Practical considerations on optimising multistage decimation and interpolation processes

Zhu, Xiaojun; Wang, Yonghao; Hu, Wei; Reiss, Joshua D.

doi:10.1109/icdsp.2016.7868581

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…Combining (16), and comparing ( 15) and ( 20), the inequality (22) demonstrates that m mf ≤ m lf for all f s .…”

Section: B For Large F S Policymentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Jun Shi. Conversion (FSRC), requiring a fractional conversion rate, which makes it challenging to design filters when the numerator and denominator of the fraction are large ( [15], [16]). The second is Arbitrary Sample Rate Conversion (ASRC), implemented by combining variable fractional delay components and FSRC with smaller numerator and denominator ( [17], [18]).…”

Section: Introductionmentioning

confidence: 99%

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A Sampling Rate Selecting Algorithm for the Arbitrary Waveform Generator

et al. 2019

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The arbitrary waveform generator is now commonly used to generate waveforms in many fields. The Nyquist sampling theorem only provides an open interval for the sampling rate selection, but how to determine a specific sampling rate within this open interval has not been discussed. In practice, the sample rate conversion is adopted to convert the arbitrarily selected sample rate to a safe one which keeps the mirror frequency out of a fixed passband of an anti-aliasing filter in an arbitrary waveform generator whose sample rate is variable. To select a safe sample rate with low computational cost and memory requirements for conversion, an algorithm, with fractional sample rate conversion in mind, is proposed in this paper to guarantee the safety of the target sample rate and the bound of resource cost. The experimental results show that not only the computational cost and memory requirements of sampling rate conversion but also the frequency stability and harmonics of the generated signal in the proposed algorithm overcome the method that arbitrary converts the sampling rate to a fixed maximum one.INDEX TERMS Arbitrary waveform generator, sampling rate conversion, memory depth.

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“…Combining (16), and comparing ( 15) and ( 20), the inequality (22) demonstrates that m mf ≤ m lf for all f s .…”

Section: B For Large F S Policymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Sampling Rate Selecting Algorithm for the Arbitrary Waveform Generator

et al. 2019

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“…In terms of building the filter, the first and foremost important part is the filter designing method that has to be chosen from the different filter design and estimation methods that can be broadly categorized as finite impulse response (FIR) filters, infinite impulse response (IIR) filters, Nyquist filters, multirate filter design, multistage filter design, and special multirate filters (Losada, 2008). Out of these filters, multistage filters can provide 10 times more efficiency than the rest of single-stage filters (Zhu et al, 2016). To benefit from these efficiency gains, we base our method on the multistage filter design, in which the first stage applies optimization and the second stage performs interpolation.…”

Section: Mops Filtermentioning

confidence: 99%

Multistage optimization filter for trend‐based short‐term forecasting

Zafar

Kellard

Vinogradov

2021

Journal of Forecasting

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A new method is proposed to estimate the long-term seasonal component by a multistage optimization filter with a leading phase shift (MOPS). It can be utilized to provide better predictions in case of the seasonal component autoregressive (SCAR) model, where data are filtered/decomposed into trend and remainder components and then forecasts for constituent components generated separately and later combined. This reinforces the importance of trend estimation filtering/decomposition methods, which are scarce and only few methods, primarily wavelet decomposition, have improved upon the forecasts generated by statistical linear models. We contribute to the literature by introducing a new trend estimation method, and the forecast results are compared with the most popular trend estimation methods, such as frequency filters, wavelet decomposition, empirical mode decomposition (EMD), and Hodrick-Prescott (HP) filter, through their performance in generating shortterm forecasts for day-ahead electricity prices. Our method for trend estimation performs better in terms of providing short-term forecasts as compared with some well-known methods, and the best forecast, according to the Diebold and Mariano (1995) test, is obtained by using our MOPS filter with annual trend period length.

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“…The solution to (15) is real. Since the values of the set {d k } need to be integers in practice, the searching algorithm in [33] has been utilized in this paper. The algorithm in [33] proposes to create a list of candidate sets utilizing the heuristic that the optimal set {d k } is in descending order, and then evaluates each candidate set in (15) to find the minimum solution.…”

Section: A Multistage Decimationmentioning

confidence: 99%

Integrating Signals With High Sampling Rates for Transmission Line Protection in Digital Substations

Hohn

Nordström

2021

IEEE Trans. Power Delivery

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Travelling wave based protection functions require significantly higher sampling rates than protection functions based on time-domain superimposed quantities or fundamental frequency phasors. Integrating these high sampling rates in digital substations leads to a significant increase of the communication load of process-level networks and causes high computational cost for centralized protection systems. This paper builds on a distributed signal processing approach, which allocates the filtering operations among standalone merging units (SAMU). In particular, the paper presents a decimation filter design to integrate signals with high sampling rates at the process-level. Thereby, signal sequences with high, medium and low sampling rates are provided according to the requirements of the respective protection function. The decimation filter structure is optimized with respect to time delay by the Remez exchange algorithm and with respect to computational cost by a multistage approach and a sparse filter design algorithm. In addition, the filter response is verified against the accuracy constraints defined in IEC 61869-6 and IEC 61869-13. The paper shows that it is feasible to distribute the filtering operations among SAMUs, while keeping the time delay below the maximum allowable processing delay of 2 ms.

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Practical considerations on optimising multistage decimation and interpolation processes

Cited by 9 publications

References 14 publications

A Sampling Rate Selecting Algorithm for the Arbitrary Waveform Generator

A Sampling Rate Selecting Algorithm for the Arbitrary Waveform Generator

Multistage optimization filter for trend‐based short‐term forecasting

Integrating Signals With High Sampling Rates for Transmission Line Protection in Digital Substations

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