Wiener Filtering Method Research Articles

Partial discharge de-noising based on hybrid particle swarm optimization SWT adaptive threshold

A hybrid particle swarm optimization stationary wavelet adaptive threshold denoising (HPO-SWT-ATD) method was proposed to improve the adaptive ability of wavelet transform in partial discharge signal denoising. In the proposed method, the partial discharge signal is decomposed by stationary wavelet transform(SWT) and the multiorder differentiable function based on the Stein unbiased likelihood estimate (SURE) is used as the denoising threshold function. In threshold selection, the improved particle swarm optimization algorithm is used for global adaptive search to select the optimal denoising threshold. The de-noising experiments are carried out on the simulated partial discharge signals and the measured partial discharge signals.The experiment results show that, compared with the hard threshold method, particle swarm optimization threshold method, genetic algorithm optimization threshold method, Wiener filter method and modal decomposition method, the proposed method can better remove the white noise in the PD signal, and the denoising signal distortion degree is smaller.

An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering.

Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.

Prediction Model of the Remaining Useful Life of the Drill Bit during Micro-Drilling of the Packaging Substrate

The packaging substrate plays a significant role in electrical connection, heat dissipation, and protection for the chips. With the characteristics of high hardness and the complex material composition of packaging substrates, drill bit failure is an austere challenge in micro-drilling procedures. In order to monitor the health state of the drill bit and predict its remaining useful life (RUL) in micro-drilling of packaging substrate, an improved RUL prediction model is established based on the similarity principle, degradation rate, and offset coefficient. And then, a micro-drilling experiment on packaging substrate is carried out to collect the axial drilling force through the precision drilling force measurement platform. Axial drilling force signals, which are processed via the Wiener filtering method, are used to analyze the effectiveness of the improved RUL prediction model. The experiment results indicate that, compared to the curves of the traditional RUL prediction model, the curves of the improved RUL prediction model present a higher fitting degree with the actual RUL curves. The average relative errors of the improved RUL prediction model are small and stable in all groups; all of the values are less than 15%, while the fluctuation of the average relative errors of the traditional model is greatly large, and the maximum value even reaches 74.43%. Therefore, taking the degradation rate and offset coefficient into account is a proper method to enhance the accuracy of the RUL prediction model. Furthermore, the improved RUL prediction model is a reliable theoretical support for the health state monitoring of drill bits during the micro-drilling of packaging substrates, which also acts as a potential method to improve micro hole processing efficiency for packaging substrates.

Real-time inverse estimation of multi-directional random waves from vessel-motion sensors using Kalman filter

The real-time inverse estimation of the ocean wave spectra and elevation from a vessel-motion sensor is highly valuable, although it is still in the early stages of development. The Kalman filter method using motion measurements has advantages over other methods in terms of real-time capability, cost savings, and easy installation. In this paper, the Kalman filter method is developed for the inverse estimation of multi-directional waves in real time. The problematic divergence in the high-frequency region encountered by previous papers using similar approaches is resolved by employing the Wiener filter method. The developed method is also shown to be robust against potential sensor noises. The real-time multidirectional-wave spectrum and elevations can be recovered, including the high-frequency region, using the developed methodology with the numerically generated motion-sensor signals with noise. The developed Kalman filter algorithm is tested and validated for various sea states of uni- and multi-directional waves. The inverse estimation of multi-directional waves is generally less accurate compared to that of unidirectional waves, mainly due to the significant increase of unknowns (component waves) in the Kalman filter processes. Nevertheless, the overall wave spectra, elevation time series, significant wave heights, peak period, main direction, and directional spreading were well recovered.

Reweighted L1-norm regularized phase retrieval for x-ray differential phase contrast radiograph.

