Local Synchronization Research Articles

Local synchronization indices for rotors detection in atrial fibrillation: A simulation study

Stable rotors have been proposed as mechanisms that maintain atrial fibrillation which is the most common arrhythmia worldwide. The information of intracardiac electrograms (EGMs), recorded through multielectrode arrays, is used to characterize the electrical conduction dynamics and thus to identify rotors or reentrant propagating waves. Most of the methods of EGMs processing are based on the assessment of the individual properties of each EGM signal. Additionally, synchronization indices have been proposed to evaluate the properties of the conduction patterns by means of multivariate analysis. However, the problem of rotor detection through EGMs remains open. We evaluate the behavior of four local synchronization indices using computational simulations of different conduction patterns and the corresponding EGMs in 2D models. The results show that phase synchronization exhibits better performance than correlation, coherence, and mutual information for detecting rotors under different fibrillatory patterns. We also show that this approach outperforms a previously reported technique based on entropy analysis of individual EGM. Synchronization maps using phase-locking values calculated from adjacent EGM highlight the vicinity of the core of stable rotors, even in the presence of multiple wavefronts and wave breaks. Therefore, phase-locking maps can be a useful tool for characterizing rotors during atrial fibrillation episodes.

An effective multi-level synchronization clustering method based on a linear weighted Vicsek model

To conquer the shortcoming that general clustering methods cannot process big data in the main memory, this paper presents an effective multi-level synchronization clustering (MLSynC) method by using a framework of “divide and collect” and a linear weighted Vicsek model. We also introduce two concrete implementations of MLSynC method, a two-level framework algorithm and a recursive algorithm. MLSynC method has a different process with SynC algorithm, ESynC algorithm and SSynC algorithm. By the theoretic analysis, we find the time complexity of MLSynC method is less than SSynC. Simulation and experimental study on multi-kinds of data sets validate that MLSynC method not only gets better local synchronization effect but also needs less iterative times and time cost than SynC algorithm. Moreover, we observe that MLSynC method not only needs less time cost than ESynC and SSynC, but also almost gets the same local synchronization effect as ESynC and SSynC if the partition of the data set is proper. Further comparison experiments with some classical clustering algorithms demonstrate the clustering effect of MLSynC method.

Polynomial Lyapunov Functions for Synchronization of Nonlinearly Coupled Complex Networks.

In this article, we search for polynomial Lyapunov functions beyond the quadratic form to investigate the synchronization problems of nonlinearly coupled complex networks. First, with a relaxed assumption than the quadratic condition, a synchronization criterion is established for nonlinearly coupled networks with asymmetric coupling matrices. Compared with the existing synchronization criteria, our results are less conservative and have a wider application. Second, the synchronization problem for polynomial networks is characterized as the sum-of-squares (SOS) optimization one. In this way, polynomial Lyapunov functions can be obtained efficiently with SOS programming tools. Furthermore, it is shown that the local synchronization of certain nonpolynomial networks can also be analyzed by using the SOS optimization method through the Taylor series expansion. Finally, three numerical examples are presented to verify the effectiveness and less conservatism of our analytical results.

Spatial patterns of intrinsic brain activity and functional connectivity in facial synkinesis patients

Objectives As one of the most objectionable sequelae of facial paralysis, patients with facial synkinesis are more likely to be depressed and have lower quality of life than other facial paralysis patients. However, there is no research on the spatial patterns of intrinsic brain activity and functional connectivity in these patients. The objective of this study was to investigate the spatial patterns and cerebral plasticity of facial synkinesis patients. Methods A total of 20 facial synkinesis patients (18 men and 2 women; mean age: 33.35 ± 6.97 years old) and 19 healthy controls (17 men and 2 women; mean age: 33.21 ± 6.75 years old) were enrolled in this study. resting-state functional magnetic resonance imaging (rs-fMRI) data were collected, and the amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated for each participant. Two-sample t-tests were performed to compare the ALFF, ReHo, and DC maps between the two groups. Results Compared with the healthy controls, facial synkinesis patients exhibited decreased ALFF in the fusiform gyrus, lingual gyrus, parahippocampal gyrus, triangular inferior frontal gyrus, precentral gyrus, postcentral gyrus, cingulate gyrus, superior frontal gyrus, precuneus, caudate nucleus and thalamus; decreased ReHo in the cingulate gyrus, superior frontal gyrus, insula, superior temporal gyrus, orbital middle frontal gyrus, caudate nucleus and thalamus; and decreased DC in the frontal lobe, insula, cingulate gyrus, superior temporal gyrus, lenticular putamen, hippocampus and parahippocampal gyrus. We found significant overlap in the superior frontal gyrus across the ALFF, ReHo and DC analyses. Conclusions In facial synkinesis patients, the neurological activity in brain areas is reduced and the local synchronization in motion-related brain regions is decreased. The superior frontal gyrus could be a crucial region in the unique spatial patterns of intrinsic brain activity and functional connectivity in these patients.

