The B.Tech in Electronics and Communication Engineering – Artificial Intelligence (ECE-AI) at Dhirubhai Ambani University is designed to address the growing convergence of electronics, communication technologies, and AI. Modern electronic and communication systems increasingly rely on intelligent algorithms to enhance performance, adaptability, and efficiency. From smart devices and IoT platforms to next-generation wireless networks and autonomous systems, AI is transforming how electronic systems are designed, optimized, and operated.

The program provides a strong foundation in core ECE disciplines, including electronic circuits, signals and systems, electromagnetics, communication engineering, and control systems, supported by rigorous training in mathematics, programming, and computing. Building on this foundation, students gain expertise in AI, machine learning, and data-driven techniques, and learn how these methods can be applied to signal processing, wireless communication, embedded systems, and intelligent electronic systems.

The curriculum emphasizes hands-on learning, modern engineering tools, and system-level design. Students develop the ability to design and implement integrated hardware–software–AI solutions, working with tools such as simulation environments, embedded platforms, and AI frameworks. The program also fosters critical thinking, teamwork, and ethical responsibility.

Graduates of the program will be well prepared for careers in the electronics, semiconductor, telecommunications, and AI industries, as well as for advanced study and research in emerging areas at the intersection of ECE and intelligent systems.

Program Outcomes (POs)

PO No.	Program Outcomes
PO1	Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
PO2	Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences
PO3	Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
PO4	Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions
PO5	Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
PO6	The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
PO7	Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
PO8	Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
PO9	Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
PO10	Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
PO11	Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
PO12	Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

The Program Specific Outcomes (PSOs) set the following goal:

After the successful completion of the BTech ECE-AI program, students will:

PSO No.	Program Specific Outcomes (PSOs)
PSO1	Apply the principles of electronics, signals and systems, electromagnetics, and communication engineering to analyze and design electronic circuits, communication systems, and embedded platforms.
PSO2	Develop and apply AI and machine learning techniques to address problems in signal processing, wireless communication, and data-driven electronic systems.
PSO3	Have the awareness of the ethical, societal, and security dimensions of AI-enabled engineering systems, with the ability to adapt to emerging technologies and contribute to innovation at the intersection of electronics, communication, and AI.

The structure of the B.Tech. ECE-AI program is designed keeping the following key points into consideration:

a) To equip students with strong competencies in Electronics and Communication Engineering (ECE) and Artificial Intelligence (AI), with a clear industry orientation, thereby enhancing their employability and placement prospects in core companies.

b) To establish a strong academic foundation that prepares students for higher studies in ECE and AI. Accordingly, the curriculum includes core courses aligned with the GATE syllabus in ECE, along with essential components in Data Science and Artificial Intelligence.

The curriculum for the B.Tech. The ECE-AI undergraduate program has been designed in accordance with the Institute's common curricular framework, which applies to all undergraduate programs.

Under this framework, all courses are categorized into the following four groups:

Institute Core (IC):

This category comprises foundational courses common to all programs under the Common Curriculum Framework. It also includes co-curricular activities, internships, projects, and other institute-mandated components

Program Core (PC):

The Program Core consists of a set of essential courses, referred to as Program Core Courses, which collectively form the academic backbone of the ECE-AI program.

Program Core	Course Title	L	T	P	C	Semester
PC1	Semiconductor Devices	3	0	2	4	I
PC2	Introduction to Engineering Electromagnetics	3	1	0	4	II
PC3	Signals and Systems	3	0	2	4	III
PC4	Transmission Lines and Antennas	3	1	0	4	III
PC5	Introduction to Artificial Intelligence	3	0	2	4	III
PC6	Convex Optimization	3	1	0	4	IV
PC7	Communication-I	3	0	2	4	IV
PC8	Analog Electronics	3	0	2	4	IV
PC9	Communication-II	3	0	2	4	V
PC10	Control Systems	3	0	2	4	V
PC11	Digital Signal Processing	3	0	2	4	V
PC12	Deep Learning	3	0	2	4	VI

Program Electives (PE):

Program Electives are elective courses offered in areas of primary relevance and specialization within the ECE-AI discipline.

Free Electives (FE):

Free Electives are courses that students may choose from disciplines outside the primary domain of the ECE-AI program.

SEMESTER-WISE COURSE ALLOCATION

Representative list of Program Electives (PE) under certain baskets are as follows:

A. AI & Machine Learning–Focused Electives (ECE-Oriented)

1. Machine Learning for Signal Processing
2. Deep Learning for Communication Systems
3. AI for Wireless and Mobile Communication
4. Intelligent Signal Processing
5. Pattern Recognition and Statistical Learning
6. Reinforcement Learning and Control
7. Graph Signal Processing and Graph Neural Networks
8. Probabilistic Models and Bayesian Learning
9. Explainable AI for Engineering Systems
10. Edge AI and TinyML for Embedded Systems

B. Signal Processing & Communications Electives

1. Advanced Digital Signal Processing
2. Adaptive Signal Processing
3. Multirate Signal Processing
4. Speech and Audio Signal Processing
5. Image and Video Signal Processing
6. Radar and Sonar Signal Processing
7. Statistical Signal Processing
8. MIMO and Massive MIMO Systems
9. Cognitive Radio and Software-Defined Radio
10. Advanced Wireless Communication Systems
11. Optical Communication Systems

C. Embedded Systems, IoT & Hardware–AI Co-Design

1. Embedded Systems Design with AI Applications
2. Internet of Things (IoT) and AI Integration
3. System Software
4. Operating Systems
5. Cyber-Physical Systems
6. FPGA-Based System Design
7. Hardware Acceleration for AI (GPUs, TPUs, FPGAs)
8. VLSI Design for AI Applications
9. Real-Time Operating Systems
10. Sensor Networks and Intelligent Systems
11. Robotics and Autonomous Systems
12. AI for Smart Sensors and Wearables

D. Data Science & Computing Electives (ECE-Relevant)

1. Data Analytics for Engineering Applications
2. Big Data Processing and Engineering
3. High-Performance Computing for AI
4. Cloud Computing for AI and Signal Processing
5. Computational Linear Algebra
6. Numerical Methods for Signal Processing and ML

E. Emerging Electronics & Interdisciplinary Electives

1. 6G Communication Systems and AI
2. AI for Biomedical Signal Processing
3. AI in Power and Smart Grid Systems
4. Intelligent Transportation Systems
5. Quantum Computing and Quantum Communication (Introductory)
6. Cybersecurity for Communication and AI Systems
7. Ethical AI and Responsible Engineering
8. Digital Twin Technology
9. Human–Machine Interaction
10. Printed and Flexible Electronics
11. Nanoelectronics and Emerging Devices
12. Innovation, Startups, and Technology Management