Time-domain Processing Research Articles

A Review of the Methods for Sudden Cardiac Death Detection: A Guide for Emergency Physicians

<p class="Abstract">Sudden cardiac death (SCD) is an unexpected death of a person with or without knowing cardiac causes are often occurring in less than an hour after the incidence of symptoms. In the case of physicians' knowledge of this incident, they can make appropriate decisions for the patients at-risk and reduce the number of such deaths significantly. The purpose of this paper is to examine different methods for predicting sudden cardiac death using electrocardiogram (ECG) signal from 1998 to recent years that can contribute to researchers to become familiar with the wide range of research conducted in this field.</p><p class="Abstract">In this paper, studies using various methods to predict sudden cardiac death that has applied the data from the Physionet and MIT-BIH databases with a sampling frequency of 256 samples per second are reviewed. Both types of data have normal and abnormal sampling labels and the data recording time varies from a few seconds to minutes. In the field of SCD prediction, various studies have addressed the processing of the electrocardiogram (ECG) signal as well as the heart rate variability (HRV) signal in different domains, including time, time-frequency, and nonlinear domain. In time-domain processing the statistical characteristics of time signal such as the mean and standard deviation of heart rate, the mean and standard deviation of RR intervals, and Root Mean Square of the Successive Differences (RSSD) are used. Also, in the frequency domain, the power spectral density (PSD) of the signal energy is used in a very-low-frequency band, low-frequency band and, high-frequency band. Similarly, in the nonlinear domain, features such as Poincare plot, detrended fluctuation analysis (DFA), common entropy, wavelet transform coefficients (WTC), and features of the recursive graph including Lmax, Lmean, correlation dimension (CD), etc. are used. In all of the proposed algorithms so far, researchers have been trying to inform the sudden death alarm in a larger interval than the time of death by separating the signals into different time periods and extracting various features.</p><p class="Abstract">To evaluate the results of the proposed methods, each of the researchers analyzed the a-few-minute intervals before the SCD. Different classification methods are available to identify the efficiency of the proposed algorithm, such as support vector machine (SVM), multilayer perceptron neural network (MLP), radial base function neural network (RBF), k-nearest neighbor (KNN) and mixture expert (ME). The use of features introduced in different domains and different classifiers has led to the observation of different horizons of prediction in various studies. The results of these predictions are often free from the interpretations of clinical symptoms, and their maximum presented time with acceptable validity eventually reaches 4 minutes before the event, which is not an acceptable time for people who have attacked outside the hospital. Accordingly, the most prominent of these evaluations is the mixture expert methodology in which the best feature extraction methods are used in a new method for selecting the optimal feature space locally. This method makes it possible to select different features every minute before the event by choosing the optimal features for each one-minute interval of the signal as an episode which increases the prediction time from 4 minutes before the death to 12 minutes and allows the interpretation of clinical symptoms in terms of multiplication of the presence of the features per minute.</p><p class="Abstract">Given the non-linear nature of the HRV signal and the similarity of the ECG signal in many time intervals, the use of the HRV signal has become more popular among scholars. The analysis of various studies shows that by approaching the time of death, linear features (time and frequency) can be predictive of death according to the sensible behavior and variations in patients’ signal. Instead of moving away from the death interval, the use of chaotic and non-linear features is more effective. Therefore, a more precise selection of features in this area can be useful for increasing the horizon of prediction of death.</p>

GNSS cloud‐data processing technique for jamming detection, identification, and localisation

Low-cost signal jammers, which are illegal in many countries but continue to be traded in the black market, emit weak global navigation satellite system (GNSS) jamming signals. This study proposes a method to detect, identify, and localise these signals based on a cloud-data processing technique in which the jamming detection, identification, and localisation are performed sequentially after gathering received signals from low-cost GNSS receivers distributed in a monitoring network. This technique consists of two steps: two-dimensional image correlation in the time-frequency domain for detection and identification, and signal correlation in the time domain and relevant processing for localisation. For this, technical descriptions of the proposed cloud-data processing method are first presented with various jamming scenarios. Then, numerical simulations are performed to evaluate the proposed method by generating satellite and jamming signals. The authors are thus able to verify the feasibility of the technique and conclude that, in practical applications, it would allow efficient and extensive monitoring of low-cost signal jammers.

