To learn the concepts of various measuring devices for measuring physical parameters such as displacement, temperature, pressure, flow. Understand the principle of operation and design of various analog signal conditioning circuits used in industrial applications. To impart knowledge about different measurement methods of physical parameters, such as speed, acceleration, torque, pressure, flow, temperature, etc.
Understand principle of operation of various signal conditioners used with temperature, displacement, optical and various miscellaneous other sensors. To explore the selection criteria of ARM processors by understanding the functional level trade-off issues. Architecture of ARM Processors: Introduction to the Architecture, Programmer's Model Operating Modes and States, Registers, Special Registers, Floating Point Registers, Application Program Status Register (APSR) Behavior - Integer Status Flags, Q Status Flag, GE Bits, Memory System - Memory System Features, memory map, stack memory, memory protection unit (MPU), exceptions and interrupts - what are exceptions?, nested vector interrupt controller (NVIC), vector table, error handling, system control block (SCB), debugging , Reset and reset sequence.
ARM Cortex-M4 and DSP Applications: DSP on a Microcontroller, Dot Product Example, Writing Optimized DSP Code for the Cortex M4-Biquad Filter, Fast Fourier Transform, FIR Filter. The Definitive Guide to ARM Cortex-M3 and Cortex-M4 Processors by Joseph Yiu, Elsevier Publications, 3rd Edition. Hardware approach to digital signal processing: Coherent and non-coherent sampling, digital signal processing techniques, DSP hardware, ALU, multipliers, accumulators, data address generators, serial ports, system interfacing with ADCs and DACs to DSPs.
Fundamentals of Wavelets- Theory, Algorithms and Applications”, Jaideva C Goswami, Andrew K Chan, John Wiley & Sons, Inc., Singapore, 1999.
TECH.- I YEAR- I SEMESTER
To provide knowledge of neural networks and fuzzy logic control and apply them to control real-time systems. To introduce students to the concepts of feedback neural networks and feedback neural networks. Course Outcomes: Upon successful completion of this course, students are expected to be able to
Introduction to Neural Networks: Introduction, Humans and Computers, Organization of the Brain, Biological Neuron, Biological and Artificial Neuron Models, Hodgkin-Huxley Neuron Model, Integrate and Fire Neuron Model, Spike Neuron Model, Characteristics of ANN, McCulloch-Pitt's Model, Historical Development, Potential Applications by ANN. Essentials of Artificial Neural Networks: Artificial Neuron Model, Operations of Artificial Neuron, Types of Neuron Activation Function, ANN Architectures, Classification Taxonomy of ANN – Connectivity, NeuralDynamics (Activation and Synaptic), Learning Strategy (Supervised, Unsupervised), Learning ReinforceR, Type of application. Feed Forward Neural Networks: Introduction, Perceptron Models: Discrete, Continuous and Multi-Category, Training Algorithms: Discrete and Continuous Perceptron Networks, Perceptron Convergence Theorem, Perceptron Model Limitations, Applications.
Forward Multilayer Neural Networks: Credit Assignment Problem, Generalized Delta Rule, Backpropagation (BP) Training Derivation, Backpropagation Algorithm Summary, Kolmogorov's Theorem, Learning Problems and Improvements. Introduction: The need for process control, Process control system planning aspects, Process degree of freedom. Gain knowledge of using virtual instruments for data acquisition and instrument control.
Apply these equations to analyze problems by making good assumptions and learning a systematic engineering method to design a good data acquisition system. Apply fundamental knowledge of mathematics to modeling and analysis of A/D & D/As, and error analysis on data acquisition systems. Conduct case studies in various data acquisition systems and interpret data from model studies to prototype cases, documenting them in technical reports.
Data loggers and data acquisition systems: data acquisition systems - configuration components, analog multiplexes and sampling and storage circuits - specifications and design considerations. Data acquisition hardware and software: Hardware-IO analog signal range specifications, analog input gain and resolution in ADC, resolution\ion in DAC and counter chips, sampling rate and maximum update rates, trigger performance. Introduction: Overview of Automation, Requirements of Automation Systems, Industrial Automation System Architecture, Introduction of PLC and Supervisory Control and Data Acquisition (SCADA).
Computer Aided Measurement and Control Systems: Role of Computers in Measurement and Control, Elements of Computer Aided Measurement and Control, Man-Machine Interface, Computer Aided Process Control Hardware, Process Related Interfaces, Communication and Networks, Industrial Communication Systems, Data Transfer Techniques, Computer Aided Process Control Software, Computer Based Data Acquisition System, Internet of Things (IoT) for Plant Automation UNIT - IV.
TECH.- I YEAR- II SEMESTER
Introduction, Natural and Nuclear Sources of EMI / EMC: Electromagnetic environment, history, concepts, practical experiences and concerns, frequency spectrum conservations, an overview of EMI / EMC, natural and nuclear sources of EMI. To acquire the practical knowledge of different controller types, control functions, tuning of controllers and control schemes. Apply control system knowledge to monitor and control industrial parameters such as flow, level, pressure, temperature, pH issues.
Identify the optimum values for the PID controller and realize the Electronic, Pneumatic and Hydraulic Control actions for various applications. Realization of PID control actions and time response analysis with electronic controllers for first and second order systems using process controller simulator. Develop control system and signal simulation applications using CDSM and DSP toolkit List of experiments.
TECH.- II YEAR- I SEMESTER
Mixture Models and the EM Algorithm: K-Means-Image Clustering and Compression, Mixtures of Gaussians-Maximum Likelihood, EM for Gaussian Mixtures, An Alternative View of EM-Gaussian Mixtures Revisited, Relation to K-means, Mixtures of Distributions Bernoulli, for Bayesian linear regression, EM Algorithm in general, Combination Models- Tree-based Models, Conditional Mixture Models- Mixtures of linear regression models, Mixtures of logistic models, Expert Mixtures. To gain knowledge about various physiological parameters both electrical and non-electrical and methods of recording and also how to transmit these parameters_. To gain knowledge of the equipment used for physical medicine and the various diagnostic and therapeutic techniques recently developed.
Understand the skills needed when writing a headline. Make sure the paper is of good quality from the first delivery. Course Objectives: Students will be able to learn to demonstrate a critical understanding of key concepts in disaster risk reduction and humanitarian response. critically evaluate the policy and practice of disaster risk reduction and humanitarian response from multiple perspectives. develop an understanding of humanitarian response standards and practical relevance in specific types of disasters and conflict situations. critically understand the strengths and weaknesses of disaster management approaches, planning and programming in different countries, especially in their country of origin or in the countries where they work. Nishith, Singh AK, "Disaster Management in India: Perspectives, Issues and Strategies"' New Royal book Company.
The technical scholars equipped with Sanskrit will be able to explore the vast knowledge of ancient literature. Understand the principles that inform the twin themes of liberty and freedom from a civil rights perspective. To address the role of socialism in India after the onset of the Bolshevik Revolution in 1917 and its impact on the original drafting of the Indian Constitution.
Discuss the growth of the demand for civil rights in India for the majority of Indians before Gandhi's arrival in Indian politics. Discuss the intellectual origins of the argumentative framework that informed the conceptualization of social reforms that led to revolution in India. History of the Making of the Indian Constitution: History Drafting Committee, (composition & . work), Philosophy of the Indian Constitution: Preamble, Salient Features.
How can teacher training (curriculum and practicum) and the school curriculum and guidance materials best support effective pedagogy. How can teacher training (curriculum and practicum) and the school curriculum and guidance materials best support effective pedagogy. Professional development: alignment with classroom practice and follow-up support, Peer support, Support of the head teacher and the community.
