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Electrical Engineering Curriculum Overview

The curriculum of the Electrical Engineering program at Manipur International University Imphal is meticulously structured to provide a balanced mix of foundational knowledge, technical depth, and practical experience. It spans eight semesters and includes core courses, departmental electives, science electives, and laboratory sessions designed to develop both theoretical understanding and hands-on skills.

The department offers a wide range of advanced departmental electives that allow students to tailor their education according to their interests and career goals. These courses are taught by faculty members who are leaders in their respective fields.

Advanced Departmental Elective Courses

Digital Signal Processing

This course delves into the mathematical foundations of digital signal processing, covering topics such as discrete-time signals and systems, Z-transforms, Fast Fourier Transform (FFT), filter design techniques, and applications in audio and image processing. Students learn to implement DSP algorithms using MATLAB and other simulation tools.

Learning Objectives:

• Understand the principles of sampling and reconstruction of continuous-time signals
• Apply digital filters for signal enhancement and noise reduction
• Design and analyze discrete-time systems using frequency-domain methods
• Implement advanced DSP algorithms in real-world scenarios

Power Electronics and Drives

This course focuses on the analysis and design of power electronic converters, inverters, and motor drives. Students explore various topologies including buck, boost, and buck-boost converters, along with their control strategies.

Learning Objectives:

• Design and analyze different types of power electronic converters
• Understand the principles of motor drive control and application
• Study energy storage systems and their integration into electrical networks
• Develop proficiency in simulation tools like PSIM and MATLAB/Simulink

Control Systems Engineering

This course covers both classical and modern control system design methods, including transfer functions, state-space representation, root locus, frequency response analysis, and PID controller design. Students also learn about robust control and adaptive systems.

Learning Objectives:

• Apply mathematical models to represent dynamic systems
• Design feedback controllers for desired system performance
• Analyze stability using various analytical techniques
• Implement control algorithms in real-time embedded systems

Renewable Energy Technologies

This course explores the principles and technologies behind solar, wind, hydroelectric, and geothermal energy systems. It includes detailed studies of photovoltaic cells, wind turbines, micro-hydro plants, and battery storage systems.

Learning Objectives:

• Understand the physics and economics of renewable energy sources
• Design and simulate renewable energy systems using specialized software
• Analyze power output and efficiency of different renewable technologies
• Evaluate environmental impacts and regulatory frameworks for renewable projects

Embedded Systems Design

This course introduces students to the architecture, design, and implementation of embedded systems. Topics include microcontrollers, real-time operating systems, hardware-software co-design, and application-specific integrated circuits (ASICs).

Learning Objectives:

• Design and program embedded systems using C/C++
• Understand hardware-software integration challenges
• Develop real-time applications for resource-constrained environments
• Implement communication protocols like UART, SPI, and I2C

Wireless Communication Systems

This course examines the fundamentals of wireless communication, including modulation schemes, channel coding, multiple access techniques, and antenna systems. It also explores recent developments in 5G and beyond technologies.

Learning Objectives:

• Understand signal propagation over wireless channels
• Design efficient communication protocols for mobile networks
• Analyze performance metrics such as BER and throughput
• Explore emerging trends like massive MIMO and edge computing

Optical Fiber Communication

This course covers the principles of optical fiber transmission, including fiber optics components, wavelength division multiplexing (WDM), and photonic integrated circuits. Students gain hands-on experience with fiber optic test equipment.

Learning Objectives:

• Understand the physics of light propagation in optical fibers
• Design and evaluate optical communication links
• Study advanced modulation techniques and error correction methods
• Implement fiber optic networks using modern simulation tools

Industrial Automation and Robotics

This course integrates concepts from control systems, sensors, actuators, and programmable logic controllers (PLCs) to build automated systems. Students also learn about robot kinematics and control algorithms.

Learning Objectives:

• Design and implement industrial automation systems
• Understand the role of PLCs in manufacturing processes
• Develop robotic control strategies for complex tasks
• Evaluate system reliability and safety standards

Smart Grid Technologies

This course focuses on smart grid concepts, including demand response, energy storage systems, distributed generation, and grid stability. It also explores cybersecurity issues in modern power systems.

Learning Objectives:

• Understand the architecture of smart grids and their components
• Implement smart metering and data analytics solutions
• Analyze security vulnerabilities in power systems
• Study regulatory policies for grid modernization

Signal Processing for Machine Learning

This course bridges the gap between traditional signal processing and machine learning. It teaches students how to apply neural networks, deep learning architectures, and statistical methods to process signals in real-time applications.

Learning Objectives:

• Apply machine learning algorithms to audio, image, and sensor data
• Design hybrid systems combining signal processing and AI techniques
• Develop models for speech recognition, image classification, and anomaly detection
• Evaluate performance of ML-based signal processing systems

Project-Based Learning Philosophy

The department places great emphasis on project-based learning as a core component of the curriculum. This approach ensures that students develop critical thinking, problem-solving, and teamwork skills essential for professional success.

Mini-projects are integrated throughout the program from the second year onwards. These projects provide students with opportunities to apply theoretical knowledge in practical settings while working in interdisciplinary teams. Projects may involve designing a circuit board, developing an embedded system, or simulating a power network.

The final-year thesis/capstone project is a culminating experience that requires students to conduct independent research under the guidance of a faculty mentor. The project must address a relevant problem in the field of electrical engineering and demonstrate mastery of advanced concepts and methodologies.

Mini-Projects Structure

Mini-projects are assigned during the third and fourth years and typically span 3-6 months. Each project has clear learning objectives, deliverables, and evaluation criteria. Students are expected to submit progress reports, present findings at departmental symposiums, and defend their work in front of a panel of experts.

Final-Year Thesis/Capstone Project

The capstone project begins in the seventh semester and continues through the eighth semester. Students select projects based on their interests and career aspirations, with faculty mentors providing guidance throughout the process. The final deliverables include a detailed technical report, a presentation to industry professionals, and an oral defense of the work.

Faculty mentors are selected based on their expertise in relevant areas and availability. The selection process involves submitting project proposals that are reviewed by departmental committees for feasibility and relevance.