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This article is dedicated to investigating the impulsive-based almost surely synchronization issue of neural network systems (NSSs) with quality-of-service constraints. First, the communication network considered suffers from random double deception attacks, which are modeled as a nonlinear function and a desynchronizing impulse sequence, respectively. Meanwhile, the impulsive instants and impulsive gains are randomly and only their expectations are available. Second, by taking two different types of random deception attacks into consideration, a novel mathematical model for vulnerable NSSs is constructed. Then, almost surely synchronization criteria are established by using Borel-Cantelli lemma. Furthermore, based on the derived strong and weak sufficient conditions, the almost surely synchronization of NSSs is achieved. Finally, the section of numerical example is shown to illustrate the effectiveness of the proposed method.

The consensus problem is relevant to different areas ranging from biology, social psychology, and physics to power systems and robotics. Two crucial aspects of the design of a consensus system are the implementation issues that arise in densely connected networks and the presence of malicious agents that try to cause a deviation from a synchronization state. In this article, we introduce a formulation to design the topology of a consensus network to improve its resilience to attacks while remaining sparse and consistent with the a priori structural relations between the agents. Through mathematical analysis and simulations on artificial and real-world cases, we show the benefits and usefulness of using this strategy to design resilient and structurally sparse consensus networks.

The emerging Federated Learning (FL) enables IoT devices to collaboratively learn a shared model based on their local datasets. However, due to end devices heterogeneity, it will magnify the inherent synchronization barrier issue of FL and result in non-negligible waiting time when local models are trained with the identical batch size. Moreover, the useless waiting time will further lead to a great strain on devices limited battery life. Herein, we aim to alleviate the negative impact of synchronization barrier through adaptive batch size during model training. When using different batch sizes, stability and convergence of the global model should be enforced by assigning appropriate learning rates on different devices. Therefore, we first study the relationship between batch size and learning rate, and formulate a scaling rule to guide the setting of learning rate in terms of batch size. Then we theoretically analyze the convergence rate of global model and obtain a convergence upper bound. On these bases, we propose an efficient algorithm that adaptively adjusts batch size with scaled learning rate for heterogeneous devices to reduce the waiting time and save battery life. We conduct extensive simulations and testbed experiments, and the experimental results demonstrate the effectiveness of our method.

This article investigates the synchronization of communication-constrained complex dynamic networks subject to malicious attacks. An observer-based controller is designed by virtue of the bounded encode sequence derived from an improved coding-decoding communication protocol. Moreover, taking the security of data transmission into consideration, the denial-of-service attacks with the frequency and duration characterized by the average dwell-time constraint are introduced into data communication, and their influence on the coder string is analyzed explicitly. Thereafter, by imposing reasonable restrictions on the transmission protocol and the occurrence of attacks, the boundedness of coding intervals can be obtained. Since the precision of data is generally limited, it may lead to the situation that the signal to be encoded overflows the coding interval such that it results in the unavailability of the developed coding scheme. To cope with this problem, a dynamic variable is introduced to the design of the protocol. Subsequently, based on the Lyapunov stability theory, sufficient conditions for ensuring the input-to-state stability of the synchronization error systems under the communication-constrained condition and malicious attacks are presented. The validity of the developed method is finally verified by a simulation example of chaotic networks.\enlargethispage-8pt.

Machine learning (ML) models are increasingly trained with distributed workers possessing heterogeneous resources. In such scenarios, model training efficiency may be negatively affected by \emph{stragglers}---workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy, called \emph{semi-dynamic load balancing}, to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at \emph{iteration boundaries}, being non-intrusive to intra-iteration execution. Based on it we further develop LB-BSP, an integrated worker coordination mechanism that adapts workers' load to their instantaneous processing capabilities---by right-sizing the sample batches at the synchronization barriers. We have designed distinct load tuning algorithms for ML in CPU clusters, in GPU clusters as well as in federated learning setups, based on their respective characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to $54\%$ .

In a time where data traffic is booming, Software-defined Networking (SDN) is becoming the most plausible solution to cope with the conventional networks shortcomings. The separation of the control (software) and the data (hardware) planes makes of SDN a technology-enabled to a multitude of new and promising applications and use cases for 5G network technology. Yet, initial SDN deployments considered having a single controller for the entire network. However, this arrangement is deemed to be counterproductive in large-scale and ultra-reliable networks based SDN as the controller might become the systems bottleneck. To address this issue, several multi-controller architectures were proposed. They are of three types: flat, hierarchical or hybrid. For theses architectures to be potent for 5G core networks, efficient path computation and flow tables update mechanisms are needed. In this paper, we propose HybCon, a hybrid SDN-based distributed Cloud architecture having the control plane distributed over different controller types (i.e., Fog, Edge, SDN). When a path computation request is received, HybCon involves some/all of the controllers and uses a multi-priority queueing model along with node parallelism to expedite the path computation and to cut down the synchronization overhead.

