Accurate Timing Information Research Articles

Travel Time Estimation for Urban Arterials Based on the Multi-Source Data

Accurate traffic information, such as travel time, becomes more important since it could help provide more efficient traffic management strategies. This paper presents a method for estimating the travel time of segments on urban arterials by leveraging multi-source data from loop detectors and probe vehicles. Travel time is defined into three distinct sections based on floating car trajectories, i.e., accelerating, constant speed, and decelerating. Considering the traffic flow characteristics, different methods are developed using various data for each section. The proposed methodology is validated using field data collected in Shanghai, China. The results validated the proposed method with absolute percentage errors (APEs) of approximately 5% in constrained traffic flow conditions and 10–20% in less constrained traffic flow. The results also show that the proposed method has better performance than the method with loop detector data and another data fusion model. It is expected that the proposed method could help improve traffic management efficiency, such as traffic signal control, by providing more accurate travel time information.

Timing Performance Testing and Regularity Analysis of eLoran System

Accurate time synchronisation is critical in modern communication, navigation, and scientific research. In this context, the eLoran receiver, which is an advanced timing device, has attracted increasing scholarly attention. This study aims to comprehensively analyse the performance and potential applications of eLoran systems for timing monitoring and to specifically explore the relevant indices of two eLoran receivers. To this end, we evaluated the performance of these receivers in receiving time signals through both simulated and empirical data and conducted a regularity analysis to uncover their potential value in practical applications. The findings demonstrate that the eLoran receiver excels at timing monitoring and provides highly accurate time information. An analysis of the timing performance of the eLoran system improved its accuracy and integrity.

Deep Dive into NTP Pool's Popularity and Mapping

Time synchronization is of paramount importance on the Internet, with the Network Time Protocol (NTP) serving as the primary synchronization protocol. The NTP Pool, a volunteer-driven initiative launched two decades ago, facilitates connections between clients and NTP servers. Our analysis of root DNS queries reveals that the NTP Pool has consistently been the most popular time service. We further investigate the DNS component (GeoDNS) of the NTP Pool, which is responsible for mapping clients to servers. Our findings indicate that the current algorithm is heavily skewed, leading to the emergence of time monopolies for entire countries. For instance, clients in the US are served by 551 NTP servers, while clients in Cameroon and Nigeria are served by only one and two servers, respectively, out of the 4k+ servers available in the NTP Pool. We examine the underlying assumption behind GeoDNS for these mappings and discover that time servers located far away can still provide accurate clock time information to clients. We have shared our findings with the NTP Pool operators, who acknowledge them and plan to revise their algorithm to enhance security.

Satellite navigation signal quality monitoring algorithm based on DBO-SVM

Satellite navigation signals can provide users with accurate and continuous time and position information. Since the satellite navigation signal is susceptible to interference from terrain and other electromagnetic signals during transmission. In this study, to improve the accuracy of the traditional satellite navigation signal quality monitoring algorithm and to solve the problem of discretization in the monitoring results, the satellite navigation signal quality monitoring algorithm based on the Dung Beetle Optimization assisted Support Vector Machine (DBO-SVM) is proposed. The algorithm uses four feature quantities (correlation loss and symmetry of correlation peaks in the correlation domain, signal power in the frequency domain, eye diagram opening in the modulation domain) to classify the quality of satellite navigation signals, and optimizing the parameters of the SVM through the application of the dung beetles’ foraging mechanism. Experimental validation was conducted by simulating satellite navigation signal data, and the results indicate that the DBO-SVM algorithm is more accurate than the traditional SVM algorithm, with an accuracy rate of over 98%.

Deep Dive into NTP Pool's Popularity and Mapping

Time synchronization is of paramount importance on the Internet, with the Network Time Protocol (NTP) serving as the primary synchronization protocol. The NTP Pool, a volunteer-driven initiative launched two decades ago, facilitates connections between clients and NTP servers. Our analysis of root DNS queries reveals that the NTP Pool has consistently been the most popular time service. We further investigate the DNS component (GeoDNS) of the NTP Pool, which is responsible for mapping clients to servers. Our findings indicate that the current algorithm is heavily skewed, leading to the emergence of time monopolies for entire countries. For instance, clients in the US are served by 551 NTP servers, while clients in Cameroon and Nigeria are served by only one and two servers, respectively, out of the 4k+ servers available in the NTP Pool. We examine the underlying assumption behind GeoDNS for these mappings and discover that time servers located far away can still provide accurate clock time information to clients. We have shared our findings with the NTP Pool operators, who acknowledge them and plan to revise their algorithm to enhance security.

