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This article presents an innovative predictive maintenance for grinding mills, aiming to enhance operational efficiency and minimize downtime. Leveraging advancements in data analytics and IoT sensor technologies, the approach integrates vibration signal forecasting, LSTM-based fast Fourier transform (FFT) analysis, and convolutional neural networks (CNNs) to detect faults early on. The method involves creating LSTM models to forecast vibration signals based on historical data and using FFT analysis to identify fault frequencies associated with the grinding process. Additionally, techniques such as Mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT), and continuous wavelet transform (CWT) are employed for spectrogram extraction, providing valuable insights into machinery conditions. Validation on real-world datasets with 99.95% of accuracy with 1 in AUC-ROC, showcases the robust predictive performance of the model and has reached 99.96% of accuracy for 16 classes with an AUC-ROC of 1 using CWRU dataset, surpassing existing approaches and demonstrating its potential for proactive maintenance across various industries beyond mining. [ABSTRACT FROM AUTHOR]

Summary: This paper develops a new image encryption algorithm based on a novel two‐dimensional chaotic map and bit‐level dynamic scrambling. First, multiple one‐dimensional chaotic maps are coupled to construct a novel two dimensions Logistic‐Sine‐coupling chaos map (2D‐LSCCM). The performance analysis shows that the 2D‐LSCCM has more complex chaotic characteristics and wider chaotic range than many extant 2D chaos maps. Second, original image matrix combines with hash algorithm SHA‐256 to generate a hash value. The initial values of 2D‐LSCCM are generated based on the hash value. Third, the original image matrix is divided into multiple sub‐matrices by wavelet transform, followed by scrambling by an improved Knuth shuffle algorithm. Fourth, the scrambled multiple sub‐matrices are stitched into an image matrix of M×N×3$$ M\times N\times 3 $$ and converted into a binary matrix. The chaotic sequence generated by 2D‐LSCCM is introduced as a control sequence to control the bit‐level scrambling of pixel points, which realizes the bit‐level dynamic scrambling. Finally, the diffusion operation is performed by parameter par and chaotic sequence to obtain the final encrypted image. The algorithm security analysis and simulation examples demonstrate the effectiveness of the proposed encryption scheme. [ABSTRACT FROM AUTHOR]

Pathologies in adipose (fat) tissue function are linked with human diseases such as diabetes, obesity, metabolic syndrome, and cancer. Dynamic, rapid release of metabolites has been observed in adipocyte cells and tissue, yet higher temporal resolution is needed to adequately study this process. In this work, a microfluidic device with precise and regular valve-automated droplet sampling, termed a microfluidic analog-to-digital converter (μADC), was used to sample secretions from ∼0.75 mm diameter adipose explants from mice, and on-chip salt water electrodes were used to merge sampled droplets with reagent droplets from two different fluorometric coupled enzyme assays. By integrating sampling and assays on-chip, either glycerol or non-esterified fatty acids (NEFA), or both, were quantified optically within merged 12 nanoliter droplets using a fluorescence microscope with as high as 20 second temporal resolution. Limits of detection were 6 μM for glycerol (70 fmol) and 0.9 μM for NEFA (10 fmol). Multiple ex vivo adipose tissue explants were analyzed with this system, all showing clear increases in lipolytic function after switching from feeding to fasting conditions. Enabled by high temporal resolution, lipolytic oscillations of both glycerol and NEFA were observed for the first time in the range of 0.2 to 1.6 min−1. Continuous wavelet transform (CWT) spectrograms and burst analyses (0.1 to 4.0 pmol bursts) revealed complex dynamics, with multiplexed assays (duplex for glycerol and NEFA) from the same explants showing mostly discordant bursts. These data support separate mechanisms of NEFA and glycerol release, although the connection to intracellular metabolic oscillations remains unknown. Overall, this device allowed automated and highly precise temporal sampling of tissue explants at high resolution and programmable downstream merging with multiple assay reagents, revealing unique biological information. Such device features should be applicable to various other tissue or spheroid types and to other assay formats. [ABSTRACT FROM AUTHOR]

Reflectivity inversion is a key step in reservoir prediction. Conventional sparse‐spike deconvolution assumes that the reflectivity (reflection coefficient series) is sparse and solves for the reflection coefficients by an L1‐norm inversion process. Spectral inversion is an alternative to sparse‐spike deconvolution, which is based on the odd–even decomposition algorithm and can accurately identify thin layers and reduce the wavelet tuning effect without using constraints from logging data, from horizon interpretations or from an initial model of the reflectivity. In seismic processing, an error exists in wavelet extraction because of complex geological structures, resulting in the low accuracy of deconvolution and inversion. Blind deconvolution is an effective method for solving the problem mentioned above, which comprises seismic wavelet and reflectivity sequence, assuming that the wavelets that affect some subsets of the seismic data are approximately the same. Therefore, we combined blind deconvolution with spectral inversion to propose blind spectral inversion. Given an initial wavelet, we can calculate the reflectivity based on spectral inversion and update the wavelet for the next iteration. During the update processing, we add the smoothness of the wavelet amplitude spectrum as a regularization term, thus reducing the wavelet oscillation in the time domain, increasing the similarity between inverted and initial wavelets, and improving the stability of the solution. The blind spectral inversion method inherits the wavelet robustness of blind deconvolution and high resolution of spectral inversion, which is suitable for reflectivity inversion. Applications to synthetic and field seismic datasets demonstrate that the blind spectral inversion method can accurately calculate the reflectivity even when there is an error in wavelet extraction. [ABSTRACT FROM AUTHOR]

The usage of propellers is increasing as an alternative to traditional aircraft and has become the primary option for new urban mobility vehicles. However, noise emissions from rotating blades can be a challenge for the certification of these new vehicles. The acoustic sources from a propeller can be decomposed into deterministic and random components. Therefore, academic research has focused on developing methods to separate these components. This study compares the performance of several methods available in the literature to decompose propeller noise signals into tonal and broadband components. Initially, the methods were applied to synthetic data that represents the main characteristics of rotating blade noise. Later, the methods were tested on realistic acoustic data collected from an isolated propeller test rig. The results showed that methods based on phase-averaging recorded signals satisfactorily detected harmonic components. The method based on cross-correlation between data blocks effectively extracted the broadband spectra, but it performed poorly on harmonic detection. Additionally, the wavelet transform and Proper Orthogonal Decomposition methods were capable of reconstructing time series related to both deterministic and random parts. However, further investigation is required regarding the separation of POD modes, as unexpected trends were observed with the wavelet-based method. [ABSTRACT FROM AUTHOR]

Due to the variable working environment of bearings, the collected data often follow different probability distributions. It is hard to directly use the trained models to identify the bearing fault with different operating conditions. In addition, it is a high cost to label the samples for every work condition. To solve these problems, a multi-scale adversarial subdomain adaptation bearing fault diagnosis method is proposed, which is based on Continuous Wavelet Transform (CWT) and our constructed Multi-scale Adversarial SubDomain Adaptation Network (MASDAN). Firstly, to extract the features of non-stationary signals with different frequency bands, CWT is used to convert continuous vibration signals into two-dimensional time-frequency images. Secondly, to enhance the correlation of the features across frequency bands, a multi-scale ConvNeXt is proposed, which adds a multi-scale module based on ConvNeXt to obtain features with different scales. Finally, to reduce the distribution discrepancy between the source domain and the target domain and to avoid feature confusion in different domains, a domain adaptive alignment network introducing domain information is constructed. Two modules are included: the Domain Alignment Classification Network Module (DACNM) based on Multi-kernel Local Maximum Mean Discrepancy (MK-LMMD) and the Domain Adversarial Network Module (DANM) based on domain discrimination. Thus, the MASDAN consists of the multi-scale ConvNeXt module, DACNM, and DANM, which can realize the multi-scale feature extraction and adaptively align the fault features at different domains. The experimental results on the Qingdao University of Technology (QUT) bearing dataset and the Case Western Reserve University (CWRU) bearing dataset demonstrate the proposed method can effectively diagnose bearing faults under different operating conditions. [ABSTRACT FROM AUTHOR]

