Module 1: Role of Interface Electronics, Analog Electronic Blocks, OPAMP – internal structure, Open-loop gain, Input R, Output R, DC noise sources and their drifts, CMRR, PSRR, Bandwidth and stability, Slew rate, Noise – general introduction, OPAMP Circuits and Analysis - Difference and Instrumentation Amplifiers (3- opamp and 2-opamp), Effect of cable capacitance and wire-resistance on CMRR, IA with guards, Biomedical application, Current-mode IA (Howland), Current-input IA, filters, Filters with underdamped response, state- variable filters, All-pass filters, Current Sources (floating and

Basic Concepts of dynamical systems and stability. Modelling of power system components for stability studies: generators, transmission lines, excitation and prime mover controllers, flexible AC transmission (FACTS) controllers.; Analysis of single machine and multi-machine systems. Small signal angle instability (low frequency oscillations): damping and synchronizing torque analysis, eigenvalue analysis.; Mitigation using power system stabilizers and supplementary modulation control of FACTS devices.

DC-AC Converters for control of AC Drives: Voltage Source Inverters, square wave operation, harmonic analysis, pulse width modulation (PWM) techniques, Space Vector PWM, Multilevel Inverters, Current Source Inverters.

Induction Motor Drives: Modelling of Induction Motors, Reference frame theory, speed-torque characteristics, Scalar control of Induction Motors, closed-loop operation, Vector control and field orientation, sensor- less control, flux observers, Direct torque and flux control.

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 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.

Review of basic probability and stochastic processes. Introduction to Markov chains. Markov models for discrete time dynamic systems, Reward, Policies, Policy evaluation, Markov decision processes, Optimality criteria, Bellman’s optimality principle, Dynamic programming, Optimality equations, Policy search, Policy iteration, Value iteration. Generalized Policy Iteration, Approximate dynamic programming.

Prerequisite: Linear algebra, Probability, and interest in programming

PR overview ‐ Feature extraction ‐ Statistical Pattern Recognition ‐ Supervised Learning ‐ Parametric methods ‐ Non-parametric methods; ML estimation ‐ Bayes estimation ‐ k NN approaches. Dimensionality reduction, data normalization. Regression, and time series analysis. Linear discriminant functions. Fisher's linear discriminant and linear perceptron. Kernel methods and Support vector machine. Decision trees for classification. Unsupervised learning and clustering. K ‐ means and hierarchical clustering. Decision Trees for classification.

The course is an introductory level computer vision course, suitable for graduate students. It will cover the basic topics 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.

Fractional Calculus: Review of basic definitions of integer‐order (IO) derivatives and integrals and their geometric and physical interpretations, Definition of Riemann‐Liouville (RL) integration, Definitions of RL, Caputo and Grunwald‐Letnikov (GL) fractional derivatives (FDs), Various geometrical and physical interpretations of these FDs, Computation of these FDs for some basic functions like constant, ramp, exponential, sine, cosine, etc., Laplace and Fourier transforms of FDs.

Introduction, discrete systems, basic signal theory, Open‐loop LTI SISO systems, time domain, frequency domain Least Squares Estimation, Covariance in Stationary, Ergodic Processes, White Noise, Detection of Periodicity and Transmission Delays, ARMA Processes.