Time-domain Data Research Articles

A Novel Hybrid Deep Neural Network for Predicting Athlete Performance Using Dynamic Brain Waves

The exploration of the performance of elite athletes by cognitive neuroscience as a research method has become an emerging field of study in recent years. In the research of cognitive abilities and athletic performance of elite athletes, the tasks of an experiment are usually performed by athletics task of closed skills rather than open skills. Thus, little has been conducted to explore the cognitive abilities and athletic performance of elite athletes with open skills. This study is novel as it attempts at predicting how table tennis athletes perform by collecting their dynamic brain waves when executing specific plays of table tennis, and then putting the data of dynamic brain waves to deep neural network algorithms. The method of this study begins with the collection of data on the dynamic brain waves of table tennis athletes and then converts the time domain data into frequency domain data before improving the accuracy of categorization using a hybrid convolutional neural networks (CNN) framework of deep learning. The findings hereof were that the algorithm of hybrid deep neural networks proposed herein was able to predict the sports performance of athletes from their dynamic brain waves with an accuracy up to 96.70%. This study contributes to the literature in cognitive neuroscience on dynamic brain waves in open skills and creates a novel hybrid deep CNN classification model for identifying dynamic brain waves associated with good elite sports performance.

Contribution analysis of road noise with transfer path analysis based on neural network

This paper introduces a new approach to transfer path analysis (TPA) combined with neural network technique. To obtain a large amount of operational data for training the neural network model, various operating conditions are considered, and the long term time domain data is converted into complex-valued frequency domain data according to the acquisition block. The number of layers and nodes of the model are determined by considering the physical characteristics of the system, and hyper-parameters such as learning rate and momentum are adjusted to improve convergency and efficiency. The reliability of the model is verified by comparing the estimated contribution and transmissibility with the existing methods. Unlike the existing TPA methods, only the operational data is needed to perform the neural network based TPA. This proposed method reduces the cost of measurement step such as impact test to identify the propagation paths of the noise.

Flexible quasi-2D inversion of time-domain AEM data, using a wavelet-based complexity measure

SUMMARYRegularization methods improve the stability of ill-posed inverse problems by introducing some a priori characteristics for the solution such as smoothness or sharpness. In this contribution, we propose a multidimensional scale-dependent wavelet-based ℓ1-regularization term to cure the ill-posedness of the airborne (time-domain) electromagnetic induction inverse problem. The regularization term is flexible, as it can recover blocky, smooth and tunable in-between inversion models, based on a suitable wavelet basis function. For each orientation, a different wavelet basis function can be used, introducing an additional relative regularization parameter. We propose a calibration method to determine (an educated initial guess for) this relative regularization parameter, which reduces the need to optimize for this parameter and, consequently, the overall computation time is under control. We apply our novel scheme to a time-domain airborne electromagnetic data set in Belgian saltwater intrusion context, but the scheme could equally apply to any other 2D or 3D geophysical inverse problem.

Review of Time Domain Electronic Medical Record Taxonomies in the Application of Machine Learning

Electronic medical records (EMRs) help in identifying disease archetypes and progression. A very important part of EMRs is the presence of time domain data because these help with identifying trends and monitoring changes through time. Most time-series data come from wearable devices monitoring real-time health trends. This review focuses on the time-series data needed to construct complete EMRs by identifying paradigms that fall within the scope of the application of artificial intelligence (AI) based on the principles of translational medicine. (1) Background: The question addressed in this study is: What are the taxonomies present in the field of the application of machine learning on EMRs? (2) Methods: Scopus, Web of Science, and PubMed were searched for relevant records. The records were then filtered based on a PRISMA review process. The taxonomies were then identified after reviewing the selected documents; (3) Results: A total of five main topics were identified, and the subheadings are discussed in this review; (4) Conclusions: Each aspect of the medical data pipeline needs constant collaboration and update for the proposed solutions to be useful and adaptable in real-world scenarios.

Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

Photoplethysmography (PPG) biometrics have received considerable attention. Although deep learning has achieved good performance for PPG biometrics, several challenges remain open: 1) How to effectively extract the feature fusion representation from time and frequency PPG signals. 2) How to effectively capture a series of PPG signal transition information. 3) How to extract time-varying information from one-dimensional time-frequency sequential data. To address these challenges, we propose a dual-domain and multiscale fusion deep neural network (DMFDNN) for PPG biometric recognition. The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics, which can learn the time-varying and multiscale discriminative features from the time and frequency domains. Meanwhile, we design a multiscale extraction module to capture transition information, which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information. In addition, the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics. Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.

Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing.

A Novel Deep Learning Method for Underwater Target Recognition Based on Res-Dense Convolutional Neural Network with Attention Mechanism

Long-range underwater targets must be accurately and quickly identified for both defense and civil purposes. However, the performance of an underwater acoustic target recognition (UATR) system can be significantly affected by factors such as lack of data and ship working conditions. As the marine environment is very complex, UATR relies heavily on feature engineering, and manually extracted features are occasionally ineffective in the statistical model. In this paper, an end-to-end model of UATR based on a convolutional neural network and attention mechanism is proposed. Using raw time domain data as input, the network model combines residual neural networks and densely connected convolutional neural networks to take full advantage of both. Based on this, a channel attention mechanism and a temporal attention mechanism are added to extract the information in the channel dimension and the temporal dimension. After testing the measured four types of ship-radiated noise dataset in experiments, the results show that the proposed method achieves the highest correct recognition rate of 97.69% under different working conditions and outperforms other deep learning methods.

Recurrent neural networks for time domain modelling of FTIR spectra: application to brain tumour detection.

Attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy alongside machine learning (ML) techniques is an emerging approach for the early detection of brain cancer in clinical practice. A crucial step in the acquisition of an IR spectrum is the transformation of the time domain signal from the biological sample to a frequency domain spectrum via a discrete Fourier transform. Further pre-processing of the spectrum is typically applied to reduce non-biological sample variance, and thus to improve subsequent analysis. However, the Fourier transformation is often assumed to be essential even though modelling of time domain data is common in other fields. We apply an inverse Fourier transform to frequency domain data to map these to the time domain. We use the transformed data to develop deep learning models utilising Recurrent Neural Networks (RNNs) to differentiate between brain cancer and control in a cohort of 1438 patients. The best performing model achieves a mean (cross-validated score) area under the receiver operating characteristic (ROC) curve (AUC) of 0.97 with sensitivity of 0.91 and specificity of 0.91. This is better than the optimal model trained on frequency domain data which achieves an AUC of 0.93 with sensitivity of 0.85 and specificity of 0.85. A dataset comprising 385 patient samples which were prospectively collected in the clinic is used to test a model defined with the best performing configuration and fit to the time domain. Its classification accuracy is found to be comparable to the gold-standard for this dataset demonstrating that RNNs can accurately classify disease states using spectroscopic data represented in the time domain.

Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few in Vivo Data

Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. One challenge of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> H-MRS is the low Signal-Noise Ratio (SNR). To improve the SNR, a typical approach is to perform a large number of Signal Averaging (SA). The data acquisition time, however, is proportional to the number of SA accordingly. A complete clinical MRS scan takes approximately 10 minutes in a common setting with the number of SA of 128. Recently, deep learning has been introduced to improve the SNR, but mostly the simulated data were used as the training set. This may hinder the MRS applications since some potential differences, such as acquisition system imperfections, physiological and psychologic conditions may exist between the simulated and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> data. In this paper, a new scheme that purely used repeated samples of realistic data was proposed. A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the time domain data with low SNR (24 SA) to the one with high SNR (128 SA). Experiments on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> brain spectra of 7 healthy, 2 tumor and 1 cerebral infarction region shows that using SA of 24, only 20% of a common setting, the spectra denoised by ReLSTM can provide the estimated concentrations of metabolites with the reliability comparable to those of the high-SNR spectra obtained commonly with 128 SA. Furthermore, compared with the state-of-the-art Low-Rank (LR) denoising method, the ReLSTM achieves lower relative errors and the Cramér-Rao lower bounds in quantifying some important biomarkers. In summary, ReLSTM can perform high-fidelity spectral denoising of the spectra with the fast acquisition (24 SA), which is valuable to MRS clinical studies.

