LTE同频小区检测及在扫频测试仪中的应用

在采用同频组网的3GPP长期演进(long term evolution,LTE)系统中,同频干扰会严重影响小区检测性能。提出在对FDD-LTE网络进行扫频测试时采用同频干扰抵消技术检测同频小区,并针对在实际组网中不同小区可能有相同的主同步码和辅同步码偶数序列的情况,提出了一种改进的同频干扰抵消技术。采用蒙特卡罗法对该方法检测同频小区的成功概率进行了仿真,仿真结果表明此方法对检测同频小区是非常有效的,当信噪比为0 dB,终端接收到的两小区信号比值达10 dB时,弱信号小区的检测成功概率超过93%。并且通过运用串行干扰抵消方法连续消除可以检测出尽可能多的同频小区,仿真结果表明在信噪比超过3 dB时,当接收到的4个同频小区信号间的最大比值为10 dB时,弱信号小区的检测成功概率都超过99%。最后在研制的FDD-LTE扫频测试仪中应用了此技术,实现了同频多小区的有效检测。     

Optimal vibration image size determination for convolutional neural network based fluid-film rotor-bearing system diagnosis

This paper suggests an image gradient based method that determines the optimal image size for convolutional neural network (CNN)-based diagnosis of fluid-film rotorbearing systems. As distinct patterns improve the diagnosis performance, a criterion is defined to measure the intensity of patterns in an image. The proposed criterion is derived by segmenting an image by the size of the CNN filter and evaluating each segment through the use of image gradient analysis. Vibration signals from a testbed are used to demonstrate the proposed method. First, the signals are transformed into vibration images by using an omnidirectional regeneration technique. Then, vibration images of four different health states are analyzed using the suggested criterion. The analyzed results are compared to the performance of CNN based diagnosis. The results indicate that the proposed criterion can determine the optimal size range of the vibration image that gives the best performance for CNN-based diagnosis.

     

改进Turbo-BLAST检测算法及其仿真

Turbo-BLAST系统基于MMSE的迭代软干扰消除检测技术可有效克服由于V-BLAST的应用带来的多流干扰,但是在迭代检测译码过程中存在错误传播,并且收敛性较差,尤其在接收天线远小于发射天线时。而MAP迭代检测算法的性能接近最优,但复杂度随发射天线和调制阶数呈指数增长。所以本文把这两种算法相结合,提出改进的SIC-MAP/MMSE检测算法。仿真结果表明,在相同频谱利用率的条件下,这种新检测算法在性能和收敛性上明显优于MMSE的迭代软干扰消除(SIC-MMSE)检测技术。     

室内覆盖直放站设计分析

通过对室内覆盖直放站系统进行了简要介绍,其对于运营商提高服务水平,增强竞争实力,树立企业形象,具有不可低估的作用,随着移动通信网络优化工作的深入展开,包括直放站在内的室内覆盖系统必将成为一种重要的优化手段运用于网络优化工作中。     

LYAPUNOV TUNING OF THE LEAKY LMS ALGORITHM FOR SINGLE-SOURCE,SINGLE-POINT NOISE CANCELLATION

Least-mean square (LMS) algorithms, which are commonly used for adaptive feedforward noise cancellation, have performance issues related to insufficient excitation, non-stationary reference inputs, finite-precision arithmetic, quantisation noise and measurement noise. Such factors cause weight drift and potential instability in the conventional LMS algorithm. Here, we analyse the stability and performance of the leaky LMS algorithm, which is widely used to correct weight drift. A Lyapunov tuning method is developed to find an adaptive leakage parameter and adaptive step size that provide optimum performance and retain stability in the presence of measurement noise on the reference input of known variance. The method accounts for non-persistent excitation conditions and non-stationary reference inputs and requires no a priori knowledge of the reference input signal characteristics other than a lower bound on its magnitude or a minimum signal-to-noise ratio. The Lyapunov tuning method is demonstrated for three candidate adaptive leakage and step size parameter combinations, each of which is a function of the instantaneous measured reference input, measurement noise variance, and/or filter length. These candidates illustrate stability vs performance tradeoffs in the leaky LMS algorithm elicited through the Lyapunov tuning method. The performance of each candidate Lyapunov tuned algorithm is evaluated experimentally in a single source, single-point acoustic noise cancellation system.     

