A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

Low-complexity signal detection for massive MIMO systems via trace iterative method

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

Mask在包装材料气体阻隔性能检测中的应用研究

目的研究和评价mask在材料气体阻隔性检测中的应用及其检测数据的重复性、准确性和数据稳定性。方法选用覆盖高阻隔、中阻隔、低阻隔等阻隔性能范围的5种样品,使用3种不同面积的mask和仪器测试腔原有面积对样品进行测试,并对测试结果重复性、稳定性和准确性进行分析评价。结果高阻隔材料PET硬片使用面积12.56 cm^(2)的mask测试时,可以得到较为稳定的检测结果,而在使用更小面积(1.77、5 cm^(2))的mask时,测试结果的相对标准偏差、相对极差和测试数据偏差都较差,不推荐使用。KOP/CPP在使用1.77 cm^(2)的mask测试时,测试结果相对标准偏差和测试数据偏差都略大于10%。PET/CPP在使用1.77 cm^(2)的mask测试时,其测试数据偏差略大于10%。BOPE/LDPE和TPU使用1.77 cm^(2)的mask测试可以得到良好的检测结果。结论Mask是解决试样材料特性、设备量程限制、试样尺寸等测试困难的优秀解决方案。对于中、低阻隔材料的透气性测试,使用mask可获得具有良好可信度和稳定性的测试数据。而在进行氧气透过率的测试时应尽量选择大的测试面积。小面积mask不适用于高阻隔材料的气体阻隔性测试。

A Distributed Newton Method for Processing Signals Defined on the Large-Scale Networks

In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.

Determining beam transverse absolute position by triangulation of multi-electrode signal phase differences

Accurate measurement of the transverse position of a beam is crucial in particle accelerators because it plays a key role in determining the beam parameters.Existing methods for beam-position measurement rely on the detection of image currents induced on electrodes or narrow-band wake field induced by a beam passing through a cavity-type structure.However,these methods have limitations.The indirect measurement of multiple parameters is computationally complex,requiring external calibration to determine the system parameters in advance.Furthermore,the utilization of the beam signal information is incomplete.Hence,this study proposes a novel method for measuring the absolute electron beam transverse position.By utilizing the geometric relationship between the center position of the measured electron beam and multiple detection electrodes and by analyzing the differences in the arrival times of the beam signals detected by these electrodes,the absolute transverse position of the electron beam crossing the electrode plane can be calculated.This method features absolute position measurement,a position sensitivity coefficient independent of vacuum chamber apertures,and no requirement for a symmetrical detector electrode layout.The feasibility of this method is validated through numerical simulations and beam experiments.

基于FFT和Masking的实时语音通话降噪算法

针对语音通话质量的提升问题,提出一种基于快速傅里叶变换(Fast Fourier Transform,FFT)和Masking技术的实时语音通话降噪算法。首先,提出一个实时语音通话降噪的基本框架,并研究了帧分割、窗函数处理及FFT的数学原理。其次,阐述了基于人耳听觉特性的Masking方法及其在频域中的应用。最后,通过逆快速傅里叶变换(Inverse Fast Fourier Transform,IFFT)将信号转换回时域,并进行实验分析。实验结果表明,该降噪算法可以有效改善语音的清晰度和整体感知质量。

Using Pearson’s System of Curves to Approximate the Distributions of the Difference between Two Correlated Estimates of Signal-to-Noise Ratios: The Cases of Bivariate Normal and Bivariate Lognormal Distributions

Background: The signal-to-noise ratio (SNR) is recognized as an index of measurements reproducibility. We derive the maximum likelihood estimators of SNR and discuss confidence interval construction on the difference between two correlated SNRs when the readings are from bivariate normal and bivariate lognormal distribution. We use the Pearsons system of curves to approximate the difference between the two estimates and use the bootstrap methods to validate the approximate distributions of the statistic of interest. Methods: The paper uses the delta method to find the first four central moments, and hence the skewness and kurtosis which are important in the determination of the parameters of the Pearsons distribution. Results: The approach is illustrated in two examples;one from veterinary microbiology and food safety data and the other on data from clinical medicine. We derived the four central moments of the target statistics, together with the bootstrap method to evaluate the parameters of Pearsons distribution. The fitted Pearsons curves of Types I and II were recommended based on the available data. The R-codes are also provided to be readily used by the readers.

