Synchronous Research Articles

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.

Finite-time H∞ synchronization of semi-Markov jump neural networks with two delay components with stochastic sampled-data control

This article investigates the finite-time H∞ synchronization for semi-Markov jump neural networks with two delay components based on stochastic sampled data control. Additionally, the parametric uncertainties are randomly varying which follows the Bernoulli distributed sequences. In the stochastic sampled data control, the sampling interval ′m′ is supposed to be two different values in the time-varying component with given probability conditions. By constructing triple and quadruple integral term in the Lyapunov-Krasovskii functional (LKF) a new integral inequality technique is addressed to derive the main results. Dissimilar from previous literature, involving the new integral inequality, a delay dependent finite-time H∞ synchronization requirements are acquired with regard to linear matrix inequalities (LMIs). In the end, the effectiveness of the considered stochastic sampled data control finite time synchronization scheme is highlighted by numerical examples.

Constrained Flooding Based on Time Series Prediction and Lightweight GBN in BLE Mesh.

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%.

A Low-Computational Burden Closed-Form Approximated Expression for MSE Applicable for PTP with gfGn Environment

The Precision Time Protocol (PTP) plays a pivotal role in achieving precise frequency and time synchronization in computer networks. However, network delays and jitter in real systems introduce uncertainties that can compromise synchronization accuracy. Three clock skew estimators designed for the PTP scenario were obtained in our earlier work, complemented by closed-form approximations for the Mean Squared Error (MSE) under the generalized fractional Gaussian noise (gfGn) model, incorporating the Hurst exponent parameter (H) and the a parameter. These expressions offer crucial insights for network designers, aiding in the strategic selection and implementation of clock skew estimators. However, substantial computational resources are required to fit each expression to the gfGn model parameters (H and a) from the MSE perspective requirement. This paper introduces new closed-form estimates that approximate the MSE tailored to match gfGn scenarios that have a lower computational burden compared to the literature-known expressions and that are easily adaptable from the computational burden point of view to different pairs of H and a parameters. Thus, the system requires less substantial computational resources and might be more cost-effective.

Source localization based on field signatures: Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services, navigation, gaming and many other applications. This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station. The existing scatterers in the target area create unique scattered field interference at each source location. The unique field interference at each source location results in a unique field signature at the base station which is used for source localization. In the proposed method, the target area is divided into a grid with a step of less than half the wavelength. Each grid node is characterized by its field signature at the base station. Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization. The normalization of the field signatures avoids the need for time synchronization between the base station and the source. When a source transmits signals, the generated field signature at the base station is normalized and then correlated with the stored fingerprints. The maximum correlation value is given by the node to which the source is the closest. Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization. The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

Optimized design of flexible sensors for snow and ice sports characterization and intelligent recognition of training technique movements

Skiing performance is affected by a variety of external factors, and real-time monitoring of skiers' skiing status is very important for improving athletes' skiing technique. There are problems in the training monitoring of snow sports athletes in a wide range of venues, such as difficulty in accurately obtaining the parameters of sports status, lack of quantitative assessment of the training effect, and inability to provide timely feedback. And traditional sensors cannot adapt to cold, humid and other weather conditions, which affects their stability. Traditional sensors can only provide limited motion data and cannot comprehensively evaluate the technical level of skiers. At the same time, it is difficult to provide sufficiently accurate data in complex snowy environments. To address this problem, this study proposes a flexible sensor that can contact with the human body and sense athletes' body postures and movements in real time. By combining the flexible sensor with ice and snow sports equipment, intelligent recognition of athletes' movements can be realized. By monitoring and analyzing athletes' movements in real time, problems can be detected in time and targeted adjustments and training can be carried out. The findings of the experiment demonstrate that the accuracy of the time synchronization model, the time-domain high-pass filtering model, the zero-speed correction model, and the improved zero-speed correction model are, respectively, 97, 94, 92, and 90 when the size of the training set is 800. At the distance of 2000 m, The CF-TENG, H-TENG and CS-TENG response times are 18 ms, 20 ms and 28 ms. The results show that the designed flexible sensors have good performance and can be used in the daily training of cross-country skiing and alpine skiing professional sports teams.