Fuzing multiple erroneous sensors to estimate travel time

Estimating accurate travel time information is one of the fundamental tasks in controlling city traffic. In general, fuzing multiple sensors can generate more accurate information to measure traffic flow characteristics than using each one separately. However, in addition to the cost of installing a new sensor system, the costly steps of data cleaning and preparation are required before using a new sensor; therefore, it is essential to estimate the marginal benefit of adding a new sensor vs its marginal cost. Three datasets are generated and analyzed in this study, namely a Hypothetical Ground Truth (HGT), an Erroneous Ground Truth (EGT), and a set of Erroneous Sensors (E-Sensors). This study also challenges the assumption of an error-free Ground Truth (GT). By computing the optimal number of detections to approximate the GT, the effect of (endogenous) error in the fusion procedure is evaluated. Furthermore, by fuzing the E-Sensors that had different levels of (exogenous) error, it is revealed that the error level has a limited effect on the result of fusion. Multiple sets of E-Sensors are assessed and the RMSE values between using Erroneous Ground Truth and Hypothetical Ground Truth are measured, which shows a significant difference. Additionally, the effect of increasing the number of sensors in estimating the travel time is investigated, which shows that adding a new sensor can improve fusion accuracy if the accuracy of the added sensor is better than a given threshold. Moreover, the optimal number of detections to approximate the ground truth is studied. Real traffic data is also used to validate the results.

Phase‐Stable Multidimensional Coherent Spectroscopy Based on Dual‐Comb Interferometry

AbstractMultidimensional coherent spectroscopy (MDCS) is a highly intriguing field for the unique capacities to unravel quantum pathways of microscopic dynamics and discern different dipole–dipole interactions. These capabilities are not simultaneously accessible through any other techniques. The foundation of MDCS lies in the stable phase and accurate time information of the multi‐pulse sequences involved. Achieving the phase stability has traditionally relied on ingenious geometric optical designs and multiple feedback control systems, albeit at the cost of complexity. In this letter, a phase stability scheme for high‐resolution MDCS is proposed by replacing traditional laser sources with two highly coherent frequency combs. By employing dual‐comb interferometry, the phase distribution across a broad spectral range and the time information of all the excitation pulses are precisely measured, even in setups that lack phase stability. In the experiment, a rephasing MDCS of rubidium atoms is obtained by applying this approach, wherein the comb resolution is utilized to differentiate the couplings of hyperfine energy levels. This scheme simplifies the structure of MDCS and holds potential for applications in other MDCS systems involving many‐body interactions and non‐absorptive resonances.

An adaptive deep multi-task learning approach for citywide travel time collaborative estimation

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network.

Research on Body Wave Extraction Technology of Mixed Active Source Ambient Noise

In complex surface environments, active source seismic exploration methods are difficult to stimulate and costly, while ambient noise detection technology is fast and convenient, with a little environmental damage and low cost. Therefore, it has aroused extensive research by scholars at home and abroad. At present, the use of passive source surface wave method for subsurface medium imaging has become mature. However, due to the low content and weak energy of the body wave signal in the actual ambient noise, it is particularly difficult to extract body waves using seismic interferometry. At the same time, body wave exploration has larger exploration depth and higher resolution. In this paper, we propose an ambient noise detection technology of mixed active sources, namely, imposing artificial seismic sources in the continuous ambient noise data acquisition process. We refer to the ambient noise of mixed active sources as "mixed ambient noise". It is found that the virtual refraction wave obtained by the ambient noise detection method of mixed active sources has high signal-to-noise ratio, strong anti-interference ability, and can obtain the real velocity information of the deeper subsurface medium, but cannot reflect the accurate time information. Supplementing the active source signal in the far field can obtain virtual refractions with higher signal-to-noise ratio, while effectively reducing the mixing phenomenon of surface wave and surface interference waves in the virtual common-shot record.