Development of the Methodology for the Choice of Polygraphy Equipment for Printing on Cloth

The article provides a comparative analysis of the main types of printing on cloth. On the basis of analysis was revealed the meaning of the main stages for each type of printing and was systematized the used printing equipment. There are the main technical characteristics proposed for the selection of printing equipment. The technological and economic criteria for the choice of equipment for printing on cloth are examined in detail. There was carried out identification of the main criteria for the selection of equipment for printing on cloth by an expert assessment of technological and economic criteria with the assistance of operational printing centers’ staff of Ukraine and Kazakhstan. There was developed a computer decision support system “the choice of equipment for printing on cloth” with local data synchronization using the Microsoft Sync Framework.

On the impact of the synchronization constraint and interconnections in quantum-dot cellular automata

Quantum-dot Cellular Automata (QCA) is an emerging nanotechnology with remarkable performance and energy efficiency. Computation and information transfer in QCA are based on field forces rather than electric currents. As a consequence, new strategies are required for design automation approaches in order to cope with the arising challenges. One of these challenges is the transport of information, which is affected by two particularities of the QCA technology. First, information flow in QCA is controlled by external clocks, and second, QCA is a planar technology in which gates, as well as interconnections, are mostly located in the same layer. The former demands proper synchronization already during the circuit design phase, while the latter results in high area costs for interconnections. This work focuses on both constraints and discusses its impact on the implementation of QCA circuits. Further, the concept of local and global synchronicity in QCA circuits is explored. The obtained results indicate that relaxing the global synchronicity constraint can reduce design size by about 70% while the throughput performance declines by similar values. Additionally, it can be shown that the impact of interconnections in QCA, like wires, fan-outs, and crossovers, is indeed substantial. That means, up to 75% of the total area is occupied by interconnections.

Privacy-preserving k-means clustering with local synchronization in peer-to-peer networks

k-means clustering, which partitions data records into different clusters such that the records in the same cluster are close to each other, has many important applications such as image segmentation and genes detection. While the k-means clustering has been well-studied by a significant amount of works, most of the existing schemes are not designed for peer-to-peer (P2P) networks. P2P networks impose several efficiency and security challenges for performing clustering over distributed data. In this paper, we propose a novel privacy-preserving k-means clustering scheme over distributed data in P2P networks, which achieves local synchronization and privacy protection. Specifically, we design a secure aggregation protocol and a secure division protocol based on homomorphic encryption to securely compute clusters without revealing the privacy of individual peer. Moreover, we propose a novel massage encoding method to improve the performance of our aggregation protocol. We formally prove that the proposed scheme is secure under the semi-honest model and demonstrate the performance of our proposed scheme.

Local adaptive pinning synchronisation for distributed secondary control of islanded microgrid

This study introduces a distributed secondary control method-based adaptive pinning control for inverter-based distributed generations (DGs) in an isolated microgrid (MG) structure with frequency and voltage compensation and power sharing. Without MG centre controller or energy management system, local DGs’ controllers have no global information of the whole communication network, which is the second smallest eigenvalue of the Laplacian matrix. In practice, the large coupling weight gains are mostly selected. However, a sufficient large coupling weight may cause instability. It is difficult to choose the coupling weight in the fully distributed complex network. To solve the above problems, the authors investigate a pinning control for synchronisation using the local adaptive pinning algorithm on coupling weight gains. The proposed method has the advantage that the consensus controller design is independent of the Laplacian matrix associated with the communication network. And the collective dynamics of the communication network can be adjusted to steady states without global information. However, the adaptive pinning control scheme is not suitable for the multi-disturbance situation, which will accumulate the coupling weight and even cause integral saturation. Therefore, an improved strategy is proposed to adapt to the load changing conditions for islanded MGs.

Local Synchronization on Asynchronous Tissue P Systems With Symport/Antiport Rules.

