Signal Analysis Research Articles

Development and Validation of a Driving Simulator for Comfort Assessment

AbstractThe passenger’s comfort is of vital importance in today’s car, even more so in the future autonomous car. Its thorough analysis requires the use of objective measures, but the subjectivity of the passenger must also be considered. The relationship between both aspects is an uncommon research avenue, and could greatly benefit from the use of a vehicle simulator. This study use a simulator to replicate a journey with diverse characteristics that numerous passengers may experience (including engaging in different activities). The appropriate variables for a proper comfort analysis are determined in this study, resulting in a comprehensive database for the same purpose. Additionally, a passenger comfort based questionnaire is developed and applied to obtain the subjective assessment of different passengers. The results of these questionnaires help not only in the identification and study of the self perception of comfort by the passengers but also in the use of the simulator for comfort experiments. The detailed comparison between both the objective approach of signal analysis and the subjective approach of questionnaires that is performed thanks to the simulator creates a foundation for future research and narrows the existing research gap.

Preparation of Deoxynivalenol Complete Antigen and Its Application in Microchannel Resistance Immune Sensing Platform with Electrical Signal Analysis

Preparation of Deoxynivalenol Complete Antigen and Its Application in Microchannel Resistance Immune Sensing Platform with Electrical Signal Analysis

Temperature and Ozone Response to Different Forcing in the Lower Troposphere and Stratosphere

To evaluate the contributions of different forcings to the temperature and atmospheric composition changes between 1980 and 2020, we exploited the chemistry-climate model (CCM) SOCOLv3. The study examined ozone content and atmospheric temperature response to (1) ozone-depleting substances; (2) greenhouse gas concentrations, ocean surface temperature, and sea ice coverage; (3) solar irradiance; and (4) stratospheric aerosol loading and, separately, (5) greenhouse gas concentrations, (6) ocean surface temperature and sea ice coverage, and (7) NOx surface emissions. To evaluate the impacts of specific factors, we performed model runs driven by each factor (1–7) variability as well as a reference experiment that accounted for the influence of all factors simultaneously. We identified the relative contribution of different factors to the evolution of the temperature and ozone content of the lower troposphere and stratosphere from 1980 to 2020. The model results were in good agreement with the reanalyses (MERRA2 and ERA5). We showed that stratospheric ozone depletion before the Montreal Protocol introduction and partial recovery after that were chiefly driven by ODS. Stratospheric aerosol from major volcanic eruptions caused only short-term (up to 5 years) ozone decline. Increased greenhouse gas emissions dominate the ongoing long-term stratospheric cooling as well as tropospheric and surface warming. Solar irradiance contributed to short-term fluctuations but had a minimal long-term impact. Furthermore, our analysis of the solar signal in the tropical stratosphere underscores the complex interplay of solar radiation with volcanic, oceanic, and atmospheric factors, revealing significant altitudinal distributions of temperature and ozone responses to solar activity. Our findings advocate further innovative methodologies to take into account the nonlinearity of the atmospheric processes.

