Comprehensive Course Structure
The engineering program at R N B Global University Bikaner is structured across 8 semesters, with a carefully designed curriculum that balances theoretical knowledge with practical application. The program includes core courses, departmental electives, science electives, and hands-on laboratory sessions to ensure comprehensive learning.
| SEMESTER | COURSE CODE | COURSE TITLE | CREDIT STRUCTURE (L-T-P-C) | PRE-REQUISITES |
|---|---|---|---|---|
| I | PHYS101 | Physics for Engineers | 3-1-0-4 | - |
| I | MATH101 | Mathematics I | 4-0-0-4 | - |
| I | CHEM101 | Chemistry for Engineers | 3-1-0-4 | - |
| I | ENGL101 | English Communication Skills | 2-0-0-2 | - |
| I | CPRO101 | Introduction to Programming | 2-0-2-3 | - |
| I | MECH101 | Engineering Mechanics | 3-1-0-4 | - |
| II | MATH201 | Mathematics II | 4-0-0-4 | MATH101 |
| II | PHYS201 | Physics II | 3-1-0-4 | PHYS101 |
| II | CHEM201 | Chemistry II | 3-1-0-4 | CHEM101 |
| II | CPRO201 | Data Structures and Algorithms | 3-0-2-5 | CPRO101 |
| II | ELEC201 | Basic Electrical Circuits | 3-1-0-4 | - |
| III | MATH301 | Mathematics III | 4-0-0-4 | MATH201 |
| III | PHYS301 | Thermodynamics | 3-1-0-4 | PHYS201 |
| III | CHEM301 | Organic Chemistry | 3-1-0-4 | CHEM201 |
| III | CPRO301 | Object Oriented Programming | 3-0-2-5 | CPRO201 |
| III | ELEC301 | Electrical Machines | 3-1-0-4 | ELEC201 |
| IV | MATH401 | Mathematics IV | 4-0-0-4 | MATH301 |
| IV | PHYS401 | Optics and Modern Physics | 3-1-0-4 | PHYS301 |
| IV | CHEM401 | Physical Chemistry | 3-1-0-4 | CHEM301 |
| IV | CPRO401 | Database Management Systems | 3-0-2-5 | CPRO301 |
| IV | ELEC401 | Power Electronics | 3-1-0-4 | ELEC301 |
| V | MATH501 | Mathematics V | 4-0-0-4 | MATH401 |
| V | PHYS501 | Quantum Mechanics | 3-1-0-4 | PHYS401 |
| V | CHEM501 | Inorganic Chemistry | 3-1-0-4 | CHEM401 |
| V | CPRO501 | Computer Networks | 3-0-2-5 | CPRO401 |
| V | ELEC501 | Control Systems | 3-1-0-4 | ELEC401 |
| VI | MATH601 | Mathematics VI | 4-0-0-4 | MATH501 |
| VI | PHYS601 | Electromagnetic Theory | 3-1-0-4 | PHYS501 |
| VI | CHEM601 | Biochemistry | 3-1-0-4 | CHEM501 |
| VI | CPRO601 | Artificial Intelligence | 3-0-2-5 | CPRO501 |
| VI | ELEC601 | Signal Processing | 3-1-0-4 | ELEC501 |
| VII | MATH701 | Mathematics VII | 4-0-0-4 | MATH601 |
| VII | PHYS701 | Nuclear Physics | 3-1-0-4 | PHYS601 |
| VII | CHEM701 | Chemical Kinetics | 3-1-0-4 | CHEM601 |
| VII | CPRO701 | Machine Learning | 3-0-2-5 | CPRO601 |
| VII | ELEC701 | Embedded Systems | 3-1-0-4 | ELEC601 |
| VIII | MATH801 | Mathematics VIII | 4-0-0-4 | MATH701 |
| VIII | PHYS801 | Relativity Theory | 3-1-0-4 | PHYS701 |
| VIII | CHEM801 | Environmental Chemistry | 3-1-0-4 | CHEM701 |
| VIII | CPRO801 | Capstone Project | 0-0-6-12 | - |
| VIII | ELEC801 | Final Year Thesis | 0-0-4-8 | - |
Detailed Course Descriptions
Advanced departmental elective courses form a crucial part of the engineering curriculum at R N B Global University Bikaner. These courses are designed to provide in-depth knowledge and specialized skills required for specific engineering domains.
