applications. CO4: Extend the artificial intelligence approach to robotics. CO5: Develop robotic systems for various applications.
Pre-requisite: 1. Embedded Systems Hrs INTRODUCTION 09 Motion - Potential Function, Road maps, Cell decomposition and Sensor and sensor planning. Kinematics. Forward and Inverse Kinematics - Transformation matrix and DH transformation. Inverse Kinematics - Geometric methods and Algebraic methods. Non-Holonomic constraints.
COMPUTER VISION 09 Projection - Optics, Projection on the Image Plane and Radiometry. Image Processing - Connectivity, Images-Gray Scale and Binary Images, Blob Filling, Thresholding, Histogram. Convolution - Digital Convolution and Filtering and Masking Techniques. Edge Detection - Mono and Stereo Vision.
SENSORS AND SENSING DEVICES 09 Introduction to various types of sensor. Resistive sensors. Range sensors – Ladar (laser distance and ranging), Sonar, Radar and Infra-red, Introduction to sensing - Light sensing, Heat sensing, Touch sensing and Position sensing.
ARTIFICIAL INTELLIGENCE 09 Uniform Search strategies - Breadth first, Depth first, Depth limited, Iterative and deepening depth first search and Bidirectional search. The A* algorithm, Planning - State-Space, Plan-Space Planning, Graphplan/ SatPlan and their Comparison, Multi-agent planning 1 and Multi-agent planning 2, Probabilistic Reasoning - Bayesian Networks, Decision Trees and Bayes net inference
INTEGRATION TO ROBOT 09 Building of 4 axis and 6 axis robot - Vision System for pattern detection - Sensors for obstacle detection – AI algorithms for path finding and decision making.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Duda, Hart and Stork, “Pattern Recognition”, Wiley-Interscience, 2000. 2. Mallot, “Computational Vision: Information Processing in Perception and Visual Behavior.” Cambridge, MA: MIT Press, 2000. 3. Stuart Russell and Peter Norvig, “Artificial Intelligence-A Modern Approach”, Pearson Education Series in Artificial Intelligence, 2004 4. I.H.Witten, “ Principles of Computer Speech”, Academic Press,1982. 5. Robert Schilling and Craig, “Fundamentals of Robotics, Analysis and control”, Prentice Hall of India Private Limied, New Delhi, 2003. 6. Forsyth and Ponce, “Computer Vision, A modern Approach “, Person Education 2003.
P15AETE19 - MICROELECTROMECHANICAL MACHINES L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the different components of MEMS. CO2: Relate electrostatic and system issues in MEMS design. CO3: Outline the various applications of MEMS. CO4: Describe Optical and RF MEMS.
Pre-requisite: 1. Basic Physics Hrs INTRODUCTION TO MEMS 09 MEMS and Microsystems, Miniaturization, Typical products, Micro sensors, Micro actuation, MEMS with micro actuators, Micro accelorometers and Micro fluidics, MEMS materials, Micro fabrication.
MECHANICS FOR MEMS DESIGN 09 Elasticity, Stress, strain and material properties, Bending of thin plates, Spring configurations, torsional deflection, Mechanical vibration, Resonance, Thermo mechanics – actuators, force and response time, Fracture and thin film mechanics.
ELECTRO STATIC DESIGN AND SYSTEM ISSUES 09 Electrostatics: basic theory, electro static instability. Surface tension, gap and finger pull up, Electro static actuators, Comb generators, gap closers, rotary motors, inch worms, Electromagnetic actuators. bistable actuators. Electronic Interfaces, Feedback systems, Noise, Circuit and system issues.
MEMS APLLICATION 09 Case studies – Capacitive accelerometer, Peizo electric pressure sensor, Microfluidics application, Modeling of MEMS systems, CAD for MEMS.
INTRODUCTION TO OPTICAL AND RF MEMS 09 Optical MEMS, - System design basics – Gaussian optics, matrix operations, resolution. Case studies, MEMS scanners and retinal scanning display, Digital Micro mirror devices. RF Memes – design basics, case study – Capacitive RF MEMS switch, performance issues.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Stephen Santeria, “Microsystems Design”, Kluwer publishers, 2000.
2. N.P.Mahalik, “MEMS”,Tata McGraw hill, 2007
3. Nadim Maluf, “An introduction to Micro electro mechanical system design”, Artech House, 2000.
4. Mohamed Gad-el-Hak, editor, “The MEMS Handbook”, CRC press Baco Raton,2000.
5. Tai Ran Hsu, “MEMS & Micro systems Design and Manufacture” Tata McGraw Hill, New Delhi, 2002.
6. Liu, “MEMS”, Pearson education, 2007.
P15AETE20 - EMBEDDED SYSTEMS IN AUTOMATIVE APPLICATIONS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Identify the various electrical and electronic components in an automobile. CO2: Explain the features of embedded systems. CO3: Outline the concepts of embedded system development using IDE. CO4: Design an embedded system for an automobile application. CO5: Distinguish the various communication protocols used in an automobile.
Pre-requisite: 1. Embedded Systems Hrs AUTOMOBILE ELECTRICAL AND ELECTRONICS 09 Basic Electrical Components and their operation in an automobile - Starting systems, Charging systems – ignition systems- Electronic fuel control- Environmental legislation for pollution- Overview of vehicle electronic systems- Power train subsystem- chassis subsystem- comfort and safety subsystems.
INTRODUCTION TO EMBEDDED SYSTEMS 09 Embedded Systems definition - Components of Embedded systems – Microprocessor - Classification of Microprocessors- Microcontrollers- Memory - Peripherals. Introduction to an embedded board (TMS470 based / ARM9 based) for hands on lab sessions (RISC processor based with standard peripherals / interfaces and I/Os)
OPERATING SYSTEM IN EMBEDDED ENVIRONMENT 09 Introduction to OS - General Purpose OS, RTOS -, Kernel - Pre-emptive & Non pre-emptive, Scheduler, Interrupt - Interrupt latency and Context Switch Latency- Board Support package, Task - Multi-tasking, Task synchronization, Inter-task communication, Features of a typical embedded RTOS (µC/OS-II)
INTEGRATED DEVELOPMENT ENVIRONMENT 09 Integrated Development Environment (IDE)- Getting Started, Hardware / Software Configuration (Boot Service, Host – Target Interaction), Booting, Reconfiguration, Managing IDE, Target Servers, Agents, Cross – Development, debugging- Introduction to an IDE for the lab board – RTOS, PC based debugger.
COMMUNICATION PROTOCOLS AND APPLICATIONS 09 Engine Management systems - Diesel / Gasoline systems, Various sensors used in system - Vehicle safety systems- electronic control of braking and traction- Introduction to control elements and control methodology- Electronic transmission control- Body electronics - Infotainment systems – Navigation systems- Introduction to CAN, LIN, FLEXRAY, MOST, KWP 2000 Protocols
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. R. K. Jurgen, “Automotive electronics handbook” McGraw Hill Professional, 1999 2. Paul Pop, Petru Eles, Zebo Peng “Analysis and Synthesis of Distributed Real-Time Embedded Systems” Springer, 21-Dec-2004 3. B. Kanta Rao “Embedded Systems” PHI Learning Pvt. Ltd.2011
P15AETE21 - ADVANCED EMBEDDED DEVELOPMENT L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Discuss the features of a generic embedded system. Distinguish the various RTOS and communication protocols for embedded CO2: systems CO3: Outline the tools used in embedded system design. CO4: Illustrate the typical applications in Automotive Embedded Systems. CO5: Recognize safety and security systems for automobiles.
Pre-requisite: 1. Embedded Systems Hrs EMBEDDED REAL TIME SYSTEMS & PROCESSOR ARCHITECTURE 09
Introduction to Embedded real time systems - Soft Real Time systems, Hard Real Time systems, Embedded hardware – Microprocessors, Micro controllers, Peripheral Interfaces. Processor architectures- Single core, Multi core.
Real time operating systems- Introduction to RTOS- Single core architectures - Multi core architectures - Hypervisor systems Embedded systems communication protocols - On Board communication – SPI, I2C. - Automotive vehicle communication – LIN, CAN, FlexRay, Ethernet, MOST.
EMBEDDED DEVELOPMENT TOOLS 09
Embedded Development Tools - Automotive s/w architecture – AUTOSAR- Model based approach for embedded software development - ASCET - Matlab/Simulink. - IDE –Integrated Development Environments - Automatic code generation- Simulators/Emulators, Debug Interfaces
EMBEDDED SYSTEMS IN AUTOMOTIVE APPLICATIONS 09
Embedded systems in Automotive applications: Power train Applications, Engine Control Module, and Transmission Control Module - Vehicle Applications: Body Control Module, Infotainment, Chassis, Airbag, and Cruise. - Driver Assistance Systems: Lane departure warning, ACC
EMBEDDED SYSTEMS SAFETY & SECURITY, CRYPTOGRAPHY 09
Safety - Introduction to Embedded Safety – Active/Passive safety - Automotive safety applications (SRS) – ABS, Airbags, Cruise control. -Overview of IEC61508/ISO26262 - Security- Introduction to IT security - Challenges of embedded security (which is quite different due to space/timing constraints) - Security building blocks such as hardware (e.g., Smart cards) and software (such as crypto library etc.).