Real-time navigation solutions of low-cost off-the-shelf GNSS receivers on board the Astrocast constellation satellites

With the ever-increasing number of small satellites launched into space, on-board orbit determination is becoming more and more important. Precise satellite positions are needed for constellation maintenance, formation flying, communication optimization, collision avoidance and Earth observation. In addition, accurate time information is required for the functioning of the instruments on board. Low-cost off-the-shelf Global Navigation Satellite System (GNSS) receivers and antennas, adapted to the small size and weight of the satellite, allow precise on-board orbit determination and time synchronization for even the smallest satellites.We analysed more than one year of on-board navigation solutions (NAVSOL) from 8 Astrocast CubeSats, which are equipped with our multi-GNSS single-frequency payload. Based on the residuals from a dynamic orbit fitting, we found that the quality of the NAVSOL from the Astrocast satellites varies strongly both over time and between satellites, with an RMS ranging from a few meters to several tens of meters.In this study, we focus on three main effects that influence the quality of the NAVSOL from the Astrocast satellites: (1) The orbit-fit residuals show a positive radial offset in the range of about 6 m to 15 m, which is caused by ionospheric refraction. Our data show that GNSS signals at negative elevations are very strongly affected by the ionospheric refraction and thus account for the major part of the radial offset. (2) Discrepancies between the NAVSOL positions and velocities, and simulations with a GNSS signal generator show a once-per-revolution periodicity in the out-of-plane component, with amplitudes of 2 m and 5 cm/s in position and velocity, respectively. This effect is probably due to an inappropriate dynamic model used in the real-time navigation algorithm of the GNSS receiver. (3) Depending on the location of the GNSS antenna on board the satellite, the noise level varies by a multiple. This is assumed to be due to the antenna orientations, interference of the GNSS signals with electromagnetic waves coming from the satellite platform, signal attenuation during the transmission from the antenna to the receiver and differences in the soldering or tuning of the individual GNSS antennas.These findings help us refine hardware configurations and firmware settings in preparation for future Astrocast satellites or other nanosatellite missions. By eliminating or minimizing the mentioned effects, we expect to consistently achieve an accuracy on the level of a few meters for the real-time navigation solution of commercial off-the-shelf single-frequency GNSS receivers in space.

High-precision digital time-amplitude analysis system for the neutron Time-Of-Flight spectrometer in HL-2M

Neutron time-of-flight (TOF) spectrometers are widely used for neutron diagnostics in large-scale fusion facilities. However, traditional amplitude and time analysis methods are limited in terms of resolution, flexibility, and other factors. To overcome these limitations, we propose the Digital Time-Amplitude Analysis (DTAA) system, which combines the advantages of both methods by using a high-performance FPGA for real-time time and amplitude analysis of particle pulses. The DTAA system provides accurate timing and pulse shape information, fast processing speed, and low implementation cost. We have also developed a multi-mode data joint time-of-flight correction algorithm and verified its performance through simulation. The DTAA system passed laboratory and tokamak tests, achieving a time measurement precision of 122 ps and accurately measuring neutron counts in tokamak experiments. This work represents a key step towards comprehensive high-performance integrated particle pulse analysis.

Assisted-GNSS positioning algorithm based on one-way fuzzy time information

In order to solve the problem that the accuracy of approximate position and approximate time information is limited by the traditional auxiliary positioning algorithm, this paper analyzes the problem that the calculation amount may increase and fail when these auxiliary information exceeds the precision requirement range and puts forward a positioning algorithm based on one-way fuzzy time assistance that is suitable for the positioning solution of GNSS satellite navigation receivers under the condition of fuzzy time information, such as the rotation condition. Methods using the reference information in the ephemeris information, the relatively accurate approximate time information can be reasonably searched and verified so as to achieve the effect of obtaining the positioning result with less calculation. The experimental results show that this method can accurately calculate the exact position even if there is an error in the approximate position information under the condition of one-way time assistance, which not only solves the problem that it cannot be positioned under the conditions of rotation but also does not increase the additional calculation amount and has practical engineering value.

Discovering conservation laws using optimal transport and manifold learning

Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, making it hard to analyze their dynamics and build stable predictive models. Current approaches for discovering conservation laws often depend on detailed dynamical information or rely on black box parametric deep learning methods. We instead reformulate this task as a manifold learning problem and propose a non-parametric approach for discovering conserved quantities. We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values. Using tools from optimal transport theory and manifold learning, our proposed method provides a direct geometric approach to identifying conservation laws that is both robust and interpretable without requiring an explicit model of the system nor accurate time information.

