Core courses are compulsory courses that provide critical foundations to the undergraduate program. These courses are mandatory in order to develop in-depth knowledge of the discipline.
Matching problems, Coloring graphs, Transport networks.
Propositional Logic and Predicate Calculus: Propositions and Logical operations, Conditional statements, First order predicate, well-formed formula of predicate, quantifiers, Inference theory of predicate logic.
Methods of proof, Mathematical induction, Recursively defined functions, Growth of Functions, Recurrence relations.
This course includes the introductory and advanced concepts and implementation of the Object Oriented Paradigm using the Java programming language. Topic would include Introduction, Elementary Programming, Selections, Loops, Methods, Arrays, Strings, Objects and Classes, Inheritance and Polymorphism, GUI Basics and Components, Graphics, Exceptions, Abstract Classes and Interfaces, Event-Driven Programming, Binary I/O, Recursion, and Java Collections Framework.
11. Course Aims
This course will enable the students to understand the concepts of object oriented programming using java, and apply its structures and techniques in various programming problems.
This course aims on the concepts used in building Internet and web systems. The students would be able to understand how a web server is built.
11. Course Aims
1. To introduce the concepts of Distributed systems, Cloud computing, web servers.
2. To understand big data and streaming data processing on web servers.
3. Understand concepts of naming and locating resources, directory systems, distributed data structures and applications.
4. Apply the concepts learnt by applying them in an end-to-end project.
Course description not available.
Circuit Analysis Review of KCL and KVL, Basic Circuit Terminology-Node, loop, mesh, circuit, branch and path. Ideal sources, Source transformation, Star-Delta transformation. AC analysis - Phasor, Complex impedance, complex power, power factor, power triangle, impedance triangle, series and parallel circuits
Network Theorems (A.C. and D.C Circuits) - Mesh and Nodal analysis, Thevenin, Norton, Maximum Power transfer, Millman, Tellegen and Superposition theorem.
Resonance and Transient Analysis
Introduction to Resonance-series and parallel, half power frequency, resonant frequency, Bandwidth, Q factor. Transient Analysis-Step response, Forced Response of RL, RC & RLC Series circuits with Sinusoidal Excitation – Time Constant & Natural frequency of Oscillation – Laplace Transform applications.
Electronic Devices and Components
Review of Energy band diagram- Intrinsic and Extrinsic semiconductors- PN junction diodes and Zener diodes – characteristics, Diode Applications-Rectifiers, Clippers and Clampers. Transistors-PNP and NPN – operation, characteristics and applications, Biasing of Transistors. Operational Amplifiers-Introduction and Applications - Inverting, Non Inverting, Voltage follower, Integrator, differentiator and difference amplifier, Summer, log and Antilog.
Three Phase and Transformers Introduction to three phase, power measurements in three phase. Transformer-Principle of operation, construction, phasor diagram of Ideal and practical transformer with load (R,L,C and their combinations) and no load, equivalent circuit, efficiency and voltage regulation of single phase transformer, O.C. and S.C. tests. Introduction to D.C. Machines.
1. Classification and representation of signals and systems, Continuous time & Discrete time signals and systems, Impulse and Step response of a system, linear systems, linearity, time invariance, causality, signal properties -LTI systems, Convolution
2. Fourier series, Fourier transform and properties, relation between Fourier transform and Fourier series, Sampling and reconstruction, FFT, DIT FFT, DIF FFT Algorithm, Inverse DFT and Convolution using FFT
3. Laplace transforms- representation of signals using continuous time complex exponentials, relation of Laplace and Fourier transform, concept of ROC and transfer function- block diagram representation, Inverse Laplace transform, properties, analysis and characterization of LTI systems using Laplace transforms
4. Z transforms- representation of signals using discrete time complex exponentials-properties, inverse Z transforms, ROC, Analysis and characterization of LTI systems using Z transforms, block diagram, transfer functions
5. Introduction to random variable and random process, State space analysis, Introduction to Two port networks and parameters
Digital Processing of Information – Basic information processing steps – logic and arithmetic; Number Systems and Arithmetic – Positional number systems, Arithmetic operations on binary numbers; Combinational Logic – Basic logic operations, Boolean algebra, Boolean functions, De Morgan’s laws, Truth table and Karnaugh map representations of Boolean functions, Combinational circuit design using gates and multiplexers; Sequential Logic – Latches and Flip-flops, Ripple counters, Sequence generator using flip-flops, State Diagram, Synchronous counters, Shift Registers; Introduction to the Microprocessor – Basic constituents of a processor, Instruction set – machine language and assembly language.
Core course for all B.Tech. Optional for B.Sc. (Research) Chemistry. Not open as UWE.
Credits (Lec:Tut:Lab)= 3:1:0 (3 lectures and 1 tutorial weekly)
Prerequisites: Class XII Mathematics.
Overview: In this course we study multi-variable calculus. Concepts of derivatives and integration will be developed for higher dimensional spaces. This course has direct applications in most engineering applications.
Past Instructors: Ajit Kumar, Sneh Lata
Core course for all B.Tech. Programs. Optional for B.Sc. (Research) Chemistry. Not available as UWE.
Credits (Lec:Tut:Lab)= 3:1:0 (3 lectures and 1 tutorial weekly)
Prerequisites: Class XII Mathematics
Overview: We will study Ordinary Differential Equations which are a powerful tool for solving many science and engineering problems. This course also covers some basic linear algebra which is needed for systems of ODEs.
