Time-frequency Signal Research Articles

Restoration of integrated circuit terahertz image based on wavelet denoising technique and the point spread function model

In recent years, terahertz (THz) imaging technology has attracted much attention in the detection of the integrated circuit (IC). However, limited by the hardware of the imaging system, THz images often contain a significant amount of noise, which impairs the quality of the image details. The THz image is also degraded due to the long wavelength. In this study, we propose a novel method for THz image restoration. We first apply a wavelet denoising technique to process the THz time-frequency signal. The point spread function (PSF) is then mathematically modeled to restore the details of the IC image, as the degradation of the THz image can be regarded as the convolution process of the object equation and PSF. Finally, we compare the performance between the restored THz images before and after wavelet denoising. The results demonstrate that the restored image after denoising performs better in peak signal-to-noise ratio and visual improvements, proving the practicability and precision of our proposed method.

Improved Fault Diagnosis of Railway Switch System Using Energy-Based Thresholding Wavelets (EBTW) and Neural Networks

An improved method with feature extraction and neural network classification is proposed and applied to address the fault diagnosis problem in railway switch systems. Features are extracted by analyzing the nonstationary signal time-frequency characteristics of the electromechanical machine under different fault severities. To enhance the fault diagnosis capability, an energy-based thresholding wavelets (EBTW) approach is proposed by reconstructing a low-dimensional feature vector with selected parameters. Different from the conventional discrete wavelet transform and soft-thresholding wavelet transform, the proposed method localizes and redistributes the signal energy to achieve an efficient dimension reduction. The first step of the proposed method is to perform a wavelet transform upon the original signals, whose wavelet coefficients are then thresholded and an extraction of features is made, on the basis of the energy conservation property of wavelet transforms. Once the features are extracted and selected, several state-of-the-art neural-network-based classifiers are applied for fault diagnosis by identifying the signal features from a machine normal operation and faulty operations with multiple severities. The effectiveness of the proposed method is verified using real-world operational railway switch data and compared with conventional feature extraction methods under different classifiers.

Securing Health Monitoring via Body-Centric Time-Frequency Signature Authorization

Identity-based attacks serve as the basis of an intruder's attempt to launch security infringements in mobile health monitoring scenarios. Wireless channel perturbations due to the presence of human body are a relative phenomenon depending heavily on the subject's dielectric properties. A new body-centric signature authorization (B-CSAI) approach based on time-frequency domain characteristics was proposed. This method utilizes multiple millimeter wave bands of 27-28 GHz, 29-30 GHz, and 31-32 GHz, thereby enhancing the security in body-centric communications exploiting benefits of subject specific channel signature. The proposed bornprint method is based on the intrinsic identity-related time-frequency domain information, which generated by the user's natural hand motion signature and resulting creeping waves and space waves. It can meet the unconditional keyless authorization requirements. A detailed measurement campaign considering radiation efficiency (ñ(dB) = -25.8, -24.7, -26.4), path-loss exponent, and shadowing factor in three millimeter wave bands, using six human subjects confirm the usability and efficiency of the proposed approach. This also shows that there is a wide space for realizing security from physical mechanisms.

Double‐adaptive chirplet transform for radar signature extraction

This study presents a novel method for time-frequency (TF) signal analysis and chirp rate estimation dedicated for electromagnetic spectrum sensing, electronic warfare, electronic intelligence and/or passive bistatic radar purposes in which frequency modulated signals occur. The approach is based on the double-adaptive chirplet transform, providing optimal analysis window parameters, which is a crucial problem during TF processing. The presented methodology is based on a Gaussian window with two degrees of freedom, which results in a strong concentration of energy on the TF plane around the main component, even if the initial window parameters were mismatched. As an example of the method's usefulness, different types of real-life radar pulses were processed: firstly, two types of non-linear frequency-modulated waveforms were examined as a suitable illustration of the changeability of the analysis window parameters. Secondly, the linear frequency modulated signals were analysed in the presence of strong interference and multipath propagation. The waveforms were processed and compared, creating individual radar signatures, which may allow transmitter classification and signal reconstruction to be carried out in further processing. Moreover, the estimation limitations were compared to the Cramer-Rao lower bound, and an appendix organising mathematical fundamentals for the analysed methods is provided.

