Synchronous State Research Articles

An in-depth assessment of the physical layer performance in the proposed B5G framework

The introduction of fifth-generation (5G) technology marks a significant milestone in next-generation networks, offering higher data rates and new services. Achieving optimal performance in 5G and beyond 5G (B5G) systems requires addressing key requirements like increased capacity, high efficiency, improved performance, low latency, support for many connections, and quality of service. It is well-known that suboptimal network configuration, hardware impairments, or malfunctioning components can degrade system performance. The physical layer of the radio access network, particularly channel estimation and synchronization, plays a crucial role. Hence, this paper offers an in-depth evaluation of the 5G Physical Downlink Shared Channel (PDSCH), along with its related channel models such as the Clustered Delay Line (CDL) and the Tapped Delay Line (TDL). This work assesses 5G network performance through practical and IA-based channel estimation and synchronization techniques, and anticipates numerologies for B5G networks. Extensive simulations leveraging the Matlab 5G New Radio (NR) toolbox assess standardized channel scenarios in both macro-urban and indoor environments, following configurations set by the 3rd Generation Partnership Project (3GPP). The numerical results offer valuable insights into achieving the maximum achievable throughput across various channel environments, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The throughput comparisons are performed under assumptions of ideal, realistic, and convolutional neural networks (CNN)-based channel estimation with both perfect and realistic synchronization conditions. Importantly, the study pinpoints certain physical layer elements that have a pronounced impact on system performance, providing essential insights for devising effective strategies or refining CNN-based methods for forthcoming mobile B5G networks.

Beat frequency induced transitions in synchronization dynamics

In neurosciences, the brain processes information via the firing patterns of connected neurons operating across a spectrum of frequencies. To better understand the effects of these frequencies in the neuron dynamics, we have simulated a neuronal network of Izhikevich neurons to examine the interaction between frequency allocation and intermittent phase synchronization dynamics. As the synchronized population of neurons passes through a bifurcation, an additional frequency mode emerges, enabling a match in the mean frequency while retaining distinct most probable frequencies among neurons. Subsequently, the network intermittently transits between two patterns, one partially synchronized and the other unsynchronized. Through our analysis, we demonstrate that the frequency changes on the network lead to characteristic transition times between synchronization states. Moreover, these transitions adhere to beat frequency statistics when the neurons’ frequencies differ by multiples of a frequency gap. Finally, our results can improve the performance in predicting transitions on problems where the beat frequency strongly influences the dynamics.

Stability and synchronization of fractional-order reaction–diffusion inertial time-delayed neural networks with parameters perturbation

This study is centered around the dynamic behaviors observed in a class of fractional-order generalized reaction–diffusion inertial neural networks (FGRDINNs) with time delays. These networks are characterized by differential equations involving two distinct fractional derivatives of the state. The global uniform stability of FGRDINNs with time delays is explored utilizing Lyapunov comparison principles. Furthermore, global synchronization conditions for FGRDINNs with time delays are derived through the Lyapunov direct method, with consideration given to various feedback control strategies and parameter perturbations. The effectiveness of the theoretical findings is demonstrated through three numerical examples, and the impact of controller parameters on the error system is further investigated.

The use of nonlinear guided-wave features for detection and assessment of intermetallic compounds within dissimilar joints – an experimental investigation

Abstract Welding dissimilar materials is widely employed in industrial construction and manufacturing to enhance cost-effectiveness and performance, often utilizing non-fusion methods like solid-state and high-energy beam welding. However, a significant challenge is the formation of intermetallic compounds (IMCs) at the joint interface, which can weaken the bond and increase brittleness, leading to hidden internal cracks. Nonlinear ultrasound detection methods are employed as advanced, non-destructive testing techniques for early damage inspection in various materials. This research investigates the assessment of the thickness of the intermetallic layer within dissimilar joints using non-linear ultrasound-wave features. Numerical and experimental investigations were performed using four friction stir welding (FSW) lap joints, between AA5052-H32 aluminum and ASTM 516-70 steel, with various intermetallic thicknesses. The methodology involved examining the generation of second-order harmonic frequency by exciting Lamb waves (LW) at specific frequencies. To determine the necessary LWs' excitation frequency, synchronism, and non-zero power flux conditions were employed. The collected signals were measured and analyzed in the time and frequency domains to understand the behavior of the non-linear parameter β′ with the thickness of the intermetallic layer. The results show that β′ changes in a linear manner with the thickness of the intermetallic compound layer (several micrometers in thickness). This provides strong evidence that nonlinear LW features are sensitive to microstructural variations in the FSW joints, which enables them to effectively evaluate their strength.

