Advanced Signal Processing Research Articles

Anomaly detection of gear wear: introducing a novel health indicator through experimentation and physical investigation

Monitoring gear wear is important for gear diagnosis, although it remains a challenging endeavor. Unlike localized tooth faults, there are still physical patterns in the vibration signature associated with distributed wear faults that should be unfolded. This study contributes a novel framework for gear wear diagnosis, supported by physical principles and validated against traditional methods through extensive experimentation. First, we characterize gear wear in the vibration signature, utilizing data from dozens of seeded, realistic degrading wear cases across various rotational speeds collected through unique controlled-degradation tests. We examine conventional features that have proven useful for diagnosing localized tooth faults and demonstrate their inadequate performance in diagnosing gear wear. We propose a sensitive spectral analysis of the gear mesh and sideband spectral energy in the spectrum to recognize wear manifestation and monitor its degradation. Drawing on the insights gained in this study, we introduce a novel health indicator for anomaly detection of incipient gear wear, grounded in physical understanding and employing sophisticated yet simple feature engineering techniques. We analyze the performance of the proposed health indicator, demonstrating its superiority over state-of-the-art methods, and underscore the importance of considering assembly and dismantling operations to avoid the issue of test-training leakage. Additionally, we highlight the potential of the proposed physical-based framework combined with a deep-learning approach to detect wear in its early stages. To the best of our knowledge, this is the first study to conduct extensive controlled-degradation tests, rather than endurance tests, to investigate gear wear and offer a hybrid physical data-driven framework that combines advanced signal processing, feature extraction, and feature engineering techniques for early anomaly detection.

Continuous monitoring of partial discharge activities in power cables and their stimulation due to the temperature rise

AbstractWith the development of the power distribution systems demands, the grid reliability becomes a strategic issue. Continuous monitoring of power transmission networks is a key component in addressing this issue. Partial discharge (PD) characterisation instruments provide a reliable solution and an important indicator of the state of cable insulation. An increase in the PD level usually indicates the development of a fault that affects the integrity of the cable and can lead to various problems related to the quality and quantity of the transmitted energy. In this context, the use of Internet of Things (IoT) systems for power networks monitoring can provide a significant improvement to the localisation and detection of faults. In this paper, the authors show the advantages of continuous monitoring of power grid cables using a new IoT based framework using an advanced signal processing technique, namely the phase diagram. Also, the authors show that variations in temperature can impact the initiation and progression of PD, affecting the reliability and lifespan of electrical insulation systems. Understanding this temperature dependence is crucial for accurate PD detection and effective maintenance of power cables. Our results show that higher temperatures can accelerate the PD activity, while lower temperatures may suppress it.

A Hybrid Data Decomposition and Deep Learning Approach for Solana Price Prediction Incorporating Market Factors

In this study, a hybrid data decomposition model for predicting the Solana (SOL) price is proposed, integrated by advanced signal processing and machine learning techniques. The model utilizes Variational Mode Decomposition (VMD) to decompose the Solana price series into 10 intrinsic modes, each representing different frequency components of the data. These 10 decomposed modes are then used as features, along with other relevant market factors, including Solana volume, halving index, Solana trend, and Bitcoin closing price. Then, the Bidirectional Long Short-Term Memory (BiLSTM) network is chosen to capture the complex temporal dependencies in these features and forecast future prices. To evaluate the efficacy of the approach, this study compares the hybrid models performance with a baseline LSTM model, which uses only raw historical price data. The results demonstrate that the hybrid model outperforms the benchmark model, showing higher predictive accuracy and robustness. A trading simulation for models was then conducted, and the findings indicate a significant potential for the experiment model to function as an effective trading support system, reinforcing its applicability in practical market environments. This work contributes to the field of cryptocurrency price prediction by offering a more sophisticated methodology that combines time series decomposition with deep learning techniques, providing valuable insights for traders and investors.

