Time-domain Signal Processing Research Articles

Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning.

Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, particularly in the field of upper limbs for stroke patients. However, there is a lack of research in the lower limb area, and standardized open-source datasets of lower limb EMG signals, especially recording data of Asian race features, are scarce. Additionally, deep learning algorithms are rarely used for human motion intention recognition based on EMG, especially in the lower limb area. In response to these gaps, we present an open-source benchmark dataset of lower limb EMG with Asian race characteristics and large data volume, the JJ dataset, which includes approximately 13,350 clean EMG segments of 10 gait phases from 15 people. This is the first dataset of its kind to include the nine main muscles of human gait when walking. We used the processed time-domain signal as input and adjusted ResNet-18 as the classification tool. Our research explores and compares multiple key issues in this area, including the comparison of sliding time window method and other preprocessing methods, comparison of time-domain and frequency-domain signal processing effects, cross-subject motion recognition accuracy, and the possibility of using thigh and calf muscles in amputees. Our experiments demonstrate that the adjusted ResNet can achieve significant classification accuracy, with an average accuracy rate of 95.34% for human gait phases. Our research provides a valuable resource for future studies in this area and demonstrates the potential for ResNet as a robust and effective method for lower limb human motion intention pattern recognition.

Inner defect detection of glass fiber reinforced polymer sandwich panel using pulsed terahertz imaging based on smoothing and derivative

We conducted an experimental investigation to localize and detect internal defects in a sandwich panel made of glass-fiber-reinforced polymer (GFRP) using terahertz (THz) time-domain spectroscopy and signal processing techniques, which included smoothing and derivative analysis to enhance the defect detection performance. A simulated defect was created using a peel ply and scanned using a pulsed THz scanner with a reflection geometry specifically designed for this purpose. The reflected THz wave was analyzed to improve defect detection performance through time-domain signal processing using smoothing and derivative analysis. To quantify the performance of the proposed signal processing method, we introduced the Defect to Noise Ratio and evaluated its effectiveness accordingly. As a result, noise was removed and defect detection performance was enhanced, enabling the visualization of foreign insert defects in the GFRP sandwich panel.

A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study

Constructional material deterioration and member damage can cause changes in the dynamic characteristics of bridge structures, and such changes can be tracked in the responses of passing vehicles via the vehicle-bridge interaction (VBI). Though data-driven methods have shown promising results in damage inspection for drive-by methods, there is still much room for improvement in their performance. Given this background, this paper proposes a novel time-domain signal processing algorithm for the raw vehicle acceleration data of data-driven drive-by inspection methods. To achieve the best data processing performance, an optimizing strategy is designed to automatically search for the optimal parameters, tuning the algorithm. The proposed method intentionally overcomes the difficulties in the application of drive-by methods, such as measurement noise, speed variance, and enormous data volumes. Meanwhile, the use of this method can greatly improve the accuracy and efficiency of Machine Learning (ML) models in vehicle-based damage detection. It consists of a filtering process to denoise the data, a pooling process to reduce data redundancy, and an optimizing procedure to maximize algorithm performance. A dataset is obtained to validate the proposed algorithm through laboratory experiments with a scale truck model and a steel beam. The results show that, compared to using raw data, the present algorithm can increase the average accuracy by 12.2-15.0%, and the average efficiency by 35.7-96.7% for different damaged cases and ML models. Additionally, the functions of filtering and pooling operations, the influence of window function parameters, as well as the performance of different sensor locations, are also investigated in the paper. The goal is to present a signal processing algorithm for data-driven drive-by inspection methods to improve their detection performance of bridge damage caused by material deterioration or structural change.

Real-Time Tracking of Time-Varying Cable Frequency Based on a Time-Domain Signal Processing Method

In this paper, a time-domain signal processing method is proposed to extract the real-time time-varying cable frequency. The proposed conjugate-pair decomposition (CPD) method uses empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs), and then the instantaneous frequency can be extracted by post-processing the conjugate-pair of IMFs. Several numerical simulations and a composite cable-stayed bridge experiment are used to validate the accuracy and capability of the proposed method for tracking time-varying cable frequency. Moreover, the proposed method may be further used to assess cable fatigue damage.