Federated Learning (FL) is a promising machine learning paradigm to cooperatively train a global model with highly distributed data located on mobile devices. Aiming to optimize the communication efficiency for gradient aggregation and model synchronization among large-scale devices, we propose a relay-assisted FL framework. By breaking the traditional transmission-order constraint and exploiting the broadcast characteristic of relay nodes, we design a novel synchronization scheme named Partial Synchronization Parallel (PSP), in which models and gradients are transmitted simultaneously and aggregated at relay nodes, resulting in traffic reduction. We prove that PSP has the same convergence rate as the sequential synchronization approaches via rigorous analysis. To further accelerate the training process, we integrate PSP with any unbiased and error-bounded compression technologies and prove that the convergence properties of the resulting scheme still hold. Extensive experiments are conducted in a distributed cluster environment with real-world datasets and the results demonstrate that our proposed approach reduces the training time up to 37\% compared to state-of-the-art methods.

This article investigates the synchronization problem of interconnected linear two-time-scale systems (TTSSs) with switching topology. By utilizing the Chang transformation, a distributed synchronization protocol is proposed with event-triggered communication. Static and dynamic event-triggered mechanisms are proposed successively, which both contain two separated event-triggering conditions corresponding to the slow and the fast subsystems. The existence of a strictly positive time period between any two successive transmissions is ensured regardless of the initial states. The main difficulty of this study lies in that the state jump and parametric uncertainty appear because of the system transformation. To overcome the difficulty, the system is first modeled as an uncertain hybrid system. Then, the control gain is properly designed by solving Riccati-like equations dependent on the rough bounds of the eigenvalues of communication graph Laplacians, and a piecewise quadratic Lyapunov function is proposed with which the jump caused by the switching topology is subtly evaluated. Sufficient conditions are thus established to achieve the event-triggered synchronization. Furthermore, the results are also extended to solve the synchronization problem of the interconnected impulsive linear TTSSs. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

Process mining aims at discovering behavioral knowledge of business processes from their event logs, which has received an increasing attention in the era of cloud computing and big data. Surprisingly, to date, discovering structural errors (e.g., deadlocks and lack of synchronization) from event logs has not been considered in state-of-the-art process mining techniques. Moreover, existing process discovery approaches cannot be directly applied to event logs of processes with structural errors due to erroneous event occurrences caused by unsynchronized activities. To address this problem, we first preprocess the event log to obtain two separate event logs that are used to discover deadlocks and lack of synchronization, respectively. Erroneous event occurrences caused by unsynchronized activities are discarded in the two processed event logs, from which our error mining algorithms can discover all process fragments involving structural errors, without the need to obtain the overall process first. We implement our approach in a ProM plugin and evaluate it on event logs of real-life business processes, the results of which demonstrate that our approach can effectively and efficiently discover deadlocks and lack of synchronization if event logs contain sufficient event sequences.

The present study focuses on designing the memory-based sampled data control approach to achieve the desired performance from switched-type of neural networks. The overall contribution of the study can be viewed from two directions. The first part is to derive the theoretical-based sufficient stability conditions that ensure the synchronization of uncontrolled and controlled systems. The idea behind the synchronization analysis is because of time complexity and computational cost. Once, we derive the closed-loop system then user can employ them in any practical application without interruptions. The existence of time-delays during the information transmission is inevitable, and in a practical sense, the delays, which are a function of time are well-effective. Besides, the sufficient conditions are derived in terms of solvable linear matrix inequalities (LMIs), which can be solved to ensure the global asymptotical stability of the error system, derived from uncontrolled–controlled systems. Technically, the work integrates with the new type of discontinuous Lyapunov–Krasovskii functional candidate with integral terms, several delay-dependent stability conditions, and Wirtinger-based integral inequality. The second part works on utilizing the controlled system into a real-life application, say, encryption/decryption process. To an evident, the proposed controlled system is considered as crypto-system in the encryption/decryption process.

