Fast Fourier Transform Based NDT Approach for Depth Detection of Hidden Defects Using HTS rf-SQUID

Rostami, Behnoush; Shanehsazzadeh, Faezeh; Fardmanesh, Mehdi

doi:10.1109/tasc.2018.2841927

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…In our study, the introduction of FFT solves the problem that binary sequences are too sparse. In addition, many studies have shown that CNN is a very suitable and effective neural network for processing spectrum features in speech recognition (O’Leary, et al, 2016; Rostami, Shanehsazzadeh, & Fardmanesh, 2018). Therefore, we abandoned the time-consuming RNNs in favor of convenient and efficient CNNs.…”

Section: Methodsmentioning

confidence: 99%

LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model

Wang

Zhao

et al. 2022

Preprint

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Long non-coding RNAs (LncRNAs) serve a vital role in regulating gene expressions and other biological processes. Differentiation of lncRNAs from protein-coding transcripts helps researchers dig into the mechanism of lncRNA formation and its downstream regulations related to various diseases. Previous works have been proposed to identify lncRNAs, including traditional bio-sequencing and machine learning approaches. Considering the tedious work of biological characteristic-based feature extraction procedures and inevitable artifacts during bio-sequencing processes, those lncRNA detection methods are not always satisfactory. Hence, in this work, we presented lncDLSM, a deep learning-based framework differentiating lncRNA from other protein-coding transcripts without dependencies on prior biological knowledge. lncDLSM is a helpful tool for identifying lncRNAs compared with other biological feature-based machine learning methods and can be applied to other species by transfer learning achieving satisfactory results. Further experiments showed that different species display distinct boundaries among distributions corresponding to the homology and the specificity among species, respectively. An online web server is provided to the community for easy use and efficient identification of lncRNA, available at http://39.106.16.168/lncDLSM.

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Section: Methodsmentioning

confidence: 99%

LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model

Wang

Zhao

et al. 2022

Preprint

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“…One method to measure the noise level of the electrode pairs is to place them in saline electrolyte and measure the potential difference between them in an electromagnetically shielded environment [7][8][9][10]. The power spectral density of the output potential difference contains valuable data to determine the noise attributed to the electrodes.…”

Section: Introductionmentioning

confidence: 99%

Development of an enhanced porosity AgAgCl reference electrode with improved stability

et al. 2019

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We have developed a new low-cost method for fabrication of an enhanced porosity ultra-low noise silver-silver chloride (Ag-AgCl) reference electrode with dramatically high stability, useful in precise electrochemical and biomedical measurements. Low noise level and high stability are the most important features when evaluating reference electrode for example when used as a half-cell at electrochemical cells. The noise was measured for bare Ag-AgCl electrode pairs in a saline electrolyte without any junction. The noise spectral density for such measurements increases at low frequencies due to flicker (1/f) noise. Thermal noise from the real part of the electrode impedance is always lower than 1/f noise and is shown as offset voltage in noise spectrum. Electrode noise is highly correlated to its offset voltage. Moreover, the variance of the potential difference in our measurements is correlated to the noise level of electrodes. In this paper, offset voltage spectrum and the variance of data while doing measurements are proposed for identification of noise in our electrodes. The results presented suggest a new low-cost process for fabrication of enhanced porosity reference electrodes with nanovolt noise level at 1 Hz, low enough for electrochemical and biomedical measurements.

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NDE Data Correlation Using Encoder–Decoder Networks with Wavelet Scalogram Images

Dargahi

Lattanzi²,

Azari³

2022

J Nondestruct Eval

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Fast Fourier Transform Based NDT Approach for Depth Detection of Hidden Defects Using HTS rf-SQUID

Cited by 9 publications

References 17 publications

LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model

LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model

Development of an enhanced porosity AgAgCl reference electrode with improved stability

NDE Data Correlation Using Encoder–Decoder Networks with Wavelet Scalogram Images

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