Stochastic resonance in a time-delayed asymmetric bistable system with mixed periodic signal

This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A general approximate Fokker-Planck equation and the expression of the signal-to-noise ratio are derived through the small time delay approximation at both fundamental harmonics and mixed harmonics. The effects of the additive noise intensity Q, multiplicative noise intensity D, static asymmetry r and delay time T on the signal-to-noise ratio are discussed. It is found that the higher mixed harmonics and the static asymmetry r can restrain stochastic resonance, and the delay time τ can enhance stochastic resonance. Moreover, the longer the delay time τ is, the larger the additive noise intensity Q and the multiplicative noise intensity D are, when the stochastic resonance appears.

Mixed Signal IC的版图设计

本文阐述Mixed Signal IC的版图基本设计方法,总结LAYOUT中的技巧。通过对布局、走线、结构的合理设计实现Mixed Signal IC的性能要求。

Analogue and Mixed-Signal Production Test Speed-Up by Means of Fault List Compression

Accurate test effectiveness estimation for analogue and mixed-signal Systems on a Chip (SoCs) is currently prohibitive in the design environment. One of the factors that sky rockets fault simulation costs is the number of structural faults which need to be simulated at circuit-level. The purpose of this paper is to propose a novel fault list compression technique by defining a stratified fault list, build with a set of “representative” faults, one per stratum. Criteria to partition the fault list in strata, and to identify representative faults are presented and discussed. A fault representativeness metric is proposed, based on an error probability. The proposed methodology allows different tradeoffs between fault list compression and fault representation accuracy. These tradeoffs may be optimized for each test preparation phase. The fault representativeness vs. fault list compression tradeoff is evaluated with an industrial case study—a DC-DC (switched buck converter). Although the methodology is presented in this paper using a very simple fault model, it may be easily extended to be used with more elaborate fault models. The proposed technique is a significant contribution to make mixed-signal fault simulation cost-effective as part of the production test preparation.

The Marginal Rao-Blackwellized Particle Filter for Mixed Linear/Nonlinear State Space Models

In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.

Stochastic resonance in an under-damped bistable system driven by harmonic mixing signal

Stochastic resonance（SR） is studied in an under-damped bistable system driven by the harmonic mixing signal and Gaussian white noise. Using the linear response theory（LRT）, the expressions of the spectral amplification at fundamental and higher-order harmonic are obtained. The effects of damping coefficient, noise intensity, signal amplitude, and frequency on spectral amplifications are explored. Meanwhile, the power spectral density（PSD） and signal-to-noise ratio（SNR） are calculated to quantify SR and verify the theoretical results. The SNRs at the first and second harmonics exhibit a minimum first and a maximum later with increasing noise intensity. That is, both of the noise-induced suppression and resonance can be observed by choosing proper system parameters. Especially, when the ratio of the second harmonic amplitude to the fundamental one takes a large value, the SNR at the fundamental harmonic is a monotonic function of noise intensity and the SR phenomenon disappears.

Analysis of RF Feedback Chain Isolation in Wireless Co-Time Co-Frequency Full Duplex

By employing a radio frequency(RF) feedback chain, the self-interference can be canceled efficiently in co-time co-frequency full duplex(CCFD). However, the evitable signal crosstalk which is caused by the imperfect RF feedback chain isolation usually damages the self-interference cancelation(SIC) performance. To deal with this problem, firstly, we analyze the impact of RF feedback chain isolation on SIC performance. Then a digital preprocessing scheme with RF feedback chain is proposed in the multiple-antenna CCFD architecture. Using both analytical and experimental methods, we find that the proposed scheme achieves a better performance on SIC.

