Average Time Error Research Articles

REAL-TIME METHOD FOR ECG R-PEAK DETECTION COMBINING AUTOMATIC THRESHOLD AND DIFFERENTIATION

This paper proposed a novel real-time algorithm for electrocardiogram (ECG) signal analysis using the first derivative and automatic threshold to locate the R-peaks. First, the ECG signals are filtered by Butterworth low pass filter to reduce the high frequency noise. Then, the first 10[Formula: see text]s datasets of the first derivative of ECG signal are analyzed to search the maximum value. Three process thresholds are computed using this maximum value, which is used to avoid the missed and false peak detections. Thus, a threshold is automatically calculated using these searched maximum values, and divide the differentiated ECG to obtain two intersection points. Recording the time of these two intersection points, a time interval is formed for the differentiated ECG. Delaying this time interval for specific sampling periods, a new time interval is acquired for the corresponding ECG cycle. Finally, the local maximum in an ECG cycle is narrowed down in this new time interval such that the R-peak can be located. The MIT-BIH Arrhythmia Database of 48 ECG recordings is used to verify the proposed algorithm. On this dataset, the proposed algorithm yielded mean sensitivity of 99.17%, positive predictivity of 97.37% and average time error of 1.01[Formula: see text]ms.

An efficient Pseudo-spectral based phase field method for dendritic solidification

In the current study, Fourier Pseudo-spectral based dealiasing scheme Random Phase Shift Method (RPSM) using the RK4 temporal scheme is implemented for simulating some benchmark problems of Phase Field based pure metal solidification. This high-resolution scheme is capable of resolving the thin solid-liquid interface accurately without using Adaptive Mesh Refinement (AMR) algorithm even using just adequate number of grid points. Both the conservative and non-conservative forms of the Phase Field equations are studied. It is found that the conservative RPSM scheme gives better computational performances as indicated by the lower values of average CPU time and different error norms calculated for the Phase Field variable. The RPSM based Phase Field model is applied for simulating dendritic solidification and it is found that dendrite shape and dimensionless tip velocity agree well with the existing literature. The RK4 based RPSM scheme is also compared with a 10th order exponential Fourier smoothing Filter in the evolution of four arm dendritic structure and it is observed that the conservative Phase Field equations with filtering scheme display numerical instabilities at later simulation time, while both forms of the RPSM scheme show stable and accurate solutions. Subsequently, the capability of the RPSM based Phase Field model in simulating microstructure evolution consisting of multiple dendrites with random location and orientation is demonstrated. The present study shows that the RPSM based Phase Field model computes dendritic growth accurately and efficiently.

A novel adaptive window based technique for T wave detection and delineation in the ECG

Abstract The electrocardiogram (ECG) morphology determines the overall activity of the heart and is the most widely used tool in the diagnostic processes. T wave is a crucial wave component that reveals very useful information regarding various cardiac disorders. In this paper we have proposed a novel T wave detection technique based on adaptive window and simple decision rule. The proposed technique uses two-stage median filters followed by the Savitzky-Golay filter at the pre-processing stage to remove the noises in the ECG signal. The QRS complex is detected for locating the T wave as a reference in one ECG cycle. An R-R interval based window is considered for detecting the T wave, and decision logic depends on the iso-electric line value. The proposed technique is tested on the QT database and self-recorded dataset for its performance evaluation. In the present work, the results achieved for T wave detection sensitivity (Se), positive predictivity (+P), detection error rate (DER), and accuracy (Acc) on the QT database are Se = 97.57%, +P = 99.63%, DER = 2.78%, and Acc = 97.22% with an average time error of (3.468 ± 5.732) ms. The proposed technique shows Se = 99.94%, +P = 99.94%, DER = 0.01%, and Acc = 99.89% on the self-recorded dataset. The proposed technique is also capable of detecting both the upward and downward T wave efficiently in the ECG signal.

Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals

It is plausible to assume that the component waves in ECG signals constitute a unique human characteristic because morphology and amplitudes of recorded beats are governed by multiple individual factors. According to the best of our knowledge, the issue of automatically classifying different ’identities’ of QRS morphology has not been explored within the literature. This work proposes five alternative mathematical models for representing different QRS morphologies providing the extraction of a set of features related to QRS shape. The technique incorporates mechanisms of combining the mathematical functions Gaussian, Mexican-Hat and Rayleigh probability density function and also a mechanism for clipping the waveform of those functions. The searching for the optimal parameters which minimize the normalized RMS error between each mathematical model and a given QRS search window enables to find an optimal model. Such modeling behaves as a robust alternative for delineating heartbeats, classifying beat morphologies, detecting subtle and anomalous changes, compression of QRS complex windows among others. The validation process evaluates the ability of each model to represent different QRS morphology classes within 159 full ECG signal records from QT database and 584 QRS search windows from MIT-BIH Arrhythmia database. From the experimental results, we rank the winning rates for which each mathematical model best models and also discriminates the most predominant QRS morphologies Rs, rS, RS, qR, qRs, R, rR’s and QS. Furthermore, the average time errors computed for QRS onset and offset locations when using the corresponding winner mathematical models for delineation purposes were, respectively, 12.87±8.5 ms and 1.47±10.06 ms.

Do Records of Sunspot Sightings Provide Reliable Indicators of Solar Maxima for 1613 – 1918?

Records of Sunspot Sightings (RSS) which were obtained by the naked eye without the aid of the telescope are essential to find the solar activity in the distant past. Intense solar activity or the solar maxima had been identified by using the RSS for several decades. However, the performance of the RSS as the indicators of the solar maxima has rarely been discussed with quantitative analysis. The RSS from 1613 to 1918 are accessed as the indicators of the solar maxima, by using the time difference between each RSS and the specific solar maxima. The results have shown that about 33% of the sunspot sightings were obtained within one year of the solar maxima and about 51% of the sunspot sightings were obtained within 2 years of the solar maxima. The average time error of identifying the solar maxima by the RSS is 2.52 years, during the period 1613 – 1918. If a certain amount of time error could be tolerated for the identification of the solar maxima, the RSS could be generally used as indicators of the solar maxima.

Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 20142016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0:7 and with average time error of 18 minutes. It is orders of magnitude better than the state-of-the-art deep learning approaches for taxi demand prediction.

Fusion Algorithm for Accurate Delineation of QRS Complex in ECG Signal

In this paper, a novel algorithm for the accurate localization of QRS complex with low average time error is proposed. The idea is thought that the various features of ECG signal like P, Q, R, S and T peaks can be independently detected from raw ECG recording and fused together to obtain a better estimate of QRS position. To explore, in this paper, an algorithm is suggested to first estimate R peak and S peak from raw ECG signal and then fused together to detect and localize QRS complex. The algorithm is validated on all the signals of MIT-BIH arrhythmia database, QT database and noise stress database taken from physionet.org. The algorithm performs reasonably well even for the signals highly corrupted with noise, and these noises are generated by adding the power line interference, electrode motion artifact, baseline wandering interference and muscle artifact to all the signals of MIT-BIH arrhythmia database and QT database. The algorithm performance is confirmed not only with a very high sensitivity and positive predictivity, but also with a very low average time error of 0.63 ms against the 3.03 ms the best results reported so far for the signals of MIT-BIH arrhythmia database and 0.85 ms against the 3.6 ms the best results reported in the literature for the signals of QT database.

Investigation on the Time Error of Detonation Acoustic in Process of Formation and Propagation

Abstract The time error of detonation acoustic in process of detonation formation and propagation in a multi-cycle gas-liquid two-phase pulsed detonation engine is experimentally investigated. Results from the tests show that before the detonation wave escapes through the open-end of PDE tube, the maximum average arrival time error of detonation acoustic is achieved in the process of overdriven detonation. After detonation wave exists of PDE tube, arrival time error at 0 deg is greater than the other directivity angles in all distances and increases dramatically first and then almost stays at a certain value. The filling fraction has a major impact on the time error of detonation acoustic. With filling fraction increasing, there are increases in arrival time error and interval time error. Arrival time error with the highest filling fraction at 30 deg is much greater than other filling fraction. The convergent nozzle exhibits a marked suppression in the time error of detonation acoustic, where the maximum reductions of 62.02 percent and 56.13 percent are obtained in arrival time error and interval time error respectively.