A Modified Range Model and Extended Omega-K Algorithm for High-Speed-High-Squint SAR With Curved Trajectory

Accurate range model with acceleration, the coupling phase terms, and spatial-variant (SV) Doppler parameters are the main issues to be solved in high-speed-high-squint SAR (HSHS-SAR) with a curved trajectory. For these issues, an extended Omega-K (EOK) algorithm is developed in this paper. The proposed EOK algorithm mainly includes the following four aspects. Firstly, a modified range model (AMRM) considering three-dimension acceleration for a curved trajectory is established. Then, the coupling between the range and azimuth direction is removed by the modified Stolt mapping (MSM). Subsequently, an improved high-order spatial-variant (SV) phase correction approach is derived to eliminate the azimuth dependence of Doppler parameters. Finally, in order to avoid zeros-padding operation, the proposed method focuses on the sub-aperture data in the range time and azimuth frequency domain through data aligning processing. The experimental results of both simulation and real data verify the effectiveness of the proposed method.

Unsupervised Detection of Tool Breakage: A Novel Approach Based on Time and Sensor Domain Data Analysis

Unsupervised Detection of Tool Breakage: A Novel Approach Based on Time and Sensor Domain Data Analysis

Brillouin Optical Correlation Domain Analysis With Over 2.5 Million Resolution Points Based on Raman Amplification and Injection Locking

We propose and experimentally demonstrate a long-range Brillouin optical correlation domain analysis (BOCDA) system based on distributed Raman amplification and injection-locking to reach over 2.6 million resolving points. The optimization of key parameters in the Raman amplification, time domain data processing, differential measurement scheme, and injection-locking are applied to achieve a spatial resolution of 2 cm over a 50 km sensing fiber. Our result corresponds to at least twice improvement compared to former records, and the best one among distributed Brillouin sensors in terms of the number of resolving points.

Cardiovascular autonomic responses in reproductive and post-menopausal women

Background: Autonomic responses are a sensitive indicator for assessing balance in sympatho-vagal system. Many studies have shown an increased response in the sympathetic system in post-menopausal women. Alteration in autonomic nerve function causes an increased incidence of hypertension, arrhythmias, or acute coronary syndrome. Thus, this study aims to find out the heart rate variability (HRV) among reproductive and post-menopausal women. Aims and Objectives: The aim of the study was to compare the cardiovascular autonomic responses among reproductive and post-menopausal women. Materials and Methods: This cross-sectional study was conducted in the department of physiology at government medical college, Kottayam on 40 reproductive and 40 post-menopausal women. Using PHYSIOPAC-PP4 (Medicaid Systems Chandigarh) machine, an electrocardiogram was recorded in both groups in the supine position for 5 min. The frequency and time domain data obtained were analyzed using SPSS software. Results: The mean age group in reproductive women was 43 and post-menopausal women was 54. In the power spectrum analysis of HRV parameters the mean value of frequency domain Low frequency (LF) in reproductive women was 72.05 (S.D = 5.706) and post-menopausal women was 79.48 (S.D = 3.789). The high frequency (HF) in reproductive women was 28 (S.D = 5.588) and post-menopausal women were 20.53 (S.D = 3.789). The LF/HF ratio mean value obtained in reproductive women was 2.723 (S. D = 0.759) and post–menopausal was 3.992 (S.D 0.785). In the various time domain parameters, we estimated the mean value of the root mean square of successive differences (RMSSD) between normal heartbeats RMSSD and obtained the value in reproductive women as 25.05 (S.D = 11.88) and post-menopausal women as 12.68 (S.D = 5.535). The RR interval triangular index mean value in reproductive women was 0.0520 (S.D = 0.013) and post-menopausal women were 0.0345 (S.D = 0.01). In all these parameters, the P-value was found to be &lt;0.001, so statistically highly significant. Conclusion: In this study, higher values in time domain and frequency domain parameters for assessing sympathetic functions were seen in postmenopausal women when compared to women of the reproductive age group which was statistically significant. This may be due to age and hormonal influences. The power spectrum analysis of HRV parameters was proved to be a noninvasive method to assess the sympathovagal balance in the study group of women.

