Peak Search Method Research Articles

Semantic mask-based two-step approach: a general framework for X-ray diffraction peak search in high-throughput molecular sieve synthetic system

X-ray diffraction (XRD) is used for characterizing the crystal structure of molecular sieves after synthetic experiments. However, for a high-throughput molecular sieve synthetic system, the huge amount of data derived from large throughput capacity makes it difficult to analyze timely. While the kernel step of XRD analysis is to search peaks, an automatic way for peak search is needed. Thus, we proposed a novel semantic mask-based two-step framework for peak search in XRD patterns: (1) mask generation, we proposed a multi-resolution net (MRN) to classify the data points of XRD patterns into binary masks (peak/background). (2) Peak search, based on the generated masks, the background points are used to fit an n-order polynomial background curve and estimate the random noises in XRD patterns. Then we proposed three rules named mask, shape, and intensity to screening peaks from initial peak candidates generated by maximum search. Besides, a voting strategy is proposed in peak screening to obtain a precise peak search result. Experiments show that the proposed MRN achieves the state-of-the-art performance compared with other semantic segmentation methods and the proposed peak search method performs better than Jade when using f1 score as the evaluation index.

High-Performance Channel Estimation for mmWave Wideband Systems With Hybrid Structures

In this paper, a channel estimation problem for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with hybrid structures is studied. Firstly, a beamspace multiple signal classification (MUSIC) algorithm for mmWave wideband channels is proposed to simultaneously estimate the angles of arrival (AOAs), angles of departure (AODs) and transmission delays. Since the traditional spectral peak search method has high complexity, a multi-spectral peak search method is skillfully designed to search for multiple spectral peaks on the MUSIC spatial spectrum more quickly and accurately. Then, the proposed channel estimator is extended to more actual systems equipped with uniform planar arrays (UPAs). Finally, the Cramér–Rao bound (CRB) results of these channel parameters are derived for evaluating the performance of the proposed channel parameter estimator. Simulation results demonstrate that the proposed channel estimator has greatly high channel estimation accuracy.

Synchro-reassigning generalized Chirplet basis transform for analyzing signals with crossover frequencies

Analysis of signals with crossover frequencies, existing in rotating machinery, is one of the challenges in time–frequency analysis (TFA). The classical TFA methods can only provide a single base in the step window, whereas the signal with crossover frequencies has at least two nonproportional fundamental frequencies. Therefore, classical methods are not suitable for addressing non-proportional multi-component signals such as those with crossover frequencies. This paper presents a novel TFA method, referred to as the synchro-reassigning generalized Chirplet basis transform (SRGCBT), which is designed for processing the signal with crossover frequencies. In the proposed SRGCBT, the peak ridges search method is utilized to find out all of the Chirplet parameters that match with multiple non-proportional fundamental frequencies in the signal. The precision and computational efficiency are enhanced by eliminating the interference of redundant parameters in the enumeration. Then, the synchro-reassigning operator is formulated and applied to extract the time–frequency (TF) coefficients at the instantaneous frequency (IF) ridges. Subsequently, the TF coefficients are reassembled to obtain a new time–frequency representation (TFR). The advantages of SRGCBT are validated by the simulation signal and the vibration signals gathered in the laboratory. The validation results indicate that the proposed SRGCBT delivers improvements in noise robustness, energy concentration, computational efficiency and accuracy in rendering the final TFR for the signal with crossover frequencies.

A novel spectral peak estimator based on underdetermined equation

This letter develops a fast spectral peak estimation method, which is inspired by the theory of compressed sensing (CS). In this work, the measurement procedure of the CS is employed to construct an underdetermined equation, and then a low complexity spectral peak search method is established by using a priori knowledge of signal amplitude. Compared with other frequency estimation methods, the proposed method requires only 10% inner products demanded to obtain the spectral peak positions, and this method can be introduced into any existing discrete Fourier transform (DFT)-based interpolation methods. Experimental results show that the proposed method yields nearly the same accuracy as other interpolation methods, but with lower computational demands.