Asynchronous tissue P systems with symport/antiport rules are a class of parallel computing models inspired by cell tissue working in a non-synchronized way, where the use of rules is not obligatory, that is, at a computation step, an enabled rule may or may not be applied. In this work, the notion of local synchronization is introduced at three levels: rules, channels, and cells. If a rule in a locally synchronous set of rules (resp., cells or channels) is used, then all enabled rules in the same locally synchronous set of rules (resp., whose involved channels or cells) should be applied in a maximally parallel manner and the implementation of these rules is finished in one computation step. The computational power of local synchronization on asynchronous tissue P systems with symport/antiport rules at the three levels is investigated. It is shown that asynchronous tissue P systems with symport/antiport rules and with locally synchronous sets of rules, channels, or cells are all Turing universal. By comparing the computational power of asynchronous tissue P systems with or without local synchronization, it can be found that the local synchronization is a useful tool to achieve a desired computational power.

Brain Structural and Functional Alterations Specific to Low Sleep Efficiency in Major Depressive Disorder.

BackgroundSleep disturbance is common in patients with major depressive disorder (MDD), but the exploration of its neural underpinnings is limited by subjective sleep measurement and single-modality neuroimaging analyses.MethodsNinety six patients with MDD underwent polysomnography examinations and multi-modal magnetic resonance imaging (MRI) scans. According to sleep efficiency, patients were subdivided into well-matched normal sleep efficiency (NSE, N = 42; 14 men; aged 43 ± 10 years) and low sleep efficiency (LSE, N = 54; 23 men; aged 45 ± 12 years) groups. Inter-group differences in brain structure and function were examined by applying voxel-based morphometry (VBM), regional homogeneity (ReHo) and functional connectivity strength (FCS), and tract-based spatial statistics (TBSS) approaches to structural, functional, and diffusion MRI data, respectively.ResultsThere was no significant difference in gray matter volume (GMV) between the NSE and LSE groups. Compared with the NSE group, the LSE group showed increased axial diffusivity in the left superior and posterior corona radiata, and left posterior limb and retrolenticular part of internal capsule. In addition, the LSE group exhibited decreased ReHo in the bilateral lingual gyri and right postcentral gyrus yet increased FCS in the left angular gyrus relative to the NSE group. Moreover, validation analyses revealed that these results remained after adjusting for the medication effect.ConclusionOur data indicate that preserved gray matter morphology, impaired white matter integrity, and decreased local synchronization degree yet increased FCS are specific to low SE in MDD patients. These findings of disassociation between structural and functional alterations might provide insights into the neural mechanisms of sleep disturbance in depression.

Distributed trajectory tracking and formation control without velocity measurements by the notion of prior bounded local neighborhood synchronization error

This paper investigates the robust consensus tracking and formation control problems of multiple second-order systems having exogenous disturbances and no velocity measurements. To account for the input saturation constraint in controller design, a novel notion of local neighborhood synchronization error is proposed, which is obtained using generalized saturation functions and can be regarded as a nonlinear variation of the well-known linear local neighborhood synchronization error. An important property of the notion is proved and then a continuous distributed controller is designed using it. To improve the robustness of the controller with respect to exogenous disturbance, a disturbance estimator–based design and a simple parameter mapping for parameter tuning are proposed. The resulting error system is proven to be small-signal [Formula: see text] stable and input-to-output stable. In particular, the synchronization errors and tracking errors converge asymptotically to zero if the disturbances converge to some constants. By the parameter mapping, the steady-state synchronization errors and tracking errors can be made arbitrarily small. The control scheme is finally modified to adapt to formation control applications by adding the desired position deviation from the leader’s trajectory. The performance of the scheme is demonstrated by the simulation results.

Global Synchronization of Sampled-Data Invariant Systems on Exponential Lie Groups

We examine the synchronization of networks of identical continuous-time agents on a matrix Lie group, controlled by a discrete-time controller with constant sampling periods and directed, weighted communication graphs with a globally reachable node. We present a smooth, distributed, nonlinear discrete-time control law that achieves global synchronization, for any sampling period, on exponential matrix Lie groups, which include simply connected nilpotent Lie groups as a special case. Synchronization is generally asymptotic, but if the Lie group is nilpotent, then synchronization is achieved at an exponential rate. We first linearize the synchronization error dynamics at identity, and show that the proposed controller achieves local exponential synchronization on any Lie group. Building on the local analysis, we show that if the Lie group is exponential, then synchronization is global. We provide conditions for finite-time synchronization when the communication graph is unweighted and complete.

