Advanced Signal Processing Research Articles

Bearing Fault Detection in Conveyor Belt Drums Using Machine Learning

In recent years, the application of machine learning techniques in condition monitoring has significantly advanced the precision and efficiency of fault detection processes. In particular, detecting bearing faults in conveyor belt drums is critical in the mining industry for maintaining operational reliability and productivity. This paper presents a case study using vibration signals and diagnostic reports provided by the company Dynamox. After meticulous data cleaning, preprocessing, and feature extraction employing advanced signal processing techniques and statistical features, several machine learning models were trained, optimized and evaluated, with the best models providing very promising results.

CMOS Pulse Oximeter

Abstract: This paper presents the design and implementation of a non-invasive pulse oximeter based on Complementary MetalOxide-Semiconductor (CMOS) technology, aimed at enhancing the accuracy and accessibility of blood oxygen saturation measurements. Pulse oximetry is a critical tool in clinical and personal health monitoring, providing vital information about respiratory function. The integration of CMOS technology allows for miniaturization and energy efficiency, facilitating the development of compact, wearable devices capable of continuous monitoring. The proposed system utilizes advanced signal processing techniques, including lock-in detection and dual-wavelength illumination, to improve the reliability of photoplethysmographic (PPG) signal acquisition under various conditions. Experimental validation demonstrates the device's capability to deliver accurate SpO2 readings even in the presence of motion artifacts and ambient light interference. Furthermore, the calibration methods employed ensure that measurements are consistent and precise across different users. By leveraging the advantages of CMOS technology, this study not only highlights the potential for improved patient comfort and compliance but also opens new avenues for the application of pulse oximetry in diverse environments. The findings underscore the effectiveness of CMOS-based pulse oximeters in advancing non-invasive health monitoring solutions, ultimately contributing to enhanced patient care and outcomes.

D-Band 4.6 km 2 × 2 MIMO Photonic-Assisted Terahertz Wireless Communication Utilizing Iterative Pruning Deep Neural Network-Based Nonlinear Equalization

In this paper, we explore the enhancement of a 4.6 km dual-polarization 2 × 2 MIMO D-band photonic-assisted terahertz communication system using iterative pruning-based deep neural network (DNN) nonlinear equalization techniques. The system employs advanced digital signal processing (DSP) methods, including down-conversion, resampling, matched filtering, and various equalization algorithms to combat signal distortions. We demonstrate the effectiveness of DNN and iterative pruning techniques in significantly reducing bit error rates (BERs) across a range of symbol rates (10 Gbaud to 30 Gbaud) and polarization states (vertical and horizontal). Before pruning, at 10 GBaud transmission, the lowest BER was 0.0362, and at 30 GBaud transmission, the lowest BER was 0.1826, both of which did not meet the 20% soft-decision forward error correction (SD-FEC) threshold. After pruning, the BER at different transmission rates was reduced to below the hard decision forward error correction (HD-FEC) threshold, indicating a substantial improvement in signal quality. Additionally, the pruning process contributed to a decrease in network complexity, with a maximum reduction of 85.9% for 10 GBaud signals and 63.0% for 30 GBaud signals. These findings indicate the potential of DNN and pruning techniques to enhance the performance and efficiency of terahertz communication systems, providing valuable insights for future high-capacity, long-distance wireless networks.

Online Quality Control of Powder Bed Fusion with High-Resolution Eddy Current Testing Inductive Sensor Arrays.

This paper presents the development of a novel eddy current array (ECA) system for real-time, layer-by-layer quality control in powder bed fusion (PBF) additive manufacturing. The system is integrated into the recoater of a PBF machine to provide spatially resolved electrical conductivity imaging of the manufactured part. The system features an array of 40 inductive sensors spaced at 1 mm pitch and is capable of performing a full array readout every 0.192 mm at 100 mm/s recoater speed. Array scalability was achieved through the careful selection of the electromagnetic configuration, miniaturized and seamlessly integrated sensor elements, and the use of advanced mixed signal processing techniques. Experimental validation was performed on stainless steel 316L parts, successfully detecting metallic structures and confirming system performance in both laboratory and real-time PBF environments. The prototype achieved a signal-to-noise ratio (SNR) of 26.5 dB, discriminating metal from air and thus demonstrating its potential for improving PBF part design, process optimization, and defect detection.

