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The extraction of phase information is crucial in moiré tomography for achieving accurate results. In this paper, a method for extracting phase information of moiré fringes based on the Morlet continuous wavelet transform is introduced. A detailed exposition of the theoretical deduction and algorithmic procedure of this method is provided. And then, to validate the feasibility and applicability of this approach, four flow fields are conducted as test objects for experiments. Based on that, the phase results provided by the Morlet continuous wavelet transform are compared with those obtained by the reported techniques such as Fourier transform and Gabor wavelet transform. It is evident that Morlet continuous wavelet transform demonstrates superior accuracy and smoothness, which proves the reliability of this method. In summary, the method presented in this study probably offers an effective method with broad applications.
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http://dx.doi.org/10.1364/AO.511443 | DOI Listing |
Front Artif Intell
August 2025
School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India.
Introduction: In recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) and its variants have been shown to be effective in the diagnosis of cardiovascular disease from ElectroCardioGram (ECG) signals. In the case of ECG as a one-dimensional signal, 1-D CNNs are deployed, whereas in the case of a 2D-represented ECG signal, i.e.
View Article and Find Full Text PDFJ Neurosci Methods
September 2025
Department of CSE, Indian Institute of Information Technology Vadodara- International Campus Diu (IIITV-ICD), 362520, Diu, India. Electronic address:
The Electroencephalogram (EEG) is a vital physiological signal for monitoring brain activity and understanding neurological capacities, disabilities, and cognitive processes. Analyzing and classifying EEG signals are key to assessing an individual's reactions to various stimuli. Manual EEG analysis is time-consuming and labor-intensive, necessitating automated tools for efficiency.
View Article and Find Full Text PDFThis paper presents a novel multiscale signal processing framework for power quality disturbance (PQD) and cyber intrusion detection in smart grids, combining Non-Subsampled Contourlet Transform (NSCT), Split Augmented Lagrangian Shrinkage Algorithm (SALSA), and Morphological Component Analysis (MCA). A key innovation lies in an adaptive weighting mechanism within NSCT's directional sub bands, enabling dynamic energy redistribution and enhanced representation of both low-frequency anomalies (e.g.
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, China.
Remote sensing object detection (RSOD) is highly challenging due to large variations in object scales. Existing deep learning-based methods still face limitations in addressing this challenge. Specifically, reliance on stride convolutions during downsampling leads to the loss of object information, and insufficient context-aware modeling capability hampers full utilization of object information at different scales.
View Article and Find Full Text PDFPLoS One
September 2025
Universiti Putra Malaysia, Serdang, Malaysia.
In this research, we have proposed a novel anomaly detection algorithm for processing hyperspectral images (HSIs), called the Graph Attention Network-Beta Wavelet Graph Neural Network-based Hyperspectral Anomaly Detection (GAN-BWGNN HAD). This algorithm treats each pixel as a node in a graph, where edges represent pixel correlations and node attributes correspond to spectral features. The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision.
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