MACHINE-LEARNING TECHNIQUES IN MULTIPLE SCLEROSIS PREDICTION USING EEG

The diagnosis and quantification of Multiple Sclerosis (MS) have typically depended on skilled doctors recognizing visual patterns, such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), which resulted in a costly, time-consuming and non-reproducible process. The application of Machine Learning (ML) in MS diagnosis has been getting a lot of attention in the last few years due to the volume of scientific data, the heterogeneity of disease courses, and the variety of diagnostic methods. The EEG has the capability to detect important changes in the brain’s inherent electrical activity, which are influenced by changes in the neural network connections associated with inflammatory demyelinating and neural damage characteristic of the MS. Utilizing multimodal machine learning over the clinically available data may be a contemporary strategy with amazing potential to facilitate early diagnosis. Considering recent EEG investigations as well as the accessibility to their datasets would be the initial steps in utilizing the ML in MS diagnosis. This paper provides a systematic review of the latest techniques for MS diagnostic based on PRISMA guidelines with the prospect of ML application in their investigations. The goal is to find if EEG could be considered a robust and accurate technique for MS diagnosis. In accordance with PRISMA guidelines, we consider 111 papers. In our review, 404 people are considered including 209 with MS and 195 healthy controls. As a result, we generated an updated investigation looking at the ML approaches as well as utilizing EEG as an accurate, but less often used method to help with early diagnosis of MS. We summarize, analyze, discuss, and synthesize the recently published works, current trends, and open research issues. The review also points out knowledge gaps about the necessity of validating results and addressing constraints such as limited sample number. Our investigation proves that the precision of the supervised strategies such as [Formula: see text]NN and SVM is typically higher than that of the unsupervised strategies. On the other hand, by utilizing various techniques such as splitting EEG signal sub-bands, signal windowing, and identifying effective features from the data analysis approaches, we have been able to achieve significant classification accuracy higher than 99%. According to the high degree of accuracy of the results, this approach is becoming the focal point of the research works. The high diagnostic accuracy of the proposed machine learning techniques on the EEG signal analysis shows its potential capacity to become a more widespread procedure as the MS diagnostic techniques. To develop the reliability of these methods, gathering, and analyzing more EEG signals from MS patients and creating more suitable EEG protocols are essential in future studies. In the same way, for the dynamic and online analyses of the EEG signals, in-depth studies are needed to determine more effective EEG protocols and machine-learning techniques.

A multiple session dataset of NIRS recordings from stroke patients controlling brain–computer interface

This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real–time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl–Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near–infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.

An adaptive 6-dimensional floating-search multi-station seismic-event detector (A6-DFMSD) and its application to low-frequency earthquakes in the East Eifel Volcanic Field, Germany

We introduce a seismic event detector that applies signal analysis in the time and frequency domains. Signals are searched for with matching coincidences at neighbouring recording stations in space and time. No a priori waveform information is needed for the Adaptive 6-Dimensional Floating-search Multi-station Seismic-event Detector (A6-DFMSD). It combines a short / long time average algorithm (STA/LTA), frequency range selection, energy envelope matching, and backprojection techniques to find a robust detection model. As a challenging test example, the new detector is tuned and applied to a dataset with five months of microearthquake (ML < 2) recordings in the East Eifel Volcanic Field (EEVF), Germany. There, both magmatic and tectonic earthquakes occur in a depth range between 3 km and 43 km. A6-DFMSD detected 4.3 times as many events as were already known and it discovered a previously unknown event cluster. After manual localization and classification of the events, we show that A6-DFMSD finds events of different origins: tectonic, magmatic, atmospheric, and anthropogenic. In particular, low-frequency (LF) earthquakes of magmatic origin with a complicated waveform coda are very well identified. We suggest that seismological networks monitoring local seismicity in similar target zones would benefit from the use of A6-DFMSD to allow the detection of a wide range of different seismic signals.

Falling weight impact acceleration-time signals analysis for road modulus detection: Theoretical and experimental investigations

Structural modulus detection is essential for construction and maintenance in road engineering. Falling weight impact loading method has significant strengths in large-scale, continuous, and rapid detection. To provide theoretical support for this, theoretical analysis and falling weight impact experiments were employed in this study to investigate the influence of road structure parameters and falling weight impact parameters on impact acceleration-time signals. First, the applicability of the Hertz model in road detection was analyzed, and theoretical formulas for peak acceleration amax and impact duration t2 were established, considering the perfectly elastic collision between falling weight and half-space. Second, the influence of layered structures’ properties on impact acceleration-time curves was studied through model box impact experiments. Third, field tests were conducted to study engineering applications. The results showed that the influence patterns were similar in both theoretical and experimental studies, with differences reflected in the time of compression and restitution phases due to the road materials’ viscosity and plasticity. Additionally, in indoor experiments, amax showed an excellent power function relationship with structural layer modulus, with R2 more than 0.95, and the contribution of each layer’s modulus to amax decreased from top to bottom. Finally, amax and resilient modulus of an old road also followed a good power function relationship, with R2 around 0.76. This study revealed that amax was recommended for road surface modulus detection based on solid theoretical and experimental support.

