Unified Filtered Operator Analysis of Experimental Switching Transients: Validation with Janda et al. (2023) Spark Discharge Dataset

We apply the unified filtered operator to experimental spark discharge data collected by Janda et al. (2023), featuring oscilloscope voltage measurements during electrical switching events. By comparing classical derivative analysis with the unified operator, we demonstrate that the proposed method selectively isolates physically significant transients—such as sparks and voltage jumps—while suppressing noise and steady-state artifacts. This work illustrates the unified operator's ability to provide clear, event-based signal detection in highly dynamic systems.The author acknowledges the use of OpenAI’s language model for assistance in drafting certain sections of this worklDisclaimer on Reference Accuracy:<br>The author acknowledges the use of OpenAI’s language model for assistance in drafting certain sections of this work. Due to the nature of generative language models, some reference years or citation details may be imprecise or outdated. All critical sources have been verified manually; however, readers are advised to cross-check dates if exact historical accuracy is required.

Publication Details

Journal
Figshare
Published
2025-01-01
DOI
https://doi.org/10.6084/m9.figshare.29782802
Primary Topic
Electrostatic Discharge in Electronics
Type
article
Field-Weighted Citation Impact
0.00
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article

Unified Filtered Operator Analysis of Experimental Switching Transients: Validation with Janda et al. (2023) Spark Discharge Dataset

Figshare
Electrostatic Discharge in Electronics
article

Unified Filtered Operator Analysis of Experimental Switching Transients: Validation with Janda et al. (2023) Spark Discharge Dataset

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Abstract

We apply the unified filtered operator to experimental spark discharge data collected by Janda et al. (2023), featuring oscilloscope voltage measurements during electrical switching events. By comparing classical derivative analysis with the unified operator, we demonstrate that the proposed method selectively isolates physically significant transients—such as sparks and voltage jumps—while suppressing noise and steady-state artifacts. This work illustrates the unified operator's ability to provide clear, event-based signal detection in highly dynamic systems.The author acknowledges the use of OpenAI’s language model for assistance in drafting certain sections of this worklDisclaimer on Reference Accuracy:<br>The author acknowledges the use of OpenAI’s language model for assistance in drafting certain sections of this work. Due to the nature of generative language models, some reference years or citation details may be imprecise or outdated. All critical sources have been verified manually; however, readers are advised to cross-check dates if exact historical accuracy is required.

Figshare
Openalex Percentile: Top 54%
Electrostatic Discharge in Electronics
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Unified Filtered Operator Analysis of Experimental Switching Transients: Validation with Janda et al. (2023) Spark Discharge Dataset · Figshare (2025) | TGRS Research Map | TGRS