Review of Ordinary Differential Equations: analytical methods, stability – Fourier series, orthog- onal functions, Fourier integrals, Fourier transform – Partial Differential Equations: first-order PDEs, method of characteristics, linear advection equation, Burgers’ equation, shock formation, Rankine-Hogoniot jump condition; classification, canonical forms; Laplace equation, min-max prin- ciple, cylindrical coordinates; heat equation, method of separation of variables, similarity transforma- tion method; wave equation, d’Alembert solution – Calculus of Variations: standard variational prob-

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

History of Microsystem Technology with overview on commercial products, Scaling laws, Microelectronic technologies for MEMS ,Materials and processing for MEMS, Micromachining modelling: Technology: Mechanics of Surface and microsystems, Bulk Micromachining, Transduction Microsystem Mechanisms (optical, piezoelectric, piezo resistive etc.), MEMS Micro sensors, and applications.

Introduction and historical background, Micro sensors : Sensors and characteristics, Integrated Smart sensors, Sensor Principles/classification-Physical sensors (Thermal sensors, Electrical Sensors, tactile sensors, accelerometers, gyroscopes , Proximity sensors, Angular displacement sensors, Rotational measurement sensors, pressure sensors, Flow sensors, MEMS microphones etc.), Chemical and Biological sensors (chemical sensors, molecule-based biosensors, cell-based biosensors), Electromagnetic,Capacitive, Electromagneticand transduction Piezoelectric, Thermal piezo micro methods (Optical,

History of aviation – types of flying machines – anatomy of an aircraft; fundamental aerodynamic variables – aerodynamic forces – lift generation – airfoils and wings – aerodynamic moments –concept of static stability – control surfaces; mechanism of thrust production – propellers – jet engines and their operation – elements of rocket propulsion; loads acting on an aircraft – load factor for simple maneuvers – Vn diagrams; aerospace materials; introduction to aerospace structures; basic orbital mechanics – satellite orbits; launch vehicles and reentry bodies.

Kernel Methods: reproducing kernel Hilbert space concepts, kernel algorithms, multiple kernels, graph kernels; multitasking, deep learning architectures; spectral clustering ; model based clustering, independent component analysis; sequential data: Hidden Markhov models; factor analysis; graphical models; reinforcement learning; Gaussian processes; motiff discovery; graph-based semisupervised learning; natural language processing algorithms.

Finite element formulations for beam, plate, shell (Kirchhoff and Mindlin-Reissner), and solid elements – 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.