High Speed Cycle-Approximate Simulation of Embedded Cache-Incoherent and Coherent Chip-Multiprocessors

The increasing density of silicon processes, coupled with the development of ever more energy and space efficient embedded core designs, has led to multi-processor system-on-chip (MPSoC) designs becoming increasingly attractive for use in embedded systems. Unfortunately this increase in core count gives rise to an explosion in design space possibilities, especially when heterogeneous designs are considered. To address this problem, new techniques in simulation are required to increase the simulation performance of these systems, while maintaining the accuracy needed to make good design decisions, and to verify the performance characteristics for real-time systems. We present a new high-speed, near cycle-accurate simulator, addressing an important but neglected category of multicore systems: deeply-embedded cache-incoherent MPSoCs. We take advantage of the unique properties of these systems to relax synchronisation constraints and increase the parallelism of the simulation. In doing so we achieve performance not possible using previous simulation techniques, without compromising the accuracy of the results. Quantitative performance results are presented across a large range of simulated MPSoC designs, comprising 1–64 cores, on average we simulate at 5.7 MIPS, with simulation speeds reaching 377 MIPS in the best case. Comparing against FPGA implementations we demonstrate that the simulator manages this with an average timing error of only 2.1%. Applying some of these techniques to coherent simulation enables even coherent 64-core designs to be simulated accurately at up to 2.2 MIPS.

An automated ECG signal quality assessment method for unsupervised diagnostic systems

In this paper, the authors present an automated method for quality assessment of electrocardiogram (ECG) signal. Our proposed method not only detects and classifies the ECG noises but also localizes the ECG noises which can play a crucial role in extracting reliable clinical features for ECG analysis systems. The proposed method is based on three stages: Wavelet decomposition of ECG signal into sub-bands; simultaneous ECG signal and noise reconstruction; extraction of temporal features such as maximum absolute amplitude, zerocrossings, kurtosis and autocorrelation function for detection, localization and classification of ECG noises including flat line (FL), time-varying noise or pause (TVN), baseline wander (BW), abrupt change (AB), power line interference (PLI), muscle artifacts (MA) and additive white Gaussian noise (AWGN). The proposed method is tested and validated against manually annotated ECG signals corrupted with aforementioned noises taken from MIT-BIH arrhythmia database, Physionet challenge database, and real-time recorded ECG signals. Comparative detection and classification results depict the superior performance of the proposed method over state of art methods. Detection results show that our method can achieve an average sensitivity (Se), average specificity (Sp) and accuracy (A) of 99.61%, 98.51%, 99.49% respectively. Also, the method achieves a Se of 98.18%, and Sp of 94.97% for real-time recorded ECG signals. The method has an average timing error of 0.14s in localizing the noise segments. Further, classification results demonstrate that the proposed method achieves an average sensitivity (Se), average positive predictivity (PP) and classification accuracy (Ac) of 98.53%, 98.89%, 97.50% respectively.

Performance comparison of bluetooth beacon and reverse RFID systems as potential tools for measuring truck travel time in open-pit mines: a simulation experiment

We compared the performance of a Bluetooth beacon system and a reverse Radio-Frequency Identification (RFID) system to investigate their potentials as tools for measuring truck travel time in open-pit mines. A university campus was chosen as the simulation experiment site, and Bluetooth beacons and RFID tags were installed at intervals along the car’s course. A car equipped with a smartphone that can recognize beacons and an RFID reader that can communicate with the tags was driven along the course; the travel time and recognition rate were measured. For comparison of the travel time measurement data, we used video data captured while driving the car. The analysis results of ten rounds of experimental measurement data show that the average time error is 1.14 and 0.80 s for the Bluetooth beacon and reverse RFID systems, respectively. The sensor recognition rate is an average of 98% for the Bluetooth beacon system and 61% for the reverse RFID system. The reverse RFID system is slightly more accurate as a tool for measuring car travel time, and the Bluetooth beacon system has a much better sensor recognition rate.

Impacts of Power Grid Frequency Deviation on Time Error of Synchronous Electric Clock and Worldwide Power System Practices on Time Error Correction

Synchronous electric clocks utilize power grid frequency as their timing reference. Power grid frequency deviation away from its nominal value results in synchronous electric clocks running fast or running slow (also known as the time error). In this article, statistical analysis on time error of synchronous electric clocks around the world is firstly presented using the power grid frequency measurements recorded by the wide-area frequency monitoring network FNET/GridEye. Then, the time error correction (TEC) process provided by electric utilities is analyzed and the worldwide TEC practice is investigated. Eventually, regions of the world where electric utilities provide TEC service are differentiated from those without TEC services. Analytical results demonstrate that the average time error of synchronous electric clocks in North America seems to be less than five seconds, and it has not changed very much over the past few years. On the other hand, the identification results present that up to the end of 2016, many electric utilities around the world, especially in North America and Europe, provided the TEC service to periodically remove the accumulative time error of synchronous electric clocks.

