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Sallen-Key Filters
Published in S. A. Pactitis, Active Filters, 2018
Several parameters are used to characterize a filter’s performance. The most commonly specified parameter is frequency response. When given a frequency-response specification, the designer must select a filter design that meets these requirements. This is accomplished by transforming the required response to a normalized low-pass specification having a cutoff of 1 rad/s. This normalized response is compared with curves of normalized low-pass filters that also have a 1 rad/s cutoff. After a satisfactory low-pass filter is determined from the curves, the tabulated normalized element values of the chosen filter are transformed or denormalized to the final design.
Force-System Resultants and Equilibrium
Published in Richard C. Dorf, The Engineering Handbook, 2018
filters, low-pass, high-pass, and band-pass, are shown. A fourth filter type, called a notch filter or bandelimination filter, is not shown because this type of filter exhibits an asymptotic response that is 0 dB at all frequencies, with a notch (area of high attenuation) around the corner frequency ωc. Most filter design procedures concentrate on designing low-pass filters and use standard transformations to convert these low-pass filters to high-pass, band-pass, or notch filters. (A good explanation of how to do this can be found in chapter 6 of Sedra and Brackett [1978].)
Filter Fundamentals
Published in T. Deliyannis, Yichuang Sun, J.K. Fidler, Continuous-Time Active Filter Design, 2019
T. Deliyannis, Yichuang Sun, J.K. Fidler
In filter design, the specifications are usually given in terms of the frequency response. However, in cases of pulse transmission, it is useful to know the response of the filter as a function of time, i.e., its transient response.
The impact of global on-line information provision on transport networks and how random early detection can help
Published in Transportmetrica B: Transport Dynamics, 2019
H. Grzybowska, S. Willmott, S. T. Waller
As described in the introduction, IP networks suffer from very similar oscillation phenomena as those observed in the previous section. One of the most common and successful ways of combating these oscillations is the Random Early Detection (RED) family of algorithms (see Sharma, Virtamo, and Lassila 2002; Trinh and Molnar 2004 for example). In communications networks, the algorithm can absorb short-lived congestion bursts by controlling the time constants used by the low-pass filter for computing the average delay. In general, a low-pass filter passes signals with a frequency lower than a certain minimum threshold and attenuates signals with frequencies higher than a certain maximum threshold. Depending on the filter design the exact frequency response is different. Low-pass filters provide a smoother form of a signal, removing the short-term fluctuations, and leaving the longer term trend.
Performance Assessment Using a Field Test of a Short-Period Monitoring System: Tun Bridge Case Study
Published in Structural Engineering International, 2019
Mosbeh R. Kaloop, Kyoung-Ho Kim, Mohamed Elsharawy, Fawzi Zarzoura, Jong Wan Hu
To reduce the effects of noise on the dynamic data, a low-pass filter was applied. The moving average (MA) filter was used to filter the strain and displacement measurements, and the finite impulse response (FIR) filter with a Kaiser window was also applied to smooth the acceleration measurements. MATLAB® was used to utilize and implement these filters. In this study, a 70-point MA filter was found to be suitable to eliminate the noise from strain and deflection measurements. The Wayne’s code of the MA filter was used to delete the shift between the measurements and filter signals.33 In addition, the FIR low-pass filter was used by considering a 50-point order and cut-off for frequencies lower than the natural frequency of the bridge; furthermore, the Kaiser window was used with a 51-point order and three parameters.30,31 The magnitude and phase responses of the FIR filter design are presented in Fig. 6. More details on the design of MA and FIR filters can be found in Refs. [16, 31].
Robust estimation method of tire torsional resonance frequency to detect decrease in tire inflation pressure
Published in Vehicle System Dynamics, 2022
The frequency ranges of the most significant dynamic parts of the vehicle are listed in Table 2 [4]. Since the torsional vibration of the tire occurs in the range of 40–50 Hz, the frequency range around the tire resonance frequency can be isolated using a band-pass filter with appropriate cut-off frequencies. In this paper, the lower and upper cut-off frequencies were set to be 30 and 60 Hz. The band pass filter was designed with a Butterworth 12th order digital filter, using filter design and analysis tool (FDATool) of ‘MATLAB’ program [9]. Through applying the band-pass filter to the wheel speed, the frequency range around the tire resonance frequency can be acquired and the others can be attenuated in the frequency domain.