50 km-Range Brillouin Optical Correlation Domain Analysis With First-Order Backward Distributed Raman Amplification

We demonstrate the enlargement of sensing range of Brillouin optical correlation domain analysis (BOCDA) system by introducing the first-order distributed Raman amplification. The time-domain data processing is adopted for the BOCDA system utilizing a gated Brillouin pump, and the Raman amplification is applied to compensate for the depletion of Brillouin pump induced by Rayleigh scattering and Brillouin interaction. The Raman pump is injected in the opposite direction to the propagation of Brillouin pump, where the pumping scheme is optimized by simulations. A differential measurement scheme is additionally introduced for improving spatial resolution and dynamic range. In experiments, we measure the strain distribution along a 52.1 km sensing fiber with 7 cm spatial resolution, where the number of resolving points of measurement is more than 744 000.

A 28-nm CMOS 12-Bit 250-MS/s Voltage-Current-Time Domain 3-Stage Pipelined ADC

This brief introduces a voltage-current-time (V-I-T) 3-domain 3-stage pipelined analog-to-digital converter (ADC) that exploits both the current-domain and the time-domain residue processing for speed enhancement. An open-loop current-time (I-T) pipeline with a calibration-free time-to-digital converter (TDC) utilizing a full-scale-matched current-to-time converter (ITC) enhances the conversion speed of a time-domain backend ADC while ensuring robustness to process, voltage, and temperature (PVT) variation. With background calibrations of voltage-to-current residue gain and sampling time skew at the first stage, the prototype 12-bit 250-MS/s ADC fabricated in a 28-nm CMOS process achieves a signal-to-noise-and-distortion ratio (SNDR) of 61.5 dB at a Nyquist input, resulting in a 22.2-fJ/conversion-step Walden figure-of-merit (FoM) under a 1.0V supply.

Validation of MA-ATI SAR Theory Using Numerical Simulation for Estimating the Direction of Moving Targets and Ocean Currents

A theory of multiaperture along-track interferometric (MA-ATI) synthetic aperture radar (SAR) was previously proposed to estimate the velocity vector of moving targets by utilizing conventional ATI SAR data. In the MA-ATI SAR method, multi/sublook processing is applied to both the raw data acquired by the fore-and-aft antennas, yielding two sets of forward-looking images and two sets of backward-looking images. From the interferograms of corresponding directions, two velocity components can be estimated, and hence the moving directions of a target are also obtained. The velocity vector is calculated by combining the estimated moving direction with the velocity in the range direction measured by the conventional ATI method. In order to show initial validation of this theory, a numerical SAR simulator, which has a function to produce a time series SAR raw data, was further developed to simulate MA-ATI SAR by taking into account the multilook processing. In the present validation study, this simulator (in time-domain processing) was applied to estimate the moving directions of a moving point target and ocean currents in the case of an airborne radar system. In particular, the effect on the subaperture azimuth beam angle was discussed since it is a key factor for the estimation. The preliminary results demonstrated that the estimation accuracy exhibited a direction error of 5°-10° for both the moving target and ocean currents, respectively, when half of the subaperture azimuth beam angle was larger than 1.25°.

High‐resolution time delay estimation via sparse parameter estimation methods

This study addresses the high-resolution time delay estimation (TDE) via sparse parameter estimation methods. Two representative algorithms, Sparse Asymptotic Minimum Variance (SAMV) and SParse Iterative Covariance-based Estimation are devised in both the time and frequency domains for application to the TDE of spread-spectrum signals and their performances are analysed in various multipath environments. The authors also proposes the combined approach of SAMV and weighted RELAX, referred to as SAMV-WRELAX, to reduce the computational load. Numerical examples demonstrate that the frequency-domain approaches with a proper type of snapshots not only outperform the corresponding time-domain approaches but also mitigate the problem of the noise correlation encountered in time-domain processing. They also show that the computational load of SAMV-WRELAX with a grid size of 0.1T c decreases up to a few tenths of that of SAMV with a fine grid, e.g. a size of 0.01T c , without any performance degradations.

