An extended state observer with adjustable bandwidth for measurement noise

In this paper,a bandwidth-adjustable extended state observer(ABESO)is proposed for the systems with measurement noise.It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise,which conflicts with observation accuracy.Therefore,we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system.The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error.When the tracking error decreases,the bandwidth decreases to suppress the noise,otherwise the bandwidth does not change.It is proven that the error dynamics are bounded and converge in finite time.The relationship between the upper bound of the estimation error and the scaling factor is given.When the scaling factor is less than 1,the ABESO has higher estimation accuracy than the linear extended state observer(LESO).Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments.The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.

Robust quantum secure direct communication and authentication protocol against decoherence noise based on six-qubit DF state

By using six-qubit decoherence-free （DF） states as quantum carriers and decoy states, a robust quantum secure direct communication and authentication （QSDCA） protocol against decoherence noise is proposed. Four six-qubit DF states are used in the process of secret transmission, however only the ｜0＇〉 state is prepared. The other three six-qubit DF states can be obtained by permuting the outputs of the setup for ｜0＇〉. By using the ｜0＇〉 state as the decoy state, the detection rate and the qubit error rate reach 81.3%, and they will not change with the noise level. The stability and security are much higher than those of the ping-pong protocol both in an ideal scenario and a decoherence noise scenario. Even if the eavesdropper measures several qubits, exploiting the coherent relationship between these qubits, she can gain one bit of secret information with probability 0.042.

Fault tolerant deterministic secure quantum communication using logical Bell states against collective noise

This study proposes two novel fault tolerant deterministic secure quantum communication （DSQC） schemes resistant to collective noise using logical Bell states. Either DSQC scheme is constructed based on a new coding function, which is designed by exploiting the property of the corresponding logical Bell states immune to collective-dephasing noise and collective-rotation noise, respectively. The secret message can be encoded by two simple unitary operations and decoded by merely performing Bell measurements, which can make the proposed scheme more convenient in practical applications. Moreover, the strategy of one-step quanta transmission, together with the technique of decoy logical qubits checking not only reduces the influence of other noise existing in a quantum channel, but also guarantees the security of the communication between two legitimate users. The final analysis shows that the proposed schemes are feasible and robust against various well-known attacks over the collective noise channel.

Effects of interface bound states on the shot noise in normal metal–low-dimensional Rashba semiconductor tunnel junctions with induced s-wave pairing potential

We consider the effects of interface bound states on the electrical shot noise in tunnel junctions formed between normal metals and one-dimensional(1 D) or two-dimensional(2 D) Rashba semiconductors with proximity-induced s-wave pairing potential. We investigate how the shot noise properties vary as the interface bound state is evolved from a non-zero energy bound state to a zero-energy bound state. We show that in both 1 D and 2 D tunnel junctions, the ratio of the noise power to the charge current in the vicinity of zero bias voltage may be enhanced significantly due to the induction of the midgap interface bound state. But as the interface bound state evolves from a non-zero energy bound state to a zero-energy bound state, this ratio tends to vanish completely at zero bias voltage in 1 D tunnel junctions, while in 2 D tunnel junctions it decreases smoothly to the usual classical Schottky value for the normal state. Some other important aspects of the shot noise properties in such tunnel junctions are also clarified.

STOCHASTIC NOISE TOLERANCE:ENHANCED FULL STATE OBSERVER VS. KALMAN FILTER FROM VIDEO TRACKING PERSPECTIVE

A control-based full state observer scheme is explored for video target tracking application, and is enhanced with a lowpass filter for improving the tracking precision, thus forming an Enhanced Full State Observer (EFSO). The whole design is based on the given lab-generated video sequence with motion of an articulate target. To evaluate the EFSO’s stochastic noise tolerance, a Kalman Filter (KF) is intentionally employed in tracking the same target with the given Gaussian white noises. The comparison results indicate that, for system noises of certain statistics, the proposed EFSO has its own noise resistance capacity that is superior to that of KF and is more advantageous for implementation.