A real-time performance improvement method for composite time scale

Abstract The composite time scale (CTS) provides a stable, accurate, and reliable time scale for modern society. The improvement of CTS’s real-time performance will improve its stability, which strengths related applications’ performance. Aiming at this goal, a method achieved by determining the optimal calculation interval and accelerating adjustment stage is proposed in this paper. The determinants of the CTS’s calculation interval (characteristics of the clock ensemble, the measurement noise, the time and frequency synchronization system’s noise and the auxiliary output generator noise floor) are studied and the optimal calculation interval is obtained. We also investigate the effect of ensemble algorithm’s initial parameters on the CTS’s adjustment stage. A strategy to get the reasonable initial parameters of ensemble algorithm is designed. The results show that the adjustment stage can be finished rapidly or even can be shorten to zero with reasonable initial parameters. On this basis, we experimentally generate a distributed CTS with a calculation interval of 500 s and its stability outperforms those of the member clocks when the averaging time is longer than ∼1700 s. The experimental result proves that the CTS’s real-time performance is significantly improved.

Achieving hybridisation between Miscanthus species: Commercially-scalable methods to manipulate flowering synchronisation and maximise seed yield

Miscanthus is a high-yielding lignocellulosic perennial biomass crop. The low multiplication rate of clonal rhizome propagation is a bottleneck to upscaling plantation areas of feedstock needed to supply and expand the bioeconomy. Novel seeded Miscanthus hybrids are currently being developed to overcome this bottleneck by increasing annual multiplication rates from approximately 10 to over 1000 times. We describe a series of field experiments in southern Italy using agronomic methods to optimise multiplication rates through (i) planting configurations and densities, (ii) ratio of seed parents to pollen parents (iii) supplemental pre-dawn misting to increase humidity during pollination. In these trials the seed-bearing M. sinensis started flowering 2–3 weeks earlier than the M. sacchariflorus pollen parent. Earlier experiments indicated that flowering in M. sacchariflorus was mostly determined by photoperiod while in M. sinensis it was modulated by endogenous signals. Consequently, a second set of experiments were conducted to delay flowering time in M. sinensis: (iv) mid-season stem cut back, (v) oversupply of nitrogen, and (vi) undersupply of water. Across all treatments and years, the multiplication rates varied from 140 to 1300 seeds m². Reducing the proportion of the pollen parent plants (M. sacchariflorus) from 50 % to 25 % did not reduce seed yield per plant. This therefore increases the seed yield per m² and reduces seed production upscaling costs. Flowering time and duration in M. sinensis was significantly impacted by mid-season cutting and water stress, but not by nitrogen supply rates. Mid-season shoot cutting reduced number of flowers per plant (77 %), seed quantity (47 %), seed size (46 %), and resulted in seeds with a low germination rate of 39 %. High M. sinensis planting densities produced higher seed yields in the first year. However, in subsequent years higher density plots were more susceptible to autumn lodging lowering seed production by loss of panicles. Pre-dawn misting to prolong pollen life and stigma receptivity had no significant effects on seed production. This study demonstrates the importance of flowering time synchronization in the open field for commercial seed production. The limited effect of agronomic efforts to reduce the interspecies flowering time gap emphasises the importance of genetic factors in controlling flowering time. The most impactful intervention to change flowering time and improve parental synchronisation was mid-season cutting, while this method reduced seed production when applied to the seed parent it could be ideal for pollen parents.

