There are various types of signals that can be processed in signal processing. Some of the common types include:

  1. Continuous-time signals: These signals are defined for all values of time within a specified interval. They can take on any value within a continuous range. Examples of continuous-time signals include audio signals, temperature measurements, and analog voltage signals.

  2. Discrete-time signals: These signals are only defined at discrete points in time. They are obtained by sampling continuous-time signals. Examples of discrete-time signals include digital audio, digital images, and sensor measurements taken at specific time intervals.

  3. Analog signals: These signals have continuous values and can take any value within a specified range. They are typically represented by continuous voltage or current levels. Examples include signals from analog sensors, such as temperature or pressure sensors.

  4. Digital signals: These signals have discrete values and can only take on specific values within a finite set. They are typically represented by binary digits (bits), where each bit can be either 0 or 1. Examples of digital signals include binary data transmitted over a communication channel or stored in computer memory.

  5. Periodic signals: These signals repeat their pattern after a fixed time interval called the period. Examples include periodic waveforms like sine waves and square waves.

  6. Non-periodic signals: These signals do not repeat their pattern over time. They are often referred to as aperiodic signals. Examples include noise signals, transient signals, and random signals.

  7. Deterministic signals: These signals can be described by a mathematical function or equation. They can be precisely predicted or generated using a mathematical model. Examples include sinusoidal signals, polynomial functions, and step functions.

  8. Random signals: Also known as stochastic signals, these signals cannot be described by a deterministic mathematical function. They exhibit some degree of randomness and are characterized by statistical properties. Examples include white noise, Gaussian noise, and random vibration signals.

  It is important to note that these are just some of the common types of signals in signal processing, and there may be other specific types depending on the application or domain of signal processing.