This article considers the fully distributed leaderless synchronization in a complex network by only utilizing local neighboring information to design and tune the coupling strength of each node such that the synchronization problem can be solved without involving any global information of the network. For an undirected network, a fully distributed synchronization algorithm is presented to adjust the coupling strength of each node based on a simple adaptive law. When the topology of a network is directed, two different types of adaptive algorithms are developed to achieve synchronization in a fully distributed manner, where the coupling strength of each node is designed to be either the sum or product of two non-negative scalar functions. The fully distributed leaderless synchronization of a directed network is investigated in a leader-follower framework, where the leader subnetwork is analyzed by using the techniques from constrained Rayleigh quotients and the follower subnetwork is addressed by employing the properties of nonsingular M -matrices. Simulations are given to illustrate the theoretical results.

This article aims at addressing the transient bipartite synchronization problem for cooperative-antagonistic multiagent systems with switching topologies. A distributed iterative learning control protocol is presented for agents by resorting to the local information from their neighbor agents. Through learning from other agents, the control input of each agent is updated iteratively such that the transient bipartite synchronization can be achieved over the targeted finite horizon under the simultaneously structurally balanced signed digraph. To be specific, all agents finally have the same output moduli at each time instant over the desired finite-time interval, which overcomes the influences caused by the antagonisms among agents and topology nonrepetitiveness along the iteration axis. As a counterpart, it is revealed that the stability can be achieved over the targeted finite horizon in the presence of a constantly structurally unbalanced signed digraph. Simulation examples are carried out to demonstrate the effectiveness of the distributed learning results developed among multiple agents.

Roadside sensors, such as camera and millimeter-wave (MMW) radar, provide traffic information beyond the visual range of intelligent vehicles in cooperative vehicle-infrastructure systems. Unlike onboard equipment, roadside sensors are affiliated with different systems and lack synchronization in both space and time. In this paper, we propose a novel spatio-temporal synchronization method of asynchronous roadside MMW radar-camera for sensor fusion, which utilizes features of the scenario to extract lane line corner points to pre-calibrate the camera. Based on the consistent time flow rate of the separate sensors, multiple virtual detection lines are set up to match the time headway of successive vehicles and conduct objective matching to track data. Finally, a synchronization optimization model is formulated and a constrained nonlinear minimization solver is applied to tune the parameters. Measure data from Donghai Bridge in Shanghai is applied to verify the feasibility and effectiveness of the method. The results determine that there are 33 frames (33*40 ms) of temporal deviation between the camera and the radar in this case. After the synchronization, the average spatial deviation is reduced from 2.47 m to 0.42 m in the X-direction and 64.06 m to 2.34 m in the Y-direction, respectively. This study provides an economical and effective way to solve the problem of spatio-temporal synchronization of roadside sensors.

In the context of streaming data, learning algorithms often need to confront several unique challenges, such as concept drift, label scarcity, and high dimensionality. Several concept drift-aware data stream learning algorithms have been proposed to tackle these issues over the past decades. However, most existing algorithms utilize a supervised learning framework and require all true class labels to update their models. Unfortunately, in the streaming environment, requiring all labels is unfeasible and not realistic in many real-world applications. Therefore, learning data streams with minimal labels is a more practical scenario. Considering the problem of the curse of dimensionality and label scarcity, in this article, we present a new semisupervised learning technique for streaming data. To cure the curse of dimensionality, we employ a denoising autoencoder to transform the high-dimensional feature space into a reduced, compact, and more informative feature representation. Furthermore, we use a cluster-and-label technique to reduce the dependency on true class labels. We employ a synchronization-based dynamic clustering technique to summarize the streaming data into a set of dynamic microclusters that are further used for classification. In addition, we employ a disagreement-based learning method to cope with concept drift. Extensive experiments performed on many real-world datasets demonstrate the superior performance of the proposed method compared to several state-of-the-art methods.

In this paper, the authors investigate the synchronization criteria of fuzzy impulsive sampled data control for Hindmarsh–Rose (H–R) neuronal system with time-delay. For the stability analysis, Lyapunov–Krasovskii functionals (LKF) are employed to deduce the conditions that guarantee the asymptotical stability of the proposed T–S fuzzy H–R neuron system. By utilizing free-matrix based inequality, some sufficient conditions are derived and expressed in terms of linear matrix inequalities. The obtained sufficient conditions can be checked easily by standard available software packages in MATLAB. Finally, some numerical simulations are given to validate the effectiveness of the proposed conditions.

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR. Firstly, we propose a heuristic mechanism for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Secondly, we introduce a motion-aware key frame selection mechanism to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

This article investigates the stability and synchronization of nonautonomous reaction-diffusion neural networks with general time-varying delays. Compared with the existing works concerning reaction-diffusion neural networks, the main innovation of this article is that the network coefficients are time-varying, and the delays are general (which means that fewer constraints are posed on delays; for example, the commonly used conditions of differentiability and boundedness are no longer needed). By Green's formula and some analytical techniques, some easily checkable criteria on stability and synchronization for the underlying neural networks are established. These obtained results not only improve some existing ones but also contain some novel results that have not yet been reported. The effectiveness and superiorities of the established criteria are verified by three numerical examples.