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Jörg Schäuffele, Thomas Zurawka “Automotive Software Engineering: Principles, Processes, Methods, And Tools”, PublisherSAE International
2. Wolfgang Ecker, Wolfgang Müller, Rainer. Dömer, “Hardware-dependent Software”, Springer, 01-Jan-2009 - Computers
3. Hermann Kopetz, “Real-time Systems: Design Principles for Distributed Embedded Applications”, Second Edition, Springer Publications
4. Nicolas Navet, Francoise Simonot-Lion, “Automotive Embedded Systems Handbook”, CRC Press, 20-Dec-2008 - Technology & Engineering
P15AETE22 - STOCHASTIC MODELS AND SIMULATION L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Apply concepts of probability theory to solve problems. Analyze properties of random variables with different distributions using CO2: simulation. CO3: Demonstrate Markov chains processes. CO4: Apply Poisson processes for reliability problems CO5: Employ renewal and queuing theory for simulation analysis.
Pre-requisite: 1. Basic Probability Theory Hrs INTRODUCTION TO PROBABILITY 09
Sample space and events; Probability axiom; Conditional probability; Independent events, Bayes formula, Simple problems.
RANDOM VARIABLE & DISTRIBUTIONS 09
Random Variables, Distribution functions, continuous and discrete random variables, Bernoulli, Binomial, Geometric, Poisson random variables, uniform, exponential, normal random variables, jointly distributed random variables, expectations and moment generating functions, – Properties, simulation samples for the above mentioned distributions.
MARKOV CHAINS 09
Markov chains, Chapman – Kolmogorov equations, Classification of states, examples of Markov chains. Gamblers ruin problem, mean time spent in transient states, Branching process.
POISSON PROCESSES 09
Properties of exponential distribution, convolution of exponential random variables, Poisson process. Inter arrival of waiting time distribution, Applications to reliability problems, Estimating software reliability.
RENEWAL THEORY, QUEUING THEORY & SIMULATION 09
Renewal Theory - examples, distribution of the counting process N(t), alternating renewal process, Regenerative process, computing the renewal function, semi – Markov process, Computation of renewal function, Poisson process as a renewal process. Single server exponential queuing system, Queue with finite capacity, Shoe shine shop, network of queues, open and closed systems. Methods of simulation of random variables – Inverse transformation method, Rejection method.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Sheldon M. Ross, “Introduction to Probability Models”, Academic press, 2005.
2. Karlin and H.M. Tailor, “A First Course in Stochastic Processes”, Academic Press, 1975.
3. Sheldon M. Ross, “A First Course in Probability”, Sixth Edition, Prentice Hall, New Jersey, 2002
4. Sheldon M. Ross, “A First Course in Probability”, Sixth Edition, Prentice Hall, New Jersey, 2002
P15AETE23 - CELLULAR AND MOBILE COMMUNICATIONS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Summarize the various mobile communication techniques. CO2: Demonstrate the cellular radio concepts. CO3: Assess the various mobile radio propagation models. Analyze the performance of different modulation, diversity and coding CO4: techniques CO5: Compare the different multiple access techniques in mobile communication. CO6: Identify design issues for wireless networks.
Pre-requisite: 1. Analog and Digital Communications Hrs INTRODUCTION TO WIRELESS MOBILE COMMUNICATIONS 09
History and evolution of mobile radio systems. Types of mobile wireless services / systems - Cellular, WLL, Paging, Satellite systems, Standards, Future trends in personal wireless systems.
CELLULAR CONCEPT AND SYSTEM DESIGN FUNDAMENTALS 09
Cellular concept and frequency reuse, Multiple Access Schemes, Channel assignment and handoff, Interference and system capacity, Trunking and Erlang capacity calculations.
MOBILE RADIO PROPAGATION 09
Radio wave propagation issues in personal wireless systems, Propagation models, Multipath fading and base band impulse response models, Parameters of mobile multipath channels, Antenna systems in mobile radio.
MODULATION AND SIGNAL PROCESSING 09
Analog and digital modulation techniques, Performance of various modulation techniques – Spectral efficiency, Error-rate, Power Amplification, Equalization Rake receiver concepts, Diversity and space-time processing, Speech coding and channel coding.
SYSTEM EXAMPLES AND DESIGN ISSUES 09
Multiple Access Techniques – FDMA, TDMA and CDMA systems, Operational systems, Wireless networking, design issues in personal wireless systems.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Feher K., “Wireless digital communications”, PHI, New Delhi, 1995.
2. Rappaport T.S., “Wireless Communications; Principles and Practice”, Prentice Hall, NJ, 1996.
3. Lee W.C.Y., “Mobile Communications Engineering: Theory and Applications”, Second Edition, McGraw-Hill, New York, 1998.
4. Schiller, “Mobile Communications”, Pearson Education Asia Ltd., 2000.
P15AETE24 - LOW POWER VLSI DESIGN L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Discuss the sources of power dissipation in CMOS logic design. CO2: Apply low power design and optimization techniques. CO3: Estimate the power at different levels.
Pre-requisite: 1. VLSI Design Hrs POWER DISSIPATION IN CMOS 0 8 Need for low power design, Hierarchy of limits of power – Sources of power consumption – Basic principle of low power design, Degree of freedom.
SUPPLY VOLTAGE SCALING 10 Challenges in supply voltage scaling, Voltage scaling approaches, Static voltage scaling approaches- Device feature size scaling, Architectural level approaches - Parallelism, Pipelining, voltage scaling through optimal transistor sizing, voltage scaling using high level transformations, multi level voltage scaling, Dynamic voltage scaling, Adaptive voltage scaling
SWITCHED CAPACITANCE MINIMIZATION 12 Hardware Software Trade-off, Bus Encoding, Use of number system, Architectural level optimization Techniques, Glitch power, Clock Gating, State encoding, Logic styles, Low power techniques for SRAM and DRAM. Special topics - Adiabatic Switching Circuits Battery-aware Synthesis Variation tolerant design CAD tools for low power synthesis.
LEAKAGE POWER MINIMIZATION 10 Standby leakage reduction- Fabrication of multiple threshold voltages, Transistor stacking, Variable-threshold-voltage CMOS (VTCMOS), Multi-threshold-voltage CMOS (MTCMOS),
Power gating, Run time leakage reduction- VDD scaling, combining power gating with Dynamic voltage and frequency scaling, multi level VDD scaling, Dual-Vt assignment approach (DTCMOS), dynamic Vth scaling.
POWER ESTIMATION 05 Power estimation techniques – Logic level power estimation – Simulation power analysis– Probabilistic power analysis.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. K.Roy and S.C. Prasad , “Low Power CMOS VLSI circuit design”, Wiley,2000 2. Gary Yeap, “Practical low power digital VLSI design”, Kluwer, 2001. 3. Dimitrios Soudris, Chirstian Pignet, Costas Goutis, “Designing CMOS Circuits For Low Power”, Kluwer,2002 4. J.B. Kuo and J.H Lou, “Low voltage CMOS VLSI Circuits”, Wiley 1999. 5. A.P.Chandrakasan and R.W. Broadersen, “Low power digital CMOS design”, Kluwer, 1995. 6. Abdellatif Bellaouar, Mohamed.I. Elmasry, “Low power digital VLSI Design”, Kluwer, 1995. 7. James B. Kuo, Shin – chia Lin, “Low voltage SOI CMOS VLSI Devices and Circuits”, John Wiley and sons, inc 2001 8. Rabaey, Pedram, “Low Power Design Methodologies” Kluwer Academic, 1997
P15AETE25 - VLSI SIGNAL PROCESSING L T P C 3 0 0 3
Course Outcomes: Upon completion of the course the student should be able to: Represent DSP algorithms, define and compute iteration bound of an CO1: algorithm. CO2: Use Pipelining and parallel processing methodologies in FIR filters CO3: Apply retiming, unfolding techniques. CO4: Design systolic architecture. CO5: Apply strength reduction in filters and transforms. CO6: Discuss synchronous, asynchronous and wave pipelining.
Pre-requisite: 1. VLSI Design 2. Digital Signal Processing
Hrs INTRODUCTION TO DSP SYSTEMS, PIPELINING AND PARALLEL 09 PROCESSING OF FIR FILTERS
Introduction to DSP systems – Typical DSP algorithms, Representation of DSP algorithms- Data flow graph and Dependence graphs - critical path, Loop bound, iteration bound, Algorithms for Computing Iteration Bound, Iteration Bound of multi rate data flow graphs. Pipelining and Parallel processing of FIR filters, Pipelining and Parallel processing for low power.
RETIMING, ALGORITHMIC STRENGTH REDUCTION 09
Retiming – definitions and properties, Solving Systems of Inequalities, Retiming Techniques Unfolding – an algorithm for unfolding, properties of unfolding, application, Critical path, Unfolding And Retiming, Application of Unfolding- sample period reduction and parallel processing, Algorithmic strength reduction in filters and transforms –parallel FIR filters, two- parallel fast FIR filter, DCT and inverse DCT, algorithm –architecture transform, parallel architectures for rank-order filters, Odd-Even merge-sort architecture, parallel rank-order filters.