Non-Negative Matrix Factorization with Averaged Kurtosis and Manifold Constraints for Blind Hyperspectral Unmixing

The Nonnegative Matrix Factorization (NMF) algorithm and its variants have gained widespread popularity across various domains, including neural networks, text clustering, image processing, and signal analysis. In the context of hyperspectral unmixing (HU), an important task involving the accurate extraction of endmembers from mixed spectra, researchers have been actively exploring different regularization techniques within the traditional NMF framework. These techniques aim to improve the precision and reliability of the endmember extraction process in HU. In this study, we propose a novel HU algorithm called KMBNMF, which introduces an average kurtosis regularization term based on endmember spectra to enhance endmember extraction, additionally, it integrates a manifold regularization term into the average kurtosis-constrained NMF by constructing a symmetric weight matrix. This combination of these two regularization techniques not only optimizes the extraction process of independent endmembers but also improves the part-based representation capability of hyperspectral data. Experimental results obtained from simulated and real-world hyperspectral datasets demonstrate the competitive performance of the proposed KMBNMF algorithm when compared to state-of-the-art algorithms.

On The Relationship Between Swirl And Unsteadiness Within Turbine Rim Seals

Abstract Unravelling the flow physics pertaining to hot gas ingress in turbines is crucial in enabling designers to realise global decarbonisation targets in aerospace. A turbine rim seal is fitted at the periphery of the rotor-stator cavity to minimise the ingress of annulus gas, which detrimentally affects cycle efficiency. The inherent unsteadiness in rim seal flows, arising from shear gradients between contiguous flow paths, introduces a consequential, yet presently unestablished, influence on sealing characteristics. A single-stage axial turbine facility in conjunction with an aeroengine architecture is employed to assess the steady and unsteady sealing characteristics of a range of industrially-relevant rim seals. Time-averaged measurements of gas concentration and swirl, acquired over a range of flow coefficients (CF), exhibited an inverse relationship between sealing performance and the purge-mainstream swirl difference (Δβ). Spectral analysis of unsteady pressure signals revealed an associated unsteadiness, induced by the strength of the annulus-cavity interaction. Across all CF, a low-frequency harmonic range consistently displayed proportionality between spectral activity and Δβ. Thus, a relationship between steady and unsteady characteristics was established. Examining a series of rim seal configurations with varying radial clearances signified that sealing performance was predominantly influenced by the radially outermost clearance. The configurations exhibiting superior performance presented heightened spectral activity, ascribed to an increased radial purge mass flux and establishing a definite relationship with concurrent steady measurements.

Transient Analysis of Flow Unsteadiness And Machine Instabilities In a Centrifugal Compressor at Surge and Second Quadrant Operation

Abstract Instabilities in turbomachinery operation originate from intrinsic flow unsteadiness, sudden variations of operating conditions, and interactions with the surrounding system, affecting both system stability and machine reliability. The flow unsteadiness, presenting a characteristic behaviour, requires a deep understanding of the underlying flow fundamentals, a careful analysis using advanced experimental methods and a clear distinction on how the system might lead to instabilities. The identified approach addresses deep surge and second quadrant characterization. The analysis presented here benefits from the responsiveness and flexibility of the experimental test rig, fine tuning of the system layout, the instrumentation installation, and the transient tests procedure. The main focus area is the time-frequency analysis of flow and pressure signals at transients, thus providing a proper reconstruction of flow mechanisms and instability evolution. Valuable techniques include: short-time fast Fourier transform, wavelet analysis, and Wigner-Ville distribution; coherence between flow and pressure, to trace waves propagation, is considered too. These, presented outlining their strengths and weaknesses, are evaluated considering real time field applicability with regards to computational resources too. The experimental campaigns and data processing are presented, with a test matrix covering a wide range of parameters, including system layout and surge volume size, rotational speeds, flow rates ranging from nominal point to full second quadrant operation. These data are vital for system modeling validation for a full compressor-system digital twin. Results indicate the importance of ensuring reliable data acquisition and analysis, with adequate instrumentation range, accuracy, synchronization, responsiveness and positioning.

