Advanced Signal Processing Methods Research Articles

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review.

This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition

Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events.

Comparison of Automated Acoustic Methods for Oral Diadochokinesis Assessment in Amyotrophic Lateral Sclerosis.

Purpose The purpose of this research note is to provide a performance comparison of available algorithms for the automated evaluation of oral diadochokinesis using speech samples from patients with amyotrophic lateral sclerosis (ALS). Method Four different algorithms based on a wide range of signal processing approaches were tested on a sequential motion rate /pa/-/ta/-/ka/ syllable repetition paradigm collected from 18 patients with ALS and 18 age- and gender-matched healthy controls (HCs). Results The best temporal detection of syllable position for a 10-ms tolerance value was achieved for ALS patients using a traditional signal processing approach based on a combination of filtering in the spectrogram, Bayesian detection, and polynomial thresholding with an accuracy rate of 74.4%, and for HCs using a deep learning approach with an accuracy rate of 87.6%. Compared to HCs, a slow diadochokinetic rate (p < .001) and diadochokinetic irregularity (p < .01) were detected in ALS patients. Conclusions The approaches using deep learning or multiple-step combinations of advanced signal processing methods provided a more robust solution to the estimation of oral DDK variables than did simpler approaches based on the rough segmentation of the signal envelope. The automated acoustic assessment of oral diadochokinesis shows excellent potential for monitoring bulbar disease progression in individuals with ALS.

Investigating parallel multi-step vibration processing pipelines for planetary stage fault detection in wind turbine drivetrains

This paper proposes a signal processing approach for wind turbine gearbox vibration signals based on employing multiple analysis pipelines. These so-called pipelines consist of combinations of various advanced signal processing methods that have been proven to be effective in literature when applied to wind turbine vibration signals. The performance of the pipelines is examined on vibration data containing different wind turbine gearbox faults in the planetary stages. Condition indicators are extracted from every pipeline to evaluate the fault detection capability for such incipient failures. The results indicate that the multipronged approach with the different pipelines increases the reliability of successfully detecting incipient planetary stage gearbox faults. The type, location, and severity of the fault influences the choice for the appropriate processing method combination. It is therefore often insufficient to only utilize a single processing pipeline for vibration analysis of wind turbine gearbox faults. Besides investigating the performance of the different processing techniques, the main outcome and recommendation of this paper is thus to employ a diversified analysis methodology which is not limited to a sole method combination, to improve the early detection rate of planetary stage gearbox faults.

Comparative study between cyclostationary analysis, EMD, and CEEMDAN for the vibratory diagnosis of rotating machines in industrial environment

The aim of this paper is to propose a comparative study between three advanced signal processing methods for the vibratory diagnosis of rotating machines working in industrial conditions. Cyclostationary analysis, empirical mode decomposition (EMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are then applied for the detection of mechanical defects of a turbofan machine in the biggest fertilizer company in Algeria. These methods proved their efficiency for the diagnosis of specific defects, like rolling bearing and gear defects in laboratory test rigs, but their application in industrial field remains limited. The application of these methods on vibratory signals measured in low, medium, and high-frequency range allowed determining the efficiency of each method to diagnose the occurrence of different defects manifested in the three considered frequency ranges. The great advantage of the modulation intensity distribution (MID), and its integration (IMID), obtained from the cyclostationary analysis, is proven compared to the envelope spectra performed from the EMD or the CEEMDAN approaches, especially for defects inducing modulation phenomena. Finally, the main result of this paper is that the advanced signal processing tools can be easily applied on signals measured in industrial environment, and can be extended to detect mechanical defects in real running conditions, more real than those simulated on laboratory test rigs.

Multilanguage-handwriting self-powered recognition based on triboelectric nanogenerator enabled machine learning

