Advanced Signal Processing Techniques Research Articles

Deep Learning Enhanced Label-Free Action Potential Detection Using Plasmonic-Based Electrochemical Impedance Microscopy.

Measuring neuronal electrical activity, such as action potential propagation in cells, requires the sensitive detection of the weak electrical signal with high spatial and temporal resolution. None of the existing tools can fulfill this need. Recently, plasmonic-based electrochemical impedance microscopy (P-EIM) was demonstrated for the label-free mapping of the ignition and propagation of action potentials in neuron cells with subcellular resolution. However, limited by the signal-to-noise ratio in the high-speed P-EIM video, action potential mapping was achieved by averaging 90 cycles of signals. Such extensive averaging is not desired and may not always be feasible due to factors such as neuronal desensitization. In this study, we utilized advanced signal processing techniques to detect action potentials in P-EIM extracted signals with fewer averaged cycles. Matched filtering successfully detected action potential signals with as few as averaging five cycles of signals. Long short-term memory (LSTM) recurrent neural network achieved the best performance and was able to detect single-cycle stimulated action potential successfully [satisfactory area under the receiver operating characteristic curve (AUC) equal to 0.855]. Therefore, we show that deep learning-based signal processing can dramatically improve the usability of P-EIM mapping of neuronal electrical signals.

Machine Learning-Based Feature Extraction and Classification of EMG Signals for Intuitive Prosthetic Control

Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM) algorithms. Signal filtering and DWT improved signal quality, facilitating better feature extraction, while the diverse feature set enhanced classification accuracy. LDA further improved accuracy by isolating the most informative features, and the SVM achieved optimal performance in decoding complex EMG patterns. Machine learning models, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), and the SVM, were evaluated, with the SVM outperforming the others. The significance of these results lies in their potential applications in prosthetic control systems and rehabilitation technologies. By accurately decoding muscle activity, the developed systems can facilitate more intuitive and responsive robotic arm movements, contributing to the advancement of innovative solutions for individuals requiring prosthetic devices or undergoing rehabilitation, hence improving the quality of life for users. This research marks a significant step forward in the integration of advanced signal processing and machine learning in the field of EMG analysis.

Low-Power Wireless Sensor Module for Machine Learning-Based Continuous Monitoring of Nuclear Power Plants.

This paper introduces the novel design and implementation of a low-power wireless monitoring system designed for nuclear power plants, aiming to enhance safety and operational efficiency. By utilizing advanced signal-processing techniques and energy-efficient technologies, the system supports real-time, continuous monitoring without the need for frequent battery replacements. This addresses the high costs and risks associated with traditional wired monitoring methods. The system focuses on acoustic and ultrasonic analysis, capturing sound using microphones and processing these signals through heterodyne frequency conversion for effective signal management, accommodating low-power consumption through down-conversion. Integrated with edge computing, the system processes data locally at the sensor level, optimizing response times to anomalies and reducing network load. Practical implementation shows significant reductions in maintenance overheads and environmental impact, thereby enhancing the reliability and safety of nuclear power plant operations. The study also sets the groundwork for future integration of sophisticated machine learning algorithms to advance predictive maintenance capabilities in nuclear energy management.

Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA.

To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp. A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment. Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects. This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.

Terahertz nondestructive layer thickness measurement and delamination characterization of GFRP laminates

Three-dimensional nondestructive location of defects, such as delaminations, in glass fiber-reinforced polymer (GFRP) laminates remains a challenge. Terahertz techniques have shown promise, but their success relies on advanced signal-processing techniques applied to the raw data. The current work presents an advance in the quantitative three-dimensional nondestructive location of delaminations in GFRP laminates. Namely, terahertz time-of-flight tomography, together with adaptive sparse deconvolution based on a two-step iterative shrinkage-thresholding algorithm, as well as the Canny edge-detection operator, are employed in nondestructive measurement of layer thicknesses and to extract the edges of delaminations in GFRP laminates. Compared with the commonly used frequency wavelet-domain deconvolution method or previous implementations of sparse deconvolution, the adaptive sparse deconvolution approach provides a clearer and rapid stratigraphic reconstruction of GFRP laminates while yielding accurate thickness information for each resin layer and low sensitivity to noise. In addition, the proposed edge-detection algorithm presents better performance in estimating the transverse size of delaminations, compared to the common −6 dB drop approach. Finally, our experiments verify the effectiveness of the proposed signal and image processing approaches for three-dimensional localization of delamination defects in GFRP laminates and the quantitative characterization of layer thickness.