Gabor Transform Associated with the Dunkl–Bessel Transform and Spectrograms

Time–frequency (or space–phase) analysis plays a key role in signal analysis. In particular, signals that have a very concentrated time–frequency content are of great importance. However, the uncertainty principle sets a limitation to the possible simultaneous concentration of a function and its Dunkl–Bessel transform. For this purpose, we introduce and study a new transformation called Dunkl–Bessel Gabor transform. For this transformation, we define the Toeplitz-type (or time–frequency localization) operators, in order to localize signals on the time–frequency plane. We study these operators; in particular, we give criteria for their boundedness and Schatten class properties. Then, using the special class of concentration operators, which are compact and self-adjoint, we show that their eigenfunctions are maximally time–frequency-concentrated in the region of interest.

Mining and analysis of adverse event signals of vandetanib based on the FAERS database.

By analyzing the signal mining of vandetanib AEs, it provides a reference for the rational clinical use of the drug. This study provides signal detection and analysis of AEs associated with vandetanib using the FAERS from Q2 2011 to Q1 2023. Disproportionality analyses were conducted using the ROR and the MHRA method, with AEs categorized and described based on SOC and PT from the MedDRA. A total of 1,721 AEs reports identified vandetanib as the primary suspect. The analyses yielded 187 AEs signals across 20 SOCs categories, with 47 signals not previously reported in the vandetanib labeling. Additionally, the study analyzes factors influencing AEs. The results are as follows: There are significant differences in Investigations based on gender and nationality (p < 0.05); skin and subcutaneous tissue disorders and gastrointestinal disorders exhibit differences based on gender, age, and nationality (p < 0.05). Common AEs signals identified align with those documented in the drug labeling. However, AEs such as tumor lysis syndrome, skin discoloration, and pigmentary disorders, which are not listed in the labeling, warrant special attention. It is essential that abnormalities in patients' skin and laboratory indicators are closely monitored and addressed promptly to safeguard medication safety.

Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition.

Dynamic control of grid-following inverters using DC bus controller and power-sharing participating factors for improved stability

Integrating Grid-Following Inverters (GFLs) into power systems presents significant stability challenges, particularly in systems with reduced strength and high renewable energy penetration. This study delves into the dynamic interactions between parallel synchronous generators (SGs) and GFLs, highlighting the impact of reduced system inertia and transient stability margins. A novel DC bus controller is proposed to enhance the inertia and stability of GFLs during grid disturbances by dynamically adjusting power references based on load demand. Through mathematical modelling, small signal analysis, and MATLAB simulations, the study evaluates the effects of inverter-based resources (IBRs) on power system stability. The proposed GFL configuration demonstrates improved stability control, achieving an 80 % increase in stability under weak grid conditions. This paper provides insights into optimized control strategies, emphasizing the importance of power-sharing participation factors (PSPF) for effective GFL integration in modern power systems with high renewable energy penetration.

Screening of functional genes for hypoxia adaptation in Tibetan pigs by combined genome resequencing and transcriptome analysis.

The high-altitude, low-oxygen environment of the Qinghai-Tibet Plateau poses significant challenges for the introduction of superior livestock breeds. However, local plateau species have adapted to thrive and reproduce under these harsh conditions. Understanding the molecular mechanisms behind plateau animals' adaptation to low-oxygen environments is essential for breeding livestock suited to high-altitude regions. Tibetan pigs, which have undergone long-term natural selection and artificial breeding, have developed the ability to survive and reproduce in hypoxic environments. In this study, we conducted whole-genome resequencing of 30 Tibetan pigs from high-altitude regions and 30 Diannan small-ear pigs from low-altitude areas, to identify candidate genes that support Tibetan pigs' adaptation to hypoxic conditions through selection signal analysis. Additionally, we performed transcriptome sequencing on five tissues (heart, liver, spleen, lung, and bone marrow) from both Tibetan pigs and Diannan small-ear pigs to identify genes with significant differential expression between the two breeds. We then integrated the genomic and transcriptomic data by examining the expression of candidate genes identified in selection signal analysis across different tissues. The selection signal analysis identified 10 genes-HES4, ANGPT1, HIF3A, SPHK2, PCK2, RCN3, HIGD2A, DNM2, IRF9, and SRF-that were under positive selection in the Tibetan pig population and are associated with hypoxia adaptation. When combined with transcriptome data, we found that five of these genes-HIF3A, RCN3, HIGD2A, PCK2, and IRF9-exhibited differential expression. Through an integrated approach of selection signal and transcriptome analysis, we identified five key functional genes that contribute to the adaptation of Tibetan pigs to hypoxic environments. These findings offer new insights into the adaptability of plateau animals.