Interval K-L expansion of interval process model for dynamic uncertainty analysis

The interval process model describes a time-variant or dynamic uncertain parameter by the upper and lower bounds rather than the precise probability distribution at each time point, providing an effective structural dynamic uncertainty quantification model with insufficient sample information. By reference to the Karhunen-Loève (K-L) expansion for stochastic process and random field models, a novel expansion method for the interval process model, namely, the interval K-L expansion is proposed in this work. The interval K-L expansion describes the continuous uncertainty of the interval process in time domain by superposition of infinite deterministic time-related functions with uncorrelated interval coefficients, which makes the interval process model much more convenient to use. By introducing a splicing technique, the interval K-L expansion method for multiple correlated interval processes is also presented. Based on the interval K-L expansion, the vibration of structures subject to interval process excitations is analyzed, where the analytic formulation of dynamic response bounds for linear elastic systems and continuum structures are derived. Finally, several numerical examples are investigated to demonstrate the effectiveness of the proposed method.

An Energy-Quality Scalable STDP Based Sparse Coding Processor With On-Chip Learning Capability.

Two main bottlenecks encountered when implementing energy-efficient spike-timing-dependent plasticity (STDP) based sparse coding, are the complex computation of winner-take-all (WTA) operation and repetitive neuronal operations in the time domain processing. In this article, we present an energy-efficient STDP based sparse coding processor. The low-cost hardware is based on the algorithmic reduction techniques as following: First, the complex WTA operation is simplified based on the prediction of spike emitting neurons. Sparsity based approximation in spatial and temporal domain are also efficiently exploited to remove the redundant neurons with negligible algorithmic accuracy loss. We designed and implemented the hardware of the STDP based sparse coding using 65nm CMOS process. By exploiting input sparsity, theproposed SNN architecture can dynamically trade off algorithmic quality for computation energy (up to 74%) for Natural image (maximum 0.01 RMSE increment) and MNIST (no accuracy loss) applications. In the inference mode of operations, the SNN hardware achieves the throughput of 374 Mpixels/s and 840.2 GSOP/s with the energy-efficiency of 781.52 pJ/pixel and 0.35pJ/SOP.

铣削过程稳定性时域分析方法研究进展

铣削过程稳定性时域分析方法研究进展

An empirical Study Investigation of Task Allocation Process Barriers in the Context of Offshore Software Development Outsourcing: An Organization Size Based Analysis

This research presented a holistic approach in determining the trade-off optimized Proportional-Integral-Derivative (PID) tunings for both servo and regulatory controls of the cascade control loop by using Genetic Algorithm (GA). Performance of GA-based PID tunings was significantly compared with the IMC-based single loop tunings and conventional cascade control tunings. GA-based PID tunings eliminated the complicated mathematic calculations in obtaining the correlation PID tuning values and also reduce the dependency on engineering knowledge, experience, and skills. The performance of transient and steady-state responses was compared through time domain specification, performance index, and process response curve. It is concluded that the GA-based PID tunings for the cascade control loop had produced the best result for both servo and regulatory control objectives, which is eventually determined.

Time- and Frequency-Domain Characteristics of Direct-Detection Acousto-Optic Delay Lines

We discuss various details regarding the practical use of direct-detection acousto-optic delay lines, and develop a mathematical model for the input signal. An expression for the transient response was obtained and used to determine the acousto-optic delay line response to an boxcar input signal. This expression for the transient response was used to determine the cutoff frequency of the acousto-optic delay line and derive an equation for the impulse response to determine the acousto-optic transmission coefficient of the acoustooptic delay line as a function of frequency. The resulting equations are confirmed by numerical examples. The numerical results were in turn experimentally validated using the direct-detection acousto-optic delay line model developed in this paper. The theoretical and experimental results were compared using two criteria: the output response to a boxcar input signal and the cutoff frequency of the frequency response curve. The cutoff frequency was obtained using an oscillogram of the output pulse and the experimentally determined frequency response. A comparison of the theoretical and experimental results indicated that the time- and frequency-domain equations for the response curves have high potential for time-domain processing of broadband analog signals.