Intercept-resend attack on six-state quantum key distribution over collective-rotation noise channels

We investigate the effect of collective-rotation noise on the security of the six-state quantum key distribution. We study the case where the eavesdropper, Eve, performs an intercept-resend attack on the quantum communication between Alice, the sender, and Bob, the receiver. We first derive the collective-rotation noise model for the six-state protocol and then parameterize the mutual information between Alice and Eve. We then derive quantum bit error rate for three interceptresend attack scenarios. We observe that the six-state protocol is robust against intercept-resend attacks on collective rotation noise channels when the rotation angle is kept within certain bounds.

State Estimation for Sound and Vibration Emitted from a Machine Based on Sound Measurement Under Existenceof Background Noise

Many studies on the diagnosis for machines have become important recently because of increased use of various complex industrial systems.The correlation information between sound and vibration is very important for machine diagnosis.Usually,vibration pickups are attached directly to the machine in order to measure vibration data.However,in some cases,the sensors can not be attached directly on highly precise devices.In this study,a method to estimate the fluctuation of sound and vibration is proposed based on the measurement data of sound emitted from the machine under existence of background noise.The effectiveness of the proposed theory is experimentally confirmed by applying it to the observed data emitted from a rotational machine driven by an electric motor.

Selection of noise parameters for Kalman filter

The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter. With state vectors and covariance matrices estimated by the Kalman filter, the likehood of the measurements can be constructed as a function of the process noise and measurement noise parameters. By maximizing the likelihood function with respect to these noise parameters, the optimal values can be obtained. Furthermore, the Bayesian probabilistic approach allows the associated uncertainty to be quantified. Examples using a single-degree-of-freedom system and a ten-story building illustrate the proposed method. The effect on the performance of the Kalman filter due to the selection of the process noise and measurement noise parameters was demonstrated. The optimal values of the noise parameters were found to be close to the actual values in the sense that the actual parameters were in the region with significant probability density. Through these examples, the Bayesian approach was shown to have the capability to provide accurate estimates of the noise parameters of the Kalman filter, and hence for state estimation.

Fault tolerant controlled quantum dialogue against collective noise

Quantum system is inevitably affected by the external environment in the real world.Two controlled quantum dialogue protocols are put forward based on logicalχ-type states under collective noise environment.One is against collectivedephasing noise,while the other is against collective-rotation noise.Compared with existing protocols,there exist several outstanding advantages in our proposed protocols:Firstly,theχ-type state is utilized as quantum channels,it possesses better entanglement properties than GHZ state,W state as well as cluster state,which make it difficult to be destroyed by local operations.Secondly,two kinds of logicalχ-type states are constructed by us in theory,which can be perfectly immune to the effects of collective noise.Thirdly,the controller can be offline after quantum distribution and permission announcement,without waiting for all the participants to complete the information coding.Fourthly,the security analysis illuminates that our protocols can not only be free from the information leakage,but also resist against the interceptand-resend attack,the entanglement-and-measure attack,the modification attack,the conspiring attack,and especially the dishonest controller’s attacks.

LINEAR FILTERING FOR VASICEK TERM STRUCTURE MODEL WITH SEQUENTIALLY CORRELATED NOISE

When Kalman filter is used in the estimation of Vasicek term structure of interest rates,it is usual to assume that the measurement noise is uncorrelated.Study results are more favorable to the assumption of correlated measurement noise.An augmented state Kalman filter form for Vasicek model is proposed to optimally estimate the unobservable state variable with the assumption of correlated measurement noise.Empirical results indicate that the model with sequentially correlated measurement noise can more accurately describe the dynamics of the term structure of interest rates.