Measuring fine-grained metro interchange time via smartphones

High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agent-operated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a two-tier classifier to infer interchange states during a metro trip, and further distinguishes 10 fine-grained cases during interchanges. Experimental results within 6months across over 14 subway lines in 3 major cities demonstrate that our approach yields an overall interchange state inference F1-measurement of 91.0% and an average time error of less than 2min at an inference interval of 20s, and an average accuracy of 89.3% to distinguish the 10 fine-grained interchange cases. We also conducted a series of case studies using measurements collected from crowdsourced users during 3months, which reveals findings previously unattainable without fine-grained interchange time measurements, such as portions of waiting time during interchange, interchange directions, usage of facilities (stairs/escalators/lifts), and the root causes of long interchange times.

Conceptual weather environmental forecasting system for identifying potential failure of under-construction structures during typhoons

In this study, we developed a conceptual weather environmental forecasting system (CWEFS) for predicting the failure of under-construction structures during typhoons. Major functions of the developed system include 1) forecasting hourly typhoon wind velocity, 2) analyzing structure reference load during the construction stage, 3) identifying potential failure of under-construction structures, and 4) evaluating the weather in future hours to determine whether the conditions are suitable for work. Data-driven models, namely support vector machines for regression (SVRs), regression, and two decision trees (namely C5.0 and CART) were employed in this study as forecasting techniques to predict the wind velocity on Orchid Island, Taiwan, the study site. Structure reference load analysis was performed using a finite element model to evaluate the reference load on an experimental tank under construction. Typhoons Nanmadol (2011) and Saola (2012) were selected for real-time simulation by using the proposed CWEFS. This study identified potential collapses by using 1- to 6-h-ahead wind speed predictions. However, prediction errors inevitably occur. The results showed that the SVRs provided excellent prediction accuracy compared with regression, C5.0, and CART regarding the average time error between the observed and predicted values in all structure scenarios. A high forecast time error might result in increased construction costs and delays in construction schedules. Thus, we suggest that shorter prediction windows (e.g., 1 and 2 h) and models with higher prediction accuracy (e.g., SVR and C5.0) be employed to create a reasonable warning system.

Evaluating Gaussian and Rayleigh-Based Mathematical Models for T and P-waves in ECG

This paper presents a comparative study of modelling and segmentation of P and T waves in electrocardiograms, using three different mathematical models: Gaussian function, a composition of two Gaussian functions and Rayleigh probability density function (Rayleigh pdf). In order to evaluate the adaptability and the matching degree between each model and each characteristic wave, we compute the normalized root mean square (RMS) error, as well as the evolution of the fitting parameters related to each kernel throughout ECG records from the well-known QT database. Concerning the delineation of P and T-waves, we apply Wavelet Transform for estimating T-wave and P-wave peak locations and combine each developed model with an approach based on the computation of Trapezium's area to locate the end point of each T-wave and the beginning and end point of each P-wave. The composition of two Gaussian functions has produced the most accurate results concerning wave modelling, providing average values of normalized RMS errors equal to 9,15% and 18,70%, respectively for T-wave and P-wave. Rayleigh pdf provided the most stable fitting parameters. For T-wave end location, the most accurate results were computed when using the kernel composition of two Gaussian functions, for which the average time error was 4,49 ± 14,32 ms. For P-wave begin and P-wave end locations, the most accurate results were computed when using kernel Rayleigh pdf, for which the average time errors were, respectively, -4,23 ± 14,84 ms and 2,26 ± 13,14 ms.

Data Receiving Method Based on Multimedia Timing in Real- Time System

Abstract Several methods, such as polling, multithread, timing, and so on, can be used in data receiving course. Low-efficiency and high level of system resource consumption may bring about data loss in polling and multithread methods when the data transmission rate is very high. Software timing methods are discussed and analyzed in Visual C++. Timing method would improve the system resource availability and decrease the risk of data loss. According to the demand ofatesting system,areal-time monitoring system based on memory sharing and multimedia timer is presented. After testing, the average timing error of the multimedia timer in the given instance is not bigger than 0.05%, so the continuity and integrity of data receiving can be assured under the conditions of high-speed data transmission.

