Synchronization performance in time-delayed random networks induced by diversity in system parameter

Synchronization rhythm and oscillating in biological systems can give clues to understanding the cooperation and competition between cells under appropriate biological and physical conditions. As a result, the network setting is appreciated to detect the stability and transition of collective behaviors in a network with different connection types. In this paper, the synchronization performance in time-delayed excitable homogeneous random networks（EHRNs） induced by diversity in system parameters is investigated by calculating the synchronization parameter and plotting the spatiotemporal evolution pattern, and distinct impacts induced by parameter-diversity are detected by setting different time delays. It is found that diversity has no distinct effect on the synchronization performance in EHRNs with small time delay being considered. When time delay is increased greatly, the synchronization performance of EHRN degenerates remarkably as diversity is increased. Surprisingly, by setting a moderate time delay, appropriate parameter-diversity can promote the synchronization performance in EHRNs, and can induce the synchronization transition from the asynchronous state to the weak synchronization. Moreover, the bistability phenomenon, which contains the states of asynchronous state and weak synchronization,is observed. Particularly, it is confirmed that the parameter-diversity promoted synchronization performance in time-delayed EHRN is manifested in the enhancement of the synchronization performance of individual oscillation and the increase of the number of synchronization transitions from the asynchronous state to the weak synchronization. Finally, we have revealed that this kind of parameter-diversity promoted synchronization performance is a robust phenomenon.

Inferring interactions of time-delayed dynamic networks by random state variable resetting

Time delays exist widely in real systems, and time-delayed interactions can result in abundant dynamic behaviors and functions in dynamic networks. Inferring the time delays and interactions is challenging due to systematic nonlinearity,noises, a lack of information, and so on. Recently, Shi et al. proposed a random state variable resetting method to detect the interactions in a continuous-time dynamic network. By arbitrarily resetting the state variable of a driving node, the equivalent coupling functions of the driving node to any response node in the network can be reconstructed. In this paper,we introduce this method in time-delayed dynamic networks. To infer actual time delays, the nearest neighbor correlation(NNC) function for a given time delay is defined. The significant increments of NNC originate from the delayed effect.Based on the increments, the time delays can be reconstructed and the reconstruction errors depend on the sampling time interval. After time delays are accurately identified, the equivalent coupling functions can also be reconstructed. The numerical results have fully verified the validity of the theoretical analysis.

Random walks in generalized delayed recursive trees

Recently a great deal of effort has been made to explicitly determine the mean first-passage time （MFPT） between two nodes averaged over all pairs of nodes on a fractal network. In this paper, we first propose a family of generalized delayed recursive trees characterized by two parameters, where the existing nodes have a time delay to produce new nodes. We then study the MFPT of random walks on this kind of recursive tree and investigate the effect of the time delay on the MFPT. By relating random walks to electrical networks, we obtain an exact formula for the MFPT and verify it by numerical calculations. Based on the obtained results, we further show that the MFPT of delayed recursive trees is much shorter, implying that the efficiency of random walks is much higher compared with the non-delayed counterpart. Our study provides a deeper understanding of random walks on delayed fractal networks.

Controller design for networked control system with data packet dropout and transmission delays

In this paper, the stabilization problem for a class of networked control systems （NCSs） with data packet dropouts and transmission time delays is considered, where the delays are time-varying and uncertain, the data packet dropout is modeled as a two-state Markov chain. To compensate the lost packet, a data packet dropout compensator is established. Thus a more realistic model for such NCSs is presented. Sufficient conditions for the stabilization of the new resulting system are derived in the form of linear matrix inequalities （LMIs）. Numerical example illustrates the solvability and effectiveness of the results.

Analysis of a Delayed Stochastic One-Predator Two-Prey Population Model in a Polluted Environment

This paper is concerned with the dynamics of a delayed stochastic one-predator two-prey population model in a polluted environment. We show that there exists a unique positive solution that is permanent in time average under certain conditions. Moreover, the global attractively of system is studied. Finally, some numerical simulations are given to illustrate the main results.

Asymptotic stability analysis of nonlinear real-time networked control systems

The paper deals with the problem of the asymptotic stability for general continuous nonlinear networked control systems （NCSs）. Based on Lyapunov stability theorem combined with improved Razumikhin technique, the sufficient conditions of asymptotic stability for the system are derived. With the proposed method, the estimate of maximum allowable delay bound （MADB） for linear networked control system is also given. Compared to the other methods, the proposed method gives a much less conservative MADB and more general results. Numerical examples and some simulations are worked out to demonstrate the effectiveness and performance of the proposed method.

