Digital_Signal_Processing_Presentation.pptx
- 1. Digital Signal Processing • Fundamentals and Applications
- 2. What is Digital Signal Processing? • - The manipulation of signals after conversion to digital form • - Mathematical analysis and transformation of discrete signals • - Enables powerful methods for analyzing, modifying, and extracting information from signals • - Foundation for modern communications, multimedia, and sensing systems
- 3. DSP vs. Analog Signal Processing • Digital Signal Processing: • - Works with discrete-time signals • - Higher precision and reproducibility • - Programmable and flexible • - Can implement complex algorithms • - Immune to environmental factors • Analog Signal Processing: • - Works with continuous-time signals
- 4. Key Components of DSP Systems • - Analog-to-Digital Converter (ADC): Samples and quantizes analog signals • - Digital Signal Processor: Executes algorithms on digital data • - Digital-to-Analog Converter (DAC): Converts processed digital signals back to analog • - Memory: Stores signal data and processing parameters • - Software/Firmware: Implements DSP algorithms
- 5. Fundamental DSP Techniques • - Filtering: Removal or enhancement of specific frequency components • - Spectral Analysis: Determining frequency content of signals (FFT) • - Correlation: Measuring similarity between signals • - Convolution: Fundamental operation for filtering and system analysis • - Modulation/Demodulation: Encoding/decoding information for
- 6. Time Domain vs. Frequency Domain • - Time Domain: Signals represented as amplitude vs. time • - Frequency Domain: Signals represented as amplitude vs. frequency • Key transforms: • - Discrete Fourier Transform (DFT) • - Fast Fourier Transform (FFT) • - Z-transform
- 7. Digital Filters • FIR Filters (Finite Impulse Response): • - Linear phase response • - Always stable • - Higher computational requirements • IIR Filters (Infinite Impulse Response): • - More efficient implementation • - Potential stability issues • - Non-linear phase response
- 8. DSP Applications in Communications • - Wireless and mobile networks (5G, LTE, WiFi) • - Modulation schemes (OFDM, QAM) • - Error correction coding • - Echo cancellation • - Equalization • - Spectrum analysis and management • - Voice over IP (VoIP)
- 9. DSP in Multimedia and Entertainment • - Audio processing (compression, enhancement) • - Image processing (filtering, compression) • - Video compression (MPEG, H.265) • - Virtual/Augmented Reality • - Computer vision • - Speech recognition and synthesis • - Music production and effects
- 10. Emerging Trends in DSP • - AI/ML Integration: Using neural networks for signal processing • - Edge Computing: DSP on IoT devices with limited resources • - Quantum Signal Processing: Leveraging quantum computing principles • - 5G and Beyond: Advanced wireless communication techniques • - Biomedical Applications: Real-time health monitoring and diagnostics