Practical method development for the separation of monoclonal antibodies and antibody-drug-conjugate species in hydrophobic interaction chromatography, part 1: optimization of the mobile phase

The goal of this work is to provide some recommendations for method development in HIC using monoclonal antibodies (mAbs) and antibody-drug conjugates (ADCs) as model drug candidates. The effects of gradient steepness, mobile phase pH, salt concentration and type, as well as organic modifier were evaluated for tuning selectivity and retention in HIC. Except the nature of the stationary phase, which was not discussed in this study, the most important parameter for modifying selectivity was the gradient steepness. The addition of organic solvent (up to 15% isopropanol) in the mobile phase was also found to be useful for mAbs analysis, since it could provide some changes in elution order, in some cases. On the contrary, isopropanol was not beneficial with ADCs, since the most hydrophobic DAR species (DAR6 and DAR8) cannot be eluted from the stationary phase under these conditions.This study also illustrates the possibility to perform HIC method development using optimization software, such as Drylab. The optimum conditions suggested by the software were tested using therapeutic mAbs and commercial cysteine linked ADC (brentuximab-vedotin) and the average retention time errors between predicted and experimental retention times were ∼1%.

Forecast Horizon of 5th 6th 7th Long Wave and Short-Period of Contraction in Economic Cycles

The purpose of this essay is to determine the forecast horizon of the fifth, sixth and seventh long wave. As the period of each long wave can change according to the data, it has been used a deterministic approach, based on historical chronologies of USA and UK economies worked out by several scholars, to determine average timing, period and forecast error of future long waves. In addition, the analysis shows that long waves have average upwave period longer than average downwave one. This result is also confirmed by US Business Cycles that have average contractions shorter than expansions phase over time.

Continuous data assimilation with stochastically noisy data**Dedicated to Professor Ciprian Foias on the occasion of his 80th birthday.

We analyse the performance of a data-assimilation algorithm based on a linear feedback control when used with observational data that contains measurement errors. Our model problem consists of dynamics governed by the two-dimensional incompressible Navier–Stokes equations, observational measurements given by finite volume elements or nodal points of the velocity field and measurement errors which are represented by stochastic noise. Under these assumptions, the data-assimilation algorithm consists of a system of stochastically forced Navier–Stokes equations. The main result of this paper provides explicit conditions on the observation density (resolution) which guarantee explicit asymptotic bounds, as the time tends to infinity, on the error between the approximate solution and the actual solutions which is corresponding to these measurements, in terms of the variance of the noise in the measurements. Specifically, such bounds are given for the limit supremum, as the time tends to infinity, of the expected value of the L2-norm and of the H1 Sobolev norm of the difference between the approximating solution and the actual solution. Moreover, results on the average time error in mean are stated.

HIGH-SPEED APPROACH FOR DELINEATING P AND T WAVES CHARACTERISTICS IN ELECTROCARDIOGRAM SIGNAL

Identifying and delineating P and T wave characteristics are greatly important in interpreting and diagnosing electrocardiogram (ECG) signals. P and T waves with high accuracy are more difficult to delineate because of their various shapes, positions, directions and boundaries. This paper proposes a high-speed approach to delineate P and T waves in a single lead using two high-speed algorithms of high detection accuracy. This approach presents a simple, adaptive and intelligent P and T wave scan method that determines the onset, peak and end time locations within an adaptive period appointed by previous records of the QRS complex. By using a translating (rising to/from falling) interval inside the scan wave, the peak time location of P and T waves and the T wave sign (upward or downward) are determined. Continuously, this time location is considered a reference point for determining the onset and the end time locations based on a series of computed outcomes related to amplitude and slope difference. The new approach is validated by 105 annotated records from the QT database collected from seven different categories of ECG signals. Simulation results show that the average detection rates of sensitivity and positive predictivity are equal to 99.97% and 99.36% for P wave and 99.98% and 99.26% for T wave, respectively. The average time errors computed by the mean and standard deviation for the P wave onset, peak and end time locations are -3.00 ± 2.94, -0.69 ± 4.42 and 0.67 ± 4.56 ms, respectively. The values for T wave are -3.33 ± 4.96, 0.24 ± 5.36 and -0.36 ± 5.68 ms. Results demonstrate the reliability, accuracy and forcefulness of the proposed approach in delineating various categories of P and T waves.