Frequency Domain Filtering Research Articles

Combining ground-penetrating radar sections with different antenna frequencies including time-frequency domain noise suppression filters

Ground-penetrating radar (GPR) is one of the most useful geophysical methods that rapidly and efficiently provide high-resolution subsurface images depending on antenna frequencies and target depth. Recently, to enrich the interpretation of GPR sections, multifrequency sections have been combined at deeper and shallower levels in a profile. In this context, the techniques of combination and image quality improving processes of GPR data also have gained importance. For this, simple summation (SS) and time-shifted balanced summation (TSBS) techniques, frequency-domain local mean notch filter (FDLMNF), frequency-space ( f- x) filter, and automatic gain control (AGC) processes are sequentially applied within a workflow. Although the FDLMNF is used to effectively remove single-frequency spike-like noises without distortion around the notch frequency, the f- x filter is applied to suppress the random noise and improve the lateral traceability of the section, and AGC provides a statistically based gain recovery. In the proposed workflow, after generating synthetic and field GPR data with 100 and 250 MHz frequencies, data were combined using SS and TSBS techniques. Finally, FDLMNF, f- x, and AGC were applied to increase the image quality of this combined section. The result sections were evaluated and compared using the Fourier average amplitude and f- k spectra. According to the spectra, the TSBS technique provides good spectral balancing and offers wider spectral content. At the same time, it was observed that the image quality of the combined GPR sections increased significantly after applying FDLMNF, f- x, and AGC processes. As a result, when the sections resulting from SS and TSBS techniques are examined, the image enhancement processes that protect and strengthen the spectral bandwidth of the data are successful. Thus, the temporal resolution and spatial continuity of GPR sections also will be increased, and a better quality interpretation will be provided.

An iterative frequency-domain envelope-tracking filter for dispersive signal decomposition in structural health monitoring

Dispersive signals are omnipresent in structural health monitoring and nondestructive testing. Due to the strong dispersion and multimodal characteristics, such signals often contain multiple components with frequency-varying group delays (GDs) overlapping in time–frequency (TF) domain, bringing great challenges to the existing signal processing methods. This paper presents a novel dispersive signal decomposition method named iterative frequency-domain envelope-tracking filter (IFETF) to accurately estimate the dispersion curves and separate overlapped components. Specifically, an envelope tracking issue is introduced in frequency domain based on dispersive signal model. Then, GDs are refined by recovered frequency envelope, and an iterative processing procedure is designed to ameliorate the TF resolution. To initialize the IFETF, an adaptive ridge detection technique is introduced to automatically estimate the number of components and their GDs with little prior knowledge. In addition, a high-quality TF representation can be constructed according to the results of IFETF, unambiguously revealing TF patterns of a multi-component dispersive signal. The proposed method was demonstrated by numerical analysis and engineering applications on broadband Lamb wave inspection and rail damage detection. Compared with traditional TF analysis techniques, the results show that the IFETF provides higher accuracy and efficiency in signal reconstruction and GD estimation.

Recaptured images detection algorithm based on frequency domain transformation1

With the raising popularity of digital devices in the current society, the present image detection system is becoming a great threaten. Especially the appearance of the recaptured images. It can be used in traditional invalid digital image detection algorithm. There is a new algorithm in this paper is presented to detect the recaptured and real image. The algorithm obtains low-frequency images, directional filtering images and high-frequency images by multiple application frequency domain filtering. Then the proposed algorithm analyzes the directional filtering images and high-frequency images by means of LBP algorithm to extract features. At last, the recaptured images were classified by the SVM. The experimental results demonstrated the algorithm in this paper could be effectively identify in the recaptured images.

A high-speed single-photon detection scheme based on frequency domain filtering

This paper presents a high-speed integrated circuit for narrow-gated single-photon detection. Due to the parasitic capacitor that exists in the avalanche photo-diode (APD), the avalanche signal pulse triggered by the received photon is easily mixed with unavoidable spike noise arising from the gated clock. The traditional double-ended architecture extracts avalanche signals by suppressing common-mode noise. In order to overcome the shortcomings of high cost and mismatch in the traditional architecture, this paper adopts a single-ended detection architecture and deals with the problem of spike noise from the perspective of the frequency domain. According to the difference between the frequency characteristics of the spike noise and the avalanche signal, the effective separation of the two signals can be realized in the frequency domain by a low-pass filter with a specific cut-off frequency. The signal-to-noise ratio of the detection system is improved by using the subsequent amplification and discrimination circuit, so as to realize sensitive detection of the avalanche signals. The proposed circuit is implemented in 180 nm complementary metal oxide semiconductor technology and occupies an effective area of 2.6 mm2. The test results show that the single-ended detection system can sensitively discriminate photon signals with an emission frequency below 20 MHz when the whole circuit, including the APD, is operated at room temperature, where the minimum voltage amplitude converted from the avalanche current of the APD is around 20 mV, and the stopband attenuation of the filter at 1 GHz frequency is −58.8 dB.