Improvement of average magnetic field measurement based on magnetic-field-sensitive Ramsey fringes

Accurate magnetic field measurement is the key to evaluating the second-order Zeeman effect. The conventional method is to deduce the magnetic field by determining the center frequency of the magnetic-field-sensitive Ramsey fringes. In this Letter, we present a more rigorous theory for this method and demonstrate that the current peak-searching method has a non-negligible sub-Hz or even larger deviation. We introduce an improved method that considers more parameters and a strict formula that can correct the deviation and suppress it to below 0.1 Hz. Corresponding experiments on the 85Rb atomic fountain demonstrate that this improved method is expected to enhance the precision of magnetic field measurement and improve the atomic fountain clock.

A Multiple Sound Source Localization and Counting Method Based on the Kernel Density Estimator

An unambiguous signal processing algorithm when using a wide intermicrophone distance is proposed in this paper for simultaneously locating and counting multiple active sound sources. Based on the kernel density estimator, a multistage structure in the time-frequency domain is used to suppress the influence of spatial aliasing, then the pooled angular spectrum is combined with a peak search method having an updated cut-off threshold and a source merging module. Complete source localization and counting is realized through the combination of these two steps. Simulation results show that the proposed method has a more robust performance than the classic counterpart, especially in adverse environments with spatial aliasing, reverberation, and interference between different sound sources.

Effect of Peak Tracking Methods on FBG Calibration Derived by Factorial Design of Experiment.

We present a calibration procedure for a humidity sensor made of a fiber Bragg grating covered by a polyimide layer. FBGs being intrinsically sensitive to temperature and strain, the calibration should tackle three variables, and, therefore, consists of a three-variable, two-level factorial design tailored to assess the three main sensitivities, as well as the five cross-sensitivities. FBG sensing information is encoded in the reflection spectrum from which the Bragg wavelength should be extracted. We tested six classical peak tracking methods on the results of the factorial design of the experiment applied to a homemade FBG humidity sensor. We used Python programming to compute, from the raw spectral data with six typical peak search algorithms, the temperature, strain and humidity sensitivities, as well as the cross-sensitivities, and showed that results are consistent for all algorithms, provided that the points selected to make the computation are correctly chosen. The best results for this particular sensor are obtained with a 3 threshold, whatever the peak search method used, and allow to compute the effective humidity sensitivity taking into account the combined effect of temperature and strain. The calibration procedure presented here is nevertheless generic and can thus be adapted to other sensors.

Preliminary Evaluation of Hai‐Nan Lightning Detection Network (HNLDN)

AbstractHai‐Nan Lightning Detection Network (HNLDN) is composed of seven sites that accept very low frequency/low frequency (VLF/LF) signals. A total of seven sites are evenly distributed on Hai‐Nan Island. The baseline distance is between 50 and 150 km, which is about five times longer than in typical short‐baseline VLF/LF imaging networks. Each detection station uses zero‐phase digital filtering to avoid the phase deviation caused by filtering. HNLDN uses an improved peak search method to increase the number of detected of lightning waveforms, applies a One‐dimensional convolutional neural network (1D‐CNN) to identify and classify the waveforms, and to the increase the detection efficiency of intracloud lightning. This article analyzes three typical thunderstorm processes. From inside to outside the network, the detection efficiency of HNLDN relative to the advanced direction‐time lightning detection system (ADTD) is 239%, 181%, and 19%, respectively. It can be seen that the detection efficiency is negatively correlated with the distance from the thunderstorm to HNLDN. The average location error of HNLDN inside the network is 190 m.

Infrared time-sensitive weak and small target detection method based on density search and gradient decision

For detecting infrared time-sensitive weak and small targets in a complex air-ground background, it is easy to be interfered by low signal-to-noise ratio and strong noise. In this paper we proposed an infrared time-sensitive weak and small target detection method based on density search and gradient decision. First, we use adaptive wiener filtering to preprocess the infrared image to reduce noise interference; then, use the density peak search method to extract points of interest, and use the eight-directional gradient method to make decision-making judgments; finally, use the adaptive threshold segmentation method to extract true targets. The experimental results show that this method has good robustness and detection performance for single infrared time-sensitive weak and small target detection in the air-ground background.