A Remote Monitoring System for Wind Speed and Direction Based on Non-Contact Triboelectric Nanogenerator

Wind speed and wind direction sensors are crucial sensor categories in the Internet of Things (IoT), particularly vital in fields such as meteorological monitoring, construction engineering, transportation engineering, and ocean engineering. However, power supply remains a key limiting factor for the widespread application of these sensors in large-scale sensor networks. In this paper, a self-powered, non-contact wind speed and direction sensor based on the triboelectric nanogenerator (SD-TENG) is proposed. The optimized wind speed measurement structure has a start-up wind speed as low as 0.2m/s and exhibits good linearity in the range of 0.2-29m/s. Additionally, the sensor demonstrates high temperature and humidity resistance, with a voltage attenuation of 2.4% at 45°C ambient temperature and 9.8% at 95% relative humidity. For wind direction measurement, the design of non-uniform electrodes enhances the recognition capability of different channels. To meet the demands of remote monitoring, we have designed an advanced signal processing circuit that can directly convert the raw output of a wind speed sensor into wind speed information and upload it to a cloud platform via a host. This system not only records and displays wind speed data in real-time but also features historical data storage and alarm functionalities, enhancing the intelligence and automation levels of wind speed monitoring. Additionally, users can access and analyze wind speed data through both computer and mobile devices, ensuring the system's efficiency and reliability. This work provides crucial technical support for the advancement of smart cities, clean energy, and environmental monitoring, thereby promoting the application and dissemination of IoT technology in the environmental field.

Design and Optimization of an Improved FIR Filter for Advanced Signal Processing

Design and Optimization of an Improved FIR Filter for Advanced Signal Processing

Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment.

Distributed fiber optic sensors (DFOSs) have become increasingly popular for intrusion detection, particularly in outdoor and restricted zones. Enhancing DFOS performance through advanced signal processing and deep learning techniques is crucial. While effective, conventional neural networks often involve high complexity and significant computational demands. Additionally, the backscattering method requires the signal to travel twice the normal distance, which can be inefficient. We propose an innovative interferometric sensing approach utilizing a Mach-Zehnder interferometer (MZI) combined with a time forest neural network (TFNN) for intrusion detection based on signal patterns. This method leverages advanced sensor characterization techniques and deep learning to improve accuracy and efficiency. Compared to the conventional one-dimensional convolutional neural network (1D-CNN), our proposed approach achieves an 8.43% higher accuracy, demonstrating the significant potential for real-time signal processing applications in smart environments.

Scalable and Secure IoT-Driven Vibration Monitoring: Advancing Predictive Maintenance in Industrial Systems

The rapid evolution of Industry 4.0 has positioned Internet of Things (IoT) technologies as key enablers for smarter industrial operations, particularly in predictive maintenance and machine monitoring. This research proposes an innovative IoT-driven vibration monitoring system that addresses limitations in traditional approaches such as high costs, limited scalability, and insufficient real-time capabilities. Employing low-cost sensors, edge computing, and LoRaWAN-based communication, the framework enables efficient fault detection and operational analysis. Data from industrial machinery was collected over two months and analyzed using advanced signal processing and machine learning techniques to extract meaningful insights. The system demonstrated an accuracy rate of 92%, a detection latency of 150 milliseconds, and extended sensor life to 12 months, marking significant improvements over conventional methods. Furthermore, scalability tests showed stable performance across setups involving up to 500 sensors, even in challenging industrial conditions. This study also highlights cost reductions of 30% and a 25% decline in machine downtime, reinforcing its practical value for industrial applications. By delivering an adaptable, energy-efficient, and secure solution, this research advances the integration of IoT into industrial systems. It lays the groundwork for future enhancements, including real-world testing and multimodal data integration

Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review

Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review

MFCC-based Classification of Carotid Artery Doppler Audio Signals Using LSTM Network

The carotid artery assessment is essential for detecting stenosis and vascular abnormalities. Traditional Doppler ultrasound, while effective, requires specialized equipment and trained operators, limiting its accessibility in primary care. This study investigates Doppler audio signal analysis as a non-invasive, cost-effective alternative for assessing carotid artery hemodynamics. Using advanced signal processing techniques like mel-frequency cepstral coefficients (MFCCs) and deep learning models such as Long Short-Term Memory (LSTM) networks, we analyze Doppler audio signals from the common carotid artery (CCA) in 216 individuals. Our findings reveal significant age-related variations in blood flow dynamics and distinct signal patterns, highlighting the potential of Doppler audio analysis for early vascular screening. The changes in MFCCs indicate their usefulness in identifying hemodynamic alterations associated with aging and disease, supporting their role in non-invasive carotid artery health assessment. We also evaluate the deep learning framework, utilizing RNNs to capture long-term dependencies in the signals and providing a comprehensive comparison of network configurations and performance relative to state-of-the-art algorithms.