Past Instructors: Ajit Kumar, Neha Gupta
The aim of this course is to bridge the gap between the various boards across the country at 10+2 level and bring everyone at the standard undergraduate level. All the engineering branches have their origin in the basic physical sciences. In this course we aim to understand the basic physical laws and to develop skills for application of various physical concepts to the science and engineering through problem solving. This will involve the use of elementary calculus like differentiation and integration.
Mechanics: The inertial reference frames, Newton’s laws of motion in vector notation, Conservation of energy, Application of Newton’s laws of motion, Dynamical stability of systems: Potential energy diagram, Collisions: Impulse, conservation of energy and linear, momentum, Conservation of angular momentum and rotation of rigid bodies in plane Thermal Physics: Averages, probability and probability distributions, Thermal equilibrium and macroscopic variables, Pressure of an ideal gas from Newton’s laws - the kinetic theory of gases. Maxwell’s velocity distribution, Laws of Thermodynamics and the statistical origin of the second law of thermodynamics, Application of thermodynamics: Efficiency of heat engines and air-conditioners, Thermodynamics of batteries and rubber bands
This is a continuation of PHY 101 and is meant for engineers and non-physics majors. The course will introduce students to Electricity and Magnetism, Maxwell’s equations, Light as an electromagnetic wave, and Wave optics.
Vector calculus: Gradient, Divergence, Curl and fundamental theorems of vector calculus. Basic laws in electricity and magnetism, Classical image problem, displacement current and continuity equation, Maxwell’s Equations, electromagnetic wave equation and its propagation in free space, conducting media and dielectric medium, Poynting theorem, Electromagnetic spectrum.
Interference of light waves: Young’s double slit experiment, displacement of fringes, Interference in thin films
Diffraction: Fresnel’s and Fraunhofer’s class of diffraction, diffraction from single, double & N- Slits, Gratings.
Polarization: Concept of Polarization in electromagnetic waves, types of polarized waves.
The department currently has 3 identified areas of growth, and students are expected to work with faculty in one of these areas. These 3 areas and some of their sub-areas of research are mentioned below: • Wireless, Mobile Computing and Networking: Internet of Things, Mobile Sensing, Cyber Physical Systems, Wireless Sensor Networks, Wireless Networks. • Data Science and Engineering: Data Mining, Machine Learning, Mobile Data Management, Data Analytics. Big Data. • Security and Privacy: Security of Cyber Physical Systems, IoT and Cyber Security.
Unit 2 (Mobile Radio Propagation):
•Types of Radio Waves.
•Free Space Propagation.
•Path loss and Fading.
•Doppler Effect, Delay Spread and Intersymbol Interference.
Unit 3 (Cellular Concept):
•Signal Strength and Cell parameters.
•Capacity of a cell.
•How to form a cluster.
•Cell Splitting and Cell Sectoring.
Unit 4 (Multiple division Techniques):
•Concepts and Models of Multiple Divisions (FDMA, TDMA, etc.)
Unit 5 (Channel Allocation):
•Static Allocation versus Dynamic Allocation.
•Fixed Channel Allocation.
•Dynamic Channel Allocation.
•Hybrid Channel Allocation.
•Allocation in specialized System Structure.
Unit 6 (Mobile Communication Systems):
•Cellular System Infrastructure.
•Handoff and Roaming Support.
•Security and Privacy.
Unit 7 (Wireless MANs, LANs and PANs):
•Wireless Metropolitan Area Networks (4G systems).
•Wireless Local Area Networks (IEEE 802.11).
•Wireless Personal Area Network (Bluetooth Networks).
Introduction to WSN, applications, and challenges.
• Understand Node and Network Architecture.
• Understand and Analyze WSN protocol stack.
• Understand and analyze MAC protocols.
• Understand Wake up strategies.
• Understand and analyze routing protocols.
• Explain clustering approaches and their applications.
• Understand various important topics: data aggregation, time synchronization, localization, topology control, node addressing,
error control, QoS.
• Examine WSN security issues.
• Laboratory classes will focus on simulating networks and various protocols; and implementing your own sensor nodes and working on them. For labs, you will be required to purchase prescribed hardware.
1 Introduction to data mining, Data Mining vs Knowledge discovery, Data Mining issues, Social Implications.
2 Introduction to data warehousing, Database/OLTP systems, Decision Support Systems, Multi-dimensional
data model, Data Warehouse components, architecture and implementation, OLAP
3 Data Preprocessing: Data Cleaning, Data Integration and Transformation, Data Reduction,
Discretization and Concept Hierarchy Generation
4 Statistical Measures: Point Estimation, Models based on Summarization, Bayes Theorem,
Hypothesis testing, Regression and Correlation; Similarity measures.
5 Mining Association Rules in large databases, Mining Multidimensional Association Rules from
Relational Databases and Data Warehouses, From Association Mining to Correlation Analysis,
Constraint Based Association Mining
6 Classification, Prediction, Clustering and Visualization
7 Introduction to Advanced Topics: Mining complex data, Mining Time-series data, Spatial data
mining, Multimedia data mining, Text mining, Mining the WWW.
8 Social impact of Data mining, Recent trends in Data mining research, Challenges and Future Scope.
The course will provide a hands on approach to the concept of IoT. Topics include:
Introduction to IoT, IoT Domains, IoT Platform Design and Methodologies, IoT Hardware Platforms and Sensors, IoT Web and Cloud Platforms, IoT Analytics, IoT Tools, IoT Security Case Studies and Projects.