Accuracy Of Time Estimation Research Articles

Wireless Picosecond Time Synchronization for Distributed Antenna Arrays

Distributed antenna arrays have been proposed for many applications ranging from space-based observatories to automated vehicles. Achieving good performance in distributed antenna systems requires stringent synchronization at the wavelength and information levels to ensure that the transmitted signals arrive coherently at the target, or scattered and received signals can be appropriately processed via distributed algorithms. In this article, we address the challenge of high-precision time synchronization to align the operations of elements in a distributed antenna array and overcome time-varying bias between platforms primarily due to oscillator drift. We use a spectrally sparse two-tone waveform, which obtains approximately optimal time estimation accuracy, in a two-way time transfer process. We also describe a technique for determining the true time delay using the ambiguous two-tone matched filter output, and we compare the time synchronization precision of the two-tone waveform with the more common linear frequency modulation (LFM) waveform. We experimentally demonstrate wireless time synchronization using a single-pulse 40-MHz two-tone waveform over a 90-cm 5.8-GHz wireless link in a laboratory setting, obtaining a timing precision of 2.26 ps.

Application of discrete event simulation model in analysis on cost-effectiveness of epidemiology screening

Discrete event simulation (DES) model is based on individual data, by which discrete events over time are simulated to reflect disease progression. The effects of individual characteristics on disease progression could be considered in the DES model. Moreover, unlike state-transition models, DES model without setting of fixed cycle can contribute to more accurate estimation of event time, especially in the evaluation of the long-term effectiveness of screening strategies for complex diseases in which time dimension needs to be considered. This article introduces the general principles, construction steps, analytic methods and other relevant issues of the DES model. Based on a research case of estimating the cost-effectiveness of screening for abdominal aortic aneurysms in women aged 65 years and above in the United Kingdom, key points in applications of the DES model in analysis on effectiveness of complex disease screening are discussed in detail, including model construction and analysis and interpretation of the results. DES model can predict occurring time of discrete events accurately by establishing the distribution function of their occurring time and is increasingly used to evaluate the screening strategies for complex diseases in which time dimension needs to be considered. In the construction of DES model, it is necessary to pay close attention to the clear presentation of model structure and simulation process and follow the relevant reporting specification to conduct cost-effectiveness analysis to ensure the transparency and repeatability of the research.

Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment

Wearable sensors and machine learning algorithms are becoming a viable alternative for biomechanical analysis outside of the laboratory. The purpose of this work was to estimate gait events from inertial measurement units (IMUs) and utilize machine learning for the estimation of ground reaction force (GRF) waveforms. Sixteen healthy runners were recruited for this study, with varied running experience. Force sensing insoles were used to measure normal foot-shoe forces, providing a proxy for vertical GRF and a standard for the identification of gait events. Three IMUs were mounted on each participant, two bilaterally on the dorsal aspect of each foot and one clipped to the back of each participant’s waistband, approximating their sacrum. Participants also wore a GPS watch to record elevation and velocity. A Bidirectional Long Short Term Memory Network (BD-LSTM) was used to estimate GRF waveforms from inertial waveforms. Gait event estimation from both IMU data and machine learning algorithms led to accurate estimations of contact time. The GRF magnitudes were generally underestimated by the machine learning algorithm when presented with data from a novel participant, especially at faster running speeds. This work demonstrated that estimation of GRF waveforms is feasible across a range of running velocities and at different grades in an uncontrolled environment.