Glimmer Synchronization Of Luciola Parvula Fireflies To External Stimulas

It is known that the Luciola parvula fireflies clearly emit the lights at regular time intervals and synchronization between multiple males are observed, but the details of synchronization have not yet been clarified. Presently, we took videos of the luminescence phenomenon of multiple male Luciola parvula fireflies and performed a correlation analysis on the details of the time change of the luminescence brightness of each male Luciola parvula fireflies. The 12 Luciola parvula fireflies are kept in glass wells so that each Luciola parvula fireflies can only moves in restricted areas and their luminescence is recorded by a digital movie camera at the video rate of 30 fps. According to the preliminary study, each glimmer lasts about 3 frames and thus the time series brightness is fitted by a parabolic function in order to give a precise timing of maximum brightness. First of all, time intervals between glimmers of each Luciola parvula fireflies are evaluated. Secondly, the glimmer correlation between different Luciola parvula fireflies are investigated. As a result, it was clarified that the light emission cycle of Luciola parvula fireflies is almost constant at 0.54 seconds, more than 70% of all light emissions are synchronized and the order of synchronization is almost fixed with very small delay. Consequently, it has been shown quantitatively that the glimmers of male Luciola parvula fireflies are well synchronized.

UIDS: A Matlab-based urban flood model considering rainfall-induced and surcharge-induced inundations

The Urban Inundation-Drainage Simulator (UIDS) is a new coupled model for simulating urban flooding dynamics, developed as an open-source, MATLAB-based platform. It integrates a rainfall-runoff model with a two-dimensional overland flow model (OFM) and a one-dimensional sewer flow model (SFM). Unlike conventional models limited to either rainfall-induced or sewer surcharge-induced flooding, UIDS captures bidirectional surface-underground interactions to simulate both processes simultaneously. The OFM employs an explicit time-stepping scheme and robust wet-dry front treatment, while a weir equation describes roof-to-ground flow exchange for numerical stability. Timing synchronization facilitates continuous OFM-SFM coupling. Benchmarking and case studies of Gangnam flood events demonstrate UIDS's ability to accurately simulate urban flooding, particularly subcritical flows. The open-source nature of UIDS allows user flexibility in accessing and modifying the MATLAB code. Ultimately, UIDS is expected to serve as an accessible and adaptable tool for urban flood modeling and risk assessment.

White Rabbit time synchronization of unmanned aerial vehicle cluster based on adaptive kalman filtering

White Rabbit time synchronization of unmanned aerial vehicle cluster based on adaptive kalman filtering

Analysis of decentralized drone control model and interception trajectory calculation

Subject matter: This article is devoted to the study of applying the innovative Cascade DataHub method for optimizing the management of automated mobile systems, especially unmanned aerial vehicles. The work analyzes both theoretical and practical aspects of implementing this method across various application sectors. Goal: The objective of the study is to conduct a comprehensive analysis of contemporary models and methods for managing a group of drones, focusing on decentralized approaches. Additionally, the study aims to develop effective algorithms for optimizing the interception trajectory, with the goal of enhancing the accuracy and reliability of managing complex automated systems through increased real-time data integration. The research is directed towards identifying the potential advantages of this method in reducing system response times and improving decision-making accuracy. Tasks: The main tasks of the research include the development of comprehensive algorithms for rapid processing and analysis of large volumes of data from various sources, creating reliable communication protocols to ensure connection stability under extreme conditions. Another important task is the integration of these developments into practical applications, which will increase their effectiveness in real operational conditions. Methods: To achieve the set goals, advanced techniques of mathematical modeling, statistical analysis, machine learning, and deep learning are used. The application of these techniques ensures high accuracy and reliability of the management systems. Results: During the research, it was found that the Cascade DataHub method significantly reduces the response time of systems to commands, increases the accuracy of task execution, and decreases data loss during their transmission. The implementation of this method also contributes to a more efficient distribution of resources among automated units, which is critically important for missions requiring high coordination and time synchronization. Conclusions: A comprehensive analysis of contemporary models and methods for managing a group of drones with a focus on decentralized approaches has been conducted. Effective algorithms for optimizing the interception trajectory have been developed, aimed at enhancing the accuracy and reliability of managing complex automated systems through real-time data integration. The study revealed the potential advantages of the proposed method in reducing system response times and improving decision-making accuracy, contributing to the more efficient functioning of automated systems.

