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。

WIDELY LINEAR RLS CONSTANT MODULUS ALGORITHM FOR COMPLEX-VALUED NONCIRCULAR SIGNALS

Based on the constant modulus criterion, a new Widely Linear(WL) blind equalizer and a novel widely linear recursive least square constant modulus algorithm are proposed to improve the blind equalization performance for complex-valued noncircular signals. The new algorithm takes advantage of the WL filtering theory by taking full use of second-order statistical information of the complex-valued noncircular signals. Therefore, the weight vector contains the complete second-order information of the real and imaginary parts to decrease the residual inter-symbol interference effectively. Theoretical analysis and simulation results show that the proposed scheme can significantly improve the equalization performance for complex-valued noncircular signals compared with traditional blind equalization algorithms.

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.

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.

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.

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.

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.

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。

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。

模拟混合视频处理电路单粒子效应测试系统设计

为开展模拟混合视频处理电路的单粒子效应研究,通过改变伪CCD信号源的输出信号,建立一款模拟混合视频处理电路的单粒子效应测试系统,实现该器件的单粒子闭锁、单粒子翻转与单粒子功能中断等单粒子效应的评估。通过在中科院近物所回旋加速器和中国原子能院串列加速器的试验测试,获取有效数据。试验结果表明该试验方法及测试系统可以有效评估器件的单粒子性能,为器件的加固设计提供有力数据支撑。

基于霍尔效应的位置传感系统设计

磁敏位置传感系统广泛应用于汽车、高端装备和先进制造等领域中。文中设计了一种基于霍尔效应的位置传感系统,可以实现被测物旋转角度的检测。该系统通过由线性霍尔芯片和磁铁组成的传感模块获取旋转角度信息,经信号调理电路处理后由12 bit ADC采样转换为数字信号,FPGA读取数据并采用拟合、标准化等算法校准,再利用Cordic算法计算得到被测物旋转角度,最后通过串口屏将测量结果显示。测试结果表明该位置传感系统精度较高,测得的旋转角度误差小于2°。

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。

FCM-FastICA的点蚀声发射信号分离识别方法

金属点蚀是一种破坏性和隐患较大的设备损伤形式。点蚀会产生声发射信号。点蚀过程中产生的多种声源类型会造成信号混叠,影响腐蚀进程的判断。针对点蚀信号混叠问题,提出一种模糊C均值聚类与快速独立分量分析算法相结合的点蚀信号分离识别方法。通过分析单、双点蚀声发射数据将点蚀分为钝化膜破裂阶段、点蚀诱导成核及发展阶段,由聚类确定信号类别并用快速独立分量分析分离混合信号,利用相关性函数验证分离效果。结果表明:单点蚀过程存在3类原信号,双点蚀过程存在7类信号,其中包含单个信号与混合信号;单个信号与原信号相关性极高,达到0.8以上,混合信号的分离分量与原信号相关性达到0.6以上,分离效果较好。该方法可对点蚀混合信号进行有效分离和识别,为腐蚀进程判断提供支持。

一种融合KPCA、FastICA及SVD的腹壁源胎儿心电 信号提取算法研究

目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singular value decomposition,SVD)的胎儿心电信号提取算法。方法:首先,采用KPCA对母体心电信号进行降维,再利用改进的基于负熵的FastICA处理降维后的数据,得到独立成分。随后,引入样本熵进行信号通道选择,挑选出包含最多母体信息的信号通道。在选中的母体通道上进行SVD,得到母体心电信号的近似估计,再用腹壁源信号减去该信号得到胎儿心电的初步估计。最后,采用改进的基于负熵的FastICA成功分离出纯净的胎儿心电信号。在腹部和直接胎儿心电图数据库(Abdominal and Direct Fetal Electrocardiogram Database,ADFECGDB)和PhysioNet 2013挑战赛数据库中对提出的算法进行验证。结果:提出的算法在主观视觉效果和客观评价指标上都表现出优越的性能。在ADFECGDB数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.74%、98.85%和99.30%;在PhysioNet 2013挑战赛数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.10%、97.87%和98.48%。结论:融合KPCA、FastICA及SVD的胎儿心电信号提取算法在提取胎儿心电信号的同时有效处理了附加噪声,为胎儿疾病的早期诊断提供了有力支持。

基于循环谱的单通道时频混叠信号识别

随着通信技术的发展和电磁环境的日益复杂,通信领域中的时频混叠信号频繁显现。一方面,这类信号会干扰通信系统的正常接收;另一方面,当混叠信号中引入虚假信号时,接收器极有可能接收虚假信号,从而导致获取欺骗信息。因此,有必要对混叠信号做出准确的识别,确保通信的可靠性,从而提出了一种基于循环谱特征的时频混叠信号识别算法。该算法基于非合作方式设计,不依赖于特定的调制参数,具备较优的普适性,并且测试表明,该算法对时频混叠信号的识别准确率优于传统算法。

潮汐干线交通信号协调控制模型

为缓解城市布局等问题导致的潮汐交通拥堵现象,提出了一种潮汐交通状态下的干线信号协调控制策略.依据交通供需关系及交通量进出平衡,构建了以干线吞吐量最大化为优化目标的混合整数线性规划模型.基于冲击波理论,构建了以过饱和方向车均延误最小化为优化目标的二次规划模型.通过引入松弛变量实现可变带宽绿波的分段优化,构建了以非饱和方向带宽最大化为目标的混合整数线性规划模型.采用三阶段优化方法求解模型,获得潮汐干线的最优控制方案.算例结果表明,本文提出的模型能优化干线吞吐量,控制过饱和方向的延误,并为非饱和方向提供可变带宽绿波,从而满足潮汐交通需求.

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%。