A novel approach for variable star classification based on imbalanced learning

Abstract The advent of time-domain sky surveys has generated a vast amount of light variation data, enabling astronomers to investigate variable stars with large-scale samples. However, this also poses new opportunities and challenges for the time-domain research. In this paper, we focus on the classification of variable stars from the Catalina Surveys Data Release 2 and propose an imbalanced learning classifier based on Self-paced Ensemble (SPE) method. Compared with the work of Hosenie et al. (2020), our approach significantly enhances the classification Recall of Blazhko RR Lyrae stars from 12% to 85%, mixed-mode RR Lyrae variables from 29% to 64%, detached binaries from 68% to 97%, and LPV from 87% to 99%. SPE demonstrates a rather good performance on most of the variable classes except RRab, RRc, and contact and semi-detached binary. Moreover, the results suggest that SPE tends to target the minority classes of objects, while Random Forest is more effective in finding the majority classes. To balance the overall classification accuracy, we construct a Voting Classifier that combines the strengths of SPE and Random Forest. The results show that the Voting Classifier can achieve a balanced performance across all classes with minimal loss of accuracy. In summary, the SPE algorithm and Voting Classifier are superior to traditional machine learning methods and can be well applied to classify the periodic variable stars. This paper contributes to the current research on imbalanced learning in astronomy and can also be extended to the time-domain data of other larger sky survey projects (LSST, etc.).

High-Precision Measurement of Brillouin Frequency Shift in Brillouin Optical Time-Domain Reflectometer Based on Half-Peak Fitting Algorithm of Power Spectrum

The Brillouin optical time-domain reflectometer (BOTDR) system, which is based on the Brillouin frequency shift (BFS) of the scattering spectrum, has been widely applied to monitor the temperature and strain in many fields. The measurement accuracy of the BFS directly affects the monitoring and location of the temperature change or strain events. In this study, the characteristics of the least-squares fitting optimal solution of the Brillouin power spectrum are theoretically investigated, and a half-peak fitting algorithm is proposed to measure the BFS with high accuracy and stability. In particular, it can precisely determine the double-peak spectrum and detect the position in the transition section of a temperature change or strain event. Furthermore, it can reduce calculation complexity and enhance measurement speed by dropping most of the data. For the fiber under test (FUT) with a length of 12-km, 200 groups of time-domain data were processed using a probe pulse of width 50 ns and sampling rate 1 GHz. The half-peak fitting algorithm increased the temperature measurement accuracy by ~1 fold, with ~20% calculation of full fitting. Meanwhile, it effectively eliminated the influence of minor peak in the double-peak spectrum, and optimized the spatial resolution of the temperature change position to 0.1-m, which is the maximum limitation by sampling rate.

Unsupervised multimodal domain adversarial network for time series classification

Unsupervised Domain Adaptation (UDA) is an ideal transfer learning method, which can use labeled source data to improve the classification performance of unlabeled target data. At present, the UDA methods for Time Series Classification (TSC) only use time-domain data or frequency-domain data as the input, and ignore fusing them, resulting in insufficient feature extraction and inaccurate source-target distribution alignment. Therefore, we propose an unsupervised Multimodal Domain Adversarial Network (MDAN) for TSC tasks. Specifically, we adopt two feature extractors for the time-domain and frequency-domain feature representations, and employ-three classifiers to perform TSC of source data for training the two feature extractors; Then, we fuse the time-domain and frequency-domain feature representations of source and target data, respectively, input them into the unified domain discriminator for unsupervised multimodal domain adversarial learning, and combine the proposed Time-Frequency-domain Joint Maximum Mean Discrepancy (TF-JMMD) to accurately align the source-target distributions; Finally, we select CNN or ResNet18 as the feature extractors to carry out comprehensive experiments, and the results demonstrate the SOTA performance of MDAN.

