B.Tech in Digital Signal Processing (DSP) – Semester-wise Syllabus

 

Year 1: Foundation in Engineering & Mathematics 

Semester 1: 

1. Mathematics-I: Calculus, Linear Algebra 

2. Engineering Physics: Waves, Oscillations, Acoustics 

3. Basic Electrical Engineering: Circuits, Network Analysis 

4. Programming Fundamentals: Python/ MATLAB Basics 

5. Engineering Graphics & CAD 

6. Lab: Basic Circuit Design, Python Programming 

 

Semester 2: 

1. Mathematics-II: Differential Equations, Probability 

2. Analog Electronics: Op-Amps, Filters, Signal Conditioning 

3. Digital Electronics: Logic Gates, ADC/DAC 

4. Signals & Systems Basics: Time/Frequency Domain Concepts 

5. Environmental Science 

6. Lab: Analog/Digital Signal Simulation (MATLAB) 

 

Year 2: Core Signal Processing 

Semester 3:

1. Signals & Systems: Continuous/Discrete Time Signals, Convolution 

2. Mathematics for DSP: Fourier Series, Transforms (FT, DFT) 

3. Analog Communication Systems: AM, FM, Modulation 

4. Microprocessors & Microcontrollers (ARM, DSP Processors) 

5. Data Structures & Algorithms 

6. Lab: FFT Implementation, Modulation/Demodulation 

 

Semester 4: 

1. Digital Signal Processing-I: Z-Transform, FIR/IIR Filters 

2. Digital Communication: PCM, QAM, Error Control Coding 

3. Random Signals & Noise: Stochastic Processes, SNR Analysis 

4. Embedded Systems: Real-Time DSP with C/Assembly 

5. Control Systems: Feedback, Stability 

6. Lab: Filter Design (MATLAB), Embedded DSP Projects 

 

Year 3: Advanced DSP & Applications

Semester 5: 

1. Digital Signal Processing-II: Multirate Systems, Wavelets 

2. Image Processing: Edge Detection, Compression (JPEG, MPEG) 

3. Statistical Signal Processing: Estimation, Detection Theory 

4. Elective-I: Audio Signal Processing / Biomedical Signal Processing 

5. Real-Time DSP: FPGA/ARM Implementation (VHDL/Verilog) 

6. Lab: Image Enhancement, Real-Time Filtering (LabVIEW) 

 

Semester 6: 

1. Adaptive Signal Processing: LMS, RLS Algorithms 

2. Speech Processing: MFCC, Speech Recognition (HMMs) 

3. Machine Learning for DSP: Neural Networks, Feature Extraction 

4. Elective-II: Radar & Sonar Signal Processing / Wireless Communications 

5. Elective-III: IoT Sensor Networks / Computer Vision 

6. Lab: Speech Recognition (Python), Adaptive Noise Cancellation 

 

Year 4: Specialization & Industry Integration 

Semester 7:

1. Advanced Topics in DSP: Sparse Signals, Compressed Sensing 

2. DSP for 5G/6G: OFDM, Massive MIMO, Beamforming 

3. Elective-IV: Quantum Signal Processing / AI in DSP 

4. Elective-V: Automotive DSP (ADAS, LiDAR) / Multimedia Systems 

5. Capstone Project-I: Industry/Research Problem (e.g., ECG Signal Analysis, Noise Reduction) 

6. Internship: Telecom, Semiconductor, or Biomedical Firms (Qualcomm, Texas Instruments, Philips) 

 

Semester 8: 

1. DSP Hardware Optimization: ASIC/FPGA Design for Low Power 

2. Ethics & Standards: Privacy in Signal Processing (e.g., GDPR) 

3. Emerging Trends: Neuromorphic DSP, Edge AI 

4. Capstone Project-II: Prototype Development (e.g., Smart Hearing Aid, Drone Navigation) 

5. Seminar/Technical Presentations

 

Electives (Sample Options): 

- Biomedical DSP: EEG/ECG Analysis, MRI Reconstruction 

- Audio Engineering: Spatial Audio, Noise Cancellation 

- Radar Systems: SAR Imaging, Target Tracking 

- Optical Signal Processing: LiDAR, Fiber Optics 

- AI-Driven DSP: Generative Models for Signal Synthesis 

- Cybersecurity: Signal Encryption, Watermarking 

 

Key Labs & Tools: 

- MATLAB/Simulink: Algorithm design, filter simulation. 

- Python Libraries: NumPy, SciPy, Librosa (audio), OpenCV (image). 

- FPGA Tools: Xilinx Vivado, Intel Quartus for hardware implementation.  

- DSP Kits: TI TMS320C6x, ARM Cortex-M for real-time processing. 

- Cloud Platforms: AWS/Azure for large-scale signal analysis. 

 

Capstone Projects: 

- Smart Noise Cancellation: Real-time adaptive filtering for headphones. 

- Medical Imaging Enhancement: MRI/CT scan reconstruction using wavelets. 

- Autonomous Vehicle Perception: LiDAR signal processing for obstacle detection. 

- AI-Based Speech Enhancement: Noise suppression in VoIP applications.