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Fees
₹1,50,000
Placement
92.0%
Avg Package
₹6,50,000
Highest Package
₹20,00,000
Fees
₹1,50,000
Placement
92.0%
Avg Package
₹6,50,000
Highest Package
₹20,00,000
Seats
200
Students
200
Seats
200
Students
200
The Signal Processing program at Electronics Service And Training Centre is structured over eight semesters, ensuring a balanced progression from foundational concepts to advanced specializations. The curriculum is designed to provide students with a solid theoretical base while emphasizing practical implementation and real-world problem-solving skills.
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus I | 3-1-0-4 | None |
| 1 | MATH102 | Linear Algebra | 3-1-0-4 | None |
| 1 | PHYS101 | Physics for Engineers | 3-1-0-4 | None |
| 1 | CS101 | Introduction to Programming | 2-1-0-3 | None |
| 1 | ELEC101 | Basic Electronics | 3-1-0-4 | None |
| 2 | MATH201 | Calculus II | 3-1-0-4 | MATH101 |
| 2 | MATH202 | Differential Equations | 3-1-0-4 | MATH101 |
| 2 | PHYS201 | Modern Physics | 3-1-0-4 | PHYS101 |
| 2 | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| 2 | ELEC201 | Electrical Circuits and Networks | 3-1-0-4 | ELEC101 |
| 3 | EC301 | Signals and Systems | 3-1-0-4 | MATH201, MATH202 |
| 3 | EC302 | Digital Signal Processing | 3-1-0-4 | EC301 |
| 3 | EC303 | Probability and Statistics | 3-1-0-4 | MATH201 |
| 3 | EC304 | Communication Systems | 3-1-0-4 | ELEC201, EC301 |
| 3 | EC305 | Electronics Lab I | 0-0-3-2 | ELEC101 |
| 4 | EC401 | Statistical Signal Processing | 3-1-0-4 | EC303, EC302 |
| 4 | EC402 | Image and Video Processing | 3-1-0-4 | EC302 |
| 4 | EC403 | Wireless Communication | 3-1-0-4 | EC304 |
| 4 | EC404 | Biomedical Signal Analysis | 3-1-0-4 | EC302, EC303 |
| 4 | EC405 | Electronics Lab II | 0-0-3-2 | EC305 |
| 5 | EC501 | Machine Learning for Signal Processing | 3-1-0-4 | EC401, EC402 |
| 5 | EC502 | Signal Processing for IoT | 3-1-0-4 | EC302, EC403 |
| 5 | EC503 | Advanced Audio Signal Processing | 3-1-0-4 | EC302 |
| 5 | EC504 | Signal Processing for Security | 3-1-0-4 | EC401, EC403 |
| 5 | EC505 | Project Lab I | 0-0-6-3 | EC402, EC403 |
| 6 | EC601 | Capstone Project | 0-0-12-6 | All previous courses |
| 6 | EC602 | Research Methodology | 3-1-0-4 | EC401 |
| 6 | EC603 | Special Topics in Signal Processing | 3-1-0-4 | EC501, EC502 |
| 6 | EC604 | Internship | 0-0-0-6 | EC601 |
| 6 | EC605 | Professional Ethics and Values | 3-1-0-4 | None |
The department offers a wide array of advanced elective courses that allow students to tailor their education to their interests and career goals. Here are detailed descriptions of several key electives:
This course introduces students to the intersection of signal processing and machine learning, focusing on how ML algorithms can be adapted to handle signal data efficiently. Topics include neural networks, deep learning architectures, supervised and unsupervised learning, and reinforcement learning techniques. Students will implement these methods using Python and TensorFlow, applying them to tasks such as speech recognition, image classification, and anomaly detection in sensor data.
This course explores the unique challenges and opportunities presented by signal processing in IoT environments. It covers topics like sensor node design, edge computing, wireless protocols, data fusion techniques, and energy-efficient algorithms. Students will work on projects involving real-world IoT devices, integrating signal processing with embedded systems programming and cloud computing platforms.
Focusing on the technical aspects of audio signal processing, this course delves into sound synthesis, noise reduction, equalization, and music information retrieval. Students will learn to use specialized software tools for audio editing, develop custom audio effects, and explore applications in virtual reality, gaming, and broadcasting.
This elective investigates how signal processing techniques can be applied to secure communication systems and cryptographic algorithms. It covers topics such as spread spectrum techniques, steganography, watermarking, and digital signatures. Students will study both theoretical foundations and practical implementations of these security measures in real-world scenarios.
This course provides an in-depth exploration of signals generated by biological systems. It covers physiological signal processing, including ECG, EEG, EMG, and MRI data analysis. Students will learn to apply signal processing methods for diagnosing medical conditions, developing diagnostic tools, and improving patient outcomes through better data interpretation.
Students will gain expertise in image enhancement, compression, segmentation, and recognition using both classical and modern techniques. The course includes hands-on experience with software libraries like OpenCV and MATLAB, focusing on real-time applications in surveillance, medical imaging, and computer vision.
This course covers the principles of wireless communication systems, including modulation schemes, channel coding, multiple access techniques, and network protocols. Students will simulate and analyze various wireless systems using tools like MATLAB and GNU Radio, preparing them for careers in telecom engineering and mobile network design.
Focused on statistical methods used in signal processing, this course teaches students how to model signals as random processes, estimate parameters, and make predictions based on observed data. It includes topics such as estimation theory, hypothesis testing, Kalman filtering, and Bayesian inference, all with practical applications in engineering and finance.
This course explores the technical side of audio signal processing, covering topics like digital filters, spectral analysis, time-frequency representations, and sound synthesis. Students will develop skills in designing audio effects, working with audio formats, and implementing real-time audio processing systems using various software platforms.
Addressing the growing importance of smart city infrastructure, this course examines how signal processing is used in urban planning, environmental monitoring, traffic management, and public safety systems. Students will work on projects involving sensor networks, data fusion, and decision support systems that integrate multiple signal sources.
The department emphasizes project-based learning as a core component of the educational experience. This approach encourages students to apply theoretical knowledge in practical settings, fostering creativity, teamwork, and problem-solving skills. Mini-projects are integrated throughout the curriculum, typically spanning one semester and involving small groups of 3-5 students.
Mini-projects begin in the third year, where students tackle challenges related to signal processing applications in real-world domains such as wireless communication, biomedical diagnostics, or audio enhancement. These projects are supervised by faculty members who guide students through the process of defining objectives, selecting appropriate methods, implementing solutions, and presenting findings.
The final-year capstone project is a significant undertaking that allows students to pursue independent research or collaborative work with industry partners. Students have the freedom to select their own topics within the scope of signal processing, subject to approval by their faculty advisor. The project must demonstrate originality, technical depth, and practical relevance.
Evaluation criteria for projects include innovation, methodology, execution quality, presentation skills, and peer collaboration. Students are required to submit detailed reports, conduct formal presentations, and participate in peer reviews. This comprehensive approach ensures that students not only acquire technical skills but also develop communication and leadership abilities essential for professional success.
