Synchronization Signal Research Articles

Accuracy study for over-the-air frequency synchronization of continuous wave signals

Abstract Future communication and radar sensing systems will require synchronization methods which are more versatile in terms of the systems involved in the synchronization process. We present an over-the-air frequency synchronization algorithm based on the standard and the generalized Kuramoto model which uses continuous wave (CW) signals. In contrast to other approaches, all nodes of the network participate equally, and synchronization can even be achieved in presence of a non-cooperative node. By changing the parameters of the radar or by modifying the synchronization algorithm, synchronization accuracy can be adjusted as well. All claims are supported by measurements conducted with CW radars. It will be demonstrated that our algorithm enables synchronization accuracies down to 1.92 ppb and thus could provide sufficient accuracy for velocity measurements on pedestrians.

Hepatic vagal afferents convey clock-dependent signals to regulate circadian food intake.

Circadian desynchrony induced by shiftwork or jet lag is detrimental to metabolic health, but how synchronous or desynchronous signals are transmitted among tissues is unknown. We report that liver molecular clock dysfunction is signaled to the brain through the hepatic vagal afferent nerve (HVAN), leading to altered food intake patterns that are corrected by ablation of the HVAN. Hepatic branch vagotomy also prevents food intake disruptions induced by high-fat diet feeding and reduces body weight gain. Our findings reveal a homeostatic feedback signal that relies on communication between the liver and the brain to control circadian food intake patterns. This identifies the hepatic vagus nerve as a potential therapeutic target for obesity in the setting of chronodisruption.

Experimental end-to-end demonstration of intersatellite absolute ranging for the Laser Interferometer Space Antenna

The Laser Interferometer Space Antenna (LISA) is a gravitational wave detector in space. It relies on a postprocessing technique named time-delay interferometry to suppress the overwhelming laser frequency noise by several orders of magnitude. This algorithm requires intersatellite-ranging monitors to provide information on spacecraft separations. To fulfill this requirement, we use on-ground observatories, optical sideband-sideband beatnotes, pseudorandom noise ranging (PRNR), and time-delay interferometric ranging (TDIR). This article reports on the experimental end-to-end demonstration of a hexagonal optical testbed used to extract absolute ranges via the optical sidebands, PRNR, and TDIR. These were applied for clock synchronization of optical beatnote signals sampled at independent phasemeters. We set up two possible PRNR processing schemes: Scheme 1 extracts pseudoranges from PRNR via a calibration relying on TDIR; Scheme 2 synchronizes all beatnote signals without TDIR calibration. The schemes rely on newly implemented monitors of local-PRNR biases. After the necessary PRNR treatments (unwrapping, ambiguity resolution, bias correction, in-band jitter reduction, and/or calibration), Schemes 1 and 2 achieved ranging accuracies of 2.0–8.1 cm and 5.8–41.1 cm, respectively, below the classical 1-m mark with margins. Published by the American Physical Society 2024

BWSAR: A Single-Drone Search-and-Rescue Methodology Leveraging 5G-NR Beam Sweeping Technologies for Victim Localization

Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for localization face limitations due to User Equipment (UE) power constraints. To overcome this, our paper introduces BWSAR, a novel three-stage Search-and-Rescue (SAR) methodology leveraging 5G-NR beam sweeping technologies. BWSAR utilizes downlink Synchronization Signal Block (SSB) for coarse-grained direction estimation, guiding the drone towards potential victim locations. Subsequently, finer-grained beam sweeping with Positioning Reference Signal (PRS) is employed within the identified direction, enabling precise three-dimensional UE coordinate estimation. Furthermore, we propose a trajectory optimization algorithm to expedite the drone’s navigation to emergency areas. Simulation results underscore BWSAR’s efficacy in reducing positioning errors and completing SAR missions swiftly, within minutes.

Multi-branch fusion graph neural network based on multi-head attention for childhood seizure detection.

The most common manifestation of neurological disorders in children is the occurrence of epileptic seizures. In this study, we propose a multi-branch graph convolutional network (MGCNA) framework with a multi-head attention mechanism for detecting seizures in children. The MGCNA framework extracts effective and reliable features from high-dimensional data, particularly by exploring the relationships between EEG features and electrodes and considering the spatial and temporal dependencies in epileptic brains. This method incorporates three graph learning approaches to systematically assess the connectivity and synchronization of multi-channel EEG signals. The multi-branch graph convolutional network is employed to dynamically learn temporal correlations and spatial topological structures. Utilizing the multi-head attention mechanism to process multi-branch graph features further enhances the capability to handle local features. Experimental results demonstrate that the MGCNA exhibits superior performance on patient-specific and patient-independent experiments. Our end-to-end model for automatic detection of epileptic seizures could be employed to assist in clinical decision-making.