SYSTOLIC ARCHITECTURE DESIGN 09
Systolic Array Design Methodology- FIR systolic array, Selection of Scheduling Vector, Matrix- Matrix Multiplication and 2D systolic Array Design, Systolic Design for space representation containing Delays.
FAST CONVOLUTION, PIPELINING AND PARALLEL PROCESSING OF 09 IIR FILTERS Fast convolution – Cook-Toom algorithm, modified Cook-Toom algorithm, Pipelined and parallel recursive filters – Look-Ahead pipelining in first-order IIR filters, Look-Ahead pipelining with power-of-2 decomposition, Clustered look-ahead pipelining, Parallel processing of IIR filters, combined pipelining and parallel processing of IIR filters.
NUMERICAL STRENGTH REDUCTION, SYNCHRONOUS, WAVE AND 09 ASYNCHRONOUS PIPELINING
Numerical strength reduction – sub expression elimination, multiple constant multiplication, iterative matching, synchronous pipelining and clocking styles, clock skew in edge-triggered single phase clocking, two-phase clocking, wave pipelining. Asynchronous pipelining - bundled data versus dual rail protocol.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Keshab K.Parhi, “VLSI Digital Signal Processing systems, Design and implementation”, Wiley Inter Science, 1999. 2. Gary Yeap, “Practical Low Power Digital VLSI Design”, Kluwer Academic Publishers, 1998.
3. Mohammed Isamail and Terri Fiez, “Analog VLSI Signal and Information Processing”, McGraw-Hill, 1994
4. S.Y. Kung, H.J. White House, T. Kailath, “VLSI and Modern Signal Processing”, PHI, 1985.
5. Jose E. France, Yannis Tsividis, “Design of Analog - Digital VLSI Circuits for Telecommunication and Signal Processing”, Prentice Hall, 1994.
P15AETE26 - ANALOG VLSI DESIGN L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Review MOS, CMOS and BiCMOS technologies. CO2: Analyze CMOS circuits for signal processing. CO3: Interpret the use of ADC, DAC, sensors and their interfaces. CO4: Examine the different approaches for testing VLSI circuits. CO5: Develop simulation models for mixed signal applications.
Pre-requisite: 1. Analog Electronics 2. Basic VLSI Design Hrs BASIC CMOS CIRCUIT TECHNIQUES, CONTINUOUS TIME AND LOW- 09 VOLTAGE SIGNAL PROCESSING
Signal VLSI Chips-Basic CMOS Circuits-Basic Gain Stage-Gain Boosting Techniques- Super MOSTransistor- Primitive Analog Cells-Linear Voltage-Current Converters-MOS Multipliers and Resistors-CMOS, Bipolar and Low-Voltage BiCMOS Op-Amp Design-Instrumentation Amplifier Design-Low Voltage Filters.
BASIC BICMOS CIRCUIT TECHNIQUES, CURRENT -MODE SIGNAL AND 09 NEURAL INFORMATION PROCESSING
Continuous-Time Signal Processing-Sampled-Data Signal Processing-Switched-Current Data Converters-Practical Considerations in SI Circuits Biologically-Inspired Neural Networks - Floating - Gate, Low-Power Neural Networks-CMOS Technology and Models- Design Methodology-Networks-Contrast Sensitive Silicon Retina.
SAMPLED-DATA ANALOG FILTERS, OVER SAMPLED A/D CONVERTERS 09 AND ANALOG INTEGRATED SENSORS First-order and Second SC Circuits-Bilinear Transformation - Cascade Design-Switched- Capacitor Ladder Filter-Synthesis of Switched-Current Filter- Nyquist rate A/D Converters- Modulators for Over sampled A/D Conversion-First and Second Order and Multibit Sigma-Delta Modulators-Interpolative Modulators –Cascaded Architecture- Decimation Filters-mechanical, Thermal, Humidity and Magnetic Sensors-Sensor Interfaces.
DESIGN FOR TESTABILITY AND ANALOG VLSI INTERCONNECTS 09
Fault modelling and Simulation - Testability-Analysis Technique-Ad Hoc Methods and General Guidelines-Scan Techniques-Boundary Scan-Built-in Self Test-Analog Test Buses- Design for Electron -Beam Testablity-Physics of Interconnects in VLSI-Scaling of Interconnects-A Model for Estimating Wiring Density-A Configurable Architecture for Prototyping Analog Circuits.
STATISTICAL MODELING AND SIMULATION, ANALOG COMPUTER- 09 AIDED DESIGN AND ANALOG AND MIXED ANALOG-DIGITAL LAYOUT
Review of Statistical Concepts - Statistical Device Modeling- Statistical Circuit Simulation- Automation Analog Circuit Design-automatic Analog Layout-CMOS Transistor Layout-Resistor Layout-Capacitor Layout-Analog Cell Layout-Mixed Analog -Digital Layout
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Mohammed Ismail, Terri Fiez, “Analog VLSI signal and Information Processing ", McGraw- Hill International Editons, 1994. 2. Malcom R.Haskard, Lan C.May, “Analog VLSI Design - NMOS and CMOS ", Prentice Hall, 1998. 3. Randall L Geiger, Phillip E. Allen, " Noel K.Strader, “VLSI Design Techniques for Analog and Digital Circuits ", Mc Graw Hill International Company, 1990. 4. Jose E.France, Yannis Tsividis, “Design of Analog-Digital VLSI Circuits for Telecommunication and signal Processing ", Prentice Hall, 1994.
P15AETE27 - MIXED SIGNALVLSI DESIGN L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Describe the various analog building blocks for VLSI design. CO2: Design active filters for VLSI circuits. CO3: Illustrate the realization of various active filters. CO4: Design various analog and digital filters. CO5: Survey the types and properties of ADC and DAC.
Pre-requisite: 1. Analog VLSI Design Hrs BASIC ANALOG BUILDING BLOCKS 09 Current mirrors – Voltage sources/references – Voltage amplifiers – Transconductance and Transresistance amplifiers – Operational amplifiers – Comparators – Multipliers.
INTRODUCTION TO ACTIVE FILTERS AND SWITCHED CAPACITOR 09 FILTERS Active RC Filters for monolithic filer design : First and Second order filter realizations - Universal active filter (KHN) – Self tuned filter – Programmable filters – Switched capacitor filters: Switched capacitor resistors – amplifiers – comparators – sample and hold circuits – Integrator – BiQuad
CONTINUOUS TIME FILTERS AND DIGITAL FILTERS 09 Introduction to Gm - C filters – bipolar transconductors – CMOS Transconductors using Triode transistors, active transistors – BiCMOS transconductors – MOSFET C Filters – Tuning Circuitry – Dynamic range performance – Digital Filters: Sampling - decimation – Interpolation – Implementation of FIR and IIR filters.
DIGITAL TO ANALOG AND ANALOG TO DIGITAL CONVERTERS 09 Non-idealities in the DAC – Types of DACs: Current switched, Resistive, Charge redistribution (capacitive), Hybrid, segmented DACs – Techniques for improving linearity – Analog to Digital Converters: quantization errors – non-idealities – types of ADCs: Flash, two step, pipelined, successive approximation, folding ADCs.
SIGMA DELTA CONVERTERS 09 Over sampled converters – Over sampling without noise and with noise – Implementation imperfections – First order modulator – Decimation filters – Second order modulator – Sigma delta DAC and ADCs, Mixed Layout: CMOS design rules – Layout of CMOS – BJT – Capacitors – Resistors – Mixed layout issues: Floor planning, power supply and ground, fully differential matching, Guard rings and shielding.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Baker R J, Li H W, and Boyce D E, “CMOS: Circuit Design, Layout and Simulation”, Prentice Hall of India,2005 2. David A Johns, Ken Martin, " Analog Integrated Circuit Design” John Wiley and Sons, 2005 3. Phillip Allen and Douglas Holmberg, “CMOS Analog Circuit Design”, 2nd Edition, Oxford University Press, 2004 4. Rudy van de Plassche, “Integrated Analog-to-Digital and Digital –to-Analog Converters“, Springer India,2005 5. Benhard Razavi, “Data Converters”, Kluwer publishers, 1999 6. Antoniou, “Digital filters analysis and design”, Tata McGraw Hill, New Delhi, 1998
P15AETE28 - VLSI TESTING AND TESTABILITY L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Discuss various fault models and fault simulation techniques. Develop models and test generation algorithms for combinational and CO2: sequential logic circuits CO3: Illustrate Delay Tests. CO4: Illustrate methods for testing analog and mixed signals. CO5: Interpret different testability methods.