Electrocardiogram-Based Driver Authentication Using Autocorrelation and Convolutional Neural Network Techniques

This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system’s performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications.

Bioacoustic Signatures - Revolutionizing User Authentication in the Digital Age

This white paper explores the concept of bioacoustic authentication in continuous user verification for cybersecurity. As traditional authentication methods become increasingly vulnerable to sophisticated attacks, we propose leveraging the unique internal sounds produced by the human body - such as heartbeats, blood flow, and joint movements as a novel biometric identifier. We examine the technical challenges and potential solutions in capturing and processing these subtle bioacoustic signals, including the development of specialized sensors and advanced signal processing algorithms. The use of machine learning techniques for real-time analysis and user verification is explored, with a focus on accuracy, adaptability, and fraud prevention. We explore practical applications across various sectors, from high-security environments to personal devices, and its potential integration with existing cybersecurity frameworks. By harnessing the body's internal rhythms, bioacoustic authentication presents a promising avenue for enhancing cybersecurity measures, offering a unique blend of security, convenience, and continuous protection against unauthorized access.

Hybrid Deep Learning Model for Fault Diagnosis in Centrifugal Pumps: A Comparative Study of VGG16, ResNet50, and Wavelet Coherence Analysis

Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification in CPs, integrating Wavelet Coherence Analysis (WCA) with deep learning architectures VGG16 and ResNet50. WCA is initially applied to vibration signals, creating time–frequency representations that capture both temporal and frequency information, essential for identifying subtle fault characteristics. These enhanced signals are processed by VGG16 and ResNet50, each contributing unique and complementary features that enhance feature representation. The hybrid approach fuses the extracted features, resulting in a more discriminative feature set that optimizes class separation. The proposed model achieved a test accuracy of 96.39%, demonstrating minimal class overlap in t-SNE plots and a precise confusion matrix. When compared to the ResNet50-based and VGG16-based models from previous studies, which reached 91.57% and 92.77% accuracy, respectively, the hybrid model displayed better classification performance, particularly in distinguishing closely related fault classes. High F1-scores across all fault categories further validate its effectiveness. This work underscores the value of combining multiple CNN architectures with advanced signal processing for reliable fault diagnosis, improving accuracy in real-world CP applications.

Recent advances in AI-powered multimedia visual computing and multimodal signal processing for metaverse era

Recent advances in AI-powered multimedia visual computing and multimodal signal processing for metaverse era

Advanced Signal Processing Techniques for Real-Time Systems in Edge Computing

With the rapid expansion of edge computing, real-time systems are increasingly deployed in environments such as IoT, autonomous vehicles, and industrial applications, where efficient signal processing is crucial for performance and decision-making. However, real-time signal processing in edge computing environments faces significant challenges, including limited computational resources, data transmission constraints, and strict latency requirements. Objective: This paper aims to explore advanced signal processing techniques specifically tailored for real-time systems in edge computing. The goal is to propose novel algorithms and strategies to enhance processing efficiency, reduce latency, and optimize resource usage in these environments. Methods: We propose a combination of traditional signal processing methods and emerging techniques, including: Adaptive Filtering: Improving real-time performance in dynamic environments. Compressed Sensing: Leveraging sparsity to reduce data processing and transmission overhead. Deep Learning Models: Applying convolutional and recurrent neural networks for accurate signal classification and anomaly detection. Distributed Signal Processing: Offloading computation tasks to distributed edge devices to balance load and reduce latency. Results: Experimental results demonstrate that the proposed techniques outperform traditional methods in terms of signal processing speed, resource utilization, and accuracy. In particular, the integration of deep learning models significantly improves classification accuracy for real-time signal detection, while compressed sensing reduces bandwidth requirements without compromising signal quality. Conclusion: The proposed signal processing methods effectively address the unique challenges posed by real-time systems in edge computing environments. These techniques can be applied to a wide range of applications, such as smart cities, industrial IoT, and autonomous systems, providing a solid foundation for the development of efficient, low-latency real-time systems.

