Signal Analysis Research Articles

Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.

Effects of expectation on face perception and its association with expertise

Perceptual decisions are derived from the combination of priors and sensorial input. While priors are broadly understood to reflect experience/expertise developed over one’s lifetime, the role of perceptual expertise at the individual level has seldom been directly explored. Here, we manipulate probabilistic information associated with a high and low expertise category (faces and cars respectively), while assessing individual level of expertise with each category. 67 participants learned the probabilistic association between a color cue and each target category (face/car) in a behavioural categorization task. Neural activity (EEG) was then recorded in a similar paradigm in the same participants featuring the previously learned contingencies without the explicit task. Behaviourally, perception of the higher expertise category (faces) was modulated by expectation. Specifically, we observed facilitatory and interference effects when targets were correctly or incorrectly expected, which were also associated with independently measured individual levels of face expertise. Multivariate pattern analysis of the EEG signal revealed clear effects of expectation from 100 ms post stimulus, with significant decoding of the neural response to expected vs. not stimuli, when viewing identical images. Latency of peak decoding when participants saw faces was directly associated with individual level facilitation effects in the behavioural task. The current results not only provide time sensitive evidence of expectation effects on early perception but highlight the role of higher-level expertise on forming priors.

SVD-Based Mind-Wandering Prediction from Facial Videos in Online Learning.

This paper presents a novel approach to mind-wandering prediction in the context of webcam-based online learning. We implemented a Singular Value Decomposition (SVD)-based 1D temporal eye-signal extraction method, which relies solely on eye landmark detection and eliminates the need for gaze tracking or specialized hardware, then extract suitable features from the signals to train the prediction model. Our thorough experimental framework facilitates the evaluation of our approach alongside baseline models, particularly in the analysis of temporal eye signals and the prediction of attentional states. Notably, our SVD-based signal captures both subtle and major eye movements, including changes in the eye boundary and pupil, surpassing the limited capabilities of eye aspect ratio (EAR)-based signals. Our proposed model exhibits a 2% improvement in the overall Area Under the Receiver Operating Characteristics curve (AUROC) metric and 7% in the F1-score metric for 'not-focus' prediction, compared to the combination of EAR-based and computationally intensive gaze-based models used in the baseline study These contributions have potential implications for enhancing the field of attentional state prediction in online learning, offering a practical and effective solution to benefit educational experiences.

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review.

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

On the properties of the anisotropic multivariate Hermite-Gauss functions

The Hermite-Gauss basis functions have been extensively employed in classical and quantum optics due to their convenient analytic properties. A class of multivariate Hermite-Gauss functions, the anisotropic Hermite-Gauss functions, arise by endowing the standard univariate Hermite-Gauss functions with a positive definite quadratic form. These multivariate functions admit useful applications in optics, signal analysis and probability theory, however they have received little attention in literature. In this paper, we examine the properties of these functions, with an emphasis on applications in computational optics.

A real-time signal processing software package for the electrophysiology laboratory.

Real-time signal processing has to date been difficult to implement in the clinical electrophysiology laboratory. To date, no open access software solutions are available in electrophysiology (EP) laboratories to facilitate real-time intraprocedural signal analysis. We aimed to develop an open access, scalable Python plug-in to allow real-time signal processing during human EP procedures. A Python-based plug in for the widely available EnsiteX mapping system was developed. This plug-in utilized the LiveSync feature of the system to allow real-time signal analysis. An open access library was developed to allow end-users to implement real-time signal analysis using this platform, implemented in the Python programming language https://github.com/anand9176/WaveWatch5000Public. We have developed and demonstrated the feasibility of a readily scalable and open-access Python-based plug in to an electroanatomic mapping system (EnSiteX) to allow real-time processing and display of electrogram (EGM) based information for the procedural electrophysiologist to view intraprocedurally in the electrophysiology laboratory. The availability, to the clinician, of traditional and novel EGM-based metrics at the time of intervention, such as atrial fibrillation ablation, allows for key mechanistic insights into critical unresolved questions regarding arrhythmia mechanism.