Decentralized adaptive neural prescribed performance control for high-order stochastic switched nonlinear interconnected systems with unknown system dynamics

In this paper, the problem of decentralized adaptive neural backstepping control is investigated for high-order stochastic nonlinear systems with unknown interconnected nonlinearity and prescribed performance under arbitrary switchings. For the control of high-order nonlinear interconnected systems, it is assumed that unknown system dynamics and arbitrary switching signals are unknown. First, by utilizing the prescribed performance control (PPC), the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, at each recursive step, only one adaptive parameter is constructed to overcome the over-parameterization, and RBF neural networks are employed to tackle the difficulties caused by completely unknown system dynamics. At last, based on the common Lyapunov stability method, the decentralized adaptive neural control method is proposed, which decreases the number of learning parameters. It is shown that the designed common controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the prescribed tracking control performance is guaranteed under arbitrary switchings. The simulation results are presented to further illustrate the effectiveness of the proposed control scheme.     

抗电磁干扰的心电信号监测系统

为了实现电磁干扰环境下的心电信号监测,提出了一种抗电磁干扰的心电信号监测系统。论文给出了输出光电流的计算公式,分析了半波电压及插入损耗对系统灵敏度的影响。利用铌酸锂电光晶体搭建实验系统,测试了5位健康志愿者的心电信号。应用提出的系统和电学心电信号采集系统分别测试了正常环境及电磁环境下志愿者的心电信号。测试结果表明:在正常环境下,本系统能获得与电学心电信号采集系统同样清晰的心电信号波形;在电磁环境下,本系统获得的心电信号优于电学心电信号采集系统。最后定量计算了两者的信噪比,计算结果表明:在电磁干扰下,本系统的信噪比变化量为0.54dB(V2)/0.49dB(V4),而电学心电信号采集系统的信噪比变化量为24.07dB(V2)/16.75dB(V4)。     

Design, modeling, and testing of a novel 6-DOF micropositioning stage with low profile and low parasitic motion

We developed a novel piezoelectrically actuated 6 degree-of-freedom (DOF) stage for micropositioning. To achieve a parallel mechanism with high stiffness, compact size, and low parasitic motion for 6-DOF positioning, an orthogonal actuator configuration is proposed and actuation units are carefully designed. For the purpose of suppressing parasitic motion in the overall stage, the ratios of the lateral stiffness to actuating directional stiffness of the actuation units are minimized under the condition that the stress at any point in the stage does not exceed the allowable strength. Flexure hinges and lever linkages are effectively applied to the horizontal and vertical actuation units. A physics-based model of the stage is developed to ensure low system order and high reliability. The model parameters are estimated by experiment and finite element analysis. Open-loop tests including sinusoidal and step responses verify low parasitic motion and reliability of the model. Closed-loop step responses with a model-based controller validate the high micropositioning performance of the stage.     

高精度动平衡测量中自适应滤波技术的应用研究

针对动平衡测量中的具体实践，发展了一种能够在强变频率结构背景噪声干扰下确保高精度提取幅值和相位的自适应消噪方法。同时提出了分段变步长的策略和初始值选取措施，使该方法具有较高的效率及可靠性。实验结果和在实际动平衡测量系统上应用表明：该文所发展的方法适合动平衡测量应用，完全满足精度和实时性的要求，具有较高的实用价值。     

A novel power amplification scheme for nuclear magnetic resonance/nuclear quadrupole resonance systems

Nuclear magnetic resonance (NMR) and nuclear quadrupole resonance (NQR)-based chemical analysis systems have been widely utilized in various areas such as medicine, security, and academic research. In these applications, the power amplifier stage plays a key role in generating the required oscillating magnetic fields within a radio frequency coil that serves as the probe. However, the bulky size and relatively low efficiency of the traditional power amplification schemes employed present a bottleneck for the realization of compact sized and portable NMR and NQR systems. To address this problem, this work presents a class D voltage-switching power amplification scheme with novel fast-start and fast-stop functions that are suitable for generating ideal NMR and NQR excitation signals. Compared to the traditional analog power amplifiers (PAs), the proposed switched-mode PA can achieve significant improvement on the power efficiency as well as the physical volume. A PA circuit for portable NQR-based explosive detection systems has been designed and built using the proposed scheme with 1 kW possible maximum output power and 10 MHz maximum operating frequency. Test results show that the presented PA achieves more than 60% measured efficiency within a highly compact volume while sustaining fast start and stop of excitation signals in the order of microseconds.     