Data fusion of multiple machine intelligent systems for the condition assessment of subway structures

Water intrusion through soil is considered the most significant structural issue and the major cause of concrete degradation in subway networks. An enormous amount of water infiltration may expedite the deterioration mechanisms, such as moisture marks, spalling, scaling, and cracks. These mechanisms can compromise the structural durability and jeopardize people’s safety. The condition assessment of concrete infrastructure is predominantly conducted based of visual inspection techniques, which are costly, time-consuming, and error prone. In this research, two main models for the condition assessment of subway networks are proposed. First, image processing techniques and machine intelligent systems are streamlined through successive operations to detect and quantify multiple surface defects automatically. Spatial and frequency domain filters are used to enhance the image clues, in tandem with artificial neural networks (ANNs) and regression analysis (RA) for defect recognition. The Monte Carlo simulation (MCS) is then leveraged to deliver advanced optimization and accurate estimation for each defect’s condition index in the subway element. The developed method was implemented on four stations in Montréal subway systems, whereby the performance of ANNs and RA was validated through R2 as 0.928 and 0.957, respectively. Moreover, the MCS forecast precision was recorded as 95% percentile, which proves the efficacy of the developed models. This research provides insights for infrastructure managers about maintenance and intervention plans in order to prioritize their spending policies.

Noise reduction in a Brillouin optical time-domain sensor by a frequency-domain feature filter.

A novel frequency-domain image processing method is proposed, to the best of our knowledge, to filter the noise from data collected by distributed optical fiber sensors based on Brillouin optical time-domain sensing (BOTDS). In the proposed method, we first divide a data image into subimages, and then we filter the noisy subimages by retaining the useful frequency information corresponding to the Lorentz-shape frequency spectrum and Brillouin frequency shift (BFS) transitions. The denoising performance improvements are verified by simulation and experiment. The performances in terms of temperature/strain measurement uncertainty, spatial resolution, and processing time achieved by the proposed filter are then compared with those by using a Gaussian filter and a nonlocal means (NLM) filter. In a proof-of-concept experiment with a 5.2 km length G657 sensing fiber, we achieve a temperature measurement uncertainty improvement of 27% compared with the results obtained by using the Gaussian filtering method. Furthermore, the processing speed of the proposed method is 22 times faster than that of the NLM filter under the same temperature measurement uncertainty.

A ResNet‐based approach for accurate radiographic diagnosis of knee osteoarthritis

Currently X-ray images are clinically graded by experienced clinicians using the Kellgren and Lawrence (KL) scoring method. However, individual scoring is subjective and error prone. This study proposes an approach for automated knee osteoarthritis classification based on deep neural networks. The knee X-ray images are first pre-processed with frequency-domain filtering and histogram normalisation, making the trabecular bone texture more obvious and benefiting the subsequent classification task. Then, a two-step classification strategy is proposed by extracting the joint centre based on the VGG network and classifying osteoarthritis grades based on the ResNet-50 network. In addition, a rebalance operation is proposed to deal with the dataset unbalance problem, and a quick search technique is proposed to improve the iterative search efficiency for the joint centre. With all of these techniques, a classification accuracy of 81.41% is obtained, which is higher compared to the state-of-the-art approaches.

Digital H∞ filter design for a low frequency multiple‐input multiple‐output system with multirate measurements

AbstractIn this article, a digital filter is designed for a low frequency multiple‐input multiple‐output (MIMO) system with multirate measurements from multiple samplers. A delay compensation is introduced to reduce estimation errors from the multiple samplers and a zero‐order holder. A single‐rate error system is obtained by converting the low frequency MIMO system using a discrete‐time lifting technique. A digital filter is proposed based on a delay‐partitioning method to guarantee asymptotic stability and performance for the single‐rate error system. A numerical example is provided to show effectiveness and superiority of the digital filter in low frequency domain.

