A time-varying signal processing method for Coriolis mass flowmeter

The mass flowrate of Coriolis mass flowmeter in practical application has slow changes with time. To solve the problem, an improved time-varying signal model whose frequency, amplitude and phase are time-varying based on the random walk model is established firstly. A new algorithm of adaptive notch filter with the capability of tracking frequency variation is applied to filter the sensor output signal of Coriolis mass flowmeter and its frequency is calculated next. An adaptive line enhancer based on the mentioned notch filter extracts fundamental frequency signal from noisy data. Then, by short window intercepting, the revised sliding DTFT recursive algorithm is introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and phase difference obtained, the time interval between the two signals is calculated and then the mass flowrate is derived. The simulations and field test results show that the proposed method can not only track the change of frequency and phase, but also ensure the calculation accuracy when measuring small phase difference. The computational load of the algorithm is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.     

Frequency tracking method and simulation for coriolismass flowmter based on a new adaptive notch filter

In order to improve the long-playing frequency tracking ability in the signal processing for coriolis mass flowmeter (CMF) , a new IIR adaptive notch filter (ANF) is developed via steiglitz-mcBride method (SMM) and applied to filter the sensor output signal whose frequency, amplitude and phase are time-varying following the random walk model. The proposed method can detect the signal frequency fleetly and track the frequency variations continuously. The tracking performances of the proposed method and the adaptive lattice notch filter (LANF) method are investigated with computer simulations. Simulations demonstrate the superiority of the proposed method and show it has advantages of fast convergence rate, insensitivity to initial phase variations, and higher long-playing tracking stability and accuracy than LANF method.     

Signal Processing Method for Coriolis Mass Flowmeter Based on Lattice Notch Filter and DTFT

A novel signal processing method for Coriolis mass flowmeter is proposed based on time-varying signal model. First, an adaptive lattice notch filter is applied to filter the output signal, whose frequency, amplitude and phase are time-varying based on the random walk model, of Coriolis mass flowmeter to get its frequency and enhanced signal. Then, by short window intercepting, the DTFT algorithm with negative frequency contribution is introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference, the time interval between two signals is calculated. Simulation results show that the proposed method is efficient. Furthermore, the computation of algorithms is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.     

Filtering Construct of Adaptive Filter Grou Pwith Multi-scale Decomposition

The principle and method of the adaptive filter and the filtering with wavelet transform were analyzed, and the model and method of adaptive filtering with wavelet transforms for the transient signal was established. The separated noise of signal by the multi-scale decomposition of wavelet transforms, was the input signal of adaptive filter, and accordingly the optimal filtering method of signal-noise decomposition was realized. By the adaptive filter grou Pbased on the wavelet transform, the optimal filtering to the multi-noise of signal is achieved at the same time, and the method presented in this paper has the excellent filtering capability. Examples of application demonstrate that this method presented is excellent to realize the optimal estimate to the valuable signal and noise of the transient signal in the same frequency segment.     

High speed, high precision digital phase sensitive detector design for electrical impedance tomography

Electrical impedance tomography (EIT) system must have the properties of high precision and speed, thus the digital phase sensitive detector (DPSD) based on the field programmable gate array(FPGA) is developed for data collection of EIT. Based on the principle of DPSD, the relationship between signal to noise ratio (SNR) and sample resolution as well as total number of samples is deduced. An implementation scheme of this system and a method of designing analog to digital converter (ADC) clock based on direct digital synthesis (DDS) technology are provided. The system adopts high speed multi channel ADC and low jitter clock conditioner for ADC. Real time DPSD is implemented with FPGA. The experiments show that the measurement accuracy reaches 0.03% and the SNR reaches 85 dB. The agar phantom experiments prove that the performance of the DPSD meet the designing requirement for EIT.     

A blind adaptive reduced-rank receiver for ultra-wideband communication systems

A low-complexity blind adaptive receiver for ultra-wideband (UWB) systems in the presence of both multiple access interference (MAI) and inter symbol interference (ISI) is proposed, which is composed of two stages. In the first stage, a reduced-rank algorithm based on the multi-stage Wiener filter (MSWF) is considered to suppress the MAI and extract multi-path components. Channel estimation using the projection approximation subspace tracking with deflation (PASTd) algorithm and multi-path combining are then performed to further enhance the signal to interference plus noise ratio (SINR) of the desired user in the second stage. The simulation results show that the proposed receiver exhibits reasonably good bit error ratio (BER) performance compared with those of matched filter, conventional Rake receiver, decorrelating Rake receiver and adaptive receiver based on constant modulus algorithm (CMA). Moreover, it has faster convergence speed and less complexity.     