Change Detection in Complex Dynamical Systems Using Intrinsic Phase and Amplitude Synchronization

We present an approach for the detection of sharp change points (short-lived and persistent) in nonlinear and nonstationary dynamic systems under high levels of noise by tracking the local phase and amplitude synchronization among the components of a univariate time series signal. The signal components are derived via Intrinsic Time scale Decomposition (ITD)–a nonlinear, non-parametric analysis method. We show that the signatures of sharp change points are retained across multiple ITD components with a significantly higher probability as compared to random signal fluctuations. Theoretical results are presented to show that combining the change point information retained across a specific set of ITD components offers the possibility of detecting sharp transitions with high specificity and sensitivity. Subsequently, we introduce a concept of mutual agreement to identify the set of ITD components that are most likely to capture the information about dynamical changes of interest and define an InSync statistic to capture this local information. Extensive numerical, as well as real-world case studies involving benchmark neurophysiological processes and industrial machine sensor data, suggest that the present method can detect sharp change points, on an average 62% earlier (in terms of average run length) as compared to other contemporary methods tested.

Research on An Improved Cluster Average Clock Synchronization Method Based on ACATS Algorithm

At present, the real-time data of electric power measurement are not the same cross-section data, which have a large error and affect the analysis effect of various advanced application software. When the power grid fails, such analysis results may cause decision-making errors, thus threatening the safe and stable operation of the power grid. The ACATS algorithm is adopted in the invention, taking into consideration the phase offset, frequency drift and random delay in the process of information transmission. Inspired by the algorithm, the monitoring idea is used to improve the local average clock synchronization method of the nodes in the cluster in the algorithm, reducing the number of information exchanges required in the same step process of the nodes in the cluster, and taking into account the phase offset, frequency drift, and The random delay in the process of information transmission is of great significance to the practical network.

Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

Background and ObjectiveRecently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject’s field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears). MethodsTwo high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method. ResultsThe data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min. ConclusionThese findings contribute to the development of more safe and practical BCI.

A local external coupling matrix solution and dynamic processing in medical cyber-physical cloud systems

Abstract Medical cyber-physical cloud systems (MCPCS) are new generation medical cyber-physical systems (MCPS), which introduce cloud computing to integrate multiple MCPS to improve quality of service (QoS) and expand service scope in hospitals. When used in large-scale distributed environment, MCPCS need to perform a stability analysis to guarantee collaboration among multiple MCPS. In this paper, we propose a local topology search algorithm (LTS) to obtain the local external coupling matrices, which are important input parameters for the local synchronization stability analysis of MCPCS. LTS generates an optimal multi-fork tree from the source end MCPS to multiple destination end MCPS. Based on the relationship between nodes in the optimal multi-fork tree, the local external coupling matrices applied to the local synchronization stability analysis of MCPCS are then solved. Considering the variability of network topology, we further propose a dynamic processing method called external coupling matrix change processing (ECMCP) to ensure the real-time update of the local external coupling matrices and dimensional consistency. Experimental results show that the proposed solution of LTS & ECMCP has lower costs of nodes and their communications than existing method.

LBS: A Beacon Synchronization Scheme With Higher Schedulability for IEEE 802.15.4 Cluster-Tree-Based IoT Applications

The IEEE 802.15.4 standard is one of the most widely used link layer technology for building Internet of Things (IoT). It specifies several physical layer options and MAC layer for meeting low-power and low-rate requirements of devices deployed in a network of IoT. The standard also specifies a synchronization scheme for devices connected in a star topology, operating in beacon-enabled (BE) mode using periodic beacons. The BE mode facilitates synchronization among devices for data transmission and is suitable for large networks to establish low duty-cycles. Absence of a such a scheme for a cluster-tree network has confined its application only to nonbeacon mode. The challenge here is to schedule beacon frame transmissions of multiple devices in a nonoverlapping manner to avoid beacon collisions. This paper tackles the problem of synchronization by proposing localized beacon synchronization (LBS) scheme, a distributed technique for beacon scheduling in cluster-tree network topologies. LBS uses 2-hop information and association order to compute beacon transmission offsets that better utilize the available time slots, incur fewer transmissions, and is highly scalable. Further, we analytically show that the schedulability of the proposed scheme is higher compared to other related schemes. In addition, we also address the important issue of resynchronization that has been ignored in all of the prior works. The proposed resynchronization mechanisms consider the interdependencies between synchronization and duty-cycling schemes and are shown to significantly lower the synchronization overhead when synchronization among devices is lost.

Spatiotemporal Oscillatory Patterns During Working Memory Maintenance in Mild Cognitive Impairment and Subjective Cognitive Decline.