Helgason–Gabor–Fourier transform and uncertainty principles

Windowing a Fourier transform is a useful tool, which gives us the similarity between the signal and time frequency signal, and it allows to get sense when/where certain frequencies occur in the input signal, this method was introduced by Dennis Gabor. In this paper, we generalize the classical Gabor–Fourier transform (GFT) to the Riemannian symmetric space calling it the Helgason–Gabor–Fourier transform (HGFT). We prove several important properties of HGFT like the reconstruction formula, the Plancherel formula and Parseval formula. Finally, we establish some local uncertainty principle such as Benedicks-type uncertainty principle.

Time-frequency features of grapevine’s xylem acoustic emissions for detection of drought stress

In cultures like grapevine, growth, yield and fruit quality depend on precise regulation of water stress using precision irrigation. In arid climates with seasonal droughts, karst areas and porous stony soils it is difficult to assess irrigation thresholds only based on soil water balance, requiring novel, plant-based techniques of early drought stress detection. In plants, water stress causes an increase in evapotranspiration rate, subjecting the water in the xylem conduits to high tension. This creates cavitations, leading to xylem embolization, loss of hydraulic conductivity and possibly plant death. A promising emerging technology for automated, non-invasive field monitoring of xylem cavitations is acoustic emission (AE) testing. Passive piezoelectric sensors on trunk are used to capture intermittently appearing AE, occurring in ultrasonic frequency-band from 100 kHz to 1 MHz. To make AE suitable as an input for precision irrigation, we present a novel AE time-frequency feature set for discrimination between normal and drought stress conditions. The proposed feature extraction method entails software-compensation of sensor's frequency response, enabling the wide-band frequency-analysis with miniature resonant high-frequency piezoelectrics. The signal analysis revealed three characteristic groups of AEs, differentiated by their spectral composition: single-component, multi-component, and broadband AEs. In conditions of increased water stress, single-component emissions cluster around 200 kHz, and multi-component emissions group between 200 and 600 kHz, forming discriminant time-frequency signatures. The study entails an extensive set of laboratory pot-plant dehydration experiments on four different grapevine cultivars, verifying the selectivity, sensitivity and repeatability of the extracted feature set. It proved sensitive enough for detection of drought stress corresponding to moderate leaf water potentials ranging between −0.7 and −1.2 MPa.

High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator

Time-frequency analysis of structural vibration responses provides essential information for structural system identification, modal updating, and condition assessment. However, spurious peaks introduced by the strong noise will significantly increase the false positive rate as well as compromise the sparse time-frequency signal representation. This paper proposes a high-resolution time-frequency representation approach for nonstationary signals polluted with strong noise. With a high sensitivity to detect slight frequency shift and an immunity to noise effect, the intermittent chaotic of Duffing oscillator system is introduced to accurately identify the time-varying instantaneous frequencies. Furthermore, an adaptive Duffing oscillator array is adopted to improve the instantaneous frequency identification efficiency. The feasibility and effectiveness of the proposed method are verified with numerical and experimental studies. Numerical studies are conducted on a multicomponent synthetic nonstationary signal as well as on a two-story shear building with time-varying stiffness under seismic loads. In experimental validations, the acceleration response of a laboratory bridge model under moving vehicle load is also analyzed by using the proposed approach to obtain the instantaneous frequency variations induced by the bridge–vehicle interaction. These results are compared with those obtained from a method based on empirical wavelet transform and Hilbert transform (EWT-HT), to highlight the superiority of the proposed approach in obtaining high-resolution time-frequency analysis results for nonstationary signals with strong noise.

Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis

Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.

Multi-label fault diagnosis of rolling bearing based on meta-learning

In practical applications, it is difficult to acquire sufficient fault samples for training deep learning fault diagnosis model of rolling bearing. Aiming at the few-shot issue and multi-label attributes of single-point faults, a novel fault diagnosis method of rolling bearing based on time–frequency signature matrix (T–FSM) feature and multi-label convolutional neural network with meta-learning (MLCML) is proposed in this paper. At the beginning, the T–FSM features sensitive to few-shot fault diagnosis of measured vibration signal are extracted. Subsequently, a designed multi-label convolutional neural network (MLCNN) with a specific architecture is employed to identify faults. Crucially, the meta-learning strategy of learning initial network parameters susceptive to task changes is incorporated to MLCNN for addressing the few-shot problem. Ultimately, the publicly available rolling bearing dataset is utilized to demonstrate the effectiveness of the proposed method. The experimental results exhibit that the trained MLCML has the capability of learning to learn few-shot fault attributes with outstanding diagnosis accuracy and generalization. More concretely, the model can adapt to new fault categories rapidly owing to that only a few samples and update steps are required to fine-tune the network.

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures.