A Time-Domain Approach to Channel Estimation and Equalization for the SC-FDM System

Similar to orthogonal frequency division multiplexing receiver, the frequency-domain (FD) channel estimation (CE) and equalization are indispensable parts in the coherent single carrier frequency division multiplexing (SC-FDM) receiver. When the channel varies slowly, the FD processing is cost effective. For the applications with high mobility, the traditional implementation of the SC-FDM receiver causes significant performance degradation. In this paper, we propose to estimate time-varying pieces of channel taps for pilot symbols based on basis expansion model (BEM), and subsequently to reconstruct time-domain (TD) channel response for data symbols by utilizing the Slepian sequences-based piece-wise interpolation. Furthermore, two simplified schemes, i.e., the Slepian sequences-based multiple-point interpolation and the segmented BEM, are developed to reduce the computational complexity of the TD-CE. The Cramer-Rao lower bound (CRLB) of channel impulse response (CIR) estimation is also analyzed. In the light of the sparsity of TD channel gain matrix, we design an iterative least-squares QR decomposition algorithm to equalize the SC-FDM symbols. In the simulated SC-FDM system, when considering the carrier frequency of 5.9 GHz and the velocity of about 510 km/h, we observe that the traditional FD methods cause the demodulation failure, while the proposed TD processing schemes achieve ideal error-probability performance and preserve a relatively low complexity.

Healthcare Informatics and Monitoring System based on Fog Computing

Wireless sensor networks and internet of things are widely used in healthcare industry. In today’s scenario wearable sensors become very popular devices to monitor patients at high risk. Patients who are suffering from long lasting sickness visits hospital at multiple time for their regular check-up. Treatment from this kind of system increases the expenditure in overall for the patients. However, with recent technological innovation such as cloud and fog computing, with minimal expense on storage and computing facilities these problems are overcome. In this paper, we provide an analysis of fog computing role over cloud computing to monitor patient’s health to provide context aware services without delay to the end user when it required to patient and medical practitioner. Three layer architecture is proposed for real time domain data storage, processing and communication. This increase the capability of cloud computing with the integration to fog computing for current and future use application.

JexoSim: a time-domain simulator of exoplanet transit spectroscopy with JWST

ABSTRACT The James Webb Space Telescope (JWST) will perform exoplanet transit spectroscopy in the coming decade, promising transformative science results. All four instruments onboard can be used for this technique that reconstructs the atmospheric transmission or emission spectrum of an exoplanet from wavelength-dependent light-curve measurements. Astrophysical and instrumental noise and systematics can affect the precision and accuracy of the final spectrum, and hence, the atmospheric properties derived from the spectrum. Correlated noise and time-dependent systematics that can bias the measured signal must be accounted for in the final uncertainties. However, quantifying these effects can be difficult with real data or simple analytic tools. Existing publicly available simulators for JWST do not adequately simulate complex time-domain processes on exoplanetary transit observations. We report JexoSim, a dedicated time-domain simulator for JWST including all the four instruments for exoplanet transit spectroscopy. JexoSim models both the astrophysics and the instrument, generating 2D images in simulated time akin to a real observation. JexoSim can capture correlated noise and systematic biases on the light curve, giving it great versatility. Potential applications of JexoSim include performance testing of JWST instruments, assessing science return, and testing data reduction pipelines. We describe JexoSim, validate it against other simulators, and present examples of its utility.

