Wavelet Transform Research Articles (Page 1)

Accurate short-term forecasting of natural phenomena-such as temperature, electricity demand, and wind-power output-is critical for reliable planning in climate science and energy management. Forecasting accuracy can be enhanced by developing advanced feature engineering techniques, particularly through the use of wavelet transformation. This paper introduces a novel method to feature construction for short-term time series forecasting of natural phenomena, which is based on the use of the Stationary Wavelet Transform (SWT) and multi-family wavelets. The developed method allows for the extraction of signal characteristics while preserving the original data dimensionality, so each time series observation is supplemented with detailed coefficients from the wavelets. This approach includes the application of several wavelet families (e.g., Daubechies, Symlets, Coiflets, Haar, and Meyer), which increases the informativeness of the time series and improves the forecasting accuracy of neural network models, in particular LSTM. Experimental evaluation on three open datasets-meteorological variables, residential electricity demand, and wind-farm output. Relative to identically configured LSTMs trained on raw observations, the wavelet-augmented models cut error consistently: MAE by 13.6%, MSE by 17.7%, RMSE by 9.5%, and SMAPE by 13.9%. These improvements confirm that multi-family SWT features offer a dataset-agnostic route to higher short-term forecasting accuracy.

Abstract As a newly emerging pillar industry in the medical field, telemedicine relies on the Internet and other transmission networks to complete the transmission of patient information and obtain the consultation results of telemedicine experts, which greatly improves the guarantee of patients’ lives. At the same time, the secure transmission of medical data is also one of the important standards of telemedicine, because any attack or theft caused by the loss of small details, changes, or information leakage will lead to the direction of treatment, resulting in serious consequences. Therefore, a new digital watermarking scheme for medical images is proposed in this paper, which combines image encryption and watermarking protection techniques. In the watermarking aspect, the Canny operator is used to obtain the self-embedding watermark image which is highly related to the plaintext, and the watermark embedding is realized by NSCT(Nonsubsampled Contourlet Transform), DWT(Discrete Wavelet Transform) transform, Fourier transform, and other operations. An efficient S-Box is constructed by using the generated chaotic sequence and the improved Z-transform, which fully disrupts the arrangement position of image pixels, and uses two-way diffusion to complete the distribution of plaintext information in ciphertext to realize image encryption. Simulation results and related tests show that the algorithm can successfully encrypt color and grayscale medical images, and has good robustness and ability to resist various external attacks.

ABSTRACT Accurate wind power prediction is essential for efficient dispatch and enhancing grid security. However, traditional methods face challenges with the non-stationary characteristics of wind energy and noise interference. This paper proposes a composite model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Dual-Tree Complex Wavelet Transform (DTCWT), a Residual Convolutional Attention Module (RCAM), Bidirectional Long Short-Term Memory (BiLSTM), and a Dynamic Weighted Multi-Head Attention (DWMHA) module. First, CEEMDAN decomposes the raw signal into high- and low-frequency components, after which DTCWT denoises the high-frequency portion. Subsequently, RCAM extracts features and BiLSTM captures long-term dependencies. Finally, the DWMHA module enables adaptive weight adjustment and feature fusion, enhancing the model’s adaptability to dynamic time series. Experiments at a Chinese wind farm show the proposed model achieves Root Mean Square Error (RMSE) values of 1.6937 MW, 5.4286 MW, and 7.6632 MW for one, six, and twelve-step-ahead predictions. Compared to the sub-optimal Informer model, our model reduces the RMSE by 3.31%, 2.83%, and 1.12% over the same horizons. These results significantly outperform competing models, confirming its strong potential for practical applications.