Peak-searching method for low count rate [formula omitted] spectrum under short-time measurement based on a generative adversarial network

In scenarios such as vehicle radioactivity monitoring and unmanned aerial vehicle radioactivity monitoring, the count rate of the γ spectrum detected by the NaI(Tl) detector is low, the characteristic peak is weak, and the statistical fluctuation is large. When such a γ spectrum is processed with the conventional peak-searching method, the characteristic peak recognition accuracy is low and the nuclide identification rate is reduced. A peak-searching method based on the generative adversarial network (GAN) is proposed in this study for low count rate and short-time measurement of a single nuclide γ spectrum. Compared with the symmetric zero-area (SZA) method, the characteristic peak recognition accuracy of the GAN method is improved, the occurrence probability of false peaks is reduced, and the number of false peaks is decreased. Furthermore, the peak position offset with different time measurement conditions of the GAN method is stable. And the performance under shielding conditions of the GAN method is also better than that of the SZA method.

Multiple Time-Frequency Curve Classification for Tacho-Less and Resampling-Less Compound Bearing Fault Detection Under Time-Varying Speed Conditions

Rotating machinery usually runs at time-varying speeds, which often brings failures, especially compound faults. However, currently, there is still a lack of satisfactory coupled fault diagnosis methods at variable speeds. Several methods have been developed, which try to eliminate rotational speed influences by converting nonstationary fault characteristic frequencies into stationary ones. But it brings new problems of computational efficiency and accuracy, and requires an auxiliary tachometer to measure the rotational speed. Time-frequency analysis can solve the problems, and extract time-frequency curves for fault diagnosis through time-frequency representation (TFR). However, for signals containing multiple faults at variable speeds, uninterested components will be generated when extracting multiple time-frequency curve (MTFC), which makes it difficult for compound fault diagnosis. To solve these problems, a novel MTFC classification method for tacho-less and resampling-less compound bearing fault detection under time-varying speed conditions is proposed in this paper. Here the TFR characteristics of a multi-fault vibration signal is analyzed, and a compound fault diagnosis strategy based on MTFC classification is developed. Firstly, MTFC is extracted by a local peak search method. Then, according to the relationship between instantaneous shaft rotational frequency, instantaneous fault characteristics frequency and their respective harmonics, two classification criteria for curves are proposed and MTFC is classified into interested curves and uninterested curves. Finally, the average ratios between interested curves are matched to theoretical fault characteristic coefficients to determine the fault type. Case studies on rolling bearing experiments verified the effectiveness and superiority of the proposed method.

An Integrated Design of Ultra-High-Speed FBG Interrogation System-Based on FDML Laser

An integrated design of ultra-high-speed Fiber Bragg Grating (FBG) interrogation system is illustrated and experimentally demonstrated. A Fourier Domain Mode Locked (FDML) laser is employed as a high-speed wavelength-swept light source. The use of the Mach-Zehnder interferometer (MZI) and the single-peak filter (SPF) overcomes the nonlinear effect and the scanning wavelength drift problem. A signal acquisition and peak-searching method based on the hardware is designed, allowing a real-time and multichannel interrogation. The controlled experiment has verified the feasibilities of the MZI and the SPF. Dynamic and static FBG experiments show that the integrated system has an accuracy of 0.02 nm, a stability of 0.006 nm, a linearity of 0.9994 and a 40 kHz demodulation frequency with 4 simultaneous interrogation channels.

Generalized Vold–Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear

Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold–Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time–frequency ridge is separated from the time–frequency representation (TFR) of the vibration signal using a peak search method; 2) according to the time–frequency ridge, GVKF parameters corresponding to all the fault characteristic frequencies (FCFs) are estimated; 3) the fault feature components are obtained using the generalized demodulation transform (GDT) and the VKF with the GVKF parameters; and 4) the spectra obtained by the fast Fourier transform (FFT) are used to fault detection. The main contributions of the proposed method are as follows: 1) the influence of speed fluctuations and the unrelated harmonic components are removed through the integration of the GDT and the VKF and 2) the tachometerless GVKF parameters are defined and calculated to quantitatively detect different fault types, which avoids missed diagnosis and misdiagnosis. The proposed multifault diagnosis algorithm is verified by both simulation and experiment data. Comparison with other commonly used techniques has shown the advantage of the new method.