In practical applications of wavelet transform, engineers and practitioners encounter challenges that arise due to the disparity between wavelet theory, which deals with continuous functions, and the digital nature of signals in engineering contexts. In particular, wavelet transform theory does not consider the effect of changes in digital signals on the result of the wavelet transform. This paper emphasizes the influence of the type of digital signals on the accuracy of wavelet transform in engineering applications and proposes an efficient wavelet function based on the derivative of the signal for better damage detection in beam structures. For this purpose, the obtained signals from the mode shapes of the steel beam are used to examine the efficiency of the proposed derivative-based wavelet transform. The effects of changes in boundary conditions, location of damage, and level of damage on the performance of the proposed method, are evaluated. Findings show that when we use the derivate of the signal in the wavelet transform, the location of damage in all damage scenarios is detected with high accuracy. This research demonstrates the importance of the type of signal used in the wavelet transform for enhancing the precision of fault and damage detection in signals. [ABSTRACT FROM AUTHOR]

An effective feature vector generation approach is herein presented based on the cointegration concept and the signal processing methods in order to improve varying speed bearing troubleshooting. Initially, each vibration signal is decomposed into its intrinsic mode functions (IMFs) using ensemble empirical mode decomposition method. Then, the cointegration relations are extracted by applying the Johansen trace test method to the obtained components. In order to form the feature matrix, the cointegration relations are analyzed by wavelet packet transform and the time-domain features are calculated for the wavelet packet coefficients. Consequently, by using a hybrid feature selection approach based on compensation distance evaluation technique, support vector machine, and binary particle swarm optimization algorithm, the optimal hybrid features were identified. The experimental results show that the optimal features are composed of the first cointegration relation, the third level of wavelet packet coefficients, the third IMF, Teager–Kaiser energy, energy, and root mean square. Also, the number of optimal features obtained from the first cointegration relation was less than other relationships. The results reveal that when the optimal feature set is computed for the first cointegration relationship, the identification accuracy of the proposed approach increases considerably. The result analysis also shows the efficiency of the proposed technique in detecting the condition of the bearing in time-varying rotational speed. [ABSTRACT FROM AUTHOR]

Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection. [ABSTRACT FROM AUTHOR]

Accurate and rapid short-term passenger flow prediction is the foundation for safe and efficient operation of urban rail transit systems. The urban rail transit passenger flow is related to the surrounding land properties and is accompanied by random interference. However, the passenger flow characteristics of stations and the role played by random disturbances in predicting passenger flow signals are not clear. A hybrid neural network named Urban Rail Transit Short-Term Flow Prediction Neural Network (URTSTFPNN) is proposed to improve the accuracy and efficiency of short-term passenger flow prediction. The network consists of three modules: feature Processing Module, Data Reconstruction Module, and Prediction Module. In this process, the urban rail transit stations are classified by dynamic time warping based on the time series attributes of entry and exit passenger flow. The noise reduction technology of wavelet transform is added to the network to increase the accuracy of the model. The analysis results of the proposed model using data from Metro Line 2 in Xi'an, Shaanxi Province, China, indicate that urban rail transit stations can be divided into commercial and official stations, high-density residential stations, low-density residential stations, and tourist and passenger transport terminal stations. The URTSTFPNN shows higher predictive accuracy in revealing the errors compared to the single Long Short-Term Memory model, the Auto-Regression and Moving Average model, and the BP neural network model. The coefficient of determination increased by 1.91 ~ 3.48%, 6.45 ~ 9.45%, and 2.72 ~ 5.69%, with a reduction of calculation time by 19.57 ~ 33.29%, 0.88 ~ 11.61%, and 27.87 ~ 36.71%, respectively. The model proposed by this research can accurately and quickly predict passenger flow, which can be used to guide various categories of urban rail stations to develop effective passenger flow management measures. [ABSTRACT FROM AUTHOR]

Recently, it has become difficult to recognize and easier to misuse digital images due to the large number of editing tools available. Detecting forgeries in images is crucial for security and forensic purposes. Therefore, this research implements a deep learning (DL) method of convolutional autoencoder (CAE) which improves colorization-based image forgery detection by leveraging spatial and color information, increasing the detection accuracy. At first, the pre-processed input forgery images are used with the wiener filtering-contrast restricted improved histogram equalization (WE-CLAHE) technique. Hybrid dual-tree complex wavelet trigonometric transform (H-DTCWT) and VGG-16 are used to extract effective features from the clustered data. Improved horse herd optimization (IHH) is employed to reduce the dimensionality of a feature. At last, the CAE model is implemented to significantly recognize the image forgery. The accuracy of CASIA V1 and GRIP datasets of 99.95% and 99.97%, respectively is achieved. Hence, this implemented method obtains a high forgery detection performance than the existing methods. [ABSTRACT FROM AUTHOR]

This study delves into the exploration of broadband measurement techniques for power systems, utilizing wavelet transform as a foundational tool for signal analysis. The research rigorously evaluates the efficacy of several machine learning algorithms, namely Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest, in interpreting and analyzing broadband signals within power systems. Through a detailed analytical process, the performance of each algorithm is meticulously assessed based on several critical metrics: accuracy, precision, recall, and F1-score. The research investigates broadband measurement methods for power systems using wavelet transform and evaluates the performance of Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest. Results show SVM achieving an accuracy of 85%, precision of 86%, recall of 82%, and F1-score of 84%. ANN yields 82% accuracy, 84% precision, 78% recall, and 81% F1 score. KNN demonstrates 87% accuracy, 88% precision, 84% recall, and 86% F1 score. DT achieves 79% accuracy, 80% precision, 75% recall, and 77% F1 score. Overall, the study provides insights into machine learning algorithms' effectiveness in broadband power system measurement. [ABSTRACT FROM AUTHOR]

A sports auxiliary evaluation system suitable for China's national conditions was established using big data and sports identification technology. First of all, this paper extends the data of normative behavior and constructs a normative library of scores and comparisons. The acquisition of 3D data is emphasized. The method based on Fourier descriptors is used to locate the motion accurately. In this way, better gait recognition results can be obtained. The Fourier characteristics before and after wavelet transform are compared with the actual object characteristics, and the results show that the proposed algorithm can extract the features with high precision. This scheme can obtain a more accurate identification effect. The system proposed in this paper provides a powerful means for judges to score. [ABSTRACT FROM AUTHOR]

