Human visual system and image perception; monochrome and color vision models; image digitization, display, and storage; 2‐D signals and systems; image transforms‐ 2D DFT, DCT, KLT, Harr transform and discrete wavelet transform; image enhancement: histogram processing.

Feature Detection and Characterization, Scale Space idea, Laplacian and Gaussian Derivatives, Differential Invariant Structure–Nonlinear Scale Space, Anisotropic Diffusion, PDE for image processing

Image Enhancement-Noise models, image de-noising using linear filters, order statistics-based filters and wavelet shrinkage methods, image sharpening, image super-resolution using Bayesian methods

Image segmentation: Graph-based techniques, Active Contours, Active Shape Models, Shape Analysis

PR overview - Feature extraction - Statistical Pattern Recognition - Supervised Learning - Parametric methods - non-parametric methods; ML estimation - Bayes estimation - KNN 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.

Introduction: What is VR, applications, basic components of VR, Success stories of VR and challenges, VR hardware, visualization, VR content generation, and storing. Human Senses and VR: Discussion on how human senses correlate to VR such as Visual system, Auditory System, Olfaction, Gustation, etc.

Three-dimensional geometry theory: coordinate system, Vectors, Line, plane transformation etc. The rendering pipeline: Geometry and vertex operations, culling and clipping, screen mapping, scan conversion or rasterization, fragment processing, texturing, etc.

International standards related to image/video/audio formulated by ISO/IEC/ITU. Short‐term Fourier Transform & Continuous Wave let Transform, CWT and its discretization, Discrete Wavelet Transforms, 2 ‐ D Wavelet Transforms, Coding Techniques in 2 ‐ D Wavelet Transforms, Family of MPEG 1/2/4 (Moving Picture Experts Group), H.26x(x=1,2,3), JPEG/JPEG‐LS/JPEG2000 (Joint Photographic Experts Group), JBIG1/2(Joint Binary Image Group), H.264/MPEG ‐ 4 Part 10 AVC (Advanced Video Coding) and the emerging H.265 standard (HEVC) and latest standard such as H.264 etc.

Basic topics of computer vision, and image processing -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.

Programming:

Soft Computing: Introduction, requirement, different tools and techniques, usefulness and applications. Fuzzy Sets and Fuzzy Logic: Introduction, Fuzzy sets versus crisp sets, operations on fuzzy sets, Extension principle, Fuzzy relations and relation equations, Fuzzy numbers, Linguistic variables, Fuzzy logic, Linguistic hedges, Applications, fuzzy controllers, fuzzy pattern recognition, fuzzy image processing, fuzzy database.

Digital Image Fundamentals: Elements of visual perception–Image sampling and quantization Basic relationship between pixels– Basic geometric transformations. Image fundamentals and image restoration: Spatial domain methods‐Spatial filtering‐ Frequency domain filters –Model of Image Degradation/restoration process – Noise models – Inverse filtering ‐Least mean square filtering –Constrained least mean square filtering – Blind image restoration – Pseudo inverse – Singular value decomposition.

Introduction: speech production and perception, information sources in speech, linguistic aspect of speech, acoustic and articulatory phonetics, nature of speech ,models for speech analysis and perception; Short ‐ term processing: need, approach, time, frequency and time ‐ frequency analysis; Short ‐ term Fourier transform (STFT): overview of Fourier representation, non ‐ stationary signals, development of STFT, transform and filter ‐ bank views of STFT; cesptrum analysis: Basis and development, delta, delta ‐ delta and mel ‐ cepstrum, homomorphic signal processing, real and complex cepstru

Vectors: Representation and Dot products, Norms, Matrices: The Four Fundamental Spaces of a Matrix, The Matrix as a Linear Operator, The Geometry associated with matrix operations, Inverses and Generalized Inverses, Matrix factorization/Decompositions, rank of a matrix, Matrix Norms.

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

Introduction and Background of state-of-art sensing and measurement techniques. Contactless potentiometer (resistance-capacitance scheme) – Methodology, Interface Circuits, Overview of Flight Instrumentation. Analog Electronic Blocks, CMRR Analysis (Non-ideal opamps) of an Instrumentation Amplifier, Linearization circuits for single- element wheatstone bridges (application to strain gauge), Direct Digital Converter for Strain gauges, Signal conditioning for Remote-connected sensor elements.