Influence of Coloured Correlated Noises on Probability Distribution and Mean of Tumour Cell Number in the Logistic Growth Model

An approximate Fokker-Planck equation for the logistic growth model which is driven by coloured correlated noises is derived by applying the Novikov theorem and the Fox approximation. The steady-state probability distribution （SPD） and the mean of the tumour cell number are analysed. It is found that the SPD is the single extremum configuration when the degree of correlation between the multiplicative and additive noises, λ is in -1 〈λ≤0 and can be the double extrema in 0〈λ〈1. A configuration transition occurs because of the variation of noise parameters. A minimum appears in the curve of the mean of the steady-state tumour cell number, （x）, versus λ The position and the value of the minimum are controlled by the noise-correlated times.

Intensity Correlation Function and Associated Relaxation Time of a Saturation Laser Model with Correlated Noises

We investigate the intensity correlation function C（s） and its associated relaxation time Tc for a saturation model of single-mode laser with correlated noises. The expressions of O（s） and Tc are derived by means of the projection operator method, and effects of correlations between an additive noise and a multiplicative noise are discussed by numerical calculation. Based on the calculated results, it is found that the correlation strength A between the additive noise and the multiplicative noise can enhance the fluctuation decay of the laser intensity.

Maximum Correntropy Kalman Filtering for Non-Gaussian Systems With State Saturations and Stochastic Nonlinearities

This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities. The stochastic nonlinearities, which take the form of statemultiplicative noises, are introduced in systems to describe the phenomenon of nonlinear disturbances. To resist non-Gaussian noises, we consider a new performance index called maximum correntropy criterion(MCC) which describes the similarity between two stochastic variables. To enhance the “robustness” of the kernel parameter selection on the resultant filtering performance, the Cauchy kernel function is adopted to calculate the corresponding correntropy. The goal of this paper is to design a Kalman-type filter for the underlying systems via maximizing the correntropy between the system state and its estimate. By taking advantage of an upper bound on the one-step prediction error covariance, a modified MCC-based performance index is constructed. Subsequently, with the assistance of a fixed-point theorem, the filter gain is obtained by maximizing the proposed cost function. In addition, a sufficient condition is deduced to ensure the uniqueness of the fixed point. Finally, the validity of the filtering method is tested by simulating a numerical example.

Deterministic remote state preparation of arbitrary three-qubit state through noisy cluster-GHZ channel

We propose a novel scheme for remote state preparation of an arbitrary three-qubit state with unit success probability,utilizing a nine-qubit cluster-GHZ state without introducing auxiliary qubits.Furthermore,we proceed to investigate the effects of different quantum noises(e.g.,amplitude-damping,phase-damping,bit-flip and phase-flip noises)on the systems.The fidelity results of three-qubit target state are presented,which are usually used to illustrate how close the output state is to the target state.To compare the different effects between the four common types of quantum noises,the fidelities under one specific identical target state are also calculated and discussed.It is found that the fidelity of the phase-flip noisy channel drops the fastest through the four types of noisy channels,while the fidelity is found to always maintain at 1 in bit-flip noisy channel.

Effective suppression of beta oscillation in Parkinsonian state via a noisy direct delayed feedback control scheme

This work explores the function of the noisy direct delayed feedback(NDDF)control strategy in suppressing the pathological oscillations in the basal ganglia(BG)with Parkinson’s disease(PD).Deep brain stimulation(DBS)alleviates the PD state fantastically.However,due to its unclear mechanism and open-loop characteristic,it is challenging to further improve its effects with lower energy expenditure.The noise stimulus performs competitively in alleviating the PD state theoretically,but it cannot adapt to the neural condition timely and automatically due to its open-loop control scheme.The direct delayed feedback(DDF)control strategy is able to disturb excessive synchronous effectively.Therefore,the NDDF control strategy is proposed and researched based on a BG computational model,which can reflect the intrinsic properties of the BG neurons and their connections with thalamic neurons.Simulation results show that the NDDF control strategy with optimal parameters is effective in removing the pathological beta oscillations.By comparison,we find the NDDF control strategy performs more excellent than DDF in alleviating PD state.Additionally,we define the multiple-NDDF control strategy and find that the multiple-NDDF with appropriate parameters performs better than NDDF.The obtained results contribute to the cure for PD symptoms by optimizing the noise-induced improvement of the BG dysfunction.