Talbot-Lau x-ray grating interferometry greatly decreases the requirements on x-ray sources to realize differential phase contrast imaging and has found many applications in industrial and medical imaging. Phase retrieval from the noisy differential signal is crucial for quantitative analysis, comparison, and fusion with other imaging modalities. In this paper, we introduce a reweighted L1-norm based nonlinear regularization method for the phase retrieval problem. Both simulation and experimental results demonstrated that, comparing with the widely used L1-norm based regularization method and Wiener filter method, the proposed method is more effective both in eliminating the strip noises and in preserving the image detail.

Research on the sharpness of combustion noise of three-cylinder GDI engine based on adaptive cyclic Wiener filter method

In order to analyze the sharpness of combustion noise of the gasoline direct injection (GDI) engine, the in-cylinder pressure and noise of different loads and speeds of a three-cylinder GDI engine are measured. An adaptive cycle Wiener filter method is proposed to separate the combustion noise. The result indicated that the proposed method can overcome the drawbacks of the original Wiener filter method and is able to separate high-frequency combustion noise components more accurately and effectively. The sharpness of the combustion noise is calculated by the Zwicker method and Aures method, and the comparison results show that the Aures method is more suitable for evaluating combustion noise. The influence of different loads and speeds on the sharpness of combustion noise is investigated by the proposed method systematically. The results indicated that at low speed (1500 r/min), the combustion noise and sharpness will increase with the load while at medium (3000 r/min) and high (5500 r/min) speeds, the combustion noise under half load (50%) has the highest sharpness. Excessive high load (100%) and too low load (0%) both suppress the generation of combustion noise. As for the same engine load, a higher engine speed will lead to higher combustion noise and sharpness.

Statement of the problem of assessing instability of passive quantum frequency standards in the presence of an error from the interaction

Construction of modern measuring complexes of the coordinate-time support system of Ukraine is impossible without improving mathematical models of quantum standards of frequency (QSF) used in group standards. This work is devoted to the analysis of methods for the stable solution of direct and inverse problems (methods for solving ill-posed problems) in models of the interaction of passive QSF in the process of their comparisons. The priority task is to use these methods for the numerical solution of problems in the design of group QSF and parallel quantum generators of random numbers. Methods for solving such problems are in demand, since they make it possible to create mathematical models of group QSF interaction. These models will enable the design of efficient parallel quantum random number generation devices for high-tech areas of cybersecurity.
 Varieties of methods such as the method of least squares or the method of the Moore-Penrose pseudo-inverse matrix are used, as a rule when evaluating the metrological parameters of QSF.Robust methods of regularization or filtering, for example, the Kalman or Wiener filter method, are used in the algorithms of group standards, due to the instability of the solution.However, these methods do not work in the presence of an error from the interaction of QSF in the process of their functioning in a group standard or in comparisons.
 The aim of this work is to analyze and substantiate the formulation of the problem of assessing the potential accuracy characteristics of passive QSF in the presence of an error from the interaction. Regularization parameters when determining the state vector of the group standard are found using signals transmitted by global navigation satellite systems such as GPS\GLONASS in the local differential correction mode.

Improved quality using newly designed algorithms in gamma- and X-ray fusion images with a photon counting CZT detector: Combining the median modified Wiener filter and edge detection method

In this study, we designed a photon-counting cadmium zinc telluride (CZT) gamma- and X-ray fusion imaging system and propose a new image processing method to improve image quality. The Geant4 Application for Tomographic Emission (GATE) platform was used to acquire gamma- and X-ray fusion images, and a stack-up parallel-hole collimator, which can greatly improve sensitivity, with a CZT detector was modeled. The newly proposed image processing algorithm to improve the image quality combines the median modified Wiener filter (MMWF), which can efficiently remove noise, and the Canny and Prewitt methods, which are representative edge detection algorithms. As a result, we confirmed that the two new algorithms improve noise and edge information compared to the conventional MMWF. In addition, when comparing the performance of the two proposed algorithms, we demonstrated that when the Canny is fused to MMWF, noise can be removed relatively efficiently, and the Prewitt can obtain improved edge information. The no-reference-based evaluation method was used to test these new algorithms with phantom images, with the same results. Thus, we demonstrate that the method combining MMWF and the edge detection algorithm provides superior image performance to the conventional filtering method in gamma- and X-ray fusion images with photon-counting CZT detectors.

