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Signal Processing
Published in Stephen Horan, Introduction to PCM Telemetering Systems, 2018
Filtering is not always performed in hardware. With the advent of digital signal processing devices as standard parts in system design, more signal processing operations are now performed in software. This has two major advantages in system design: the part count can be significantly reduced and the programming can be changed. The latter is difficult to do with fixed components. The software filter can be as complicated as the code space and processor speed allow. The software filter can be a simple low pass filter to remove noise from the signal to complicated predictive analysis techniques such as Kalman filters or Box-Jenkins analysis. The filtering process in hardware can only be a causal design. With software, a noncausal filter can be realized by applying a suitable delay in the processing. In the filter realization, a causal filter would only use data from the current sample and previous samples. By using a selective delay, noncausal filters can use not only the current sample and past samples but they can also employ future samples. When this is done, the output is not immediate and has a delay.
Signal Processing
Published in Stephen Horan, Introduction to PCM Telemetering Systems, 2017
The filtering process in hardware can only be a causal design. A suitable delay in the processing software produces a noncausal filter. In the filter realization, a causal filter only uses data from the current sample and previous samples. By using a selective delay, noncausal filters use not only the current sample and past samples, but they can also employ future samples beyond the current output sample point. When this is done, the output is not immediate and has a delay. The advantage is improved processing filtering improvement.
Optical Filter Synthesis
Published in Kenichi Iga, Yasuo Kokubun, Encyclopedic Handbook of Integrated Optics, 2018
Physically realizable filters are stable and causal. A causal filter produces no output before an input occurs. A stable filter means that for any finite energy input, the output will also be finite energy. For FIR filters, causality means that the impulse response has only nonzero terms in z−n, where n ≤ 0 are physically realizable and stability is guaranteed by the finite sum of square magnitudes of the impulse response coefficients.
Bilinear observer-based robust adaptive fault estimation for multizone building VAV terminal units
Published in Journal of Building Performance Simulation, 2023
Mona Subramaniam A., Tushar Jain, Joseph J. Yamé
The bilinear observer-based robust adaptive fault estimator (RAFE) is proposed as where ‘’ represents the convolution operator, denotes the state estimate, the output estimate. Here, it is worth noticing that the unknown fault is viewed as a time-varying parameter whose estimate is obtained by the adjusting law given by in (8), where the to-be-designed parameters are constant matrices L and G, and the impulse response of a causal filter. Let the state estimation error be defined as , then from (7) and (8) the error dynamics reads
Low-cut Filter Frequency Quantification and Its Influence on Inelastic Response Spectrum
Published in Journal of Earthquake Engineering, 2022
Yabin Chen, Longjun Xu, Pangang Wu
It can be found in Fig. 19 that the low-cut frequencies determined by the study of Guan et al. (2004) changes the acceleration signal most (the second row panel). It may be attributed to the Chebyshev II causal filter or the convergent criteria they used. On the contrary, the PGA of records filtered with the low-cut frequencies quantified by the proposed method has marginal change after filtering. The result from the proposed method and Guan et al. (2004) implies that the quantification of the filter low-cut frequency for the Chebyshev II filter should be cautiously designed. By contrast, the solid and empty squares denote the PGA ratio of the records filtered with the low-cut frequencies obtained by PEER and Xu et al. (2018), respectively. And they are very similar to those from the proposed method. It reveals the fact that the acausal filter either in the frequency or time domain for the consistency of acceleration is superior to that for the causal filter.
Optimal Corner Frequency in High-Pass Filtering of Strong Ground Motions and Its Effect on Seismic Intensity
Published in Journal of Earthquake Engineering, 2021
Mohammad Reza Falamarz-Sheikhabadi, A. Zerva
Fig. 10 presents the effect of the filter order on the acceleration, velocity and displacement time series of the x-component of the processed SGM recorded at the Erzincan-Meteorologij Mudurlugu station, high-pass filtered with fc = 0.25 Hz and two different values of the filter order, i.e. n = 3 and 8. An approximate negative polarity can be observed in the displacement and velocity time series due to the change of the filter order, due to the use of a causal filter (Section 2). In addition, the waveform is partly modified, which indicates that the selection of the optimal filter order to prevent the excessive elimination of the low-frequency signals is also important.