Improved-Speed Parameter Tuning of Deconvolution Algorithm

Bako, T.B.; Dabóczi, T.

doi:10.1109/tim.2016.2534668

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…where " X sw in (31) is given by (24). Therefore, in compliance with the interpretation given in Subsection IV-A, the MSE of the SW estimator at high output SNR is nearly the MMSE (12) of the optimal solution (11), as expected.…”

Section: Mse Analysis Of the Proposed Estimatorsupporting

confidence: 78%

“…Evidently, (24) has the same structure as the optimal solution (11), but uses the estimated output SNR rather than the true output SNR. Hence, our proposed estimator is a nearly optimal fully data-driven approximation of (11) when X[k] is unknown.…”

Section: A the Self-wiener Estimator In The High Snr Regimementioning

confidence: 99%

“…Some of these methods also require tuning parameters, such as the λ parameter in [19], controlling the balance between noise reduction and filtration errors, caused by the deviation from the ideal inverse filter when λ is non-zero. For a detailed discussion on this topic, see [24].…”

Section: Introductionmentioning

confidence: 99%

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"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals

Weiss¹,

Nadler²

2020

Preprint

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We consider the fundamental problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequencydomain, where it tries to mimic the optimal unrealizable Wienerlike filter as if the unknown deterministic signal were known. This leads to a threshold-type regularized estimator, where the threshold value at each frequency is found in a fully datadriven manner. We provide an analytical performance analysis, and derive approximate closed-form expressions for the residual Mean Squared Error (MSE) of our proposed estimator in the low and high Signal-to-Noise Ratio (SNR) regimes. We show analytically that in the low SNR regime our method provides enhanced noise suppression, and in the high SNR regime it approaches the performance of the optimal unrealizable solution. Further, as we demonstrate in simulations, our solution is highly suitable for (approximately) bandlimited or frequency-domain sparse signals, and provides a significant gain of several dBs relative to other methods in the resulting MSE.

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Section: Mse Analysis Of the Proposed Estimatorsupporting

confidence: 78%

Section: A the Self-wiener Estimator In The High Snr Regimementioning

confidence: 99%

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"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals

Weiss¹,

Nadler²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, several studies have been done to characterize and remove the effect of a system or fixture between external ports. The time-domain input and output waveforms of an overall system were measured to achieve a transfer function using a deconvolution method with parameter tuning [21][22][23][24]. A de-embedding method was used to extract the design parameters of an antenna system or to present the S-parameters of a fixture [25,26].…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Pulse Waveform Correction for Pulsed Electric Field Measurement in Microwave Cable with Frequency-Dependent Loss

Jeong

Kim

Ryu

2021

J Electromagn Eng Sci

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This paper presents a method that corrects pulse waveforms distorted by the frequency-dependent loss of microwave cables in measuring pulsed electric fields (PEFs). Because the distortion resulting from the microwave cable disrupts accurate PEF measurements, the distorted pulse should be corrected for precise PEF effect testing. The proposed correction method is achieved by a transfer function that is determined by ABCD parameters calculated from the scattering parameters of the cable. A 10-m microwave cable is tested to validate the proposed method, where the input pulse is a 2-ns sine pulse of a single cycle. Here, the output pulse, scattering parameters, and cable resistance are measured. These measurement results are used to represent the transfer function in MATLAB for the proposed correction method. The test results show that the corrected pulse obtained from the transfer function has an error of 4.5% in the peak-to-peak voltage and an error of 0.8% in the bipolar pulse width compared to the reference input pulse. The errors of PEF measurement decrease dramatically by using the proposed correction method. Moreover, the correction method is validated for various pulse durations, pulse shapes, and cable types.

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“…If the relation between input and output is linear, it is a problem of deconvolution and there are many studies including super-resolution [4]. Subtractive deconvolution [5] may be applied if available data are impulsive.…”

Section: Introductionmentioning

confidence: 99%

Hybrid backward simulator for determining causal heater state with resolution improvement of measured temperature data through model conformation

2017

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<p class="Abstract">We are developing a backward simulator, which determines the unknown system input from the system output by using a system model. However, its processing time would increase enormously if the simulation model requires the multiple case branching, which is typical for backward simulations. In some target applications, we can use forward simulation processing in the backward data flow with significant reduction of processing time. This paper shows an example of such application to determine system input of heater operation from measured data of room temperature. Although the resolution of measurement restricts the performance of the simulation result, we also used the model to improve the resolution of measured data and show its effect to simulation. Furthermore, we show the result of reduction of noise caused by quantizing LSB jitters.</p>

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Improved-Speed Parameter Tuning of Deconvolution Algorithm

Cited by 10 publications

References 25 publications

"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals

"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals

Time-Domain Pulse Waveform Correction for Pulsed Electric Field Measurement in Microwave Cable with Frequency-Dependent Loss

Hybrid backward simulator for determining causal heater state with resolution improvement of measured temperature data through model conformation

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