A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet by Muhammad Waqas Ahmed, Moneerah Alotaibi, Sultan Refa Alotaibi, Dina Abdulaziz Alhammadi, Asaad Algarni, Ahmad Jalal, Jeongho Cho

“…We employ Azimuthal Average Feature Extraction, Haar Wavelet Transform, and Maximally Stable Extremal Regions (MSER) to capture a rich set of features encompassing texture, frequency, and shape information. …”
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A Synchronized Hybrid Brain-Computer Interface System for Simultaneous Detection and Classification of Fusion EEG Signals by Dalin Yang, Trung-Hau Nguyen, Wan-Young Chung

“…Feature extraction is performed by the wavelet transform, which is extracted by the features in the frequency and time domains. …”
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Identifying etiologies of heart failure using non-contrast cardiac magnetic resonance imaging: cine imaging, T1 and T2 mapping, and texture analysis for T1 mapping by Yasuo Amano, Yasuyuki Suzuki, Xiaoyan Tang, Chisato Ando

“…Vertical run length nonuniformity, vertical gray level nonuniformity (vGLNU), wavelet energy LL(3) and HH (4) on T1 mapping were estimated at the mid-septal segment using open-access software. …”
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Dynamics‐Oriented Underwater Mechanoreception Interface for Simultaneous Flow and Contact Perception by Hua Zhong, Yaxi Wang, Jiahao Xu, Yu Cheng, Sicong Liu, Jia Pan, Wenping Wang, Zheng Wang

“…Therefore, by evaluating the oscillation dynamics with tailored wavelet‐based indices, DOUMI can distinguish between contact‐ and flow‐induced oscillations at each receptor unit with 90.5% accuracy. …”
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Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model by Suvashisa Dash, Mohammed Siddique, Satyasis Mishra, Demissie J. Gelmecha, Sunita Satapathy, Davinder Singh Rathee, Ram Sewak Singh

“…The IFF-FLICM algorithm is utilized to accurately segment the brain’s magnetic resonance (MR) images to identify the tumor regions. The Mexican hat wavelet transform is employed for feature extraction from the segmented images. …”
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NIGWO-iCaps NN: A Method for the Fault Diagnosis of Fiber Optic Gyroscopes Based on Capsule Neural Networks by Nan Lu, Huaqiang Zhang, Chunmei Dong, Hongtao Li, Yu Chen

“…To address these challenges, this paper proposes a new fault diagnostic model that performs a fault diagnosis of gyroscopes using the enhanced capsule neural network (iCaps NN) optimized by the improved gray wolf algorithm (NIGWO). The wavelet packet transform (WPT) is used to construct a two-dimensional feature vector matrix, and the deep feature extraction module (DFE) is added to extract deep-level information to maximize the fault features. …”
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Descriptores espacio-frecuencia para identificación automática de patrones de textura en productos textiles utilizando aprendizaje supervisado by Arley Bejarano Martínez, Andres Felipe Calvo Salcedo, Carlos Alberto Henao Baena

“…En la etapa de caracterización se utilizan descriptores como la transformada Wavelet, la transformada de Fourier y la adaptación de la Transformada corta de Fourier en espacio para la generación de un vector de características, a este vector se le computa los momentos estadísticos como Kurtosis, sesgo, media y desviación estándar. …”
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Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of Concrete by Koki Shoda, Jun Younes Louhi Kasahara, Qi An, Atsushi Yamashita

“…To address this challenge, we propose a novel method that enhances defect discrimination accuracy by integrating statistical processing and physical property analysis within a machine learning framework designed for force signal characteristics. Our method employs wavelet transformation to convert short-duration force signals into high-resolution time-frequency features, capturing their non-stationary behavior in detail. …”
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Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer by Shrey S. Sukhadia, Christoph Sadée, Olivier Gevaert, Shivashankar H. Nagaraj

“…Results Our ML‐based radiogenomic modeling identified specific imaging features—wavelet, three‐dimensional local binary patterns, and logarithmic sigma of gray‐level variance—as predictive indicators for high (1) vs. low (0) gene expression of pivotal NSCLC‐related genes: SLC35C1, BCL2L1, and MAPK1. …”
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A comparative study on different machine learning approaches with periodic items for the forecasting of GPS satellites clock bias by Longjiang Song, Jiahao Liu, Leilei Wang, Ziyi Wang, Yibo Yuan

“…Specifically, we utilize precision satellite clock bias data from the International GNSS Service forecast experiments and assess the predictive effects of various models including backpropagation neural network (BPNN), wavelet neural network (WNN), long short-term memory (LSTM), and gated recurrent units (GRUs). …”
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Resnet-1DCNN-REA bearing fault diagnosis method based on multi-source and multi-modal information fusion by Xu Chen, Wenbing Chang, Yongxiang Li, Zhao He, Xiang Ma, Shenghan Zhou