A high-speed true random number generator based on Ag/SiNx/n-Si memristor

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness,this variability is valuable for safe applications in hardware,such as the true random number generator(TRNG).However,the speed of TRNG is still be further improved.Here,we propose a reliable Ag/SiNx/n-Si volatile memristor,which exhibits a typical threshold switching device with stable repeat ability and fast switching speed.This volatile-memristor-based TRNG is combined with nonlinear feedback shift register(NFSR)to form a new type of high-speed dual output TRNG.Interestingly,the bit generation rate reaches a high speed of 112 kb/s.In addition,this new TRNG passed all 15 National Institute of Standards and Technology(NIST)randomness tests without post-processing steps,proving its performance as a hardware security application.This work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security.

Loss of time-delay signature in a ring of three unidirectionally coupled semiconductor lasers

A ring of three unidirectionally coupled semiconductor lasers （RTUC-SLs） is used to generate broadband chaos with no pronounced time-delay （TD） signature. Using the autocorrelation function and pernmtation entropy as the TD measures, we demonstrate that under suitable coupling strength, the loss of the TD signature of the lasers in the RTUC-SL configuration is achieved both for the intensity and the phase. These findings should prove valuable for developing high-quality optical chaos for potential applications, such as chaos-based communica- tions and random number generation.

A class of anomalous diffusion epidemic models based on CTRW and distributed delay

In recent years,the epidemic model with anomalous diffusion has gained popularity in the literature.However,when introducing anomalous diffusion into epidemic models,they frequently lack physical explanation,in contrast to the traditional reaction-diffusion epidemic models.The point of this paper is to guarantee that anomalous diffusion systems on infectious disease spreading remain physically reasonable.Specifically,based on the continuous-time random walk(CTRW),starting from two stochastic processes of the waiting time and the step length,time-fractional space-fractional diffusion,timefractional reaction-diffusion and fractional-order diffusion can all be naturally introduced into the SIR(S:susceptible,I:infectious and R:recovered)epidemic models,respectively.The three models mentioned above can also be applied to create SIR epidemic models with generalized distributed time delays.Distributed time delay systems can also be reduced to existing models,such as the standard SIR model,the fractional infectivity model and others,within the proper bounds.Meanwhile,as an application of the above stochastic modeling method,the physical meaning of anomalous diffusion is also considered by taking the SEIR(E:exposed)epidemic model as an example.Similar methods can be used to build other types of epidemic models,including SIVRS(V:vaccine),SIQRS(Q:quarantined)and others.Finally,this paper describes the transmission of infectious disease in space using the real data of COVID-19.

Real-Time nonlinear predictive controller design for drive-by-wire vehicle lateral stability with dynamic boundary conditions

Due to flexible drive-by-wire technology,vehicle stability control can improve handling and lateral stability under extreme conditions.However,this technology can also increase the probability of random transmission delay.This paper proposes a nonlinear model predictive control(NMPC)strategy to improve vehicle stability and compensate for the random time delay.First,by combining the nonlinear dynamic characteristics and driver behavior,we obtain a stable region of the yaw rate and the sideslip angle under complex driving conditions.Second,an NMPC controller is designed to track the reference values in the identified stable region to improve the handling and lateral stability.Finally,the actuator receives the optimized control sequence and compensates for the random time delay of the transmission channel.CarSim/Simulink simulation and hardware-in-the-loop experiment results show that the proposed controller with dynamic boundary conditions can better track the expected value of the yaw rate and suppress the sideslip angle under low adhesion road conditions.

Receding horizon estimation to networked control systems with multirate scheme

This paper investigates the problem of receding horizon state estimation for networked control systems （NCSs） with random network-induced delays less than one sample period, which are formulated as multirate control systems. Based on a batch of recent past slow rate measurements in a finite horizon window, the initial state estimation in this window is solved by minimizing a receding-horizon objective function, and then the fast rate state estimations are calculated by the prediction of dynamic equation to compensate for the network-induced time delays. Furthermore, convergence results and unbiasedness properties are analyzed. An upper bound of estimation error is presented under the assumption of bounded disturbances acting on the system and measurement equations. A simulation example shows the effectiveness of the proposed method.