This article investigates the fixed-time synchronization problem of a class of delayed complex-valued inertial neural networks (CVINNs). The analysis does not rely on the traditional reduced-order transformation, but constructing Lyapunov functions directly focused on the original system. Based on the direct method and the separation method, different control strategies are proposed, under which the addressed CVINNs can achieve synchronization perfectly in a fixed time. The corresponding synchronization criteria in terms of matrix inequalities are derived, which are more concise and easier to verify than algebraic inequalities conditions, and the estimation of the settling times. The direct method makes full use of some innovative inequalities in the complex field; the exponential parameters in the designed controllers are independent. Finally, in order to fully support the theoretical results, based on two typical activation functions, the proposed theoretical results are numerically validated and compared.

Grid synchronization stability is an important research topic for grid-forming converters. To prevent instability and undesired oscillations, the small-signal modelling and stability analysis must be performed, which require the line impedance information. Nevertheless, the line impedance may be unknown in most cases. As a consequence, the designed control parameters may fail to satisfy the stability requirement. To fill in this research gap, this paper presents a geometrical approach to evaluate and improve the small-signal synchronization stability of grid-forming converters. The proposed analytical approach maps the line impedance variation on a two-dimension plane and identifies the stability boundary in a geometrical way. As a benefit, the controller of grid-forming converters can be explicitly designed to achieve the robust synchronization stability. Finally, the feasibility of the proposed technique is verified by simulation and hardware experimental results.

Fuzzing has become an essential means of finding software bugs. Bug finding through fuzzing requires two partsexploring code paths to reach bugs and exposing bugs when they are reached. Existing fuzzing research has primarily focused on improving code coverage but not on exposing bugs. Sanitizers such as AddressSanitizer (ASAN) and MemorySanitizer (MSAN) have been the dominating tools for exposing bugs. However, sanitizer-based bug exposing has the following limitations. (1) sanitizers are not compatible with each other. (2) sanitizers incur significant runtime overhead. (3) sanitizers may generate false positives, and (4) exposed bugs may not be exploitable. To address these limitations, we propose EXPOZZER, a fuzzing system that can expose bugs comprehensively, efficiently, and faithfully. The intuition of EXPOZZER is to detect bugs through divergences in a properly diversified dual-execution environment, which does not require maintaining or checking execution metadata. We design a practical and deterministic dual-execution engine, a co-design for dual-execution and fuzzers, bug-sensitive diversification, comprehensive and efficient divergence detection to ensure the effectiveness of EXPOZZER. The results of evaluations show that EXPOZZER can detect not only CVE-assigned vulnerabilities reliably, but also new vulnerabilities in well-tested real-world programs.EXPOZZER is 10 times faster than MemorySanitizer and is similar to AddressSanitizer.

This article studies the pinning synchronization problem with edge-based decentralized adaptive schemes under link attacks. The link attacks considered here are a class of malicious attacks to break links between neighboring nodes in complex networks. In such an insecure network environment, two kinds of edge-based decentralized adaptive update strategies (synchronous and asynchronous) on coupling strengths and gains are designed to realize the security synchronization of complex networks. Moreover, by virtue of the edge pinning technique, the corresponding secure synchronization problem is considered under the case where only a small fraction of coupling strengths and gains is updated. These designed adaptive strategies do not require any global information, and therefore, the obtained results in this article are developed in a fully decentralized framework. Finally, a numerical example is provided to verify the availability of the achieved theoretical outcomes.

With the growing effort to reduce power consumption in machines, fault tolerance becomes more of a concern. This holds particularly for large-scale computing, where execution failures due to soft faults waste excessive time and resources. These large-scale applications are normally parallel in nature and rely on control structures tailored specifically for parallel computing, such as locks and barriers. While there are many studies on resilient software, to our knowledge none of them focus on protecting these parallel control structures. In this work, we present a method of ensuring the correct operation of both locks and barriers in parallel applications. Our method tracks the memory locations used within parallel sections and detects a violation of the control structures. Upon detecting any violation, the violating thread is rolled back to the beginning of the structure and reattempts it, similar to rollback mechanisms in transactional memory systems. We test the method on representative samples of the BigDataBench kernels and find it exhibits a mean error reduction of 93.5% for basic mutex locks and barriers with a mean 3.8% overhead at 12 threads. Additionally, we provide a comparison to transactional memory methods and demonstrate up to 26% overhead reduction.