Modeling and prediction of lithium-ion battery thermal runaway via multiphysics-informed neural network

In this study, a multiphysics-informed neural network (MPINN) is proposed for the estimation and prediction of thermal runaway (TR) in lithium-ion batteries (LIBs). MPINNs are encoded with the governing laws of physics, including the energy balance equation and Arrhenius law, ensuring accurate estimation of time and space-dependent temperature and dimensionless concentration in comparison to a purely data-driven approach. Specifically, the network is trained using data from a high-fidelity model of an LIB, which describes TR by addressing several coupled partial differential equations. Quantitative analysis reveals that the mean absolute error (MAE) and root mean squared error (RMSE) of the MPINN for TR estimation are less than an artificial neural network (ANN) by 0.71 and 1.57, respectively, when using fully labeled data for training. It outperforms the ANN in terms of MAE and RMSE by 90.56 and 118.64, when only a small portion of labeled data (semi-supervision) are used for TR prediction. Importantly, it predicts TR without any labeled data when the decomposition of reactive species is modeled in the positive electrode. The MPINN exhibits promising results in surrogate modeling, implying it can be successfully implemented in practical scenarios and stimulate further research related to TR modeling using physics-informed deep learning.

Fine-Grained CPU Power Management Based on Digital Frequency Divider

Dynamic voltage and frequency scaling (DVFS) is a widely used method to improve the energy efficiency of the CPU. Reducing the voltage and frequency during memory-intensive workloads can minimize power consumption without affecting performance, thereby improving overall energy efficiency. A finer-grained DVFS strategy leads to better energy efficiency. However, due to the limitation of voltage regulators, the implementation granularity of the current DVFS strategies is 100 μs or more. This paper proposes that managing the CPU’s power through a more fine-grained load-aware approach can improve CPU energy efficiency, even with limitations of the voltage regulators. This paper adds a more fine-grained dynamic frequency divider to the DVFS system. This mechanism can improve the processor’s energy efficiency in scenarios where DVFS does not take effect. This paper also proposes a DVFS management strategy based on finer-grained sampling. In order to improve the accuracy of performance estimation, we enhanced the state-of-the-art CRIT method to complete accurate memory time estimation in a shorter interval. The power management strategy was verified on the ChampSim and McPAT simulating platforms. In the SPEC CPU 2017 benchmark, this work saves an average of 16.36% energy consumption and improves energy efficiency by 13.57%. Compared with the state-of-the-art CRIT of 9.77% and 6.79%, this work improved energy consumption and efficiency by 6.20% and 6.35%, respectively. This method brings a 2.04% performance reduction, only a 0.16% drop in performance compared to CRIT.

Incremental use of FFT as a solution for low BT-product reverberation time measurements

The limitations in performance of band-pass filters to accurately process rapid decaying signals in lower frequency bands is an obstacle for some measurements within building acoustics. For instance, it would be beneficial to be able to accurately measure reverberation times down to the 20 Hz one-third octave band for impact sound in timber buildings. Here, it is tested whether calculations with FFT with small incremental steps may be a way to achieve discrete frequency time signals with faster performance than traditional band-pass filters. The tests show that incremental FFT gives accurate estimations of the reverberation time corresponding down to 0.1 s at 20 Hz with a spectral resolution of 2 Hz. Using the one-third octave limits it is possible to form approximate one-third octave band results. It is seen that accurate estimations of reverberation time are achievable for BT⩾0.5 (T=0.1 seconds for the 20 Hz one-third octave band) and possibly even lower, if the dynamic range in the interrupted noise signal is sufficient. The higher one-third octave results show to work as well. A disadvantage with the method is that during short reverberation times (0.1 s) there is a severe spectral leakage to the side bands. Also, the method requires higher dynamic range decay signals compared to band-pass filtered signals. If a one-third octave resolution is requested, a dynamic range of 50 dB or greater is preferable. With a coarse resolution of e.g., 10 Hz and having no averaging into one-third octave bands, it is possible to measure short reverberation times (0.1 s) with signals having close to the same dynamic range used in classical band-pass filtered reverberation time measurements.

Synergistic effect of alloying on thermoelectric properties of two-dimensional PdPQ (Q = S, Se).