This study presents an innovative approach to evaluate the implementation effects of the Rural Revitalization Strategy R2S, utilising the robust capabilities of the wavelet analysis algorithm. The proposed methodology integrates the strength of wavelet transform, an advanced mathematical tool for signal processing, with the entropy weighting method and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) model. This integration creates a comprehensive framework for assessing the multifaceted impacts of rural revitalisation initiatives. The wavelet analysis algorithm is the cornerstone of this approach, enabling the decomposition of complex rural development data into different frequency components, thus facilitating a more nuanced analysis. The entropy weighting method contributes by objectively determining the weights of various evaluation indicators, ensuring that the most relevant factors in rural revitalisation are emphasised in the assessment process. The TOPSIS model complements this by clearly ranking the analysed strategies based on their proximity to an ideal solution, thereby enabling decision-makers to identify the most effective strategies for rural development. Together, these techniques form a powerful tool for evaluating the effectiveness of rural revitalisation strategies, offering critical insights for policy formulation and implementation in rural areas. This study's methodology not only enhances the accuracy of evaluation but also provides a replicable model for similar assessments in other contexts, contributing significantly to rural development and policy analysis. [ABSTRACT FROM AUTHOR]

Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistant against various distortions such as noise contamination, pixel missing and block missing, so that the forgers cannot deceive the detector by adding some distortions to the faked fingerprint. In this paper, we propose a fingerprint forgery detection algorithm based on a suggested adaptive thresholding pattern. The anisotropic diffusion of the input image is passed through three levels of the wavelet transform. The coefficients of different layers are adaptively thresholded and concatenated to produce the feature vector which is classified using the SVM classifier. Another contribution of the paper is to investigate the effect of various distortions such as pixel missing, block missing, and noise contamination. Our suggested approach includes a novel method that exhibits improved resistance against a range of distortions caused by environmental phenomena or manipulations by malicious users. In quantitative comparisons, our proposed method outperforms its counterparts by approximately 8% and 5% in accuracy for missing pixel scenarios of 90% and block missing scenarios of size 70 × 70 , respectively. This highlights the novelty approach in addressing such challenges. [ABSTRACT FROM AUTHOR]

High signal-to-noise ratio (SNR) seismic waveform data are conductive to various studies in seismology. Seismic denoising aims to enhance SNR by eliminating additive noise through signal processing while preserving important features of the seismic signal. Conventional parametric seismic denoising methods often require selecting appropriate parameters to achieve optimal results, which may be limiting when dealing with various types and scales of seismic data. Here, we develop an adaptive parameter-free denoising method by combining general cross-validation (GCV) thresholding and pixel connectivity in synchrosqueezed (SS) domain. In this denoising framework, the synchrosqueezed continuous wavelet transform (SS-CWT) is first applied to obtain a high-resolution time–frequency representation. Then, the GCV approach, which allows for choosing the (nearly) optimal threshold without relying on any prior knowledge about the noise level, is employed to attenuate most of the low-energy noise. After that, the relatively isolated high-energy residual noise remaining in the SS-CWT spectrum is removed using pixel connectivity thresholding. Finally, the inverse SS-CWT is applied to the thresholded spectrum to obtain the denoised seismic record. As the thresholds for GCV and pixel connectivity are derived from the spectrum characteristics of the data being analyzed, the proposed denoising approach is highly adaptive and parameter-free. We demonstrate the effectiveness and versatility of the proposed denoising framework using synthetic data and real seismic data from diverse monitoring scenarios, including land, ocean, and emerging distributed acoustic sensing (DAS). The results indicate that the method is a stable and efficient tool for seismic data denoising. [ABSTRACT FROM AUTHOR]

The document is a guest editorial discussing the importance of multiresolution analytics in analyzing data with fractal, chaotic, or complex characteristics. The editorial highlights how these techniques reveal hidden patterns and dependencies in various fields such as financial markets, economic indices, environmental data, and medical imaging. By examining data at different resolutions, multiresolution analytics enhance our understanding of complex systems and phenomena exhibiting self-similarity or irregular behavior. The editorial emphasizes the need for advanced statistical and computational tools to handle the complexity of modern data and the importance of integrating multiresolution analytics with machine learning and artificial intelligence for comprehensive data analysis. [Extracted from the article]

ABSTRACT This paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore the feasibility of using continuous wavelet transform to extract spectral difference information of different vigor desiccated cotton species.A method of filtering wavelet features (WFs) based on correlation and feature importance is proposed to extract the fine structure and complex information of spectral features of seeds with different vigor.Different vigor classes of desiccated cotton seeds were obtained through artificial aging experiments. And its high spectral image was collected.The raw spectra are preprocessed with Savitzky-Golay smoothing, multivariate scattering correction, first-order differentiation, and second-order differentiation.Then, the WFs extracted by wavelet basis functions such as gauss4, mexh and bior6.8 were compared.Spectral wavelet features (SFs) and WFs were downscaled using principal component analysis.Based on machine learning algorithms such as support vector machines (SVM), random forest (RF), extreme learning machines (ELM), and back propagation neural network (BPNN), a seed vigor detection model was developed for SFs principal components and WFs principal components. The accuracy of the seed vigor detection model was compared between SFs principal components and WFs principal components.The fine spectral information in WFs was further extracted based on correlation analysis and random forest feature importance evaluation. Including the 1% | R | -WFs feature set with the correlation with seed vigor at the top 1%, the 1% Importance-WFs feature set with the feature importance at the top 1% in seed vigor recognition, and the 1% | R | + 1% Importance-WFs feature set with the combination of the two, and bring these three WFs feature sets into the above machine learning model.The results showed that: 1) The bior6.8 function extracted better WFs for different vigor desiccated cotton species.Other wavelet basis functions show a clear ringing effect when extracting WFs.2) The modeling accuracy of the WFs principal components is higher than that of the SFs principal components in all machine learning models for each species.The model based on 1% |R|+1% Importance-WFs has the highest accuracy. 3) The optimal models for seed vigor detection of Jinke 21 and Jinke 20 were : 1% | R | + 1% Importance-WFs + ELM. The optimal model of Xinluzao 64 seed vigor detection is : 1% | R | + 1% Importance-WFs + any machine model and PCA-WFs + ELM / BPNN.The accuracies of the training set and test set of the optimal model of Jinke 21 are 99.63% and 98.28%, and the accuracies of the training set and test set of the optimal model of Jinke 20 and Xinluzao 64 are both 100%. The results indicate that the method proposed in this paper based on correlation and feature importance can effectively extract spectral difference information of different vitality dried cottonseeds, providing a new spectral characterization approach for seed vitality hyperspectral detection. [ABSTRACT FROM AUTHOR]

In the degradation evaluation process of the crane girder structure under moving loads, the complex vibration response and low accuracy of health index standards are the main factors limiting the performance of the evaluation method. A method for identifying and evaluating damage to crane girder structures under moving loads has been proposed. In this method, optimized empirical wavelet transform (EWT) is used for decomposing the vibration signal of the crane girder structure to obtain sensitive components. A health indicator based on power spectrum maximum sequence is constructed by combining structural vibration simulation and response characteristics analysis under moving loads. A degradation index acquisition scheme based on an optimized sparse autoencoder is proposed, which utilizes a simulated annealing algorithm to optimize the model parameters of the sparse autoencoder. The effectiveness of the proposed method is verified through girder structure crack tests under moving loads. [ABSTRACT FROM AUTHOR]