Continuous Human Motion Recognition With a Dynamic Range-Doppler Trajectory Method Based on FMCW Radar

Radar-based human motion recognition is crucial for many applications, such as surveillance, search and rescue operations, smart homes, and assisted living. Continuous human motion recognition in real-living environment is necessary for practical deployment, i.e., classification of a sequence of activities transitioning one into another, rather than individual activities. In this paper, a novel dynamic range-Doppler trajectory (DRDT) method based on the frequency-modulated continuous-wave (FMCW) radar system is proposed to recognize continuous human motions with various conditions emulating real-living environment. This method can separate continuous motions and process them as single events. First, range-Doppler frames consisting of a series of range-Doppler maps are obtained from the backscattered signals. Next, the DRDT is extracted from these frames to monitor human motions in time, range, and Doppler domains in real time. Then, a peak search method is applied to locate and separate each human motion from the DRDT map. Finally, range, Doppler, radar cross section (RCS), and dispersion features are extracted and combined in a multidomain fusion approach as inputs to a machine learning classifier. This achieves accurate and robust recognition even in various conditions of distance, view angle, direction, and individual diversity. Extensive experiments have been conducted to show its feasibility and superiority by obtaining an average accuracy of 91.9% on continuous classification.

Direction-of-Arrival Estimation for Both Uncorrelated and Coherent Signals in Coprime Array

Direction-of-arrival (DOA) estimation in coprime array involves the problem that there is the coexistence of both uncorrelated signals and coherent signals in the multipath environment. An approach to estimate DOA of uncorrelated and coherent signals separately, based on the coprime linear array and coprime planar array, is proposed in this paper. The uncorrelated signals are estimated first, where the root multiple signal classification (root-MUSIC) is applied in the coprime linear array and unitary estimating signal parameters via rotational invariance techniques (Unitary-ESPRIT) is utilized in the coprime planar array. Those subspace algorithms are of low complexity compared with the spectral peak search method. We then eliminate the components of noises and uncorrelated signals and reconstruct a covariance matrix of coherent signals. Finally, we use the root-MUSIC or Unitary-ESPRIT to resolve the one-dimensional or two-dimensional DOAs of coherent signals, respectively. The simulation results demonstrate the computational efficiency and high accuracy of the proposed algorithm. The results also prove that this algorithm can estimate the number of signals more than that of subarray sensors and separate two signals from the same angle.

Development of pulse shape algorithms for discrimination between gamma and X-ray radiation pulses.

Influence of x-ray pulse generated from gamma spectrometers should be eliminated in applications, which typically uses pulse shape techniques between gamma and x-ray pulses. In this study, we proposed and tested several algorithms aiming to eliminate this influence. The algorithms are based on curve fitting (CF), artificial neural network (ANN), system identification, peak shape, amplitude search with curve fitting and pulse tracking methods. Gamma pulses and X-ray pulses are detected by NaI(TI) scintillator detector and Silicon lithium Si(Li) detector, respectively. The developed algorithms are tested using 32,000 total instantaneous detector events of acquired gamma pulses and 65,536 total instantaneous detector events of x-ray source. An algorithm using the least square curve fitting method is applied for differentiation between gamma and x-ray pulses. ANN is employed as a classifier for identification of extracted spectrum and Bispectrum features of gamma and x-ray pulses. A comparison between identification results due to extracted spectrum and Bispectrum features is established. System identification algorithm is then built to determine the detection system response of each radiation pulse, which includes various models to attain best fitting. These models are Auto-regressive model with external input (ARX), the linear parametric model (IV) and process models (P1D). The peak shape algorithm is also tried, which depends on the individual classification of pulse width. The amplitude search with curve fitting algorithm is implemented. Moreover, the pulse tracking algorithm is investigated for PSD between gamma and x-ray pulses. The maximum peak of contaminated pulse is tracked using a suggested peak search method. Then, pulse position is estimated using matrix method. Comparison between these algorithms is conducted based on the evaluation of light of residuals, fitting error and processing time. The results confirm that peak shape algorithm is the best one from computational speed point of view, while ANN algorithm using Bispectrum feature extraction method is the most appropriate one that yields 100% accuracy over noisy environment with longer processing time. In addition, the system identification algorithm is the optimal algorithm that achieves zero fitting error under clean environment. These proposed algorithms for PSD between gamma and x-ray pulses lead to design efficient spectrometers with optimal applicability in various environments.