Arabic Diacritic-Aware Text-Audio Segmentation and Alignment Model (DASAM)

Abstract: This paper introduces the Diacritic-Aware Segmentation and Alignment Model for Arabic (DASAM). Diacritics are vital for pronunciation and meaning in the Arabic language but are often ignored by current speech recognition systems. DASAM is designed for word-level segmentation and alignment in unseen audio and associating them with diacritic-marked Arabic text. The DASAM approach uses linguistic analysis based on intonation rules. DASAM then applies Dynamic Time Warping (DTW) to match the reference audio word with its position in the unseen sentence audio. The model outputs a list of words with their start and end times in the recording. Tested on the Qur’an dataset, DASAM outperforms Google Speech-to-Text (STT) in predicting word timings. It achieves higher accuracy in text-audio alignment, with values of 0.959 and 0.957 for word start and end times, respectively (compared to Google STT’s 0.870 and 0.849). Additionally, DASAM employs advanced signal processing techniques and demonstrates robustness across various audio variations. These results establish that DASAM constitutes a fundamental building block for speech-to-text conversion and linguistic research in Arabic, particularly for applications involving diacritics.

Innovative Bearing Fault Diagnosis Method: Combining Swin Transformer Deep Learning and Acoustic Emission Technology

Abstract Wind power generation, as a paragon of clean energy, places great importance on the reliability of its equipment. Bearings, in particular, as the core components of wind turbines, have a direct correlation with the stable operation and economic benefits of the entire system. Against this backdrop, addressing the core challenges in the field of bearing fault diagnosis, an innovative fault diagnosis method has been proposed. For the first time, the Swin Transformer deep learning model is combined with Acoustic Emission (AE) technology, and through advanced signal processing techniques, bearing signals are transformed into FBank feature inputs for the model, effectively achieving precise fault detection in low-speed, heavy-load bearings. With extensive validation on laboratory data of low-speed, heavy-load bearings and the CWRU bearing dataset, this method has achieved significant results in identifying four main damage categories. In-depth comparative analysis shows that 1) the improved Swin-Transformer achieved an accuracy of 98.6% on the acoustic emission signal laboratory dataset, performing well under data imbalance conditions. 2) It achieved an accuracy of 95.63% on the vibration signal CWRU dataset, demonstrating good generalization capabilities.

Adaptive multi-band modulation for robust and low-complexity faster-than-Nyquist non-orthogonal FDM IM-DD systems

Faster-than-Nyquist non-orthogonal frequency-division multiplexing (FTN-NOFDM) has been shown to have good robustness against the steep frequency roll-off in a bandwidth-limited optical communication system. Among various FTN-NOFDM techniques, the non-orthogonal matrix precoding (NOM-p) based FTN has relatively high compatibility, which can easily utilize the existing advanced digital signal processing (DSP) techniques in the conventional OFDM. In this work, we propose what we believe to be a novel FTN-NOFDM scheme with adaptive multi-band modulation. By dividing the single-band NOM-p into multiple-band NOM-p, the proposed scheme is able to assign different quadrature amplitude modulation (QAM) levels to different sub-bands, leading to better utilization of the low-pass-like channel as well as reduced computational complexity. The impacts of sub-band number and bandwidth compression factor on the bit-error-rate (BER) performance and complexity are experimentally analyzed in a 32.23-Gb/s and 20-km intensity modulation-direct detection (IM-DD) optical transmission system. Results show that the proposed scheme with a proper sub-band number can lower the BER and greatly reduce the complexity compared to the conventional single-band scheme.

Unique Hyperspectral Response Design Enabled by Periodic Surface Textures in Photodiodes.

The applications of hyperspectral imaging across disciplines such as healthcare, automobiles, forensics, and astronomy are constrained by the requirement for intricate filters and dispersion lenses. By utilization of devices with engineered spectral responses and advanced signal processing techniques, the spectral imaging process can be made more approachable across various fields. We propose a spectral response design method employing photon-trapping surface textures (PTSTs), which eliminates the necessity for external diffraction optics and facilitates system miniaturization. We have developed an analytical model to calculate electromagnetic wave coupling using the effective refractive index of silicon in the presence of PTST. We have extensively validated the model against simulations and experimental data, ensuring the accuracy of our predictions. We observe a strong linear relationship between the peak coupling wavelength and the PTST period along with a moderate proportional relation to the PTST diameters. Additionally, we identify a significant correlation between inter-PTST spacing and wave propagation modes. The experimental validation of the model is conducted using PTST-equipped photodiodes fabricated through complementary metal-oxide-semiconductor-compatible processes. Further, we demonstrate the electrical and optical performance of these PTST-equipped photodiodes to show high speed (response time: 27 ps), high gain (multiplication gain, M: 90), and a low operating voltage (breakdown voltage: ∼ 8.0 V). Last, we utilize the distinctive response of the fabricated PTST-equipped photodiode to simulate hyperspectral imaging, providing a proof of principle. These findings are crucial for the progression of on-chip integration of high-performance spectrometers, guaranteeing real-time data manipulation, and cost-effective production of hyperspectral imaging systems.