Evaluating antenna phase center variation effects on tropospheric delay retrieval using a low-cost dual-frequency GNSS receiver

Abstract This study examines the impact of Phase Center Variation (PCV) corrections on Zenith Wet Delay (ZWD) accuracy using a low-cost U-blox ZED-F9P receiver paired with three different antenna configurations: the high-grade TRM57971 antenna, the moderate-grade AS-ANT3BCAL antenna, and the low-cost ANN-MB-00 antenna. Among the three antennas evaluated, the low-cost antenna exhibited the largest PCV magnitude and a pronounced elevation angle dependence. In contrast, the other two antennas demonstrated lower levels of PCV variation. Without PCV corrections, the low-cost antenna showed significant ZWD biases compared to reference values. Applying PCV corrections significantly improved its accuracy, reducing bias and RMS by 88% and 79%, respectively. Moderate- and high-grade antennas experienced minimal improvement with correction. All antennas exhibited remarkable day-to-day repeatability in their residual patterns, despite variations observed in the RMS of phase residuals. This observed repeatability is likely attributable to the presence of unmodeled multipath contributions. The variations in RMS, in turn, can be primarily ascribed to inherent differences in multipath resistance among the antenna designs. This study highlights the critical role of PCV corrections for accurate ZWD estimation with low-cost GNSS receivers. Future research should prioritize the acquisition of manufacturer-provided calibration data for low-cost antennas to streamline and enhance the accuracy of PCV correction applications. Moreover, efforts should be directed toward developing innovative solutions, such as low-cost, multipath-resistant antennas or advanced signal processing algorithms, to mitigate the impact of multipath errors. By addressing these areas, low-cost GNSS solutions can become more reliable and cost-effective tools for tropospheric delay estimation.

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.

Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.

High‐Accuracy Liquid Flow Monitoring via Triboelectric Nanogenerator Combined with Bionic Design and Common‐Mode Interference Suppression

AbstractIn the development of smart cities, accurate liquid flow monitoring is essential for the efficient operation of water supply systems. Current flow sensors often face limitations in sensitivity and environmental adaptability, affecting measurement accuracy, and restricting their application in smart city infrastructure. To address these challenges, this study proposes a high‐accuracy flow monitoring method. Specifically, by combining the bionic design with advanced signal processing techniques, the sensitivity and anti‐interference ability are improved, respectively, to enhance the measurement accuracy. Based on this method, a self‐powered flow sensor (SPFS) is developed using noncontact triboelectric nanogenerators (NC‐TENGs) as the sensing unit. The SPFS achieves a sensitivity of 2.07 Hz L−1 min−1 and improves the signal‐to‐noise ratio by more than 13 times over the initial sensing signal. In addition, an intelligent system is developed to accurately measure water resources. The maximum flow rate error rate is less than 0.97% compared to commercial flow sensors. The SPFS demonstrates higher sensitivity and accuracy compared to the existing TENG flow sensors. This study addresses the limitations of existing flow sensors and pioneers a novel solution for enhanced water resource management in smart cities.

Non-invasive detection of hydraulic cylinder leakage using computer vision and time-frequency analysis

ABSTRACT This study presents a meticulous investigation facilitated by a bespoke high-pressure test rig. The rig stands as a cornerstone of the research endeavour, providing the capability to simulate diverse leakage scenarios under controlled conditions. With its capacity to replicate conditions akin to real-world hydraulic systems, including various levels of severity such as healthy operation, moderate leakage and severe leakage. The study delves into the intricate analysis of time-series discharge flow rate signals within this experimental framework. The study utilizes sophisticated signal processing techniques, particularly the Continuous Wavelet Transform (CWT), to observe frequency components and temporal occurrences. Despite prevalent challenges distinguishing between leakage severity levels, the CWT-based analysis provides crucial insights into the dominant low frequencies (2.3–3.3 hz) characterising all leakage conditions. An advanced computer vision-based methodology is devised to address the complexities inherent in leakage differentiation, integrating cutting-edge models such as You Only Look Once (YOLO-V7) and You Only Look Once-Neural Architecture Search (YOLO-NAS). These models are meticulously trained using annotated CWT images of flow rates corresponding to different leakage conditions. Notably, the superiority of YOLO-NAS in terms of both speed and accuracy underscores its efficacy in automated leakage detection. In summary, this comprehensive approach, underpinned by the innovative hydraulic test rig and advanced signal processing coupled with state-of-the-art computer vision techniques, presents a significant advancement in the realm of internal leakage detection in hydraulic systems.