Research on iterative repair algorithm of Hyperchaotic image based on support vector machine

The damaged area of the hyperchaotic image is prone to lack of texture information. It needs to make image restoration design to improve the information expression ability of the image. In this paper, an iterative restoration algorithm of hyperchaotic image based on support vector machine is proposed. The sample blocks in the damaged region of hyperchaotic images are divided into smooth mesh structures according to block segmentation method, and the neighborhood pixels of which points need to repair are ranked efficiently according to gradient values. According to the edge fuzzification features, the position of the important structural information of the damaged area is located. A multi-dimensional spectral peak search method is applied to construct the information feature subspace of image texture, so as to find the best matching block for restoring the damaged region of hyperchaotic image. Considering the features of structural information and texture information, the maximum likelihood algorithm is used to reconstruct the pixel elements in the image region by piecewise fitting. Through the support vector machine algorithm, the image iterative restoration is carried out. The simulation results show that the restoration method for hyperchaotic image can achieve effective restoration of image damaged area, the quality of restorationed image is better, and the computation speed is fast. The image restoration method can effectively ensure the visual effect of the reconstructed image.

Influence of Crack on Modal Parameters of Cantilever Beam Using Experimental Modal Analysis

In this paper, the dynamic behavior of a cantilever beam without and with crack is observed. An elastic Aluminum cantilever beams having surface crack at various crack positions are considered to analyze dynamically. Crack depth, crack length and crack location are the foremost parameters for describing the health condition of beam in terms of modal parameters such as natural frequency, mode shape and damping ratio. It is crucial to study the influence of crack depth and crack location on modal parameters of the beam for the decent performance and its safety. Crack or damage of structure causes a reduction in stiffness, an intrinsic reduction in resonant frequencies, variation of damping ratios and mode shapes. The broad examination of cantilever beam without crack and with crack has been done using Numerical analysis (Ansys18.0) and experimental modal analysis. To observe the exact higher modes of beam, discretize the beam into small elements. An experimental set up was established for cantilever beam having crack and it was excited by an impact hammer and finally the response was obtained using PCB accelerometer with the help sound and vibration toolkit of NI Lab-view. After obtaining the Frequency response functions (FRFs), the natural frequencies of beam are estimated using peak search method. The effectiveness of experimental modal analysis in terms of natural frequency is validated with numerical analysis results. This paper contains the study of free vibration analysis under the influence of crack at different points along the length of the beam.

Spatial resolution cell based centralized target detection in multistatic radar

Abstract The problem of spatial resolution cell (SRC) based centralized target detection in multistatic radar is considered. Due to the fact that using the observation data from different radar sites can result in a much smaller uncertainty volume, the SRC of multistatic radar is usually an intersection of the overlapping range, azimuth and elevation resolution cells of all spatial diversity channels (SDCs). Then, the target detection problem in multistatic radar can be considered as deciding whether a target is present in each probed SRC. In this paper, a general scheme of the SRC based centralized target detection in multistatic radar is proposed. Considering that different neighboring SRCs of multistatic radar may correspond to the same range, azimuth and elevation resolution cell of one SDC, the desired signals or noises from these neighboring SRCs hence can be mutually dependent. Motivated by this, the symbiosis relationship of these neighboring SRCs in exceeding the test threshold during the process of centralized target detection is specifically studied. Furthermore, the performance of the peak search method to decide which SRC on earth contains the target of interest is accordingly analyzed.

A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks

In this paper, a Reconfigurable Mesh-Ring (RMR) algorithm is proposed for Bluetooth sensor networks. The algorithm is designed in three stages to determine the optimal configuration of the mesh-ring network. Firstly, a designated root advertises and discovers its neighboring nodes. Secondly, a scatternet criterion is built to compute the minimum number of piconets and distributes the connection information for piconet and scatternet. Finally, a peak-search method is designed to determine the optimal mesh-ring configuration for various sizes of networks. To maximize the network capacity, the research problem is formulated by determining the best connectivity of available mesh links. During the formation and maintenance phases, three possible configurations (including piconet, scatternet, and hybrid) are examined to determine the optimal placement of mesh links. The peak-search method is a systematic approach, and is implemented by three functional blocks: the topology formation block generates the mesh-ring topology, the routing efficiency block computes the routing performance, and the optimum decision block introduces a decision-making criterion to determine the optimum number of mesh links. Simulation results demonstrate that the optimal mesh-ring configuration can be determined and that the scatternet case achieves better overall performance than the other two configurations. The RMR topology also outperforms the conventional ring-based and cluster-based mesh methods in terms of throughput performance for Bluetooth configurable networks.