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The dynamic mode decomposition (DMD)—a popular method for performing data-driven Koopman spectral analysis—has gained increased popularity for extracting dynamically meaningful spatiotemporal descriptions of fluid flows from snapshot measurements. Often times, DMD descriptions can be used for predictive purposes as well, which enables informed decision-making based on DMD model forecasts. Despite its widespread use and utility, DMD can fail to yield accurate dynamical descriptions when the measured snapshot data are imprecise due to, e.g., sensor noise. Here, we express DMD as a two-stage algorithm in order to isolate a source of systematic error. We show that DMD’s first stage, a subspace projection step, systematically introduces bias errors by processing snapshots asymmetrically. To remove this systematic error, we propose utilizing an augmented snapshot matrix in a subspace projection step, as in problems of total least-squares, in order to account for the error present in all snapshots. The resulting unbiased and noise-aware total DMD (TDMD) formulation reduces to standard DMD in the absence of snapshot errors, while the two-stage perspective generalizes the de-biasing framework to other related methods as well. TDMD’s performance is demonstrated in numerical and experimental fluids examples. In particular, in the analysis of time-resolved particle image velocimetry data for a separated flow, TDMD outperforms standard DMD by providing dynamical interpretations that are consistent with alternative analysis techniques. Further, TDMD extracts modes that reveal detailed spatial structures missed by standard DMD.

Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these components often leads to costly downtime and potential safety risks, further emphasizing the importance of monitoring their health state. Vibration signal analysis is now a common approach for this purpose, as it provides useful information related to the dynamic behavior of machines. This research aimed to conduct a comprehensive examination of the current methodologies employed in the stages of vibration signal analysis, which encompass preprocessing, processing, and post-processing phases, ultimately leading to the application of Artificial Intelligence-based diagnostics and prognostics. An extensive search was conducted in various databases, including ScienceDirect, IEEE, MDPI, Springer, and Google Scholar, from 2020 to early 2024 following the PRISMA guidelines. Articles that aligned with at least one of the targeted topics cited above and provided unique methods and explicit results qualified for retention, while those that were redundant or did not meet the established inclusion criteria were excluded. Subsequently, 270 articles were selected from an initial pool of 338. The review results highlighted several deficiencies in the preprocessing step and the experimental validation, with implementation rates of 15.41% and 10.15%, respectively, in the selected prototype studies. Examination of the processing phase revealed that time scale decomposition methods have become essential for accurate analysis of vibration signals, as they facilitate the extraction of complex information that remains obscured in the original, undecomposed signals. Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. Given the conclusions drawn, future researchers are urged to investigate the interpretability and integration of the diagnosis and prognosis models developed with the aim of applying them in real-time industrial contexts. Furthermore, there is a need for experimental studies to disclose the preprocessing details for datasets and the operational conditions of the machinery, thereby improving the data reproducibility. Another area that warrants further investigation is differentiation of the various types of fault information present in vibration signals obtained from bearings, as the defect information from the overall system is embedded within these signals.

Electroencephalogram (EEG) signal analysis has become an interesting and required area in the medical industry to analyze brain function for different diseases. But, the EEG signal’s noise features might degrade the signal prediction's exactness score. So, the presented article aims to develop a novel EEG signal analysis system named a novel Firefly-based Deep Belief Signal Specification (FbDBSS). In addition, the disease signal considered in this research work is Schizophrenia (SZ) signal. Initially, the SZ signal with a normal EEG signal is trained to the system, and preprocessing function is performed. Then the filtered signal is entered into the classification layer for the feature extraction and signal analysis function. Furthermore, the proposed design is executed in the python environment, and the robustness score has been measured in terms of accuracy, sensitivity, and error rate. The chief parameter of the proposed FbDBSS design is compared with other models and has gained the finest 3% of improved signal analysis accuracy and sensitivity score.