Fourier ptychographic microscopy (FPM) breaks through the resolution limitations of conventional optical systems, which offer a full‐field view and high resolution without additional mechanical scanning. However, conventional image‐domain optimizations require trade‐offs between correction efficacy, data redundancy, and reconstruction accuracy. Furthermore, the existing linear time‐invariant model for actual nonlinear, time‐varying optical systems leads to forward model mismatch, complicating the corrections of the vignetting effect. To overcome these challenges and achieve stitching‐free FPM, a family of forward wavelet‐transform models (WL‐FPM) is proposed. WL‐FPM employs the reversibility of the wavelet transform for high‐fidelity reconstruction in the multiscale feature domain. The wavelet loss function is updated in each iteration, and non‐convex optimization is solved by complex back diffraction. WL‐FPM offers stitching‐free, high‐resolution, and robust reconstruction under various challenging conditions, including vignetting effects, LED position mismatch, intensity fluctuations, and high‐level noise environments, which outperform conventional FPM methods. Under a 4X objective with NA 0.1, WL‐FPM achieves a 435‐nm resolution and stitching‐free full‐field reconstruction of a 3.328 × 3.328 mm2 pathological section with distinct subcellular organelles. In live cell imaging, it provides a full‐field observation with distinct lipids in a single cell. A large number of simulation and experimental results demonstrate its potential for biomedical applications. [ABSTRACT FROM AUTHOR]

Introduction: Tobacco is a critical economic crop, yet its cultivation heavily relies on chemical pesticides, posing health risks to consumers, therefore, monitoring pesticide residues in tobacco is conducive to ensuring food safety. However, most current research on pesticide residue detection in tobacco relies on traditional chemical methods, which cannot meet the requirements for real-time and rapid detection. Methods: This study introduces an advanced method that combines hyperspectral imaging (HSI) technology with machine learning algorithms. Firstly, a hyperspectral imager was used to obtain spectral data of tobacco samples, and a variety of spectral pre-processing technologies such as mean centralization (MC), trend correction (TC), and wavelet transform (WT), as well as feature extraction methods such as competitive adaptive reweighted sampling (CARS) and least angle regression (LAR) were used to process the spectral data, and then, grid search algorithm (GSA) is used to optimize the support sector machine (SVM). Results: The optimized MC-LAR-SVM model achieved a pesticide classification accuracy of 84.1%, which was 9.5% higher than the original data model. The accuracy of the WT-TC-CARS-GSA-SVM model in the fenvalerate concentration classification experiment was as high as 91.8 %, and it also had excellent performance in other metrics. Compared with the model based on the original data, the accuracy, precision, recall, and F1-score are improved by 8.3 %, 8.2 %, 7.5 %, and 0.08, respectively. Discussion: The results show that combining spectral preprocessing and feature extraction algorithms with machine learning models can significantly enhance the performance of pesticide residue detection models and provide robust, efficient, and accurate solutions for food safety monitoring. This study provides a new technical means for the detection of pesticide residues in tobacco, which is of great significance for improving the efficiency and accuracy of food safety detection. [ABSTRACT FROM AUTHOR]

Among the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy, which has been shown to lower the risk of the disease. As a result, Computer-aided polyp classification technique is applied to identify colorectal cancer. But visually categorizing polyps is difficult since different polyps have different lighting conditions. Different from previous works, this article presents Enhanced Scattering Wavelet Convolutional Neural Network (ESWCNN), a polyp classification technique that combines Convolutional Neural Network (CNN) and Scattering Wavelet Transform (SWT) to improve polyp classification performance. This method concatenates simultaneously learnable image filters and wavelet filters on each input channel. The scattering wavelet filters can extract common spectral features with various scales and orientations, while the learnable filters can capture image spatial features that wavelet filters may miss. A network architecture for ESWCNN is designed based on these principles and trained and tested using colonoscopy datasets (two public datasets and one private dataset). An n-fold cross-validation experiment was conducted for three classes (adenoma, hyperplastic, serrated) achieving a classification accuracy of 96.4%, and 94.8% accuracy in two-class polyp classification (positive and negative). In the three-class classification, correct classification rates of 96.2% for adenomas, 98.71% for hyperplastic polyps, and 97.9% for serrated polyps were achieved. The proposed method in the two-class experiment reached an average sensitivity of 96.7% with 93.1% specificity. Furthermore, we compare the performance of our model with the state-of-the-art general classification models and commonly used CNNs. Six end-to-end models based on CNNs were trained using 2 dataset of video sequences. The experimental results demonstrate that the proposed ESWCNN method can effectively classify polyps with higher accuracy and efficacy compared to the state-of-the-art CNN models. These findings can provide guidance for future research in polyp classification. [ABSTRACT FROM AUTHOR]

This study presents a meticulous investigation facilitated by a bespoke high-pressure test rig. The rig stands as a cornerstone of the research endeavour, providing the capability to simulate diverse leakage scenarios under controlled conditions. With its capacity to replicate conditions akin to real-world hydraulic systems, including various levels of severity such as healthy operation, moderate leakage and severe leakage. The study delves into the intricate analysis of time-series discharge flow rate signals within this experimental framework. The study utilizes sophisticated signal processing techniques, particularly the Continuous Wavelet Transform (CWT), to observe frequency components and temporal occurrences. Despite prevalent challenges distinguishing between leakage severity levels, the CWT-based analysis provides crucial insights into the dominant low frequencies (2.3–3.3 hz) characterising all leakage conditions. An advanced computer vision-based methodology is devised to address the complexities inherent in leakage differentiation, integrating cutting-edge models such as You Only Look Once (YOLO-V7) and You Only Look Once-Neural Architecture Search (YOLO-NAS). These models are meticulously trained using annotated CWT images of flow rates corresponding to different leakage conditions. Notably, the superiority of YOLO-NAS in terms of both speed and accuracy underscores its efficacy in automated leakage detection. In summary, this comprehensive approach, underpinned by the innovative hydraulic test rig and advanced signal processing coupled with state-of-the-art computer vision techniques, presents a significant advancement in the realm of internal leakage detection in hydraulic systems. [ABSTRACT FROM AUTHOR]

Identification of retinal diseases in automated screening methods, such as those used in clinical settings or computer-aided diagnosis, usually depends on the localization and segmentation of the Optic Disc (OD) and fovea. However, this task is difficult since these anatomical features have irregular spatial, texture, and shape characteristics, limited sample sizes, and domain shifts due to different data distributions across datasets. This study proposes a novel Multiresolution Cascaded Attention U-Net (MCAU-Net) model that addresses these problems by optimally balancing receptive field size and computational efficiency. The MCAU-Net utilizes two skip connections to accurately localize and segment the OD and fovea in fundus images. We incorporated a Multiresolution Wavelet Pooling Module (MWPM) into the CNN at each stage of U-Net input to compensate for spatial information loss. Additionally, we integrated a cascaded connection of the spatial and channel attentions as a skip connection in MCAU-Net to concentrate precisely on the target object and improve model convergence for segmenting and localizing OD and fovea centers. The proposed model has a low parameter count of 0.8 million, improving computational efficiency and reducing the risk of overfitting. For OD segmentation, the MCAU-Net achieves high IoU values of 0.9771, 0.945, and 0.946 for the DRISHTI-GS, DRIONS-DB, and IDRiD datasets, respectively, outperforming previous results for all three datasets. For the IDRiD dataset, the MCAU-Net locates the OD center with an Euclidean Distance (ED) of 16.90 pixels and the fovea center with an ED of 33.45 pixels, demonstrating its effectiveness in overcoming the common limitations of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Spur gearboxes are an integral component in the operation of rotary machines. Hence, the early determination of the severity level of a failure is crucial. This manuscript delineates a methodology for selecting essential mother wavelets and filters from the wavelet transform (WT) to process the vibration signal within the time‐frequency domain, aiming to ascertain the severity level of failures in spur gearboxes. Initially, information is garnered from the gearbox through vibration signals in the time domain, utilising six accelerometers. Subsequently, the signal is partitioned into various levels, and information from each level is extracted using diverse mother wavelets and their respective filters. The signal is segmented into sub‐bands, from which the condition state is ascertained using an energy operator. After that, the appropriate level of wave decomposition is determined through ANOVA tests and post‐hoc Tukey analyses, evaluating performance in failure classification via the Random Forest (RF) model. Upon establishing the decomposition level, the analysis proceeds to identify which mother wavelets and filters are most suitable for determining the severity level of different types of failure in spur gearboxes. Moreover, this study investigates the impact of sensor positioning and inclination on acquiring the vibration signal. This aspect is explored through factorial ANOVA tests and multiple comparisons of the data derived from the sensors. The RF classification model achieved exceedingly favourable results (accuracy >$\,>$96% and AUC >$\,>$98%), with minimal practical influence from the positioning and inclination of a sensor, thereby affirming the proposed methodology's suitability for this type of analysis. [ABSTRACT FROM AUTHOR]