Hosts of 2D materials exist, yet few allow compositional and structural tailoring as the MQ2 (M = Mo, W; Q = S, Se) family does, for which various structural superlattices have been synthesized. Using thorough first-principles calculations, we show how bonding hierarchy contributes to the structural resilience of 2D PdPQ and allows for full-range alloying of sulfur and selenium. Within the structural unit of Pd2P2Q2, the covalently-bonded [P2Q2]4- polyanions hold the structure together with their molecular-like P-P bonds while ionically bonded Pd-Qs allow the S/Se substitution. Here, the bonding hierarchy imparts superior electronic and structural features to the PdPQ monolayers. As such, the flat-and-dispersive valence band and the eight degenerate valleys of the conduction band benefit the p-type and n-type thermoelectricity of pristine PdPQ, which can be further enhanced by alloying. The high-entropy alloying synergistically suppresses the lattice heat transport from 75 to 30 W m-1 K-1 and increases the band degeneracy of PdPQ monolayers, resulting in an overall improvement in zT. Combining these features, in a naïve approach, results in a large zT approaching two for both p-type and n-type doping. However, accurate fully-fledged electron-phonon calculations rebut this promise, showing that at high temperatures, the increased electron scattering results in a stagnant power factor in the flat-and-dispersive valence band. Using a realistic first-principles scattering, we finally calculate the thermoelectric efficiency of PdPQ (Q = S, Se) and highlight the importance of an accurate estimation of electron relaxation time for thermoelectric predictions.

Problems of forecasting the length of the assembly cycle of complex products realized in the MTO (make-to-order) model

This article presents the problem of forecasting the length of machine assembly cycles in make-to-order production (Make-to-Order). The model of Make-to-Order production and the technological process of manufacturing the finished product are presented. The possibility of developing a novel method, using artificial intelligence solutions, to estimate machine assembly times based on historical company data on manufacturing times for structurally similar components, is described. It is assumed that the result of the developed method will be an intelligent system supporting efficient and accurate estimation of machine assembly time, ready for implementation in production conditions. Such data as part availability, human resource availability and novelty factor will be used as input data for learning the neural network, while the output variable during learning the neural network will be the actual machine assembly time.

Significance of Providing Index-based Accurate Treatment Time Estimates to Patients Before Orthodontic Treatment

Introduction: Orthodontic treatment duration has always been a topic of concern to patients and one of the aims of orthodontic treatment is to minimize the overall treatment time. Aim and Objective: To determine the benefits of providing index-based accurate treatment duration to patients before orthodontic treatment in practice management. Materials and Method: A cross-sectional questionnaire survey was the chosen method of research. A multiple choice and dichotomous question questionnaire was structured with 15 relevant questions to be filled in by the respondents to deduce the required information. The survey was circulated to 100 postgraduate students and orthodontists using various social media platforms who responded to the survey. Result: In general, young adults (51.0%) demanded the approximate treatment time before treatment and 50% of the clinicians provided an accurate estimate of treatment time to the patients before treatment. For simple orthodontic cases, PAR Score was the index of choice and for complex orthodontic cases, ABO-DI index was preferred. Conclusion: Accurate estimation of treatment duration before treatment resulted in highly motivated, happy and satisfied patients. It also saved the time and resources of orthodontists.

Recognition of Intersection Traffic Regulations from Crowdsourced Data

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%.

ExtendedSketch+: Super host identification and network host trust evaluation with memory efficiency and high accuracy