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

There are a large number of time domain, frequency domain and time-frequency signal processing methods available for univariate feature extraction. However, there is no consensus in SHM on which feature, or feature sets, are best suited for the identification, localisation and prognosis of damage. This paper attempts to address this problem by providing a comprehensive benchmark of feature selection & reduction methods applied to an extensive set of univariate features. These univariate features are extracted using multiple statistical, temporal and spectral methods from the benchmark S101 and Z24 bridge datasets. These datasets contain labelled accelerometer recordings from full scale bridges as they are progressively subjected to multiple damage scenarios. To identify the minimal set of features that best distinguishes between the multiple damage states, a supervised machine learning approach is used in combination with multiple feature selection methods. The ability of these reduced feature sets to distinguish between damage states is benchmarked using the prediction performance of the classification models, with the training and test sets obtained through stratified k-fold cross validation. The results obtained show that reduced sets of univariate features, extracted from a single accelerometer sensor, are capable of accurately distinguishing between multiple classes of healthy and damaged states. This work provides a benchmark for SHM practitioners and researchers alike for the choice, comparison and validation of feature extraction and feature selection methods across a wide range of systems.

A 0.4 V, 6.4 nW, −75 dBm Sensitivity Fully Differential Wake-Up Receiver for WSNs Applications

This paper presents a fully differential wake-up receiver (WuRX) with ultra-low power and high sensitivity. Implementing the fully differential framework achieves differential signal processing without an off-chip balun, which achieves noise suppression and sensitivity improvement. The low voltage baseband signal processing (LVBSP) is proposed to cut down the power consumption, which is achieved by the low-voltage fast-response differential amplifier (LFDA) and the time-domain signal processing circuit. Moreover, the periodic offset cancellation technique (POCT) in the baseband circuit is proposed to alleviate the offset and low-frequency noise impacts. This WuRX is implemented in the TSMC 65-nm CMOS process occupying an active area of 0.18 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . When operating at 434 MHz, the proposed WuRX has a −75-dBm sensitivity at 250 bps while consuming 6.4 nW from 0.4 V supply voltage.

Influence of Meteorological Processes on cGPS Measurements of Crustal Movements

Surface displacement measurements collected using continuous Global Positioning System (cGPS) stations include the contribution of both endogenic processes (e.g., plate tectonics, isostasy) and exogenic processes that show seasonal variation (e.g., climate). These seasonal variations distort the tectonic signals and limit the usefulness of short-term investigations. This paper explores cGPS and meteorological time series collected in southern Africa and investigates whether the usefulness of cGPS time series can be improved by accounting for the seasonal effects of the dominant meteorological processes. The study is carried out using time, frequency, and time-frequency domain signal processing (inferential) analysis techniques. It was found that from the considered atmospheric processes, thermally-induced effects, which are not corrected for in the cGPS data, are the most prominent meteorological contributors in the vertical annual deformation component observed in cGPS time series. The effects of heave action (due to seasonal subsurface water infiltration and absorption, as well as changes in the water table) and changes of water mass distribution (caused by infiltration, transportation, extractions, and evaporation) on the displacement time series are much smaller. This suggests that correcting for thermal expansion, contraction, and thermal-induced errors could reduce the annual seasonal deformation component observed in cGPS position measurements in southern Africa and, most probably, in other parts of the world subjected to large seasonal variations in atmospheric temperatures. Reducing the magnitude of the seasonal components would increase the usefulness of short-term cGPS campaigns.

A Time-Domain z−1 Circuit with Digital Calibration

This paper presents a novel circuit of a z−1 operation which is suitable, as a basic building block, for time-domain topologies and signal processing. The proposed circuit employs a time register circuit which is based on the capacitor discharging method. The large variation of the capacitor discharging slope over technology process and chip temperature variations which affect the z−1 accuracy is improved using a novel digital calibration loop. The circuit is designed using a 28 nm Samsung FD-SOI process under 1 V supply voltage with 5 MHz sampling frequency. Simulation results validate the theoretical analysis presenting a variation of capacitor voltage discharging slope less than 5% over worst-case process corners for temperature between 0 °C and 100 °C while consuming only 30 μA. Also, the worst-case accuracy of z−1 operation is better than 33 ps for input pulse widths between 5 ns and 45 ns presenting huge improvement compared with the uncalibrated operator.