Finding synchronization state of higher-order motif networks by dynamic learning

Synchronization of action potentials is one of the important phenomena for neural networks to achieve biological functions. How to find the optimal parameter space of neural networks in a synchronized state is of great significance for studying their synchronization. This study explores the parameter space of triplet motif-based higher-order networks in a synchronized state through the dynamic learning of synchronization (DLS) technique, which dynamically modulates the connection weights between motifs to alter their firing patterns. Our study delves into regular, Erdós-Rënyi random graphs, small-world, and scale-free networks, emphasizing the high-order motif interactions that characterize these networks. Our key findings indicate that the DLS technique successfully promotes synchronization within high-order motif networks with various connection patterns, although the degree of synchronization in networks where motifs are interconnected by chemical synapses is slightly weaker than those interconnected by electrical synapses. Additionally, we demonstrate the pattern of weight changes during the regulation of network firing states by DLS, finding that the evolution of weight distributions correlates with the network's topological structure. This work might provide new insights into complex network synchronization and lays the foundation for further exploration of using DLS technology to synchronize higher-order networks through external factors. Published by the American Physical Society 2024

The role of local bounds on neighborhoods in the network for scale‐free state synchronization of multi‐agent systems

AbstractThis article provides necessary and sufficient conditions for the existence of solutions to the state synchronization problem of homogeneous multi‐agent systems (MAS) via scale‐free linear dynamic non‐collaborative protocol in both continuous and discrete time. These conditions guarantee for which class of MAS, one can achieve scale‐free state synchronization. We investigate protocol design with and without utilizing local bounds on the neighborhood of agents. The results shows that the availability of local bounds on neighborhoods plays a key role.

Distributed delayed impulsive control for [formula omitted]-synchronization of multi-link structure networks with bounded uncertainties and time-varying delays of unmeasured bounds: A novel Halanay impulsive inequality approach

This study discusses the μ−synchronization of multi-link structure networks with parameter uncertainties and nonlinear couplings. Three types of variable delays, including internal, coupling, and distributed delays at sampling moments are deliberated in the synchronization control process. Different from existing multi-link models, the bulk of delays can be non-differentiable and unmeasured, which also brings new challenges to our research. To conquer the difficulties caused by such multiple generalized time delays, a novel Halanay-like impulsive inequality is established by utilizing the properties of μ−function and mathematical induction recipes. For letting the multi-link networks achieve μ−synchronization, a distributed-delay impulsive controller is designed, and multiple coupling matrices are decomposed into diagonal matrices and residual matrices. Based on the stability function method, the derived impulsive inequality, and matrix decomposition techniques, important synchronization conditions for the concerning multi-link networks are obtained, which releases the traditional constraints that delays should be smaller than impulsive intervals, and popularizes the existing synchronization results respecting multi-link systems with unbounded delays.

Scalable synchronization cluster in networked chaotic oscillators.

Cluster synchronization in synthetic networks of coupled chaotic oscillators is investigated. It is found that despite the asymmetric nature of the network structure, a subset of the oscillators can be synchronized as a cluster while the other oscillators remain desynchronized. Interestingly, with the increase in the coupling strength, the cluster is expanding gradually by recruiting the desynchronized oscillators one by one. This new synchronization phenomenon, which is named "scalable synchronization cluster," is explored theoretically by the method of eigenvector-based analysis, and it is revealed that the scalability of the cluster is attributed to the unique feature of the eigenvectors of the network coupling matrix. The transient dynamics of the cluster in response to random perturbations are also studied, and it is shown that in restoring to the synchronization state, oscillators inside the cluster are stabilized in sequence, illustrating again the hierarchy of the oscillators. The findings shed new light on the collective behaviors of networked chaotic oscillators and are helpful for the design of real-world networks where scalable synchronization clusters are concerned.