The advanced development of the Internet of Things (IoT) technology has made it possible to monitor health conditions continuously and make decisions based on data in individual nursing practice. Nevertheless, biomedical signals are multidimensional, nonstationary and complex, which creates great challenges to traditional data analytics. To deal with this, we suggest an Advance Quantum Wavelet Transformation (AQWT)-based smart analytics model, which incorporates IoT-based health tracking with quantum-inspired wavelet operations to provide accurate and responsive nursing care. Physiological parameters (heart rate, blood oxygen level, body temperature and respiratory activity) are gathered in real-time by the wearable IoT devices employed in the framework. To break down and examine such signals within a variety of resolutions, AQWT is used, which allows detecting small anomalies and enhances the interpretability of patient health status. The data that have been processed are then combined with machine learning models to produce custom nursing advice, early risk anticipation and patient-centered care planning. Through experimental analysis, it can be shown that the AQWT-based framework can provide more accurate predictions, minimize the computational load by efficiently compressing data and enable privacy-preserving information transmission over IoT networks. This paper presents the opportunities of AQWT-based intelligent analytics to change personalized nursing care into a proactive, accurate and patient-centered health model.

Portable electroencephalogram (EEG) systems are increasingly used in healthcare due to their user-friendly and wearable design. However, accurate diagnosis can be hindered by electrooculogram (EOG) artifacts-low-frequency, high-amplitude signals caused by eye blinks. These artifacts are especially problematic in single-channel (SCL) EEG systems, necessitating robust artifact removal techniques. This study proposes an automated method for eliminating EOG artifacts from EEG signals using a Fixed Frequency Empirical Wavelet Transform (FF-EWT) integrated with a finely tuned Generalized Moreau Envelope Total Variation (GMETV) filter. The approach effectively separates artifact sources from the single-channel EEG by identifying contaminated components at the decomposition stage using kurtosis (KS), dispersion entropy (DisEn), and power spectral density (PSD) metrics. These components are then removed using the GMETV filter. The method was validated on both synthetic and real EEG datasets, demonstrating its capability to suppress EOG artifacts while preserving essential low-frequency EEG information. Performance evaluation revealed substantial improvements using the FF-EWT+GMETV technique, with lower Relative Root Mean Square Error (RRMSE) and higher Correlation Coefficient (CC) on synthetic data, and improved Signal-to-Artifact Ratio (SAR) and Mean Absolute Error (MAE) on real EEG recordings. This advancement offers strong potential for brain signal analysis, serving as an effective preprocessing tool in both clinical and research settings.

ABSTRACT The detection of small objects in remote sensing imagery presents significant challenges, mainly attributed to intricate background complexities, inadequate feature extraction performance, and constrained receptive field dimensions. Moreover, under constrained computational resources, improving detection accuracy while reducing the model's computational complexity and parameter count has become an urgent issue. In response to these challenges, a novel lightweight model specifically designed for small object detection, named DWT‐YOLO, is proposed in this study. By introducing the Discrete Wavelet Transform (DWT) combined with convolutional down‐sampling operations, the model effectively expands the receptive field and enhances feature extraction capabilities. Simultaneously, an innovative Wavelet Convolution Fusion module (WCFM) is designed, which improves multi‐scale feature fusion and cross‐domain information association, enhancing the feature representation for small objects while keeping model complexity relatively low. Additionally, the model incorporates a Spatial‐Channel Decoupling Down‐sampling (SCDown) module, which effectively reduces redundant parameters and improves detection accuracy. We first validated the effectiveness of DWT‐YOLO on the RSOD dataset and then further assessed its small object detection performance on the VisDrone2019 dataset. The proposed model demonstrates a 5.4% enhancement in detection accuracy compared to the baseline model. The model has only 5.8M parameters, and it outperforms several advanced models in detection performance. Experimental results demonstrate that DWT‐YOLO not only achieves significant improvements in small object detection tasks but also exhibits lightweight characteristics, providing new solutions and technical insights for this field. The implementation source code of the proposed method is publicly accessible at the following repository: http://github.com/zzgithubFly/DWT‐YOLO .