Neural functional rehabilitation: exploring neuromuscular reconstruction technology advancements and challenges.

Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders, traumatic injuries, and neurological diseases. Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor, sensory, and cognitive functions, significantly improving patients' quality of life. This review analyzes the chronological development and integration of various neural machine interface technologies, including regenerative peripheral nerve interfaces, targeted muscle and sensory reinnervation, agonist-antagonist myoneural interfaces, and brain-machine interfaces. Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and high-resolution electrodes, which enhance the performance and longevity of neural machine interface technology. However, significant challenges remain, such as signal interference, fibrous tissue encapsulation, and the need for precise anatomical localization and reconstruction. The integration of advanced signal processing algorithms, particularly those utilizing artificial intelligence and machine learning, has the potential to improve the accuracy and reliability of neural signal interpretation, which will make neural machine interface technologies more intuitive and effective. These technologies have broad, impactful clinical applications, ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation. This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering, clinical surgery, and neuroengineering to develop more sophisticated and reliable interfaces. By addressing existing limitations and exploring new technological frontiers, neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation, promising enhanced mobility, independence, and quality of life for individuals with neurological impairments. By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles, researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.

Real-Time Acoustic Measurement System for Cutting-Tool Analysis During Stainless Steel Machining

This study presents a sound-based tool-wear monitoring system designed to overcome the limitations of conventional methods that focus solely on gradual and predictable wear patterns. The proposed system employs low-cost, high-frequency microphones and advanced signal processing—featuring analog/digital filtering, oversampling, signal conditioning, PLL-based synchronization, and feature extraction (ZCR, RMS)—to capture acoustic emissions during machining. Key innovations include optimized microphone placement, a custom PCB, and real-time data transfer via WiFi to MATLAB for analysis. Using the TreeBagger machine-learning algorithm, the system accurately predicts tool wear, detecting both gradual and abrupt wear patterns. Tested on EN 1.4307 (AISI/ASTM 304L) stainless steel, the system demonstrated robust performance in real-time tool-condition assessment. Its scalable and cost-effective design allows for the integration of additional sensors and features, providing a non-invasive and adaptive solution to enhance machining efficiency and reduce operational costs.

Exploring the Molecular Structure and Treatment Dynamics of Cellulose Fibres with Photoacoustic and Reversed Double-Beam Spectroscopy.

In this study, we explored the structural and chemical modifications of cellulose fibres subjected to chemical and mechanical treatments through an innovative analytical approach. We employed photoacoustic spectroscopy (PAS) and reversed double-beam photoacoustic spectroscopy (RDB-PAS) to examine the morphological changes and the chemical integrity of the treated fibres. The methodology provided enhanced sensitivity and specificity in detecting subtle alterations in the treated cellulose structure. Additionally, we applied Coifman wavelet transformation to the PAS signals, which facilitated a refined analysis of the spectral features indicative of chemical and mechanical modifications at a molecular level. This advanced signal processing technique allowed for a detailed decomposition of the PAS signals, revealing hidden characteristics that are typically overshadowed in raw data analyses. Further, we utilised the concept of energy trap distribution to interpret the wavelet-transformed data, providing insights into the distribution and density of energy states within the fibres. Our results indicated significant differences in the energy trap spectra between untreated and treated fibres, reflecting the impact of chemical and mechanical treatments on the fibre's physical properties. The combination of these sophisticated analytical techniques elucidated the complex interplay between mechanical and chemical treatments and their effects on the structural integrity and chemical composition of cellulose fibres.