Saliva has great promise as a background fluid for noninvasive biomarker monitoring at the point of care. However, detection in saliva can be challenging due to its complex composition, which can potentially interfere with analyte signal quantification. In the context of electrochemical sensing, the complexity of saliva can lead to a high and variable background current such that robust quantification of the analyte signal is challenging. Simple algorithms that work for quantification in well-defined buffer backgrounds may not be ideal for analysis in complex biofluids. To address this, we investigated an analysis method for robust signal extraction from voltammograms measured in minimally processed saliva. Our sequence of voltammogram manipulations consists of (1) log-transformation for improved handling of systematic variation, (2) smoothing for high-frequency noise reduction, (3) normalization via subtraction of an interpolated spline fit of the analyte-peak-censored voltammogram, and (4) extraction of a peak feature as the analyte signal—peak curvature, height, or area. In the context of measuring the concentration of the drug carbamazepine in saliva, we systematically determined reasonable parameter values for the manipulations, and evaluated the analysis method using multiple metrics: signal coefficient of variation, Welch’s t -statistic, and percent difference between predicted and actual signal. We found that log-transformation of the voltammogram current data resulted in overall improved metrics for positive drug concentrations across multiple datasets and for the peak features considered. Comparison across the different peak features indicated that using peak area or peak height resulted in improved resolution and accuracy over peak curvature. Graphical abstract

Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.

The number of fluorescent labels that can unambiguously be distinguished in a single image when acquired through band pass filters is severely limited by the spectral overlap of available fluorophores. The recent development of spectral microscopy and the application of linear unmixing algorithms to spectrally recorded image data have allowed simultaneous imaging of fluorophores with highly overlapping spectra. However, the number of distinguishable fluorophores is still limited by the unavoidable decrease in signal to noise ratio when fluorescence signals are fractionated over multiple wavelength bins. Here we present a spectral image analysis algorithm to greatly expand the number of distinguishable objects labeled with binary combinations of fluorophores. Our algorithm utilizes a priori knowledge about labeled specimens and imposes a binary label constraint on the unmixing solution. We have applied our labeling and analysis strategy to identify microbes labeled by fluorescence in situ hybridization and here demonstrate the ability to distinguish 120 differently labeled microbes in a single image.

In this paper, we have proposed complex Fourier–Bessel series expansion-based empirical wavelet transform (CFBSE-EWT) and Hilbert spectral analysis (HSA) for time-frequency analysis of complex-valued signals. The proposed method obtains the real-valued positive and negative frequency components of the complex-valued signal using a suitable filter. Further, the obtained real-valued components are decomposed into corresponding set of subband signals using the Fourier–Bessel series expansion-based empirical wavelet transform (FBSE-EWT) method. The HSA is applied on the subband signals to obtain the time-frequency distribution (TFD). The effectiveness of the proposed CFBSE-EWT has been evaluated on two synthetic multicomponent complex-valued signals and a real-life wind signal. The decomposition results of CFBSE-EWT method are also compared with complex empirical mode decomposition (CEMD), complex flexible analytic wavelet transform (CFAWT), complex variational mode decomposition (CVMD), and complex improved eigenvalue decomposition of Hankel matrix (CIEVDHM) using the quality of reconstruction factor as performance objective measure. Additionally, the TFD of the synthetic complex-valued signals and real-life complex-valued wind signal is obtained from the proposed CFBSE-EWT-based HSA and compared with the CEMD-based HSA, CFAWT-based HSA, CVMD-based HSA, and CIEVDHM-based HSA methods. The CFBSE-EWT-based HSA provides improved TFD and it is useful for analysis of real-life complex-valued signals.

Bearings represent crucial components within rotating machinery, and unexpected failures can lead to significant damage and unplanned breakdowns. This paper introduces a novel approach to diagnose bearing faults under variable working conditions, leveraging the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sequential backward selection (SBS). CEEMDAN automatically selects intrinsic mode functions (IMFs) from vibration and current signals to establish a comprehensive set of health indicators. Subsequently, the SBS algorithm identifies the most pertinent indicators for different bearing failure modes. The accuracy of the proposed method is evaluated on both vibration and electrical signals using data from a dedicated test bench at the Signal and Industrial Process Analysis Laboratory (LASPI). Results demonstrate the effectiveness of the proposed method in accurately identifying and classifying bearing faults across various working conditions, utilizing both types of signals. This approach holds promise for real-world industrial applications, offering a reliable method for condition monitoring and diagnostics in bearing systems.