Effect of Signal Filtering on Image Quality of Projection-Based Magnetic Particle Imaging

Purpose: Magnetic particle imaging (MPI) allows for imaging of the spatial distribution of magnetic nanoparticles (MNPs) in positive contrast, with high sensitivity, high spatial resolution, and high imaging speed. It is necessary to increase the signal-to-noise ratio to enhance the reliability of MPI. The purpose of this study was to investigate the effect of signal filtering on the image quality and quantitativity in projection-based MPI using phantoms. Materials and Methods: We fabricated two kinds of phantom (cylindrical tube phantom with a diameter of 6 mm and A-shaped phantom) and evaluated the effect of signal filtering in terms of root-mean-square (RMS) granularity and the correlation coefficient between iron concentrations of MNPs and average MPI values for four filter modes (THRU, BPF, BEF, and LPF). In the THRU mode, the signal input was output without passing through the filter. In the BPF mode, only the third-harmonic signal was passed using a band-pass filter (central frequency: 1200 Hz, band width: 1/3 octave). In the BEF mode, the first-harmonic signal was eliminated using a band-elimination filter (central frequency: 400 Hz, band width: 1/3 octave). In the LPF mode, only the signal with a frequency less than the third-harmonic frequency was passed using a low-pass filter (cut-off frequency: 1200 Hz, -24 ± 2 dB/octave). The RMS granularity was obtained by calculating standard deviations of the pixel values in the MPI image without MNPs, whereas average MPI values were obtained by drawing a circular region of interest with a diameter of 6 mm on the MPI image of the cylindrical tube phantom. Results: When using the filtered back-projection (FBP) method with a ramp filter for image reconstruction, the RMS granularity and correlation coefficient decreased in the order of THRU, BPF, BEF, and LPF. In the BPF mode, however, some artifacts were observed. When using the maximum likelihood-expectation maximization (ML-EM) algorithm with an iteration number of 15, the correlation coefficient decreased in the order of THRU, BPF, BEF, and LPF, whereas the RMS granularity did not largely depend on the filter mode and was significantly (p Conclusion: The BEF mode is adequate for the FBP method in projection-based MPI, whereas THRU is a best option in use of the ML-EM algorithm.

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks （WSNs）, a new signal reconstruction algorithm based on a cubature Kalman particle filter （CKPF） is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter （CKF） to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter （UKPF） and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.

STUDY OF MISMATCHED FILTERING OF PASSIVE RADAR USING TV SIGNAL

This letter demonstrates the structure of the passive radar using TV signals. Because the TV signal is a kind of pseudoperiodic signal, the matched filtering of color TV signals would yield high sidelobes which cause the range ambiguity. To overcome this problem, the mismatched filter is proposed to suppress the correlation sidelobes of matched filtering of TV signals. By utilizing the iteration process, this method could achieve the required peak sidelobe level. The impacts of the noise and target movement on mismatched filtering are also analysed. Simulation results are included to demonstrate the effectiveness of the proposed technique.

Generalized Parseval’s Theorem on Fractional Fourier Transform for Discrete Signals and Filtering of LFM Signals

This paper investigates the generalized Parseval’s theorem of fractional Fourier transform (FRFT) for concentrated data. Also, in the framework of multiple FRFT domains, Parseval’s theorem reduces to an inequality with lower and upper bounds associated with FRFT parameters, named as generalized Parseval’s theorem by us. These results theoretically provide potential valuable applications in filtering, and examples of filtering for LFM signals in FRFT domains are demonstrated to support the derived conclusions.

RECURSIVE FILTERING RADON-AMBIGUITY TRANSFORM ALGORITHM FOR DETECTING MULTI-LFM SIGNALS

In multi-LFM signal condition, Radon-Ambiguity Transform (RAT) of the strongLFM component has strong suppression effect on that of the weak LFM component. A methodnamed as Recursive Filtering RAT (RFRAT) Mgorithm is proposed for solving this problem. Byfully using of the Maximum Likelihood (ML) estimation value of the frequency modulation rategot by RAT. RFRAT can detect the noisy multi-LFM signals out step by step. The merit of thisnew method is validated by an illustrative example in low Signal-to-Noise-Ratio (SNR) condition.

LOOP-SHIFT S&G ADAPTIVE FILTER AND ITS APPLICATION IN SEPARATION OF OVERLAPPED PERIODIC BIOMEDICAL SIGNALS

A Loop-Shift Stop & Go Adaptive Filter(LSSGAF) with applications in period detection and signal separation of overlapped periodic signals is presented. The filter places no constraints on the overlapped forms in frequency domain and requires no priori knowledge except for approximate period range of signals to be separated, therefore it provides for an efficient and feasible method to separate periodic signals overlapped both in time and frequency domains. The convergence conditions of the filter and the requirements of all parameter selections are discussed, meanwhile a rough scanning and channel auto-shut algorithm is proposed to reduce the amount of calculation in the searching stage. Experiments show that the filter can separate all kinds of overlapped periodic signals with different initial phases and different relative magnitudes.