Renewable sources-based automatic load frequency control of interconnected systems using chaotic atom search optimization

This paper proposes an improved form of chaotic based atom search optimization (IASO) algorithm by adapting one-dimensional (1D) chaotic map (tent, sine and logistic) to improve the search ability by intensifying the exploration and exploitation phase. The IASO avoids premature convergence and trapping into local optima. Initially, the proposed IASO is validated using a classical benchmark function and its performance is compared with ASO algorithm. Test results indicate that the proposed algorithm outperforms in terms of mean, standard deviation, and best values. Further, the proposed technique is used to design the parameters of fractional-order proportional integral derivative controller for automatic load frequency control (ALFC) of multi-area, multi-source hybrid power system (HPS) by minimizing the integral time absolute error. The results obtained show that the proposed control scheme improves the frequency response of the system by 48 %, 70 %, 15 % and 69 % in terms of settling time, peak undershoot, steady state error value and control effort, respectively compared to ASO. Moreover, the sensitivity analysis is carried out by considering ±25 % variation in HPS parameters and the real-time applicability is tested with Malaysian meteorological data of solar radiation and wind speed variation. These analysis indicates that the transient oscillations are damped out with minimum settling time and the system regains to stable operating conditions. Further, the evaluation of transient and steady-state performance indices shows that the tent map-based IASO is found to be more efficient for obtaining the optimal solution in solving the ALFC problems. In addition, the stability of the system is analysed by approximating the fractional-order transfer function based on the oustaloup filter in frequency domain.

Implementation of Modified Wiener Filtering in Frequency Domain in Speech Enhancement

The most common complaint about Digital Hearing Aids is feedback noise. Many attempts have been undertaken in recent years to successfully reduce feedback noise. A wiener filter, which calculates the wiener gain using before and after filtering SNR, is one technique to reduce background noise. Modified Noise Reduction Method (MNRM), a new way for reducing feedback noise Reduction, is presented in this work. In the Modified Noise Reduction Strategy, the advantages of a wiener filter are merged with a decision-directed approach and a twin-stage noise suppression technique The Modified Noise Reduction method can reduce the noise more successfully, according to comprehensive MATLAB programming, investigation, and findings analysis. After being modelled in MATLAB for seven distinct noise types, the SNR of the two architectures is compared.

DCT and DST Filtering With Sparse Graph Operators

Graph filtering is a fundamental tool in graph signal processing. Polynomial graph filters (PGFs), defined as polynomials of a fundamental graph operator, can be implemented in the vertex domain, and usually have a lower complexity than frequency domain filter implementations. In this paper, we focus on the design of filters for graphs with graph Fourier transform (GFT) corresponding to a discrete trigonometric transform (DTT), i.e., one of 8 types of discrete cosine transforms (DCT) and 8 discrete sine transforms (DST). In this case, we show that multiple sparse graph operators can be identified, which allows us to propose a generalization of PGF design: multivariate polynomial graph filter (MPGF). First, for the widely used DCT-II (type-2 DCT), we characterize a set of sparse graph operators that share the DCT-II matrix as their common eigenvector matrix. This set contains the well-known connected line graph. These sparse operators can be viewed as graph filters operating in the DCT domain, which allows us to approximate any DCT graph filter by a MPGF, leading to a design with more degrees of freedom than the conventional PGF approach. Then, we extend those results to all of the 16 DTTs as well as their 2D versions, and show how their associated sets of multiple graph operators can be determined. We demonstrate experimentally that ideal low-pass and exponential DCT/DST filters can be approximated with higher accuracy with similar runtime complexity. Finally, we apply our method to transform-type selection in a video codec, AV1, where we demonstrate significant encoding time savings, with a negligible compression loss.

Phase-Based Motion Magnification for Structural Vibration Monitoring at a Video Streaming Rate