The satellite component is recognized as a promising solution to complement and extend the coverage of future Internet of things (IoT) terrestrial networks (TNs). In this context, a study item to integrate satellites into narrowband-IoT (NB-IoT) systems has been approved within the 3rd Generation Partnership Project (3GPP) standardization body. However, as NB-IoT systems were initially conceived for TNs, their basic design principles and operation might require some key modifications when incorporating the satellite component. These changes in NB-IoT systems, therefore, need to be carefully implemented in order to guarantee a seamless integration of both TN and non-terrestrial network (NTN) for a global coverage. This paper addresses this adaptation for the random access (RA) step in NB-IoT systems, which is in fact the most challenging aspect in the NTN context, for it deals with multi-user time-frequency synchronization and timing advance for data scheduling. In particular, we propose an RA technique which is robust to typical satellite channel impairments, including long delays, significant Doppler effects, and wide beams, without requiring any modification to the current NB-IoT RA waveform. Performance evaluations demonstrate the proposal’s capability of addressing different NTN configurations recently defined by 3GPP for the 5G new radio system.

Synchronization control is an essential technique in networked multi-axis motion systems (NMAMSs). However, the compound disturbances caused by the delay-induced uncertainty and external disturbances such as system coupling and load mutation have great influence on performance of the motion control systems. The purpose of this paper is to propose an equivalent-input-disturbance (EID) Based position synchronization control strategy for NMAMSs to solve the influence of compound disturbances on synchronization control performance in NMAMSs. First, a position synchronization error model is established for the NMAMSs, and the delay-induced uncertainty is treated as a network disturbance (ND). Next, the EID is designed to suppress the overall effect. Then, an EID-based synchronization controller is designed to achieve both position synchronization and disturbance rejection. The effect of the compound disturbances in the synchronization performance is significantly reduced. Finally, simulations and experiments on a position synchronization control platform of a four-motor system are presented to demonstrate the effectiveness and superiority of the proposed method.

A novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC) emerges recently, which integrates Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies. It enables mobile clients to both extend their computing capacities by task offloading, and charge from edge servers via energy transmission. Existing studies generally focus on the centralized design of task scheduling and energy charging in WP-MEC networks. To meet the decentralization requirement of the near-coming 6G network, we propose an online learning algorithm for computation offloading in WP-MEC networks with a distributed execution manner. Specifically, we first define the delay minimization problem by considering task deadline and energy constraints. Then, we transform it into a primal-dual optimization problem based on the Bellman equation. After that, we design a novel neural model that learns both offloading and time division decisions in each time slot to solve the formulated optimization problem. To train and execute the designed algorithm distributivity, we form multiple learning models decentralized on edge servers and they work coordinately to achieve parameter synchronization. At last, both theoretical and performance analyses show that the designed algorithm has significant advantages in comparison with other representative schemes.

This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given.

This article focuses on a dissipativity and synchronization control method for a class of delayed quaternion-valued memristor-based recurrent neural networks. On the basis of Lyapunov functionals, analytical techniques, and novel control algorithms, sufficient conditions are established to achieve dissipativity and synchronization of fuzzy quaternion-valued memristive systems. Moreover, the lexicographical order method is introduced, and can be used to determine the “magnitude” of two different vectors. Finally, corresponding simulation results are provided to show the effectiveness of the proposed method.

In this article, a periodic self-triggered impulsive (PSTI) control scheme is proposed to achieve synchronization of neural networks (NNs). Two kinds of impulsive gains with constant and random values are considered, and the corresponding synchronization criteria are obtained based on tools from impulsive control, event-driven control theory, and stability analysis. The designed triggering protocol is simpler, easier to implement, and more flexible compared with some previously reported algorithms as the protocol combines the advantages of the periodic sampling and event-driven control. In addition, the chaotic synchronization of NNs via the presented PSTI sampling is further applied to encrypt images. Several examples are also utilized to illustrate the validity of the presented synchronization algorithm of NNs based on PSTI control and its potential applications in image processing.

The issues of complete synchronization (CS) and finite-time synchronization (F-TS) for a class of fractional-order fuzzy neural networks are addressed based on nonlinear feedback control in this paper. First, a fractional-order finite-time convergence principle is established by virtue of fractional calculus basic theory and reduction to absurdity. Next, two novel nonlinear controllers, namely the adaptive nonlinear controller and discontinuous nonlinear controller, are designed. Then some easily validated criteria to guarantee CS and F-TS are derived with the help of some useful analysis techniques and our newly established convergence principle. Moreover, the settling time of F-TS is effectively estimated. Finally, some numerical results are presented to show the validity of derived theoretical results.