ABSTRACT Although Sustainable Development Goals (SDGs) give significance to alleviating energy poverty (EP), the former literature is considerably silent on how renewable energy investments (REI), which are vital in providing clean and reliable energy and ensuring a sustainable development path, impact EP in China, one of the most prominent countries in renewable energy. Therefore, the primary objective of this study is to uncover time‐, frequency‐ and quantile‐based interactions between REI and EP in China from 2004 M1 to 2020 M6 using various EP indicators by utilising Wavelet Transform Coherence (WTC) and Quantile‐on‐Quantile Regression (QQR) as a baseline estimator, while Quantile Regression (QR) is utilised as robustness checks. The empirical results denote that (i) there is a time‐ and frequency‐dependent interaction between REI and EP. (ii) REI can alleviate EP by fostering access to primary energy, electricity and clean energy technologies. (iii) The EP‐alleviating impact of REI through increasing access to electricity is more dominant. (iv) Robustness checks denote that empirical findings are robust. Chinese policymakers could prevalently use REI as an effective tool for alleviating EP and achieving SDG‐7.

ABSTRACT Medical image analysis is highly susceptible to noise during acquisition and transmission, significantly degrading diagnostic accuracy. This study proposes a robust Attention-Based Squeeze-Excitation Residual Autoencoder (ASE-RAE) for denoising medical images corrupted by Gaussian and Salt-and-Pepper (SP) noise. The network integrates residual skip connections in both encoder and decoder paths, combined with Squeeze-and-Excitation (SE) blocks and a Convolutional Block Attention Module (CBAM) to improve channel and spatial feature representation. The model is benchmarked against traditional denoising techniques, including Non-Local Means Filter (NLMF), Median Filter (MF), Wavelet Transform (WT), Gaussian Filter (GF), and Convolutional Neural Networks (CNNs). Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Mean Squared Error (MSE) demonstrates that the proposed method consistently outperforms all baseline models across varying noise intensities (5%–75%) on the IQ-OTH/NCCD lung CT dataset. Furthermore, ablation studies confirm the contribution of each module (SE and CBAM) to performance gains. The proposed architecture preserves diagnostic features while suppressing both random and structured noise, and its convolutional SE implementation reduces computational overhead compared to conventional fully connected attention blocks. This work highlights a scalable, interpretable solution for image denoising in clinical workflows and sets a foundation for real-time deployment in AI-assisted diagnostics.

This paper addresses the long-standing question of understanding the origin and evolution of low-frequency unsteadiness interactions associated with shock waves impinging on a turbulent boundary layer in transonic flow (Mach: 1.1 to 1.3). To that end, high-speed experiments in a blowdown open-channel wind tunnel have been performed across a convergent–divergent nozzle for different expansion ratios (PR = 1.44, 1.6, and 1.81). Quantitative evaluation of the underlying spectral energy content has been obtained by processing time-resolved pressure transducer data and Schlieren images using the following spectral analysis methods: Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), as well as coherence and time-lag evaluations. The images demonstrated the presence of increased normal shock-wave impact for PR = 1.44, whereas the latter were linked with increased oblique λ-foot impact. Hence, significant disparities associated with the overall stability, location, and amplitude of the shock waves, as well as quantitative assertions related to spectral energy segregation, have been inferred. A subsequent detailed spectral analysis revealed the presence of multiple discrete frequency peaks (magnitude and frequency of the peaks increasing with PR), with the lower peaks linked with large-scale shock-wave interactions and higher peaks associated with shear-layer instabilities and turbulence. Wavelet transform using the Morlet function illustrates the presence of varying intermittency, modulation in the temporal and frequency scales for different spectral events, and a pseudo-periodic spectral energy pulsation alternating between two frequency-specific events. Spectral analysis of the pixel densities related to different regions, called spatial FFT, highlights the increased influence of the feedback mechanism and coupled turbulence interactions for higher PR. Collation of the subsequent coherence analysis with the previous results underscores that lower PR is linked with shock-separation dynamics being tightly coupled, whereas at higher PR values, global instabilities, vortex shedding, and high-frequency shear-layer effects govern the overall interactions, redistributing the spectral energy across a wider spectral range. Complementing these experiments, time-resolved numerical simulations based on a transient 3D RANS framework were performed. The simulations successfully reproduced the main features of the shock motion, including the downstream migration of the mean position, the reduction in oscillation amplitude with increasing PR, and the division of the spectra into distinct frequency regions. This confirms that the adopted 3D RANS approach provides a suitable predictive framework for capturing the essential unsteady dynamics of shock–boundary layer interactions across both temporal and spatial scales. This novel combination of synchronized Schlieren imaging with pressure transducer data, followed by application of advanced spectral analysis techniques, FFT, CWT, spatial FFT, coherence analysis, and numerical evaluations, linked image-derived propagation and coherence results directly to wall pressure dynamics, providing critical insights into how PR variation governs the spectral energy content and shock-wave oscillation behavior for nozzles. Thus, for low PR flows dominated by normal shock structure, global instability of the separation zone governs the overall oscillations, whereas higher PR, linked with dominant λ-foot structure, demonstrates increased feedback from the shear-layer oscillations, separation region breathing, as well as global instabilities. It is envisaged that epistemic understanding related to the spectral dynamics of low-frequency oscillations at different PR values derived from this study could be useful for future nozzle design modifications aimed at achieving optimal nozzle performance. The study could further assist the implementation of appropriate flow control strategies to alleviate these instabilities and improve thrust performance.