Research on X‐Ray Inspection of Basin Insulators and Wireless Image Sensing Technology

This paper presents an in‐depth study and analysis of X‐ray inspection of basin insulators by wireless sensing technology. Aiming at the characteristics of low contrast and many kinds of noise in the basin insulator ray image, this paper proposes an X‐ray basin insulator image denoising method based on improved 3D block matching. Using the RF microcontroller CC2530 chip as the core hardware and networked by ZigBee protocol, the sensor senses and collects various parameters and transmits this information to the monitoring end in real time through wireless. The method proposes an improved wavelet thresholding denoising method to overcome the pseudo‐Gibbs phenomenon caused by the wavelet hard thresholding method in the 3D block matching algorithm cofiltering and retain more details of the image. Aiming at the ringing effect caused by the Wiener filtering method used in the three‐dimensional block matching algorithm collaborative filtering, an improved Kalman filtering method based on anisotropic diffusion is proposed, which avoids the ringing effect, and has clear edges and complete details. An improved Kalman filtering method based on anisotropic diffusion is proposed to avoid the ringing effect, and the edges are clear, and the details are complete. The proposed method is a safe, efficient, accurate, and feasible method for detecting defects in basin insulators by combining X‐ray and improved wireless image sensing technology to detect the internal equipment without disassembling or touching the GIS equipment.

Wearable Hearing Device Spectral Enhancement Driven by Non-Negative Sparse Coding-Based Residual Noise Reduction.

This paper proposes a novel technique to improve a spectral statistical filter for speech enhancement, to be applied in wearable hearing devices such as hearing aids. The proposed method is implemented considering a 32-channel uniform polyphase discrete Fourier transform filter bank, for which the overall algorithm processing delay is 8 ms in accordance with the hearing device requirements. The proposed speech enhancement technique, which exploits the concepts of both non-negative sparse coding (NNSC) and spectral statistical filtering, provides an online unified framework to overcome the problem of residual noise in spectral statistical filters under noisy environments. First, the spectral gain attenuator of the statistical Wiener filter is obtained using the a priori signal-to-noise ratio (SNR) estimated through a decision-directed approach. Next, the spectrum estimated using the Wiener spectral gain attenuator is decomposed by applying the NNSC technique to the target speech and residual noise components. These components are used to develop an NNSC-based Wiener spectral gain attenuator to achieve enhanced speech. The performance of the proposed NNSC–Wiener filter was evaluated through a perceptual evaluation of the speech quality scores under various noise conditions with SNRs ranging from -5 to 20 dB. The results indicated that the proposed NNSC–Wiener filter can outperform the conventional Wiener filter and NNSC-based speech enhancement methods at all SNRs.

Spectral sensitivity estimation of trichromatic camera based on orthogonal test and window filtering.

The three-channel spectral sensitivity of a trichromatic camera represents the characteristics of system color space. It is a mapping bridge from the spectral information of a scene to the response value of a camera. In this paper, we propose an estimation method for three-channel spectral sensitivity of a trichromatic camera. It includes calibration experiment by orthogonal test design and the data processing by window filtering. The calibration experiment was first designed by an orthogonal table of the 9-level and 3-factor. A rough estimation model of spectral sensitivity is established on the data pairs of the system input and output in calibration experiments. The data of rough estimation is then modulated by two window filters on frequency and spatial domain. The Luther-Ives condition and the smoothness condition are introduced to design the window, and help to achieve the optimal estimation of the system spectral sensitivity. Finally, the proposed method is verified by some comparison experiments. The results show that the estimated spectral sensitivity is basically consistent with the measured results of the monochromator experiments, the relative full-scale errors of the RGB three-channel is obviously lower than the Wiener filtering method and the Fourier band-limitedness method. The proposed method can estimate the spectral sensitivity of the trichromatic digital camera very well, which is of great significance for the colorimetric characterization and evaluation of imaging systems.