Host cardinality estimation is one crucial task in network traffic measurement. Super host is the host that exhibits anomalies in host cardinality and it is usually related to network abnormal events. Therefore, accurate host cardinality estimation is the premise of super host identification and it can be used to measure the trust degree of a network. Sketches have been widely used in super host identification. However, expanding network scale and increasing link rate bring challenges to the efficiency and accuracy of sketch-based super host identification. The size of the counters in most sketches requires a trade-off between memory resources and accuracy. To meet the need of measuring high cardinalities, large size counters are applied, which leads to memory waste in monitoring low cardinality hosts. On the other hand, most sketches have high computational overhead when tracking superhosts, resulting in inefficiency. In order to address these issues, we propose a novel memory efficient and reversible sketch, named ExtendedSketch+, to provide accurate host cardinality estimation with the purpose of super host identification and network host trust evaluation. ExtendedSketch+ achieves both high memory utilization efficiency and high accuracy, in addition to high super host identification efficiency. It applies extensible counters to record unbalanced host cardinality distribution, by adaptively expanding the counters with the increase of cardinality. It adopts a lossless traffic information transfer strategy during counter extension to ensure the accuracy of cardinality estimation. It can directly tracks the host with the highest cardinality in each bucket, which greatly improves the identification efficiency of super hosts. Based on accurate cardinality estimation of ExtendedSketch+, we can further accurately evaluate host trust degree. We theoretically analyze ExtendedSketch+ with regard to its space and time complexities and estimation accuracy. We conduct performance evaluation based on real network traffic traces. Experimental results show that ExtendedSketch+ performs better than state-of-the-art sketches regarding super host identification and provides accurate host trust evaluation.

Correcting sampling bias in speckle contrast imaging.

When performing spatial or temporal laser speckle contrast imaging (LSCI), contrast is generally estimated from localized windows containing limited numbers of independent speckle grains NS. This leads to a systematic bias in the estimated speckle contrast. We describe an approach to determine NS and largely correct for this bias, enabling a more accurate estimation of the speckle decorrelation time without recourse to numerical fitting of data. Validation experiments are presented where measurements are ergodic or non-ergodic, including in vivo imaging of mouse brain.

SISTEM INFORMASI PENJADWALAN PROYEK MENGGUNAKAN METODE PERT (PROGRAM EVALUATION AND REVIEW TECHNIQUE) DI CV. INFOTECHMEDIA

CV. Infotech Media is a legally established national private company specializing in website, desktop, and Android-based software development services. During project execution, delays often occur due to several factors, particularly in estimating the work time. These time estimates frequently encounter discrepancies because they rely on rough calculations and fail to account for both optimistic and pessimistic scenarios. Additionally, there is no system in place to determine which tasks should be prioritized and which can be postponed. To address these issues, CV. Infotech Media requires a project scheduling information system. The proposed solution utilizes the Program Evaluation and Review Technique (PERT) to calculate accurate time estimates and identify the critical path, highlighting tasks that must be prioritized and cannot be delayed. Based on the test results, the project scheduling information system successfully addresses the company's challenges by providing accurate time estimates, offering critical path information, and generating schedules in the form of a Gantt Chart.

Decomposing stress wave interference in a cylindrical wooden medium using an inverse deconvolution filter.

Excitation of an embedded waveguide in the cross section plane of a cylindrical structure produces Rayleigh modes propagating primarily in circumferential regions of the propagating medium. In many applications, this region is critical to the overall strength of the structure. Using the through-transmission technique, the Rayleigh mode in cylindrical symmetry produces clockwise and counterclockwise trajectories that interfere with each other at the receiver, producing a composite energy response in the temporal domain that hinders the accurate mode characterization. This work presents a decomposition algorithm consisting of three processing steps: (1) analytical continuous wavelet transforms of the raw waveform; (2) frequency polynomial approximation of the inverse point spread function using L2 norm optimization; and (3) derivation of the inverse deconvolution filter via the dual derivative operator. This work demonstrates the mathematical formulation and algorithm implementation with numerical and empirical validations. The proposed decomposition filter can achieve the overall time and energy estimation accuracy with a relative root mean square error of 0.07. Its broader impact will enhance the parametric estimation of the diagnostic stress wave for material characterization.