Synthetic image data generation via rendering techniques for training AI-based Instance Segmentation

Within the scope of the development of the project, the acceleration and gyro signal, VR implementation, and User Control for VR were realized. The starting point was a list of MPU9250 data, which are acceleration and gyro data. The goal is to do the signal processing of the signal that is got from MPU9250 and implement the data with a specific format so that the LINAK LA36 actuator can be controlled in such a way that it performs as desired position. For this purpose, a multi-axis acceleration and position sensor is mounted on a surfing board which then collects the acceleration and Gyro data. A MATLAB Program had been written to convert the desired position into a CAN message. Before that, the raw acceleration data and gyro must be converted and filtered into actual data. First, the acceleration data was collected from another group. A signal processing method has been implemented. This includes gravitational force filter, noise filter, integration, and complementary filter method was used in order to get the accurate actual data that is able to be implemented into the system. For those methods, it was necessary to deal extensively with the characteristics of signal processing. VR application was implemented in this project to give the user a sensational surfing experience in the virtual world. User control has been implemented as well to allow users to control the video. Unity with Arduino work together so that both Arduino and Unity can communicate with each other, and signal messages will be sent to the Arduino from Unity and vice versa to achieve the synchronization of the video signal and Unity VR application.

Physics-based digital twin updating and twin-based explainable crack identification of mechanical lap joint

The mechanical joints, including the lap joint, weld, bolt, and pin, are vulnerable to fatigue failure because of stress concentration and internal flaws. Digital twin (DTw) strategies were proposed to prevent catastrophic system failure by fatigue damage in mechanical joints. In previous studies, the data-driven approach, such as deep learning and machine learning were utilized to estimate severity of the damage. However, it needs to improve its prediction accuracy because of insufficient data and physical interpretability. In this study, the physics-based digital twin model updating and twin-based crack identification of fatigue damage in riveted lap joints were proposed using lamb waves with consideration of uncertain crack growth path. The proposed approach is based on three techniques; (i) Data pre-processing, including filtering and optimization-based signal synchronization, (ii) Lamb-wave propagation analysis with sensor dynamics model and uncertain crack path, and (iii) Optimization based physics-based model updating and inference. In data pre-processing, the excitation frequency magnitude and truncation time are estimated using the observed actuator signal in the Lamb-wave test. The sensor dynamic model and model parameters are updated using the Bayesian optimization method to minimize both the errors in the predicted (y^t) and observed (yt) wave signal and the errors in the inferred (l*) and observed (l) crack length. The crack growth path is sampled based on angular and spline schemes to consider uncertain crack propagation paths. The validity of the proposed method is demonstrated using an open data set (2019 PHM society data challenge). The results conclude that the proposed digital twin approach can improve estimation accuracy considering both the crack growth path and sensor dynamics model.

Development of an accurate synchronous transport signal hand-held sensing platform for fluorescence-based berberine on-site detection

BackgroundBerberine is widely used in clinical treatment because of its wide antibacterial spectrum and low toxic side effects. However, its abuse could lead to bacterial resistance and several other adverse effects. In addition, measuring the content of berberine in environmental water samples helps to monitor its accumulation and metabolism in ecosystems. Traditional detection methods usually need to be carried out in the laboratory, involving complex processing procedures, which are not only time-consuming, but also unfavorable for rapid response and decision-making. Therefore, it is necessary to develop portable instruments to provide reasonable guidance on the addition and intake of berberine to reduce the harm caused by its abuse. ResultsIn this work, an accurate synchronous transport signal hand-held sensing platform (STSHSP) with a low-cost, easy-to-manufacture, independent use was developed by using photoelectric conversion technology, Bluetooth technology, remote synchronous signal technology, electrical technology, and 3D printing technology. To verify the performance of STSHSP, a 5-oxo-2,3-dihydro-5H-thiazolo [3,2-a] pyridine-3,7-dicarboxylic acid (TPDCA) with ultra-high quantum yield was designed and synthesized as a probe. TPDCA exhibited bright blue fluorescence under the ultraviolet light of 365 nm which could be quenched by berberine through the inner filter effect. In the range of 0.1–80 μg/mL, the voltage displayed by the prepared STSHSP has a good linearity with the berberine concentration (R2 = 0.9997) with a detection limit of 28.32 ng/mL. The portable sensor demonstrated good stability, accuracy, and reliability in detecting actual river water, urine, traditional Chinese medicine, and its preparation samples. SignificanceThe sensor with its compact structure, portability, and simple operation was suitable for in-situ detection with accurate, reliable, and feasible results, which is beneficial for improving drug quality and ensuring human health. Fortunately, the device could transmit the information to the control center and/or a third-party supervision institution in real-time, which could effectively eliminate the trust crisis. The sensor has broad application prospects in the field of environmental water quality detection and drug safety.

Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation.

The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic inter-play between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.

Analysis and Justification of Data Structure Selection for Traffic Flow Management Tasks

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.

Three-phase balance relay using numerical techniques experimentally verified on synchronous machines

In this paper, a multifunction three-phase balance relay based on normalized correlation coefficients is proposed to detect and estimate imbalances and perturbations in synchronous machine output signals. Furthermore, fresh definitions of imbalance and disturbance indicators derived using the correlation estimators are introduced, taking into account the changes in the waveform phase displacement, frequency, amplitude, and shape of the machine three-phase waves. Experimental tests are performed on a motor–generator set connected to a three-phase load, which is used to identify and evaluate the imbalance and disturbance conditions of the voltage and current measurements. Extensive tests for different fault types have been presented. The practical results show that the proposed protection can respond quickly to faults, and assess online the level of the imbalance/disturbance with high accuracy. Its running time is within one cycle. In addition, the proposed algorithm’s reliability and accuracy are its most significant attributes, whose percentages exceed 96.6%. The present algorithm considers the impact of both negative and zero sequence components when measuring di-symmetry factors, while some conventional approaches merely rely on the negative sequence component computed for the machine voltages and currents.

IGF-I concentration determines cell fate by converting signaling dynamics as a bifurcation parameter in L6 myoblasts

Insulin-like growth factor (IGF)-I mediates long-term activities that determine cell fate, including cell proliferation and differentiation. This study aimed to characterize the mechanisms by which IGF-I determines cell fate from the aspect of IGF-I signaling dynamics. In L6 myoblasts, myogenic differentiation proceeded under low IGF-I levels, whereas proliferation was enhanced under high levels. Mathematical and experimental analyses revealed that IGF-I signaling oscillated at low IGF-I levels but remained constant at high levels, suggesting that differences in IGF-I signaling dynamics determine cell fate. We previously reported that differential insulin receptor substrate (IRS)-1 levels generate a driving force for cell competition. Computational simulations and immunofluorescence analyses revealed that asynchronous IRS-1 protein oscillations were synchronized during myogenic processes through cell competition. Disturbances of cell competition impaired signaling synchronization and cell fusion, indicating that synchronization of IGF-I signaling oscillation is critical for myoblast cell fusion to form multinucleate myotubes.

Simplified coherent chaotic optical secure communication scheme based on the Kramers-Kronig receiver.

Enhancing physical layer encryption in fiber-optic networks remains a challenging yet vital task. In this Letter, we propose a simplified coherent chaotic secure optical communication scheme based on the Kramers-Kronig (KK) receiver. This scheme incorporates a semiconductor laser with a phase-conjugated optical feedback serving as a common chaotic source, and its chaotic output is directly injected into the two slave lasers arranged separately at the transmitter and receiver end to achieve high-quality synchronization of chaotic signals, with a corresponding chaotic bandwidth of 30.6 GHz. By virtue of the common-signal-induced broad chaotic synchronization, a proof-of-principle demonstration is successfully conducted. It involves the secure transmission of a 20 Gbaud 16-level quadrature amplitude modulation (16QAM) signal over a 50 km standard single-mode fiber (SSMF) link. At the receiver end, we deploy a KK receiver to reconstruct the field of the optical signal and hence enable signal compensation and recovery with offline digital signal processing (DSP). This method simplifies device requirements in the current chaotic coherent optical secure communication, offering a cost-effective mode and promising path for advancing physical layer encryption in inter-data center communications.

Research and implementation of a large-bandwidth real-time radar jamming simulator.

