Otis Singleton
Analog-to-digital conversion (ADC) is an electronic process that changes a continuously variable analog signal into a multilevel digital signal. This process involves converting analog signals, such as sound and light waves, into digital signals that can be used in computing. The conversion is performed by analog-to-digital converters (ADCs), which change the continuous nature of the analog signal in terms of time and amplitude to a digital signal with discrete levels or states. Digital signals are more efficient for propagation and are easier for electronic circuits to distinguish from noise. The reverse process, converting digital to analog, is performed by digital-to-analog converters (DACs).
Converts Analog Signals to Electrical Signals
| Characteristics | Values |
|---|---|
| Name | Analog-to-digital converter (ADC) |
| Function | Converts a continuously variable or analog signal into a multilevel digital signal without altering its essential content |
| Examples | Sine waves, waveforms representing human speech, signals from a conventional television camera |
| Output | Defined levels or states, usually a power of two (2, 4, 8, 16, etc.) |
| Sampling Rate | Must be at least twice its frequency to accurately depict the original signal |
| Use Cases | Digital audio and video, telephone modems, TV tuner cards, digital audio workstations, digital imaging systems, data communication |
| Resolution | Indicates the number of discrete values the converter can produce, usually expressed as audio bit depth |
| Time-Stretch ADC | Digitizes wide bandwidth analog signals by time-stretching the signal prior to digitization |
| Digital-to-Analog Converter | DAC, or D/A, performs the reverse function of converting digital to analog |
| DAC Use Cases | Music players, televisions, mobile phones, military radar systems, electric motor speed control, LED lamp dimming, high-end hi-fi systems |
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What You'll Learn
Analog-to-digital conversion (ADC)
The output of the analog-to-digital converter has defined levels or states, which are almost always a power of two, such as 2, 4, 8, or 16. The simplest digital signals have only two states and are called binary. All whole numbers can be represented in binary form as strings of ones and zeros. The rate of new values is called the sampling rate or sampling frequency of the converter. The Nyquist-Shannon sampling theorem states that a faithful reproduction of the original signal is only possible if the sampling rate is higher than twice the highest frequency of the signal.
The process of analog-to-digital conversion involves sampling the input wave of the analog signal, which varies continuously like a sine wave. This is done by measuring the amplitude of a continuous-time signal at discrete instants, converting the continuous signal into a discrete one. There are three sampling methods: ideal sampling, natural sampling, and time-stretch analog-to-digital conversion. Ideal sampling is an instantaneous sampling of pulses from the analog signal, but it is difficult to implement in practice. Natural sampling is a more practical method, where the pulse has a finite width and retains the shape of the analog signal. Time-stretch analog-to-digital conversion (TS-ADC) digitizes a very wide bandwidth analog signal that cannot be digitized by a conventional ADC. It uses a photonic preprocessor to time-stretch the signal, slowing it down and compressing its bandwidth.
ADC technology is widely used to process video signals into digital bit streams for transmitting visual images and voice communications. It is also used in digital audio and video to reduce aliasing, or the production of a false frequency. A sample rate that is too low will result in distortion or aliasing, while a rate that is too high will use more storage and processing resources than necessary.
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Digital-to-analog conversion (DAC)
DACs rely on a constant reference voltage or current to create their output value. A multiplying DAC takes a variable input voltage or current as a conversion reference. Modern high-speed DACs have an interleaved architecture, in which multiple DAC cores are used in parallel. Their output signals are combined in the analog domain to enhance performance. The combination of signals can be performed in the time domain or the frequency domain. The number of possible output levels a DAC is designed to reproduce is usually stated as the number of bits it uses. For example, a 1-bit DAC is designed to reproduce 2 levels, while an 8-bit DAC is designed for 256 levels.
DACs are widely used in modern communication systems, enabling the generation of digitally defined transmission signals. They can be found in digital speakers, sound cards, and high-end hi-fi systems. In high-end hi-fi systems, DACs convert the digital output of a CD player into an analog line-level output that can be fed into an amplifier to drive speakers.
There are several DAC architectures, and the suitability of a DAC for a particular application is determined by figures of merit, including resolution and maximum sampling frequency. DACs can degrade a signal, so it is important to select a DAC with insignificant errors in terms of the application. Due to the complexity and the need for precisely matched components, most DACs are implemented as integrated circuits (ICs).
The R-2R Ladder DAC is a type of DAC that contains only two values of resistors: R and 2R. This makes it easier to select and design more accurate resistors. It overcomes the disadvantages of a binary-weighted resistor DAC, producing an analog output almost equal to the digital input.
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Sampling rate
The sampling rate, also known as the sample rate or sampling frequency, is the frequency at which an analog signal is sampled during analog-to-digital conversion. It is measured in samples per second or Hertz (Hz).
