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    +91 88943 57155
    Pune, Maharashtra, India

    Duration

    4 Years

    Electrical Engineering

    Al Karim University Katihar
    Duration
    4 Years
    Electrical Engineering UG OFFLINE

    Duration

    4 Years

    Electrical Engineering

    Al Karim University Katihar
    Duration
    Apply

    Fees

    ₹1,05,500

    Placement

    93.5%

    Avg Package

    ₹9,50,000

    Highest Package

    ₹18,00,000

    OverviewAdmissionsCurriculumFeesPlacements
    4 Years
    Electrical Engineering
    UG
    OFFLINE

    Fees

    ₹1,05,500

    Placement

    93.5%

    Avg Package

    ₹9,50,000

    Highest Package

    ₹18,00,000

    Seats

    120

    Students

    600

    ApplyCollege

    Seats

    120

    Students

    600

    Curriculum

    Curriculum Overview

    The Electrical Engineering program at Al Karim University Katihar is structured to provide a comprehensive and progressive educational experience that balances theoretical knowledge with practical application. The curriculum spans eight semesters, with each semester building upon the previous one to ensure students develop a deep understanding of electrical engineering principles.

    Throughout the program, students are exposed to foundational courses in mathematics, physics, and computer programming during their first year. These subjects lay the groundwork for more advanced topics in later semesters. The second year introduces core electrical engineering concepts such as electrical machines, network analysis, and digital logic design, preparing students for specialized areas of study.

    The third and fourth years offer increasing specialization through elective courses that allow students to tailor their education to their interests and career goals. Advanced electives cover topics ranging from power systems to signal processing, communication systems, VLSI design, and artificial intelligence in electrical engineering. This flexibility ensures that graduates are well-prepared for diverse career paths.

    Project-based learning is a key component of the curriculum, with students engaging in both mini-projects and capstone projects throughout their academic journey. These projects encourage creativity, problem-solving, and collaboration while reinforcing theoretical concepts through practical implementation.

    Course Structure

    The following table provides a detailed breakdown of all courses offered across eight semesters:

    SemesterCourse CodeCourse TitleCredit Structure (L-T-P-C)Pre-requisites
    1MATH-101Mathematics I3-1-0-4-
    1MATH-102Mathematics II3-1-0-4MATH-101
    1PHYS-101Physics I3-1-0-4-
    1PHYS-102Physics II3-1-0-4PHYS-101
    1CSE-101Introduction to Computer Programming2-0-2-3-
    1EE-101Basic Electrical Circuits3-1-0-4-
    1EE-102Basic Electronics3-1-0-4-
    1ENGL-101English for Engineering2-0-0-2-
    2MATH-201Mathematics III3-1-0-4MATH-102
    2MATH-202Mathematics IV3-1-0-4MATH-201
    2PHYS-201Chemistry I3-1-0-4-
    2PHYS-202Chemistry II3-1-0-4PHYS-201
    2EE-201Electrical Machines I3-1-0-4EE-101
    2EE-202Network Analysis3-1-0-4EE-101
    2EE-203Digital Logic Design3-1-0-4-
    2EE-204Electronics Devices and Circuits3-1-0-4EE-102
    2ENGL-201Technical Communication2-0-0-2-
    3MATH-301Probability and Statistics3-1-0-4MATH-202
    3MATH-302Differential Equations3-1-0-4MATH-202
    3EE-301Power System Analysis3-1-0-4EE-201
    3EE-302Control Systems3-1-0-4EE-201
    3EE-303Digital Signal Processing3-1-0-4EE-203
    3EE-304Microprocessor and Microcontroller3-1-0-4EE-204
    3EE-305Electromagnetic Fields3-1-0-4MATH-302
    3EE-306Embedded Systems3-1-0-4EE-304
    4MATH-401Linear Algebra3-1-0-4MATH-301
    4MATH-402Numerical Methods3-1-0-4MATH-301
    4EE-401Power Electronics3-1-0-4EE-201
    4EE-402Signal and Systems3-1-0-4EE-303
    4EE-403VLSI Design3-1-0-4EE-306
    4EE-404Communication Systems3-1-0-4EE-302
    4EE-405Renewable Energy Technologies3-1-0-4EE-301
    4EE-406Artificial Intelligence in Electrical Engineering3-1-0-4EE-303
    5EE-501Advanced Power Systems3-1-0-4EE-301
    5EE-502Modern Control Theory3-1-0-4EE-302
    5EE-503Image Processing3-1-0-4EE-303
    5EE-504Computer Architecture and Organization3-1-0-4-
    5EE-505Smart Grid Technologies3-1-0-4EE-501
    5EE-506Machine Learning in Engineering3-1-0-4EE-303
    6EE-601Power System Protection3-1-0-4EE-501
    6EE-602Optical Fiber Communications3-1-0-4EE-404
    6EE-603Advanced Embedded Systems3-1-0-4EE-306
    6EE-604RF and Microwave Engineering3-1-0-4EE-305
    6EE-605Renewable Energy Integration3-1-0-4EE-505
    6EE-606Neural Networks and Deep Learning3-1-0-4EE-506
    7EE-701Research Methodology2-0-0-2-
    7EE-702Capstone Project I3-1-0-4-
    7EE-703Advanced Topics in Power Systems3-1-0-4EE-501
    7EE-704Advanced Signal Processing3-1-0-4EE-402
    7EE-705Project Management in Engineering2-0-0-2-
    8EE-801Capstone Project II3-1-0-4EE-702
    8EE-802Internship3-0-0-3-
    8EE-803Professional Ethics and Social Responsibility2-0-0-2-
    8EE-804Advanced VLSI Design3-1-0-4EE-403

