Low complexity, low latency resampling of asynchronously sampled signals

Abhijit, Patki; Thiagarajan, Ganesan

doi:10.1109/spcom.2016.7746686

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…Only the method described in [21] focused both on processing and group delay. The Akima algorithm provides low-complexity and fast calculation.…”

Section: Research Gapmentioning

confidence: 99%

“…The Akima algorithm provides low-complexity and fast calculation. It requires a low number of future samples, equivalent to the group delay, namely three for standard implementation, and 2 for the improved version in [21]. These advantages must be paid for by a high OSR, which determines how many more samples are needed compared to the Nyquist rate.…”

Section: Research Gapmentioning

confidence: 99%

“…These advantages must be paid for by a high OSR, which determines how many more samples are needed compared to the Nyquist rate. A study of the relation between OSR and resampling error was only found regarding Akima [21] and in [17]. Both articles recommend for their low complexity algorithm an OSR > 4.…”

Section: Research Gapmentioning

confidence: 99%

“…Truncating the signal results in reconstruction errors, especially at the margins of the available sampled time window. The WSI error can be reduced by multiplying the input samples with a window function, e.g., a Blackman Harris window, as suggested by [21].…”

Section: Mathematical Implementations and Limitationsmentioning

confidence: 99%

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Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method

Jedermann¹

2023

Algorithms

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Wireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite impulse response (FIR) filters, entail a group delay of 10 s of samples, which is not acceptable regarding the typical interval of wireless sensors of seconds or minutes. The purpose of this paper is to verify the feasibility of single-delay resampling, i.e., the algorithm resamples the data without waiting for future samples. A new method to parametrize Kriging interpolation is presented and compared with two variants of Lagrange interpolation in detailed simulations for the resulting prediction error. Kriging provided the most accurate resampling in the group-delay scenario. The single-delay scenario required almost double the OSR to achieve the same signal-to-noise ratio (SNR). An OSR between 1.8 and 3.1 was necessary for single-delay resampling, depending on the required SNR and signal distortions in terms of jitter, missing samples, and noise. Kriging was the least noise-sensitive method. Especially for signals with missing samples, Kriging provided the best accuracy. The simulations showed that single-delay resampling is feasible, but at the expense of higher OSR and limited SNR.

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“…Only the method described in [21] focused both on processing and group delay. The Akima algorithm provides low-complexity and fast calculation.…”

Section: Research Gapmentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

Section: Mathematical Implementations and Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method

Jedermann¹

2023

Algorithms

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“…Most of these methods are difficult to perform with asynchronous so it is desirable to convert them to uniform sampled data using resampling algorithms. For example, in [32], two algorithms were proposed for an asynchronous-to-synchronous resampling scheme. Signal resampling is one of the steps of the data-preprocessing process, used in many applications, such as prognosis and health management (e.g., [33]) and state identification (e.g., [34]).…”

Section: Related Workmentioning

confidence: 99%

Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

Escobet

Quevedo

et al. 2020

ASI

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This paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.

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