Quantum Nature of Distortion and Delay of Satellite Signals II

A detailed analysis of the influence of Rydberg states to the behavior of GPS satellite signals in the D and E atmospheric layers has been carried out. It is demonstrated that these states are the main reason for the GPS signal distortion. It is shown that the behavior of satellite signals is associated with the spectral characteristics of the UHF radiation of the Rydberg states depending on the geomagnetic conditions of ionosphere. The foundations of the quantum theory of distortion and delay of GPS satellite signal propagation through D and E atmospheric layers are analyzed and expounded. The problem reduces to the resonant scattering of photons, moving in the electromagnetic field of the signal, to the Rydberg complexes populated in a two-temperature non-equilibrium plasma. The processes of creation of additional photons because of stimulated emission and resonance scattering of photons are considered. In the present work, the quantum theory of the propagation of a satellite signal in the Earth’s upper atmosphere, firstly earlier proposed by the same authors, is described in detail. The general problems of the theory and possible theoretical and applied consequences are discussed. It is explained that two main processes occurring here, are directly related to the resonant quantum properties of the propagation medium. The first process leads to a direct increase in the power of the received signal, and second—to a shift in the signal carrier frequency??and the time delay??of its propagation. The main reasons of the processes are scattering of the Rydberg electron by the ion core and presence of the neutral medium molecule in the intermediate autoionization states of the composite system populated by the strong non-adiabatic coupling of electron and nuclear motions. The main purposes of our investigation are the physical justification of the formation of parameters ?and??using the quantum dynamics of the electron behavior in the intermediate state of the Rydberg complex A**M and the estimation of the quantities of??and??in the elementary act of elastic (Rayleigh) photon scattering.

Traffic Conflict Technique Development for Traffic Safety Evaluation under Mixed Traffic Conditions of Developing Countries

In developing countries, the numbers of traffic accidents, injuries and fatalities are very high and tend to increase atsignalized intersections. For example, in Ho Chi Minh City （HCMC） of Vietnam, the number of accidents at signalized intersectionsaccounted for 45% of the total accidents at all the intersections. This fact leads to strong necessity for analyzing traffic safety atsignalized intersections. Nevertheless, the historical accident data in HCMC is not available for deep analysis, this study uses videocameras to capture and analyze conflicts that potentially lead to accidents using TCT （traffic conflict technique）. Conflict severityidentification is one of the most significant steps to evaluate traffic safety at signalized intersections using TCT. Six zones （seriousconflict, common conflict, non-conflict, highest potential serious conflict, potential serious conflict, and potential common conflict） areexplored in this study to clarify conflict severity. This result is based on being the cut off value between serious conflicts and commonconflicts, according to 85% cumulative frequency of TTC （time to collision） and CS （conflict speed） under 3,050 samples size whichwere observed at 10 signalized intersections during August-November, 2014. Such a deep understanding is a scientific basis to studyhow to apply TCT to evaluate traffic safety at signalized intersections under mixed traffic conditions.

Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

Identification of fractional order Hammerstein models based on mixed signals

An algorithm based on mixed signals is proposed,to solve the issues of low accuracy of identification algorithm,immeasurable intermediate variables of fractional order Hammerstein model,and how to determine the magnitude of fractional order.In this paper,a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently.The nonlinear part is fitted by the neural fuzzy network model,which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models.In addition,the multi-innovation Levenberg-Marquardt(MILM)algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results.A simulation example is given to verify the accuracy and effectiveness of the proposed method.

A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

Tunable microwave signal generation based on an Opto-DMD processor and a photonic crystal fiber

Frequency-tunable microwave signal generation is proposed and experimentally demonstrated with a dual-wavelength single-longitudinal-mode （SLM） erbium-doped fiber ring laser based on a digital Opto-DMD processor and four-wave mixing （FWM） in a high-nonlinear photonic crystal fiber （PCF）. The high-nonlinear PCF is employed for the generation of the FWM to obtain stable and uniform dual-wavelength oscillation. Two different short passive sub-ring cavities in the main ring cavity serve as mode filters to make SLM lasing. The two lasing wavelengths are electronically selected by loading different gratings on the Opto-DMD processor controlled with a computer. The wavelength spacing can be smartly adjusted from 0.165 nm to 1.08 nm within a tuning accuracy of 0.055 nm. Two microwave signals at 17.23 GHz and 27.47 GHz are achieved. The stability of the microwave signal is discussed. The system has the ability to generate a 137.36-GHz photonic millimeter signal at room temperature.

