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In MATLAB, discrete-time sinusoidal signals can be generated using the sin function along with a time vector. For example, you can create a time vector t using 0:Ts:duration, where Ts is the sampling period and duration is the total time. The sinusoidal signal can be defined as x = A * sin(2 * pi * f * t + phi), where A is the amplitude, f is the frequency, and phi is the phase shift. You can then plot the signal using the plot function to visualize the discrete-time sinusoidal waveform.
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What are the examples of discrete time signals?
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.
Can discrete signals have fractional periods?
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.
Why there is need to study discrete time signals?
Studying discrete time signals is essential because they are fundamental to digital signal processing, which is widely used in modern technology, including telecommunications, audio and video processing, and data compression. Discrete signals allow for easier manipulation, storage, and transmission using digital systems, making them more efficient and reliable. Additionally, analyzing these signals aids in understanding sampling theory, filtering, and system stability, which are crucial for designing effective digital systems.
How do you convulate two pulse signals?
To convolve two pulse signals, you can use the convolution integral if they are continuous signals or the discrete convolution sum for discrete signals. Essentially, you flip one of the signals, shift it across the other, multiply the overlapping values, and integrate (for continuous) or sum (for discrete) the results at each shift. This process combines the two signals, resulting in a new signal that represents the area of overlap at each point in time. The convolution operation captures how the shape of one signal affects the other.
What is Fast Fourier Transform in matlab?
The Fast Fourier Transform (FFT) in MATLAB is an efficient algorithm used to compute the discrete Fourier transform (DFT) and its inverse. It allows for the transformation of a time-domain signal into its frequency-domain representation, facilitating analysis and processing of signals. MATLAB provides built-in functions like fft for performing FFT, making it easy to analyze signal frequencies, perform filtering, and apply other signal processing techniques. The FFT significantly reduces computational complexity compared to directly calculating the DFT, especially for large datasets.
What is the difference between a continuous signal and a discrete signal?
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.
What are examples of periodic signals?
Examples of the periodic signals include exponential and sinusoidal signal.
Difference between z transform and laplace transform?
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.
Why FDM is for analog signals and TDM is for digital signals?
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.
What are two forms of electrical signals?
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.
What is the difference between Discrete-time Fourier transform and Discrete Fourier transform?
The Discrete Fourier Transform (DFT) is a specific mathematical algorithm used to compute the frequency spectrum of a finite sequence of discrete samples. In contrast, the Discrete-time Fourier Transform (DTFT) represents a continuous function of frequency for a discrete-time signal, allowing for the analysis of signals in the frequency domain over an infinite range. Essentially, the DFT is a sampled version of the DTFT, applied to a finite number of samples, whereas the DTFT provides a broader, continuous frequency representation of the signal.
What is mean by bounded input and bounded output?
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)
