ABSTRACT
Signals are traditionally classified as being analog (continuous-time), discrete-time (sample-data), or digital. A continuous-time signal has infinite precision in both the time-and amplitude-domain. Discrete-time signals have infinite amplitude precision, but are discretely resolved in time (sampled). Digital signals are of finite precision in both the time (sampled) and amplitude (quantized). Digital signals are either synthesized by a digital system (e.g., computer) or by digitizing an analog signal using an analog-to-digital converter (ADC). A digital-to-analog converter (DAC) converts a digital signal into an analog signal. Signal processing refers to the science of analyzing, synthesizing, and manipulating audio, acoustic, speech, video, image, geophysical, radar, radio signals, plus a host of other waveforms using mathematics or technology. Signals may be an array of one-, two-, or Af-dimensional samples, of finite or infinite duration. Digital signal processing (DSP) refers to the processing of digital or digitized signals exclusively with digital technologies and techniques. DSP systems and elements can be linear or nonlinear, and reside in the time (e.g., filter) or transform domain (e.g., frequency). DSP processing agents range from specialized mathematical and statistical abstractions, to software or hardware. In practice, DSP systems are often designed to meet very restrictive real-time speed, precision, dynamic range requirements, and operate in multisignal, multisystem environments. The design and study of a DSP solution, therefore, requires a concurrent knowledge of signal processing theory, application, and technology.