Using monthly data from 19 meteorological stations in Guangxi Zhuang Autonomous region from 1961 to 2020, this study calculated the standardized precipitation evapotranspiration index (SPEI) based on the Penman-Monteith evapotranspiration model (SPEI_PM). The temporal and spatial variation of meteorological drought were analyzed via linear tendency estimation, and Mann-Kendall trend test. The correlation between El Niño-Southern Oscillation (ENSO) and meteorological drought in Guangxi was also explored by cross-correlation function, cross wavelet transform and wavelet coherence. The values for summer, autumn, and annual SPEI_PM from 1961 to 2020 exhibited a significant linear trend of increasing wetness over time. Annual and seasonal droughts throughout the region were predominately mild and moderate, with less frequent severe and extreme droughts. Spring droughts were concentrated in the central part of the region, summer droughts in the southwest, autumn droughts in the southeast and northeast, and winter droughts spread throughout the region. Annual droughts were concentrated in the northeast. The rate of drought occurrence as measured by the stations in Guangxi exhibited a distinctly chronological character. Large-scale droughts (especially severe and extreme droughts) across the entire region occurred most often in the 1960s, 1970s, and 2000s. The impact of ENSO on the meteorological drought in Guangxi was characterized by spatial heterogeneity. Correlation between the ocean Niño index (ONI) and meteorological drought was highest in the northeast and southeast, as well as the northwest. There was no significant correlation between drought and ONI in coastal areas and most southwestern parts of the region. As a typical example from meteorological stations in northeastern Guangxi, SPEI-3 and ONI at the Guilin Meteorological station showed interannual oscillation cycles of 16-48 months in the period 1962-1976, and 12-64 months in the period 1990-2019. These results can serve as a useful reference for future drought forecasting and assessment in Guangxi. [ABSTRACT FROM AUTHOR]

We employ the IllustrisTNG simulation data to investigate the turbulent and thermal motions of the cosmic baryonic fluid. With continuous wavelet transform techniques, we define the pressure spectra, or density-weighted velocity power spectra, as well as the spectral ratios, for both turbulent and thermal motions. We find that the magnitude of the turbulent pressure spectrum grows slightly from |$z=4$| to 2 and increases significantly from |$z=2$| to 1 at large scales, suggesting progressive turbulence injection into the cosmic fluid, whereas from |$z=1$| to 0, the spectrum remains nearly constant, indicating that turbulence may be balanced by energy transfer and dissipation. The magnitude of the turbulent pressure spectra also increases with environmental density, with the highest density regions showing a turbulent pressure up to six times that of thermal pressure. We also explore the dynamical effects of turbulence and thermal motions, discovering that while thermal pressure provides support against structure collapse, turbulent pressure almost counteracts this support, challenging the common belief that turbulent pressure supports gas against overcooling. [ABSTRACT FROM AUTHOR]

Semantic segmentation is crucial for a wide range of downstream applications in remote sensing, aiming to classify pixels in remote sensing images (RSIs) at the semantic level. The dramatic variations in grayscale and the stacking of categories within RSIs lead to unstable inter-class variance and exacerbate the uncertainty around category boundaries. However, existing methods typically emphasize spatial information while overlooking frequency insights, making it difficult to achieve desirable results. To address these challenges, we propose a novel dual-domain fusion network that integrates both spatial and frequency features. For grayscale variations, a multi-level wavelet frequency decomposition module (MWFD) is introduced to extract and integrate multi-level frequency features to enhance the distinctiveness between spatially similar categories. To mitigate the uncertainty of boundaries, a type-2 fuzzy spatial constraint module (T2FSC) is proposed to achieve flexible higher-order fuzzy modeling to adaptively constrain the boundary features in the spatial by constructing upper and lower membership functions. Furthermore, a dual-domain feature fusion (DFF) module bridges the semantic gap between the frequency and spatial features, effectively realizes semantic alignment and feature fusion between the dual domains, which further improves the accuracy of segmentation results. We conduct comprehensive experiments and extensive ablation studies on three well-known datasets: Vaihingen, Potsdam, and GID. In these three datasets, our method achieved 74.56%, 73.60%, and 81.01% mIoU, respectively. Quantitative and qualitative results demonstrate that the proposed method significantly outperforms state-of-the-art methods, achieving an excellent balance between segmentation accuracy and computational overhead. [ABSTRACT FROM AUTHOR]

Objective: Abnormal tumor vascular network contributes to aberrant blood perfusion and reduced oxygenation in tumors, which lead to poor efficacy of chemotherapy and radiotherapy. We aimed to explore the effects of the tumor‐derived exosomes (TDEs) and C188‐9 (a small molecule inhibitor of signal transducer and activator of transcription 3, STAT3) on tumor microvascular hemodynamics and determine which blood flow oscillations for various frequency intervals are responsible for these changes. Methods: Microvascular hemodynamics parameters were recorded using a PeriFlux 6000 EPOS system in tumor surface in a nude mouse subcutaneous xenograft model. Oscillations of laser Doppler flowmetry (LDF) signal were investigated by wavelet transform analysis. Results: TDEs facilitated tumor growth at least partially was associated with increasing blood flow in smaller vessels with lower speed and decreasing the blood flow at larger vessels with higher speed. Lower oxyhemoglobin saturation (SO2) on tumor surface was aggravated by TDEs, and C188‐9 treatment significantly alleviated this decrease. Wavelet transform spectral analysis revealed that TDEs increased the amplitude of oscillations in four frequency intervals related to endothelial (NO‐dependent and ‐independent), myogenic and neurogenic activities, and C188‐9 had no effect on this increase. Conclusions: TDEs facilitated tumor growth partially was associated with increasing blood flow in distributing vessels, reducing blood perfusion in larger vessels, and lowering SO2 on tumor surface. Enhanced vascular smooth muscle, endothelial and neurogenic activities occurred in tumor superficial zone. [ABSTRACT FROM AUTHOR]

Although the classical spectral representation method (SRM) has been widely used in the generation of spatially varying ground motions, there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice. The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function. Another difficulty is the estimation of evolutionary power spectral density (EPSD) with quite a few samples. To tackle these problems, the wavelet packet transform (WPT) algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain. The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process. For the simulation of spatially varying ground motions, the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models. Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques. In addition to a good match with the target coherency model, two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording, including waveform, temporal/spectral non-stationarity, normalized energy buildup, and significant duration. [ABSTRACT FROM AUTHOR]

