Introduction

The Digital Signal Processing (DSP) Training Course provides a comprehensive understanding of the fundamental and advanced principles of DSP, which is essential in modern engineering applications such as communications, biomedical signal processing, audio/video processing, radar, and artificial intelligence.

This course covers discrete-time signals and systems, sampling theory, Fourier analysis, filter design, spectral estimation, real-time DSP implementation, and emerging trends such as deep learning for signal processing and quantum DSP. Participants will gain hands-on experience with MATLAB, Python (NumPy/SciPy), and industry-standard DSP hardware platforms.

Objectives

By the end of this course, participants will:

1. Understand the fundamental concepts of digital signals and systems.
2. Learn about discrete-time signal processing and transformations.
3. Apply Fourier and z-transforms for spectral analysis.
4. Design and implement digital filters (FIR and IIR).
5. Explore advanced topics such as wavelets, multi-rate DSP, and adaptive filtering.
6. Gain hands-on experience with real-time DSP implementation using MATLAB and Python.
7. Examine AI-powered DSP applications, including deep learning for signal processing.
8. Investigate emerging fields like quantum DSP and neuromorphic computing.

Who Should Attend?

This course is ideal for:

• Electrical and Electronics Engineers working with signals and communications.
• DSP Engineers and Researchers focusing on advanced filtering and signal analysis.
• Software Developers and AI/ML Engineers working on speech and image processing.
• Biomedical Engineers dealing with medical imaging and biosignal processing.
• Radar, Sonar, and Telecommunications Professionals designing DSP-based systems.
• Academics and Students interested in signal processing fundamentals and applications.

Course Outline

Day 1: Fundamentals of DSP and Signal Representations

Session 1: Introduction to DSP and Applications

• Why DSP? The Role of Digital Signal Processing in Modern Technology.
• Comparison: Analog vs. Digital Signal Processing.
• Real-World DSP Applications (Speech, Image, Biomedical, IoT, Radar).

Session 2: Discrete-Time Signals and Systems

• Classification of Signals (Deterministic, Random, Periodic, Aperiodic).
• Basic Operations on Signals (Time Shifting, Scaling, Folding).
• Discrete-Time Systems: Linear Time-Invariant (LTI) Systems and Convolution.

Session 3: Sampling and Quantization

• Nyquist-Shannon Sampling Theorem.
• Aliasing and Anti-Aliasing Filters.
• Quantization Noise and Bit Resolution.

Hands-On Workshop: Signal representation and sampling using MATLAB/Python.

Day 2: Fourier Analysis and Transform Techniques

Session 1: The Discrete-Time Fourier Transform (DTFT) and Discrete Fourier Transform (DFT)

• Introduction to the Fourier Series and Fourier Transform.
• DTFT and Its Properties.
• Computing the DFT Using the Fast Fourier Transform (FFT).

Session 2: The z-Transform and Its Applications

• Definition of z-Transform and Region of Convergence (ROC).
• Properties and Applications in System Analysis.
• Relationship Between z-Transform, Fourier Transform, and Laplace Transform.

Session 3: Spectral Analysis and Applications

• Power Spectral Density (PSD) and Spectral Estimation.
• Applications in Speech Processing and Image Analysis.
• Case Study: DSP in Seismology and Biomedical Engineering.

Hands-On Workshop: Fourier and z-transform computations using MATLAB/Python.

Day 3: Digital Filter Design and Implementation

Session 1: Digital Filter Basics and FIR Filters

• Introduction to Filters: FIR vs. IIR Filters.
• FIR Filter Design: Windowing, Frequency Sampling, and Parks-McClellan Algorithm.
• Applications of FIR Filters in Audio and Biomedical Processing.

Session 2: Infinite Impulse Response (IIR) Filter Design

• Butterworth, Chebyshev, and Elliptic Filters.
• Bilinear Transformation and Impulse Invariant Methods.
• Stability Analysis of IIR Filters.

Session 3: Advanced Filter Design and Optimization

• Adaptive Filtering (LMS, RLS Algorithms).
• Wavelet Transform and Multi-Resolution Signal Analysis.
• Digital Filter Optimization Using AI and Deep Learning.

Hands-On Workshop: FIR/IIR filter design and implementation using MATLAB/Python.

Day 4: Advanced DSP Applications and Real-Time Processing

Session 1: Multi-Rate DSP and Wavelets

• Decimation and Interpolation.
• Polyphase Filters and Efficient Resampling Techniques.
• Introduction to Wavelet Transform and Applications in Image Compression.

Session 2: DSP for Communications and AI Applications

• Modulation and Demodulation Using DSP.
• AI and Machine Learning for Signal Processing (Neural Networks, CNNs, RNNs).
• Case Study: Deep Learning for Speech Recognition and Image Processing.

Session 3: Real-Time DSP and Hardware Implementation

• Introduction to Real-Time DSP on Embedded Platforms (DSP Processors, FPGA, ARM).
• Hardware Implementation of Filters and FFT on DSP Chips.
• Challenges in Low-Power DSP for IoT and Edge Devices.

Hands-On Workshop: Implementing DSP algorithms on embedded platforms (TI DSP Kit or FPGA).

Day 5: Emerging Trends and DSP Project Development

Session 1: Quantum DSP and Neuromorphic Computing

• Basics of Quantum Signal Processing.
• Neuromorphic DSP and Spiking Neural Networks.
• Case Study: Brain-Inspired Signal Processing for Edge AI.

Session 2: DSP in 5G/6G and IoT

• OFDM, MIMO, and DSP in Wireless Communications.
• DSP for Radar, LiDAR, and Automotive Applications.
• Case Study: Signal Processing for 6G and Beyond.

Session 3: Final Project Presentation and Certification

• Participants work in teams to develop a DSP-based solution.
• Presentation of project findings and discussion on real-world deployment.
• Expert feedback and recommendations for industry applications.

Final Hands-On Workshop: Participants develop and simulate a DSP system using MATLAB/Python.

Final Assessment & Certification

• Knowledge Check: A final quiz covering all course topics.
• Final Project Presentation: Teams present their DSP project concepts.
• Certification: Participants receive a certificate upon successful completion.