Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method

We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.     

Improved Quantum Evolutionary Computation Based on Particle SwarmOptimization and Two-Crossovers

A quantum evolutionary computation （QEC） algorithm with particle swarm optimization （PSO） and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.     

Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization

This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.     

Refractivity estimations from an angle-of-arrival spectrum

This paper addresses the probability of atmospheric refractivity estimation by using field measurements at an array of radio receivers in terms of angle-of-arrival spectrum.Angle-of-arrival spectrum information is simulated by the ray optics model and refractivity is expressed in the presence of an ideal tri-linear profile.The estimation of the refractivity is organized as an optimization problem and a genetic algorithm is used to search for the optimal solution from various trial refractivity profiles.Theoretical analysis demonstrates the feasibility of this method to retrieve the refractivity parameters.Simulation results indicate that this approach has a fair anti-noise ability and its accuracy performance is mainly dependent on the antenna aperture size and its positions.     

New control algorithm for automatic PMD compensation system

A new cross-tracking method is proposed to improve the convergent speed of the control algorithm in real-time polarization mode dispersion（PMD）compensation systems.The cross-tracking algorithm is compared with the previously used dithering particle swarm optimization（DPSO）and gradient particle swarm optimization（GPSO）algorithms,and it is proven to offer the best performance among the three algorithms.The transmission of a 43-Gb/s differential quadrature phase-shift keying（DQPSK）signal over a 1 200-km fiber span using a compensator based on digital signal processing（DSP）is demonstrated via the cross-tracking algorithm.     

Frequency invariability of acoustic field and passive source range estimation in shallow water

The frequency invariability of the warped modal signal and the warped signal autocorrelation function in shallow water is discussed.A method is proposed for passive source-range estimation based on the frequency invariability and warping transform of signal autocorrelation function received by a single hydrophone in a range-independent or weak range-dependent shallow water environment.In the method,a guided source with a known range is employed to provide the crucial and relative invariant scaled features.The experimental data in shallow water with an iso-speed profile and a fluctuated thermocline are used to verify this approach.The relative errors of the source range estimation are basically less than 10%.     

Estimation of spatially distributed processes using mobile sensor networks with missing measurements

This paper investigates the estimation problem for a spatially distributed process described by a partial differential equation with missing measurements.The randomly missing measurements are introduced in order to better reflect the reality in the sensor network.To improve the estimation performance for the spatially distributed process,a network of sensors which are allowed to move within the spatial domain is used.We aim to design an estimator which is used to approximate the distributed process and the mobile trajectories for sensors such that,for all possible missing measurements,the estimation error system is globally asymptotically stable in the mean square sense.By constructing Lyapunov functionals and using inequality analysis,the guidance scheme of every sensor and the convergence of the estimation error system are obtained.Finally,a numerical example is given to verify the effectiveness of the proposed estimator utilizing the proposed guidance scheme for sensors.     

A genetic resampling particle filter for freeway traffic-state estimation

On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control.Because a nonlinear feature exists in the traffic state,and because particle filters have good characteristics when it comes to solving the nonlinear problem,a genetic resampling particle filter is proposed to estimate the state of freeway traffic.In this paper,a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object.By analysing the traffic-state characteristics of the freeway,the traffic is modeled based on the second-order validated macroscopic traffic flow model.In order to solve the particle degeneration issue in the performance of the particle filter,a genetic mechanism is introduced into the resampling process.The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail,and the filter estimation performance is validated and evaluated by the achieved experimental data.     

Fast filtering algorithm based on vibration systems and neural information exchange and its application to micro motion robot

This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering performance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.     

Geoacoustic Inversion Based on a Vector Hydrophone Array

We propose a geoacoustic inversion scheme employing a vector hydrophone array based on the fact that vector hydrophone can provide more acoustic field information than traditional pressure hydrophones. Firstly, the transmission loss of particle velocities is discussed. Secondly, the sediment sound speed is acquired by a matchedfield processing （MFP） procedure, which is the optimization in combination of the pressure field and vertical particle velocity field. Finally, the bottom attenuation is estimated from the transmission loss difference between the vertical particle velocity and the pressure. The inversion method based on the vector hydrophone array mainly has two advantages： One is that the MFP method based on vector field can decrease the uncertain estimation of the sediment sound speed. The other is that the objective function based on the transmission loss difference has good sensitivity to the sediment attenuation and the inverted sediment attenuation is independent of source level. The validity of the inverted parameters is examined by comparison of the numerical results with the experimental data.     

Classical and modern power spectrum estimation for tune measurement in CSNS RCS

Precise measurement of betatron tune is required for good operating condition of CSNS RCS. The fractional part of betatron tune is important and it can be measured by analyzing the signals of beam position from the appointed BPM. Usually these signals are contaminated during the acquisition process, therefore several power spectrum methods are used to improve the frequency resolution. In this article classical and modern power spectrum methods are used. In order to compare their performance, the results of simulation data and IQT data from J-PARC RCS are discussed, It is shown that modern power spectrum estimation has better performance than the classical ones, though the calculation is more complex.     

Enhancing the precision of phase estimation by weak measurement and quantum measurement reversal

The enhancement of the precision of phase estimation in quantum metrology is investigated by employing weak measurement （WM） and quantum measurement reversal （QMR）. We derive the exact expressions of the optimal quantum Fisher information （QFI） and success probability of phase estimation for an exactly solving model consisting of a qubit interacting with a structured reservoir. We show that the QFI can be obviously enhanced by means of the WM and QMR in different regimes. In addition, we also show that the magnitude of the decoherence involved in the WM and QMR can be a general complex number, which extends the applicable scope of the WM and QMR approach.     