A TIME-SERIES AND SPATIAL ANALYSIS OF 56 YEARS (1961–2017) OF RAINFALL HISTORICAL DATA FROM MALAYBALAY, BUKIDNON

Abstract. This paper focuses on using time series and spatial analysis methods to detect climate change indicators in Malaybalay, Bukidnon. We look at 56 years of historical rainfall data between the years 1961 to 2017 and perform a computational method for data processing to arrive at spatial statistics and provide data visualization. We demonstrate the use of the Augmented Dickey-Fuller test (ADF), where a p-value is tested versus a threshold to reject or accept the null hypothesis for a stationarity test. For the seasonality test, we perform a time-domain signal processing by an autocorrelation function. The time-series analysis shows that for Malaybalay, Bukidnon rainfall data shows ADF statistic of −16.348964, a p-value = 0.000000 with critical values 1%:−3.431, 5%:−2.862, 10%:−2.567. Hence, the significant negative values indicate more likely to reject the null hypothesis. We showed that rainfall does not demonstrate periodicity, is not seasonal, and is non-stationary. This work does not cover those that can be detected and attributed to anthropogenic causes.

Discrete time domain analysis of radiation detector noise

In this paper, the analysis of radiation detector noise is carried out based on digital signal processing in the discrete time domain. To generate accurate noise models, detector noise sources are analyzed and modeled in the discrete region. Poisson noise generator was used for shot noise, and White Gaussian Noise Generator was used for Johnson Noise and Channel Thermal Noise. For flicker noise, White Gaussian Noise Generator with 1/f filter was applied. The output noise was calculated by convolution sum of the system impulse function and noise input. In order to improve the accuracy noise analysis, the time domain equation was derived based on the advanced model compared to the existing noise model. CSA model is based on a realistic limited-bandwidth OP-AMP. For the realistic detector model, Laplace transformed function of CSA and shaper out signal for parallel and series noise were derived and inversed for time domain signal. The results of the existing frequency domain analysis match well with the discrete time domain analysis. The discrete time domain analysis can increase the accuracy of noise investigation because it can visualize results of existing frequency domain models and provide different representation methods for the same results.

Engine Fault Detection Approach Based on Angle Domain Signal Model

Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

A 310-nA Quiescent Current 3-fs-FoM Fully Integrated Capacitorless Time-Domain LDO With Event-Driven Charge Pump and Feedforward Transient Enhancement

In this article, a fully integrated capacitorless low-dropout regulator (LDO) is presented for Internet-of-Things (IoT) edge sensor application. To achieve sub-1-V operation and fast transient response with low quiescent current, the conventional operational transconductance amplifier (OTA)-based error amplifier (EA) is replaced with oscillator-based voltage-to-time converter and time-domain signal processing, including time-domain edge-based frequency comparator (FC) and event-driven voltage mode charge pump (CP). Compared with the conventional phase frequency detector (PFD), the proposed clock-edge-based FC achieved more than six times power reduction. Event-driven CP is adopted to drive analog power transistor and the transient response is enhanced by feedforward capacitor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C_{\mathrm {FD}}$ </tex-math></inline-formula> and coarse–fine CP control. To further reduce the power consumption, multi-voltage domain and clock frequency optimization are implemented. A prototype chip is fabricated in a standard 65-nm CMOS process. The design only consumes 310-nA quiescent current while achieving 0.5–1.2-V input range, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.0\times 10^{6}$ </tex-math></inline-formula> load dynamic range, and 3-fs figure of merit (FoM).

Digital-to-time converter for test equipment implemented using FPGA DSP blocks

This paper presents a novel digital-to-time converter (DTC) implemented using digital signal processing (DSP) blocks built in field-programmable gate array (FPGA) devices. The converter was implemented in a low-cost Xilinx Spartan-6 FPGA chip manufactured in the 45 nm CMOS process and can be applied in any other programmable logic device that contains DSP blocks. The designed DTC time interval jitter is below 2.8 ps. This is the lowest time interval jitter so far reported in such kind of circuits implemented in a programable device. The obtained resolution of up to 8.6 ps is about twice as good as that achieved in a single carry-chain-based delay-line. Furthermore, the designed circuit saves FPGA general-purpose logic (programmable logic blocks). This kind of converter can be especially useful as a part of test instrumentations as well as time-domain signal processing systems implemented in the latest generation of programmable logic devices.