Pre-requisite: 1. Digital Electronics 2. VLSI Design Hrs FAULT MODELLING AND SIMULATION 09 Introduction to Testing - Faults in digital circuits - Modeling of faults - Logical Fault Models - Fault detection - Fault location - Fault dominance – Single stuck fault model and multiple stuck fault model-Logic Simulation - Types of simulation - Delay models - Gate level Event-driven simulation- Fault Simulation Techniques- Serial , Parallel and Deductive
TESTING FOR SINGLE STUCK AT FAULTS 09 Test Generation algorithms for combinational circuits – Fault oriented ATG - D-Algorithm- Examples – PODEM - Fault independent ATG - Random Test generation - ATGs for SSFs in sequential circuits – TG using iterative array models- Random Test Generation.
DELAY TEST 09 Delay test problem – Path delay test – Test generation for Combinational circuits, Number of paths in a circuit-Transition faults – Delay test methodologies-Slow clock combinational test, Enhanced scan test, normal scan sequential test, Variable- clock Non-scan sequential test, Rated- clock Non-scan sequential test.
ANALOG AND MIXED SIGNAL TEST 09 DSP based analog and mixed signal test – Static ADC and DAC testing methods - Model based Analog and Mixed signal Test - Analog fault models-Analog fault simulation – Analog ATPG
DESIGN FOR TESTABILITY 09 Testability- Controllability and observability, Ad-hoc design for testability Techniques – Controllability and observability by means of scan registers- Storage cells for scan design- Level sensitive scan design(LSSD)-Partial scan using I-Paths – Boundary scan standards.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Abramovici, M., Brever, A., and Friedman, D., "Digital Systems Testing and Testable Design", Jaico Publishing House. 2. Michael L Bushnell and Vishwani D Agarwal, “Essentials of Electronic Testing for Digital, Memory and Mixed Signal Circuits”, Springer, verlag 2000. 3. Stanley L Hurst “VLSI Testing : Digital and Mixed Analogue Digital Techniques”, Institute of Electrical Engineers,1998 4. Xiaoqing Wen, Cheng Wen Wu and Laung Terng Wang “VLSI Test Principles and Architectures: Design for Testability”, Cambridge University Press, 2000 5. Parag K Lala, “Fault Tolerant and Fault Testable Hardware Design” BS Publications, 2002
P15AETE29 - COMPUTER AIDED DESIGN OF VLSI L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Discuss data structures and algorithms for VLSI design automation. CO2: Apply algorithms for physical design automation. Discuss algorithms for simulation and synthesis at logic and higher level CO3: abstraction. Pre-requisite: 1. VLSI Design Hrs VLSI DESIGN METHODOLOGIES 09
Introduction to VLSI Design methodologies - Review of Data structures and algorithms - Review of VLSI Design automation tools - Algorithmic Graph Theory and Computational Complexity - Tractable and Intractable problems - general purpose methods for combinatorial optimization.
LAYOUT & PARTITIONING 09
Layout Compaction - Design rules - problem formulation - algorithms for constraint graph compaction - placement and partitioning - Circuit representation - Placement algorithms – partitioning
FLOORPLANNING & ROUTING 09
Floor planning concepts - shape functions and floor plan sizing - Types of local routing problems - Area routing - channel routing - global routing - algorithms for global routing.
SIMULATION & SYNTHESIS 09
Simulation - Gate-level modeling and simulation - Switch-level modeling and simulation - Combinational Logic Synthesis - Binary Decision Diagrams - Two Level Logic Synthesis.
HIGH LEVEL SYNTHESIS 09
High level Synthesis - Hardware models - Internal representation - Allocation assignment and scheduling - Simple scheduling algorithm - Assignment problem – High level transformations.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. S.H. Gerez, "Algorithms for VLSI Design Automation", John Wiley & Sons, 2002.
2. N.A. Sherwani, "Algorithms for VLSI Physical Design Automation", Kluwar Academic Publishers, 2002
3. Drechsler, R., “Evolutionary Algorithms for VLSI CAD”, Kluwer Academic Publishers, Boston, 1998.
4. Hill, D., D. Shugard, J. Fishburn and K. Keutzer, “Algorithms and Techniques for VLSI Layout Synthesis”, Kluwer Academic Publishers, Boston, 1989.
P15AETE30 - DESIGN AND ANALYSIS OF ALGORITHMS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Identify the complexity of algorithms. CO2: Assess the performance of the different optimization algorithms. CO3: Assess the performance of searching and sorting algorithms. CO4: Apply graph algorithms to solve real life problems CO5: Review special algorithms for solving problems.
Pre-requisite: 1. Data Structures and Algorithms Hrs INTRODUCTION 09
Polynomial and Exponential algorithms, big "o" and small "o" notation, exact algorithms and heuristics, direct / indirect / deterministic algorithms, static and dynamic complexity, stepwise refinement.
DESIGN TECHNIQUES 09
Subgoals method, working backwards, work tracking, branch and bound algorithms for traveling salesman problem and knapsack problem, hill climbing techniques, divide and conquer method, dynamic programming, greedy methods.
SEARCHING AND SORTING 09
Sequential search, binary search, block search, Fibonacci search, bubble sort, bucket sorting, quick sort, heap sort, average case and worst case behavior, FFT.
GRAPH ALGORITHMS 09
Minimum spanning, tree, shortest path algorithms, R-connected graphs, Even's and Kleitman's algorithms, ax-flow min cut theorem, Steiglitz's link deficit algorithm.
SPECIAL ALGORITHMS 09
NP Completeness Approximation Algorithms, NP Hard Problems, Strasseu's Matrix Multiplication Algorithms, Magic Squares, Introduction To Parallel Algorithms and Genetic Algorithms, Monti-Carlo Methods, Amortised Analysis.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Sara Baase, "Computer Algorithms: Introduction to Design and Analysis", Addison Wesley, 1988.
2. T.H.Cormen, C.E.Leiserson and R.L.Rivest, "Introduction to Algorithms", Mc Graw Hill, 1994.
3. E.Horowitz and S.Sahni, "Fundamentals of Computer Algorithms", Galgotia Publications, 1988.
4. D.E.Goldberg, "Genetic Algorithms: Search Optimization and Machine Learning", Addison Wesley, 1989.
P15AETE31 - DSP PROCESSOR ARCHITECTURE AND PROGRAMMING L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Describe the fundamentals of Digital Signal Processors. Explain the architecture, addressing modes and instruction set of generic DSP CO2: devices. Illustrate algorithms for implementation in Digital Signal Processors to solve CO3: real-time problems. CO4: Compare the features and performance of DSP devices. CO5: Identify salient features of advanced DSP devices.
Pre-requisite: 1. Digital Signal Processing and Processors Hrs FUNDAMENTALS OF PROGRAMMABLE DSPs 09
Multiplier and Multiplier accumulator – Modified Bus Structures and Memory access in P-DSPs – Multiple access memory – Multi-port memory –VLIW architecture- Pipelining –Special Addressing modes in P-DSPs – On-chip Peripherals.
TMS320C5X PROCESSOR 09
Architecture – Assembly language syntax - Addressing modes – Assembly language Instructions - Pipeline structure, Operation – Block Diagram of DSP starter kit – Application Programs for processing real time signals.
TMS320C3X PROCESSOR 09
Architecture – Data formats - Addressing modes – Groups of addressing modes- Instruction sets - Operation – Block Diagram of DSP starter kit – Application Programs for processing real time signals – Generating and finding the sum of series, Convolution of two sequences, Filter design – Introduction to code composer studio
ADVANCED PROCESSORS I 09
Architecture of ADSP-21XX and ADSP-210XX series of DSP processors- Addressing modes and assembly language instructions – Application programs –Filter design, FFT calculation.
ADVANCED PROCESSORS II 09
Architecture of TMS320C54X: Pipe line operation, Code Composer studio - Architecture of TMS320C6X - Architecture of Motorola DSP563XX – Comparison of the features of DSP family processors.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. B.Venkataramani and M.Bhaskar, “Digital Signal Processors – Architecture, Programming and Applications”, Tata McGraw – Hill Publishing Company Limited, New Delhi, 2003. 2. “User guides”- Texas Instruments, Analog Devices, Motorola.
3. Lapsley et al, “DSP Processor Fundamentals, Architectures & Features”, S. Chand & Co, 2000.
P15AETE32 - DSP INTEGATED CIRCUITS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Review the basics of digital signal processing. CO2: Categorize filters and their structures CO3: Employ the concepts of multirate signal processing CO4: Analyze the finite word length effects in digital signal processors CO5: Compare VLSI fabrication technologies
Pre-requisite: 1. Digital Signal Processing 2. VLSI Design Hrs NUMBER SYSTEMS, ARITHMETIC UNITS AND INTEGRATED CIRCUIT 09 DESIGN
Conventional number system, Redundant Number system, Residue Number System .Bit-parallel and Bit-Serial arithmetic, Distributed Arithmetic, Basic shift accumulator, Reducing the memory size, Complex multipliers, Improved shift-Accumulator.
DIGITAL SIGNAL PROCESSING 09
Digital signal processing, Sampling of analog signals, Selection of sample frequency, Signal- processing systems, Frequency response, Transfer functions, Signal flow graphs, Filter structures, Adaptive DSP algorithms, FFT-The Fast Fourier Transform Algorithm, Image coding, Discrete cosine transforms.