Face forgery detection (FFD) on digital images has become increasingly challenging with the proliferation of sophisticated manipulation techniques. In this study, we propose a novel approach, named Adaptive Wavelet-based Transformer with Self-paced Auto Augmentation (SA 3 WT), which naturally combines the global representation capabilities of visual transformers with adaptive enhancement of fine-grained artifacts in the frequency domain to effectively capture forgery patterns. In particular, to adequately handle various clues, the network incorporates Wavelet-based Mixed Attention (WMA) Transformer block to better leverage the information residing in all frequency sub-bands and a Residual Reserve Fine-grained Sampler (RRFS) to enhance detailed forgery artifacts while learning hierarchical global representations. By deeply mixing the modeling processes of global representations and fine-grained features throughout the network, the model captures rich forgery clues while simultaneously bypassing the fusion issue arising from their separate extraction. Furthermore, Self-paced Auto Augmentation Strategy (SAAS) facilitates model learning by unifying data augmentation and active learning in a coupled manner. Extensive experiments conducted on several benchmarks demonstrate the superiority of SA 3 WT compared to state-of-the-art methods. The ablation studies and cross-dataset evaluations confirm the significance of the specifically designed modules, in terms of both effectiveness and generalization. Our findings suggest that the pure visual transformers also provide a promising direction for advanced forgery detection in real-world scenarios. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions In medical image segmentation, a domain gap often exists between training and testing datasets due to different scanners or imaging protocols, which leads to performance degradation in deep learning‐based segmentation models. Given the high cost of manual labeling and the need for privacy protection, it is often challenging to annotate the testing (target) domain data for model fine‐tuning or to collect data from different domains to train domain generalization models. Therefore, using only unlabeled target domain data for test‐time adaptation (TTA) presents a more practical but challenging solution.To improve the segmentation accuracy of deep learning‐based models on unseen datasets, and especially to enhance the efficiency and stability of TTA for individual samples from heterogeneous domains.In this study, we proposed to dynamically adapt a wavelet‐VNet (WaVNet) to unseen target domains with a hybrid objective function, based on each unlabeled test sample during the test time. We embedded multiscale wavelet coefficients into a V‐Net encoder and adaptively adjusted the spatial and spectral features according to the input, and the model parameters were optimized by three loss functions. We integrated a shape‐aware loss to focus on the foreground segmentations, a Refine loss to correct the incomplete and noisy segmentations caused by domain shifts, and an entropy loss to promote the global consistency of the segmentations. We evaluated the proposed method on multidomain liver and prostate segmentation datasets to assess its advantages over other TTA methods. For the source domain model training of the liver dataset, we used 15 3D MR image samples for training and 5 for validation. Correspondingly, for the prostate dataset, we used 22 3D MR image samples for training and 7 for validation. In the target domain, we used a single 3D MR image sample for adaptation and testing. The total number of testing samples is 60 in the liver dataset (for 3 different domains) and 116 in the prostate dataset (for 6 different domains).The proposed method showed the highest segmentation accuracy among all methods, achieving a mean (± SD) Dice coefficient (DSC) of 78.10 ± 5.23% and a mean 95th Hausdorff distance (HD95) of 15.52 ± 5.84 mm on the liver dataset; and a mean DSC of 80.02 ± 3.89% and a mean HD95 of 9.18 ± 3.47 mm on the prostate dataset. The DSC is 11.67% (in absolute terms) and 15.27% higher than that of the baseline (no adaptation) method, for the liver and the prostate datasets, respectively.The proposed adaptive WaVNet enhanced the image segmentation accuracy from unseen domains during the test time via unsupervised learning and multi‐objective optimization. It can benefit clinical applications where data are scarce or with changing data distributions, including online adaptive radiotherapy. The code will be released at: https://github.com/sanny1226/WaVNet. [ABSTRACT FROM AUTHOR]

Speech enhancement, or SE, is a method of converting an input speech signal into a target signal with improved quality of voice and readability. To hear the voice, the skeleton bone vibrates ultra smooth thanks to bone conduction. The benefits of Bone-Conducted Microphone (BCM) speech include noise reduction and enhanced communication quality in high-noise environments. To acquire signals and precisely model word phonemes, BCM relies on the placement of bones. Certain computer techniques are expensive and ineffective in simulating signal phonemes. Three wavelet transform techniques are presented in this work: complex continuous wavelet transforms (CCWT), steady wavelet transforms (SWT), and discrete wavelet transforms (DWT). The right ramp, the voice box, and the mastoid were the three distinct bony locations for which the speech intelligibility of the BCM signal was evaluated. The listener evaluated the comprehension of the speech after obtaining the BCM signal for Tamil words. Speech quality is enhanced by the location of the larynx bone in comparison to alternative calculation methods. [ABSTRACT FROM AUTHOR]

As an important piece of equipment in industrial production, the health of an air compressor directly impacts the success of production. Therefore, researching fault diagnosis methods for air compressors is of significant importance in improving the continuity, reliability, and safety of production. Traditional fault diagnosis methods, however, struggle to obtain accurate fault features. The measurement of feature distribution differences between various working conditions lacks sufficient domain adaptability, making it challenging to achieve high recognition accuracy. Additionally, the operation of air compressors generates background noise, which can introduce interference and impact the accuracy in fault detection. In order to overcome these limitations, a fault diagnosis method for air compressors based on feature fusion is proposed. Firstly, the Mel-frequency cepstral coefficients (MFCC) features and wavelet transform features of the air compressor are extracted separately. Then, late fusion is applied at the decision level to combine confidence scores and predicted bounding boxes. The best network model is determined based on evaluation metrics to complete the classification. Based on the experimental results analysis, the feature fusion method demonstrated superior recognition performance. [ABSTRACT FROM AUTHOR]

The primary goal of the present paper is to examine variations in frequency and intensity of cyclonic storms over Arabian Sea using Accumulated cyclone energy (ACE) and Genesis Potential Index (GPI) from 1991–2020.For this purpose wavelet analysis is applied to surface and sub-surface ocean parameters viz Sea surface temperature (SST), Latent heat flux (LHF), Mixed layer depth (MLD), Depth of D26 Isotherm and Tropical cyclone heat potential (TCHP) to delve the probable changes. It is observed that the ACE signifies the intensity of tropical storms, has risen by 17.6% during the recent decade (2011–2020). LHF and SST are in phase with the intensification of cyclonic storms from Continuous wavelet transform (CWT) power spectrum analysis. Both SST and GPI were strongly correlated(0.8, 95% significant level) during pre-monsoon season.Siginifcant coherence was observed between frequency of cyclonic storms and TCHP in the recent decades through wavelet coherence transform technique. The cross wavelet transform (XWT) shows the frequency of cyclonic storms and SSTs are in phase during Indian Ocean dipole events. By these observations,this study helps in advance forecasting about formation and intensification of cyclonic storms over Arabian Sea. [ABSTRACT FROM AUTHOR]

Tool wear state recognition is a challenging problem because the various types of signals corresponding to different wear states have similar features, making classification difficult. Convolutional neural networks are widely used in the field of tool wear state recognition. To address this problem, this study proposes a milling cutter wear state monitoring model that combines KANS and deep residual networks (ResNet). The traditional ResNet-34's top linear classifier was replaced with a nonlinear convolutional classifier including _top_kan, and the data were preprocessed using continuous wavelet transform (CWT) to enhance the model's immunity to interference and feature characterization. The experimental results based on the PHM dataset show that the improved KANS-ResNet-34 model improves accuracy by 1.07% compared to ResNet-34, making it comparable to ResNet-50, while its computation time is only 1/33.68 of the latter. This significantly improves computational efficiency, reduces the pressure on hardware resources, and provides an effective tool wear state recognition solution. [ABSTRACT FROM AUTHOR]