Stability for an inverse source problem of the diffusion equation

We prove an increasing stability estimate for an inverse source problem of the diffusion equation with a nontrivial variable absorption coefficient in . The stability estimate consists of two parts: the Lipschitz type data discrepancy and the high frequency tail of the source function. The latter decreases when the upper bound of the frequency increases. The proof is based on an exact observability bound for the heat equation and the resolvent estimates for the elliptic operator. In particular, to justify the Fourier transform and obtain Plancherel’s theorem for the time-domain Cauchy data on the boundary, we derive certain appropriate time decay estimates for the heat equation using semigroup theory.

Are Infinite-Failure NHPP-Based Software Reliability Models Useful?

In the literature, infinite-failure software reliability models (SRMs), such as Musa-Okumoto SRM (1984), have been demonstrated to be effective in quantitatively characterizing software testing processes and assessing software reliability. This paper primarily focuses on the infinite-failure (type-II) non-homogeneous Poisson process (NHPP)-based SRMs and evaluates the performances of these SRMs comprehensively by comparing with the existing finite-failure (type-I) NHPP-based SRMs. In more specific terms, to describe the software fault-detection time distribution, we postulate 11 representative probability distribution functions that can be categorized into the generalized exponential distribution family and the extreme-value distribution family. Then, we compare the goodness-of-fit and predictive performances with the associated 11 type-I and type-II NHPP-based SRMs. In numerical experiments, we analyze software fault-count data, collected from 16 actual development projects, which are commonly known in the software industry as fault-count time-domain data and fault-count time-interval data (group data). The maximum likelihood method is utilized to estimate the model parameters in both NHPP-based SRMs. In a comparison of the type-I with the type-II, it is shown that the type-II NHPP-based SRMs could exhibit better predictive performance than the existing type-I NHPP-based SRMs, especially in the early stage of software testing.

Numerical approach to quantify depth-dependent blood flow changes in real-time using the diffusion equation with continuous-wave and time-domain diffuse correlation spectroscopy.

Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique that can measure brain perfusion by quantifying temporal intensity fluctuations of multiply scattered light. A primary limitation for accurate quantitation of cerebral blood flow (CBF) is the fact that experimental measurements contain information about both extracerebral scalp blood flow (SBF) as well as CBF. Separating CBF from SBF is typically achieved using multiple source-detector channels when using continuous-wave (CW) light sources, or more recently with use of time-domain (TD) techniques. Analysis methods that account for these partial volume effects are often employed to increase CBF contrast. However, a robust, real-time analysis procedure that can separate and quantify SBF and CBF with both traditional CW and TD-DCS measurements is still needed. Here, we validate a data analysis procedure based on the diffusion equation in layered media capable of quantifying both extra- and cerebral blood flow in the CW and TD. We find that the model can quantify SBF and CBF coefficients with less than 5% error compared to Monte Carlo simulations using a 3-layered brain model in both the CW and TD. The model can accurately fit data at a rate of <10 ms for CW data and <250 ms for TD data when using a least-squares optimizer.

Anomaly Detection Method in Railway Using Signal Processing and Deep Learning

In this paper, anomaly detection of wheel flats based on signal processing and deep learning techniques is analyzed. Wheel flats mostly affect running stability and ride comfort. Currently, domestic railway companies visually inspect wheel flats one by one with their eyes after railway vehicles enter the railway depots for maintenance. Therefore, CBM (Condition-Based Maintenance) is required for wheel flats resolution. Anomaly detection for wheel flat signals of railway vehicles using Order analysis and STFT (Short Time Fourier Transform) is studied in this paper. In the case of railway vehicles, it is not easy to obtain actual failure data through running vehicles in a university laboratory due to safety and cost issues. Therefore, vibration-induced acceleration was obtained using a multibody dynamics simulation software, SIMPACK. This method is also proved in the other paper by rig tests. In addition, since the noise signal was not included in the simulated vibration, the noise signal obtained from the Seoul Metro Subway Line 7 vehicle was overlapped with the simulated one. Finally, to improve the performance of both detection rate and real-time of characteristics based on existing LeNet-5 architectures, spectrogram images transformed from time domain data were proceeded with the LeNet deep learning model modified with the pooling method and activation function. As a result, it is validated that the method using the spectrogram with a deep learning approach yields higher accuracy than the time domain data.