Artificial Intelligence and Machine Learning
This course delves into the theoretical foundations and practical applications of artificial intelligence and machine learning. Students will explore algorithms such as neural networks, decision trees, and support vector machines. The course emphasizes hands-on implementation using Python and TensorFlow frameworks. Students will work on real-world datasets to develop AI solutions for various domains including computer vision, natural language processing, and robotics.
The learning objectives include understanding the mathematical principles behind machine learning algorithms, implementing and training models on large datasets, and evaluating model performance using appropriate metrics. This course prepares students for careers in AI research, data science, and machine learning engineering roles at leading technology companies.
Computer Networks
Computer networks form the backbone of modern communication systems. This course covers fundamental concepts such as network architectures, protocols, and security mechanisms. Students will study TCP/IP stack, routing algorithms, and wireless networking technologies. The course includes practical sessions on network simulation using tools like Wireshark and GNS3.
The learning outcomes include understanding network topologies, designing and implementing network protocols, and troubleshooting network issues. Students will gain hands-on experience in configuring routers and switches, implementing security measures, and analyzing network performance. This knowledge is essential for careers in network engineering, cybersecurity, and telecommunications industries.
Data Structures and Algorithms
This course provides a comprehensive overview of fundamental data structures and algorithmic techniques. Students will study arrays, linked lists, stacks, queues, trees, graphs, and hash tables. The course emphasizes algorithm design principles including divide and conquer, dynamic programming, and greedy algorithms.
Students will implement these concepts using programming languages like C++, Java, or Python. Practical sessions involve solving coding problems on platforms like LeetCode and HackerRank. This course is crucial for developing problem-solving skills required in software engineering and competitive programming.
Database Management Systems
This course explores the design, implementation, and management of database systems. Students will study relational databases, SQL queries, normalization, and transaction processing. The course covers advanced topics such as indexing, query optimization, and distributed databases.
Students will gain hands-on experience with industry-standard database management systems like MySQL, PostgreSQL, and Oracle. Practical sessions include designing database schemas, writing complex queries, and implementing database security measures. This knowledge is essential for careers in data engineering, database administration, and software development roles that involve data management.
Signal Processing
Signal processing is fundamental to modern communication systems and multimedia applications. This course covers mathematical foundations of signals and systems, Fourier transforms, and digital signal processing techniques. Students will study filter design, sampling theory, and spectral analysis methods.
The course includes practical sessions on implementing signal processing algorithms using MATLAB and Python libraries. Students will work on projects involving audio processing, image enhancement, and biomedical signal analysis. This knowledge is crucial for careers in telecommunications, audio/video engineering, and biomedical engineering.
Power Electronics
Power electronics deals with the conversion and control of electrical power using semiconductor devices. This course covers rectifiers, inverters, DC-DC converters, and AC-AC converters. Students will study switching devices like thyristors, MOSFETs, and IGBTs.
Practical sessions involve designing power electronic circuits and simulating their behavior using tools like LTspice and MATLAB/Simulink. Students will work on projects related to renewable energy systems, electric vehicle charging stations, and industrial drive applications. This course prepares students for careers in power system engineering and renewable energy industries.
Control Systems
Control systems are essential in automation and robotics applications. This course covers mathematical modeling of dynamic systems, transfer functions, and feedback control principles. Students will study stability analysis, root locus techniques, and frequency response methods.
The course includes practical sessions on designing controllers using MATLAB and Simulink. Students will work on projects involving servo motor control, temperature regulation, and robotic arm positioning. This knowledge is crucial for careers in automation engineering, robotics, and process control industries.
Embedded Systems
Embedded systems are specialized computing systems that perform dedicated functions within larger systems. This course covers microcontroller architectures, real-time operating systems, and embedded software development. Students will study ARM-based microcontrollers and programming languages like C and Assembly.
Practical sessions involve designing embedded applications using development boards like Arduino and Raspberry Pi. Students will work on projects involving sensor integration, communication protocols, and real-time system design. This knowledge is essential for careers in IoT development, automotive engineering, and embedded software engineering.
Machine Learning
This advanced course builds upon foundational machine learning concepts to explore cutting-edge techniques and applications. Students will study deep learning architectures, reinforcement learning, and natural language processing methods. The course emphasizes practical implementation using Python frameworks like TensorFlow and PyTorch.
Students will work on end-to-end machine learning projects involving data preprocessing, model selection, training, and evaluation. The course includes hands-on sessions with real-world datasets from domains such as healthcare, finance, and autonomous vehicles. This prepares students for advanced roles in AI research and development.
