Semester-wise syllabus for an M.Tech in Digital Signal Processing (DSP)

 

Semester 1:

 Core Foundations 

1. Advanced Digital Signal Processing 

   - Discrete-time signals, Z-transform, DFT/FFT algorithms, FIR/IIR filter design, multirate systems. 

2. Statistical Signal Processing 

   - Random processes, estimation theory (MMSE, ML), detection theory, Wiener/Kalman filters. 

3. Signal Processing Algorithms 

   - Adaptive filters (LMS, RLS), spectral estimation (periodogram, parametric methods). 

4. Programming for DSP 

   - MATLAB/Python (NumPy, SciPy), C/C++ for real-time DSP, GPU acceleration (CUDA). 

5. Lab Work 

   - FIR/IIR filter design, FFT implementation, noise reduction algorithms, and audio/image processing basics. 

 

Semester 2:

 Advanced Techniques & Applications 

1. Real-Time DSP Systems

   - DSP processor architectures (TI C6x, SHARC), fixed-point arithmetic, real-time OS integration. 

2. Image and Video Processing 

   - 2D transforms (DCT, wavelet), compression (JPEG, MPEG), edge detection, object recognition. 

3. Speech and Audio Processing 

   - Speech recognition (MFCC, HMMs), audio coding (MP3, AAC), noise cancellation. 

4. FPGA-Based DSP Design 

   - HDL programming (VHDL/Verilog), Xilinx/Vivado tools, FPGA implementations of DSP algorithms. 

5. Elective 1 

   - Options: Biomedical Signal Processing, Wireless Communications, Radar Signal Processing. 

6. Lab Work 

   - FPGA-based FIR filters, speech recognition (Python/TensorFlow), image compression (OpenCV). 

 

Semester 3:

Specialization & Research 

1. Wavelet Transforms and Sparse Representations 

   - Multiresolution analysis, wavelet-based compression/denoising, compressive sensing. 

2. Machine Learning for Signal Processing 

   - Deep learning for audio/image analysis (CNNs, RNNs), anomaly detection, time-series forecasting. 

3. Elective 2 

   - Options: Radar/Sonar Systems, IoT Signal Processing, Biomedical Imaging. 

4. Elective 3 

   - Options: 5G/6G Signal Processing, Quantum Signal Processing, Embedded DSP Systems. 

5. Research Project (Phase 1) 

   - Proposal development (e.g., ECG signal classification, radar target detection, or AI-driven noise reduction). 

6. Lab Work

   - ECG signal analysis (MATLAB), radar signal simulation (GNU Radio), deep learning for denoising (PyTorch). 

 

Semester 4:

Dissertation & Industry Integration 

1. Dissertation/Thesis 

   - Focus areas: AI-enhanced DSP, medical imaging, wireless communication systems, or defense applications. 

2. Industry Internship (Optional) 

   - Collaborations with telecom firms (Ericsson, Qualcomm), medical device companies, or defense labs (DRDO, ISRO). 

3. Emerging Trends Seminar 

   - Topics: AI/ML in DSP, brain-computer interfaces, terahertz signal processing. 

4. Seminar & Viva Voce 

   - Presentation and defense of thesis, peer reviews, and industry expert feedback. 

 

Electives (Across Semesters 2–3) 

- Biomedical Signal Processing: EEG/ECG analysis, MRI reconstruction, wearable sensor data. 

- Wireless Communications: OFDM, MIMO systems, channel equalization, 5G NR protocols. 

- Radar Signal Processing: SAR imaging, Doppler processing, target tracking. 

- IoT Signal Processing: Edge computing, sensor fusion, low-power DSP for IoT devices. 

- Quantum Signal Processing: Quantum Fourier transform, quantum noise mitigation. 

 

Tools & Technologies

- Software: MATLAB, Python (Librosa, OpenCV), Simulink, GNU Radio, LabVIEW. 

- Hardware: TI DSP kits (TMS320C6x), Xilinx FPGAs, Raspberry Pi/Arduino for prototyping. 

- Machine Learning: TensorFlow, PyTorch, Keras for signal classification/analysis. 

- Cloud/Edge: AWS IoT, TensorFlow Lite, ONNX for deployment. 

 

Industry Applications 

- Telecom: 5G/6G modulation, beamforming, channel coding. 

- Healthcare: Medical imaging (MRI/CT), wearable health monitors, prosthetics control.  

- Defense/Aerospace: Radar/sonar systems, satellite communication, electronic warfare. 

- Consumer Electronics: Noise-canceling headphones, image enhancement (smartphones), voice assistants.