DIGITAL FILTERS AND FINITE WORD LENGTH EFFECTS 09
FIR filters, FIR filter structures, FIR chips, IIR filters, Specifications of IIR filters, Multirate systems, Interpolation with an integer factor L, Sampling rate change with a ratio L/M, Multirate filters. Finite word length effects -Parasitic oscillations, Scaling of signal levels, Round-off noise, Measuring round-off noise, Coefficient sensitivity, Sensitivity and noise.
DSP INTEGRATED CIRCUITS AND VLSI CIRCUIT TECHNOLOGIES 09
Standard digital signal processors, Application specific IC’s for DSP, DSP systems, DSP system design, Integrated circuit design. MOS transistors, MOS logic, VLSI process technologies, Trends in CMOS technologies.
DSP ARCHITECTURES AND SYNTHESIS 09
DSP system architectures, Standard and Ideal DSP architecture, Multiprocessors and multi computers, Systolic and Wave front arrays, Mapping of DSP algorithms onto hardware, Implementation based on complex PEs, Shared memory architecture with Bit – serial PEs, Layout of VLSI circuits, FFT processor, DCT processor and Interpolator as case studies.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Lars Wanhammer, “DSP Integrated Circuits”, Academic press, New York 1999.
2. A.V.Oppenheim et.al, “Discrete-time Signal Processing”, Pearson education, 2000.
3. Emmanuel C. Ifeachor, Barrie W. Jervis, “Digital signal processing – A Practical Approach”, 2nd edition, Prentice Hall, 2001.
4. Keshab K.Parhi, ‘VLSI digital Signal Processing Systems design and Implementation”, John Wiley & Sons, 1999.
P15AETE33 - SENSORS AND SIGNAL CONDITIONING L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Analyze static and dynamic characteristics of measurement systems Explain the working of the various types of sensors - Resistive, Reactive, Self- CO2: generating. CO3: Explain the operation of digital sensors and semiconductor device sensors. CO4: Examine the different sensors for various applications.
Pre-requisite: 1. Measurements and Instrumentation Hrs INTRODUCTION TO MEASUREMENT SYSTEMS 09
Introduction to measurement systems: general concepts and terminology, measurement systems, sensor classification, general input-output configuration, methods of correction performance characteristics: static characteristics of measurement systems, accuracy, precision, sensitivity, other characteristics: linearity, resolution, systematic errors, random errors, dynamic characteristics of measurement systems: zero-order, first-order, and second-order measurement systems and response
RESISTIVE SENSORS 09
Resistive sensors: potentiometers, strain gages and types, resistive temperature detectors (rtds) , thermistors , magneto resistors, light-dependent resistors (ldrs); Signal conditioning for resistive sensors: measurement of resistance , voltage dividers , Wheatstone bridge. Balance and deflection measurements , sensor bridge calibration and compensation instrumentation amplifiers , interference types and reduction
REACTIVE SENSORS 09
Reactance variation and electromagnetic sensors : capacitive sensors – variable & differential, inductive sensors – reluctance variation, eddy current, linear variable differential transformers (lvdts) , variable transformers: synchros, resolvers, inductosyn , magneto elastic sensors, electromagnetic sensors – sensors based on faraday’s law, hall effect sensors, Signal conditioning for reactance variation sensors : problems and alternatives, ac bridges, carrier amplifiers – application to the lvdt, variable oscillators, resolver-to-digital and digital-to-resolver converters
SELF-GENERATING SENSORS 09
Self-generating sensors: thermoelectric sensors, piezoelectric sensors, pyroelectric sensors, photovoltaic sensors , electrochemical sensors, Signal conditioning for self-generating sensors: chopper and low-drift amplifiers, offset and drifts amplifiers , electrometer amplifiers, charge amplifiers, noise in amplifiers
DIGITAL SENSORS AND SEMICONDUCTOR DEVICE SENSORS 09
Digital sensors: position encoders, variable frequency sensors – quartz digital thermometer, vibrating wire strain gages , vibrating cylinder sensors, saw sensors, digital flow meters, Sensors based on semiconductor junctions : thermometers based on semiconductor junctions, magneto diodes and magneto transistors, photodiodes and phototransistors, sensors based on mosfet transistors, chargecoupled sensors – types of CCD imaging sensors , ultrasonic-based sensors , fiber-optic sensors
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Ramon Pallas Areny, John G. Webster, “Sensors and Signal Conditioning”, 2nd edition, John Wiley and Sons, 2000 2. D.Patranabis, “Sensors and Transducers”, TMH 2003
3. Jon Wilson , “Sensor Technology Handbook”, Newne 2004.
4. Herman K.P. Neubrat, “Instrument Transducers – An Introduction to Their Performance and Design”, Oxford University Press. 5. E.O. Doeblin, “Measurement System : Applications and Design”, McGraw Hill Publications 6. D. Johnson, “Process Control Instrumentation Technology”, John Wiley and Sons
7. Kevin James, PC Interfacing and Data acquisition, Elsevier, 2011
8. Graham Brooker, Introduction to Sensors for ranging and imaging, Yesdee, 2009
9. Ian Sinclair, Sensors and Transducers, Elsevier, 3rd Edition, 2011
P15AETE34 - MACHINE VISION AND LEARNING L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the concepts of machine vision. CO2: Review the concepts of image optics CO3: Relate the various machine learning methods. CO4: Apply Bayesian and Computational Learning methods. CO5: Employ advanced learning concepts.
Pre-requisite: 1. Digital Image Processing
Hrs MACHINE VISION 09
Introduction – Machine vision –Relationship to other fields –Image definitions levels of computation- Binary image processing – Thresholding - Geometric properties – position – orientation –Run length encoding -Binary algorithms – Definitions - Component labeling –Size filter –Euler number –Region boundary –Area perimeter – compact Distance measures- Distance transforms – Medial axis – Thinning expanding and shrinking –morphological operators.
OPTICS SHADING 09
Optics – lens equation –Image resolution –Depth of Field view volume –Exposure- shading – Image Inductance –Illumination – Reflector –Surface orientation –shape from shading depth – Stereo imaging –Cameras in arbitrary position and orientation –Stereo matching –Edge matching – Region correlation shape from X – Range imaging – structural lighting – Imaging Radar- Active vision.
INTRODUCTION TO LEARNING 09
Learning Problems – Perspectives and Issues – Concept Learning – Version Spaces and Candidate Eliminations – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search. K- Nearest Neighbour Learning – Locally weighted Regression – Radial Bases Functions – Case Based Learning
BAYESIAN AND COMPUTATIONAL LEARNING 09
Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum Description Length Principle – Bayes Optimal Classifier – Gibbs Algorithm – Naïve Bayes Classifier – Bayesian Belief Network – EM Algorithm – Probability Learning – Sample Complexity – Finite and Infinite Hypothesis Spaces – Mistake Bound Model.
ADVANCED LEARNING 09
Learning Sets of Rules – Sequential Covering Algorithm – Learning Rule Set – First Order Rules – Sets of First Order Rules – Induction on Inverted Deduction – Inverting Resolution – Analytical Learning – Perfect Domain Theories – Explanation Base Learning – FOCL Algorithm – Reinforcement Learning – Task – Q Learning – Temporal Difference Learning
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Ramesh Jain, Rangachar Kasturi and Brian G. Schunck, “Machine Vision”, McGraw Hill International Edition, 2006.
2. Gregory A Baxes, “Digital Image Processing, John Wiley & Sons”, 1994.
3. W.K. Pratt, “Digital Image Processing, John Wiley and Sons”, 2001.
4. Tom M. Mitchell, “Machine Learning”, New Delhi: McGraw-Hill Science/Engineering/Math, 1997.
5. Ethem Alpaydin, “Introduction to Machine Learning (Adaptive Computation and Machine Learning)” New Delhi: The MIT Press 2004.
6. T. Hastie, R. Tibshirani and J. H. Friedman, “The Elements of Statistical Learning”, New York: Springer; 2001.
7. Bishop, C. “Pattern Recognition and Machine Learning”, Berlin: Springer-Verlag.2006.
P15AETE35 - PATTERN RECOGNITION L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: Apply parametric estimation and supervised learning techniques for pattern CO1: classification CO2: Compare various clustering methods for pattern classification CO3: Describe the structural pattern recognition methods. CO4: Demonstrate the feature extraction and selection methods Apply neural networks, fuzzy systems and Genetic algorithms to pattern CO5: recognition and classification.
Pre-requisite: 1. Digital Image Processing
Hrs PATTERN CLASSIFIER 09
Overview of pattern recognition -Discriminant functions-Supervised learning –Parametric estimation- Maximum likelihood estimation –Bayesian parameter estimation- Perceptron algorithm-LMSE algorithm – Problems with Bayes approach –Pattern classification by distance functions-Minimum distance pattern classifier.
UNSUPERVISED CLASSIFICATION 09
Clustering for unsupervised learning and classification - Clustering concept-C-means algorithm- Hierarchical clustering procedures- Graph theoretic approach to pattern clustering - Validity of clustering solutions.
STRUCTURAL PATTERN RECOGNITION 09
Elements of formal grammars-String generation as pattern description - recognition of syntactic description- Parsing-Stochastic grammars and applications - Graph based structural representation.