Federated Learning (FL) serves privacy-preserving collaborative learning among multiple isolated parties, while retaining their privacy data locally. Cross-device and cross-silo FL have achieved great success in cross-domain applications, in which the scarce communication resource is the primary bottleneck. Driven by the need to combine heterogeneous machines from different parties to build a shared data center, we found intra-domain FL, a new type of FL in which isolated parties collaborate in the shared data center, and strong computational heterogeneity becomes the primary bottleneck. To mitigate the training inefficiency caused by stragglers, this paper proposes an efficient synchronization algorithm ESync, which allows parties to train different iterations locally under the coordination of a novel scheduler State Server. We give the boundaries of weight divergence and optimality gap of ESync, and analyze the trade-off between convergence accuracy and communication efficiency. Extensive experiments are conducted to compare ESync with SSGD, ASGD, DC-ASGD, FedAvg, FedAsync, TiFL and FedDrop under strong computational heterogeneity. Numerical results show that ESync achieves great speed up without loss of accuracy, and therefore demonstrate the effectiveness of ESync in both training efficiency and converged accuracy.

This paper investigates the heterogeneous impulsive synchronization for T-S fuzzy probabilistic coupled delayed neural networks (CDNNs) with mode-dependent parameters and piecewise membership functions. To begin with, a novel CDNNs model considering probabilistic coupling delays and adjustable coupling strength is designed to ensure the accuracy of the CDNNs model and the reliability of synchronization analysis. Meanwhile, a generalized isolated node (regarded as a neural network) with four types of mismatched parameters, named heterogeneous isolated delayed neural network (HIDNN), is firstly considered to extend the synchronization problem. Then, the mode-dependent fuzzy rules are introduced to design the novel model, which implies that switching signals and fuzzy processes are interdependent and can share information to communicate. To improve the hybrid controller's reliability, the mode-dependent impulses are also developed here, which the impulsive effects with different properties can occur at any moment in the switching interval. The exponential synchronization conditions are derived by means of the methods of auxiliary state variables, Lyapunov-Krasovskill functional, average switching interval, and mode-dependent average impulsive interval (MAII). Moreover, the improved mode-dependent piecewise approximated membership functions (IMPAMFs) are proposed to reduce the main results' conservatism. Finally, a numerical example is provided to illustrate the effectiveness of the main results.

Computer vision systems have become a key element of modern companies. However, its implantation not only requires specialized hardware components but also software to control the correct performance of all these components. The purpose of this article is to present the design and deployment of a software created to manage the different computer vision systems of any company. First, using our experience in mechanization and industrialization of company processes, a general company specification will be proposed as well as the main software requirements that have to be satisfied. Then, a three-level modular design composed of the core, a configuration module, and user interfaces with functionalities capable of satisfying the defined requirements will be presented. Special attention will be given to time restrictions and components' synchronization. In addition, the different tests that have been carried out to control the correct performance of the software will be shown. The development process will end with a generic, modular, and scalable software able to fit different industrial scenarios by simply modifying a set of input parameters. To illustrate the correct performance of the proposal, the details of its installation in four real companies with different needs will be presented. The proposed work has a practical use in industry and it also provides a thorough description of the main components involved in computer vision systems of real company environments and how to manage them.

With the development of computer architecture, even for embedded systems, GPU devices can be integrated, providing outstanding performance and energy efficiency to meet the requirements of different industries, applications, and deployment environments. Data analytics is an important application scenario for embedded systems. Unfortunately, due to the limitation of the capacity of the embedded device, the scale of problems handled by the embedded system is limited. In this paper, we propose a novel data analytics method, called G-TADOC, for efficient text analytics directly on compression on embedded GPU systems. A large amount of data can be compressed and stored in embedded systems, and can be processed directly in the compressed state, which greatly enhances the processing capabilities of the systems. Particularly, G-TADOC has three innovations. First, a novel fine-grained thread-level workload scheduling strategy for GPU threads has been developed, which partitions heavily-dependent loads adaptively in a fine-grained manner. Second, a GPU thread-safe memory pool has been developed to handle inconsistency with low synchronization overheads. Third, a sequence-support strategy is provided to maintain high GPU parallelism while ensuring sequence information for lossless compression. Moreover, G-TADOC involves special optimizations for embedded GPUs, such as utilizing the CPU-GPU shared unified memory. Experiments show that G-TADOC provides 13.2× average speedup compared to the state-of-the-art TADOC. G-TADOC also improves performance-per-cost by 2.6× and energy efficiency by 32.5× over TADOC.