Working memory (WM) is a crucial cognitive process and its disruption is among the earliest symptoms of Alzheimer's disease. While alterations of the neuronal processes underlying WM have been evidenced in mild cognitive impairment (MCI), scarce literature is available in subjective cognitive decline (SCD). We used magnetoencephalography during a WM task performed by MCI , SCD and healthy elders to examine group differences during the maintenance period (0-4000ms). Data were analyzed using time-frequency analysis and significant oscillatory differences were localized at the source level. Our results indicated significant differences between groups, mainly during the early maintenance (250-1250ms) in the theta, alpha and beta bands and in the late maintenance (2750-3750ms) in the theta band. MCI showed lower local synchronization in fronto-temporal cortical regions in the early theta-alpha window relative to controls and SCD , and in the late theta window relative to controls and SCD . Early theta-alpha power was significantly correlated with memory scores and late theta power was correlated with task performance and functional activity scores . In the early beta window, MCI showed reduced power in temporo-posterior regions relative to controls and SCD . Our results may suggest that these alterations would reflect that memory-related networks are damaged.

Frequency-specific oscillations synchronization in primary angle-closure glaucoma.

Background Previous neuroimaging studies demonstrated that patients with primary angle-closure glaucoma were accompanied by abnormal neuronal activity. Purpose To investigate frequency-dependent local oscillations synchronization in primary angle-closure glaucoma using the regional homogeneity method. Material and Methods In total, 37 individuals with primary angle-closure glaucoma (20 men, 17 women) and 37 normal-sighted controls (20 men, 17 women) closely matched in age, sex, and education underwent resting-state MRI scans. We compared the different regional homogeneity values in full band (0.01–0.08 Hz) and two different frequency bands (slow-4: 0.027–0.073 Hz and slow-5: 0.010–0.027 Hz) between two groups. Results Compared to the normal-sighted group, the primary angle-closure glaucoma group showed decreased regional homogeneity values in the left calcarine and left postcentral in full band. The primary angle-closure glaucoma group showed increased regional homogeneity values in the bilateral superior medial frontal lobe in the slow-4 band. The primary angle-closure glaucoma group exhibited decreased regional homogeneity values in the right calcarine in the slow-5 band. Specifically, we found that the regional homogeneity values in the right superior frontal lobe were greater in the slow-4 than in the slow-5 band, whereas regional homogeneity in the left calcarine, right pallidum, left inferior occipital gyrus, left superior occipital gyrus, left postcentral/angular gyrus, left paracentral lobule, left superior parietal gyrus, and right precuneus gyrus were greater in the slow-5 than in the slow-4 band. Conclusion Primary angle-closure glaucoma groups showed abnormal regional homogeneity in visual network (calcarine) and default mode network (superior medial frontal lobe) at two frequency bands. Moreover, the regional homogeneity signals in slow-5 band showed closely related to the severity of individuals with primary angle-closure glaucoma.

Acute Focal Seizures Start As Local Synchronizations of Neuronal Ensembles.

Understanding seizure formation and spread remains a critical goal of epilepsy research. We used fast in vivo two-photon calcium imaging in male mouse neocortex to reconstruct, with single-cell resolution, the dynamics of acute (4-aminopyridine) focal cortical seizures as they originate within a spatially confined seizure initiation site (intrafocal region), and subsequently propagate into neighboring cortical areas (extrafocal region). We find that seizures originate as local neuronal ensembles within the initiation site. This abnormal hyperactivity engages increasingly larger areas in a saltatory fashion until it breaks into neighboring cortex, where it proceeds smoothly and is then detected electrophysiologically (LFP). Interestingly, PV inhibitory interneurons have spatially heterogeneous activity in intrafocal and extrafocal territories, ruling out a simple role of inhibition in seizure formation and spread. We propose a two-step model for the progression of focal seizures, where neuronal ensembles activate first, generating a microseizure, followed by widespread neural activation in a traveling wave through neighboring cortex during macroseizures.SIGNIFICANCE STATEMENT We have used calcium imaging in mouse sensory cortex in vivo to reconstruct the onset of focal seizures elicited by local injection of the chemoconvulsant 4-aminopyridine. We demonstrate at cellular resolution that acute focal seizures originate as increasingly synchronized local neuronal ensembles. Because of its spatial confinement, this process may at first be undetectable even by nearby LFP electrodes. Further, we establish spatial footprints of local neural subtype activity that correspond to consecutive steps of seizure microprogression. Such footprints could facilitate determining the recording location (e.g., inside/outside an epileptogenic focus) in high-resolution studies, even in the absence of a priori knowledge about where exactly a seizure started.