GEDI: Gammachirp envelope distortion index for predicting intelligibility of enhanced speech

In this study, we propose a new concept, the gammachirp envelope distortion index (GEDI), based on the signal-to-distortion ratio in the auditory envelope, SDRenv, to predict the intelligibility of speech enhanced by nonlinear algorithms. The objective of GEDI is to calculate the distortion between enhanced and clean-speech representations in the domain of a temporal envelope extracted by the gammachirp auditory filterbank and modulation filterbank. We also extend GEDI with multi-resolution analysis (mr-GEDI) to predict the speech intelligibility of sounds under non-stationary noise conditions. We evaluate GEDI in terms of the speech intelligibility predictions of speech sounds enhanced by a classic spectral subtraction and a Wiener filtering method. The predictions are compared with human results for various signal-to-noise ratio conditions with additive pink and babble noises. The results showed that mr-GEDI predicted the intelligibility curves better than short-time objective intelligibility (STOI) measure, extended-STOI (ESTOI) measure, and hearing-aid speech perception index (HASPI) under pink-noise conditions, and better than HASPI under babble-noise conditions. The mr-GEDI method does not present an overestimation tendency and is considered a more conservative approach than STOI and ESTOI. Therefore, the evaluation with mr-GEDI may provide additional information in the development of speech enhancement algorithms.

A novel 3D position measurement and structure prediction method for RFID tag group based on deep belief network

In the influencing factors of reading distance of RFID tags, the tags’ geometric spatial structures have an important aspect on the corresponding reading distance. In order to achieve the goal of improving the reading distance of RFID tags, a novel three-dimensional (3D) position measurement and structure prediction method for RFID tag group based on deep belief network (DBN) is proposed. First, a 3D structure prediction system for RFID tags, which is based on stereovision, is designed. Second, an image deblurring scheme which combines the knife-edge with Wiener filtering method is adopted to restore the blur image in the 3D position measurement process. Then, RFID tags’ 3D positions are acquired by using the image matching method. Finally, the relationship model between RFID tags’ 3D positions and reading distance is built by DBN. The experimental results show that the relationship between tag group’s 3D positions and reading distance can be well built by DBN. The established DBN can forecast the reading distance accurately. The MAPE and RMSE of DBN prediction results are 1.64% and 0.168. The DBN can find the best 3D spatial distribution structure of RFID tag group corresponding to the maximum reading distance, which can guide the 3D distribution of the tag group.

Memory Effect Based Filter to Improve Imaging Quality Through Scattering Layers

Recent extensive studies have promoted the imaging techniques through scattering layers to a reality but the imaging quality still needs to be improved. We demonstrate a high-speed method to image objects hidden behind a thin scattering medium. By analyzing the imaging degradation model of the speckle correlation methods, a memory effect (ME) based filter is proposed to improve the image quality of reconstructed images with deconvolution operation. Comparing to Wiener filter and other related methods, the ME-based filter can optimize the recovered images with the smaller mean square error, suppression of noise background, and central maximum. The proposed ME filter is also applied to improve the imaging method proposed for dynamic imaging through scattering layer with a reference object. The artifact generated by the deviation of retrieved point spread function can be effectively eliminated with a better imaging quality. This ME-based filter is promising to improve the imaging quality for other deconvolution and speckle correlation methods.

Noise segmentation for improving performance of Wiener filter method in spectral reflectance estimation

AbstractNoise is an indispensable part of an imaging system. For having acceptable performance of reflectance estimation from digital signals, noise effects should be controlled. In this study a new method based on segmentation of noise is presented. Simulation and real experiments on four reflectance data sets showed that noise segmentation improves Wiener filter estimation method. However, oversegmentation can have reverse effect on the reflectance estimation results.