Satellite orbit determination and time synchronization using GPS single-frequency observables in low and high solar activities

We assess the orbit accuracy and time synchronization error using the L1 and C1 observables during the different solar activities. In general, GPS single-frequency (SF) observable can be used for commercial applications in satellite industry. The accuracy of satellite orbit determination using the SF observations is dominated by solar activities. The solar activities are indexed by the F10.7 value. The different solar activities lead to the ionosphere perturbation, triggering off the occurrence probability of ionospheric irregularities. The ionospheric irregularity affects the amplitude and phase of GPS signal. The affected amplitude and phase are indexed by the S4 value. We determine the GRACE satellite orbit using the SF GPS observations and compare the resulting orbit to that derived by dual-frequency observations for the effectiveness. The SF phase data are very sensitive to the variation in electron density and indirectly affects both the orbit accuracy and the time synchronization error. This is most likely caused by the phase ambiguity disturbed by the ionosphere. However, the C1 is relatively free from such a disturbance due to the strong signal-to-noise ratio (SNR) and the phase shift keying technique. The C1 performs the consistent solution over the low and high solar activities. However, this is not the case for the L1. The L1-derived orbit solution during the high solar activities is worse than that during the low solar activities. On the other hand, the time synchronization errors derived by the L1 and C1 are also different. The L1-derived time synchronization error has a relatively large perturbation as compared to the C1-derived one, which shows a consistent solution for a long-term period. This work suggests that the C1 observable is able to produce a consistent the orbit solution and time synchronization for the commercial applications of the satellite industry.Graphical

Development of a simulation model and adjustment of hyperparameters of a neural network for predicting possible states of a network of small space carriages

The purpose of the research is to develop a simulation model and tune the hyperparameters of a neural network to predict possible states of a network of small spacecraft.The research methods are based on the concepts of the theory of artificial intelligence to control a group of small spacecraft (SSV) – the use of adaptive methods and tools to make decisions, similar to the mechanisms of human thinking. In relation to space communication systems with a heterogeneous structure, artificial intelligence methods and technologies are aimed at predicting the state of communication channels between network nodes and automatically reconfiguring a network of devices based on neural network learning processes. One of the most important functions of network software for the application of cognitive algorithms is to predict the quality of communication between pairs of SSVs.Results. A method has been developed for using the Transformer architecture neural network to predict possible states of the SSV network, which provides aggregation and time synchronization of data on the state of the SSV network, their use for training the neural network, as well as using the neural network to predict the quality of communication. The data format for the training sample has been created, based on the representation of the state of the SSV network, which ensures the generation of the initial state of the network, modeling the proactive mode of its operation, collecting SSV network state markers to generate training data sets in the form of chronological sequences grouped into frames, and allowing to reduce the amount of data transferred between SSVs when creating a training set. A simulation model of the SSV network has been developed, which provides generation of the initial state of the network, modeling of the proactive mode of its operation, and collection of information about the state of the SSV network to generate sets of synthetic training data.Conclusion. The article develops a simulation model of the SSV network for generating synthetic training data and predicting possible states of the SSV network, as well as a method for using a neural network to predict possible states of the SSV network.

Multi-wing chaotic system based on smooth function and its predefined time synchronization

In this paper, the construction of multi-wing chaotic system and predefined time synchronization are investigated. First, a new 4D chaotic system is constructed, and the existence of chaotic attractors is proved by dissipativity, boundedness, and Lyapunov exponents. Then the existence of a saddle focus of index-2 is proved by equilibrium point analysis, and a multi-winged hyperchaotic system is generated by extending the saddle focus with the constructed smooth function, which is found to be more complex and more conducive to synchronization applications by complexity analysis. Then the simulation of the circuit also proves the chaotic properties of the system in a physical sense. Finally, a new robust controller is designed using the fast terminal sliding mode algorithm to achieve predefined time synchronization between heterogeneous chaotic systems, and the numerical simulation results indicate that this synchronization method has a rapid time of convergence and achieves the predefined time effect.