Global synchronization in generalized multilayer higher-order networks

Networks incorporating higher-order interactions are increasingly recognized for their ability to introduce novel dynamics into various processes, including synchronization. Previous studies on synchronization within multilayer networks have often been limited to specific models, such as the Kuramoto model, or have focused solely on higher-order interactions within individual layers. Here, we present a comprehensive framework for investigating synchronization, particularly global synchronization, in multilayer networks with higher-order interactions. Our framework considers interactions beyond pairwise connections, both within and across layers. We demonstrate the existence of a stable global synchronous state, with a condition resembling the master stability function, contingent on the choice of coupling functions. Our theoretical findings are supported by simulations using Hindmarsh-Rose neuronal and Rössler oscillators. These simulations illustrate how synchronization is facilitated by higher-order interactions, both within and across layers, highlighting the advantages over scenarios involving interactions within single layers. Published by the American Physical Society 2024

Digital Twin-Enhanced Adaptive Traffic Signal Framework under Limited Synchronization Conditions

Unmanned traffic signal control is regarded as a sustainable intelligent management methodology. However, it faces the challenge of unpredictable traffic flow due to stochastic arrivals. The digital twin (DT) has emerged as a promising approach to address the challenges of time-varying traffic demand in urban transportation. Previous studies of DT-based adaptive traffic signal control (ATSC) methods all assume ideal synchronization conditions between the DT and the physical twin (PT). It means that the DT can immediately figure out the next neglecting limitation of realistic conditions, i.e., discrepancies between the DT and PT and computational ability. This paper proposes a DT-ATSC framework aimed at reducing the total delay at isolated intersections under limited synchronization conditions. The framework contains two parts: (1) a cell transmission model-based intersection simulation model featuring less computational consumption and the parameter self-calibration mechanism; and (2) scheme searching algorithms that can guide the DT to create optimization-oriented signal timing scheme candidates. Three options are provided for the scheme searching algorithms, i.e., grid search (GS), the genetic algorithm (GA), and Bayesian optimization (BO). A testing platform is created to validate the effectiveness of the proposed DT-ATSC. Experimental results indicate that the proposed DT-ATSC-BO outperforms the DT-ATSC-GA and DT-ATSC-GS. Meanwhile, the average vehicle delay of the DT-ATSC-BO is up to 53% lower than that of the current adaptive signal control method, which indicates that the proposed DT-ATSC has achieved the expected effect. Moreover, compared to the previous related work, the proposed DT-ATSC framework is more likely to be able to be applied in realistic situations because synchronization issues are incorporated in the design of the DT-ATSC by assuming a limited margin time for a decision.

Critical expert analysis of permanent magnet synchronous motors identification methods and state variable observers

Relevance. In recent years, the oil industry has seen a tendency to transfer low-yield oil wells to intermittent mode. Unregulated electric drive and the open-loop controlled electric drive of electric centrifugal pumping with permanent magnet synchronous motors are typically not suitable for tasks of intermittent operation of wells. Due to objective reasons, there may not be smooth starts and required quality indicators of transient processes, which leads to even more reduction in oil production. The synthesis of a closed-loop electric drive control system of submersible installations is possible only using algorithms for indirect estimation of non-measured mechanical coordinates of the electric drive – state variables identification methods, observers of the rotor angular velocity and the load torque. The installation of sensors of angular velocity and torque on the shaft of the submersible electric motor is associated with technical difficulties, therefore, there is no need for significant modernization of the hardware solutions of the existing electrical equipment system. To date, the theory of identifying non-linear dynamic systems is widely used to derive an indirect estimate of the non-measured state variables of the permanent magnet synchronous motors. The large number of methods presented in scientific sources requires comparative analysis regarding their benefits and disadvantages in order to select the best identification method that meets the requirements of the oil production and takes into account its specificity and features. In this case, identification systems that are focused on minimizing computing power or operating in real time are given priority. Aim. To determine the best in terms of computational complexity and the convenience of practical use method of identifying state variables of permanent magnet synchronous motors operating in submersible oil well pumping units based on comparative analysis of existing methods presented in Russian and foreign sources. Methods. Methods for system analysis and identification of dynamic systems, a method of critical expert analysis. Result and conclusions. The most common methods for identifying and observing the state variables of synchronous motors with permanent magnets have been described in detail, given the general performance of each group of methods, their benefits and disadvantages were reviewed. By comparative critical expert analysis, it was found that for tasks of submersible electric centrifugal pump units electric drive a Luenberger observer, characterized by relatively low computational complexity and ease of setup in engineering practice can be recommended.