Optimum deconvolution algorithm for system with multiplicative white noise and additive correlative noise

The optimum state filter and fixed-interval smoother and the optimum deconvolution algorithm for system with multiplicative noise are derived upon the condition that the dynamic noise correlates itself in one-step and correlates with the measurement noise at the present step as well as one past step, and the multiplicative noise is white and statistically independent of the dynamic noise and the measurement noise. A simulation example demonstrates the effectiveness of the above-mentioned deconvolution algorithm.

Artificial Noise Based Security Algorithm for Multi-User MIMO System

The existing physical layer security algorithm, which is based on artificial noise, could affect legitimate receivers negatively when the number of users is no less than sending antennas in multi-user MIMO system. In order to improve security of multi-user MIMO system under this scenario, we propose a new multi-user MIMO system physical layer security algorithm based on joint channel state matrix. Firstly, multiple users are processed together, thus a multi-user joint channel state matrix is established. After achieving Singular Value Decomposition (SVD) of the joint channel state matrix, the minimum singular value is obtained, which can be utilized for precoding to eliminate the interference of artificial noise to legitimate receivers. Further, we also present an approach to optimize the power allocation. Simulation results show that the proposed algorithm can increase secrecy capacity by 0.1 bit/s/HZ averagely.

Bifurcation in most probable phase portraits for a bistable kinetic model with coupling Gaussian and non-Gaussian noises

The phenomenon of stochastic bifurcation driven by the correlated non-Gaussian colored noise and the Gaussian white noise is investigated by the qualitative changes of steady states with the most probable phase portraits.To arrive at the Markovian approximation of the original non-Markovian stochastic process and derive the general approximate Fokker-Planck equation(FPE),we deal with the non-Gaussian colored noise and then adopt the unified colored noise approximation(UCNA).Subsequently,the theoretical equation concerning the most probable steady states is obtained by the maximum of the stationary probability density function(SPDF).The parameter of the uncorrelated additive noise intensity does enter the governing equation as a non-Markovian effect,which is in contrast to that of the uncorrelated Gaussian white noise case,where the parameter is absent from the governing equation,i.e.,the most probable steady states are mainly controlled by the uncorrelated multiplicative noise.Additionally,in comparison with the deterministic counterpart,some peculiar bifurcation behaviors with regard to the most probable steady states induced by the correlation time of non-Gaussian colored noise,the noise intensity,and the non-Gaussian noise deviation parameter are discussed.Moreover,the symmetry of the stochastic bifurcation diagrams is destroyed when the correlation between noises is concerned.Furthermore,the feasibility and accuracy of the analytical predictions are verified compared with those of the Monte Carlo(MC)simulations of the original system.

A Tree Formulation for Signaling Games with Noise

The paper provides an analysis of a sender-receiver sequential signaling game. The private information of the sender is transmitted with noise by a Machine, i.e. does not always correctly reflect the state of nature. Hence, a truthful revelation by the sender of his information does not necessarily imply that the signal he sends is correct. Also, the receiver can take a correct action even if the sender transmits an incorrect signal. The payoffs of the two players depend on their combined actions. Perfect Bayesian Equilibria which can result from different degrees of noise is analysed. The Bayesian updating of probabilities is explained. The fixed point theorem which makes the connection with the idea of rational expectations in economics is calculated. Given a number of equilibria, we comment on the most credible one on the basis of the implied payoffs for both players. The equilibrium signals are an example of the formation of a language convention discussed by D. Lewis.

Echo State Network With Probabilistic Regularization for Time Series Prediction

Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world tasks.This work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness guaranteed.Specifically,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the PRESN.Furthermore,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical analyses.Finally,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN.The source code is publicly available at https://github.com/LongJinlab/probabilistic-regularized-echo-state-network.