“…The one-dimensional vibration signals were converted into two-dimensional time-frequency images by continuous wavelet transform (CWT), and then they were fed into the Resnet network for fault diagnosis. …”
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An Improved ConvNeXt With Multimodal Transformer for Physiological Signal Classification by Jiajian Zhu, Yue Feng, Qichao Liu, Hong Xu, Yuan Miao, Zhuosheng Lin, Jia Li, Huilin Liu, Ying Xu, Fufeng Li

“…The proposed ICMT-Net utilizes continuous wavelet transform to partition 5-second ECG and WPS segments into spectrograms. …”
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Predicting Screening Efficiency of Probability Screens Using KPCA-GRNN with WP-EE Feature Reconstruction by Qingtang Chen, Yijian Huang

“…Subsequently, empirical mode decomposition energy entropy (EMD-EE), variational mode decomposition energy entropy (VMD-EE), and wavelet packet energy entropy (WP-EE) features are extracted from the time series vibration signals, and three single input energy entropy-generalized regressive neural network (GRNN) prediction accuracy models are established and compared. …”
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Dynamic Response of Graphitic Targets with Tantalum Cores Impacted by Pulsed 440-GeV Proton Beams by Pascal Simon, Philipp Drechsel, Peter Katrik, Kay-Obbe Voss, Philipp Bolz, Fiona J. Harden, Michael Guinchard, Yacine Kadi, Christina Trautmann, Marilena Tomut

“…Using advanced post-processing techniques, such as fast Fourier transformation and continuous wavelet transformation, different pressure wave components are identified and their contribution to the overall dynamic response of a two-body target and their failure mode are discussed. …”
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Mechanical Properties of Bump-Prone Coal with Different Porosities and Its Acoustic Emission-Charge Induction Characteristics under Uniaxial Compression by Xin Ding, Xiaochun Xiao, Xiangfeng Lv, Di Wu, Jun Xu

“…Both of them originated from cracks and belong to homologous signals, crack development bound to be accompanied by stress wavelet, not necessarily free charge; meanwhile, charge pulse being emerged means there must be cracks interaction and the acoustic emission signals are generated prior to charge induction.…”
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A 1.5D Spectral Kurtosis-Guided TQWT Method and Its Application in Bearing Fault Detection by Xiong Zhang, Ming Zhang, Shuting Wan, Rujiang Hao, Yuling He, Xiaolong Wang

“…In this paper, the signal is processed by the tunable Q-factor wavelet transform (TQWT), the midfrequency band of the signal is tightly divided by selecting different Q-values, and the 1.5D spectral kurtosis defined in frequency domain is used to select the optimal subband. …”
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Altered Brain Activity and Effective Connectivity within the Nonsensory Cortex during Stimulation of a Latent Myofascial Trigger Point by Xinglou Li, Meiling Luo, Yan Gong, Ning Xu, Congcong Huo, Hui Xie, Shouwei Yue, Zengyong Li, Yonghui Wang

“…The data investigated the latent MTrP-induced changes in brain activity and effective connectivity (EC) within the nonsensory cortex. The parameter wavelet amplitude (WA) was used to describe cortical activation, EC to show brain network connectivity, and main coupling direction (mCD) to exhibit the dominant connectivity direction in different frequency bands. …”
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Estimating the safe mud weight window for drilling operations through pre-stack seismic inversion, a case study in one of the Southwestern Iran oil fields by Mohammad Sadegh Mahmoudian, Yousef Shiri, Ahmad Vaezian

“…The pre-stack seismic inversion is conducted by constructing a velocity model and utilizing angle gather aggregation, statistical wavelet extraction, and initial model creation. Seismic inversion analysis revealed that the studied sandstone reservoir layer, known as the Ghar formation, possesses lower P-wave or S-wave acoustic impedance (P-/S-AI) and density compared to adjacent layers, likely due to the presence of porosity and potentially intra-formational fluids. …”
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The Theory and Applications of Hölder Widths by Man Lu, Peixin Ye

“…The fact that Hölder widths are smaller than the known widths implies that the nonlinear approximation represented by deep neural networks can provide a better approximation order than other existing approximation methods, such as adaptive finite elements and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi></mrow></semantics></math></inline-formula>-term wavelet approximation. In particular, we show that Hölder widths for Sobolev and Besov classes, induced by deep neural networks, are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">O</mi><mo>(</mo><msup><mi>n</mi><mrow><mo>−</mo><mn>2</mn><mi>s</mi><mo>/</mo><mi>d</mi></mrow></msup><mo>)</mo></mrow></semantics></math></inline-formula> and are much smaller than other known widths and entropy numbers, which are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">O</mi><mo>(</mo><msup><mi>n</mi><mrow><mo>−</mo><mi>s</mi><mo>/</mo><mi>d</mi></mrow></msup><mo>)</mo></mrow></semantics></math></inline-formula>.…”
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Trends and Spatiotemporal Patterns of the Meteorological Drought in the Ili River Valley from 1961 to 2023: An SPEI-Based Study by Su Hang, Alim Abbas, Bilal Imin, Nijat Kasim, Zinhar Zunun

“…The SPEI drought index, along with Sen’s trend analysis, the Mann–Kendall test, the cumulative departure method, and wavelet analysis, were employed to assess drought patterns. …”
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