Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm by Kai Zhang, Yujun Zhang, Kun You, Ying He, Qiankun Gao, Guohua Liu, Chungui He, Yibing Lu, Boqiang Fan, Qixing Tang, Wenqing Liu

“…But in the conventional methods for noise reduction and baseline correction, such as wavelet transform, derivative, interpolation, polynomial fitting, and so forth, the basic functions of these algorithms, the number of decomposition layers, and the way to reconstruct the signal have to be adjusted according to the characteristics of different components in the transmission spectra. …”
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Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme by Xiaohui Yu, Meng Wang, Chaolie Ning, Kun Ji

“…Subsequently, we employ a wavelet-based technique to generate synthetic aftershock accelerograms that align with the spectrum of the mainshock, using the mainshock ground motion as a reference input. …”
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A multiresolution approach with method-informed statistical analysis for quantifying lymphatic pumping dynamics by Mohammad S. Razavi, Katarina J. Ruscic, Elizabeth G. Korn, Marla Marquez, Timothy T. Houle, Dhruv Singhal, Lance L. Munn, Timothy P. Padera

“…Here, we address this unmet need by integrating near-infrared fluorescence lymphangiography imaging with an innovative analytical workflow that combines data acquisition, signal processing, and statistical analysis to integrate traditional peak-and-valley analysis with advanced wavelet time-frequency analyses. Variance component analysis was used to evaluate the drivers of variance attributable to each experimental variable for each lymphangiography measurement type. …”
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Early prediction of Li-ion cell failure from EIS derived from current–voltage time series by M T Wilson, V Farrow, C J Dunn, L Cowie, M J Cree, J Bjerkan, A Stefanovska, J B Scott

“…Analysis of voltage and current time series data using both parametric (equivalent circuit model) and non-parametric (wavelet-based analysis) approaches allowed us to successfully reconstruct the EIS data. …”
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WCMU-net: An Effective Method for Reducing the Impact of Speckle Noise in SAR Image Change Detection by Yu Duan, Kaimin Sun, Wangbin Li, Jinjiang Wei, Song Gao, Yingjiao Tan, Wanghui Zhou, Jun Liu, Junyi Liu

“…The model initially employs discrete wavelet decomposition to extract high-frequency bands for noise reduction. …”
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A Pervasive Approach to EEG-Based Depression Detection by Hanshu Cai, Jiashuo Han, Yunfei Chen, Xiaocong Sha, Ziyang Wang, Bin Hu, Jing Yang, Lei Feng, Zhijie Ding, Yiqiang Chen, Jürg Gutknecht

“…After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. …”
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EEG alpha/beta features as a biomarker for quantifying pain in patients with lumbar disk herniation by Rumei Li, Wanqi Shao, Shumei Zhao, Lingli Wang, Chao Yu, Lanying Liu, Kuiying Yin

“…Following the spectral analysis of the EEG signals with continuous wavelet transform, power ratios were extracted for four frequency bands (θ, α, β and γ). …”
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An Experimental Investigation on the Relationship between MS Frequency Response and Coal and Gas Outburst by Quanjie Zhu, Qingsong Li, Yu Feng, Xianwei Heng, Shaoquan Li, Tao Yang

“…Based on these laws, CGOB experiments were carried out using a large CGOB physical simulation system with a MS monitoring system. Notching filter and wavelet packet transform technique were used in the denoising and feature extraction of six typical MS events (signals). …”
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A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection by Xiaoshuai Cao, Shaojie Zheng, Jincan Zhang, Wenna Chen, Ganqin Du

“…Methods A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study. First, the Discrete Wavelet Transform (DWT) is applied to perform a five-level decomposition of the raw EEG signals, from which time–frequency and nonlinear features are extracted from the decomposed sub-bands. …”
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Multiwavelength Variability Analysis of the Blazar PKS 0727-11: An ~ 168 day Quasiperiodic Oscillation in the γ-Ray by Yuncai Shen, Tingfeng Yi, Vinit Dhiman, Lisheng Mao, Liang Dong

“…It is the first time that periodic variations have been detected in this source, and further supported by other methods: weighted wavelet z -transform, phase dispersion minimization, REDFIT, autoregressive integrated moving average model, and structure function analysis. …”
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Using apparent diffusion coefficient maps and radiomics to predict pathological grade in upper urinary tract urothelial carcinoma by Rile Nai, Kexin Wang, Shuai Ma, Zuqiang Xi, Yaofeng Zhang, Xiaodong Zhang, Xiaoying Wang