In brain–computer interface (BCI) systems, motor imagery (MI) electroencephalogram (EEG) is widely used to interpret the human brain. However, MI classification is challenging due to weak signals and a lack of high-quality data. While deep learning (DL) methods have shown significant success in pattern recognition, their application to MI-based BCI systems remains limited. To address these challenges, we propose a novel deep learning algorithm that leverages EEG signal features through a two-branch parallel convolutional neural network (CNN). Our approach incorporates different input signals, such as continuous wavelet transform, short-time Fourier transform, and common spatial patterns, and employs various classifiers, including support vector machines and decision trees, to enhance system performance. We evaluate our algorithm using the BCI Competition IV dataset 2B, comparing it with other state-of-the-art methods. Our results demonstrate that the proposed method excels in classification accuracy, offering improvements for MI-based BCI systems. [ABSTRACT FROM AUTHOR]

Bird research contributes to understanding species diversity, ecosystem functions, and the maintenance of biodiversity. By analyzing bird images and the audio of birds, we can monitor bird distribution, abundance, and behavior to better understand the health of ecosystems. However, bird images and audio involve a vast amount of data. To improve the efficiency of data transmission and storage efficiency and save bandwidth, compressive sensing can overcome this challenge. Compressive sensing is a technique that uses the sparsity of signals to recover original data from a small number of linear measurements. This paper introduces a deep neural network based on the Iterative Shrinkage Thresholding Algorithm (ISTA) and a Convolutional Block Attention Module (CBAM), CBAM_ISTA-Net+, for the compressive reconstruction of bird images, audio Mel spectrograms and wavelet transform spectrograms. Using 45 bird species as research subjects, including 20 bird images, 15 audio-generated Mel spectrograms, and 10 audio wavelet transform (WT) spectrograms, the experimental results show that CBAM_ISTA-Net+ achieves a higher peak signal-to-noise ratio (PSNR) at different compression ratios. At a compression ratio of 50%, the average PSNR of the three datasets reaches 33.62 dB, 55.76 dB, and 38.59 dB, while both the Mel spectrogram and wavelet transform spectrogram achieve more than 30 dB at compression ratios of 25–50%. These results highlight the effectiveness of CBAM_ISTA-Net+ in maintaining high reconstruction quality even under significant compression, demonstrating its potential as a valuable tool for efficient data management in ecological research. [ABSTRACT FROM AUTHOR]

Recently neural style transfer (NST) has drawn a lot of interest of researchers, with notable advancements in color representation, texture, speed, and image quality. While previous studies focused on transferring artistic style across entire content images, a new approach proposes to transfer style specifically to objects within the content image based on the style image and maintain photorealism. Recent techniques have produced intriguing creative effects, but often only work with artificial effects, leaving real flaws visible in photographs used as references for styles. The suggested approach employs a two-dimensional wavelet transform (WT) to achieve style transfer by adjusting image structure with high-pass and low pass filters (LPF). Preserving the information content and numerical attributes of VGGNet19 through WT-based style transfer using the db5 WT at level 5, we can achieve a peak signal-to-noise ratio (PSNR) value of up to 96.76725. The qualitative result of the proposed methodology is compared with other existing algorithm. Also, the time complexity of the proposed methodology on different hardware platforms has been calculated and presented in the paper. The proposed methodology able to maintains appealing and precise quality of resultant image. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (MSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the M5 model tree, a decision tree-based model. The M5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-M5-ANN method compared to other available hybrid methods. Specifically, the calculated R2 was 0.93 for the proposed WT-M5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN (WANN) and WT-M5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale. [ABSTRACT FROM AUTHOR]

Hidden capacity, concealment, security, and robustness are essential indicators of hiding algorithms. Currently, hiding algorithms tend to focus on algorithmic capacity, concealment, and security but often overlook the robustness of the algorithms. In practical applications, the container can suffer from damage caused by noise, cropping, and other attacks during transmission, resulting in challenging or even impossible complete recovery of the secret image. An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms. In this proposed algorithm, a secret image of size 256 × 256 is first decomposed using an eight-level Haar wavelet transform. The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands, which are then embedded into the carrier image via a hiding network. During the recovery process, the container image is divided into four non-overlapping parts, each employed to reconstruct a low-resolution secret image. These low-resolution secret images are combined using dense modules to obtain a high-quality secret image. The experimental results showed that even under destructive attacks on the container image, the proposed algorithm is successful in recovering a high-quality secret image, indicating that the algorithm exhibits a high degree of robustness against various attacks. The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain, making it suitable for practical applications. In conclusion, the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms. Its ability to recover high-quality secret images even in the presence of destructive attacks makes it an attractive option for various applications. Further research and experimentation can explore the algorithm's performance under different scenarios and expand its potential applications. [ABSTRACT FROM AUTHOR]

On rainy days the uncertainty of the shape and distribution of rain streaks can cause the images captured by RGB image-based measurement tools to be blurred and distorted. Thanks to the wavelet transform ability to provide spatial and frequency domain information about an image and its multidirectional and multiscale nature, it is widely used in traditional image enhancement methods. In image deraining the distribution of rain streaks is not only related to spatial domain features but is also closely related to frequency domain spatial features. However, deep learning-based rain removal models mainly rely on the spatial features of the image, and RGB data can hardly distinguish rain marks from image details, which leads to the loss of crucial image information during rain removal. We have developed a lightweight single-image rain removal model called the deep wavelet transform network (DWTN) to address this limitation. This method separates image details from rain images and can more effectively remove rain marks. The proposed DWTN has three significant contributions. First, DWTN uses the feature components after the wavelet transform as the input to the model and assigns a separate frequency-aware enhancement block (FAEB) to each element. These blocks focus on specific frequency features that benefit the rain removal task. Second, we introduce a frequency feature fusion block (FFFB) that fuses different wavelet components to reduce noise and enhance the image background through a channel attention mechanism while attenuating rain streaks. Finally, we design a spatial feature enhancement block (SFEB), which uses a spatial attention mechanism to calibrate the spatial position of features to improve the rain removal performance. We evaluate the performance of DWTN using PSNR and SSIM on four synthetic datasets and NIQE and BRISQUE on two real datasets. The results of the evaluation of the six datasets and at least four performance metrics show that the proposed DWTN is superior to existing methods. [ABSTRACT FROM AUTHOR]

Vibration data from mechanical equipment contain extensive information distributed across multiple dimensions. Single‐scale analysis fails to comprehensively reflect its damage characteristics, thereby reducing fault diagnosis accuracy. This study proposes a novel signal vibration feature extraction method called hierarchical refined composite generalized multiscale fluctuation dispersion entropy (HRCGMFDE). This method simultaneously extracts fault information from both low‐frequency and high‐frequency components of the data, addressing the drawback of high‐frequency information loss in refined composite generalized multiscale fluctuation dispersion entropy (RCMFDE). Comparative results on two simulated signals demonstrate the method's advantages of high stability and more accurate complexity measurement. Furthermore, low‐frequency and high‐frequency components of the data are comprehensively extracted using dual‐tree complex wavelet packet transform (DTCWPT), and high‐dimensional features are downscaled using t‐distributed stochastic neighbor embedding (t‐SNE) to obtain low‐dimensional sensitive fault features. Subsequently, a Random Forest (RF) classifier is employed for fault identification. Finally, the effectiveness of the proposed method is validated using three typical mechanical datasets. Results confirm the method's capability to effectively determine the fault states of bearings, gearboxes, and centrifugal pumps, showcasing significant advantages over comparative methods. [ABSTRACT FROM AUTHOR]