Image denoising based on adaptive graph regularization

Adaptive regularization can select different parameters based on the features of local areas in an image, which can differentiate the edges and noise in an image flexibly. An adaptive graph regularization is proposed based on graph spectral theory and adaptive regularization, which uses the Non local means to generate the weighting function of graph. The adaptive graph regularization equation is used to filter the noisy image. Simulation results show that the proposed method can effectively remove the noise and is superior to other graph theory based partial differential equation methods.     

State of charge estimation of lithium iron phosphate batteriesbased on adaptive Kalman filters

The Kalman filter algorithm can be used to estimate the state of charge (SOC) of power batteries, however, it easily causes divergence due to uncertain of system noise and its estimation performance is affected by model. An adaptive Kalman filter algorithm is adopted to dynamically estimate SOC of lithium iron phosphate batteries for application in electric vehicles. At first, an equivalent circuit model, appropriate for SOC estimation is built after studying battery models. Then some charging and discharging experiments are carried out for parameter identification and the results are verified. At last, the adaptive Kalman filter algorithm is used on this model for on-line SOC estimation under unknown interfering noise. Simulation results show that adaptive Kalman filter method can correct SOC estimation error caused by tiny model error online, and the estimate accuracy is higher than Kalman filter method. Adaptive Kalman filter algorithm can also correct the initial error. Full-cycle test in electric vehicles proves that the algorithm is appropriate for SOC estimation of lithium iron phosphate battery.     

Real time monitoring for multivariate statistical process with on line multiscale filtering

By analyzing shortages of current MSPCA model, an on line multi variable statistical process monitoring method is proposed, which uses some concepts from online multi scale filtering and can be applied to sensor fault diagnosis. In the method, wavelet decomposition is employed to the signals using edge correction filter in a fixed length data window, and then wavelet denoising is conducted with wavelet threshold filtering. Next, an on line multi scale model is constructed for data combining wavelet transformation and adaptive PCA in the previous data window. This model avoids time waste in direct signal denoising and reduces time cost in multi scale data with conventional PCA, which eventually increases accuracy in fault diagnosis. Experiments on eight vibration signals of 6135D diesel engine under severe leak condition prove the practicability and feasibility of the proposed method.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

The Application of Narrowband Interference Suppression in Direct Sequence Spread Spectrum

In order to improve the system effect, it is necessary take the technique of digital signal process to suppress interference, because of the finite gain and the narrow-band interference in system of direct sequence spread spectrum. The authors design a new filter structure improving on tunable digital heterodyne adaptive notch filters. This filter can adaptively track the interference frequency and control the bandwidth and depth of notch, which has the excellent agility and utility. The experiment proves this method overcomes limitation of other interference suppressions, hardly weaken the system performances, enhances the stability of the filter system, and can quickly track the interference frequency.     

汽车转弯自动照明控制系统数据采集滤波方法

为了减少汽车转弯自动照明控制系统数据滤波处理中的延时,在滑动中值滤波方法基础上,提出了一种带补偿的限幅滑动中值滤波算法,通过补偿的方式跟随信号的变化趋势。该算法通过2个步骤完成滤波,先对数据进行带补偿方式的限幅处理,再进行滑动中值滤波处理。该算法综合了滑动中值滤波法的实时性以及限幅滤波法对偶然性干扰滤除的有效性,实现更准确的滤波。仿真与实测结果表明,与滑动中值滤波方法相比较,改进后的算法滤波能够实时地滤除数据中的干扰,处理时间可减少62%,滤波效果较好,且速度信号的数据滞后时间较短,实时性较高,满足系统设计要求。     

Adaptive Enhancementt of Heart Sound Based on Simulink

In the course of recording heart sound, it is inevitable that many kinds of noise will be merged in the main signal. These kinds of noise disbennifit the heart sounds, even import awful influence to analysis result. So, before further processings of phonocardiographic records, noise must be suppressed. Because heart-sound is a highly nonstationary signal, this paper presents the application of adaptive filter method to the elimination of noise,and at the same time, employs the datum of heart-sound plot as the reference noise. With simulink, a modeling and simulation tool for dynamical systems, the model of LMS adaptive heart-sound enhancement is established.Tests prove this method makes effective denoising and heart-sound enhancement.     