Here we propose a novel approach to image magnification that enables frame-by-frame motion magnification application at a video streaming rate of around 30 fps. This novel approach can be used instead of batch processing an image file in conventional phase-based magnification (PBM). This new PBM method can instantly show a magnified video streaming on display at 30 fps, which is helpful for vibration measurement and for monitoring tasks where magnified images need to be viewed simultaneously. To accomplish video streaming-rate magnification, the proposed PBM uses time-domain convolution in temporal bandpass filtering for frame-by-frame operation whereas conventional PBM employs a frequency-domain filter that is applied to the entire image file at once. An experiment was conducted to monitor the vibration of a cantilever using a webcam streaming at the same frame rate, and data were collected simultaneously using a laser Doppler vibrometer for comparison. The experiment confirmed that the proposed PBM approach is more effective than the conventional magnification method and it also analyzed the system performance for vibration measurement. Additionally, the proper orthogonal mode could be found through the singular value decomposition from vibration displacement data of the cantilever that was collected instantly from the magnified image frames. Furthermore, the dominant mode could be effectively extracted from excitation at the resonance frequency. Because of magnification factors, the vibration displacements from the proposed method were estimated using linear regression and the accuracy of the estimated displacements was within the permissible error bound. Since the proposed PBM is a frame-by-frame operation, instantaneous adjustment of magnification parameters is available, even while the magnification is being processed. In addition, the proposed PBM is independently adjustable to the number of image frames for temporal FIR filter order.

Study on organic molecular vibrational dynamics by TR-box-CARS spectroscopy

This paper reported a comparative study of molecular vibrational dynamics in different organic molecules by femtosecond time-resolved folded box-coherent anti-Stokes Raman scattering (TR-box-CARS) spectroscopy. As model system methanol and ethanol molecules are chosen, the coherence transfer between stretching vibration modes of C-H bonds in them are excited and detected at the same time. Different vibration periods and wavenumber spacing are found for the amplitudes of the coherent vibrations of C-H stretch modes in methanol and ethanol molecules. This study makes femtosecond TR-box-CARS possible as a prospective technique for detection and identification of organic molecular species in ambient environments. A simple spatial magic filter and frequency domain filter method with folded TR-box-CARS approach of suppressing the non-resonant FWM background is demonstrated.

Proton Exchange Membrane Fuel Cell Prognosis Based on Frequency-Domain Kalman Filter

The degradation seriously affects the durability and cost of the proton exchange membrane fuel cell (PEMFC). This article presents a novel model-driven method based on the frequency-domain Kalman filter (FDKF) and voltage degradation model to predict the degradation of PEMFC in the frequency domain. The advantage of the proposed FDKF method is that it can process the data in groups; thus, the computation time can be greatly reduced with high accuracy. Two degradation experiments under constant and quasi-dynamic currents have been used to demonstrate its prognosis performances under different conditions and different training times. Compared with the traditional time-domain extended Kalman filter method and literature, it has been demonstrated that the proposed one has higher accuracy and requires much less calculation time.

Image Denoising Using Nonlocal Regularized Deep Image Prior

Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep neural network in a supervised fashion usually requires the collection of a great number of examples and the consumption of a significant amount of time. However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging. In this paper, we overcome the problem of a lack of training data by using an unsupervised deep-learning-based method. Specifically, we propose a deep-learning-based method based on the deep image prior (DIP) method, which only requires a noisy image as training data, without any clean data. It infers the natural images with random inputs and the corrupted observation with the help of performing correction via a convolutional network. We improve the original DIP method as follows: Firstly, the original optimization objective function is modified by adding nonlocal regularizers, consisting of a spatial filter and a frequency domain filter, to promote the gradient sparsity of the solution. Secondly, we solve the optimization problem with the alternating direction method of multipliers (ADMM) framework, resulting in two separate optimization problems, including a symmetric U-Net training step and a plug-and-play proximal denoising step. As such, the proposed method exploits the powerful denoising ability of both deep neural networks and nonlocal regularizations. Experiments validate the effectiveness of leveraging a combination of DIP and nonlocal regularizers, and demonstrate the superior performance of the proposed method both quantitatively and visually compared with the original DIP method.

Joint Inspection of HD Video and Robot in Substation Based on OCR Technology

The research on the joint inspection technology of HD video and robot in substation is of great significance in the operation and maintenance management of substation. The related research on multidimensional collaborative inspection technology of substation has attracted great attention. The substation monitoring system is designed to realize multidimensional collaborative inspection of HD video and inspection robot. OCR technology is used to improve the abnormal recognition system of inspection robot equipment. According to the collection of video information, DSP comprehensive information processing is carried out, and the detection information is analyzed by frequency domain filtering method through human-computer interaction interface. Meanwhile, the main component features of substation detection components are extracted by pattern recognition method, and the resolution model of similar features of video monitoring images is constructed. It plays an important role in the daily operation and maintenance of substation to construct the reliability visual tracking and identification model and optimize the multidimensional cooperative inspection system model.