Drought is a natural disaster that often remains unnoticed until ecosystem impacts become severe. Therefore, monitoring and detecting droughts are important research topics. Consequently, drought indices with different focuses, such as precipitation or soil moisture, have been developed. Yet, the utility of the indices is limited before the beginning of the drought. To overcome this shortcoming, drought forecasting and providing decision-makers with an early warning to mitigate the effects is an important research topic. This study aims to take on the forecasting of the droughts with its novelty on the spatial focus, Norway (Drammen, Hamar, and Lillehammer). We forecast the Effective Drought Index (EDI) across spatially diverse Norwegian regions without hydrological constraints. To achieve this, we have utilized precipitation data between 1980 and 2025 and trained our machine learning models, namely, Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGboost), Long-Short Term Memory network (LSTM), and Categorical Boosting Algorithm (Catboost). Moreover, the latent feature space is extended by wavelet transformation (WT). The innovative aspect of this study and its contribution to the literature is its novel application of the WT to some algorithms. Furthermore, unlike the literature, EDI was chosen as the drought index in this study, further increasing its innovative nature. Our results indicate that long short-term memory networks enhanced by wavelet transformation provide the best forecasts. Here, the best performance, LSTMW-M04, is achieved over Drammen (r = 0.9765, NSE = 0.9510, KGE = 0.8641, PI = 0.3211, and RMSE = 0.2207). Although LSTM is already an innovative and successful algorithm, we have further improved the model performance. This result will help decision-makers in a future drought study with both the model input structure and the algorithm used.

Accurate differentiation between microseismic signals induced by hydraulic fracturing and those from roof fracturing is vital for optimizing fracturing efficiency, assessing roof stability, and mitigating mining-induced hazards in coal mining operations. We propose an automatic identification method for microseismic signals generated by hydraulic fracturing in coal seam roofs. This method first transforms the microseismic signals induced by hydraulic fracturing and roof fracturing into time-frequency feature images using the Frequency Slice Wavelet Transform (FSWT) technique, and then employs a sliding window (Swin) Transformer network to automatically identify and classify these two types of time-frequency feature maps. A comparative analysis is conducted on the performance of three methods—including the signal energy distribution method, Residual Network (ResNet) model, and VGG Network (VGGNet) model—in identifying microseismic signals from hydraulic fracturing in coal seam roofs. The results demonstrate that the Swin Transformer recognition model combined with FSWT achieves an accuracy of 92.49% and an F1-score of 92.96% on the test set of field-acquired microseismic signals from hydraulic fracturing and roof fracturing. These performance metrics are significantly superior to those of the signal energy distribution method (accuracy: 64.70%, F1-score: 64.70%), ResNet model (accuracy: 88.04%, F1-score: 89.24%), and VGGNet model (accuracy: 88.47%, F1-score: 89.52%). This advancement provides a reliable technical approach for monitoring hydraulic fracturing effects and ensuring roof safety in coal mines.

