Discrete time signals are sequences of values or samples that are defined at distinct intervals. Examples include digital audio signals, where sound is sampled at regular time intervals, and digital images, which consist of pixel values sampled at specific grid points. Other examples include time-series data like stock prices recorded at hourly intervals or temperature readings taken daily. Each of these signals is represented as a series of discrete points rather than a continuous waveform.
A discrete control system is a type of control system that operates on discrete-time signals, meaning it processes data at distinct intervals rather than continuously. In such systems, the input and output signals are sampled at specific time points, allowing for analysis and control using digital methods. Discrete control systems are commonly used in digital computers and embedded systems, where algorithms can be implemented to manage and optimize system performance effectively. Examples include digital PID controllers and various automation systems in industrial applications.
The linear discrete time interval is used in the interpretation of continuous time and discrete valued: Quantized signal.
discrete fourier transformer uses digital signals whereas the fast fourier transform uses both analog and digital.
A discrete data control system is a type of control system that processes distinct, separate values or signals, rather than continuous data. It typically involves the use of digital signals to represent information at specific intervals, allowing for precise control and decision-making. Such systems are commonly used in applications like automation, robotics, and digital signal processing, where actions are triggered based on specific discrete inputs. Examples include digital controllers that manage processes like on/off switches and feedback loops in various engineering fields.
The Discrete Fourier Transform (DFT) is used in digital signal processing to analyze the frequency content of discrete signals. It converts time-domain signals into their frequency-domain representations, enabling the identification of dominant frequencies, filtering, and spectral analysis. By efficiently transforming data, the DFT facilitates various applications, including audio and image processing, communication systems, and data compression. Its computational efficiency is further enhanced by the Fast Fourier Transform (FFT) algorithm, making it practical for real-time processing tasks.
A discrete control system is a type of control system that operates on discrete-time signals, meaning it processes data at distinct intervals rather than continuously. In such systems, the input and output signals are sampled at specific time points, allowing for analysis and control using digital methods. Discrete control systems are commonly used in digital computers and embedded systems, where algorithms can be implemented to manage and optimize system performance effectively. Examples include digital PID controllers and various automation systems in industrial applications.
No, discrete signals cannot have fractional periods. In signal processing, a period is defined as the smallest positive integer ( N ) such that ( x[n+N] = x[n] ) for all integer values of ( n ). Since the signal is discrete, it can only repeat at integer multiples of the period. Fractional periods would imply a non-integer number of samples between repetitions, which is not possible in discrete signals.
A continuous signal is one that is measured over a time axis and has a value defined at every instance. The real world is continuous (ie. analog). A discrete signal is one that is defined at integers, and thus is undefined in between samples (digital is an example of a discrete signal, but discrete does not have to imply digital). Instead of a time axis, a discrete signal is gathered over a sampling axis. Discrete signals are usually denoted by x[k] or x[n], a continuous signal is x(t) for example. Laplace transforms are used for continuous analysis, Z-transforms are used for discrete analysis. Fourier transforms can be used for either.
discrete & continuous
analog (continuous) and discrete (discontinuous)
The Laplace transform is used for analyzing continuous-time signals and systems, while the Z-transform is used for discrete-time signals and systems. The Laplace transform utilizes the complex s-plane, whereas the Z-transform operates in the complex z-plane. Essentially, the Laplace transform is suited for continuous signals and systems, while the Z-transform is more appropriate for discrete signals and systems.
Analog signals are continuous while digital signals are discrete
FDM stnds for frequency division multiplexing and it is used only in case of analog signals because analog signals are continuous in nature and the signal have frequency. TDM-stands for time division multiplexing and it is used only in case of digital signals because digital signals are discrete in nature and are in the form of 0 and 1s. and are time dependent.
Two forms of electrical signals are analog signals, which vary continuously over time, and digital signals, which represent data as discrete values. Analog signals can take on any value within a range, while digital signals have specific voltage levels to represent binary data.
Examples of the periodic signals include exponential and sinusoidal signal.
A signal is bounded if there is a finite value such that the signal magnitude never exceeds , that is for discrete-time signals, or for continuous-time signal (Source:Wikipedia)
The linear discrete time interval is used in the interpretation of continuous time and discrete valued: Quantized signal.