Hybrid emerging model predictive data-driven forecasting of three-phase electrical signals of photovoltaic systems using GBR-LSTM

In numerous industrial contexts, precise analysis and forecasting of electrical signals within three-phase systems are indispensable. As a result, this work presents DeepPhase, a hybrid framework that combines Long Short-Term Memory (LSTM) neural networks with gradient-boosted regression (GBR) to predict the current, voltage, and power of electrical signals. The performance of the model is evaluated in comparison to benchmark models, namely Bidirectional LSTM (BiLSTM), K-Nearest Neighbors (KNN), and LSTM, which utilize essential Key Performance Indicators (KPIs). As demonstrated by its highest Coefficient of Determination (R2) of 0.999, Mean Absolute Error (MAE) of 6.94 × 10−5, Mean Absolute Percentage Error (MAPE) of 0.07 %, and Root Mean Square Error (RMSE) of 0.000156, DeepPhase consistently exhibits predictive precision. For Three-Phase Current, MAE is 2.13 × 10−3, MAPE is 0.01 %, RMSE is 0.062432, and R2 is 0.960596; and for Three-Phase Voltage, MAE is 9.52E-03, MAPE is 0.03 %, RMSE is 0.014, and R2 is 0.999. The results of this study highlight the effectiveness of DeepPhase in analyzing the dynamics of complex Three-Phase electrical signals. This has significant implications for improving decision-making and control strategies in complex electrical systems.

COMPUTER SIMULATION OF THE DIAGNOSTIC SYSTEM OF BRAKING CIRCUITS OF FREQUENCY-CONTROLLED ASYNCHRONOUS ELECTRIC DRIVES CONVERTERS

This research is essential for improving the reliability and efficiency of frequency-controlled asynchronous electric drives, especially in industrial environments where uninterrupted production processes are critical. The primary goal of the research is to develop a computer model for a brake system diagnostic system that allows for the timely detection of issues and failures in these systems. One of the key aspects of your research is the use of computer modeling, particularly in the MatLab Simulink environment. This enables engineers and researchers to more easily develop and test models, reducing research time and costs. An important element of such a model is the use of logic-time functions and wavelet diagnostics. These methods allow for the analysis of signals from brake systems from different perspectives and the detection of anomalies in their operation. The research also focuses on mathematical models of brake systems that are integrated into computer programs for simulating the operation of electric drives. Analyzing critical parameters and characteristics of brake circuits helps identify potential problems or failures in advance. Diagnostic algorithms based on data obtained from computer models enable the timely determination of the brake system's condition and the necessary decision-making to enhance safety and efficiency in electric drive operation. The results obtained from your research have significant practical importance as they help maintain the reliability and safety of industrial systems at a high level. Brake system diagnostics in frequency-controlled asynchronous electric drives become more accessible and efficient through the use of mathematical models and computer modeling in MatLab Simulink. This contributes to increased productivity and reduced risk of accidents in industrial systems.

Application of variational mode decomposition in vibration-based bridge damage detection

Bridges are critical infrastructure that require continuous structural health monitoring to ensure safety. Vibration-based methods utilize changes in dynamic response for damage detection. This study applies variational mode decomposition (VMD), a signal analysis technique, to detect damage in a three-span beam bridge model subjected to moving loads. A finite element model is developed to represent the bridge-vehicle interaction, and damage is simulated by reducing flexural stiffness. Sensors placed on each span record vibrations during vehicle passage. VMD decomposes the signals to analyze for damage indications. Different damage levels (30-50% stiffness reduction) and locations (each span) are evaluated. Vehicle speed is also varied to assess its effect. Damage is identified as peaks in the instantaneous energy diagrams of decomposed signals, correctly indicating the axle locations. Detection improves with sensors nearer the damage and lower speed. The results demonstrate VMD's ability to detect and locate damage without a baseline, indicating its potential as a non-baseline vibration-based damage detection method for bridges.

Enhancement of Brain Computer Interface System Based on Artificial Intelligent Technique

With the rapid development of information technology, the EEG has become an increasingly popular method for analyzing brain signals. EEG signals greatly contribute to(BCI) brain-computer communication . BCI outputs are used to restore many functions in relation to individuals with a motor impairment, and these signals are complex random signals produced from hundreds of millions of neurons in EEG mixtures in the brain that contain many data about brain activity. its include artefacts which have a great influence on the diagnosis; Hence, these unwanted signals become the most important problem in EEG signal analysis. Therefore, used four blind source separation techniques (BSS), STONE, FICA, BEFICA and EFICA. The proposed system is using one of the new Antlion (ALO) optimization algorithms to improve the performance of the previous algorithms and find out which ones are the most responsive, by comparing them and choosing the best ones according to the (PSD) standard. With the use of real data, noticed through the results that the EFICA algorithm is the best response to the improvement and the most efficient, as the ratio of (PSD) was the least possible, as ALO worked to reduce thevariance in the distribution of the frequency spectrum because it relied on solving constrained problems using various searches.