FEATURE EXTRACTION AND SELECTION 09
Entropy minimization – Karhunen - Loeve transformation-feature selection through functions approximation- Binary feature selection.
RECENT ADVANCES 09
Neural network structures for Pattern Recognition –Neural network based Pattern associators- Unsupervised learning in neural Pattern Recognition-Self organizing networks-Fuzzy logic-Fuzzy classifiers-Pattern classification using Genetic Algorithms.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. R.O Duda, P.E Hart and Stork, “Pattern Classification”, Wiley, 2012.
2. Robert J. Sehalkoff, “Pattern Recognition: Statistical, Structural and Neural Approaches”, John Wiley & Sons Inc., 2007.
3. Tou & Gonzales, “Pattern Recognition Principles”, Wesley Publication Company, 2000.
4. Morton Nadier and P. Eric Smith, “Pattern Recognition Engineering”, John Wiley & Sons, 2000.
Other References :
5. IEEE Transaction on Pattern Recognition Technique, 2006.
6. IEEE Engineering Medicine and Biology Magazine, 2006
P15AETE36 - SOLID STATE DEVICE MODELLING AND SIMULATION L T P C 3 0 0 3
Course Outcomes: Upon completion of the course the student should be able to: CO1: Analyze the various.MOS devices using models. CO2: Apply mathematical techniques for device simulations. CO3: Explain the fundamentals of building device and circuit simulators. CO4: Use simulation techniques effectively for analyzing devices. Pre-requisite: 1. Electronic Devices and Circuits Hrs MOSFET DEVICE PHYSICS 09
MOSFET capacitor, Basic operation, Basic modeling, Advanced MOSFET modeling, RF modeling of MOS transistors, Equivalent circuit representation of MOS transistor, High frequency behavior of MOS transistor and A.C small signal modeling, model parameter extraction, modeling parasitic BJT, Resistors, Capacitors, Inductors.
DEVICE MODELLING 09
Prime importance of circuit and device simulations in VLSI; Nodal, mesh, modified nodal and hybrid analysis equations. Solution of network equations: Sparse matrix techniques, solution of nonlinear networks through Newton-Raphson technique, convergence and stability.
MULTISTEP METHODS 09
Solution of stiff systems of equations, adaptation of multistep methods to the solution of electrical networks, general purpose circuit simulators.
MATHEMATICAL TECHNIQUES FOR DEVICE SIMULATIONS 09
Poisson equation, continuity equation, drift-diffusion equation, Schrodinger equation, hydrodynamic equations, trap rate, finite difference solutions to these equations in 1D and 2D space, grid generation.
SIMULATION OF DEVICES 09
Computation of characteristics of simple devices like p-n junction, MOS capacitor and MOSFET; Small-signal analysis.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Arora, N., “MOSFET Models for VLSI Circuit Simulation”, Springer-Verlag, 1993.
2. Selberherr, S., “Analysis and Simulation of Semiconductor Devices”, Springer-Verlag., 1984
3 Fjeldly, T., Yetterdal, T. and Shur, M., “Introduction to Device Modeling and Circuit Simulation”, Wiley-Interscience., 1997 4 Grasser, T., “Advanced Device Modeling and Simulation”, World Scientific Publishing Company., 2003 5. Chua, L.O. and Lin, P.M., “Computer-Aided Analysis of Electronic Circuits: Algorithms and Computational Techniques”, Prentice-Hall., 1975 6. Trond Ytterdal, Yuhua Cheng and Tor A. Fjeldly Wayne Wolf, “Device Modeling for Analog and RF CMOS Circuit Design”, John Wiley & Sons Ltd.
P15AETE37 - NANOELECTRONICS L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the basic concepts of Nano electronics. CO2: Explain the techniques of fabrication and measurement. CO3: Identify the properties of materials. CO4: Analyze Nanostructure devices. CO5: Choose logic devices for necessary applications.
Pre-requisite: 1. Engineering Physics 2. Electron Devices Hrs INTRODUCTION TO NANOELECTRONICS 09
Microelectronics towards biomolecule electronics-Particles and waves- Wave-particle duality- Wave mechanics- Schrodinger wave equation- Wave mechanics of particles: - Atoms and atomic orbitals- Materials for nanoelectronics- Semiconductors- Crystal lattices: Bonding in crystals- Electron energy bands- Semiconductor heterostructures- Lattice-matched and pseudomorphic heterostructures- Inorganic-organic heterostructures- Carbon nanomaterials: nanotubes and fullerenes
FABRICATION AND MEASUREMENT TECHNIQUES 09
Growth, fabrication, and measurement techniques for nanostructures- Bulk crystal and heterostructure growth- Nanolithography, etching, and other means for fabrication of nanostructures and nanodevices- Techniques for characterization of nanostructures- Spontaneous formation and ordering of nanostructures- Clusters and nanocrystals- Methods of nanotube growth- Chemical and biological methods for nanoscale fabrication- Fabrication of nano- electromechanical systems
PROPERTIES 09
Dielectrics-Ferroelectrics-Electronic Properties and Quantum Effects-Magnetoelectronics – Magnetism and Magnetotransport in Layered Structures-Organic Molecules – Electronic Structures, Properties, and Reactions-Neurons – The Molecular Basis of their Electrical Excitability-Circuit and System Design- Analysis by Diffraction and Fluorescence Methods- Scanning Probe Techniques
NANO STRUCTURE DEVICES 09
Electron transport in semiconductors and nanostructures- Time and length scales of the electrons in solids- Statistics of the electrons in solids and nanostructures- Density of states of electrons in nanostructures- Electron transport in nanostructures-Electrons in traditional low-dimensional structures- Electrons in quantum wells- Electrons in quantum wires- Electrons in quantum dots- Nanostructure devices- Resonant-tunneling diodes- Field-effect transistors- Single-electron- transfer devices- Potential-effect transistors- Light-emitting diodes and lasers- Nano- electromechanical system devices- Quantum-dot cellular automata
LOGIC DEVICES AND APPLICATIONS 09
Logic Devices-Silicon MOSFETs-Ferroelectric Field Effect Transistors-Quantum Transport Devices Based on Resonant Tunneling-Single-Electron Devices for Logic Applications- Superconductor Digital Electronics-Quantum Computing Using Superconductors-Carbon Nanotubes for Data Processing- Molecular Electronics
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Vladimir V. Mitin, Viatcheslav A. Kochelap, Michael A. Stroscio, “Introduction to Nanoelectronics: Science, Nanotechnology, Engineering, and Applications”, Cambridge University Press 2011 2. Supriyo Datta,“Lessons from Nanoelectronics: A New Perspective on Transport”, World Scientific 2012 3 George W. Hanson,“Fundamentals of Nanoelectronics”, Pearson 2009
4. Korkin, Anatoli; Rosei, Federico (Eds.), “Nanoelectronics and Photonics”, Springer 2008
5. Mircea Dragoman, Daniela Dragoman, “Nanoelectronics: principles and devices”, CRC Press 2006 6 Karl Goser, Peter Glosekotter, Jan Dienstuhl, “Nanoelectronics and Nanosystems: From Transistors to Molecular and Quantum Devices“, Springer 2004 7. W. R. Fahrner, Nanotechnology and Nan electronics: Materials, Devices, Measurement Techniques (SpringerVerlag Berlin Heidelberg 2005) 8. Mark A. Reed, Takhee Lee,“Molecular Nanoelectronics”, American Scientific Publishers 2003 9. Jaap Hoekstra, “Introduction to Nanoelectronic Single-Electron Circuit Design”, Pan Stanford Publishing 2010 10. W. Ranier, “Nano Electronics and Information Technology”, John Wiley & Sons 2012
SPECIAL ELECTIVES
P15AESE01/ P15COSE01 -RESEARCH METHODOLOGY (Common to both Communication Systems and Applied Electronics) L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Recognize the importance of literature review. CO2: Identify the different types of research. CO3: Develop mathematical models for different problems. CO4: Formulate models for experimental analysis CO5: Analyze the results using statistical methods CO6: Prepare technical reports.
Pre-requisite: NIL Hrs RESEARCH CONCEPTS 09
Concepts, meaning, objectives, motivation, types of research, approaches, research (Descriptive research, Conceptual, Theoretical, Applied & Experimental). Formulation of Research Task – Literature Review, Importance & Methods, Sources, quantification of Cause Effect Relations, Discussions, Field Study, Critical Analysis of Generated Facts, Hypothetical proposals for future development and testing, selection of Research task.
MATHEMATICAL MODELING AND SIMULATION 09
Concepts of modeling, Classification of Mathematical Models, Modeling with Ordinary differential Equations, Difference Equations, Partial Differential equations, Graphs, Simulation, Process of formulation of Model based on Simulation.
EXPERIMENTAL MODELING 09
Definition of Experimental Design, Examples, Single factor Experiments, Guidelines for designing experiments. Process Optimization and Designed experiments, Methods for study of response surface, determining optimum combination of factors, Taguchi approach to parameter design.