Background texture suppression and defect extraction of high-speed milling surface image

This paper proposed a method to extract the machine defect from the surface images of high-speed milling workpiece and reduced the impact of workpiece surface background texture. Firstly, Wiener filtering method is used to denoise the high-speed milling workpiece surface images. Then, the potential texture images are obtained by using the non-negative matrix decomposition algorithm for learning denoising images unsupervisedly. Next, original images and potential texture images are used to convolute with the imaginary part function of Gabor filter respectively and apply image differencing method to obtain the energy difference images. The binary defect images are extracted after the threshold segmentation method is applied. Application example shows the capability of this method to extract machine defect.

Background texture suppression and defect extraction of high-speed milling surface image

This paper proposed a method to extract the machine defect from the surface images of high-speed milling workpiece and reduced the impact of workpiece surface background texture. Firstly, Wiener filtering method is used to denoise the high-speed milling workpiece surface images. Then, the potential texture images are obtained by using the non-negative matrix decomposition algorithm for learning denoising images unsupervisedly. Next, original images and potential texture images are used to convolute with the imaginary part function of Gabor filter respectively and apply image differencing method to obtain the energy difference images. The binary defect images are extracted after the threshold segmentation method is applied. Application example shows the capability of this method to extract machine defect.

Linear Reconstruction Methods for Large Thick Aperture Imaging

Large thick aperture imaging method is proposed to measure the radiation intensity distribution of radiation source whose size is several centimetres. The new method contains two steps which are coded imaging and image reconstruction. Geant4 is used to simulate the coded imaging process. Wiener filter, Lucy-Richardson, Regularized filter, blind deconvolution and CT reconstruction method are realized using Matlab software. Wiener filter and Regularized filter produce artifacts. CT reconstruction method can’t reveal the right size of60Co-γ source because of the overlapping of knife edge images. Both L-R and blind deconvolution are effective. If the aperture is moved 0 mm, 2 mm, 4 mm, 6 mm along x-axis, the reconstruction result using L-R method has a larger correlation coefficient with source image. However, L-R method gets bad result if the aperture rotates more than 1.4°. Under this condition, blind deconvolution is the better choice.

Speech Noise Reduction Algorithm in Digital Hearing Aids Based on an Improved Sub-band SNR Estimation

To improve the speech intelligibility in noisy environments for persons with hearing impairments, a new method for reducing noise, based on improved sub-band signal-to-noise ratio (SNR) estimation, is proposed. First, the input signal is decomposed into several sub-band signals with an analysis filter bank. Then, under the assumption of a Gaussian model, maximum a posterior probability is applied to estimate the information embedded in adjacent frames in each sub-band, which is in the form of a joint probability density function, and the minimum of the noise spectrum is tracked to estimate the noise. Subsequently, the gain of each sub-band, which changes with the noise in the corresponding sub-band, is calculated with a linear proportional gain function. The obtained gains of the sub-bands are multiplied by the sub-band noisy signals to obtain the enhanced sub-band speech signals. Finally, all the sub-band signals are spliced to obtain the estimated speech signals. In this algorithm, the gains are calculated in the time domain, which avoids the process of the inverse Fourier transform and leads to a decrease in computational complexity. Compared with the traditional spectral subtraction and basic Wiener filtering method, the delay in this algorithm is reduced by 40.4 and 60.6%, respectively. It is also compared with the modulation depth integrated into hearing aids under an experimental simulation and a real scenario. The results indicate that the output SNR is improved by 1 dB under the software simulation and 3.1 dB in the real scenario when the input SNR is set as 10 dB. Compared with the simulation environment, the proposed algorithm only fell by 1.5% in the real scenario. Furthermore, the distance of the logarithmic spectrum and quality of speech perception are improved by 20.6 and 9.3%, respectively.

Combined adaptive multiple subtraction based on optimized event tracing and extended wiener filtering

The surface-related multiple elimination (SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.