Introduction to Digital and Analog Signal Processing

Introduction to Digital Signal Processing

• Digital Signal Processing (DSP) is the analysis, interpretation, and manipulation of signals like sound, images, and sensor data.
• It encompasses a wide range of applications, for example biological data such as electrocardiograms, control system signals, and telecommunication transmission signals like radio signals.
• DSP addresses challenges in signal transmission, such as signal contamination or deformation due to external noise.
• DSP retrieves the original signal at the receiver using suitable filters.
• DSP is a powerful tool for processing both analog and digital signals.

Analog Signal Processing

• Analog signals are signals that are continuous in time.
• Analog signal processing involves using electronic circuits.
• Examples of analog signal processing include:
  • Traditional radio, telephone, radar, and television systems.
  • Electronic circuits such as passive filters, active filters, additive mixers, integrators and delay lines.

Digital Signal Processing

• Digital signals are discrete representations of analog signals.
• Digital signal processing involves using computers or digital circuits.
• Examples of digital signal processing include:
  • General-purpose computers.
  • Digital circuits such as field-programmable gate arrays (FPGA) or specialized digital signal processors (DSP chips).
• A simplified block diagram illustrating the concept of DSP includes:
  • An analog filter.
  • An analog-to-digital conversion (ADC) unit.
  • A digital signal (DS) processor.
  • A digital-to-analog conversion (DAC) unit.
  • A reconstruction filter.
• Digital signal processors can be large programmable digital computers or small microprocessors programmed for specific signal operations.

Advantages of Digital Signal Processing

• Accuracy: Digital circuits are more accurate than analog circuits as they are less prone to temperature and external influences.
• Flexibility: Digital circuits can be easily reconfigured by changing program coefficients, unlike complex analog circuit reconfiguration.
• Cost-effectiveness: Digital implementations of signal processing systems can be cheaper than their analog counterparts in some cases.
• Storage: Digital signals can be easily stored on magnetic or optical media using semiconductor chips.
• Easy Operation: Computers can perform complex mathematical operations easily, which is not a simple task in analog processing.
• Multiplexing: Digital signal processing allows for the integration of digitized signals with other digital data for transmission through the same channel.

Limitations of Digital Signal Processing

• Bandwidth restrictions: High-speed analog-to-digital converters (ADCs) are required for signals with wide bandwidths.
• Speed limitations: Processing speeds are limited by the capacity of the digital system.
• Cost: DSP systems can be expensive for small applications.

Analog vs. Digital Systems

• Analog systems:
  • Constructed using active and passive components, such as resistors, capacitors, and operational amplifiers.
  • Represented by differential equations.
  • Analyzed using Laplace transforms.
• Digital systems:
  • Constructed using adder, multiplier, and delay elements.
  • Represented by difference equations.
  • Analyzed using Z-transforms.

DSP Basic Algorithms

• These algorithms are widely used in signal processing applications:
  • FIR filters and IIR filters: Used to remove unwanted noise from signals.
  • Convolution algorithms: Used to find similarities in signals.
  • Discrete Fourier Transforms: Used to represent signals in easier-to-process formats.
  • Discrete Cosine Transforms: Used in image processing applications.

DSP Applications

• DSP applications extend to various fields, including:
• Robotics: Voice recognition, autonomous navigation, sensor data processing.
• Amazon: Speech recognition, product recommendations, smart home devices.
• Smart Mirror: Image recognition for personalized content, facial tracking for interactive experiences.
• Noise filtering: Removing unwanted noise from signals.
• Text to speech: Synthesizing speech from text.
• Image filtering: Enhancing or modifying images.
• Image recognition: Identifying objects and patterns in images.

Course Prerequisites

• This course assumes prior knowledge of Signals and Systems.
• This includes:
  • Linear system theory for continuous-time signals and systems.
  • Fourier and Laplace Transforms.

Course Resources

• Alan V. Oppenheim, Discrete Time-Signal Processing, Prentice Hall; 3rd Edition, 2009.
• Luis F. Chaparro, Signals and Systems Using MATLAB, Department of Electrical and Computer Engineering, University of Pittsburgh.
• Li Tan, Digital Signal Processing Fundamentals and Applications, DeVry University Decatur, Georgia.