Investigation of Synchronization Control of the Memristive/Resistive-Coupled Neural Network with Noise Effect

This study comprehensively investigates synchronization control of the memristive- and resistive-coupled neural network with noise effect. In this context, a sub-network is constructed with three chaotic memristive Hindmarsh–Rose neurons coupled by the memristive synapses. Then, two equivalent sub-networks are linked by the diffusive resistive synapse under a distinctive global network structure including two sub-networks. Thus, a double (memristive/resistive) control mechanism is used to achieve the synchronization state. The first mechanism is the memristive synaptic coupling in each sub-network, and the second one is the diffusive resistive coupling between two sub-networks constructing the global network. The synchronization between the neurons is proven theoretically through the Lyapunov stability and Lipschitz theorems and this proof process is verified with numerical experiments. According to the theoretical and numerical results, by adjusting memristive/resistive synaptic coupling weights, a synchrony behavior is generated successfully in these sub-networks and global networks. Additionally, this paper also handles the Gaussian white noise effect on the global network to examine continuity capability and stability of synchronization. Two different frameworks are built by applying the noise with multiple noise power levels and periods to (i) one of the resistive coupling neurons in each sub-network directly and (ii) one neuron in a sub-network without direct contact with the other sub-network. With these two characteristic designs, it is proven that the second design provides more robust characteristics since it utilizes the double control mechanism, which enhances the stability throughout external interferences enabling the network to sustain synchronous states.

Photonic device programmability in support of autonomous optical networks

The emergence and consolidation of increasingly programmable optical devices such as transceivers, amplifiers, multiplexers, or ROADMs—which allow their remote configuration and control by adopting software-defined networking principles such as model-driven development—is enabling the evolution toward gradually more autonomous networks. Such networks leverage device programmability and are able to adapt and react to traffic and network condition changes, e.g., changing modes of operation or reconfiguring the network state, paving the way for the increased adoption of AI/ML models in support of enhanced network operation. In this paper, after a short review of some key elements in the control and orchestration systems of optical networks in support of autonomous networking, we present in detail a proof-of-concept validation of autonomous, closed-loop dynamic adaptation of transceiver operational modes. This includes (i) the design and development of an SDN agent of a multi-band sliceable bandwidth variable transceiver, based on extended OpenConfig terminal device data models; (ii) an SDN controller that performs discovery and management of transceivers’ operational modes and maps to transport API (TAPI) profiles enabling efficient physical layer impairment-aware path computation; (iii) a dedicated externalized path computation element/digital twin that performs adaptation recommendations; and (iv) an MQTT-based telemetry platform for publisher/subscriber based state synchronization between the control plane functional entities to avoid systematic polling.

Kuramoto model subject to subsystem resetting: How resetting a part of the system may synchronize the whole of it.

We introduce and investigate the effects of a new class of stochastic resetting protocol called subsystem resetting, whereby a subset of the system constituents in a many-body interacting system undergoes bare evolution interspersed with simultaneous resets at random times, while the remaining constituents evolve solely under the bare dynamics. Here, by reset is meant a reinitialization of the dynamics from a given state. We pursue our investigation within the ambit of the well-known Kuramoto model of coupled phase-only oscillators of distributed natural frequencies. Here, the reset protocol corresponds to a chosen set of oscillators being reset to a synchronized state at random times. We find that the mean ω_{0} of the natural frequencies plays a defining role in determining the long-time state of the system. For ω_{0}=0, the system reaches a synchronized stationary state at long times, characterized by a time-independent nonzero value of the synchronization order parameter that quantifies macroscopic order in the system. Moreover, we find that resetting even an infinitesimal fraction of the total number of oscillators, in the extreme limit of infinite resetting rate, has the drastic effect of synchronizing the entire system, even in parameter regimes in which the bare evolution does not support global synchrony. By contrast, for ω_{0}≠0, the dynamics allows at long times either a synchronized stationary state or an oscillatory synchronized state, with the latter characterized by an oscillatory behavior as a function of time of the order parameter, with a nonzero time-independent time average. Our results thus imply that the nonreset subsystem always gets synchronized at long times through the act of resetting of the reset subsystem. Our results, analytical using the Ott-Antonsen ansatz as well as those based on numerical simulations, are obtained for two representative oscillator frequency distributions, namely, a Lorentzian and a Gaussian. Given that it is easier to reset a fraction of the system constituents than the entire system, we discuss how subsystem resetting may be employed as an efficient mechanism to control attainment of global synchrony in the Kuramoto system.