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

A robust and efficient instantaneous frequency estimator of multi-component signals with intersecting time-frequency signatures

An algorithm for multi-component instantaneous frequency (IF) estimation of signals with intersecting signatures in the time-frequency domain is proposed. The proposed algorithm exploits the slow variation in the IF and the direction of the IF curve to accurately track the IF without computing the complete time-frequency representation. The performance of the algorithm is illustrated using both synthetic and real-life signals. The proposed algorithm outperforms the state of art, such as Viterbi and adaptive directional time-frequency distribution based methods, in terms of the accuracy of IF estimates and computational complexity.

RESEARCH ON ELECTROMAGNETIC RADIATION DURING THE EXPLOSION PROGRESS OF COMPOSITION B EXPLOSIVES

The electromagnetic radiation interference, which can be obviously observed during the explosives process, has attracted attention in many fields. However, the corresponding generation mechanism and theoretical model are still immature, experiments are still the key approach to study this phenomenon. This paper designs experiments to collect the electromagnetic radiation signals, generated by different charges of composition B explosives (Comp B), and uses the wavelet transform method to obtain these signals time-frequency characteristics, namely the main spectrum distribution is in the range of 0$\sim$50 kHz. Furthermore, the self-developed EXPOSION-3D software is used to simulate the experimental conditions to obtain the characteristics of the flow field during the explosion. By comparing the experimental results with the numerical simulations, series of conclusions are given in the following. The first pulse signal is the electromagnetic pulse directly generated by the high-temperature and high-pressure plasma generated by the detonation of Comp B; the second pulse signal is an electromagnetic pulse generated by the plasma formed at the air shock wave front which is caught up by the reflected shock wave from the ground; the third pulse signal is an invalid signal caused by the shock wave hitting the measurement coils. The amplitude of the first electromagnetic pulse has a linear relationship with the 1/3 power of the charge, and its arrival time is not sensitive to the charge of explosive. The time of the second electromagnetic pulse is in an exponential relationship with the charge of explosive. Overall, this paper put forward the characteristics of the explosion wave flow field when the shock wave reflection forms the electromagnetic wave signal, which provides verification data for the subsequent theoretical research.

Novel LDoS attack detection by Spark‐assisted correlation analysis approach in wireless sensor network

Low-rate denial of service (LDoS) attack is a special DoS attack type of wireless sensor network (WSN). Routing protocol is the critical component of the WSN. Routing flood attack is a novel LDoS attack pattern in WSN. However, the attack is difficult to be detected by traditional intrusion detection algorithm. A novel LDoS attack detection method based on big data and signal analysis is proposed. Hilbert–Huang Transform (HHT) time–frequency signal analysis method is used to analyse the small non-linear signal from LDoS attack traffic signal. Spark-based Pearson and Spearman correlation coefficient calculation approaches are used to recognise the false intrinsic mode functions (IMFs) components decomposed by the HHT method. The effective threshold value of Pearson correlation coefficient is set to 0.2, the effective threshold value of Spearman correlation coefficient is set to 0.3, which are united to identify the false IMF components. SunSpot wireless nodes are used to build the wireless sensor nodes. If the difference between the IMF component and the normal IMF component is more than 40%, the LDoS attack will be detected. Experimental results show that this approach is effective to detect the LDoS attack in ZigBee WSN. This is a quantitative LDoS attack detection experimental research in WSN.

Differential Timing Method Based on Modified Traceability Model

The common view time transfer and two-way time and frequency transfer methods are currently the main means for achieving time synchronisation at nanosecond level. However, these methods have some limitations in real time and cost, which limit their wide applications in many fields, such as time synchronisation among base stations of the upcoming 5G network. In order to meet the requirements of nanosecond time synchronisation, a low-cost differential timing method is proposed in this paper by changing the manner of generation of traceability model parameters in GNSS navigation messages. The time deviation between GNSS system time and the timing laboratory that maintains Coordinated Universal Time (UTC) kept by timing laboratory named k (UTC(k)) is monitored by receiving the GNSS signal in space with monitoring receivers. The new traceability model parameters are generated with the monitored time deviations and then broadcast to users through the GNSS navigation message. The precision of the one-way timing method can be improved from tens of nanoseconds to the order of several nanoseconds with the proposed method. In addition, there are obvious advantages to carry out this method on the geostationary satellites in the BeiDou navigation satellite (BDS) constellation. The proposed method is verified on an experimental platform based on the UTC(NTSC) time frequency signal and the geostationary satellites in the BDS-3 constellation.