INTEGRATED MEASUREMENT SYSTEM FOR ATHLETE HEALTH: HEART RATE, BLOOD OXYGEN, AND BODY TEMPERATURE MONITORING

Athlete health monitoring is critical for optimizing performance and ensuring physical well-being. This study presents an integrated non-invasive system designed to measure key physiological parameters: heart rate, blood oxygen saturation (SPO2), and body temperature. Utilizing infrared LED and photodiode sensors placed on the fingertip, the system detects changes in blood volume, correlating them with heart rate fluctuations. Pulse oximetry, employing red and infrared light wavelengths, accurately measures SPO2 levels by analyzing light absorption through finger tissues. Results from testing show promising accuracy with an average error of 0.89% for SPO2 measurements and 3.095% for heart rate measurements. Body temperature readings exhibit an average error of 0.78%, underscoring the system's reliability in non-invasively monitoring temperature changes. Challenges such as environmental influences and sensor calibration are addressed to ensure consistent and precise measurements. Future enhancements may incorporate advanced signal processing techniques and artificial intelligence to further refine measurement accuracy and reliability. This integrated system holds significant potential for enhancing athlete health management, aiding in early detection of physiological anomalies, and optimizing performance through real-time health monitoring

Toward Alleviating the Stigma of Hearing Aids: A Review.

Despite the significant advancements in hearing aid technology, their adoption rates remain low, with stigma continuing to be a major barrier for many. This review aims to assess the origins and current state of hearing aid stigma, as well as explore potential strategies for alleviating it. This review examines the societal perceptions, psychological impacts, and recent technological advancements that can influence hearing aid adoption and reduce stigma. Methods: A narrative-focused review of the literature from peer-reviewed journals and reputable sources was conducted, analyzing papers on hearing aid stigma, adoption rates, and technological solutions. The research works were categorized based on their focus on the drivers and alleviation strategies for the stigma of hearing aids. Results: This review identifies stigma as a complex, multifaceted issue driven primarily by ageism, disability perception, and the association of hearing aids with aging and incapability. Despite technological improvements, the studies surveyed listed stigma as a major factor in non-adoption. Technological advancements such as artificial intelligence in sound processing, multifunctional features, and innovative design have shown potential in reducing stigma and improving user experience. Conclusions: Alleviating the stigma of hearing aids requires a multi-pronged approach, combining improvements in technology with changes in societal perceptions. Multifunctional devices including both health and communications functions, advanced signal processing, and esthetic improvements can drive their adoption, but broader public health awareness and education are also essential to changing societal attitudes and fostering greater acceptance of hearing aids.

Cardiac Pacemaker: Fractional Equations and Frequency Analysis

Understanding and researching the human body has been fundamental due to its importance for living beings. As one of the vital organs of the human body, the heart has always been of great academic relevance. One of the areas of study of the heart is the study of the electrical stimuli that provide blood pumping. These electrical stimuli come from the Sinoatrial Node (SA), known as the natural pacemaker, which transmits the stimuli to the other parts of the heart, thus allowing blood to be pumped. A model to represent the signals of a pacemaker has been developed using a relaxation oscillator, the Van der Pol oscillator, given by a second-order differential equation. In this particular study, a more comprehensive investigation is proposed by introducing fractional order differential equations. Varying the order of the system allows for a more refined analysis of the dynamic properties of the Van der Pol oscillator and, by extension, of the cardiac pacemaker modeled by it. This approach is especially relevant considering the complex and nonlinear nature of the cardiovascular system. The practical implementation of this model is carried out by means of numerical simulation, using algorithms developed in the Python programming language. This choice of platform allows for efficient and flexible analysis of the resulting signals under different conditions and system parameters. Signal analysis in the time domain is complemented by advanced signal processing techniques in the frequency domain. The Discrete Fourier Transform (DFT) and Continuous Wavelet Transform (CWT) are employed to investigate the system's response at different frequencies, providing an in-depth understanding of its stability and dynamic behavior. In addition, the graphical representation of the results using phase spaces and bifurcation diagrams provides a visual understanding of the complex interactions between the system components and their dependencies on the model parameters.