Mixed Signals

The Chinese and the U.S. central banks are sending out two completely different messages to the global market. China, with looming inflation, is raising interest rates and the reserve requirement ratio to curb runaway consumer prices. The United States, on the other hand,

基于改进等效电路模型的直流微电网大信号稳定性分析

对高阶非线性直流微电网进行简化建模是大信号稳定性分析的有效方法。传统简化模型主要关注LC滤波器和线路阻抗等参数,难以分析换流器控制参数的影响,所建立的判据存在难以准确反映直流微电网稳定域的问题。因此,该文提出直流电压下垂控制换流器的具备完整状态变量集的改进等效电路模型,相比于传统简化模型,能更准确地刻画系统低频响应特性。基于混合势理论推导了直流微电网大信号稳定判据及其稳定域,并结合LaSalle不变集定理得到能量指标,以反映系统稳定性变化趋势。建立的稳定判据可指导控制参数优化,且能拓展到多端直流微电网的大信号稳定性分析。在多场景下仿真验证了所提改进等效电路模型和大信号稳定判据的有效性。

降速方式对混流泵压力脉动特性的影响

混流泵在降速过程中的内部流动特性变化对其性能和稳定性有重要影响。针对这一问题,该研究探究了混流泵在降速过程中内部流动的复杂性,尤其关注不同降速方式对泵内压力脉动特性的影响。通过构建多通道测试系统,收集了混流泵在不同降速方式下的外特性参数信号和泵内压力脉动信号。并采用非稳态信号处理技术,从时域、频域和时频三方面探究不同降速方式对叶轮出口和导叶内部的压力脉动特性的影响以及二者之间的相关性。结果表明,泵内压力脉动的峰值和样本熵的变化趋势与不同降速方式的幂指数呈正相关关系;在整个降速过程中叶轮出口压力脉动对导叶内压力脉动起主导作用;叶轮出口和导叶内部压力脉动之间的相干频率主要集中于80~120 Hz之间;在不同降速过程中叶轮出口和导叶内部的压力脉动之间强相关区域的频带范围保持不变,但是强相关区域在整个降速过程频带范围中的占比有所变化;对于具有固定初始转速和目标转速的降速过程,降速方式的改变不会影响叶轮出口和导叶内压力脉动的相干频率。研究结论可为揭示混流泵瞬态运行特性及提高混流泵降速运行中的稳定性提供参考。

DSP口袋实验箱设计

数字信号处理(DSP)是当前电子信息类专业学生必修的一门重要课程,其实验是理解和巩固理论的重要途经。但是当前的实验大都使用体积大且移动不便的实验箱,限制学生必须在实验室做实验,便携性和易用性不高。针对这一需求,研究设计了一款便携的“DSP口袋实验箱”。该实验箱集成两路模拟信号发生器及混频单元、高速高精度ADC采样单元以及TMS320VC5509A处理器、仿真器等模块。软件部分具备DSP课程的相关算法实验。设计的实验箱体积小、操作简便,便于学生在宿舍里开展实验。该设计对本科DSP实验教学便利化和激发学生深入研究学习具有较高的意义。

智能网联环境下信号交叉口车辆轨迹重构模型

车辆轨迹数据提供了大量的时空交通流信息,可用于各类交通研究.传统车辆轨迹模型多以人工驾驶环境为研究对象,普遍未考虑由常规车(RV)、网联人工驾驶车(CV)以及智能网联车(CAV)组成的混合交通流的影响.为解决该问题,构建智能网联环境下信号交叉口全样本车辆轨迹重构模型.首先,介绍并分析智能网联环境下城市道路交叉口处车辆组成及排队通过情况;然后,构建城市道路混合交通流轨迹数量估计模型,并针对前后车的排队情况提出虚拟车的概念,用于估计不同车辆的交通状态;最后,设计数值仿真实验分析交通流密度和网联车渗透率对模型的影响,并基于NGSIM数据进行实例验证.结果表明:轨迹重构模型的数量误差和位置误差均随着交通流密度和网联车渗透率的增大而减小,如交通流密度由20 veh/km增大至50 veh/km的过程中,模型数量误差和位置误差均呈现下降趋势,且最大误差分别不超过6.88%和8.02 m;与网联人工驾驶车渗透率相比,智能网联车的渗透率对模型结果影响更大.