Enhanced Performance Analysis of 120 Gbps Single‐Channel IQ Modulation Based PDM‐FSO Transmission System Using 64‐QAM Signals

ABSTRACTThe ever‐augmenting demand of data channel‐bandwidth by the end users has resulted in the exploration of new transmission techniques for wireless transmission networks. In this work, we have demonstrated the modeling of a single‐channel free space optics (FSO) transmission system by employing polarization‐division‐multiplexing (PDM) technique. Further, in‐phase‐quadrature modulation (IQM) has been used to increase the transmission efficiency of the system. To decrease the baud‐rate of the system, we have proposed the use of 64‐level quadrature amplitude modulation (QAM) signals. The net transmission rate of the system is 120 Gbps with a baud‐rate of 10 Gbps. Furthermore, the overcome the signal degradation of the 120 Gbps signal propagating through atmospheric channel, we have reported the use of advanced digital signal processing (DSP) algorithms. The system's performance is investigated for adverse external climate weather conditions and the results reported show reliable 120 Gbps data transmission along range between 870 m and 16.25 km with good performance of BER and EVM 7.5%.

EddyBot: A multichannel FPGA-based Eddy Current Testing mobile robot

EddyBot: A multichannel FPGA-based Eddy Current Testing mobile robot

Use of operational track dynamics logger measurement data on Norwegian railway tracks to analyze rail joints and rail welding degradation

Since 2022, the Bane Nor Measurement train is equipped with a TDL (Track Dynamics logger) designed by SD&M - France. The TDL captures, by means of 4 triaxial accelerometers on the bearings of a bogie and 4 water resistant microphones, both the forces and noise emission generated by the complete Norwegian railway network, automatically and without need for any user interaction. The acquired data (0-20kHz) is processed towards parameters usable for noise mitigation and maintenance planning, and both raw and processed data are transferred through a 4G/5G connection to a dedicated cloud repository. In Norway, about 4000 kilometers of tracks are covered by the TDL, recording parameters for protection of the infrastructure (rails, sleepers, ballast), for troubleshooting annoyance of citizens (noise, vibrations), and for verification of maintenance activities (grinding, tamping, switches, etc.). Roughness spectra (1-50 cm wavelengths), squat detection, and railpad damping properties are some examples of parameters that can be extracted from the data. Here we will focus mainly on detection or rail defects such as squats, studs, and weldings, by applying advanced signal processing such as band filtering, wavelets, and specific issue-detection algorithms. A comparison can be made with other measurement techniques, e.g., laser scanning or high-speed photography.

Multi-band and polarization-insensitive electromagnetically-induced transparency based on coupled-resonators in a metamaterial operating at GHz frequencies

The analogy of electromagnetically-induced transparency (EIT) has emerged as an intriguing phenomenon in metamaterials research, enabling precise control over electromagnetic wave propagation. In this work, a metamaterial is investigated to achieve polarization-insensitive and multi-band transparency in microwave region. The metamaterial unit-cell consists of three coupled resonators, engineered to exhibit distinct resonance frequencies within the GHz spectrum and their coupling results in transparency windows. The functionality of the metamaterial is validated experimentally, demonstrating dual-band transparency windows at 4.6 and 6.4 GHz. Additionally, under oblique incidence, a third transparency peak arises, and the achieved triple-band EIT peaks can be maintained above 67% up to an incident angle of 60°. Our work contributes a metamaterial platform offering multi-band EIT behavior and polarization insensitivity at GHz frequencies. The proposed structural design leverages coupled-resonator configurations to achieve enhanced control over electromagnetic wave propagation, promising significant advancements in both fundamental research and practical applications. These include advanced signal processing, high-efficiency filters, and sensors operating in GHz communication and radar systems.