This article concentrates on the synchronization of discrete-time persistent dwell-time (PDT) switched bidirectional associative memory inertial neural networks with time-varying delays. Through the use of the switched system theory related to the PDT, the convex optimization technique together with some straightforward decoupling methods, an appropriate mode-dependent controller with nonfragility is developed to acclimatize itself to some practical circumstances. Simultaneously, sufficient conditions of ensuring the performance and exponential stability for the resulting switched synchronization error system are derived. Finally, a numerical example is utilized to show the validity of the model constructed and the influence of the PDT on the performance. In addition, an image encryption example is employed to show the potential application prospect of the investigated system.

In networks of identical linear oscillators (e.g. pendulums undergoing small vibrations) coupled through both dissipative connectors (e.g. dampers) and restorative connectors (e.g. springs) the relation between asymptotic synchronization and coupling structure is studied. Conditions on the interconnection under which synchronization can be achieved for some selection of coupling strengths are established. How to strengthen those conditions so that synchronization is guaranteed for all admissible parameter values is also presented.

In this research, the fuzzy pinning adaptive event-triggered control (FPAETC) problem related to complex networked systems (CNSs) is investigated. In light of the fact that the underlying controllers are fuzzy pinning, and the threshold parameters can flexibly update their states based on the latest and previous sampled signals rather than transmitted signals, a novel state-dependent FPAETC protocol is devised to reduce the frequency of event-triggering and save more communication resources. Moreover, a more general time-dependent AETC protocol is proposed compared to the existing related results. Then, in combination with the graph theory, looped Lyapunov functional (LLF) method and fuzzy logic pinning technique, some relaxed criterion which establish the relationship between the synchronization and desired $\mathcal{H}_{\infty}$ performance for the considered system are derived. Simulation results validate the superiority of derived theoretical results.

The attitude synchronization problem for multiple spacecraft with angular velocity and input constraints is investigated in this article, under the mild assumptions that the leader's states can reach each follower through a path and the communications between followers are bidirectional. An adaptive finite-time distributed observer is designed to estimate the leader's states for each follower spacecraft, which has the superiority in the fully distributed that the communication topology or some other global information is not required, and the finite-time convergence can be guaranteed. Furthermore, the novel auxiliary systems are first proposed to avoid the angular velocity and input constraints being violated, while the finite-time command filtered backstepping controller is designed to track the leader's attitude motion. The remarkable features of the developed algorithm are the global finite-time attitude consensus of multiple spacecraft, with the constraints satisfied. Moreover, the efficiency of the proposed method is illustrated by numerical simulations and real-time platform verification.

A grand challenge for the state estimation (SE) method in large-scale distribution networks lies in how to deal with the increasing computational tasks. This paper addresses the issue and proposes a novel multi-area architecture for the unbalanced distribution network SE method. The first step of the method is to introduce an innovative multi-area state estimation (MASE) model using micro-phasor measurement units (uPMU) mixed with conventional Supervisory Control And Data Acquisition (SCADA) systems, with both the coordinate tensions and synchronization issues considered. The proposed model contains a SCADA measurement delay estimator and a MASE algorithm. Then, the hybrid state estimation (HSE) model is solved in a distributed way. In each subarea, the HSE problem is solved locally with minimal data exchanges among neighbor subareas. Case studies show the accuracy and efficiency enhancements obtainable of the proposed MASE method with respect to existing ones.

The security of data is a major concern in this digital era. In general text and image data are well protected with the help of existing standards, meanwhile, securing voice communications is a challenging task. In the literature, there are some methods found using oscillators and chaotic signals for protecting direct voice communications, but they have limited functionalities due to synchronization problems between chaotic signals and oscillators. This paper gives an idea of encrypting voice signals over peer to peer communication with the help of a modified and lightweight AES algorithm. This algorithm is similar to the traditional AES algorithm in most of the aspects but does not imply the use of mix column that is used in the traditional AES algorithm. The lightweight algorithm is tested on two different Field Programmable Gate Array (FPGA) and the designs are analyzed with respect to time delay and effective utilization of resources.

As a promising technology, the Internet of Vehicle (IoV) enables vehicles connected to the network and sends announcements to roadside units (RSUs) or other vehicles. Nevertheless, because of the open nature of IoV, trust and privacy are confronted with challenges from cyber attacks. Motivated by addressing the above-mentioned problems, a privacy-preserve announcement protocol is constructed in this article, in which an identity-based group signature is used to achieve anonymity of vehicles. In addition, a novel blockchain-based trust management system is designed to guarantee the authenticity of the transmitted messages while realizing the message synchronization. In the meanwhile, a joint proof of work and improved practical Byzantine fault tolerance consensus mechanism is adopted in our scheme to enhance the efficiency of verification. Security analysis as well as simulation results indicate that our proposal is suitable and effective for IoV environment.