ZOOM FFT Technology Based on Analytic Signal and Band Pass Filter

This paper deals on the method of ZOOM FFT based on analytic signal and band pass filter, which is different from other methods of ZOOM FFT. It is used on computer with larger zoom ratio; higher compute speed and so on than other ways for offline testing. It provides a new method for the development of Virtual Instruments on ZOOM FFT analysis.     

黄烷酮类衍生物的抗菌活性QSAR研究

为了研究黄烷酮衍生物分子结构与杀菌活性的关系,本文以34种黄烷酮衍生物的杀菌活性为指标,以量子化学计算软件PCLIENT提取化合物的初始分子描述符,经高维特征筛选以及多轮末尾淘汰进行特征选择,获得了14个重要分子描述符,并基于保留描述符构了建多元线性回归、支持向量回归模型,获得了较高的预测精度。最后基于支持向量机非线性解释体系,对保留分子描述符的作用进行解析,各描述符对影响黄烷酮衍生物的杀菌活性的重要性依次为SssO > MATS6e > SeaC2C2aa > EEig04d > EEig03x > C-028 > R7v+ > L2e > X4A > Mor25m > G1m > Mor17m >BEHv1 > RDF045m,为指导黄烷酮类杀菌剂的合成提供了重要指导。     

Application of digital phase-locked loop and filtering technique in PWM rectifier

VHS-ADC high speed digital signal processing platform based on MATLAB and FPGA could be used to build three-phase voltage source rectifier with space voltage vector PWM(SVPWM).The initial phase of coordinate transformation from three phase static coordinate to two phase static coordinate which is called Park transformation and from two phase rotating reference frame to two phase static coordinate is uncertain. There existed noise in acquiring grid voltage and current. Digital phase-locked loop is added to follow the frequency and phase of grid voltage, FIR filter is added to process signal and reduce the interference. External voltage control loop, internal current control loop and the model of coordinate transformation are designed on the VHS-ADC platform. The small power experiment is conducted to verify the stability and feasibility of the control system. The experimental results show that the digital phase-locked loop and FIR digital filter can be applied to three-phase voltage source rectifier.     

Adaptive RBF networks backstepping control of robots with filtering reducer transmission

A mathematic model for filtering reducer transmission robots which have the characteristics of parameter uncertaintiesexternal disturbancestrong couplingnonlinearities and multi-variable is establishedand then an adaptive RBF networks backstepping method is proposed to control the robot.RBF is employed to approach the nonlinear item of the filtering reducer transmission robot on lineand an adaptive RBF network controller is developed based on backstepping method which effectively suppresses the effect of parameter uncertainties and external disturbance etc.It is combined with Lyapunov stabilization analytical method to demonstrate the close loop system convergence.Simulation results verify that the proposed controller not only satisfies high performance position trackingbut also has good accuracy and robustness.     

Application of Intelligent Neural Network on Prediction of Time Series Signal

The research of neural network has been maturated both in theory and practical application since 1980's, and also been employed into the prediction and analysis of nonlinear time series signal in the field of signal process system. Concerning with the problem of time series signal prediction based on traditional neural network, such as black box, poor accuracy, and facing the shortage of post knowledge, this paper presents a different neural network prediction model from the traditional ones, based on intelligent neural cell model and employing the iterative prediction method. Through the example on stock price prediction, the prediction accuracy and practical value are proved.     

Filter Design of Wavelet Transform for 2-D Image

The filter design is the key to 2-D image wavelet transform. Based on the studying of image properties and 1-D wavelet theory,the authors describe a parasymmetry boundary extension method and realize 2-D discrete wavelet transform by means of 1-D wavelet transform according to correlation of adjacent pixels. Also,it is proved that the discrete wavelet transform of inverse data stream in row and the sign of discrete detail signal will be inversed,and that a filter of wavelet transform based on symmetry of bi_orthogonal filter is constructed. The test has proved the fine reconstruction and perfect SNR.     

Design of Vision System Based on Service Robot

A vision system based on the service robot is involved. In the system, picture signal is acquired by the picture sensor OV7635. Frame memory AL422B is used as data buffer memory, while CPLD controlled the time order DSP performed. In the software system of image processing, to accomplish color image segmentation and recognition, the threshold vector judgment and improved seed-fill algorithm is introduced, and the image geometric moment is calculated during the segmenting. In order to achieve a vision servo system which composed of image-based feedback and adaptive compensation, the deduced matrix-based Jacobian from the image moment is taken as image feature. Adopted the TFT LCD is adopted to straightly disolav the result of vision recognition and vision tracing.