The electromagnetic environment faced by modern radar is becoming increasingly complex. One effective means to improve the performance of radar systems is testing in an anti-jamming ability test chamber, where the increased complexity has also led to higher performance requirements for radar jamming simulators. Based on the requirements for modern radar system testing, this paper presents a study of a large-bandwidth real-time radar jamming simulator and describes its overall design architecture; the simulator covers the L-Ku and Ka frequency bands and the instantaneous bandwidth is ≥2GHz, which means that the system is able to simulate 11 interference patterns. Synchronous control of the system is realized in 1ms through use of the reflection memory interrupt mechanism, the synchronous pulse signal mechanism, synchronous timing design, and a real-time control software architecture. An overall design scheme for real-time simulation of a radar target jamming echo is given and baseband signal processing resources are saved through information preprocessing, a large-capacity high-speed storage board is designed to improve the data reading speed, a multiphase filtering structure is used to achieve high sampling rates and save hardware resources, and a high-speed parallel computing method is used to improve computing efficiency; the actual measured baseband signal processing time is less than 500ns. Finally, a measurement platform is built, and the main interference patterns are verified through experimental measurements.

Hybrid quantum key distribution: A novel approach for secure end-to-end communication in network infrastructure

The Quantum Key Distribution (QKD) network approach has emerged as a promising solution and has garnered significant interest across various domains. QKD networks can be broadly categorized into two main types: those based on measurement-device-dependent (MDD) protocols and those based on measurement-device-independent (MDI) protocols. A novel network scheme is proposed combining these two protocols to guarantee end-to-end secure communication. It comprises two interconnected networks — the first uses a measurement-device-dependent protocol to connect routers with nearby nodes, while the second employs a measurement-device-independent protocol to interconnect the routers. In the first network, the process begins with Alice (Router) generating two types of weak quantum signals — the first has intensity, frequency and polarization or phase randomly varied, while the second with different delay times. Alice randomly transmits either signal states carrying information or decoy states with varying properties to the receivers (Bobs) over the quantum channels. She also sends a synchronization signal along with the quantum signals. At the receiver end, the synchronization pulse timestamps the incoming quantum signals. Each Bob then randomly shifts the frequencies of the received signals and measures their phase/polarization of the first signal and measures the delay times of the second signal. The secret key is derived by combining the frequency, the delay time and the phase/polarization information of the quantum signals, resulting in longer key lengths. Encoding information across multiple degrees of freedom enables extended key generation for secure communication. However, between the routers, MDI-QKD protocols are used to securely share the keys. This hybrid approach aims to ensure secure end-to-end communication across the network.

Beta bursts in the parkinsonian cortico-basal ganglia network form spatially discrete ensembles

Defining spatial synchronisation of pathological beta oscillations is important, given that many theories linking them to parkinsonian symptoms propose a reduction in the dimensionality of the coding space within and/or across cortico-basal ganglia structures. Such spatial synchronisation could arise from a single process, with widespread entrainment of neurons to the same oscillation. Alternatively, the partially segregated structure of cortico-basal ganglia loops could provide a substrate for multiple ensembles that are independently synchronized at beta frequencies. Addressing this question requires an analytical approach that identifies groups of signals with a statistical tendency for beta synchronisation, which is unachievable using standard pairwise measures. Here, we utilized such an approach on multichannel recordings of background unit activity (BUA) in the external globus pallidus (GP) and subthalamic nucleus (STN) in parkinsonian rats. We employed an adapted version of a principle and independent component analysis-based method commonly used to define assemblies of single neurons (i.e., neurons that are synchronized over short timescales). This analysis enabled us to define whether changes in the power of beta oscillations in local ensembles of neurons (i.e., the BUA recorded from single contacts) consistently covaried over time, forming a “beta ensemble”. Multiple beta ensembles were often present in single recordings and could span brain structures. Membership of a beta ensemble predicted significantly higher levels of short latency (<5 ms) synchrony in the raw BUA signal and phase synchronisation with cortical beta oscillations, suggesting that they comprised clusters of neurons that are functionally connected at multiple levels, despite sometimes being non-contiguous in space. Overall, these findings suggest that beta oscillations do not comprise of a single synchronisation process, but rather multiple independent activities that can bind both spatially contiguous and non-contiguous pools of neurons within and across structures. As previously proposed, such ensembles provide a substrate for beta oscillations to constrain the coding space of cortico-basal ganglia circuits.