Velocity-independent Marchenko focusing in time- and depth-imaging domains for media with mild lateral heterogeneity

The Marchenko method retrieves Green’s functions between the acquisition surface and any arbitrary point in the medium. The process generally involves solving an inversion starting with an initial focusing function, e.g., a direct-wave Green’s function from the desired subsurface position, typically obtained using an approximate velocity model. We have formulated the Marchenko method in the time-imaging domain. In that domain, we recognize that the traveltime of the direct-wave Green’s function is related to the Cheop’s traveltime pyramid commonly used in time-domain processing, which in turn can be readily obtained from the local slopes of the common-midpoint gathers. This observation allows us to substitute the velocity-model-based initial focusing operator with that from a data-driven slope estimation process. Moreover, we found that working in the time-imaging domain allows for the specification of the desired subsurface position in terms of vertical time, which is connected to the Cartesian depth position via the time-to-depth conversion. Our results suggest that the prior velocity model is only required when specifying the position in depth, but this requirement can be circumvented by making use of the time-imaging domain within its usual assumptions (e.g., mild lateral heterogeneity). Provided that those assumptions are satisfied, the estimated Green’s functions from the proposed method have comparable quality to those obtained with the knowledge of a prior velocity model.

Uses and mis-uses of energy operators for machine diagnostics

Abstract The Teager Kaiser Energy Operator (TKEO) was originally proposed for use in speech analysis as representing the total energy (i.e. kinetic plus potential energy) in a signal. It was shown that for a mono-component carrier, with slowly changing amplitude and frequency, the TKEO is approximately equal to the product of the squares of the instantaneous amplitude and frequency. The TKEO is only strictly defined for mono-components, i.e. signals that can be modelled as a single carrier frequency, modulated in amplitude and frequency in such a way that they can be represented as the real part of an analytic signal, with a one-sided spectrum. The traditional way of estimating the TKEO was by an efficient time domain operation involving only three adjacent samples, and this can be done in real time, but this implies that all filtering and other processing must use causal processing to retain this advantage. However, causal filters give phase distortion and non-ideal filter characteristics. It is easily shown that the TKEO is approximately equal to the squared envelope of the derivative of the signal, which can alternatively be calculated by efficient non-causal Hilbert transform techniques via the frequency domain, incidentally giving a more accurate result, as well as being virtually as efficient. When combined with other non-causal processing, such as ideal filtering by choice of a specified band in the frequency domain, and ideal differentiation/integration by jω operations in the frequency domain, this approach has many advantages in cases where real-time processing is not required, and where the processing can be carried out by post-processing of recorded signals, which can be very long. Machine diagnostics is one area where real-time processing gives no advantage, and even numerous disadvantages, which accompany causal processing, such as mentioned above. Even in the single situation in machine monitoring where a result might be required rapidly, online monitoring of critical equipment, there is little practical difference in the processing time for causal and non-causal techniques (a maximum of a second or so) as this would rarely be sufficient time to make a decision on whether to shut a machine down, or for its speed to reduce significantly even if it were. The disadvantage of non-causal (batch) processing via Fourier transforms comes from the intrinsic circularity of the latter, where all functions in both time and frequency domains are assumed periodic. However, this has been dealt with since the birth of the FFT algorithm in 1965, and usually means that time records (or spectra) just have to be extended a small amount to allow truncation of wraparound effects. There are already a considerable number of papers published recommending the use of the TKEO and its variants for machine diagnostics, many claiming that this gives advantages over traditional approaches, for example of amplitude and frequency demodulation based on Hilbert transforms. However, this paper demonstrates that the claimed advantages are invariably false, for the following reasons: 1) The formulas derived for estimating the instantaneous amplitude and frequency of a mono-component using the TKEO actually give the values for the derivative of the signal, which are not the same. It is true that the time domain TKEO gives better results for a single chirp sweeping over a wide frequency range from zero (because of huge wraparound effects) but this situation does not apply to machine signals because of interference between multiple harmonics of shaft speeds, meaning that the maximum speed range in one record is 2:1. 2) By employing Hilbert transform and non-causal processing techniques, the errors and excessive time of causal time domain processing (for example time domain convolutional filtering, differentiation, etc) are avoided and virtually all other parameters and features of machine faults are estimated more accurately and faster, with considerably more control of frequency bandwidth and waveforms. Multiple differentiations can be achieved with equal accuracy in one operation. 3) Many of the proposed applications of the TKEO do not require the signal to be mono-component, such as the application to bearing diagnostics (since bearing signals do not have continuous phase) and where the only advantage of differentiation (increasing weighting with frequency) can only be realised with a frequency range much greater than the maximum 2:1 limit for a mono-component. This paper demonstrates all the above claims with a range of typical signals and applications to gear and bearing diagnostics, and rebuts many of the false claims previously made.