Evacuation Time Estimation Model in Large Buildings Based on Individual Characteristics and Real-Time Congestion Situation of Evacuation Exit

Fire is one of the most common and harmful disasters in real life. In 2021, firefighting teams in China reported 748,000 fires, resulting in 1987 deaths, 2225 injuries and CNY 6.75 billion of direct property losses, which account for 0.05‰ of GDP. Scientific and accurate estimation of evacuation time can provide decision support for intelligent fire evacuation. This paper aims to effectively improve the evacuation efficiency of people in large buildings, especially for a scenario with intricate evacuation passages. There are many factors that make a difference in evacuation time, such as individual behavior, occupant density, exit width, and so on. The people distribution density is introduced to effectively assess the impact of unstable pedestrian flow and unbalanced distribution in the process of evacuation. The verification results show that there is a strong positive correlation between people distribution density and evacuation time. Combining the people distribution density with many other factors, the training dataset is built by Pathfinder to learn the relationship between evacuation time and influencing factors. Finally, an evacuation time prediction model is established to estimate the consumption time that occupants spend on moving in the evacuation process based on stacking integration. The model can assist occupants in choosing different channels for evacuation in advance. After testing, the average error between the predicted evacuation consumption time and the reference time is 3.63 s. The result illustrates that the model can accurately predict the time consumed in the process of evacuation.

Accounting for cognitive time in activity-based costing: A technology for the management of digital economy

Human cognitive time has become a key asset of the digital economy, yet we lack the means to manage it. In response to that need, we propose a technology to manage cognitive time in economic organizations. This technology is called cognitive time-driven activity-based costing (CTABC), and it extends the established time-driven activity-based costing technology. CTABC accounts for human agents' cognitive time and the fact that cognitive time typically does not equal physical clock time for a given economic activity. CTABC also unearths a hidden lever effect that leads to considerable economic inefficiencies. An illustration of the proposed CTABC shows the limitations of contemporary approaches to cost assessments, which ignore errors caused by workers' cognitive time estimation. This paper contributes to the literature on cost accounting technology and the future of the digital economy. Specifically, it enriches the literature on the problem of the accuracy of employees' time estimates.

Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments

Abstract. Landslides and flash floods are geomorphic hazards (GHs) that often co-occur and interact. They generally occur very quickly, leading to catastrophic socioeconomic impacts. Understanding the temporal patterns of occurrence of GH events is essential for hazard assessment, early warning, and disaster risk reduction strategies. However, temporal information is often poorly constrained, especially in frequently cloud-covered tropical regions, where optical-based satellite data are insufficient. Here we present a regionally applicable methodology to accurately estimate GH event timing that requires no prior knowledge of the GH event timing, using synthetic aperture radar (SAR) remote sensing. SAR can penetrate through clouds and therefore provides an ideal tool for constraining GH event timing. We use the open-access Copernicus Sentinel-1 (S1) SAR satellite that provides global coverage, high spatial resolution (∼10–15 m), and a high repeat time (6–12 d) from 2016 to 2020. We investigate the amplitude, detrended amplitude, spatial amplitude correlation, coherence, and detrended coherence time series in their suitability to constrain GH event timing. We apply the methodology on four recent large GH events located in Uganda, Rwanda, Burundi, and the Democratic Republic of the Congo (DRC) containing a total of about 2500 manually mapped landslides and flash flood features located in several contrasting landscape types. The amplitude and detrended amplitude time series in our methodology do not prove to be effective in accurate GH event timing estimation, with estimated timing accuracies ranging from a 13 to 1000 d difference. A clear increase in accuracy is obtained from spatial amplitude correlation (SAC) with estimated timing accuracies ranging from a 1 to 85 d difference. However, the most accurate results are achieved with coherence and detrended coherence with estimated timing accuracies ranging from a 1 to 47 d difference. The amplitude time series reflect the influence of seasonal dynamics, which cause the timing estimations to be further away from the actual GH event occurrence compared to the other data products. Timing estimations are generally closer to the actual GH event occurrence for GH events within homogenous densely vegetated landscape and further for GH events within complex cultivated heterogenous landscapes. We believe that the complexity of the different contrasting landscapes we study is an added value for the transferability of the methodology, and together with the open-access and global coverage of S1 data it has the potential to be widely applicable.

