Dataset Information

Auxiliary Diagnostic Signal for Deep-Level Detection.

ABSTRACT: We propose and demonstrate that temperature-dependent curve-fitting error values of the Schottky diode I-V curve in the forward regime can be an auxiliary diagnostic signal as the temperature-scan Capacitance DLTS (CDLTS) signals and helps to work time-efficiently with high accuracy when using the Laplace Transform (LT)-DLTS or Isothermal Capacitance transient spectroscopy (ICTS) method. Using Be-doped GaAs showing overlapping DLTS signals, we verify that the LT-DLTS or ICTS analysis within a specific temperature range around the characteristic temperature Tpeak coincides well with the results of the CDLTS and Fourier Transform DLTS performed within the whole temperature range. In particular, we found that the LT-DLTS signals appeared intensively around Tpeak, and we confirmed it with the ICTS result. The occurrence of the curve fitting error signal is attributed to the relatively increased misfit error by the increased thermal emission from the deep-level trap in the case near the Tpeak, because the applied transport model excludes defect characteristics.

SUBMITTER: Ahn IH

PROVIDER: S-EPMC10648165 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Auxiliary Diagnostic Signal for Deep-Level Detection.

Ahn Il-Ho IH Lee Dong Jin DJ Kim Deuk Young DY

Nanomaterials (Basel, Switzerland) 20231029 21

We propose and demonstrate that temperature-dependent curve-fitting error values of the Schottky diode I-V curve in the forward regime can be an auxiliary diagnostic signal as the temperature-scan Capacitance DLTS (CDLTS) signals and helps to work time-efficiently with high accuracy when using the Laplace Transform (LT)-DLTS or Isothermal Capacitance transient spectroscopy (ICTS) method. Using Be-doped GaAs showing overlapping DLTS signals, we verify that the LT-DLTS or ICTS analysis within a sp ...[more]

PMID: 37947711

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Project description:Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensive, error-prone, and becomes unfeasible with large data volumes. Therefore, machine-driven algorithms are increasingly applied to various bioacoustic signal identification challenges. Nevertheless, biologists still have major difficulties trying to transfer existing animal- and/or scenario-related machine learning approaches to their specific animal datasets and scientific questions. This study presents an animal-independent, open-source deep learning framework, along with a detailed user guide. Three signal identification tasks, commonly encountered in bioacoustics research, were investigated: (1) target signal vs. background noise detection, (2) species classification, and (3) call type categorization. ANIMAL-SPOT successfully segmented human-annotated target signals in data volumes representing 10 distinct animal species and 1 additional genus, resulting in a mean test accuracy of 97.9%, together with an average area under the ROC curve (AUC) of 95.9%, when predicting on unseen recordings. Moreover, an average segmentation accuracy and F1-score of 95.4% was achieved on the publicly available BirdVox-Full-Night data corpus. In addition, multi-class species and call type classification resulted in 96.6% and 92.7% accuracy on unseen test data, as well as 95.2% and 88.4% regarding previous animal-specific machine-based detection excerpts. Furthermore, an Unweighted Average Recall (UAR) of 89.3% outperformed the multi-species classification baseline system of the ComParE 2021 Primate Sub-Challenge. Besides animal independence, ANIMAL-SPOT does not rely on expert knowledge or special computing resources, thereby making deep-learning-based bioacoustic signal identification accessible to a broad audience.

Dataset Information

Auxiliary Diagnostic Signal for Deep-Level Detection.

Publications

Auxiliary Diagnostic Signal for Deep-Level Detection.

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