ANALYSIS OF RESULTS 09
Parametric and Non-parametric, descriptive and Inferential data, types of data, collection of data (normal distribution, calculation of correlation coefficient), processing, analysis, error analysis, different methods, analysis of variance, significance of variance, analysis of covariance, multiple regression, testing linearity and non-linearity of model.
REPORT WRITING 09
Types of reports, layout of research report, interpretation of results, style manual, layout and format, style of writing, typing, references, tables, figures, conclusion, appendices.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. R. Panneerselvam, “Research Methodology”, PHI 2004.
2. Douglas Montgomary, “Design of Experiments, Statistical Consulting Services”, 1990.
3. Douglas H. W. Allan, “Statistical Quality Control: An Introduction for Management”, Reinhold Pub Corp, 1959.
4. Cochran and Cox, “Experimental Design”, John Willy & Sons, 2nd Edition , May 1992
5. S. S. Rao, “Optimization Theory and Application”, Wiley Eastern Ltd., New Delhi, 1996.
6. C. R. Kothari, “Research Methodology”, New Age Publishers, 2005.
P15AESE02/ P15COSE02 -MULTIRATE SIGNAL PROCESSING L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Discuss the fundamental concepts of multirate systems. CO2: Design QMF filter banks. CO3: Design perfect reconstruction filter banks and analyze their properties. CO4: Implement filters using polyphase structures. CO5: Design filters banks using cosine modulation techniques. CO6: Apply the concepts of multirate signal processing to real time signals.
Pre-requisite: 1. Digital Signal Processing Hrs FUNDAMENTALS OF MULTIRATE SYSTEMS 09
Sampling theorem - sampling at sub Nyquist rate - Basic Formulations and schemes-Multirate operations- Decimation and Interpolation - Digital Filter Banks –Interconnection of Building Blocks- Decimation with transversal filters – Interpolation with transversal filters – Polyphase representation -Decimation with Polyphase filters – Interpolation with polyphase filters – Decimation and Interpolation with Rational sampling factors
MAXIMALLY DECIMATED FILTER BANKS 09
Quadrature mirror filter banks -Errors in the QMF bank- Perfect reconstruction (PR) QMF Bank - Design of an alias free QMF Bank - Biorthogonal and linear phase filter banks – Transmuliplexer filter banks
PERFECT RECONSTRUCTION FILTER BANKS 09
Paraunitary PR Filter Banks- Filter Bank Properties induced by paraunitarity - Two channel FIR paraunitary QMF Bank- Linear phase PR Filter banks- Necessary conditions for Linear phase property- Quantization Effects: -Types of quantization effects in filter banks. - coefficient sensitivity effects, dynamic range and scaling.
FILTER BANKS WITH POLYPHASE STRUCTURE 09
Fundamental polyphase structures – polyphase QMF banks – General two channel polyphase filter banks – General M-channel polyphase filter banks – Paraunitary polyphase filter banks – DFT polyphase filter banks. Application: Digital audio.
COSINE MODULATED FILTER BANKS 09
Cosine Modulated pseudo QMF Bank- Alias cancellation- Elimination of Phase distortion- Closed form expression- Cosine modulated PR Systems-Sub band coding of speech and Image signals
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Vaidyyanathan P P, "Multirate Systems and Filter Banks", Prentice Hall Inc., 2011
2. Fliege N J, "Multirate Digital Signal Processing", John Wiley and sons, 1994
3. J.G. Proakis. D.G. Manolakis. “Digital Signal Processing: Principles. Algorithms and Applications”, 3rd Edition, Prentice Hall India, 1999
4. Sanjit K Mitra, "Digital Signal Processing-A Computer Based Approach", Tata McGraw Hill, 2003
5. R.E. Crochiere. L. R. “Multirate Digital Signal Processing”, Prentice Hall. Inc.1983.
P15AESE03 - MULTI-SENSOR DATA AND IMAGE FUSION L T P C Course Outcomes: 3 0 0 3 Upon completion of the course the student should be able to: CO1: Apply Baye’s theorem for image fusion CO2: Describe various data association methods CO3: Apply Kalman algorithm for multi sensor image fusion CO4: Describe various image fusion methods and architectures
Pre-requisite: NIL Hrs PROBABILISTIC DATA FUSION 09
Baye’s Theorem, Data Fusion using Baye’s Theorem, Recursive Baye’s updating, Data Dependency and Baye’s Networks, Distributed Data Fusion with Baye’s Theorem.
MULTI-SENSOR ESTIMATION 09
State and Sensor Models - Kalman Algorithm - Extended Kalman Filter – Multi – Sensor Kalman Filter - Observation Models, Distributed Multi Sensor Kalman Filter, Track-to-Track Fusion - Non-Linear Data Fusion Method - Likelihood Estimation Method, Particle Filter, Sum-of- Gaussians Method, Distribution Approximation Filter (DAF).
MULTI –SENSOR MULTI TARGET ESTIMATION 09
Data Association – Nearest-Neighbor standard Filter, Probabilistic Data Association Filter, Track Splitting Filter, Multiple–Hypothesis Filter. Multi sensor Data Association - Single to multiple sensor Associations, Deterministic Track-to- Track Assignment, Probabilistic Track-to-Track Assignment, Decentralized Data Association.
DATA FUSION ARCHITECTURES 09
Hierarchical Data Fusion Architectures, Distributed Data Fusion Architectures, Centralized Data Fusion Architectures, Decentralized Estimation –information Filter, Decentralized Information Filter, Decentralized multi-Target Tracking, Decentralized Identification, Decentralized Management – Sensor Management, Communications Management and System Design.
MULTISENSOR IMAGE FUSION 09
An overview of image fusion, Image fusion levels, Image fusion using Laplacian pyramid, radient pyramid, Bayesian approach, Wavelet transforms, Neural network and Fuzzy logic, Gradient based multiresolution image fusion, Fusion using Independent Component Analysis.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Y. Bar-Shalom and X. Li, Multitarget-Multisensor Tracking: Principle and Techniques, YBS Publishing, 1995. 2. D.D. Blackman, Multiple Target Tracking with Radar applications, Artech House, 1986. 3. Y. Bar-Shalom and X. Li, Estimation with Application to Tracking and Navigation, John Wiley, 2001. 4. E. Waltz and J. Linas, Multisensor Data Fusion, Artech House, 1990. 5. Y. Bar-Shalom and X. li, Multitarget multisensor tacking: Applications and advances Vol. I and II, Academic Press, 1990, 1992. 6. Y. Bar-Shalom and Dale Blair, Multitarget multisensor: Applications and advances, Vol III, Artech House, 2000. 7. Rick S Blum & Zheng Liu, Multisensor Image Fusion and its Applications, CRC press 2006. RESEARCH PAPERS
1. Cvejic, N.; Bull, D.; Canagarajah,N., “Region-Based Multimodal Image Fusion Using ICA Bases”, IEEE Sensors Journal, Volume 7, Issue 5, May 2007 Page(s):743 – 751 2. Hui Li Manjunath, B.S. Mitra, S.K., “Multi-sensor image fusion using the wavelet transform”, Proceedings of IEEE International Conference in Image Processing, 1994. ICIP- 94.; 3. Nunez, J.; Otazu, X.; Fors, O.; Prades, A.;Pala, V.; Arbiol, R., „Multiresolutionbased image fusion with additive wavelet decomposition“, IEEE Transactions on Geoscience and Remote Sensing, Volume 37, Issue 3, May 1999 Page(s):1204 – 1211 4. Valdimir S. Petrovic, Costas S.Xydeas, “Gradient based multiresolution Image Fusion”, IEEE transactions on Image Processing, Volk.13, No. 2., February 2004. 5. Mallat, (1989). “A Theory for Multiresolution Signal Decomposition: the Wavelet Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11:674- 693.
P15AESE04 - HYPERSPECTRAL IMAGE PROCESSING L T P C 3 0 0 3 Course Outcomes: Upon completion of the course the student should be able to: CO1: Explain the fundamentals of Hyperspectral data acquisition systems CO2: Apply mutual information for image registration Understand the fundamentals of Independent component analysis and apply it to CO3: hyperspectral imagery. Understand the application of SVM and Markov Random field to hyperspectral CO4: imagery.
Pre-requisite: 1. Digital Image processing 2. Advanced Digital Image Processing Hrs INTRODUCTION 09 Hyperspectral imaging, Multi-spectral scanning systems, Hyperspectral Systems- Airborne and Spaceborne Sensors, Ground Spectroscopy, Applications of Hyperspectral imaging, Overview of image processing- Image file formats, Image Distortion and Rectification, Image Registration, Image Enhancement, Image Classification, Image Change Detection, Image Fusion, Automatic Target Recognition.
MUTUAL INFORMATION: A SIMILARITY MEASURE FOR INTENSITY 09 BASED IMAGE REGISTERATION Mutual information similarity measure, Joint histogram estimation measures, interpolation induced artifacts, generalized partial volume estimation of joint histograms, optimisation issues in maximisation of mutual information.
