Curriculum Overview
The Electronics Engineering program at NAGAJI INSTITUTE OF TECHNOLOGY AND MANAGEMENT GWALIOR is structured to provide students with a robust foundation in core principles while offering flexibility to explore specialized areas. The curriculum spans eight semesters and integrates theoretical knowledge with practical applications through laboratory sessions, mini-projects, and capstone initiatives.
Year 1 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | ENG101 | English for Communication | 3-0-0-3 | - |
| I | MAT101 | Mathematics I | 4-0-0-4 | - |
| I | PHY101 | Physics | 3-0-0-3 | - |
| I | CHM101 | Chemistry | 3-0-0-3 | - |
| I | BE101 | Basic Electrical Engineering | 3-0-0-3 | - |
| I | CS101 | Introduction to Programming | 2-0-2-3 | - |
| I | L101 | Engineering Graphics and Design | 1-0-4-3 | - |
| I | EP101 | Introduction to Electronics | 2-0-0-2 | - |
| I | SE101 | Soft Skills & Ethics | 1-0-0-1 | - |
| II | MAT102 | Mathematics II | 4-0-0-4 | MAT101 |
| II | PHY102 | Physics Lab | 0-0-2-2 | PHY101 |
| II | CHM102 | Chemistry Lab | 0-0-2-2 | CHM101 |
| II | BE102 | Circuit Analysis | 3-0-0-3 | BE101 |
| II | DME101 | Engineering Mechanics | 3-0-0-3 | - |
| II | CS102 | Data Structures & Algorithms | 2-0-2-3 | CS101 |
| II | L102 | Basic Electronics Lab | 0-0-4-3 | - |
| II | EP102 | Electronic Devices & Circuits | 3-0-0-3 | EP101 |
Year 2 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| III | MAT201 | Mathematics III | 4-0-0-4 | MAT102 |
| III | DME201 | Strength of Materials | 3-0-0-3 | DME101 |
| III | EC201 | Signals and Systems | 3-0-0-3 | MAT102 |
| III | EE201 | Electromagnetic Fields | 3-0-0-3 | BE102 |
| III | EC202 | Digital Logic Design | 3-0-0-3 | EP102 |
| III | CS201 | Object-Oriented Programming | 2-0-2-3 | CS102 |
| III | L201 | Digital Logic Lab | 0-0-4-3 | EP102 |
| III | L202 | Electronic Devices Lab | 0-0-4-3 | EP102 |
| IV | MAT202 | Mathematics IV | 4-0-0-4 | MAT201 |
| IV | EC203 | Network Analysis | 3-0-0-3 | EC201 |
| IV | EE202 | Electromagnetic Lab | 0-0-2-2 | EE201 |
| IV | EC204 | Microprocessor Architecture | 3-0-0-3 | EC202 |
| IV | EC205 | Analog Circuits | 3-0-0-3 | EP102 |
| IV | L203 | Microprocessor Lab | 0-0-4-3 | EC204 |
| IV | L204 | Analog Circuits Lab | 0-0-4-3 | EC205 |
Year 3 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| V | EC301 | Control Systems | 3-0-0-3 | EC203 |
| V | EC302 | Communication Systems | 3-0-0-3 | EC201 |
| V | EC303 | Digital Signal Processing | 3-0-0-3 | EC201 |
| V | EC304 | Embedded Systems | 3-0-0-3 | EC204 |
| V | EC305 | Electronics Workshop | 1-0-4-3 | - |
| V | L301 | Control Systems Lab | 0-0-4-3 | EC301 |
| V | L302 | Communication Systems Lab | 0-0-4-3 | EC302 |
| V | L303 | DSP Lab | 0-0-4-3 | EC303 |
| V | L304 | Embedded Systems Lab | 0-0-4-3 | EC304 |
| VI | EC306 | Power Electronics | 3-0-0-3 | EC205 |
| VI | EC307 | VLSI Design | 3-0-0-3 | EC205 |
| VI | EC308 | Wireless Communication | 3-0-0-3 | EC202 |
| VI | EC309 | Computer Networks | 3-0-0-3 | EC204 |
| VI | L305 | Power Electronics Lab | 0-0-4-3 | EC306 |
| VI | L306 | VLSI Design Lab | 0-0-4-3 | EC307 |
Year 4 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| VII | EC401 | Final Year Project I | 2-0-6-8 | - |
| VII | EC402 | Project Management | 2-0-0-2 | - |
| VII | EC403 | Advanced Topics in Electronics | 3-0-0-3 | - |
| VII | EC404 | Capstone Project | 0-0-12-12 | - |
| VII | L401 | Final Year Project Lab | 0-0-8-8 | - |
| VIII | EC405 | Final Year Project II | 2-0-6-8 | EC401 |
| VIII | EC406 | Industry Internship | 0-0-12-12 | - |
| VIII | EC407 | Electronics Engineering Seminar | 1-0-0-1 | - |
| VIII | EC408 | Research Methodology | 2-0-0-2 | - |
Advanced Departmental Elective Courses
The department offers several advanced elective courses designed to deepen students' understanding of specialized domains within Electronics Engineering. These courses are taught by faculty members with extensive industry experience and research background.
Course 1: Machine Learning for Signal Processing
This course explores the application of machine learning techniques in signal processing tasks such as pattern recognition, classification, regression, and anomaly detection. Students learn to implement algorithms using Python libraries like scikit-learn, TensorFlow, and Keras.