Internet of Things (IoT)
The Internet of Things represents the convergence of computing, networking, and physical systems. This course covers IoT architectures, sensor networks, wireless communication protocols, and cloud integration. Students will study smart home systems, industrial IoT applications, and wearable technology.
Practical sessions involve building IoT projects using platforms like Arduino, Raspberry Pi, and cloud services like AWS IoT and Google Cloud IoT. Students will work on projects involving data collection, processing, and visualization in real-time applications. This knowledge is crucial for careers in IoT development and smart systems engineering.
Advanced Computer Architecture
This course explores modern computer system design principles and performance optimization techniques. Students will study processor design, memory hierarchy, cache organization, and parallel computing architectures. The course covers both theoretical concepts and practical implementation aspects.
Students will work on projects involving microarchitecture design, performance analysis, and system optimization. Practical sessions involve using simulation tools like Gem5 and conducting benchmarking experiments. This knowledge is essential for careers in computer architecture research, semiconductor industry, and high-performance computing.
Software Engineering
Software engineering encompasses the systematic approach to software development and maintenance. This course covers software development life cycle, requirements analysis, design patterns, and testing methodologies. Students will study agile development, version control systems, and project management techniques.
The course includes practical sessions on developing software projects using industry-standard tools and frameworks. Students will work on group projects involving full software development cycles from planning to deployment. This prepares students for careers in software development, system architecture, and project management roles.
Renewable Energy Systems
This course addresses the design and implementation of sustainable energy solutions. Students will study solar photovoltaic systems, wind turbines, hydroelectric power generation, and energy storage technologies. The course covers both theoretical principles and practical applications.
Practical sessions involve designing renewable energy systems and simulating their performance using specialized software tools. Students will work on projects related to grid integration, energy management, and environmental impact assessment. This knowledge is crucial for careers in renewable energy industries and sustainable development sectors.
Biomedical Engineering
Biomedical engineering integrates engineering principles with medical sciences to develop innovative healthcare solutions. This course covers medical device design, bioinformatics, and bioprocess engineering. Students will study topics such as medical imaging, tissue engineering, and drug delivery systems.
The course includes practical sessions involving laboratory experiments and prototyping of biomedical devices. Students will work on projects related to healthcare technology development and regulatory compliance. This prepares students for careers in medical device industry, pharmaceutical research, and healthcare technology innovation.
Advanced Materials Science
This course explores the structure, properties, and applications of advanced materials. Students will study nanomaterials, composite materials, smart materials, and biomaterials. The course covers synthesis techniques, characterization methods, and material selection principles.
Practical sessions involve laboratory experiments on material processing and testing. Students will work on projects related to developing new materials for specific applications in aerospace, automotive, and electronics industries. This knowledge is essential for careers in materials research, manufacturing, and product development.
Advanced Thermodynamics
This advanced course delves into complex thermodynamic principles and their applications in engineering systems. Students will study non-equilibrium thermodynamics, chemical thermodynamics, and thermodynamic cycles. The course covers both theoretical analysis and practical problem-solving techniques.
Students will work on projects involving energy system optimization, environmental impact assessment, and industrial process design. Practical sessions involve using thermodynamic software tools for simulation and analysis. This prepares students for careers in energy systems engineering, environmental consulting, and industrial research.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is rooted in the belief that hands-on experience is crucial for developing practical engineering skills. Projects are designed to mirror real-world challenges and provide students with opportunities to apply theoretical knowledge in practical contexts.
Mini-projects are introduced in the second year, allowing students to explore specific areas of interest while building foundational skills. These projects typically span 4-6 weeks and involve working in small teams under faculty guidance. The evaluation criteria include project execution, documentation quality, and presentation skills.
The final-year capstone project is a comprehensive endeavor that integrates all knowledge and skills acquired throughout the program. Students select projects based on their interests and career aspirations, often aligning with ongoing research initiatives or industry collaborations. Faculty mentors are assigned based on expertise matching and project requirements.
Project selection involves a detailed proposal process where students must articulate the problem statement, proposed methodology, expected outcomes, and timeline. The department provides resources including laboratory access, software licenses, and mentorship support to ensure successful project completion.
Evaluation of projects is conducted through multiple stages including progress reviews, interim presentations, and final demonstrations. The assessment criteria emphasize innovation, technical depth, practical applicability, and team collaboration skills. This approach ensures that students graduate with a portfolio of practical work that demonstrates their capabilities to potential employers.