SYNCHRONOUS AND DIACHRONIC ANALYSIS OF COMPLEX SUFFIXAL FORMANTS AS A METHOD OF THEIR STUDY

Particular characteristics of suffixal complication, which has an articulated dynamic nature, require the application of synchronous and diachronic approaches to their study. Synchronous and diachronic study of complex formants implies the consideration of the previous historical changes when analysing synchronous states, as well as the study of dynamic processes in the word-formational, lexical and semantic subsystems of a language in determinate historical stages of its development. The method of synchronous and diachronic analysis of word-formation models with genetically complex word-formation affixes is presented by the example of the suffix -nits(a) with an object meaning. Synchronous and diachronic analysis of derived words with the suffix -nits(a), which implies the study of systemic relations between the nouns with the suffix -nits(a) in the Old Russian in the different stages of its development, reveals a genetic connection and a historical interaction of the modern Russian word-formation types with the suffixes -nits(a) and -n(ya), which are close semantically and include the derived words with the meaning of container and premises. The suffix -nits(a) with an object meaning is formed in the suffixal complication models “-n(ya) + -its(a)” in the nomina loci group. During its historical development, an extension of the semantic scope of application of the complex formant -nits(a) is observed, and its meaning transcends the boundaries of the names of premises group. The structural and functional evolvement of a new complex formant is empowered both by the peculiarities of nomina loci derivation and by the analogous influence of structurally and semantically close lexical units. The historical development of the suffix -nits(a) results in the following changes in its relations with the suffix -n(ya): the transition from the motivation relations to the relations of co-derivation, the increased productivity of the new complex formant, its prevalence in the competition with the genetically related suffix -n(ya).

Entrainment: Transcendental Condition that Affected with Musical

This research examines the phenomenon of entrainment, which is associated with atmospheric phenomenon in meteorology, and is now also used in a person’s psychological and neurological state that leads to a state of brainwave synchronization (neural entrainment). In the most extreme conditions, this neural oscillation process will make a person enter the transcendental phase. This research is examined through a qualitative method by conducting a literature study of a number of studies on the phenomenon of entrainment. The findings obtained by researchers and academics related to entrainment can be a source of evidence for this research. The results of the research conducted found that entrainment can be the most logical explanation to explain a number of transcendental phenomenon associated with traditional or modern music. Entrainment is also a scientific explanation for the phenomenon of being possessed by spirit in a number of traditional arts in Indonesia and entrainment also affects human behavior which can reach the most extreme phase when affected by music to the subconscious level. This article is not sufficient to provide a significant explanation for the phenomenon of trance in some traditional Indonesian arts. More proofs, experiments, and literature studies are needed to strengthen the explanation of the phenomenon scientifically.

Cardio-respiratory motion compensation for coronary roadmapping in fluoroscopic imaging.

Inferring the shape and position of coronary artery poses challenges when using fluoroscopic image guidance during percutaneous coronary intervention (PCI) procedure. Although angiography enables coronary artery visualization, the use of injected contrast agent raises concerns about radiation exposure and the risk of contrast-induced nephropathy. To address these issues, dynamic coronary roadmapping overlaid on fluoroscopic images can provide coronary visual feedback without contrast injection. This paper proposes a novel cardio-respiratory motion compensation method that utilizes cardiac state synchronization and catheter motion estimation to achieve coronary roadmapping in fluoroscopic images. For more accurate cardiac state synchronization, video frame interpolation is applied to increase the frame rate of the original limited angiographic images, resulting in higher framerate and more adequate roadmaps. The proposed method also incorporates a multi-length cross-correlation based adaptive electrocardiogram (ECG)matching to address irregular cardiac motion situation. Furthermore, a shape-constrained path searching method is proposed to extract catheter structure from both fluoroscopic and angiographic image. Then catheter motion is estimated using a cascaded matching approach with an outlier removal strategy, leading to a final corrected roadmap. Evaluation of the proposed method on clinical x-ray images demonstrates its effectiveness, achieving a 92.8% F1 score for catheter extraction on 589 fluoroscopic and angiographic images. Additionally, the method achieves a 5.6-pixel distance error of the coronary roadmap on 164 intraoperative fluoroscopic images. Overall, the proposed method achieves accurate coronary roadmapping in fluoroscopic images and shows potential to overlay accurate coronary roadmap on fluoroscopic image in assisting PCI.