Characterizing anomalous peaks in the resistance–temperature profile of Bi2Sr2CaCu2O8+δ flakes featuring surface degeneration

Abstract This study focuses on an observed anomalous resistance peak in the temperature-dependent resistance (RT) curves of Bi2Sr2CaCu2O8+δ (BSCCO), attributed to surface degradation and pronounced electrical resistance anisotropy. Employing a standard four-point probe technique on the ab-plane, this research circumvents conventional c-axis testing limitations, enhancing the understanding of BSCCO’s electrical behavior by avoiding contact resistance and etching issues. A comprehensive three-dimensional model, developed using the finite element method, captures the strong resistive anisotropy and correlates the depth of surface degradation with the anomalous resistance peaks, explaining this phenomenon from a quantitative perspective, providing a more specific reference for future analysis of relevant signals. The fabrication process involved pre-patterning and mechanical exfoliation techniques to minimize atmospheric exposure and ensure device integrity. Despite these efforts, surface degradation impacting the superconductivity of surface layers was inevitable. The study’s experimental results, complemented by numerical modeling, reveal the intricate relationship between surface layer thickness and the anomalous resistance peak, providing an approach to gauge the extent of degradation in BSCCO devices. Moreover, it underscores the potential necessity of employing some critical techniques to avoid degradation, such as low-temperature exfoliation in other literatures where degradation signal is notably absent from RT curves. This work advances the understanding of BSCCO’s electrical properties and highlights the critical need for precise fabrication and environmental controls in developing high-temperature superconducting technologies.

Mining and analysis of adverse event signals for alendronate based on the real-world data of FDA adverse event reporting system database

ABSTRACT Objective Our study aims to assess alendronate-related adverse events (AEs) from the US FDA adverse event reporting system database. Methods The AE data associated with alendronate between the first quarter of 2004 and the first quarter of 2024 were selected. Various signal quantification methods, including the ROR, PRR, BCPNN, and EBGM, were applied for analysis. Results In 34,943 reports where alendronate was the primary suspected drug for the AE, 24 affected system organ classes and 1046 significant preferred terms were identified in this study. Several significant AEs beyond drug instructions with strong signals were determined, including low turnover osteopathy, fracture delayed union, fracture nonunion, loss of anatomical alignment after fracture reduction, fracture malunion, periprosthetic fracture, carotid bruit, oral fibroma, traumatic occlusion, and phlebolith. The median time to onset of alendronate-related AEs was 306 days (interquartile range [IQR] 12–1,461 days), and the majority of cases occurred 2 years later (18.80%) and within 30 days (14.49%). Conclusions The current study detected multiple potential new AE signals for alendronate, and more clinical researches are required to further validate our results and clarify their associations.

Analysis of Physiological Signals to Detect the Symptoms of Paralysis Disorder using Neural Network Approach: a Cohort Study

As per World Medical Associations, Paralysis attack is one of the leading causes of disorder in the human subjects after other chronic disorders such as Cancer and Heart Attack. Due to the attack of Paralysis, the body parts of human body become un responsive due to which the person may be physically disabled from hands or legs-based locomotion. There is scarcity in the methodologies who can early indicate the symptoms and alert the medical practitioners or human subject under surveillance. In this study, the health-related signals were studied and as per consultation of medical experts, total eight key parameters were identified and then based on that the output decision that weather person has symptoms of paralysis or not. The observatory data base has been prepared from more than 40,000 human subjects for the duration January 2018 to December 2023. As per categorical analysis based on training, validation and testing of three-layer Recurrent Neural Network, it has been observed that the ECG, Blood pressure, Heart Rate and PPG signals are key indicators to judge the susceptance of Paralysis disorder in the human subjects. Based on the evaluation parameters of machine learning models, the conjunction of response in association with selected input parameters gives 94% accuracy than other models. Our study helps research community to develop such methodologies by which the early indication of such disorders can be helpful to human health.

Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN’s utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results.

Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 kHz. This interface provides real-time data visualization and communication with the ML server, creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.