“…Compared with the mean ADC values, the ADC-based radiomics model, which incorporates features such as log-sigma-1-0-mm-3D_glcm_ClusterProminence and wavelet-LLL_firstorder_10Percentile, obtained a significantly greater AUC in the training set (AUC: 1.000, 95% CI: 1.000–1.000, p < 0.001), and a trend towards statistical significance in the test set (AUC: 0.786, 95% CI: 0.651–0.921, p = 0.071). …”
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The use of low-density EEG for the classification of PPA and MCI by Panteleimon Chriskos, Panteleimon Chriskos, Kyriaki Neophytou, Christos A. Frantzidis, Christos A. Frantzidis, Jessica Gallegos, Alexandros Afthinos, Chiadi U. Onyike, Argye Hillis, Panagiotis D. Bamidis, Kyrana Tsapkini, Kyrana Tsapkini

“…Utilizing the Relative Wavelet Entropy method, we derived (i) functional connectivity, (ii) graph theory metrics and extracted (iii) various energy rhythms. …”
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Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders by Upeka De Silva, Samaneh Madanian, Sharon Olsen, John Michael Templeton, Christian Poellabauer, Sandra L Schneider, Ajit Narayanan, Rahmina Rubaiat

“…From these tasks, conventional speech features (such as fundamental frequency, jitter, and shimmer), advanced digital signal processing–based speech features (such as wavelet transformation–based features), and spectrograms in the form of audio images were analyzed. …”
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Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals&#x2019; Electroencephalography Signals by Illia Pidvalnyi, Anna Kostenko, Oleksandr Sudakov, Dmytro Isaev, Oleksandr Maximyuk, Oleg Krishtal, Olena Iegorova, Ievgen Kabin, Zoya Dyka, Steffen Ortmann, Peter Langendorfer

“…Hjorth&#x2019;s parameters and Daubechies discrete wavelet transform coefficients were found to be the most informative features of EEG data. …”
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Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces by Xin Deng, Boxian Zhang, Nian Yu, Ke Liu, Kaiwei Sun

“…After that, this work considers that the 1-D convolution of EEGNet can be explained by a special Discrete Wavelet Transform (DWT), and the depthwise convolution of EEGNet is similar to the Common Spatial Pattern (CSP) algorithm. …”
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A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition by Jiawen Li, Guanyuan Feng, Chen Ling, Ximing Ren, Xin Liu, Shuang Zhang, Leijun Wang, Yanmei Chen, Xianxian Zeng, Rongjun Chen

“…Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.e., delta, theta, alpha, beta, and gamma, from EEG signals, followed by the acquisition of multi-entropy features, including Spectral Entropy (PSDE), Singular Spectrum Entropy (SSE), Sample Entropy (SE), Fuzzy Entropy (FE), Approximation Entropy (AE), and Permutation Entropy (PE). …”
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Biparametric MRI-based radiomics for noninvastive discrimination of benign prostatic hyperplasia nodules (BPH) and prostate cancer nodules: a bio-centric retrospective cohort study by Yangbai Lu, Runqiang Yuan, Yun Su, Zhiying Liang, Hongxing Huang, Qu Leng, Ang Yang, Xuehong Xiao, Zhaoqi Lai, Yongxin Zhang

“…A total of 1130 radiomic features were extracted from each MRI sequence, including shape-based features, gray-level histogram-based features, texture features, and wavelet features. The clinical model was constructed using logistic regression analysis. …”
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A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling by Hao Yang, Yubin Zhai, Mengkun Zheng, Tan Wang, Dongliang Guo, Jianhui Liang, Xincheng Li, Xianliang Liu, Mingtao Jia, Rui Zhang

“…For each round, two binary classifier models are trained by features extracted by the proposed method and the empirical mode decomposition (EMD)–auto regressive (AR) spectrum method, fast Fourier transform (FFT) and continuous wavelet transform (CWT), respectively; the classification precision, recall ratio, accuracy and F1 ratio are obtained on the testing set by contrasting the overall performances of the five-round cross-validation, and the proposed method is proved to be more effective in noise reduction and significant feature extraction, which is able to improve the accuracy and efficiency of binary classification for piston pin wear identification.…”
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Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy by Jing Xue, Menghan Wu, Jing Zhang, Jiayang Yang, Guannan Lv, Baojun Qu, Yanping Zhang, Xia Yan, Xia Yan, Jianbo Song, Jianbo Song

“…Key features associated with RP included D1cc, T1_wavelet-LLL_glcm_MCC, D2cc, and T2_original_firstorder_90 Percentile.ConclusionsThe MRI-based delta radiomics model shows significant promise in predicting RP severity in cervical cancer patients following radiotherapy, with enhanced predictive performance when combined with clinical features.…”
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Historical snow measurements in the central and southern Apennine Mountains: climatology, variability, and trend by V. Capozzi, F. Serrapica, A. Rocco, C. Annella, C. Annella, G. Budillon

“…In addition, using a cross-wavelet analysis, we found a close in-phase linkage on a decadal timescale between the investigated snow indicators and the Eastern Mediterranean teleconnection Pattern. …”
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