SC-PSNET: A deep neural network for automatic P- and S-phase detection and arrival-time picker using 1C recordings

It is important to autopick an event’s arrival time and classify the corresponding phase for seismic data processing. Traditional arrival-time picking algorithms usually need 3C seismograms to classify event phase. However, a large number of borehole seismic data sets are recorded by arrays of hydrophones or distributed acoustic sensing elements whose sensors are 1C and cannot be analyzed for particle motion or phase polarization. With the development of deep learning techniques, researchers have tried data mining with the convolutional neural network (CNN) for seismic phase autopicking. In the previous work, CNN was applied to process 3C seismograms to detect phase and pick arrivals. We have extended this work to process 1C seismic data and focused on two main points. One is the effect of the label vector on the phase detection performance. The other is to propose an architecture to deal with the challenge from the insufficiency of training data in the coverage of different scenarios of [Formula: see text] ratios. Two novel points are summarized after this analysis. First, the width of the label vector can be designed through signal time-frequency analysis. Second, a combination of CNN and recurrent neural network architecture is more suitable for designing a P- and S-phase detector to deal with the challenge from the insufficiency of training data for 1C recordings in time-lapse seismic monitoring. We perform experiments and analysis using synthetic and field time-lapse seismic recordings. The experiments show that it is effective for 1C seismic data processing in time-lapse monitoring surveys.

Detection of Sealant Delamination in Integrated Circuit Package Using Nondestructive Evaluation

Abstract This paper presents the development of a nondestructive evaluation method for delamination detection in integrated circuit (IC) packages. The developed method allows for quantitative detection of sealant delamination between integrated heat sink (IHS) and substrate, which is considered a potential failure mechanism in IC packages. The proposed method is expected to overcome the destructive nature of most existing techniques, while maintaining low cost of development. The reported development consists of the following processes: First, flash thermographic analysis is investigated and shown to be incapable of detecting a realistic sealant delamination. Then, ultrasonic guided waves (UGWs) are used as the chosen interrogation method due to their sensitivity to small-size damage and structural thickness variations. The complexity of the received ultrasonic signals caused by the geometric heterogeneity is resolved using a time–frequency signal processing technique. The extracted ultrasonic information, including time-of-arrival (TOA) and amplitude of wave modes received from different sensing paths under multiple excitation frequencies, is then used to construct a feature space. A multivariate Gaussian model is implemented as an information fusion and delamination detection tool, which investigates the distribution of feature space including correlations between features. Results from the developed model are compared with two existing evaluation methods, including pullout force measurement and a delamination metric. The developed method is seen to possess a similar level of accuracy with a nondestructive nature.

The Second-Order Synchrosqueezing Continuous Wavelet Transform and Its Application in the High-Speed-Train Induced Seismic Signal

Thousands of high-speed trains (HSTs) run on over 30000-km high-speed railway every day in China. These running trains not only cause vibrations but also stimulate all kinds of seismic waves. The receiver seismic signal's spectrum has one special equal-interval, multinarrowband feature. Some commonly used time-frequency (TF) analyzing methods, such as the short-time Fourier transform, continuous wavelet transform, and the Stockwell transform, can capture a signal's TF features and reconstruct signal without loss. However, their TF resolution is limited by the uncertainty principle. In this letter, we apply the second-order synchrosqueezing continuous wavelet transform (SSCWT), which is one improved SSCWT, to the HST induced seismic signal and capture its TF spectrum features. The synthetic data example and real data example have proved the second-order SSCWT can provide a more concentrated TF spectrum than the commonly used SSCWT.

Time Frequency Signal Classification Using Continuous Wavelet Transformation

Time-frequency analysis can provide useful information in digital signal seismic data processing and interpretation. The energy concentration of the spectrum depends on the consistency of function of the time-frequency analysis and instantaneous frequency variation digital signal. In this case, we used the digital signal seismic from selected seismometer broadband which deployed in Sumatera Island. The aim of this study to classify the waveform based on the time-frequency analysis using continuous wavelet transform (CWT). The sample data used the earthquake of 20 February 2018 in North Sumatera. The result indicated the classification between the horizontal and vertical components from the seismometer broadband is different. The classification of vertical is affected by seismic source and horizontal component affected the site effect.

Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex frequency modulation signals compared with existing handcrafted features. Numerous results demonstrate that the synchrosqueezing TF feature can offer a better recognition accuracy, especially for the signals with intricate time-varying information. Moreover, a linear relevance propagation algorithm is employed to attest to the SST feature importance from the perspective of deep learning.