Basics of probability theory: axioms, definitions, random variable – probability structure of random variable – joint distributions – functions of random variables – some common random variables – random processes/random fields.

Structural reliability – fundamental concepts – first order reliability methods – second order reliability methods – probabilistic sensitivity – system reliability – simulation techniques – high dimensional model representation techniques for reliability analysis.

Linear elastic fracture mechanics; energy release rate, stress intensity factor (SIF), relation between SIF and energy release rate, anelastic deformation at the crack tip – J-integral, CTOD, test meth- ods for fracture toughness – crack growth and fracture mechanisms, mixed-mode fracture, fracture at nanoscale – numerical methods for analysing fracture, applications – fatigue and design against fatigue failure – prediction of fatigue life.

Introduction – materials deformation and fracture phenomena – strength of materials: flaws, defects, and a perfect material, brittle vs. ductile material behavior, the need for atomistic simulations – ap- plications basic atomistic modeling – classical molecular dynamics – interatomic potential-numerical implementation – visualisation – atomistic elasticity, the virial stress and strain – multiscale model- ing and simulation methods – deformation and dynamical failure of brittle and ductile materials – applications.

Finite element formulations for beam, plate, shell (Kirchhoff and Mindlin-Reissner), and solid ele- ments – large deformation nonlinearity – nonlinear bending of beams and plates – stress and strain measures – total Lagrangian and updated Lagrangian formulations – material nonlinearity – ideal and strain hardening plasticity – elastoplastic analysis – boundary nonlinearity – general contact formulations – solution procedures for nonlinear analysis, Newton-Raphson iteration method.

The variational principle and the derivation of the governing equations of static and dynamic systems – different energy methods: Rayleigh-Ritz, Galerkin etc. – applications: problems of stress analysis, determination of deflection in determinate and indeterminate structures, stability and vibrations of beams, columns and plates of constant and varying cross-sectional area.

Review of planar motion of rigid bodies and Newton-Euler equations of motion; constraints – holo- nomic and non-holonomic constraints, Newton-Euler equations for planar inter connected rigid bod- ies; D’Alembert’s principle, generalized coordinates; alternative formulations of analytical mechanics and applications to planar dynamics – Euler-Lagrange equations, Hamilton’s equations and ignorable coordinates, Gibbs-Appel and Kane’s equations; numerical solution of differential and differential al- gebraic equations; spatial motion of a rigid body – Euler angles, rotation matrices, quaternions

Review of tensor algebra – tensor analysis – concept of continuum – kinematics of a deformable body – deformation and strain – motion and flow – analysis of stress-stress tensors – conservation laws, mass and momentum conservation – continuum thermodynamics – first and second laws applied to a continuum – Clausius-Duhem inequality – constitutive relations – applications.

Introduction to static and dynamic aeroelastic phenomena – divergence, control efficiency and control reversal – two dimensional analysis – divergence of unswept wings – effect of sweep on divergence and control reversal – two-dimensional (airfoil) flutter analysis with quasi-steady and unsteady aero- dynamic loads – introduction to buffeting, stall flutter, galloping and vortex-induced oscillations problems.

Basics of computer vision, and introduce some fundamental approaches for computer vision research: Image Filtering, Edge Detection, Interest Point Detectors, Motion and Optical Flow, Object Detection and Tracking, Region/Boundary Segmentation, Shape Analysis, and Statistical Shape Models, Deep Learning for Computer Vision, Imaging Geometry, Camera Modeling, and Calibration. Recent Advances in Computer vision.

Introduction to data mining concepts; linear methods for regression; classification methods: k- nearest neighbourclassifiers, decision tree, logistic regression, naive Bayes, Gaussian discriminant analysis; model evaluation & selection; unsupervised learning: association rules; apriori algorithm, FP tree, cluster analysis, self-organizing maps, google page ranking; dimensionality reduction methods: supervised feature selection, principal component analysis; ensemble learning: bagging, boosting, AdaBoost; outlier mining; imbalance problem; multi class classification; evolutionary computa

Evolution of the Internet and Big Data. Introduction to the Internet of Things (IoT). The Internet protocol stack. IPv4 and IPv6. TCP and UDP. DNS and the IoT Protocol stack, Layers in the Internet of Things. Sensing and Actuator Layer, Network Layer, and Application Layer. Wireless Sensor Networks. Communication Technologies for the Internet of Things. CoAP, MQTT, and HTTP Protocols for IoT. Data aggregation and fusion. Operating Systems for IoT. Contiki OS, Tiny OS, and other IoT OSs. Databases for the Internet of things. Data mining for the Internet of Things.

Classical scaling in CMOS, Moore’s Law, Clean room concept, Material properties,crystal struc- ture, lattice, Growth of single crystal Si, Cleaning and etching, Thermaloxidation, Dopant diffu- sion in silicon, Deposition & Growth (PVD, CVD, ALD,epitaxy, MBE, ALCVD etc.),Ion-im- plantation, Lithography (Photolithography, EUVlithography, X-ray lithography, e-beam lithog- raphy etc.), Etch and Cleaning, CMOS Process integration, Back end of line processes (Copper damascene process, Metalinterconnects; Multi-level metallization schemes), Advanced technologies (SOIMOSFETs, Strained Si, Silic