Coursework Done

Ref: Subject | Code

SEMESTER-I

Autonomous Navigation | CP 313

Instructor: Dr. Raghu Krishnapuram

Perception and sensor technologies (such as IMU, GPS, LiDAR, and wheel odometry), behaviour modelling, trajectory prediction, localization and mapping methods (including visual odometry), and motion/path planning in the presence of obstacles, SLAM techniques.

Navigation, Guidance & Control | AE 205

Instructors: Prof. Debasish Ghose, Prof. Radhakant Padhi and Dr. Ashwini Rathnoo

Navigation: Continuous waves and frequency modulated radars, MTI and Doppler radars;

Hyperbolic navigation systems: INS, GPS, SLAM; Guidance: Guided missiles, guidance laws:

pursuit, LOS and PN laws, Guidance of UAVs; Control: Linear time invariant systems, transfer functions and state space modeling, analysis and synthesis of linear control systems, applications to aerospace engineering.

Mathematics for Electrical Engineers | E2 243

Instructor: Prof. R Vittal Rao

Analysis: The Real Number System, Euclidean Spaces, Metric Spaces, Closed and open sets,

Numerical sequences and series, Limits, Continuity. Probability Theory: The axioms of probability theory, Independence and conditional probability, Random variables and their distribution, Expectation, Conditional distribution, Convergence of sequences of random variables, Laws of large numbers and Central limit theorem. Linear Algebra: Vector Spaces, Subspaces, Linear independence, Basis and dimension, Orthogonality; Matrices, Determinants, Eigenvalues and Eigenvectors, Positive definite matrices, Singular Value Decomposition.

Digital Signal Processing | IN 270

Instructor: Prof. T.K. Mondal

Fourier analysis, Fourier Integral, Discrete Fourier transform multiplications of two signals, Z transform, convolution, correlation Digital filtering, Discrete transformation modulation, FIR, IIR filters, Analog I/O interphase for real time DSP system, application of TMS320 C6713DSK to evaluate convolution, IIR and FIR filter. Multirate digital signal processing, Linear prediction and optimum linear filters , Power spectrum estimation.

SEMESTER-II

Introduction to Robotics | ME 246

Instructor: Prof. Ashitava Ghosal

Robot manipulators: representation of translation, rotation, links and joints, direct and inverse kinematics and workspace of serial and parallel manipulators, dynamic equations of motion, position and force control and simulation.

Topics in Neural Computation | AE 274

Instructor: Prof. Suresh Sundaram

Radial Basis Function Network and its Learning Algorithms, Review on Recurrent Neural Network, Neural Networks for Classification Problems, Evolving Neural Networks and its Sequential Learning Algorithms, Sequential Learning Algorithm for Pattern Classification, Meta-Cognitive Neural Networks, Complex-valued Neural Networks.

Control Systems Design | IN 227

Instructor: Prof. Jayanth G R

Dynamics of linear systems, Laplace transforms, analysis of feedback control systems using Nyquist plots, Bode plots and Root Locus, design of control systems in single-degree of-freedom configuration using direct design, proportional-integral-derivative control, lead-lag ompensation, design of control systems in two-degree of-freedom configuration to achieve robustness, Quantitative feedback theory control of non-minimum phase systems, Bode sensitivity integrals, use of describing functions to analyze and compensate nonlinearities.

Computer Vision | E1 216

Instructor: Prof. Venu Madhav Govindu

Image Formation : Camera Geometry, Radiometry, Colour; Image Features : Points, Lines, Edges,

Contours, Texture; Shape : Object Geometry, Stereo, Shape from cues; Motion : Calibration, Registration, Multiview Geometry,Optical Flow; Approaches to Grouping and Segmentation; Representation and Methods for Object Recognition; Applications

SEMESTER-III

Computational Methods of Optimisation | E0 230

Instructor: Prof. Chiranjeeb Bhattacharya

Weierstrass Theorem, Taylor series, small-oh notation, Multivariate Taylor Series, Local/Golobal Minima, Coercive function, Necessary and Sufficient condition for unconstrained Optimization, Descent methods, Exact line search, Minimization of convex functions by steepest descent, Inexact line search, Global convergence Theorem, Rate of convergence of Steepest descent, Coordinate Descent (Gauss-Southwell) and Conjugate Gradient Algorithms, Newton's method, Quasi Newton Methods, Constrained Optimisation: Projection onto a convex set, Farka's Lemma, KKT conditions, Gradient Projection, Active Set Methods for Convex QP, Duality, Linear Programming, Subgradient Projection.

SEMESTER-IV

Robot Learning & Control | CP314

Instructor: Dr. Shishir NYK

Robot kinematics and dynamics, Non-linear control and stability techniques, Lyapunov theory, PD control, Reinforcement learning basics, Imitation learning, Model-based and model-free methods, Impedance control, Trajectory optimization, Online learning