BeiDou B1I/B3I Signals Joint Tracking Algorithm Based on Kalman Filter

Dual-frequency satellite positioning receivers are widely used because they can eliminate ionospheric delay and solve the full-circumference ambiguity quickly.However,in traditional dual-frequency receivers,the relevance of dual-frequency signals are not considered,and,with no improvement imposed to the tracking loop,two independent tracking loops are used to achieve the tracking of dual-frequency signals.In this paper,the Bei Dou dual-frequency signals joint tracking algorithm based on Kalman filter is proposed for the tracking of Bei Dou B1I and B3I dual-frequency signals.Taking the relevance of B1I and B3I signals into consideration,the algorithm adds a Kalman filter between the phase detector and carrier loop filter of the traditional dual-frequency independent tracking loop.The output results of the phase detectors of the B1I and B3I branches are then combined and filtered by the Kalman filter,and the results are input to the carrier loop filters of the corresponding branches.Proved by experiments,the algorithm not only enables the loop to enter a stable tracking state quickly,but also reduces the noise bandwidth of the two loop filters by about 10 Hz with the same tracking performance obtained.

Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Digital FIR filter design for bio-signal processing system

Recently,real-time processing systems for bio-signal of the muscles generated by the movement of the user have been developed.Finite impulse response(FIR)filter for bio-signal processing in bio-signal process systems is composed of multiple multiplier and adder of high-area.This makes the chip area increase significantly.To solve this problem,a low-area digital FIR filter is proposed in this paper,which can reduce the chip area.

Fuzzy Digital Filtering: Signal Interpretation

The paper makes a description of the fuzzy filter properties considering its operational principles. A digital filter interacts with a reference model signal into real process in order to get the best corresponding answer, having the minimum error at the filter output using the mean square criterion. Adding into this filter structure a fuzzy mechanism, to obtain an intelligent filtering because adaptively select and emit a decision answer according with the external reference signal changes, in order to actualize the best correct new conditions updating a process dynamically. The interpretation of the input signal level describes the operation of the reference model, to update the filter weights giving the answers approximation in accordance with the reference signal in natural form. Finally the paper shows the simulations results of the fuzzy filter into the Kalman structure using the Matlab? tool.

Signal Processing Circuit Design of Infrared Detection System with SO2 Concentration Based on Correlation Filter Technology

Signals from infrared detector are very weak in SO2 concentration measuring system.In order to improve the sensitivity of detection,combining with filter correlation technology and infrared absorption principle,the weak signal processing circuit is designed according to correlation detection technology.Under laboratory conditions,system performance of SO2 concentration is tested,and the experimental data are analyzed and processed.Then relationship of SO2 concentration and the measuring voltage is provided to prove that the design improves measuring sensitivity of the system.

Restoration of Noisy 1/f Family Fractal Signal Based on Haar Wavelets and Wiener Filter

In order to restore noisy fractal Brownian motion(FBM),discrete fractional gaussiannoise(DFGN) combined with noise increments is decomposed by Haar wavelets based on Mallatalgorithm.Considering the correlation of detail coefficients,a bank of Wiener filters are used to estimatethe detail coefficients to reconstruct DFGN considering the estimated approximation coefficients in thecoarsest scale in the minimum mean square sense.Then,the reconstructed DFGN is used to restore FBM.In the digital simulation,in light of the restoration mean square error,we show that the suppose that thecorrelation of detail coefficients and the approximation coefficients in the coarsest scale for any Hurstcould be avoided is unrealistic.Moreover,we calculate the estimation root mean square error of the hurstparameter of the restored FBM to show the validity of our algorithm.

DESIGN OF INTERPOLATION FILTER FOR ALL DIGITAL RECEIVER-ON THE INTERPOLATION PROBLEM OF FULL RESPONSE MODULATION SIGNALS

All digital implementation of receiver is a main topic on digital communication recently. The design of interpolation filter is one of the important problems for all digital implementation of receiver. In this paper, for full response linear modulation signal, a interpolation criterion is proposed. An interpolation formula is presented on bandwidth-limited transmission signal. For example, using the raised cosine roll off function as the system pulse response, the feasibility and effectiveness on the interpolation formula are certified by theoretical and numerical analysis. The computer simulation result on 16-QAM signal is given.