Introduction: Patients with aortic valve disease, such as bicuspid aortic valve (BAV), require regular echocardiography or cardiovascular (CV) MRI to monitor for complications such as valve stenosis (AS) and aortic dilation. However, repeated imaging can be burdensome and incur substantial cost. Seismocardiogram (SCG) chest acceleration measurements recorded by inexpensive wearable devices can give indicators of valve-mediated hemodynamic changes, and as such may have supplemental value for such patients. This study investigated using SCG recordings coupled with a novel machine-learned (ML) classifier for SCG signals to identify patient valve type and presence/absence of aortic valve stenosis (AS). Hypothesis: We hypothesize that accurate classification of aortic valve type and AS can be made from SCG recordings with ML analysis compared to those from standard-of-care imaging (ground truth: cardiac MRI or echo). Methods: Healthy controls (no known CV disease) and aortic valve disease patients with tricuspid (TAV), BAV, or post-repair mechanical valve who received echo or MRI (clinical CV protocol) were enrolled for same-day 2-minute wearable SCG measurement (fig. A). Standard clinical assessment of valve/flow function was used (fig. B). Informed consent was given with IRB oversight. Clinical imaging used 4D flow MRI (1.5T,1-3mm3/30-40ms) or 2D Doppler echo (1.7-3.3MHz,12-40FPS). From clinical read of valve type/function, subjects were grouped in four classes: AS (any degree), BAV no-AS, TAV no-AS, mechanical. A hybrid network with convolutional neural network and multi-layer perceptron was trained (80/20 train/test) to classify patient valve status from SCG wavelet coefficients and demographics (age/sex/height/weight). Performance was evaluated by 20-fold cross-validation. Results: Enrolment was 129 subjects (97 MRI/32 echo): 46 controls (45.9±17.4y/20F) and 83 patients (22.4±15.8y/20F; 67 BAV/6 TAV/10 mech.). Classification area-under-curve (AUC) was high for all classes (AUC≥0.79). Across all ML validations, correct classification was achieved for ≥75% of subjects. Conclusion: This evaluation of a machine-learned classifier for SCG indicate potential utility in screening for valve-mediated hemodynamic changes, which reverberate through the chest and cause altered vibrations. The low cost and ease of acquisition for SCG would make it an appealing complement to imaging as the current standard for aortic valve abnormality screening and management.

Multiresolution analysis and orthonormal linear canonical wavelets in generalized Sobolev spaces

Cognitive workload refers to the mental effort required to perform a task and plays a vital role in cognitive functioning and daily decision-making. The precise estimation of cognitive workload can increase efficiency and decrease mental errors. EEG signals are non-invasive and trustworthy, containing useful information about mental and cognitive tasks, and are very effective in measuring cognitive workload. This study aims to classify various cognitive workload levels using EEG signals, primarily by channel selection based on the Pearson Correlation Coefficient, to reduce computational complexity and facilitate real-time applications. As time-frequency decomposition techniques can provide simultaneous time and frequency information for more accurate analysis, three techniques were adopted: Maximal Overlap Discrete Wavelet Transform (MODWT), Empirical Mode Decomposition (EMD), and a hybrid approach combining both. After decomposition, ten statistical features were extracted, and the Improved Distance Evaluation technique was employed to select the most critical features. Classification was performed on these features using three classifiers: Support Vector Machine (SVM), K-Nearest Neighbors, and Decision Tree. The findings revealed the important role of frontal EEG channels in assessing cognitive workload. Additionally, the combined use of MODWT and EMD with the SVM classifier yielded the best classification accuracy for both binary and three-class classification scenarios. The results indicate that the optimal choice of channels, combined with time-frequency decomposition methods, can significantly enhance classification accuracy while reducing system complexity in estimating cognitive workload.