Pathogenesis of sleep disordered breathing in the setting of opioid use: A multiple mediation analysis using physiology.

Opioid medications are commonly used and are known to impact both breathing and sleep, and are linked with adverse health outcomes including death. Clinical data indicate that chronic opioid use causes central sleep apnea, and might also worsen obstructive sleep apnea. The mechanisms by which opioids influence sleep-disordered breathing pathogenesis are not established. Patients who underwent clinically-indicated polysomnography confirming sleep-disordered breathing (SDB) (AHI≥5/hr) were included. Each patient using opioids was matched by sex, age, and BMI to three control individuals not using opioids. Physiology known to influence SDB pathogenesis were determined from validated polysomnography-based signal analysis. PSG and physiology paramters of interest were compared between opioid and control individuals, adjusted for covariates. Mediation analysis was used to evaluate the link between opioids, physiology, and polysomnographic metrics. 178 individuals using opioids were matched to 534 controls (median [IQR] age 59 [50,65] years, BMI 33 [29,41] kg/m2, 57% female, daily morphine equivalent 30 [20,80] mg). Compared with controls, opioids were associated with increased central apneas (2.8 vs 1.7 events/hr; p=0.001) and worsened hypoxemia (5 vs 3% sleep with SpO2<88%; p=0.013), with similar overall AHI. Use of opioids was associated with higher loop gain, a lower respiratory rate and higher respiratory rate variability. Higher loop gain and increased respiratory rate variability mediated the effect of opioids on central apnea, but did not mediate the effect on hypoxemia. Opioids have multi-level effects impacting SDB. Targeting these factors may help mitigate deleterious respiratory consequences of chronic opioid use.

Non-Nutritive Suck Parameters Measurements Using a Custom Pressure Transducer System.

The non-nutritive suck (NNS) device is a transportable, user-friendly pressure transducer system that quantifies infants' NNS behavior on a pacifier. Recording and analysis of the NNS signal using our system can provide measures of an infant's NNS burst duration (s), amplitude (cmH2O), and frequency (Hz). Accurate, reliable, and quantitative assessment of NNS has immense value in serving as a biomarker for future feeding, speech-language, cognitive, and motor development. The NNS device has been used in numerous research lines, some of which have included measuring NNS features to investigate the effects of feeding-related interventions, characterizing NNS development across populations, and correlating sucking behaviors with subsequent neurodevelopment. The device has also been used in environmental health research to examine how exposures in utero can influence infant NNS development. Thus, the overarching goal in research and clinical utilization of the NNS device is to correlate NNS parameters with neurodevelopmental outcomes to identify children at risk for developmental delays and provide rapid early intervention.

Decomposition via elastic-band transform

In this paper, we propose a novel decomposition method using elastic-band transform (EBT), which mimics eye scanning and is utilized for multiscale analysis of signals. The proposed EBT-based method can efficiently extract the features of various signals with the following three advantages. First, it is a data-driven approach that extracts several important modes based solely on data without using predetermined basis functions. Second, it does not assume that the signal consists of (locally) sinusoidal intrinsic mode functions, which is a common assumption in existing methods. Therefore, the proposed method can handle a wide range of signals. Finally, it is robust to noise. A practical algorithm for decomposition is presented, along with some theoretical properties. Simulation examples and real data analysis results show promising empirical properties of the proposed method.

Comparative utility of omnipolar and bipolar electroanatomic mapping methods to detect and localize dual nodal substrate in patients with atrioventricular nodal reentrant tachycardia.