Proposal of Single Sideband Modulation Scheme Using Frequency Domain Filtering

With the rapid development of wireless systems, the demand for frequency resources has been increasing in recent years. Therefore, it is necessary to consider the high-quality communication method that efficiently utilizes finite frequency resources. In this paper, Single Sideband 16 Pulse Amplitude Modulation (SSB 16PAM) scheme for the uplink communication is proposed. It transmits data in only Lower Sideband (LSB) without extra Hilbert components. Under Additive White Gaussian Noise (AWGN) channel environment, Bit Error Rate (BER) performance of the proposed scheme is superior by 3 dB in terms of Carrier-to-Noise Ratio (CNR) to 256 Quadrature Amplitude Modulation (256QAM) scheme with the same frequency efficiency and the same Peak-to-Average Power Ratio (PAPR). Our proposed scheme employs the original frequency domain filter on the transmitter side to form an ideal spectrum. The configuration of its process is almost similar to Single Carrier-Frequency Division Multiple Access (SC-FDMA), moreover, half of the input data on the frequency domain is removed. The proposed frequency domain filter produces the SSBmodulated spectrum with a roll-off rate of zero without degrading the BER performance.

Dense false target jamming suppression for airborne superimposed stepped frequency radar

Traditional space-time adaptive processing (STAP) suffers from severe performance degradation in dense false target jamming scenarios, especially mainlobe deceptive jamming. To solve this problem, the superimposed stepped frequency (SSF) radar is explored and its ability in suppressing the mainlobe deceptive jamming is studied in this work. SSF radar can provide a controllable degree of freedom in range domain besides the traditional freedom in angle and Doppler domains, which can be used to distinguish true and false targets. Thus, the range cascaded STAP approach is proposed to suppress the deceptive jamming and the clutter. First, the true target and clutter are blocked in carrier frequency domain. Then, the jamming is attenuated by the adaptive filter in carrier frequency domain with the jamming covariance matrix being constructed by the blocked data. At last, the clutter can be suppressed by the cascaded STAP in the space-time domain. Simulation results demonstrate the effectiveness of the proposed radar framework and the processing approach in suppressing the clutter and jamming.

Stochastic analysis of locally resonant linear and hysteretic metamaterials for seismic isolation of process equipment

Periodic metafoundations have proven to inherit valuable properties from wave propagating in phononic periodic structures in the very low-frequency regime. Therefore, finite locally resonant metafoundations (LRMs) represent a novel type of seismic isolation for ultralow-frequency applications. In this context, it is still unknown the impact that massive resonators with varying frequencies or devices with hysteretic behavior can entail on the whole system performance. For this purpose, we develop and optimize two finite locally resonant multiple degrees of freedom (MDoF) metafoundations in this paper: i) a foundation endowed with resonators, linear springs and linear viscous dampers tuned to multiple frequencies; and ii) a foundation equipped with fully nonlinear hysteretic dampers. Both are optimized considering the stochastic nature of ground motion, modelled with a modified Kanai-Tajimi filter in the stationary frequency domain, and a massive MDoF superstructure, chosen to be a fuel storage tank. In order to take all of the above-mentioned effects into account, we establish a procedure based on nonlinear programming that is able to optimize any number of parameters. More precisely, to optimize the nonlinear behavior of damper devices we employ a Bouc-Wen hysteretic model. Therefore, we reduce the nonlinear differential equations of Bouc-Wen models to a system of linear equations through the stochastic (equivalent) linearization technique. Moreover, we test the optimized systems against natural seismic records both with linear and nonlinear time history analyses. To investigate the role of hysteresis on the nonlinear band structure, we derive linearized and nonlinear dispersion relationships for the uncoupled periodic metafoundation. Finally, we obtain further detailed information on the nonlinear wave propagation by means of a spectro-spatial analysis.

Vibration Response Testing and Evaluation of A Hydropower Station’s Gate

We use standard vibration source to verify the method that vibration displacement was obtained by filter and integral in frequency domain. The feasibility and reliability of this method was confirmed. A gate’s vibration response such as acceleration, dynamic stress and displacement was tested. The vibration acceleration is less than 3 m/s2, the vibration displacement is less than 0.25 mm, and the dynamic stress is less than 153.2 MPa. In the initial stage of the gate lifting, the maximum vibration displacement over the standard requirements. Vibration evaluation of the gate was given by the acceleration, displacement and dynamic stress.