Two synchronization problems, synchronization with probability one and synchronization in probability, are investigated for an array of coupled probabilistic Boolean networks (CPBNs). Compared with the former one, the in-probability problem considers a more general situation, in which synchronization can be achieved with a positive probability, instead of strictly 100%. It reflects the intrinsic randomness of biological systems. For both problems, some necessary and sufficient conditions are proposed, based on which two feasible algorithms are presented for checking two kinds of synchronization, respectively. CPBNs can be seemed as a combination of coupled Boolean networks (CBNs) with assigned probabilities. There are also detailed discussions on the impact of the synchronism of CBNs on the two addressed synchronization problems. The existence of invariant synchronization subsets is studied, which deepens our understanding on the difference and difficulty of these problems. Finally, numerical simulations show the effectiveness of the theoretical results.

Non-Volatile Memory (NVM) offers byte-addressability and persistency Because NVM can be plugged into memory and provide low latency, it offers a new opportunity to build new database systems with a single-layer storage design. A single-layer NVM-Native DataBase (N2DB) provides zero copy and log freedom. Hence, all data are stored in NVM and there is no extra data duplication and logging during execution. N2DB avoids complex data synchronization and logging overhead in the two-layer storage design of disk-oriented databases and in-memory databases. Garbage Collection (GC) is critical in such an NVM-based database because memory leaks on NVM are durable. Moreover, data recovery is equally essential to guarantee atomicity, consistency, isolation, and durability properties. Without logging, it is a great challenge for N2DB to restore data to a consistent state after crashes and recoveries. This paper presents the GC and data recovery mechanisms for N2DB. Evaluations show that the overall performance of N2DB is up to 3.6× higher than that of InnoDB. Enabling GC reduces performance by up to 10%, but saves storage space by up to 67%. Moreover, our data recovery requires only 0.2% of the time and half of the storage space of InnoDB.

We investigate the stability properties of a multi-converter power system model, defined on a high-order manifold. For this, we identify its symmetry (i.e., rotational invariance) generated by a static angle shift and rotation of AC signals. We characterize the steady state set, primarily determined by the steady state angles and DC power input. Based on eigenvalue conditions of its Jacobian matrix, we show asymptotic stability of the multi-converter system in a neighborhood of the synchronous steady state set by applying the center manifold theory. We guarantee the eigenvalue conditions via an explicit approach. Finally, we demonstrate our results based on a numerical example involving a network of identical DC/AC converter systems.

In this article, the sampled-data-based event-triggered synchronization control for fractional and impulsive complex networks (CNs) with time-varying delay is investigated and a class of more general network structure based on the switching topologies at impulsive instants is considered. First, a class of novel fractional-order integral inequalities is produced to obtain depend-delay synchronization criteria and estimate Lyapunov-Krasovskii functions. Then, a sampled-data-based event-triggered control is designed, which can ensure synchronization of fractional and impulsive CNs (FICNs) with time-varying delay. Next, by using the Lyapunov direct method, some criteria are obtained to guarantee the synchronization of FICNs. Numerical simulations are given to demonstrate that the designed sampled-data-based event-triggered synchronization strategy can effectively not only achieve synchronization of FICNs but reduce the frequency of controller update compared to the previous related works.

Time synchronization is one of the important services in wireless sensor networks (WSN), and Bluetooth Low Energy (BLE) is one of the wireless protocols that has drawn a lot of attention recently, with applications in Internet of Things (IoT) and sensor fusion. Although, non-connectable beacons are the most energy efficient advertising mode of BLE, no synchronization service is defined for them in the BLE standard. Here, we present BlueSync, a BLE compatible time synchronization protocol, which uses beacons for data transfer. As part of this study, we investigate the trade-offs between energy efficiency, synchronization error and speed by considering different drift-management methods and various synchronization packet configurations. Additionally, we propose Discrete Adjustment technique that can improve energy efficiency in calculating timer-ticks updates up to 15x without sacrificing accuracy. BlueSync is implemented on two commercial BLE System-on-Chips, and tested in 10-minute long sessions without any re-synchronization. Experimental results show that, depending on the settings, BlueSync can achieve average synchronization errors as low as 320 ns per 60 s (single hop), and is the most accurate time synchronization reported on BLE to date.