ABSTRACT The present study investigates the impact of Sea Surface Temperature (SST) extremes on precipitation extremes in India's six homogeneous climate regions from 1981 to 2020. A wavelet‐based complex network analysis was used to explore the connections between precipitation extremes and SST extremes. The Maximum Overlap Discrete Wavelet Transform (MODWT) technique was employed for time series decomposition to identify these links across different temporal scales. Spearman correlation was then used to determine the relationships between SST extremes and precipitation extremes at the grid level. Complex networks were then constructed for each of India's climate regions, focusing on both positive and negative correlations with various global sea regions. Results reveal that precipitation extremes at shorter time scales are predominantly driven by proximal oceanic regions such as the Bay of Bengal (BOB), Arabian Sea (ARS), and Eastern Indian Ocean (EIO), reflecting strong monsoon–SST coupling through moisture convergence and synoptic convection processes. As the time scale increases, remote SST influences from the Atlantic (NAO, EAO), Pacific (EPO, NPO, SPO), and Southern Hemisphere oceans (SIO, SAO, SOO) become increasingly dominant, highlighting the role of atmospheric teleconnections, jet stream modulation, and cross‐equatorial moisture transport. Interannual scales show the widest and most diverse SST control, whilst decadal‐scale influence is generally weak and region‐specific. The regional analysis demonstrates distinct propagation pathways of SST influence, with West Central India showing consistent multiscale sensitivity, Central Northeast and Northeast India transitioning from local to global control, and South Peninsular India retaining strong regional dominance. These insights underscore the necessity of integrating both regional and global SST indices into forecasting frameworks for improved prediction and climate adaptation strategies in India.

Reliable fault diagnosis of milling machines is essential for maintaining operational stability and cost-effective maintenance; however, it remains challenging due to limited labeled data and the highly non-stationary nature of acoustic emission (AE) signals. This study introduces an optimized Few-Shot Learning framework (FSL) that integrates time–frequency analysis with attention-guided representation learning and distribution-aware classification for data-efficient fault detection. The framework converts AE signals into Continuous Wavelet Transform (CWT) scalograms, which are processed using a self-attention-enhanced ResNet-50 backbone to capture both local texture features and long-range dependencies in the signal. Adaptive prototype computation with learnable importance weighting refines class representations, while Mahalanobis distance-based matching ensures robust alignment between query and prototype embeddings under limited sample conditions. To further strengthen discriminability, contrastive loss with hard negative mining enforces compact intra-class clustering and clear inter-class separation. Comprehensive experiments under 7-way 5-shot settings and 5-fold stratified cross-validation demonstrate consistent and reliable performance, achieving a mean accuracy of 98.86% ± 0.97% (95% CI: [98.01%, 99.71%]). Additional evaluations across multiple spindle speeds (660 rpm and 1440 rpm) confirm that the model generalizes effectively under varying operating conditions. Grad-CAM++ activation maps further illustrate that the network focuses on physically meaningful fault-related regions, enhancing interpretability. The results verify that the proposed framework achieves robust, scalable, and interpretable fault diagnosis using minimal labeled data, offering a practical solution for predictive maintenance in modern intelligent manufacturing environments.

Memory forensics is an essential cybersecurity tool that comprehensively examines volatile memory to detect the malicious activity of fileless malware that can bypass disk analysis. Image-based detection techniques provide a promising solution by visualizing memory data into images to be used and analyzed by image processing tools and machine learning methods. However, the effectiveness of image-based data for detection and classification requires high computational efforts. This paper investigates the efficacy of texture-based methods in detecting and classifying memory-resident or fileless malware using different image resolutions, identifying the best feature descriptors, classifiers, and resolutions that accurately classify malware into specific families and differentiate them from benign software. Moreover, this paper uses both local and global descriptors, where local descriptors include Oriented FAST and Rotated BRIEF (ORB), Scale-Invariant Feature Transform (SIFT), and Histogram of Oriented Gradients (HOG) and global descriptors include Discrete Wavelet Transform (DWT), GIST, and Gray Level Co-occurrence Matrix (GLCM). The results indicate that as image resolution increases, most feature descriptors yield more discriminative features but require higher computational efforts in terms of time and processing resources. To address this challenge, this paper proposes a novel approach that integrates Local Interpretable Model-agnostic Explanations (LIME) with deep learning models to automatically identify and crop the most important regions of memory images. The LIME’s ROI was extracted based on ResNet50 and MobileNet models’ predictions separately, the images were resized to 128 × 128, and the sampling process was performed dynamically to speed up LIME computation. The ROIs of the images are cropped to new images with sizes of (100 × 100) in two stages: the coarse stage and the fine stage. The two generated LIME-based cropped images using ResNet50 and MobileNet are fed to the lightweight neural network to evaluate the effectiveness of the LIME-based identified regions. The results demonstrate that the LIME-based MobileNet model’s prediction improves the efficiency of the model by preserving important features with a classification accuracy of 85% on multi-class classification.