Improvement of Capacitive Accelerometers: Integrating Modeling, Electrode Design, Signal Processing, and Material Selection

This work offers a thorough method for integrating advanced modeling, electrode design, signal processing, and material selection techniques to maximize the performance of capacitive accelerometers. Key performance measures, including noise reduction, sensitivity, and range, can be systematically analyzed and simulated to forecast how design decisions would affect the overall behavior of the sensor. Advanced electrode designs greatly increase capacitance, which results in more precise and trustworthy sensor readings. These designs include optimizing the shape and using high-permittivity materials. Furthermore, to guarantee that the accelerometer’s output precisely represents actual motion even in noisy surroundings, noise reduction strategies including filtering and digital signal processing methods are essential. Additionally, calibration is emphasized as a critical step in preserving measurement accuracy over time and accounting for environmental changes and sensor drift. The choice of material, with an emphasis on thermally stable and high-permittivity materials, is crucial in determining the capacitance, sensitivity, and durability of the sensor. The study offers a paradigm for the creation of capacitive accelerometers that perform better across a variety of applications by striking a balance between these variables.

A threshold activation-based simplified Lv's transform algorithm for transient multi-component linear frequency modulation signals analysis.

The high sampling rate in modern digital systems generates a large scale of data. To address the computational burden, this paper proposes a threshold activation-based simplified Lv's transform (SLVT) algorithm to analyze the transient multi-component linear frequency modulation signals. Only the signal arrival can trigger the signal analysis. This mechanism alleviates the computation pressure because of the sparsity of signals. The threshold activation mechanism enables transient signal detection and sampling rate adjustment, thereby enhancing the efficiency and effectiveness of the analytical process. The simplified Lv's transform (LVT) removes redundant computations on stretch weighting and the Discrete Fourier Transform (DFT) in Lv's transform. SLVT uses the efficient Bluestein chirp-z algorithm to implement the stretch keystone transform. The comparison results show that SLVT reduces the computational complexity of the original LVT by at least 30.8%. This algorithm exhibits superior performance compared to other advanced signal processing methods, such as discrete chirp Fourier transform, fractional Fourier transform, and Radon Wigner transform algorithms, in terms of parameter extraction accuracy, computational complexity, and execution time. Moreover, the implementation of a field programmable gate array accelerates SLVT computing by a factor of 116 in comparison to the CPU (Central Processing Unit) platform.

Automated ship noise measurement system and Enhancing Cetacean Habitat and Observation program ship noise database

An automated ship Underwater Radiated Noise (URN) system has been developed and applied to process measurements of tens of thousands of commercial vessel transits, to produce the Enhancing Cetacean Habitat and Observation (ECHO) Program’s Ship URN database—believed now to be the largest existing ship URN database. The system integrates environmental data (ocean current, wind speed), vessel AIS broadcasts, vessel classification metadata obtained from commercial ship databases, and acoustic data from JASCO’s Underwater Listening Stations (ULS). It utilizes advanced signal processing methods (e.g., image symmetry detection, cepstrum, harmonic analysis, DEMON) to automatize analysis tasks such as estimating vessel closest point of approach and propeller rotation rates. Full wave models are applied to calculate propagation loss. Vessel noise emissions (Radiated Noise Level and Source Level) are determined in compliance with ANSI standard 12.64-2009 Grade C protocols, correcting for background noise in all frequency bands. Additionally, ShipSound offers a comparative ranking system for vessel noise emissions relative to peers of the same category. All data and metadata are stored in a database and are accessible through a full-featured web interface. In this paper, we will describe the processing system details and provide examples of measurement reports and metadata displays.

