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Magnitude response of a low pass filter with 6 dB per octave or 20 dB per decade roll-off. Measuring the frequency response typically involves exciting the system with an input signal and measuring the resulting output signal, calculating the frequency spectra of the two signals (for example, using the fast Fourier transform for discrete signals), and comparing the spectra to isolate the ...
For some filter classes, such as the Butterworth filter, the insertion loss is still monotonically increasing with frequency and quickly asymptotically converges to a roll-off of 20n dB/decade, but in others, such as the Chebyshev or elliptic filter the roll-off near the cut-off frequency is much faster and elsewhere the response is anything ...
It is commonly applied to sonar, radar, and laser systems, and to other applications, such as in spread-spectrum communications (see chirp spread spectrum). This signal type is biologically inspired and occurs as a phenomenon due to dispersion (a non-linear dependence between frequency and the propagation speed of the wave components).
The frequency response can be classified into a number of different bandforms describing which frequency bands the filter passes (the passband) and which it rejects (the stopband): Low-pass filter – low frequencies are passed, high frequencies are attenuated. High-pass filter – high frequencies are passed, low frequencies are attenuated.
Analog discrete-time signal processing is a technology based on electronic devices such as sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. This technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals. [7]
Equalization was also applied to correct the response of the transducers, for example, a particular microphone might be more sensitive to low frequency sounds than to high frequency sounds, so an equalizer would be used to increase the volume of the higher frequencies (boost), and reduce the volume of the low frequency sounds (cut).
Signals are converted from time or space domain to the frequency domain usually through use of the Fourier transform. The Fourier transform converts the time or space information to a magnitude and phase component of each frequency. With some applications, how the phase varies with frequency can be a significant consideration.
A few examples of joint sensing-communications applications are listed below. Intelligent Transport Systems (Vehicle-to-vehicle Communications) [6] [7] Commercial Flight Control [8] Communications & Military Radar [9] Remote Medical Monitoring and Wearable Medical Sensors [10] [11] High Frequency Imaging and Communications [12] [13] Li-Fi and ...