    Advanced Departmental Electives

    The department offers several advanced departmental elective courses that allow students to specialize in areas of interest and gain deeper insights into cutting-edge technologies:

    • Advanced Power Systems: This course delves into complex topics such as power system stability analysis, load flow studies, fault analysis, and protection schemes. It prepares students for careers in power utilities and engineering firms involved in power generation and distribution.
    • Modern Control Theory: Students learn about state-space representation, controllability, observability, and optimal control techniques. The course includes practical applications using MATLAB/Simulink tools.
    • Digital Signal Processing: This course covers digital filtering, transform methods, and spectral analysis. It is crucial for students pursuing careers in telecommunications, audio processing, and biomedical engineering.
    • VLSI Design: The course introduces students to the principles of Very Large Scale Integration (VLSI) design, including logic synthesis, layout design, and verification techniques.
    • Communication Systems: Students explore analog and digital communication systems, modulation techniques, and error correction codes. This course is essential for careers in wireless communications and network engineering.
    • Renewable Energy Technologies: The focus is on solar panels, wind turbines, energy storage systems, and grid integration strategies. It prepares students for roles in the growing renewable energy sector.
    • Machine Learning in Engineering: This course teaches students how to apply machine learning algorithms to solve engineering problems. Topics include supervised and unsupervised learning, neural networks, and deep learning.
    • Smart Grid Technologies: Students learn about smart grid components, communication protocols, and control strategies. It is designed for those interested in the future of power systems.
    • Neural Networks and Deep Learning: This advanced course covers neural network architectures, backpropagation algorithms, and deep learning frameworks such as TensorFlow and PyTorch.
    • Optical Fiber Communications: The course explores the principles of optical fiber transmission, components, and systems. It prepares students for careers in telecommunications and data networking.

    Project-Based Learning Approach

    The department places significant emphasis on project-based learning as a core component of its educational philosophy. Projects are integrated throughout the curriculum to ensure that students apply theoretical concepts in real-world scenarios.

    Mini-projects begin in the second year and involve solving real-world problems under faculty supervision. These projects are evaluated based on design quality, functionality, presentation skills, and teamwork abilities. They provide students with early exposure to collaborative work environments and professional expectations.

    The final-year thesis or capstone project is a significant undertaking that allows students to explore a specific area of interest in depth. Projects are selected based on student preferences, faculty expertise, and industry relevance. Students work closely with assigned mentors throughout the process, receiving guidance and feedback at regular intervals.

    The structure and scope of these projects are carefully defined to ensure they challenge students while remaining achievable within the timeframe. Evaluation criteria include originality, technical depth, clarity of presentation, and overall impact of the solution or innovation.