Bus Travel Time Prediction Based on the Similarity in Drivers’ Driving Styles

Providing accurate and real-time bus travel time information is crucial for both passengers and public transportation managers. However, in the traditional bus travel time prediction model, due to the lack of consideration of the influence of different bus drivers’ driving styles on the bus travel time, the prediction result is not ideal. In the traditional bus travel time prediction model, the historical travel data of all drivers in the entire bus line are usually used for training and prediction. Due to great differences in individual driving styles, the eigenvalues of drivers’ driving parameters are widely distributed. Therefore, the prediction accuracy of the model trained by this dataset is low. At the same time, the training time of the model is too long due to the large sample size, making it difficult to provide a timely prediction in practical applications. However, if only the historical dataset of a single driver is used for training and prediction, the amount of training data is too small, and it is also difficult to accurately predict travel time. To solve these problems, this paper proposes a method to predict bus travel times based on the similarity of drivers’ driving styles. Firstly, the historical travel time data of different drivers are clustered, and then the corresponding types of drivers’ historical data are used to predict the travel time, so as to improve the accuracy and speed of the travel time prediction. We evaluated our approach using a real-world bus trajectory dataset collected in Shenyang, China. The experimental results show that the accuracy of the proposed method is 13.4% higher than that of the traditional method.

Semi-supervised calibration of noisy event risk (SCANER) with electronic health records

ObjectiveElectronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliably. Proxies of PFS time such as time to the first mention of progression in the notes are at best good approximations to the true event time. This leads to difficulty in efficiently estimating event rates for an EHR patient cohort. Estimating survival rates based on error-prone outcome definitions can lead to biased results and hamper the power in the downstream analysis. On the other hand, extracting accurate event time information via manual annotation is time and resource intensive. The objective of this study is to develop a calibrated survival rate estimator using noisy outcomes from EHR data. Materials and MethodsIn this paper, we propose a two-stage semi-supervised calibration of noisy event rate (SCANER) estimator that can effectively overcome censoring induced dependency and attains more robust performance (i.e., not sensitive to misspecification of the imputation model) by fully utilizing both a small-labeled set of gold-standard survival outcomes annotated via manual chart review and a set of proxy features automatically captured via EHR in the unlabeled set. We validate the SCANER estimator by estimating the PFS rates for a virtual cohort of lung cancer patients from one large tertiary care center and the ICU-free survival rates for COVID patients from two large tertiary care centers. ResultsIn terms of survival rate estimates, the SCANER had very similar point estimates compared to the complete-case Kaplan Meier estimator. On the other hand, other benchmark methods for comparison, which fail to account for the induced dependency between event time and the censoring time conditioning on surrogate outcomes, produced biased results across all three case studies. In terms of standard errors, the SCANER estimator was more efficient than the KM estimator, with up to 50% efficiency gain. ConclusionThe SCANER estimator achieves more efficient, robust, and accurate survival rate estimates compared to existing approaches. This promising new approach can also improve the resolution (i.e., granularity of event time) by using labels conditioning on multiple surrogates, particularly among less common or poorly coded conditions.

Carrier Phase Residual Modeling and Fault Monitoring Using Short-Baseline Double Difference and Machine Learning

Global Navigation Satellite Systems (GNSS) are used to provide accurate position, navigation, and time (PNT) information to users in various sectors of our society including transportation. Augmentation systems such as differential GNSS (DGNSS), real-time kinematics (RTK), and Precise Point Positioning (PPP) improve the GNSS performance, and providing reliable measurements from its reference station is very crucial. To ensure safe and accurate PNT solutions, code and carrier measurements must be monitored for potential faults or a performance degrade. Although there exist numerous methods to model and monitor the measurements, research on the carrier phase measurements is not as extensive as the code measurements. This paper introduces a split of residuals into receiver noise and multipath components to customize their estimation according to their respective statistical properties. This study also proposes a method to use machine learning-based non-linear regression to effectively model and monitor potential faults in the GNSS measurements including the carrier phase. A training dataset is used to model the nominal quantities of GNSS measurement residuals, and inflation factors are applied to over-bound the fault-free residuals. These inflated residuals are coupled with uncertainty factors to compute thresholds for monitoring carrier phase residuals, and the effectiveness of the thresholds is validated with a test dataset by achieving the false alarm rate of 6.61×10−6, slightly lower than the desired level of 10−5.