Specific Factors Affecting Operating Room Efficiency: An Analysis of Case Time Estimates

Operating room (OR) efficiency has an impact on surgeon productivity and patient experience. Accuracy of case duration estimation is important to optimize OR efficiency. The purpose of this study was to identify factors associated with inaccurate case time estimates in outpatient hand surgery. A better understanding of these findings may help to improve OR efficiency and scheduling. All outpatient hand surgical cases from 2018 to 2019 were reviewed. Poorly-estimated cases (i.e., poor scheduling accuracy) were defined as those cases where the actual operative time differed from the predicted time by >50% (either quicker by >50% or slower by >50% than the predicted time). The percentages of poorly-estimated cases were analyzed, categorized, and compared by surgeon, procedure type, and scheduled case length. A total of 6,620 cases were identified. Of 1,107 (16.7%) cases with poorly estimated case durations, 75.2% were underestimated. There was no difference in the likelihood of poor estimation related to start time. Well-estimated cases tended to have longer scheduled case duration, but shorter realized case duration and surgical time. Our systems analysis identified specific surgeons and procedures as predictable outliers. Cases scheduled for 15-30 minutes frequently were inaccurate, whereas cases scheduled for 30-45 and 106-120 minutes had accurate estimates. The accuracy of case time estimations in a standard outpatient hand surgery practice is highly variable. Nearly one-fifth of outpatient hand surgery case durations are poorly estimated, and inaccurate case time estimation can be predicted based on surgeon, procedure type, and case time. Maximizing OR efficiency should be a priority for surgeons and hospital systems. With multiple surgeries done per day, the efficiency of the OR has an impact on surgeon productivity and patient experience.

Timing estimates for complex programmed surveys

Developing a method for estimating average survey completion time---easily, accurately, and repeatedly---was a many-years-long aspiration for the International Tobacco Control Policy Evaluation (ITC) Project. This article describes the three survey length estimation methods used for the ITC Project's complex programmed surveys, with their respective merits and disadvantages. Method I, Read-Throughs, required staff to read through different versions of the survey. Automating this approach in Method II, Word Counts, replaced staff reading with question word counts and a postulated reading speed, but still required creation of multiple versions that were timed separately. Method III, Weighted Questions, also automated, was streamlined by weighting individual questions, rather than survey versions, by the proportion of respondents likely to answer them. The two automated methods greatly reduced staff time cost, provided surprisingly accurate time estimates, and made it possible to quickly re-estimate the length after each survey revision. Other survey researchers using surveys with complex branching may benefit from our experimentation and results.

Accurate Direction–of–Arrival Estimation Method Based on Space–Time Modulated Metasurface

A metasurface (MTS)-based direction of arrival (DoA) estimation method is presented. The method exploits the properties of space–time modulated reflective metasurfaces to estimate in real-time the impinging angle of an illuminating monochromatic plane wave. The approach makes use of the amplitude unbalance of the received fields at broadside at the frequencies of the two first-order harmonics generated by the interaction between the incident plane wave and the modulated metasurface. Here, we first describe analytically how to generate the desired higher order harmonics in the reflected spectrum and how to realize the breaking of the spatial symmetry of each order harmonic scattering pattern. Then, the 1-D omnidirectional incident angle can be analytically computed using +1st- and −1st-order harmonics. The approach is also extended to 2-D DoA estimation by using two orthogonally arranged 1-D DoA modulation arrays. The accuracy of 1-D DoA estimation is verified through full-wave numerical simulations. Compared to conventional DoA estimation methods, the proposed approach simplifies the computation and hardware complexity, ensuring at the same time estimation accuracy. The proposed method may have potential applications in wireless communications, target recognition, and identification.