Lock-in Thermography for the Localization of Security Hard Blocks on SoC Devices

AbstractLocalizing security-relevant hard blocks on modern System-on-Chips for physical attacks, such as side-channel analysis and fault attacks, has become increasingly time-consuming due to ever-increasing chip-area and -complexity. While this development increases the effort and reverse engineering cost, it is not sufficient to withstand resolute attackers. This paper explores the application of camera-based lock-in thermography, a nondestructive testing method, for identifying and localizing security hard blocks on integrated circuits. We use a synchronous signal to periodically activate security-related functions in the firmware, which causes periodic temperature changes in the activated die areas that we detect and localize via an infrared camera. Using this method, we demonstrate the precise detection and localization of security-related hard blocks at the die level on a modern SoC.

Improved echo signal detection for pseudo-random single-photon counting laser ranging

In this paper, a new echo signal detection method, to the best of our knowledge, for pseudo-random single-photon counting ranging (PSPCR) LiDAR systems is proposed, which is applied for long distances, low repetition rates, and system cost reduction. First, in order to achieve a comparable temporal resolution as that in time-correlated single-photon counting (TCSPC) systems, we extend the pseudo-random code to discriminate the minimal time slot in time correlation. Second, we use the full width at half maxima (FWHM) in the duration of each pseudo-random code for correlation to reduce the impact of pulse width variation and timing jitters on ranging accuracy. Third, we study the bias and errors caused by using synchronous signals as the “START” signal, and propose to use the time of flight (ToF) at half energy to reduce the walk error. Simulation results show that, compared with existing PSPCR methods, the proposed method improves ranging accuracy with a lower repetition rate and lower peak and average power—centimeter-level ranging accuracy over tens of kilometers can be achieved using a laser with a repetition rate of 400 kHz, peak power of up to 1 kW, and average power of up to 1 W.

Global topological synchronization of weighted simplicial complexes.

Higher-order networks are able to capture the many-body interactions present in complex systems and to unveil fundamental phenomena revealing the rich interplay between topology, geometry, and dynamics. Simplicial complexes are higher-order networks that encode higher-order topology and dynamics of complex systems. Specifically, simplicial complexes can sustain topological signals, i.e., dynamical variables not only defined on nodes of the network but also on their edges, triangles, and so on. Topological signals can undergo collective phenomena such as synchronization, however, only some higher-order network topologies can sustain global synchronization of topological signals. Here we consider global topological synchronization of topological signals on weighted simplicial complexes. We demonstrate that topological signals can globally synchronize on weighted simplicial complexes, even if they are odd-dimensional, e.g., edge signals, thus overcoming a limitation of the unweighted case. These results thus demonstrate that weighted simplicial complexes are more advantageous for observing these collective phenomena than their unweighted counterpart. In particular, we present two weighted simplicial complexes: the weighted triangulated torus and the weighted waffle. We completely characterize their higher-order spectral properties and demonstrate that, under suitable conditions on their weights, they can sustain global synchronization of edge signals. Our results are interpreted geometrically by showing, among the other results, that in some cases edge weights can be associated with the lengths of the sides of curved simplices.

Joint detection and state estimation based on GPS spoofing attack in smart grids

Phasor Measurement Units (PMUs) are widely used in power system state estimation due to its high sampling rate and real-time estimation of power state in power systems. However, it is also vulnerable to cyberattacks (GPS spoofing attacks) since it uses unencrypted GPS civil signals for synchronization. The GPS spoofing attacks (GSAs) will intentionally manipulate the time reference of PMUs, which is equivalent to falsifying the phase angle of the PMU measurements. In this paper, we consider the secure power state estimation and attacks detection with the unknown GSAs in the power systems. Particularly, we derived the PMU-based GSAs model in the power state estimation problem, which is formulated as a mixed maximum likelihood problem. In the formulated problem, the involved unknown parameters are coupled and the objective function is non-convex, which are of significant challenges in finding optimal solutions. To tackle these challenges, a joint maximum a posteriori and maximum likelihood (JMAP-ML) algorithm is proposed to securely estimate the power state and detect the GSAs in the mixed measurements of PMUs. Different testing scenarios in IEEE 14 bus system are simulated to show the proposed algorithm’ performance on GSAs detection and state estimation. Numerical examples demonstrate the improved accuracy of our algorithm compared with classical algorithms when GSAs are present. And we conclude that when a sufficient number of PMUs are deployed in the system, the impact of GSAs will be largely compensated in the estimation stage.