Learning Objectives:
- Understand fundamental concepts in supervised and unsupervised learning
- Apply neural networks for time series forecasting and signal classification
- Design feature extraction pipelines for complex signals
- Develop real-time inference systems using embedded platforms
Course 2: Advanced VLSI Design Techniques
This course covers advanced topics in VLSI design including floorplanning, placement, routing, and synthesis optimization. Students work with industry-standard tools to design complex integrated circuits.
Learning Objectives:
- Understand physical design flow for modern ICs
- Implement layout design rules and design-for-testability (DFT) techniques
- Optimize performance metrics such as power consumption and timing closure
- Use EDA tools for circuit simulation and verification
Course 3: Wireless Sensor Networks
This course examines the design and implementation of wireless sensor networks used in environmental monitoring, healthcare, smart cities, and industrial automation. Students learn about protocols, architectures, and deployment strategies.
Learning Objectives:
- Design energy-efficient communication protocols for low-power nodes
- Implement routing algorithms in dynamic network topologies
- Analyze performance metrics such as throughput, delay, and packet delivery ratio
- Deploy sensor networks in real-world applications
Course 4: Embedded Systems Security
This course addresses security challenges in embedded systems including hardware-level attacks, firmware vulnerabilities, and secure boot processes. Students develop secure embedded software using cryptographic libraries.
Learning Objectives:
- Identify common threats to embedded devices
- Implement secure communication protocols in resource-constrained environments
- Design secure authentication mechanisms for IoT systems
- Evaluate security frameworks and standards such as ISO/IEC 27030
Course 5: Neural Network Architectures
This course focuses on advanced architectures in deep learning including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students implement these models for image recognition, natural language processing, and time series analysis.
Learning Objectives:
- Understand architecture design principles for CNNs and RNNs
- Implement transformer-based models for sequence modeling tasks
- Optimize neural network architectures using pruning and quantization techniques
- Deploy trained models on edge devices using TensorFlow Lite or ONNX Runtime
Course 6: Renewable Energy Systems
This course covers the design and implementation of renewable energy systems including solar panels, wind turbines, and battery storage units. Students learn to model and simulate power generation and grid integration.
Learning Objectives:
- Model photovoltaic cell behavior under different environmental conditions
- Design maximum power point tracking (MPPT) controllers for solar systems
- Analyze energy storage solutions for hybrid renewable systems
- Evaluate grid integration challenges and economic feasibility of solar projects
Course 7: Biomedical Instrumentation
This course explores the design and application of electronic devices in healthcare settings. Students work with medical sensors, data acquisition systems, and diagnostic equipment to develop real-time monitoring solutions.
Learning Objectives:
- Design analog front-end circuits for biomedical signals
- Implement signal processing algorithms for ECG, EEG, and EMG analysis
- Develop portable medical devices using microcontroller platforms
- Integrate wireless communication modules for remote patient monitoring
Course 8: Quantum Electronics
This course introduces quantum mechanics principles applied to electronic devices and circuits. Students explore quantum dots, quantum wells, and photonic crystals used in next-generation electronics.
Learning Objectives:
- Understand quantum confinement effects in semiconductor heterostructures
- Model quantum transport phenomena using Schrödinger equations
- Design quantum devices for optical communication and sensing applications
- Analyze performance limitations of quantum electronic components
Course 9: Advanced Robotics and Control Systems
This course covers advanced control strategies for robotic systems including adaptive control, fuzzy logic, and model predictive control. Students build and program robots to perform complex tasks autonomously.
Learning Objectives:
- Design feedback controllers for multi-variable systems
- Implement path planning algorithms for autonomous navigation
- Develop robotic systems using ROS (Robot Operating System)
- Integrate sensors and actuators in robotic platforms
Course 10: Internet of Things (IoT) Applications
This course focuses on IoT architecture, protocols, and applications. Students build end-to-end IoT solutions using cloud platforms, microcontrollers, and wireless communication technologies.
Learning Objectives:
- Design IoT networks with low-power wide-area (LPWAN) technologies
- Implement cloud-based data analytics for sensor networks
- Develop secure communication protocols for distributed IoT systems
- Deploy scalable IoT applications using edge computing frameworks
Project-Based Learning Philosophy
The Electronics Engineering program at NAGAJI INSTITUTE OF TECHNOLOGY AND MANAGEMENT GWALIOR emphasizes project-based learning as a core component of education. This approach encourages students to apply theoretical knowledge in practical contexts, fostering innovation and problem-solving skills.
Mini-Projects Structure
Mini-projects are integrated into the curriculum from the second year onwards. Each mini-project spans 3-4 weeks and involves a team of 3-5 students working under faculty supervision. Projects are selected based on current industry trends, emerging technologies, or research challenges.
Final-Year Thesis/Capstone Project
The final-year capstone project is a comprehensive initiative that integrates all learned concepts and serves as the culmination of undergraduate education. Students select projects from faculty research areas or propose original ideas aligned with their interests.
Project selection process:
- Faculty mentorship sessions to identify suitable topics
- Proposal submission with literature review and methodology
- Advisory board evaluation and approval
- Regular progress reports and milestone assessments
Evaluation Criteria
Projects are evaluated based on:
- Technical depth and complexity of implementation
- Innovation and originality of approach
- Effectiveness in solving real-world problems
- Documentation quality and presentation skills
- Team collaboration and leadership abilities