INDEPENDENT COMPONENT ANALYSIS 09 Independent component analysis (ICA) introduction, ICA Algorithms – Preprocessing using PCA, Information minimization solution for ICA, ICA solution through non-gaussianity maximisation, Application of ICA to hyperspectral imagery – Feature extraction based model, Linear mixture model based model, An ICA algorithm for hyperspectral imagery.
SUPPORT VECTOR MACHINES 09 Statistical Learning theory – Empirical Risk Minimisation, structural risk minimisation, Design of Support vector machines – Linearly separable case, linearly non-separable case, non-linear support vector machines, SVMs for multiclass classification – one against the rest classification, pairwise classification, classification based on decision directed acyclic graph and decision tree structure, multiclass objective function, optimization methods.
MARKOVS RANDOM FIELD MODELS 09 MRF and Gibbs Distribution, MRF Modelling in remote sensing applications, optimisation algorithms – simulated annealing, Metropolis Algorithm, iterated condition mode algorithm.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs
References: 1. Varshney P K and Aurora M K, Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data.
2. John R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd Edition, 1995.
3. Robert Shcowebgerdt, Remote sensing models & methods for image processing, 3rd edition, 2004.
4. John A.Richards, Springer – Verlag, Remote Sensing Digital Image Analysis 1999.
5. Digital Image Processing (3rd Edition) Rafael C. Gonzalez, Richard E. Woods Prentice Hall, 2007. 5. W.G.Rees - Physical Principles of Remote Sensing, Cambridge University Press, 2nd edition, 2001.
P15AESE05 MISSILE GUIDANCE AND CONTROL L T P C
3 0 0 3
Course Outcomes: Upon completion of the course the student should be able to: CO1: Select suitable coordinate system for aerospace vehicle guidance methods. CO2: Design and analyse controllers for missile autopilot. CO3: Interpret and modify the existing guidance algorithms for Missiles. CO4: Estimate the trajectory of Ballistic missiles. CO5: Analyze the performance of Weapon Delivery Systems.
Pre-requisite: 1. Advanced Control Engineering
MISSILE SYSTEMS INTRODUCTION 08 History of guided missile for defence applications – Classification of missiles – The Generalized Missile Equations of Motion – Coordinate Systems – Lagrange’s Equations for Rotating Coordinate Systems – Rigid-Body Equations of Motion – Missile system elements, missile ground systems.
MISSILE AIRFRAMES, AUTOPILOTS AND CONTROL 09 Missile aerodynamics – Force Equations, Moment Equations, Phases of missile flight – Missile control configurations – Missile Mathematical Model – Autopilots – Definitions, Types of Autopilots, Example Applications – Open-loop autopilots – Inertial instruments and feedback – Autopilot response, stability, and agility – Pitch Autopilot Design, Pitch-Yaw-Roll Autopilot Design.
MISSILE GUIDANCE LAWS 10 Tactical Guidance Intercept Techniques, Derivation of the Fundamental Guidance Equations, explicit, Proportional Navigation, Augmented Proportional Navigation, beam riding, bank to turn missile guidance, Three-Dimensional Proportional Navigation, comparison of guidance system performance, Application of Optimal Control of Linear Feedback Systems.
STRATEGIC MISSILES 10 Introduction, The Two-Body Problem, Lambert’s Theorem, First-Order Motion of a Ballistic Missile, Correlated Velocity and Velocity-to-Be-Gained Concepts, Derivation of the Force Equation for Ballistic Missiles, Atmospheric Reentry, Ballistic Missile Intercept, Missile Tracking Equations of Motion, Introduction to Cruise Missiles, The Terrain-Contour Matching (TERCOM) Concept.
WEAPON DELIVERY SYSTEMS 08 Weapon Delivery Requirements, Factors Influencing Weapon Delivery Accuracy, Unguided Weapons, The Bombing Problem, Guided Weapons, Integrated Flight Control in Weapon Delivery, Missile Launch Envelope, Mathematical Considerations Pertaining to the Accuracy of Weapon Delivery Computations.
Theory 45 Hrs Tutorial - Hrs Total 45 Hrs References: 1. Siouris, G.M., “Missile Guidance and Control Systems”, Springer, 2003.
2. Blakelock, J. H., “Automatic Control of Aircraft and Missiles”, 2nd Edition, John Wiley and Sons, 1990.
3. Fleeman, Eugene L., “Tactical Missile Design”, First Edition, AIAA Education series, 2001.
4. Garnell, P., “Guided Weapon Control Systems”, 2nd Edition, Pergamon Press, 1980.
5. Joseph Ben Asher and Isaac Yaesh, “Advances in Missile Guidance Theory”, AIAA Education series, 1998.
6. Paul Zarchan, “Tactical and Strategic Missile Guidance”, AIAA Education series, 2007.
ONE CREDIT COURSES
P15AEIN01 -ADVANCED EMBEDDED SYSTEM DESIGN USING ARM L T P C 3 0 0 1 Pre-requisite: 1. Microprocessors and Microcontrollers Hrs OVERVIEW 03 Review of ARM v7 core and its architecture, Introduction to Advanced ARM CORTEX M4 architecture, Peripherals overview, Advantages of using Cortex M4,Instruction set implementation, CPU timers introduction
FLOATING POINT UNIT 03 Introduction to Floating Point Architecture , Advantages of FPU, Need for FPU,IEEE Standards for implementing FPU, Various FPU Modules in Cortex M4 Processors, Software flow for FPU implementation
MOTION CONTROL 03 Introduction to motion control, advantages for using using motion control modules, Implantation of motion control overview, introduction to PWM Modules, PWM Concepts for Motion Control, Configuration of PWM Modules, Introduction to encoders , types of encoders , QEP Module
SERIAL INTERFACE 06 Types of Serial Interface, Advantages of using serial interface, Comparisons between various serial communication standards, Introduction to USB, Types of USB Interfacing Standards, Modes of Interfacing.CAN BUS –Advantage of CAN bus ,Overview of CAN Bus, Implementation of CAN in ARM Cortex M4
Theory 15 Hrs Tutorial - Hrs Total 15 Hrs
References: 1. Jonathan W Valvano, Introduction to Arm(r) Cortex -M Microcontrollers,2012. 2. Andrew Sloss , Dominic Symes,Chris Wright, ARM System Developer's Guide,2004. 3. Datasheet, Technical Documents and Application Notes http://www.ti.com/product/tm4c123gh6pm
P15AEIN02 -ADVANCED ANALOG SYSTEM DESIGN L T P C 3 0 0 1 Pre-requisite: 1. Electronic Devices and Circuits Hrs AUTOMATIC VOLUME CONTROL (AVC) 03 Introduction –Circuit and Description-Need for AVC Applications - Benefits.
DC-DC CONVERTER 03 Introduction-conversion methods- Circuit and Description - Applications.
LOW DROPOUT REGULATOR (LDO) 03 Brief theory and description-Need for LDO- Comparison – Specifications – Applications - Introduction to WEBENCH.
LAB EXPERIMENTS USING ASLK PRO 06 (1)Design of Automatic Volume control Obtain transfer characteristics (2) Design of DC-DC converters Simulation Obtain time response Obtain transfer function Implementation Obtain time response using hardware Obtain transfer function using hardware (3) Design of Low Dropout Regulator Simulation Obtain output characteristics Transfer characteristics Measure rippled rejection Design of LDO using WEBENCH Implementation Obtain output characteristics using hardware Transfer characteristics Measure rippled rejection AUTOMATIC VOLUME CONTROL (AVC) Introduction –Circuit and Description-Need for AVC Applications - Benefits.
Theory 15 Hrs Tutorial - Hrs Total 15 Hrs
P15AEIN03 -CONCEPTS OF MODERN SENSOR TECHNOLOGY L T P C 3 0 0 1
Hrs TRANSDUCTION PRINCIPLES 03 Capacitive actuation and sensing, Piezoelectric actuation and sensing, Piezoresistive sensing, Quantum Mechanical Tunneling based sensing, Electro-thermal actuation, Magnetic actuation, Magnetoresistance, Magnetostriction.
MICROFABRICATION TECHNOLOGIES 03 Deposition - Spin Casting, Physical vapor deposition, Chemical vapor deposition Lithography – Optical/UV, Electron beam, Ion Beam, Scanning probe, Lift-off Removal - Wet etching, Dry and Plasma etching, Surface Micromachining, Bulk Micromachining, Deep reactive ion etch Packaging techniques.
MEMS SENSORS AND APPLICATIONS 03 Pressure sensor, Accelerometer, Gyroscope, Magnetometer, Microphone – Construction and Principle of operation. Cantilever based devices - Bio-molecular sensing, Scanning probe microscopy Example Sensors – ICs from Industry
MEASUREMENT CONCEPTS 06 Types of noise – Johnson noise, 1/f noise, shot noise, Signal-to-noise DC measurement, AC measurement, Phase-sensitive detection Data acquisition - Sampling, Aliasing, ADC & DAC, Resolution, Buffering Digital Signal Processing – Filtering, Interpolation, Decimation, Frequency domain analysis Example Systems – Typical system design(s) used in Industry
Theory 15 Hrs Tutorial - Hrs Total 15 Hrs