Signal Analysis Research Articles

Global Positioning System Signal Verification through Correlation Function Distortion and Received Power Tracking

Purpose: This study proposes a significantly improved method for detecting, classifying, and isolating Global Navigation Satellite System (GNSS) signals using the relationship between the measured power and the distortion of the correlation function to achieve the signal verification required for Global Positioning System (GPS) civil applications such as safe civil aircraft navigation. Methodology: The suggested approach uses power and distortion measurements in the received signal to identify it as jammed, multipath, spoofing, or no-interference. By adding an isolator scaling factor to the detector, the signal patterns will be induced with a unique temporary factor that will set it apart from the rest and make it possible to easily position each signal in its own zone. The detector divides the four signal types into distinct zones for verification. Findings: The sufficient signal data is analyzed and the extensive simulation conducted indicates that about 94% detection accuracy is achieved which is relatively high. Unique contribution to theory, practice and policy: This study is implemented through the development of relevant detection software tools with a user-friendly interface for GNSS signal detection, validation and analysis.

Multiscale dynamic graph signal analysis

We consider the problem of decomposing a time-varying graph signal into its constituent amplitude- and frequency-modulated components and inferring their dynamic functional connectivity structures in a data-driven manner, referred to as graph mode decomposition (GMD). The currently available method for GMD obtains static connectivity structures, limiting its applicability to real-life data. Further, the performance of the method relies critically on the accurate prior knowledge of the number of components to be extracted. We address those key limitations by presenting two approaches: (1) a two-stage method for obtaining the graph modes as well as corresponding connectivity structures by solving two separate optimization problems, and (2) a joint optimization formulation to achieve that task. The proposed optimization formulations include a prior to account for the time variation of graph edge weights. Moreover, we use a successive scheme to solve the problems which alleviates the limitation of having to specify the number of decomposed components a priori — improving the accuracy and data-driven capability while reducing the computational requirement of the methods. The performance of these methods is validated on a wide range of synthetic and real data sets. We also provide a public toolbox containing the MATLAB codes of the proposed methods.

Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0.

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material's susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool.

Enhancing Electric Vehicle Performance with a Hybrid PI-Sliding Mode Controller for Battery Supercapacitor Integration

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

Epilepsy Unveiled using a Multi-Classifier Approach integrated in EAS system for Enhanced Detection Using EEG Signal Analysis.

The study focuses on the advantages of using non-invasive approaches to detect epileptic seizures, aiming to prevent injuries from sudden falls and improve medical diagnosis and management. It presents an EEG Acquisition System (EAS) that integrates a multi-level amplifier with four machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multilayer Perceptron Neural Networks (MLPNN), and Convolutional Neural Network (CNN)—for signal classification. These methods are chosen for their accuracy and rapid processing, making them ideal for real-time use. The research analyzed data from 310 participants to detect epileptic patterns under various conditions. The results show that the SVM model achieved the highest accuracy at 96.37%±2.68 in the small data, followed by the CNN with 95.25%±2.25 in real-time detection, MLPNN at 89.87%, and KNN with 86.37% . This demonstrates CNN's strong performance in seizure detection, complemented by a detailed comparison of sensitivity and specificity across all models to assess their predictive efficiency. The system tells the scientist if there is an epilepsy , dementia ,or a normal brain activity, if the system detects the abnormal activities, it could map it on the brain map with a precision of 98%.

Real-Time Signal Analysis with Wider Dynamic Range and Enhanced Sensitivity in Multiplex Colorimetric Immunoassays Using Encoded Hydrogel Microparticles.

The simultaneous quantification of multiple proteins is crucial for accurate medical diagnostics. A promising technology, the multiplex colorimetric immunoassay using encoded hydrogel microparticles, has garnered attention, due to its simplicity and multiplex capabilities. However, it encounters challenges related to its dynamic range, as it relies solely on the colorimetric signal analysis of encoded hydrogel microparticles at the specific time point (i.e., end-point analysis). This necessitates the precise determination of the optimal time point for the termination of the colorimetric reaction. In this study, we introduce real-time signal analysis to quantify proteins by observing the continuous colorimetric signal change within the encoded hydrogel microparticles. Real-time signal analysis measures the "slope", the rate of the colorimetric signal generation, by focusing on the kinetics of the accumulation of colorimetric products instead of the colorimetric signal that appears at the end point. By developing a deep learning-based automatic analysis program that automatically reads the code of the graphically encoded hydrogel microparticles and obtains the slope by continuously tracking the colorimetric signal, we achieved high accuracy and high throughput analysis. This technology has secured a dynamic range more than twice as wide as that of the conventional end-point signal analysis, simultaneously achieving a sensitivity that is 4-10 times higher. Finally, as a demonstration of application, we performed multiplex colorimetric immunoassays using real-time signal analysis covering a wide concentration range of protein targets associated with pre-eclampsia.

Sensorless Speed Estimation of Induction Motors through Signal Analysis Based on Chaos Using Density of Maxima.

Three-phase induction motors are widely used in various industrial sectors and are responsible for a significant portion of the total electrical energy consumed. To ensure their efficient operation, it is necessary to apply control systems with specific algorithms able to estimate rotation speed accurately and with an adequate response time. However, the angular speed sensors used in induction motors are generally expensive and unreliable, and they may be unsuitable for use in hostile environments. This paper presents an algorithm for speed estimation in three-phase induction motors using the chaotic variable of maximum density. The technique used in this work analyzes the current signals from the motor power supply without invasive sensors on its structure. The results show that speed estimation is achieved with a response time lower than that obtained by classical techniques based on the Fourier Transform. This technique allows for the provision of motor shaft speed values when operated under variable load.

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.

A Cross-Process Signal Integrity Analysis (CPSIA) Method and Design Optimization for Wafer-on-Wafer Stacked DRAM.

A multi-layer stacked Dynamic Random Access Memory (DRAM) platform is introduced to address the memory wall issue. This platform features high-density vertical interconnects established between DRAM units for high-capacity memory and logic units for computation, utilizing Wafer-on-Wafer (WoW) hybrid bonding and mini Through-Silicon Via (TSV) technologies. This 3DIC architecture includes commercial DRAM, logic, and 3DIC manufacturing processes. Their design documents typically come from different foundries, presenting challenges for signal integrity design and analysis. This paper establishes a lumped circuit based on 3DIC physical structure and calculates all values of the lumped elements in the circuit model with the transmission line model. A Cross-Process Signal Integrity Analysis (CPSIA) method is introduced, which integrates three different manufacturing processes by modeling vertical stacking cells and connecting DRAM and logic netlists in one simulation environment. In combination with the dedicated buffer driving method, the CPSIA method is used to analyze 3DIC impacts. Simulation results show that the timing uncertainty introduced by 3DIC crosstalk ranges from 31 ps to 62 ps. This analysis result explains the stable slight variation in the maximum frequency observed in vertically stacked memory arrays from different DRAM layers in the physical testing results, demonstrating the effectiveness of this CPSIA method.

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.

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.