Simplified and approximation autofocus back‐projection algorithm for SAR

Due to accuracy limited inertial measurement unit systems, residual synthetic aperture radar (SAR) motion errors need to be compensated by autofocus method. Back-projection (BP) algorithm is a time-domain imaging process suitable for arbitrary trajectory and imaging on digital elevation model. However, traditional SAR autofocus algorithm requires a Fourier transform relationship between image domain and phase history domain. This study introduces an autofocus algorithm based on phase-error estimation. By selecting the image sharpness as target cost function, the exact phase error is directly estimated in closed form by solving the fourth-order polynomial with simplified approach. Another straightforward approximation autofocus method is also proposed to simplify calculation with high accuracy. The vast storage requirement for filtered BP data is reduced through a trade between time and space. Computer simulation and airborne experimental results validate the efficiency and efficacy of the proposed method.

A weighted auto regressive LSTM based approach for chemical processes modeling

Data-driven methods have been regarded as effective methods for modeling in chemical processes. However, with the increasing complexity of chemical processes in spatial domain and time domain, how to extract meaningful features and build corresponding models are keys for accurate modeling tasks. To retain temporal features of original inputs, a Recurrent Denosing Autoencoder (RDAE) is built to extract meaningful features and reduce input dimension in the spatial domain, and Cumulative Percent Variance (CPV) is introduced to decide the number of extracted features. Considering correction and prior knowledge effects of real history outputs, a Weighted Auto Regressive Long Short Term Memory (WAR-LSTM) structure is proposed as the basic cell, then multiple WAR-LSTMs are stacked as deep WAR-LSTM to extract high level representations from multi variables. Hence, spatial and temporal information are sufficiently used both in extracting features and building model. The benchmarked Tennessee Eastman process data and real process data from a Fluid Catalytic Cracking (FCC) unit verify the effectiveness of our work.

A modeling of dispersive tensorial second-order nonlinear effects for the finite-difference time-domain method

A generalized formalism is developed to model second-order nonlinear processes in finite-difference time-domain (FDTD) simulations. The method is capable of modeling frequency-conversion from all 18 elements of the second-order nonlinear tensor, where dispersion of the tensor elements is included at both the pump and generated frequencies. The model is validated by considering frequency-conversion in a LiNbO3 crystal, which has highly dispersive second-order nonlinear susceptibilities near the phonon resonances. The developed nonlinear formalism is able to model any arbitrary excitation polarization state and can be applied to investigate second-order nonlinear processes in type I or type II phase-matching. This generalized second-order nonlinear formalism represents an advancement for the FDTD computational technique and can provide more realistic modeling of second-order nonlinear interactions in nanoscale devices and waveguiding structures.

Dim small moving target detection and tracking method based on spatial-temporal joint processing model

In order to solve the problem of weak target detection in complex moving background, a joint algorithm model of space-time domain is proposed in this paper. The algorithm first selects appropriate length of the time domain window and loop step size. In each time domain window: (1) In time domain processing stage, the algorithm fully considers the factors of background movement, and uses neighborhood similarity measure based on Pearson correlation coefficient to suppress most part of background area. (2) In space domain processing stage, a target detection method based on regional gray level (RGL) is proposed to suppress residual background and re-enhance the target. In each iteration, this joint detection model can obtain part of the trajectory of the target in real time. Therefore, after all iterations are completed (all images in the sequence have been processed), the target can be detected and the target trajectory can be efficiently extracted. The experimental results in this paper show that the proposed algorithm can effectively detect targets with maximum speed of 5 pixel/frame in the image sequence with average SCR ≤ 1 and background moving speed ≤0.8 pixel/frame.