Joint Vertex-Time Filtering on Graphs With Random Node-Asynchronous Updates

In graph signal processing signals are defined over a graph, and filters are designed to manipulate the variation of signals over the graph. On the other hand, time domain signal processing treats signals as time series, and digital filters are designed to manipulate the variation of signals in time. This study focuses on the notion of vertex-time filters, which manipulates the variation of a time-dependent graph signal both in the time domain and graph domain simultaneously. The key aspects of the proposed filtering operations are due to the random and asynchronous behavior of the nodes, in which they follow a collect-compute-broadcast scheme. For the analysis of the randomized vertex-time filtering operations, this study first considers the random asynchronous variant of linear discrete-time state-space models, in which each state variable gets updated randomly and independently (and asynchronously) in every iteration. Unlike previous studies that analyzed similar models under certain assumptions on the input signal, this study considers the model in the most general setting with arbitrary time-dependent input signals, which lay the foundations for the vertex-time graph filtering operations. This analysis shows that exponentials continue to be eigenfunctions in a statistical sense in spite of the random asynchronous nature of the model. This study also presents the necessary and sufficient condition for the mean-squared stability and shows that stability of the underlying state transition matrix is neither necessary nor sufficient for the mean-squared stability of the randomized asynchronous recursions. Then, the proposed filtering operations are proven to be mean-square stable if and only if the filter, the graph operator and the update probabilities satisfy a certain condition. The results show that some unstable vertex-time graph filters (in the synchronous case) can be implemented in a stable manner in the presence of randomized asynchronicity, which is also demonstrated by numerical examples.

Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing

In this paper, the estimation of the remaining useful life (RUL) of angular contact ball bearing using time-domain signal processing method is discussed. An experimental setup based on acoustic emission (AE) signal is used to extract and collect the desired data. The residual life test is performed on the SKF 7202 BEP angular contact ball bearing. Sixty-time domain features have been introduced and used for fault detection. Improved Distance Evaluation (IDE) method has been used for feature dimensionality reduction and the best 10 features have been selected. K-Nearest Neighbors (KNN) algorithm has been used to investigate the classification accuracy of IDE based on selected features for classifying healthy and faulty bearings. The results show that the IDE method enables natural fault detection in bearings with high precision. To validate the performance of the KNN classifier, performance indices such as accuracy, precision, and specificity are applied. The results show that kurtosis, FM4, k factor, energy, and peak are the best features and kurtosis has the highest KNN rank with accuracy, precision, and specificity of 97%, 93%, and 94%, respectively.

A Statistical Approach of Analog Circuit Fault Detection Utilizing Kolmogorov–Smirnov Test Method

This work presents a testing technique based on ‘Kolmogorov–Smirnov’ (K–S) test for detection of parametric faults in analog circuits. The proposed method is a time-domain signal processing technique that compares the statistical similarity in terms of ‘Empirical Cumulative Distribution Function’ (ECDF) of the outputs of the circuit when the input of the circuit is a random analog signal. ‘Multivariate Adaptive Regression Splines’ (MARS) technique is used to map the tolerances of functional metrics to the components of the circuit under test (CUT). Two benchmark circuits, i.e., second-order Sallen–Key band-pass filter and weakly nonlinear cascade amplifier are tested to validate the proposed fault detection technique. The proposed statistical approach with the use of random analog signal as input excitation results reduction of complexity for designing input test signal and increases fault coverage in the analog circuit testing. The proposed method is testified experimentally for Sallen–Key band-pass filter. The experimental results are in good agreement with the simulated results.

Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique

Determining the type of flow pattern and gas volumetric percentage with high precision is one of the vital topics for researchers in this field. For this, in this paper, three different types of liquid–gas two-phase flow regimes, namely annular, stratified, and homogenous were simulated in various gas volumetric percentages ranging from 5% to 90%. Simulations were performed by Monte Carlo N Particle (MCNP) code. Metering system includes one 137Cs sources, one Pyrex glass, and two NaI detectors to register the transmitted photons. Because the signals which are received from the MCNP simulations contain high-frequency noises, the Savitzky-Golay filter has been applied to solve this problem. Then, thirteen characteristics in time domain were extracted from the recorded data of both detectors. Since none of features were capable of completely separating the flow regimes, two methods as “extracting two different features from the recorded data of both detectors” and “extracting three features from the recorded data of both detectors” were proposed. Using these methods, many different separator cases were found and the best separator cases were distinguished via S parameter. Finally, two artificial neural network (ANN) models of multilayer perceptron (MLP) were implemented for each method to identify the flow regimes and approximate the gas volumetric percentages. The proposed methodology and networks could diagnose all flow patterns properly, and also predict the volumetric percentage with a root mean square error (RMSE) of less than 0.60. Increasing the precision of two-phase flow meter by extracting time-domain features and signal processing techniques is the most important advantage of this study.