Assessment of Quasi-Stationary Power Quality Phenomena in DC Power Systems

Assessment of Quasi-Stationary Power Quality Phenomena in DC Power Systems

Optimization of monitoring systems power cable lines

RELEVANCE. The length and complexity of the geography of medium voltage cable lines is at a high level. Such lines extend underground, on supports in the air. In order to constantly maintain the reliability of city power supply at a high level, any interruptions and accidents should be promptly corrected.TARGET. The main goal of the work is to develop the theory of medium voltage CL research, to practically and theoretically substantiate the search for the most convenient and effective installation for CL diagnostics, to study and develop possible modifications of CL diagnostic installations.METHODS. A variant of CL diagnostics based on the CPDA-60 installation is proposed, which makes it possible to find and localize the places where defects occur in the insulation based on the measurement and analysis of partial discharges (PD). Suitable for insulation monitoring in all types of high voltage cables. The CPDA installation can be used when testing new cable lines being put into operation, and to analyze the condition of old cables in operation.RESULTS. Cable lines require an integrated approach to diagnostics and monitoring, since the reliability of modern authentication systems for the generation and distribution of electricity is largely determined by the electrical reliability of electrical equipment. Technical diagnostics of equipment is a key link, the quality of which determines the efficiency of the processes of organizing production activities, strategic planning and renovation of electric grid assets.CONCLUSION. The study and analysis of the presented data and research allows us to form a conclusion regarding the method of measuring and localizing partial discharges (PD) in power cable lines (CL) using the Online Wire Testing System (OWTS) diagnostic system. The OWTS system allows real-time measurements without interrupting cable lines, making it especially valuable to the energy industry. Thanks to the introduction of advanced technologies and signal processing algorithms, the method has high accuracy and sensitivity to minimal manifestations of private discharges, which allows not only to detect, but also to accurately localize the location of defects in the insulation. The use of this method can significantly increase the service life of power cables, reduce the likelihood of sudden accidents and, as a result, reduce the cost of repair and maintenance of electrical power equipment. Ultimately, improvements in diagnostic and monitoring techniques, including the method of measuring and localizing PD in power lines using OWTS, represent a significant step towards improving the reliability and safety of electrical power systems. This will not only reduce operating costs, but also ensure uninterrupted and high-quality power supply to consumers.

Test and evaluation of radar systems operating in the modern electromagnetic spectrum

AbstractModern radar systems are critical infrastructure in defence and civilian applications. In the defence domain, modern radars operate in a highly contested, congested, and constrained electromagnetic environment. In the context of operating in this challenging environment, the challenges across the system life cycle are examined in testing modern radar systems which are characterised by advanced features, such as multi‐function capability, advanced digital signal processing, high resolution, wideband, and frequency agile systems that incorporate phase array and active electronically scanned array capabilities. The challenges and key objectives in testing these sophisticated modern radar systems are highlighted, as well as the enabling technologies that can support Test & Evaluation of such systems. An architecture for Hardware‐in‐the‐Loop testing, which leverages modular and flexible commercial‐of‐the‐shelf hardware, is proposed together with an application software that allows the testing of radar with a radar target generator.

EEG-based deception detection using weighted dual perspective visibility graph analysis.

Deception detection is a critical aspect across various domains. Integrating advanced signal processing techniques, particularly in neuroscientific studies, has opened new avenues for exploring deception at a deeper level. This study uses electroencephalogram (EEG) signals from a balanced cohort of 22 participants, consisting of both males and females, aged between 22 and 29, engaged in a visual task for instructed deception. We propose a novel approach in the realm of deception detection utilizing the Weighted Dual Perspective Visibility Graph (WDPVG) method to decode instructed deception by converting average epochs from each EEG channel into a complex network. Six graph-based features, including average and deviation of strength, weighted clustering coefficient, weighted clustering coefficient entropy, average weighted shortest path length, and modularity, are extracted, comprehensively representing the underlying brain dynamics associated with deception. Subsequently, these features are employed for classification using three distinct algorithms: K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). Experimental results reveal promising accuracy rates for KNN (66.64%), SVM (86.25%), and DT (82.46%). Furthermore, the features distributions of EEG networks are analyzed across different brain lobes, comparing truth-telling and lying modes. These analyses reveal the frontal and parietal lobes' potential in distinguishing between truth and deception, highlighting their active role during deceptive behavior. The findings demonstrate the WDPVG method's effectiveness in decoding deception from EEG signals, offering insights into the neural basis of deceptive behavior. This research could enhance the understanding of neuroscience and deception detection, providing a framework for future real-world applications.