A review of signal processing techniques for electrocardiogram (ECG) signals

The accurate diagnosis of cardiovascular conditions relies heavily on Electrocardiogram (ECG) signals, yet persistent interference challenges, including baseline wander, powerline interference, and muscle artifacts, compromise clinical accuracy. This study comprehensively explores cutting-edge preprocessing techniques aimed at addressing multifaceted challenges in ECG signal processing. Investigating noise removal, baseline correction, feature extraction, arrhythmia detection, and heart rate variability (HRV) analysis, we synthesize insights from recent research to provide a thorough understanding of current state-of-the-art methodologies. Each facet plays a crucial role in enhancing the reliability of ECG signals for accurate cardiovascular diagnoses. In a landscape where clinical accuracy is paramount, this review critically assesses advancements in signal processing techniques, shedding light on innovative strategies and potential breakthroughs.

Mitigation of atmospheric losses using dual‐polarized 16‐QAM MIMO technique in free‐space optical system in various weather conditions

SummaryThe technique to overcome the challenge while implementing the fiber optical link is investigated using an alternative optical link called a free‐space optical (FSO) link with advanced modulation and signal processing techniques. A dual‐polarized 16‐quadrature amplitude‐modulated signal is transmitted over single and multi‐FSO channels under various atmospheric attenuation conditions. High bandwidth and data rates (up to 10 Gbps), unlicensed spectrum (above 300 GHz), and simplicity of deployment are significant benefits of the FSO system. However, the weather conditions of haze, rain, and fog cause signal attenuation when the optical carrier wave is transmitted over free space, and atmospheric turbulence is a challenge to FSO implementation, which degrades the transmission performance of optical signals for long‐distance communication. For 120 Gbps data transmission, the performance of a multi‐input and multi‐output (MIMO) FSO system is evaluated by observing the bit error rate (BER) and received signal constellation diagram. Optical signal‐to‐noise ratio (OSNR) parameters were iterated to determine the optimal value for back‐to‐back, single‐input, single‐output (SISO), and MIMO systems. Moreover, we examined the proposed system's output with recent articles, including MIMO, multiplexing, and various modulation techniques. The comparison of results shows that the suggested MIMO‐FSO model has enhanced the performance with the lower BER as less than 3.8 × 10−3 (log of BER is −2.42) forward error correction (FEC) limit for 18.7 km link range in clear air with 0.54 dB/km attenuation, 4.8, 4.5, 1.35, and 0.57 km under haze, light fog, medium, and heavy fog with 4.6, 6.9, 28.9, and 75 dB/km attenuations.

Flexible Self-Powered Low-Decibel Voice Recognition Mask.

In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts.

App Based Digital Audiometer

Hearing impairment is a widespread health concern on a global scale that affects individuals across all age groups. The timely identification and regular monitoring of one's hearing ability are crucial factors for prompt intervention and an improved overall quality of life. However, the traditional method of audiometry comes with limitations such as the need for specialized equipment and professional supervision making it inaccessible and expensive for many individuals seeking hearing assessments. To address this issue a cutting-edge approach has been developed in the form of a Digital Audiometer App offering a convenient cost-efficient and user-friendly means of assessing one's auditory function. This innovative tool utilizes the widespread use of smartphones and wearable devices allowing for a portable and accessible method of hearing assessment. The system integrates advanced signal processing techniques intuitive interfaces and reliable calibration processes to guarantee precise and consistent results. Additionally, the audiometer is compatible with both iOS and Android platforms ensuring its availability to users worldwide

Development and Deployment of a High-Precision Power Factor Measurement Meter

This research article presents the development and deployment of a high-accuracy power factor measurement meter. Power factor measurement is crucial for assessing the efficiency of electrical systems and ensuring optimal power usage. The proposed meter employs advanced signal processing techniques to accurately measure both leading and lagging power factors with high precision. The design incorporates robust hardware components such as ATMega8 micro- controlling chip and sophisticated algorithms to minimize errors and provide reliable measurements even in challenging operating conditions. Experimental results demonstrate the effectiveness of the proposed meter in accurately measuring power factor across a wide range of electrical loads. The developed meter offers significant improvements in accuracy and reliability compared to existing solutions, making it suitable for various industrial and commercial applications where precise power factor measurement is essential for efficient energy management.