Catheter-based slow pathway modification (SPM) for atrioventricular nodal reentrant tachycardia (AVNRT) is traditionally performed at empiric sites using anatomical landmarks and test ablation feedback within the triangle of Koch (TK). While studies have described more tailored techniques such as bipolar low voltage bridge (LVB) and wavefront collision identification, few have systematically compared the diagnostic yields of each and none have investigated whether omnipolar mapping technology provides incremental benefit. The objective of this study was to compare the utility of omnipolar andbipolar-derived qualitative and quantitative measurements in identifying and localizing dual AVN substrate in patients with versus without AVNRT. A retrospective case-control study of consecutive patients with paroxysmal supraventricular tachycardia undergoing electrophysiology study with both omnipolar and bipolar mapping from 2022-2023. Thirteen AVNRT cases (median age 16.1 years, 512 TK points) were compared to nine non-AVNRT controls (median age 15.7 years, 332 TK points). Among qualitative variables, an omnipolar activation vector pivot, defined as a ≥45 degree change in activation direction within the TK, had the highest positive (81%) and negative predictive values (100%) for identifying AVNRT cases and had a median distance of 1 mm from SPM sites. Among quantitative variables, the optimal discriminatory performance for successful SPM sites was observed using bipolar voltage restricted to a peak frequency >340 Hz (c statistic 0.75). Omnipolar vector pivot analysis represents an automated, annotation-independent qualitative technique that is sensitive and specific for AVNRT substrate and co-localizes with successful SPM sites. Bipolar voltage quantitatively describes SP anisotropy better than omnipolar voltage, and the addition of peak frequency signal analysis further optimizes the selection of SPM sites.

Anti-Freezing and Ultrasensitive Zwitterionic Betaine Hydrogel-Based Strain Sensor for Motion Monitoring and Human-Machine Interaction.

Ultrasensitive and reliable conductive hydrogels are significant in the construction of human-machine twinning systems. However, in extremely cold environments, freezing severely limits the application of hydrogel-based sensors. Herein, building on biomimetics, a zwitterionic hydrogel was elaborated for human-machine interaction employing multichemical bonding synergies and experimental signal analyses. The covalent bonds, hydrogen bonds, and electrostatic interactions construct a dense double network structure favorable for stress dispersion and hydrogen bond regeneration. In particular, zwitterions and ionic conductors maintained excellent strain response (99 ms) and electrical sensitivity (gauge factor = 14.52) in the dense hydrogel structure while immobilizing water molecules to enhance the weather resistance (-68 °C). Inspired by the high sensitivity, zwitterionic hydrogel-based strain sensors and remote-control gloves were designed by analyzing the experimental signals, demonstrating promising potential applications within specialized flexible materials and human-machine symbiotic systems.

Site-specific mutagenesis screening in KRASG12D mutant library to uncover resistance mechanisms to KRASG12D inhibitors

KRAS plays a crucial role in regulating cell survival and proliferation and is one of the most commonly mutated oncogenes in human cancers. The novel KRASG12D inhibitor, MRTX1133, demonstrates promising antitumor efficacy in vitro and in vivo. However, the development of acquired resistance in treated patients presents a considerable challenge to sustained therapeutic effectiveness. In response to this challenge, we conducted site-specific mutagenesis screening to identify potential secondary mutations that could induce resistance to MRTX1133. We screened a range of KRASG12D variants harboring potential secondary mutations, and 44 representative variants were selected for in-depth validation of the pooled screening outcomes. We identified eight variants (G12D with V9E, V9W, V9Q, G13P, T58Y, R68G, Y96W, and Q99L) that exhibited substantial resistance, with V9W showing notable resistance, and downstream signaling analyses and structural modeling were conducted. We observed that secondary mutations in KRASG12D can lead to acquired resistance to MRTX1133 and BI-2865, a novel pan-KRAS inhibitor, in human cancer cell lines. This evidence is critical for devising new strategies to counteract resistance mechanisms and, ultimately, enhance treatment outcomes in patients with KRASG12D-mutant cancers.

Helicopter Planet Gear Rim Crack Diagnosis and Trending Using Cepstrum Editing Enhanced with Deconvolution.