The sampling rate is one of the most important characteristics to consider when selecting an analog-to-digital converter (ADC). The rate at which an analog signal is sampled affects the accuracy of the digital representation of the original signal. The higher the sampling rate, the more accurately the digital signal will represent the original analog signal.
The Nyquist-Shannon sampling theorem sets a theoretical lower limit for the sampling rate, known as the Nyquist rate or Nyquist frequency. According to the theorem, to capture all possible frequencies in the original signal, the sampling rate must be at least twice the highest frequency component in the continuous signal. In practice, however, the sampling rate must often be much greater than twice the highest frequency for the signal to be accurately converted. This is known as the Nyquist criterion.
For example, humans can hear sounds in the 20-20,000 Hz range, so music and other sound waves are often sampled at 44.1 kHz or 48 kHz, which is slightly above the Nyquist frequency. In some cases, audio is recorded at even higher rates, such as 88.2 kHz or 96 kHz, in a process known as oversampling, which improves resolution and the signal-to-noise ratio.
In digital video, the sampling rate is defined as the frame rate or field rate, and the image sampling frequency refers to the repetition rate of the sensor integration period. Video digital-to-analog converters operate in the megahertz range, with higher-resolution outputs requiring higher sampling rates.
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Nyquist-Shannon theorem
The Nyquist-Shannon theorem, also known as the Nyquist-Shannon sampling theorem, is a critical principle in digital signal processing. It provides a sufficient condition for the sampling and reconstruction of band-limited signals, helping to prevent aliasing in waveforms. Aliasing refers to the distortion or unwanted noise that may destroy the integrity of a signal during its conversion from analog to digital.
The theorem was first introduced by Harry Nyquist of Bell Labs in 1928, and later by Claude Shannon in the late 1940s. The theorem is also associated with E. T. Whittaker, who published a similar concept in 1915, and was cited by Shannon in his work. Due to these contributions, the theorem is sometimes referred to as the Whittaker-Shannon or Whittaker-Nyquist-Shannon theorem.
The Nyquist-Shannon theorem states that to accurately reconstruct a continuous analog signal from its digital samples, the sampling rate must be at least twice the highest frequency present in the signal. This principle is essential for faithfully reproducing signals and avoiding aliasing. If the sampling rate is lower than twice the frequency of the signal, information may be permanently lost, and perfect reconstruction becomes impossible.
The theorem has been applied in various fields, including audio, digital image sampling, and FM radio signals. It is also relevant to the creation of continuous time analog waveforms from digital, discrete samples, as described by Shannon's sampling theorem. Shannon's theorem states that a digital waveform must be updated at least twice as fast as the signal's bandwidth for accurate generation.
The Nyquist-Shannon theorem has been further explored and expanded upon by various researchers, including Blackman and Tukey, who cited Nyquist's work in their sampling theorem of information theory. The theorem has also been generalized for non-uniform sampling, where samples are not taken at equally spaced intervals, providing additional flexibility in signal processing and reconstruction.
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Digital signal processing
Analog-to-digital converters (ADCs) are used to convert analog signals into digital signals. This process is known as analog-to-digital conversion (ADC). It involves changing a continuously variable, or analog, signal into a multilevel digital signal without altering its essential content.
DSP is used to process real-world signals such as voice, audio, video, temperature, pressure, or position. These signals are detected by analog products and are then converted into digital form by ADCs. The digital signals are then fed into a DSP, which captures and processes the digitised information. This occurs at very high speeds.
The information processed by a DSP can be used by a computer to control things like security, telephone, home theatre systems, and video compression. DSP can be used to enhance or manipulate signals to improve their quality or provide information that cannot be sensed by humans, such as echo cancellation for cell phones or computer-enhanced medical images. DSP is also used in applications such as audio and speech processing, sonar, radar, biomedical engineering, and seismology.
DSP can involve linear or nonlinear operations. Linear operations consist of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. Nonlinear signal processing is implemented in the time, frequency, and spatio-temporal domains. DSP also uses mathematical techniques such as Fourier Transform (FT) to convert time-domain signals into frequency-domain representations.
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Frequently asked questions
Analog-to-digital converters (ADCs) are used to convert analog signals to digital signals.
An analog-to-digital converter changes a continuous analog signal in terms of time and amplitude to a digital signal that is discrete in time and amplitude.
The Nyquist-Shannon sampling theorem states that a faithful reproduction of the original signal is only possible if the sampling rate is higher than twice the highest frequency of the signal.
Analog signals vary continuously over time, while digital signals are discrete and non-continuous. Digital signals are also more efficient for propagation and are easier for electronic circuits to distinguish from noise.
Analog-to-digital conversion is used in digital audio and video to reduce aliasing or the production of false frequencies. For example, in a telephone call, the caller's voice is converted into an analog electrical signal by a microphone, and then the analog signal is converted to digital for transmission.