Background: Scrub typhus (ST, also known as tsutsugamushi disease) is a common febrile vector-borne disease in South Korea and commonly known as autumn- and female-dominant disease. Although understanding changes in seasonality and sex differences in ST is essential for preparing health interventions, previous studies have not dealt with variations in periodicity and demographic characteristics in detail. Therefore, we aimed to quantify the temporal dynamics of seasonal patterns and sex differences in the incidence of ST in South Korea. Methods: We extracted epidemiological week (epi-week)-based ST cases from 2003 to 2019 Korean National Health Insurance Service data (ICD-10-CM code: A75.3). To determine changes in seasonality and sex differences, year-, sex-, and age-group-stratified male-to-female ratios and wavelet transform analyses were conducted. Results: Between 2003 and 2019, 213,976 ST cases were identified. The incidence per 100,000 population increased by 408.8% from 9.1 in 2003 to 37.2 in 2012, and subsequently decreased by 59.7% from 2012 to 15.0 in 2019. According to the continuous wavelet transform results, ST exhibited a dual seasonal pattern with dominant seasonality in autumn and smaller seasonality in spring from 2005 to 2019. Overall, the periodicity of seasonality decreased, whereas its strength decreased in autumn and increased in spring. With an overall male-to-female ratio being 0.68:1, the ratio has increased from 0.67:1 in 2003 to 0.78:1 in 2019 (Kendall's τ = 0.706, p < 0.001). However, interestingly, the ratio varied significantly across different age groups. Conclusions: Our findings quantitatively demonstrated changes in seasonality with dual seasonal pattern and shortened overall periodicity and a decrease in sex differences of ST in South Korea. Our study suggests the need for continuous surveillance on populations of vector and host to address ST dynamics to preemptively prepare against global warming. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) diseases are a significant global health issue, causing millions of deaths annually. This study presents a novel method for classifying GI diseases using endoscopy videos. The proposed method involves three major phases: image processing, feature extraction, and classification. The image processing phase uses wavelet transform for segmentation and an adaptive median filter for denoising. Feature extraction is conducted using a concatenated recurrent vision transformer (RVT) with two inputs. The classification phase employs an ensemble of four classifiers: support vector machines, Bayesian network, random forest, and logistic regression. The system was trained and tested on the Hyper–Kvasir dataset, the largest publicly available GI tract image dataset, achieving an accuracy of 99.13% and an area under the curve of 0.9954. These results demonstrate a significant improvement in the accuracy and performance of GI disease classification compared to traditional methods. This study highlights the potential of combining RVTs with standard machine learning techniques and wavelet transform to enhance the automated diagnosis of GI diseases. Further validation on larger datasets and different medical environments is recommended to confirm these findings. [ABSTRACT FROM AUTHOR]

In this work, a blind watermarking scheme has been proposed for medical image authentication. The scheme can embed patients' records in digital format into a digital copy of a computerized scan. In this scheme, an Integer Wavelet Transform (IWT) is applied to the medical image prior to the embedding procedure. Here LH, HH, and HL sub-bands of the IWT are used for watermark embedding. To ensure the security of the scheme a SHA-512 hash algorithm is applied to the watermark, and the hash value is embedded within the digital copy of the scanned image to ensure the authenticity of the scanned copy. Depending on a shared secret key, the IWT coefficients are selected from random positions before embedding the watermark. The results show that the proposed scheme achieved 78.5 dB PSNR with an NCC value of 0.9805, and SSIM is near about 1 after embedding 65,280 bits of watermark information. The experimental outcomes are compared with some state-of-the-art schemes to show the superiority of the proposed scheme in terms of robustness and imperceptibility. [ABSTRACT FROM AUTHOR]

Suspension bridge hangers over 100m are prone to wind-induced vibration. To investigate the vibration characteristics of a suspension bridge hanger over 140m with two cables on a day affected by typhoon Sura, the field measurement of the wind field and acceleration of two cables at different heights was performed. The wavelet transform method was introduced to analyze the time–frequency characteristics of the hanger and girder, while based on the stochastic subspace identification method, the damping ratios of the hanger were identified. The cluster analysis was performed to investigate the relationship between the wind field and hanger vibration. The result of measurement at the site indicated that under the nonstationary wind, the hanger exhibited three different forms of vibration: vortex-induced vibration, buffeting, and another high-frequency vibration. The first two forms may be caused by the incoming wind, and the last one may be due to the vibration of the bridge deck. The vibration was the same between two cables, but different in multiple heights of a cable. For the same cable, there existed a linear relationship between the in-plane and out-of-plane vibration. The hanger exhibited single-mode and multimode vibrations with an elliptical or eight-shaped motion trajectory. For the multimode vibration, a clear energy alternation phenomenon between the dominant frequency and subdominant frequency was found. The out-of-plane acceleration was greater than the in-plane vibration due to the damping difference in the two directions. The wind field was divided into five clusters, and the clusters of the wind field were related to the mode of hanger vibration. [ABSTRACT FROM AUTHOR]

In the structural health monitoring system, a large amount of testing data from sensors has been used to detect the health status of a structure. However, due to factors such as sensor failure and electromagnetic interference, the data collected by the structural health monitoring system may exhibit various abnormal states, which will affect the accuracy of structural damage identification and state assessment. How to process large amounts of data with high accuracy and recognize those abnormal data with high efficiency poses significant challenges. This paper proposes an innovative deep-learning method for data abnormality diagnosis by designing and establishing a self-attention residual network (SARN) model. In the method, all the monitoring data, including the abnormal data, are first converted into a time–frequency diagram by continuous wavelet transform, which are then used as the input of the SARN model for training. Then the convolutional layer, batch normalization, max pooling layer, the set self-attention residual building unit-1, and unit-2 are established successively to process the data, and finally the abnormal data can be identified by deep-learning technique. The proposed method broadens the receptive field of convolutional neural networks limited by the size of convolution kernels, allowing for the extraction of richer global features of data, thereby enabling high-precision automatic diagnosis and classification of a large amount of abnormal monitoring data. As an application case, a long-span cable-stayed bridge being monitored is introduced, and its acceleration monitoring data during one month are used as a dataset, and the developed SARN model is used for data anomaly diagnosis. The test accuracy of the model is found to be 97.80%, indicating its efficiency and accuracy in identifying abnormal data in the structural health monitoring dataset. [ABSTRACT FROM AUTHOR]

The digital voice is multimedia content of great importance, given the range of applications where it can be found. This paper addresses the shortcomings of existing voice authentication algorithms, presenting a completely blind speech authentication and recovery method based on fragile watermarking using the Least Significant Bit (LSB) method. This scheme obtains a compressed version of the original speech signal by Adaptive Differential Pulse Code Modulation (ADPCM) coding and the Discrete-Time Wavelet Transform (DTWT). Authentication bits are then generated by the SHA256 hash function, and the watermark is afterward embedded in the last three LSBs of the original audio samples. Experimental results evaluated on five different audio databases, each comprising speech signals recorded in different situations, contexts, and languages, have demonstrated a high embedding payload and imperceptibility of the watermark, obtaining an average Signal-to-Noise Ratio (SNR) value above 40 d B . Furthermore, the proposed method demonstrates a strong ability to accurately locate and restore up to 50% of a speech signal that has been tampered with, using no additional information. Moreover, the recovered speech signal is intelligible and has an SNR value higher than other recovery schemes, justifying the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]