Detecting gear rim fatigue cracks using vibration signal analysis is often a challenging task, which typically requires a series of signal processing steps to detect and enhance fault features. This task becomes even harder in helicopter planetary gearboxes due to the complex interactions between different gear sets and the presence of vibration from sources other than the planetary gear set. In this paper, we propose an effectual processing algorithm to isolate and enhance rim crack features and to trend crack growth in planet gears. The algorithm is based on using cepstrum editing (or liftering) of the hunting-tooth synchronous averaged signals (angular domain) to extract harmonics and sidebands of the planet gears and low-pass filtering and minimum entropy deconvolution (MED) to enhance extracted fault features. The algorithm has been successfully applied to a vibration dataset collected from a planet gear rim crack propagation test undertaken in the Helicopter Transmission Test Facility (HTTF) at DSTG Melbourne. In this test, a seeded notch generated by an electric discharge machine (EDM) was used to initiate a fatigue crack that propagated through the gear rim body over 94 load cycles. The proposed algorithm demonstrated a successful isolation of incipient fault features and provided a reliable trending capability to monitor crack progression. Results of a comparative analysis showed that the proposed algorithm outperformed the traditional signal processing approach.

Analysis of Ethylene Signal Regulating Sucrose Metabolism in Strawberry Fruits Based on RNA-seq.

Ethylene is a key hormone that regulates the maturation and quality formation of horticultural crops, but its effects on non-respiratory climacteric fruits such as strawberries are not yet clear. In this study, strawberry fruits were treated with exogenous ethephon (ETH) and 1-methylcyclopropene (1-MCP). It was found that ETH treatment increased the soluble solids and anthocyanin content of the fruits, reduced hardness, and decreased organic acid content, while 1-MCP treatment inhibited these processes. Transcriptome analysis revealed that differentially expressed genes (DEGs) were enriched in the starch-sucrose metabolism pathway. qRT-PCR results further showed significant changes in the expression levels of sucrose metabolism genes, confirming the influence of ethylene signals on soluble sugar accumulation during strawberry fruit development. This study elucidates the quality changes and molecular mechanisms of ethylene signal in the development of strawberry fruits, providing some key targets and theoretical support for guiding strawberry cultivation and variety improvement.

A green chemical analysis of iron (III) in water sample using a smart phone

In this study, a smartphone was used as the photometer for determination of iron (III) by forming a complex with thiocyanate. After color formation at concentrations of, image capture with mobile phone, signal analysis of each sample was performed by the application and converted to the absorption number. The calibration curve was completely linear in the range of 10 to 80 mg L–1 used and the linear coefficient was better than 0.9833. The limits of detection (LOD) and quantification (LOQ) were 0.1 and 0.3 mg L–1, respectively. Finally, this method was successfully used to measure iron in real samples.•A smartphone was used for the determination of iron (III), showcasing its potential in color compound analysis.•The method demonstrated superior performance in terms of calibration curve range and measurement speed compared to traditional atomic absorption devices.•The technique was successfully applied in the measurement of iron in real samples, indicating its practical applicability.

Comprehensive software suite for functional analysis and synaptic input mapping of dendritic spines imaged in vivo.

Advances in genetically encoded sensors and two-photon imaging have unlocked functional imaging at the level of single dendritic spines. Synaptic activity can be measured in real time in awake animals. However, tools are needed to facilitate the analysis of the large datasets acquired by the approach. Commonly available software suites for imaging calcium transients in cell bodies are ill-suited for spine imaging as dendritic spines have structural characteristics distinct from those of the cell bodies. We present an automated tuning analysis tool (AUTOTUNE), which provides analysis routines specifically developed for the extraction and analysis of signals from subcellular compartments, including dendritic subregions and spines. Although the acquisition of in vivo functional synaptic imaging data is increasingly accessible, a hurdle remains in the computation-heavy analyses of the acquired data. The aim of this study is to overcome this barrier by offering a comprehensive software suite with a user-friendly interface for easy access to nonprogrammers. We demonstrate the utility and effectiveness of our software with demo analyses of dendritic imaging data acquired from layer 2/3 pyramidal neurons in mouse V1 in vivo. A user manual and demo datasets are also provided. AUTOTUNE provides a robust workflow for analyzing functional imaging data from neuronal dendrites. Features include source image registration, segmentation of regions-of-interest and detection of structural turnover, fluorescence transient extraction and smoothing, subtraction of signals from putative backpropagating action potentials, and stimulus and behavioral parameter response tuning analyses. AUTOTUNE is open-source and extendable for diverse functional synaptic imaging experiments. The ease of functional characterization of dendritic spine activity provided by our software can accelerate new functional studies that complement decades of morphological studies of dendrites, and further expand our understanding of neural circuits in health and in disease.