Handwriting signature is widely used and the main challenge for handwriting recognition is how to obtain comprehensive handwriting information. Triboelectric nanogenerator is sensitive to external triggering force and can be used to record personal handwriting signals and associated characteristics. In this work, micro/nano structure textured TENG acting as a smart self-powered handwriting pad is developed and its effectiveness for handwriting recognition is demonstrated. Three individuals' handwriting signals of English words, Chinese characters and Arabic numerals are acquired by leaf-inspired TENG, and the other three people's handwriting signals of English sentences and the corresponding Chinese sentences are obtained by cylindrical microstructured PDMS based TENG, and these signals exhibit unique features. Combined with the machine learning method, the people's handwriting was successfully identified. The classification accuracies of 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% were reached for English words, Arabic numerals, Chinese characters, English sentences, and the corresponding Chinese sentences, respectively. The results strongly suggested that the textured TENG exhibited great potential in personal handwriting signature identification, security defense, and private information protection applications. This work presented a killer approach for multilanguage handwriting signal sensing and recording. We reported a smart self-powered handwriting pad based on micro/nano-structures textured TENG for sensing different individuals’ handwriting. The obtained signals exhibited unique features both in time domain and frequency domain which could be used in personal handwriting signature identification. For the first time, a novel solution for recognizing personal handwriting combing TENG based smart self-powered device, advanced signal processing and machine learning method was reported. We successfully demonstrated that extremely high classification accuracies achieved by using the proposed advanced technology. • TENG based self-powered handwriting pad was designed and fabricated. • The handwriting pad was sensitive and rapidly-responding to handwriting behavior. • Handwriting was recognized by combing the handwriting pad and machine learning method. • Extremely high classification accuracies were obtained for three kinds of characters.

Advanced algorithms for signal processing scintillation gamma ray detectors at high counting rates

Gamma ray spectrometry measurements at high detector counting rates (on the order of 107 s−1 and higher) are relevant for high-temperature plasma diagnostics for existing tokamaks and during the development of gamma ray diagnostic systems for the ITER tokamak under construction. At high loads, to obtain spectra without distortion and with a small amount of dead time, it is necessary to use advanced scintillation detector signal processing methods, which can resolve superimposed pulses. Two algorithms that can be used for digital signal processing of scintillation gamma ray detectors with many piled-up pulses are considered in this article, the fitting and deconvolution methods. These algorithms are compared with both one another and two less sophisticated pulse-height analysis algorithms (maximum height and total sum under the peak) in applications that process model and measured signals. Baseline detection algorithms are also considered, which are necessary when processing signals from detectors. The algorithms are applied to LaBr3(Ce) detector signal processing. For the modeled signals, the best results in terms of the number of resolved events and energy resolution at counting rates up to 2 × 107 s−1 of the LaBr3(Ce) detector are demonstrated by the fitting method. In the real gamma ray measurements at a loading of 5.1 106 s−1, the deconvolution method demonstrated the best energy resolution.

Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis

Myocardial infarction (MI), usually referred as heart attack, takes place when blood circulation stops to specific portion of the heart resulting permanent damage to the heart muscles. It is an important task to identify the occurrence of MI from the ECG recordings efficiently. Most of the detection procedures include advanced signal processing methods, more ECG features and composite classifiers, making the overall procedure complex. This paper aims at automated identification of MI using modified Stockwell transform (MST) based time-frequency analysis and a phase information distribution pattern method. The morphological, pathological and temporal alterations in ECG waveforms resulting from the onset of MI are noticed in the phase distribution pattern of the ECG signal. Two discriminating features, utterly reflecting these alterations, are recognized for 12 leads of the MI affected ECG signal. Prior informations regarding the pathological characteristics of the specific disease are required for the correct detection of MI using few numbers of ECG leads. Thus, in this paper 12 lead ECG signals have been considered for identification of MI. The two-class classification problem with MI class and healthy individual class is performed using the threshold based classification regulation. Both healthy control and MI affected ECG signals are collected from the PTB diagnostic ECG database. The accuracy, sensitivity and specificity are found to be 99.93%, 99.97% and 99.30% for detection of MI. The proposed method has got the superiority in terms of simplicity of features, small feature dimension and simpler classification rule ensuring faster, accurate and easier MI detection.

Advanced Signal Processing Methods for Inspection of Aircraft Structural Materials

Abstract Aircraft, their assemblies, and units must provide high durability and reliability, and maintain mechanical and technological characteristics throughout the life span of the aircraft. Different elements of aircraft structures work under mechanical loads, over a wide temperature range, with varying degrees of exposure to corrosive environments. Aircraft structural materials have a variation in the characteristics values and require the various testing methods for their inspection. In many NDT methods applied in aviation materials testing, signals that could be represented by a narrowband processes model are used. Known methods of their processing are focused on determining and analyzing the signals amplitude characteristics, but the information resource contained in phase characteristics is not used. In the article, the methodology for signal processing and determining phase characteristics in the time domain are discussed. It is based on the combination of the discrete Hilbert transform and the deterministic and statistical methods of the phase measurement. There are given examples of the application of the methodology for pulsed eddy current testing of electrically conductive materials and products, ultrasonic thickness measurement of products made of materials have significant ultrasonic attenuation, the realization impulse variant of acoustic impedance flaw detection of products made of composite materials. The examples have shown that the proposed signal processing methodology enables to determine new information parameters and signal characteristics for the industry, and extend the scope of known NDT methods.