Earthquakes are sudden and destructive natural events caused by tectonic movements in the Earth’s crust. Although they cannot be predicted with certainty, rapid and reliable detection is essential to reduce loss of life and property. This study aims to automatically distinguish earthquake and noise signals from real seismic data by analyzing time-frequency features. Signals were scaled using z-score normalization, and extracted with Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT), and combined EMD+DWT methods. Feature selection methods such as Lasso, ReliefF, and Student’s t-test were applied to identify the most discriminative features. Classification was performed with Ensemble Bagged Trees, Decision Trees, Random Forest, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). The highest performance was achieved using the RF classifier with the Lasso-based EMD+DWT feature set, reaching 100% accuracy, specificity, and sensitivity. Overall, DWT and EMD+DWT features yielded higher performance than EMD alone. While k-NN and SVM were less effective, tree-based methods achieved superior results. Moreover, Lasso and ReliefF outperformed Student’s t-test. These findings show that time-frequency-based features are crucial for separating earthquake signals from noise and provide a basis for improving real-time detection. The study contributes to the academic literature and holds significant potential for integration into early warning and earthquake monitoring systems.

With the rapid development of digital multimedia technology, images, as an important carrier of information dissemination, have been widely applied in fields such as healthcare, security, commerce, and social networking. However, images are highly susceptible to tampering, duplication, and illegal use during transmission and storage, posing severe challenges to their authenticity and integrity. Traditional image authentication techniques exhibit significant deficiencies in terms of security, robustness, and invisibility, making them difficult to meet the increasing security demands. This paper proposes a novel image authentication method that integrates Sparse Approximation (SA) and Quantum Encryption (QE), aiming to enhance the security and anti-attack capabilities of digital images. The method first performs subsampling and sparsification on the watermark image, extracts multi-scale features of the image using Discrete Wavelet Transform (DWT), and generates a highly random measurement matrix through quantum logic mapping to achieve encryption and exchange of sparse coefficients. Subsequently, Singular Value Decomposition (SVD) is employed to embed the encrypted watermark information into the low-frequency components of the host image, ensuring the invisibility and robustness of the watermark. Experimental results demonstrate that the proposed method exhibits excellent performance in resisting noise, geometric transformations, and enhancement attacks. When the correct key is used, the watermark can be accurately recovered, while the use of an incorrect key results in complete distortion of the watermark, effectively preventing illegal extraction. The research presented in this paper provides an efficient and secure technical path for digital image copyright protection and content authentication.

Accurate fault detection for Permanent Magnet Synchronous Motors (PMSMs) prevents costly failures and improves overall reliability. This paper presents a hybrid one-dimensional convolutional neural network (1DCNN)–bidirectional gated recurrent unit (BiGRU) deep learning model for PMSM fault detection. Inverter-driven short-circuit, open-circuit, and thermal faults, as well as stator faults, can cause electrical and thermal disturbances that affect PMSMs. Significant harmonic distortions, current and voltage peaks, and transient fluctuations are introduced by these faults. The proposed architecture utilizes handcrafted features, including statistical analysis, fast Fourier transform (FFT), and Discrete Wavelet Transform (DWT), extracted from the raw PMSM signals to efficiently capture these faults. 1DCNN effectively extracts local and high-frequency fault-related patterns that encode the effects of peaks and harmonic distortions, while the BiGRU of this enriched representation models complex temporal dependencies, including global asymmetries across phase currents and long-term fault evolution trends seen in stator faults and thermal faults. The proposed model reveals the highest metrics for inverter-driven and stator winding fault datasets compared to the other approaches, achieving an accuracy of 99.44% and 99.98%, respectively. As a result, the study with realistic and comprehensive datasets guarantees high accuracy and generalizability not only in the laboratory but also in industry.