一种压阻式传感器信号调理电路的设计

由于压阻式MEMS传感器输出信号极其微弱,极易受到测试环境的噪声干扰,还需要外接调理电路,为了提升信号的采集精度,提出一种压阻式传感器信号ASIC调理电路的设计方案,实现输出信号的放大、滤波功能。测试结果表明,调理电路可以在10~200倍的范围内调节增益,-0.5 dB截止频率调节范围覆盖0.1~20 kHz;低频范围内,芯片的等效噪声为45 nV/√Hz。将调理电路与传感器相结合,改进的测试系统响应速度快,滤波效果更好,测试精度优于5%,传感器非线性为5.4%。同时,调理电路的大小和体积也很小,功耗低,对后续继续研究传感器测试系统有重要意义。

Kalman filter of the force signal of identifying weld seam in remote teaching

For reasons of the vibration of robot, the rough surface of weld seam and electromagnetic disturbance of welding machine, the force signals of identifying weld seam become unstable. The position error of remote teaching point is too big to meet teaching requirements in remote welding. The force signals of identifying weld seam can be filtered by Kalman. The force signals of identifying weld seam of next teaching point is accurately predicted according to predicting algorithms, such as the equation of the state, the equation of the observation, the gain matrix of the filter and the covariance matrix of predicting state. The experimental results show that the precision of identifying weld seam is improved by Kalman.filter.

An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter

Analysis of long-term EEG signals needs that it be segmented into pseudo stationary epochs. That work is done by regarding to statistical characteristics of a signal such as amplitude and frequency. Time series measured in real world is frequently non-stationary and to extract important information from the measured time series it is significant to utilize a filter or smoother as a pre-processing step. In the proposed approach, the signal is initially filtered by Moving Average (MA) or Savitzky-Golay filter to attenuate its short-term variations. Then, changes of the amplitude or frequency of the signal is calculated by Modified Varri method which is an acceptable algorithm for segmenting a signal. By using synthetic and real EEG data, the proposed methods are compared with original approach (simple Modified Varri). The simulation results indicate the absolute advantage of the proposed methods.

Time-Varying Bandpass Filter Based on Assisted Signals for AM-FM Signal Separation: A Revisit

In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.

基于反馈机制的光子微波信号级联滤波方法

光子微波通信过程中,受噪声影响造成带宽变窄,降低了通信效率。为了优化光子微波通信质量,提出基于反馈机制的光子微波信号级联滤波方法。利用形态学腐蚀和膨胀去除信号模糊程度后,将相似度较高的信号聚集为连续区域,完成微波信号去噪。选用卡尔曼滤波器对微波信号测量噪声初次滤波,使用均值滤波以及双门限输出方案二次滤波双路光电反馈下生成的微波信号。通过两种滤波器的级联处理,去除微波信号中不同的杂波,实现窄线宽光子微波信号级联滤波。实验结果表明,所提方法级联滤波波动幅度小,能够平稳输出滤波结果,且不同温度下的级联滤波衰减特性相对平稳。

Elegant and Practical Method of Fir Decimation Using Comb Filters in the Field of Digital Signal Processing

This paper deals with the technology of using comb filters for FIR Decimation in Digital Signal Processing. The process of decreasing the sampling frequency of a sampled signal is called decimation. In the usage of decimating filters, only a portion of the out-of-pass band frequencies turns into the pass band, in systems wherein different parts operate at different sample rates. A filter design, tuned to the aliasing frequencies all of which can otherwise steal into the pass band, not only provides multiple stop bands but also exhibits computational efficiency and performance superiority over the single stop band design. These filters are referred to as multiband designs in the family of FIR filters. The other two special versions of FIR filter designs are Halfband and Comb filter designs, both of which are particularly useful for reducing the computational requirements in multirate designs. The proposed method of using Comb FIR decimation procedure is not only efficient but also opens up a new vista of simplicity and elegancy to compute Multiplications per Second (MPS) and Additions per Second (APS) for the desired filter over and above the half band designs.

基于EM-KF算法的微地震信号去噪方法

针对微地震信号能量较弱,噪声较强,使微地震弱信号难以提取问题,提出了一种基于EM-KF(Expectation Maximization Kalman Filter)的微地震信号去噪方法。通过建立一个符合微地震信号规律的状态空间模型,并利用EM(Expectation Maximization)算法获取卡尔曼滤波的参数最优解,结合卡尔曼滤波,可以有效地提升微地震信号的信噪比,同时保留有效信号。通过合成和真实数据实验结果表明,与传统的小波滤波和卡尔曼滤波相比,该方法具有更高的效率和更好的精度。