Multiple dipole source localization of EEG measurements using particle filter with partial stratified resampling.

Tracking and detection of neural activity has numerous applications in the medical research field. By considering neural sources, it can be monitored by electroencephalography (EEG). In this paper, we focus primarily on developing advanced signal processing methods for locating neural sources. Due to its high performance in state estimation and tracking, particle filter was used to locate neural sources. However, particle degeneracylimits the performance of particle filters in the most utmost situations. A few resampling methods were subsequently proposed to ease this issue. These resampling methods, however, take on heavy computational costs. In this article, we aim to investigate the Partial Stratified Resampling algorithm which is time-efficient that can be used to locate neural sources and compare them to conventional resampling algorithms. This work is aimed at reflecting on the capabilities of various resampling algorithms and estimating the performance of locating neural sources. Simulated data and real EEG data are used to conduct evaluation and comparison experiments.

Neural Response Selectivity to Natural Sounds in the Bat Midbrain

Little is known about the neural mechanisms that mediate differential action–selection responses to communication and echolocation calls in bats. For example, in the big brown bat, frequency modulated (FM) food-claiming communication calls closely resemble FM echolocation calls, which guide social and orienting behaviors, respectively. Using advanced signal processing methods, we identified fine differences in temporal structure of these natural sounds that appear key to auditory discrimination and behavioral decisions. We recorded extracellular potentials from single neurons in the midbrain inferior colliculus (IC) of passively listening animals, and compared responses to playbacks of acoustic signals used by bats for social communication and echolocation. We combined information obtained from spike number and spike triggered averages (STA) to reveal a robust classification of neuron selectivity for communication or echolocation calls. These data highlight the importance of temporal acoustic structure for differentiating echolocation and food-claiming social calls and point to general mechanisms of natural sound processing across species.

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative low-cost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|ΔT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> | ), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC > 95$ % was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73 %, SE = 91.57 %, PPV = 94.80 % and $\text {F1} = 93.12$ %. Using the EEMD method, ACC > 95$ % was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49 %, SE = 97.89 %, PV = 99.53 % and F1 = 98.69 %. In this study, the best results were achieved using the AWT method, which provided ACC > 95 % in all 12 types and levels of interference with average values of ACC = 99.34 %, SE = 99.49 %, PPV = 99.85 % a F1 = 99.67 %.

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach.

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.

Ground penetrating radar use in flood prevention

The main goal of the work is to create an automatic method of locating weak zones within flood embankments structure based on ground penetrating radar (GPR) measurements. The presented research shows the possibilities of using advanced methods of GPR signal processing and its analysis with the help of signal attributes for detecting zones threatening the stability of the structure of flood embankments. Obtained results may help in quick detection of potential weak zones of the embankments and consequently give means to ameliorate them, which may prevent damage to the embankments during rise in the level of river water. The presented analyses were carried out on GPR data obtained for the flood banks of the Rudawa River (Kraków, Poland) in the area of their visible degradation. The use of signal attributes, such as Energy, instantaneous frequency, similarity, curvature gradient, dominant frequency, allowed initial indication of anomalous zones threatening the stability of embankment. Advanced processing supported by the use of advanced filters such as GLCM, Grubbs filter threshold and Convolve Prewitt helped in the analysis of the structure of the embankments. Artificial neural networks (ANNs) in the supervised and unsupervised variants were used to perform the automatic classification of weakened zones within the embankments. The results demonstrated the usefulness of GPR geophysical method through integration of ANN in the analysis of the data.

Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method.

For high-speed trains, out-of-roundness (OOR)/defects on wheel tread with small radius deviation may suffice to give rise to severe damage on both vehicle components and track structure when they run at high speeds. It is thus highly desirable to detect the defects in a timely manner and then conduct wheel re-profiling for the defective wheels. This paper presents a wayside fiber Bragg grating (FBG)-based wheel condition monitoring system which can detect wheel tread defects online during train passage. A defect identification algorithm is developed to identify potential wheel defects with the monitoring data of rail strain response collected by the devised system. In view that minor wheel defects can only generate anomalies with low amplitude compared with the wheel load effect, advanced signal processing methods are needed to extract the defect-sensitive feature from the monitoring data. This paper explores a Bayesian blind source separation (BSS) method to decompose the rail response signal and to obtain the component that contains defect-sensitive features. After that, the potential defects are identified by analyzing anomalies in the time history based on the Chauvenet’s criterion. To verify the proposed defect detection method, a blind test is conducted using a new train equipped with defective wheels. The results show that all the defects are identified and they concur well with offline wheel radius deviation measurement results. Minor defects with a radius deviation of only 0.06 mm are successfully detected.