Fixed/Prescribed-time synchronization of multi-layer complex networks under switching control

The fixed/preassigned time (FT/PT) synchronization issue of multi-layer networks via a discontinuous control strategy is addressed in this paper. Firstly, the inventiveness FT theorem is established, and a precise estimation of setting time is given by employing mathematical induction, proof by contradiction, and maximum value theory. Subsequently, based on the FT stability results, several FT synchronization criteria for multi-layer networks are proposed by designing a periodically switching protocol. Additionally, the topological connectivity constraints are eliminated through the implementation of synchronized states as virtual nodes. Correspondingly, the PT synchronization issue is investigated by developing several extraordinary control strategies in which the control gains are finite. Ultimately, the feasibility of the developed control design and the established criteria is illustrated by two numerical examples.

Advancing Convergence Speed of Distributed Consensus Time Synchronization Algorithms in Unmanned Aerial Vehicle Ad Hoc Networks

Time synchronization is a critical prerequisite for unmanned aerial vehicle ad hoc networks (UANETs) to facilitate navigation and positioning, formation control, and data fusion. However, given the dynamic changes in UANETs, improving the convergence speeds of distributed consensus time synchronization algorithms with only local information poses a major challenge. To address this challenge, this study first establishes a convex model on the basis of graph theory and relevant theories of random matrices to approximate the original problem. Subsequently, three acceleration schemes for consensus algorithms are derived by minimizing the Frobenius norm of the iteration matrix. Additionally, this study provides a new upper bound for constant communication weights and discusses the limitations of existing metrics used to measure the convergence speeds of consensus algorithms. Finally, the proposed schemes are compared with existing ones through simulation. Our results indicate that the three proposed schemes can achieve faster convergence while maintaining high-precision synchronization in scenarios with static or known topological structures of networks. In scenarios where the topological structure of a UANET is time-varying and unknown, the scheme proposed in this paper achieves the fastest convergence speed.

CRT-Based Clock Synchronization for Millimeter-Wave Communication with Asymmetric Propagation Delays

The rapid advancement of millimeter-wave communication technology has presented new challenges for time synchronization, driven by the need for high-speed and low-latency data transmission. Ethernet synchronization technologies, such as Precise Time Protocol (PTP), have emerged to overcome the limitations of point-to-point architecture and enable precise synchronization across multiple devices. A key drawback of conventional PTP is its reliance on symmetric packet exchange delays, which may not hold in real-world scenarios where there is relative mobility between master and slave nodes, leading to asymmetric propagation delays and clock offset errors. To address this issue, a novel clock synchronization protocol that integrates Chinese Remainder Theory (CRT) has been proposed. This protocol enhances conventional PTP by incorporating distance estimation based on CRT using multi-carrier phase measurement. By combining a coarse estimation of one-way propagation delay from conventional PTP with a fine estimation of remainder distance from CRT, the protocol accurately determines the distance between master and slave nodes, reducing motion errors and enhancing clock synchronization accuracy. Simulation results indicate that the Root Mean Square Error (RMSE) of distance estimation remains below 10−5 m, with a corresponding motion time error of approximately 0.01 picosecond.

Enhancing interpretability in data-driven modeling of photovoltaic inverter systems through digital twin approach

The utilization of data-driven modeling techniques has been extensively employed in the simulation analysis, power prediction, and condition monitoring of photovoltaic power generation systems. However, the absence of interpretability regarding the intrinsic mechanisms in the modeling process has resulted in numerous constraints in practical implementation and subsequent promotion. To this end, we propose a novel digital twin modeling approach that eliminates the need for injecting additional signals or sensors, estimating unknown parameters in the mechanism model solely by using operational data from physical systems. A time synchronization filter was added to address the frequency mismatch between the actual sampling frequency and the solution step size. The results of numerical research indicate that the proposed digital twin model has the ability to accurately simulate the dynamic characteristics of photovoltaic grid connected inverters. The digital twin model of photovoltaic inverters has achieved good results in the cross experiment of device degradation trend monitoring, indicating that the proposed method is expected to make significant contributions to the simulation, power prediction, and degradation monitoring of grid connected photovoltaic systems.

Time synchronization between parietal–frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks

BackgroundIn post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.MethodsTen participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson’s correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification.ResultsParietal–frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients.ConclusionsIn this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.