A novel direct method to [formula omitted] synchronization of switching inertial neural networks with mixed time-varying delays

In this paper, the H∞ synchronization problem of switching inertial neural networks with mixed time-varying delays is discussed. A parameterized system solution-based direct analysis method combined with minimum residence time method is proposed to solve the considered problem. The advantage of this method is that no model transformation or construction of Lyapunov–Krasovskii functional is required, thus reducing the complexity of derivation process. In addition, the resulting synchronization conditions composed of some simple scalar inequalities can be easily solved via MATLAB. Finally, the reliability of the theoretical results is illustrated by numerical simulations.

Accuracy of Volumetric Bone Mineral Density Measurement in Weight Bearing, Cone Beam Computed Tomography

Background: Weight bearing computed tomography (WBCT) utilizes cone beam CT technology to provide assessments of lower limb joint structures while they are functionally loaded. Grey-scale values indicative of X-ray attenuation that are output from cone beam CT are challenging to calibrate, and their use for bone mineral density (BMD) measurement remains debatable. To determine whether WBCT can be reliably used for cortical and trabecular BMD assessment, we sought to establish the accuracy of BMD measurements at the knee using modern WBCT by comparing them to measurements from conventional CT.Methods: A hydroxyapatite phantom with three inserts of varying densities was used to systematically quantify signal uniformity and BMD accuracy across the acquisition volume. We evaluated BMD in vivo (n = 5, female) using synchronous and asynchronous calibration techniques in WBCT and CT. To account for variation in attenuation along the height (z-axis) of acquisition volumes, we tested a height-dependent calibration approach for both WBCT and CT images.Results: Phantom BMD measurement error in WBCT was as high as 15.3% and consistently larger than CT (up to 5.6%). Phantom BMD measures made under synchronous conditions in WBCT improved measurement accuracy by up to 3% but introduced more variability in measured BMD. We found strong correlations (R = 0.96) as well as wide limits of agreement (-324 mgHA/cm3 to 183 mgHA/cm3) from Bland-Altman analysis between WBCT and CT measures in vivo that were not improved by height-dependent calibration.Conclusion: Whilst BMD accuracy from WBCT was found to be dependent on apparent density, accuracy was independent of the calibration technique (synchronous or asynchronous) and the location of the measurement site within the field of view. Overall, we found strong correlations between BMD measures from WBCT and CT and in vivo measures to be more accurate in trabecular bone regions. Importantly, WBCT can be used to distinguish between anatomically relevant differences in BMD, however future work is necessary to determine the repeatability and sensitivity of BMD measures in WBCT.

Practical exponential stability and applications of switched homogeneous nonlinear systems with external inputs and impulses

The practical exponential stability of switched homogeneous nonlinear systems (SHNS) with external inputs and impulses is studied in this paper, which includes two types of continuous-time systems and discrete-time systems. Specifically, we add a kind of nonnegative impulses which is consistent with the switching behaviours for the systems. We first study switched homogeneous positive nonlinear systems (SHPNS) case, then we extend the SHPNS to a time-varying SHNS case. The primary difficulty of this research is the estimation of Lyapunov function value at switching-impulse points under the coupling effects of synchronous switches and state jumps. By employing the max-separable Lyapunov function approach, we present new sufficient stability criteria and design new switching-impulse signals, and successfully establish the new relation between the average dwell time parameter and impulse strength. Then, we apply the obtained results to the time-varying vehicle formation model with impulses. At last, four examples are given to demonstrate the feasibility of our results, which includes the specific algorithm verification process.