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography.

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

Order Frequency Spectral Correlation Based Cyclo-nonstationary Analysis of Surface EMG Signals in Biceps Brachii Muscles.

Surface Electromyogram (sEMG) is an indicator of fatigue progression during isometric or dynamic contraction of skeletal muscle. Estimation of fatigue index provides clinically relevant data for the diagnosis of neuromuscular disorders. The major challenge is that the signal is highly nonstationary upon dynamic contraction of muscles. Therefore, an advanced signal processing method is essential for the analysis of such signals to measure fatigue indices. Cyclo-nonstationary (CNS) analysis reveals the hidden periodicities of a highly nonstationary signal which is not firmly established for muscle fatigue analysis. In this work, an attempt has been made for the cyclo-nonstationary analysis of sEMG signal in biceps brachii muscle using Order-Frequency Spectral Correlation function (OFSC) method. For this, signals are recorded from fifty healthy volunteers with well-defined protocol. The preprocessed signals are equally partitioned into three segments namely, nonfatigue, progression of fatigue and fatigue. Further, OFSC is computed using the Welch-based estimator. In addition, the OFSC statistical features such as area under the curve, skewness, kurtosis and six entropies are estimated to evaluate fatigue condition with CNS analysis. The preliminary results show that OFSC features are able to differentiate the fatigue condition. The obtained results are statistically significant with p <; 0.002. Therefore, OFSC-based CNS analysis can be used for the fatigue index estimation to diagnose neuromuscular disorders.

Advanced Signal Processing Methods for Ground-Penetrating Radar: Applications to Civil Engineering

Ground-penetrating radar (GPR) is a common technique for evaluating the structure and quality of civil engineering materials. The ever-increasing demand for higher GPR time resolution and better interpretation of GPR data has motivated the use of advanced signal processing methods for GPR applications. In this article, we review the major advances in signal processing techniques employed in civil engineering for different tasks, such as estimation of thickness, permittivity, and roughness. Their performance is tested and compared through numerical testing and using experimental data from laboratory measurements.

Drivetrain reliability improvements from long-term field data processed in the cloud

This work describes an autonomous condition monitoring framework to process and analyze data measured on wind turbine gearboxes. Industry 4.0 and the Industrial Internet of Things open the door for much more elaborate measurement and data analysis campaigns thanks to the reduction in cost of sensors and of processing power. This increase in data acquisition and handling potential is especially useful considering that most current state-of-the-art methods in signal processing often lead to large quantities of health indicators due to the multiple processing steps. Such large numbers of indicators become unfeasible to inspect manually when the data volume and the number of monitored turbines increases. Therefore, this paper illustrates a hybrid analysis approach that combines advanced signal processing methods with machine learning and anomaly detection. This approach is validated on an experimental wind turbine gearbox vibration data set.

Application of the cyclostationarity analysis in the detection of mechanical defects: comparative study

The analysis of vibratory signals is an important subject in the field of researches on the diagnosis of mechanical breakdowns in rotating machinery. A number of signal processing methods are classified as classical tools, such as statistical analysis, FFT, envelope analysis (EA), and some time-frequency methods such as wavelet multi-resolution analysis (WMRA). These methods have shown their limitations when it is desired to analyze non-stationary and non-linear signals. In this paper, we introduce an advanced signal processing method, called cyclostationary analysis, which is probably one of the most recent tools used in vibratory diagnosis, especially in the case of non-stationary running conditions. A comparative study between five different methods and the cyclostationarity analysis is first conducted, choosing as criteria the advantages, disadvantages, and their contributions to the detection of mechanical defects. This comparative study is carried out on vibratory signals measured on defective rolling bearings kindly provided by the Bearing Data Center. In response to the request of a great industrial group, the cyclostationarity is used to analyze vibratory signals measured on a turbo-alternator working in real conditions. The experimental results obtained from this study confirm the ability of the proposed methodology to diagnose real mechanical defects in an industrial environment, in comparison with the results obtained by the application of the other classical methods mentioned previously.