Classification of ECG Signals Using Machine Learning Techniques

Cardiovascular diseases are one of the leading causes of mortality in contemporary society. With the growth in the accumulation of medical data, new opportunities have arisen to enhance diagnostic accuracy using machine learning techniques. Heart diseases present symptoms that can be similar to other disorders or be mistaken for signs of aging. Furthermore, diagnosing based on electrocardiogram (ECG) signals can be challenging due to the variability in signal length and characteristics. This article has developed a methodology for classifying ECG signals using the k-Nearest Neighbor (kNN) algorithm and statistical techniques. 9000 ECG signal samples from the PhysioNet database were processed. The signals were normalized to a length of 9000 samples, and relevant features for classification, such as median, standard deviation, skewness, among others, were extracted. Multiple kNN models with different parameters were trained and evaluated on a test set. The models exhibited high performance in classifying normal signals but faced difficulties in correctly classifying signals with arrhythmias. The weighted kNN algorithm demonstrated the best accuracy, although all models showed a tendency to misclassify abnormal signals due to data imbalance. While significant accuracy was achieved in ECG signal classification, there is still room for improvement. Future strategies could involve extracting more relevant features, addressing data imbalance, and fine-tuning model hyperparameters. Integrating domain knowledge from the medical field and advanced signal processing techniques could further enhance classification accuracy. Received: 3 January 2024 / Accepted: 7 April 2024 / Published: 5 May 2024

The Perspective of Metallic Access Networks Based on MELT Parameter Measurements on Homogeneous Symmetric Lines

In this paper, attention is focused on the perspective of metallic access networks utilizing homogeneous symmetric lines as a transmission medium. This miscellaneous environment has a strong influence on transmitted Digital Subscriber Line (DSL) signals and hence it is necessary to know its specific parameters in the given case. Fast and precise measurements on homogeneous symmetric lines are still essential, meaningful and imperative for many reasons: solving interference problems, designing advanced signal processing techniques and realizing appropriate simulation models. There are various ways of testing measurements with meaningful differences. In this contribution, we are focusing on the importance of measurements on homogeneous symmetric lines by means of metallic ended line testing. Subsequently, possibilities for new evaluation techniques for Metallic Ended Line Testing (MELT) parameters and for analyzing of DSL service provision are presented. Additionally, the realization of a mobile application functionality for facilitating the jobs of technicians is introduced, together with the possible availability of homogeneous symmetric lines for DSL service provision.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. PRR1-10.2196/54388.

Railroad Infrastructure Management: A Novel Tool for Automatic Interpretation of GPR Imaging to Minimize Human Intervention in Railroad Inspection

Regular monitoring and inspection of military railroad tracks are necessary to ensure the safe transportation of military freight. Manual railroad inspection has drawbacks and limitations that can impact accuracy and efficiency. This study introduces a novel tool designed to automate Ground Penetrating Radar (GPR) imaging interpretation for railroad ballast condition assessment, aiming to reduce human intervention in inspections. The tool uses advanced signal processing techniques, such as the Short-Time Fourier Transform (STFT) and Wavelet Transform (WT), to quantify ballast fouling levels accurately, enhancing maintenance and safety protocols for railroad tracks. Validation through comprehensive testing, including two case studies, demonstrates the tool’s superior efficacy over traditional manual inspection methods. This research represents a pivotal step towards more efficient and reliable infrastructure management, ensuring critical railroad systems’ safety and operational integrity.

Reinforcement Learning-Based Automation of Time-Frequency Domain Reflectometry

Advanced reflectometry, which can be used to estimate the location of defects in electrical cables with high accuracy, has undergone continuous development based on advanced signal processing techniques. However, in actual fields, non-experts find it difficult to use advanced reflectometry owing to the difficulty of the application process. Therefore, in this paper, a novel technique that can automate the signal-design process in reflectometry, enabling anyone to use advanced reflectometry in the actual field, is proposed. In this work, the signal-design process is automated by utilizing reinforcement learning (RL), and the application methodology is explained using time-frequency domain reflectometry. The agent of the proposed RL model is trained in a simulation-based environment. Finally, the trained agent is applied to a real-world cable to demonstrate its performance.

Emerging Data Processing Methods for Single‐Entity Electrochemistry

AbstractSingle‐entity electrochemistry is a powerful tool that enables the study of electrochemical processes at interfaces and provides insights into the intrinsic chemical and structural heterogeneities of individual entities. Signal processing is a critical aspect of single‐entity electrochemical measurements and can be used for data recognition, classification, and interpretation. In this review, we summarize the recent five‐year advances in signal processing techniques for single‐entity electrochemistry and highlight their importance in obtaining high‐quality data and extracting effective features from electrochemical signals, which are generally applicable in single‐entity electrochemistry. Moreover, we shed light on electrochemical noise analysis to obtain single‐molecule frequency fingerprint spectra that can provide rich information about the ion networks at the interface. By incorporating advanced data analysis tools and artificial intelligence algorithms, single‐entity electrochemical measurements would revolutionize the field of single‐entity analysis, leading to new fundamental discoveries.