Local feature descriptors based ECG beat classification.

ECG beat type analysis is important in the detection of various heart diseases. The ECG beats give useful information about the status of the monitored heart condition. Up to now, various artificial intelligence-based methods have been proposed for ECG based heart failure detection. These methods were generally based on either time or frequency domain signal processing routines. In this study, we propose a different approach for ECG beat classification. The proposed approach is based on image processing. Thus, the initial step of the proposed work is converting the ECG beat signals to the ECG beat images. To do that, the ECG beat snapshots are initially saved as ECG beat images and then local feature descriptors are considered for feature extraction from ECG beat images. Eight local feature descriptors namely Local Binary Patterns, Frequency Decoded LBP, Quaternionic Local Ranking Binary Pattern, Binary Gabor Pattern, Local Phase Quantization, Binarized Statistical Image Features, CENsus TRansform hISTogram and Pyramid Histogram of Oriented Gradients are considered for feature extraction. The Support Vector Machines (SVM) classifier is used in the classification stage of the study. Linear, Quadratic, Cubic and Gaussian kernel functions are used in the SVM classifier. Five types of ECG beats from the MIT-BIH arrhythmia dataset are considered in experiments and the classification accuracy is used for performance measure. To construct a balanced training and test sets, 5000 and 10,000 ECG beat samples are randomly selected and are used in experiments in tenfold cross-validation fashion. The obtained results show that the proposed method is quite efficient where the calculated accuracy score is 99.9% and the comparisons with the state-of-the-art method show that the proposed method outperforms other methods.

Analysis of electrooculography signals for the detection of Myasthenia Gravis

ObjectiveA precursor to more severe forms of Myasthenia Gravis (MG) is ocular MG (OMG) in which the MG symptoms are localized to the eyes. Current MG diagnostic methods are often invasive, painful, and not always specific. The objective of the proposed work was to extract quantifiable features from electrooculography (EOG) signals recorded around the eyes and develop an alternative non-invasive screening method for detecting MG. MethodsEOG signals acquired from MG and Control subjects were analyzed for eye movement characteristics and quantified using time and wavelet domain signal processing techniques. The ability of the proposed approaches to classify MG vs. control subjects was evaluated using a linear discriminant analysis (LDA) based classifier. ResultsThe range of overall classification accuracies achieved by the proposed time and wavelet domain approaches for different groupings were between 82.1–83.3% (Rise Rate feature: P < 0.01, AUC ≥ 0.87) and 82.1–87.2% (Mean Scale Band Energy feature: P < 0.01, AUC ≥ 0.89), respectively. ConclusionOur results demonstrate that an EOG-based signal analysis is a potentially viable non-invasive alternative for MG screening. SignificanceThe proposed approach could lead to early detection of MG and thereby improve clinical outcomes in this population.

Review of the state of the art in cardiovascular endoscopy imaging of atherosclerosis using photoacoustic techniques with pulsed and continuous-wave optical excitations.

.Intravascular photoacoustics (IV-PA) is an emerging atherosclerosis imaging modality that provides chemical-specific optical information of arterial walls with acoustic depth penetration and resolution. As lipid composition of atherosclerotic plaques is considered to be one of the primary indicators for plaque vulnerability, many IV-PA applications are calibrated so as to target plaque necrotic cores. Based on the mode of optical excitation and the corresponding signal processing technique, IV-PA is categorized into two different modalities. The pulse-based IV-PA has been the universal IV-PA imaging mode with its high peak power and straightforward time-domain signal processing technique. As an alternative, the low power continuous-wave (CW)-based IV-PA has been under intense development as a radar-like frequency-domain signal processing modality. The two state-of-the-art types of IV-PA are reviewed in terms of their physics and imaging capabilities, with major emphasis on frequency-swept CW-based IV-PA that has been recently introduced in the field.