A new denoising algorithm for MFL data obtained from seamless pipeline inspection

The magnetic flux leakage (MFL) nondestructive evaluation technique is used extensively for in-service inspection of gas and oil pipelines. Unfortunately, the MFL data obtained from seamless pipeline inspection is usually contaminated by various sources of noise, which considerably reduces the detectability of defect signals in MFL data. In this paper, a new denoising algorithm is presented for removing seamless pipe noise (SPN) and system noise contained in MFL data. The algorithm first utilizes the new wavelet domain adaptive filtering method proposed by combining wavelet transform with the adaptive filtering technique to remove SPN contained in MFL data and then exploits the coefficient denoising approach with wavelet transform to cancel the system noise in the output of the wavelet domain adaptive SPN cancellation system. Theoretical analysis shows that the proposed denoising algorithm has a better overall performance than the existing denoising algorithm. Results of application of the proposed algorithm to MFL data from field tests are presented to demonstrate the performance of the proposed algorithm compared with the existing denoising algorithm. The text was submitted by the author in English.     

Equalization of rotationally variant signals

Several equalization algorithms utilizing the rotationally variant nature of the received signals are presented in this paper to combat the detrimental effect of intersymbol interference (ISI) introduced by frequency selective channels. Their adaptive implementations and application to a time-reversal space-time block coded (TR-STBC) system are also considered. In addition, a turbo equalization algorithm is derived for systems employing the error correction code. The proposed equalizers and turbo equalizer are evaluated over broadband fixed wireless access channels, and are shown to yield superior performance compared to the conventional equalization schemes.     

Adaptive noise cancellation based on EMD in water-supply pipeline leak detection

In water-supply pipeline leak detection and location, both the leak signals and blurred noises are closely related to the pipeline states and surroundings and most of the conventional noise-cancellation methods have to depend on the empirical parameters of either signals or noises. EMD (Empirical Mode Decomposition) is an adaptive signal decomposition method and is exclusive of base functions. A signal is decomposed into several IMFs (Intrinsic Mode Functions) in EMD, then the noise in a signal can be cancelled through removing uncorrelated IMFs. The existing EMD noise cancellation methods need to know the characteristics of either the wanted signal or the noise for rebuilding the noise-removed signal. However the characteristics of leak signals and noises are not fixed in various pipeline conditions, so the existing EMD noise cancellation methods can’t be directly applied in water-supply pipeline leak detection. This paper proposes an adaptive noise cancellation method based on EMD, in which the IMFs that don’t or less contain the components related to the leak can be removed through the cross-correlation between the IMFs and another signal collected at the either side of a suspect leak. In simulation analysis, the adaptive noise cancellation method can increase the SNRs (Signal to Noise Ratios) of leak signals as high as 16 dB. In processing practical pipeline vibro-acoustic signals, with the proposed method the peak of adaptive time delay estimate of leak signals, which determines the location of a leakage, becomes more distinguished, and thus the error of leakage location is improved.     

Investigation of FE model size definition for surface coating application

An efficient prediction mechanical performance of coating structures has been a constant concern since the dawn of surface engineering. However, predictive models presented by initial research are normally based on traditional solid mechanics, and thus cannot predict coating performance accurately. Also, the high computational costs that originate from the exclusive structure of surface coating systems (a big difference in the order of coating and substrate) are not well addressed by these models. To fill the needs for accurate prediction and low computational costs, a multi-axial continuum damage mechanics (CDM)-based constitutive model is introduced for the investigation of the load bearing capacity and fracture properties of coatings. Material parameters within the proposed constitutive model are determined for a typical coating (TiN) and substrate (Cu) system. An efficient numerical subroutine is developed to implement the determined constitutive model into the commercial FE solver, ABAQUS, through the user-defined subroutine, VUMAT. By changing the geometrical sizes of FE models, a series of computations are carried out to investigate (1) loading features, (2) stress distributions, and (3) failure features of the coating system. The results show that there is a critical displacement corresponding to each FE model size, and only if the applied normal loading displacement is smaller than the critical displacement, a reasonable prediction can be achieved. Finally, a 3D map of the critical displacement is generated to provide guidance for users to determine an FE model with suitable geometrical size for surface coating simulations. This paper presents an effective modelling approach for the prediction of mechanical performance of surface coatings.     