Application of clan member signal method in structural damage detection

It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （CMSM） is proposed for use in structures consisting of groups （or clans） that have the same geometry, i.e., the same cross section and length, and identical boundary conditions. It is expected that signals measured on any undamaged member in a clan after an event could be used as a reference for any other members in the clan. To verify the applicability of the proposed method, a steel truss model is tested and the results show that the CMSM is very effective in detecting local damage in structures composed of identical slender members.

Probabilistic small signal stability analysis of power system with wind power and photovoltaic power based on probability collocation method

Recently, with increasing improvements in the penetration of wind power and photovoltaic power in the world, probabilistic small signal stability analysis(PSSSA) of a power system consisting of multiple types of renewable energy has become a key problem. To address this problem, this study proposes a probabilistic collocation method(PCM)-based PSSSA for a power system consisting of wind farms and photovoltaic farms. Compared with the conventional Monte Carlo method, the proposed method meets the accuracy and precision requirements and greatly reduces the computation; therefore, it is suitable for the PSSSA of this power system. Case studies are conducted based on a 4-machine 2-area and New England systems, respectively. The simulation results show that, by reducing synchronous generator output to improve the penetration of renewable energy, the probabilistic small signal stability(PSSS) of the system is enhanced. Conversely, by removing part of the synchronous generators to improve the penetration of renewable energy, the PSSS of the system may be either enhanced or deteriorated.

Totally Coded Method for Signal Flow Graph Algorithm

After a code-table has been established by means of node association information from signal flow graph, the totally coded method (TCM) is applied merely in the domain of code operation beyond any figure-earching algorithm. The code-series (CS) have the holo-information nature, so that both the content and the sign of each gain-term can be determined via the coded method. The principle of this method is simple and it is suited for computer programming. The capability of the computer-aided analysis for switched current network (SIN) can be enhanced.

Distributed wide field electromagnetic method based on high-order 2^(n) sequence pseudo random signal

To make three-dimensional electromagnetic exploration achievable,the distributed wide field electromagnetic method(WFEM)based on the high-order 2^(n) sequence pseudo-random signal is proposed and realized.In this method,only one set of high-order pseudo-random waveforms,which contains all target frequencies,is needed.Based on high-order sequence pseudo-random signal construction algorithm,the waveform can be customized according to different exploration tasks.And the receivers are independent with each other and dynamically adjust the acquisition parameters according to different requirements.A field test in the deep iron ore of Qihe−Yucheng showed that the distributed WFEM based on high-order pseudo-random signal realizes the high-efficiency acquisition of massive electromagnetic data in quite a short time.Compared with traditional controlled-source electromagnetic methods,the distributed WFEM is much more efficient.Distributed WFEM can be applied to the large scale and high-resolution exploration for deep resources and minerals.

Application of quartz crystal nanobalance in conjunction with a net analyte signal based method for simultaneous determination of leucine, isoleucine and valine

The aim of the present investigation was to develop a biosensor for the detection of amino acids, Leucine, Isoleucine and Valine based on a quartz crystal nanobalance. leucine (Leu), isoleucine (Ile), and valine (Val) were selectively determined by quartz crystal nanobalance (QCN) sensor in conjunction with net analyte signal (NAS)-based method called HLA/GO. An orthogonal design was applied for the formation of calibration and prediction sets including Leu, Ile and Val compounds. The selection of the optimal time range involved the calculation of the net analyte sig-nal regression plot in any considered time window for each test sample. The searching of a region with maximum linearity of NAS regression plot (minimum error indicator) and minimum of PRESS value was carried out by applying a moving window strategy. On the base of obtained results, the differences on the adsorption profiles in the time range between 1 and 300 s were used to determine mixtures of compounds by HLA/GO method. The results showed that the method was successfully applied for the determina-tion of Leu, Ile and Val.