Quantum information processing and metrology with color centers in diamonds

The Nitrogen Vacancy （NV） center is becoming a promising qubit for quantum information processing. The defect has a long coherence time at room temperature and it allows spin state initialized and read out by laser and manipulated by microwave pulses. It has been utilized as a ultra sensi- tive probe for magnetic fields and remote spins as well. Here, we review the recent progresses in experimental demonstrations based on NV centers. We first introduce our work on implementation of the Deutsch- Jozsa algorithm with a single electronic spin in diamond. Then the quantum nature of the bath around the center spin is revealed and continuous wave dynamical decoupling has been demonstrated. By applying dynamical decoupling, a multi-pass quantum metrology protocol is realized to enhance phase estimation. In the final, we demonstrated NV center can be regarded as a ultra-sensitive sensor spin to implement nuclear magnetic resonance （NMR） imaging at nanoscale.     

一种深度学习的立体阵波达方向估计方法

针对常规波束形成主瓣宽且目标分辨能力低的问题,提出一种基于深度卷积神经网络的波达方向估计方法。算法使用常规波束形成计算二维空间功率谱,将预处理后的空间功率谱图输入深度卷积神经网络。该文利用神经网络学习解卷积映射关系,输出主瓣宽度更窄的空间功率谱图,从而实现高分辨率二维波达方向估计。该算法对阵列结构没有限制,适用于立体阵。仿真结果表明该文方法在不同目标个数、快拍数及信噪比参数下均能准确估计目标方向。该文方法目标分辨能力优于常规波束形成方法。在低快拍情况下,目标方向估计误差低于自适应波束形成方法。     

Two Avowable Quantum Communication Schemes

Two avowable quantum communication schemes are proposed. One is an avowable teleportation protocol based on the quantum cryptography. In this protocol one teleports a set of one-particle states based on the availability of an honest arbitrator, the keys and the Einstein Podolsky-Rosen pairs shared by the communication parties and the arbitrator. The key point is that the fact of the teleportation can neither be disavowed by the sender nor be denied by the receiver. Another is an avowable quantum secure direct communication scheme. A one-way Hash function chosen by the communication parties helps the receiver to validate the truth of the information and to avoid disavowing for the sender.     

Optimal State Estimation of Pure Qubits on Circles

We consider the problem of state estimation of qubits chosen from circles. It is shown that any qubit encoded in pairs chosen from a fixed circle parailel to the x-y equator with different phases contains the same information. We also investigate the problem of state estimation of qubits from three circles. The optimai estimation fidelity is derived.     

A design strategy of achievable linear optics for a complex storage ring lattice

A design strategy is discussed in this paper,and it provides much convenience for effectively exploring achievable linear optics and globally investigating the flexibility of a complex lattice with super-periodicity.A matching method of fractional steps,which means separately finding the standard cell setting and the matching cell setting,is adopted to simplify the complexity of the linear beam optics design in the complex lattice.The multi-objective genetic algorithm is used to find most of all the stable linear optics,and reach a target solution after multi-generational propagation,both in the standard cell and the matching cell.A fitting algorithm with gradient information is used to restore the periodicity and symmetry of the lattice,and finely adjust the linear optics for further optimization.This design strategy is applied in the Shanghai Synchrotron Radiation Facility（SSRF） storage ring,and the results are presented.     

Optimization of magnet sorting in a storage ring using genetic algorithms

In this paper, the genetic algorithms are applied to the optimization problem of magnet sorting in an electron storage ring, according to which the objectives are set so that the closed orbit distortion and beta beating can be minimized and the dynamic aperture maximized. The sorting of dipole, quadrupole and sextupole magnets is optimized while the optimization results show the power of the application of genetic algorithms in magnet sorting.     

100-nm T-gate InAIAs/InGaAs InP-based HEMTs with fT= 249 GHz and fmax ： 415 GHz

InA1As/InGaAs high electron mobility transistors （HEMTs） on an InP substrate with well-balanced cutoff frequency fT and maximum oscillation frequency frnax are reported. An InA1As/InGaAs HEMT with 100-nm gate length and gate width of 2 × 50 μm shows excellent DC characteristics, including full channel current of 724 mA/mm, extrinsic maximum transconductance gm.max of 1051 mS/mm, and drain-gate breakdown voltage BVDG of 5.92 V. In addition, this device exhibits fT = 249 GHz and fmax = 415 GHz. These results were obtained by fabricating an asymmetrically recessed gate and minimizing the parasitic resistances. The specific Ohmic contact resistance was reduced to 0.031 0.mm. Moreover, the fT obtained in this work is the highest ever reported in 100-nm gate length InA1As/InGaAs InP-based HEMTs. The outstanding gm.max, fT, fmax, and good BVDG make the device suitable for applications in low noise amplifiers, power amplifiers, and high speed circuits.     

Bidirectional reflectance one-dimensional rough distribution function modeling of surface in the microwave band

In this study, the bidirectional reflectance distribution function （BRDF） of a one-dimensional conducting rough surface and a dielectric rough surface are calculated with different frequencies and roughness values in the microwave band by using the method of moments, and the relationship between the bistatic scattering coefficient and the BRDF of a rough surface is expressed. From the theory of the parameters of the rough surface BRDF, the parameters of the BRDF are obtained using a genetic algorithm. The BRDF of a rough surface is calculated using the obtained parameter values. Further, the fitting values and theoretical calculations of the BRDF are compared, and the optimization results are in agreement with the theoretical calculation results. Finally, a reference for BRDF modeling of a Gaussian rough surface in the microwave band is provided by the proposed method.