自由曲面五轴变步长数控加工刀轨生成方法

为了在满足逼近误差要求的同时最大程度减少冗余刀轨,对自由曲面提出了一种五轴变步长数控加工刀轨生成方法.首先对刀触点轨迹基于线性误差计算出初始刀触点点集,再以局部干涉调整前倾角的方式计算出无干涉刀位点和刀轴矢量;以最大非线性误差刀位处到刀触点轨迹的最小值作为相邻刀位点之间的逼近误差,并基于数据点自适应离散法计算逼近误差;...     

A new computing approach for power signal modeling using fractional adaptive algorithms

Estimating the harmonic parameters is fundamental requirement for signal modelling in a power supply system. In this study, exploration and exploitation in fractional adaptive signal processing (FrASP) is carried out for identification of parameters in power signals. We design FrASP algorithms based on recently introduced variants of generalized least mean square (LMS) adaptive strategies for parameter estimation of the model. The performance of the proposed fractional adaptive schemes is evaluated for number of scenarios based on step size and noise variations. Results of the simulated system for sufficient large number of independent runs validated the reliability and effectiveness of the given methods through different performance measures in terms of mean square error, variance account for, and Nash Sutcliffe efficiency.     

量子模糊形态学联想记忆网络及在齿轮振动信号识别中的应用

针对传统模糊形态学联想记忆网络(FMAM)不能自适应选择结构元素的形状和大小,在对样本进行分类时存在错分问题,以量子的叠加、坍塌性质为基础,提出量子模糊形态学联想记忆网络(QFMAM),用量子位系统构造结构元素,量子位概率代表相应的隶属度,获取具有自适应特性的结构元素,在分类前先对样本进行处理从而降低无用干扰信息对分类精度的影响。利用QFMAM分别对仿真数据和齿轮箱台架实验信号进行分类,并与FMAM、支持向量机(SVM)、朴素贝叶斯分类器(NBC)的分类性能作比较,验证了提出的QFMAM训练效率高、学习能力强、分类精度高,是一种很好用的智能分类器。     

Development of a practical foolproof system using ultrasonic local positioning

The objective of this research is to develop a prototype for practical foolproof system which can be used in manual assembly processes. For this purpose, a high-performance and low-cost ultrasonic system is proposed to measure 3D positions of an indoor mobile object. Composed of an ultrasonic sender and a receiver, the system employs ultrasonic time-of-flight (TOF) and trilateration to estimate the positions of the object with an accuracy of few centimeters. To calculate three TOFs, ultrasonic signals are processed full-digitally with a low-cost FPGA, which provides high design flexibility keeping both high performance and low noise. Proposed system is autonomous, so there is no need of an external PC and the system development cost becomes low. As an improved thresholding method to calculate the TOFs, this paper proposes a debounce module, designed in the FPGA, to remove the pulse noises generated during the thresholding. The resulting time delay from the debounce is compensated by a microprocessor for calculating actual TOFs. Lastly, the positions of the mobile object are calculated from the TOFs values by trilateration in the microprocessor. In order to remove measurement noises, both moving average filters and Kalman filters are adopted in calculating the TOFs and positions.     

基于奇异值和奇异向量的振动信号降噪方法

针对复杂的转子振动信号中同时存在随机噪声干扰和工频噪声干扰的问题,提出了基于奇异值和奇异向量相结合的降噪方法。首先,对振动信号进行奇异值分解(singular value decomposition,简称SVD),根据奇异值谱确定振动信号有效奇异值阶次;其次,对有效阶次范围内的奇异向量进行快速傅里叶变换(fast Fourier transform,简称FFT),依据幅值谱筛选出对应于工频噪声的奇异向量;最后,利用其余的奇异值和奇异向量进行重构得到降噪的时域信号。通过仿真信号和工程试验信号对该方法进行了验证,结果表明,基于奇异值和奇异向量相结合的降噪方法,不但能有效降低振动信号中的随机噪声干扰,还能有效降低工频噪声干扰,同常用的陷波器方法相比所提出方法具有明显优势。     

移动心电监护系统QRS波的实时检测算法研究

针对移动心电监护系统对ECG信号实时分析的需要,提出了利用动态心电图的二阶差分极小值和一阶差分过零点来实现QRS波精确定位的实时检测算法。该算法能克服或抑止各类噪声对ECG信号的影响,并设计了具有自学习和自适应功能的信号检测门限。经过对正常人和临床病人的动态ECG信号进行测试验证,QRS波检测准确率可达99.8%以上,试验结果表明该算法适合于移动心电监护系统QRS波的实时检测和分析。