Applying Address Encryption and Timing Noise to Enhance the Security of Caches

Encrypting the mapping relationship between physical and cache addresses has been a promising technique to prevent conflict-based cache side-channel attacks. However, this method is not foolproof and the attackers can still build a side-channel despite the increased difficulty of finding the minimal eviction set. To address this issue, we propose a new protection method that integrates both address encryption and timing noise extension mechanisms. By adding the timing noise extension mechanism to the address encryption method, we can randomly generate cache misses that prevent the attackers from pruning the eviction set. Our analysis shows that the timing noise extension mechanism can cause the attackers to fail in obtaining accurate timing information for accessing memory. Furthermore, our proposal reduces the timing noise generating rate, minimizing performance overhead. Our experiments on SPEC CPU 2017 show that the integrated mechanism only resulted in a tiny performance overhead of 2.9%.

External Measurement System for Aircraft Structural Parts Based on Digital Image Correlation in Signal-limited Spaces

An experimental study was performed on the application of the digital image correlation (DIC) technique to characterize aircraft components for which accurate time information is required. This study builds an aircraft external measurement system based on Stereo DIC. This system obtains real-time satellite time information from a GPS clock source and transmits it to a trigger unit. A camera acquires this information and displays it on the image. This solves the problem that some closed laboratories can use only computer time and cannot obtain accurate satellite time when using camera measurements. Moreover, we verify the reliability and accuracy of this approach, finding an average angular accuracy of better than 0.02 degrees and a relative error of less than 1%. Using the binocular Stereo DIC system, the accuracy of the measurements is clarified for specific conditions. Furthermore, we use this system to measure simulated aircraft landing gear, which moves at an angular velocity of 52.4 °/ s over a distance of 5 m with a wide field of view of 16.8°. The satellite time information acquired by the camera and the time information of the captured time targets are highly stable. The system’s reliability is demonstrated for use in practical engineering, contributing to the engineering application of Stereo DIC for external aircraft measurements.

Mitigating Software Integrity Attacks With Trusted Computing in a Time Distribution Network

Time Distribution Networks (TDNs) evolve as new technologies occur to ensure more accurate, reliable, and secure timing information. These networks typically exploit several distributed time servers, organized in a master-slave architecture, that communicate via dedicated timing protocols. From the security perspective, timing data must be protected since its modification or filtering can lead to grave consequences in different time-based contexts, such as health, energy, finance, or transportation. Thus, adequate countermeasures must be employed in all the stages and systems handling timing data from its calculation until it reaches the final users. We consider a TDN offering highly accurate (nanosecond level) time synchronization through specific time unit devices that exploit terrestrial atomic or rubidium clocks and Global Navigation Satellite Systems (GNSS) receivers. Such devices are appealing targets for attackers, who might exploit various attack vectors to compromise their functionality. We individuate three possible software integrity attacks against time devices, and we propose a solution to counter them by exploiting the cryptographic Trusted Platform Module (TPM), defined and supported by the Trusted Computing Group. We used remote attestation software for cloud environments, namely the Keylime framework, to verify (periodically) the software daemons running on the time devices (or their configuration) from a trusted node. Experiments performed on a dedicated testbed set up in the ROOT project with customized time unit devices from Seven Solutions (currently Orolia Spain) allow us to demonstrate that exploiting TPMs and remote attestation in the TDNs is not only helpful but is fundamental for discovering some attacks that would remain otherwise undetected. Our work helps thus TDN operators build more robust, accurate, and secure time synchronization services.

Exploitation of the Timing Capabilities of Metallic Magnetic Calorimeters for a Coincidence Measurement Scheme

In this report, we compare two filter algorithms for extracting timing information using novel metallic magnetic calorimeter detectors, applied to the precision X-ray spectroscopy of highly charged ions in a storage ring. Accurate timing information is crucial when exploiting coincidence conditions for background suppression to obtain clean spectra. For X-rays emitted by charge-changing interactions between ions and a target, this is a well-established technique when relying on conventional semiconductor detectors that offer a good temporal resolution. However, until recently, such a coincidence scheme had never been realized with metallic magnetic calorimeters, which typically feature much longer signal rise times. In this report, we present optimized timing filter algorithms for this type of detector. Their application to experimental data recently obtained at the electron cooler of CRYRING@ESR at GSI, Darmstadt is discussed.