不同模型对MMC-HVDC系统大信号稳定性分析准确性影响的对比研究

模块化多电平换流器高压直流输电(MMC-HVDC)系统运行中不可避免地遭受大信号扰动,可能导致不稳定现象的发生。基于混合势理论(MPT)对比分析了MMC-HVDC系统3种不同模型下的大信号稳定性。首先,建立了MMC-HVDC系统的全阶模型、降阶模型和等效恒功率负载(CPL)模型,并从理论层面对比分析了三者之间的差异性。利用MPT分别推导了基于3种模型的MMC-HVDC系统大信号稳定性判据,对比研究了3种不同模型对大信号稳定性分析结果的影响,并做出了系统在电网电压暂降大扰动下的稳定运行边界。最后,通过Matlab/Simulink时域仿真验证了理论分析的正确性。

基于全光谱主成分分析的拉曼信号提取方法

拉曼光谱(Raman spectra)基于拉曼散射效应的散射光谱。由于不同种类物质的振动、转动能量特征是唯一的,由此产生的拉曼散射光谱也是唯一的,因此拉曼光谱在鉴别物质成分方面极具优势。更因为无损耗、非接触、快速、简单、可重复等特点备受青睐,被广泛应用于化学、物理、生物和医学等各个领域。但受限于被测信号微弱、光学仪器加工精度以及混合物组分之间的相互作用,混合物的拉曼光谱不但存在谱峰重叠的现象,部分较弱信号的特征峰还可能会淹没在背景噪声中,影响了对混合物进行拉曼光谱分析的精度。为了解决拉曼光谱弱信号分析识别的困难,该研究将主成分分析应用于拉曼光谱分析,提出一种基于全光谱信息的拉曼信号提取方法。将实测拉曼光谱看作不同物质组分光谱的线性叠加,通过不同组分比例的多组拉曼光谱进行主成分分析,分离背景噪声和随机噪声并提取不同物质组分的拉曼信号。根据物质组分拉曼光谱非必要满足正交性的特点,分析讨论了光谱主成分与物质组分拉曼光谱成分之间的关系,给出了采用光谱主成分修正为物质组分拉曼光谱的一般性方法。根据光谱主成分与物质组分浓度之间的线性关联特征,给出了物质组分拉曼光谱、线性误差以及随机噪声的判定依据。通过不同浓度的甲醇、乙醇混合溶液拉曼光谱进行的实验验证,实现了甲醇和乙醇拉曼信号的提取,并成功分离了背景噪声和随机噪声,最终结果与参考信号匹配良好,同时验证了对物质组分拉曼信号、线性误差以及随机噪声的判断结果。提出了采用拉曼光谱主成分提取实际光谱组分的有效方法,此方法具有快速方便,成本低,精确度高的优点,是拉曼光谱数据处理技术的一种有益补充与尝试,在物质识别和浓度检测方面具有极大的应用潜力。

基于多特征融合的MIMO-OFDM系统单-混信号调制识别算法

为解决非协作通信中多输入多输出正交频分复用(multiple-input multiple-output orthogonal frequency division multiplexing, MIMO-OFDM)系统的单-混信号调制识别问题,提出一种基于多特征融合和决策融合的调制识别方法。首先,利用特征矩阵的联合近似对角化(joint approximate diagonalization of eigenvalue matrix, JADE)算法从接收端恢复发送信号;然后,提取恢复后信号的四次方谱、循环谱和高阶累积量作为输入样本,利用多特征融合网络对输入样本进行训练;最后,使用决策融合策略根据每条支路输出得到最终判决结果。仿真实验结果表明,所提方法可以对MIMO-OFDM系统中的7种信号进行有效识别,在信噪比为10 dB时识别精度可达99.4%。