Correction of cosine oscillation to the improved correlation method of estimating the amplitude of gravitational background signal

In the measurement of G with the angular acceleration method,the improved correlation method developed by Wu et al.（Wu W H,Tian Y,Luo J,Shao C G,Xu J H and Wang DH 2016 Rev.Sci.Instrum.87 094501） is used to accurately estimate the amplitudes of the prominent harmonic components of the gravitational background signal with time-varying frequency.Except the quadratic slow drift,the angular frequency of the gravitational background signal also includes a cosine oscillation coming from the useful angular acceleration signal,which leads to a deviation from the estimated amplitude.We calculate the correction of the cosine oscillation to the amplitude estimation.The result shows that the corrections of the cosine oscillation to the amplitudes of the fundamental frequency and second harmonic components obtained by the improved correlation method are within respective errors.

A FAST SEARCH METHOD BASED ON THE STATISTICAL PROPERTIES OF SIGNAL

The concepts of ordered code-book and the priority of code-vector are proposedin this paper.The statistical properties of a signal are investigated through its coded sequence.Experimental results are presented which provide some insight into the statistical properties ofvector quantized sequences.Based on the given concepts and experimental results,a fast searchmethod for the vector quantization of correlated information sources,such as Gauss-Markovsources,is proposed and has shown its efficiency in simulation results.

Signal-to-noise ratio comparison of angular signal radiography and phase stepping method

Grating-based x-ray phase contrast imaging has the potential to be applied in future medical applications as it is compatible with both laboratory and synchrotron source. However, information retrieval methods are important because acquisition speed, scanning mode, image quality, and radiation dose depend on them. Phase-stepping （PS） is a widely used method to retrieve information, while angular signal radiography （ASR） is a newly established method. In this manuscript, signal-to-noise ratios （SNRs） of ASR are compared with that of PS. Numerical experiments are performed to validate theoretical results. SNRs comparison shows that for refraction and scattering images ASR has higher SNR than PS method, while for absorption image both methods have same SNR. Therefore, our conclusions would have guideline in future preclinical and clinical applications.

A novel wavelet method for electric signals analysis in underwater arc welding

Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavelet （MMW） method. A novel threshold algorithm, which compromises the hard-threshold wavelet （HTW） and soft-threshold wavelet （STW） methods, is investigated to eliminate welding current noise. Finally, advantages over traditional wavelet methods are verified by both simulation and experimental results.

A Critical Eigenvalues Tracing Method for the Small Signal Stability Analysis of Power Systems

The continuation power flow method combined with the Jacobi-Davidson method is presented to trace the critical eigenvalues for power system small signal stability analysis. The continuation power flow based on a predictor- corrector technique is applied to evaluate a continuum of steady state power flow solutions as system parameters change;meanwhile, the critical eigenvalues are found by the Jacobi-Davidson method, and thereby the trajectories of the critical eigenvalues, Hopf bifurcation and saddle node bifurcation points can also be found by the proposed method. The numerical simulations are studied in the IEEE 30-bus test system.

Single Phase-to-Ground Fault Line Identification and Section Location Method for Non-Effectively Grounded Distribution Systems Based on Signal Injection

A diagnostic signal current trace detecting based single phase-to-ground fault line identifica- tion and section location method for non-effectively grounded distribution systems is presented in this paper.A special diagnostic signal current is injected into the fault distribution system,and then it is detected at the outlet terminals to identify the fault line and at the sectionalizing or branching point along the fault line to locate the fault section.The method has been put into application in actual distribution network and field experience shows that it can identify the fault line and locate the fault section correctly and effectively.

基于Mask R-CNN和样方密度法的城市功能区识别

因传统方法单一使用遥感影像或兴趣点(point of interest,POI)数据识别城市功能区时,存在城市特征信息利用不完全、识别精度不高的问题,提出利用POI数据、遥感影像等多源数据,并将自然特征和人文特征相结合,采用基于掩膜区域卷积神经网络和样方密度法(mask region based convolutional neural network and quadrat density method,Mask R-CNN-QDM)模型识别城市功能区的方法。首先基于遥感影像采用Mask R-CNN模型识别建筑物,然后将识别结果与POI数据进行补充校验,得到结合自然特征和人文特征的分类结果,再引入面积要素对分类结果进行赋分,以计算样方密度,并采用随机抽样方式对所提方法功能区的识别精度进行评价。研究结果表明,Mask R-CNN-QDM模型的识别精确度高达0.900,平均Kappa系数为0.802,说明该方法能较好地区分单一城市功能区和混合城市功能区。