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Speckle noise in ultrasound images (UIs) significantly reduces the accuracy of disease diagnosis. The aim of this study was to quantitatively evaluate its feasibility in salivary gland ultrasound imaging by modeling the adaptive non-local means (NLM) algorithm. UIs were obtained using an open-source device provided by SonoSkills and FUJIFILM Healthcare Europe. The adaptive NLM algorithm automates optimization by modeling the isotropic search window, eliminating the need for manual configuration in conventional NLM methods. The coefficient of variation (COV), contrast-to-noise ratio (CNR), and edge rise distance (ERD) were used as quantitative evaluation parameters. UIs of the salivary glands revealed evident visualization of the internal echo shape of the malignant tumor and calcification line using the adaptive NLM algorithm. Improved COV and CNR results (approximately 4.62 and 2.15 times, respectively) compared with noisy images were achieved. Additionally, when the adaptive NLM algorithm was applied to the UIs of patients with salivary gland sialolithiasis, the noisy images and ERD values were calculated almost similarly. In conclusion, this study demonstrated the applicability of the adaptive NLM algorithm in optimizing search window parameters for salivary gland UIs. [ABSTRACT FROM AUTHOR]

Purpose: The working environment of the mine is complex, and there are many interference noises. Microseismic signals and noises are mixed together, which interferes with the analysis of microseismic signals and affects the subsequent work of phase arrival picking and source location. Therefore, it is necessary to study the noise reduction of microseismic signals. Method: In view of the randomness and non-stationarity of microseismic signals, a signal denoising method based on the multi-layer joint is proposed in this paper. SVD (singular value decomposition) is used as the first layer denoising method to remove the white noise in the signal. EMD( Empirical Mode Decomposition) is used as the second layer noise reduction method to deal with random and non-stationary microseismic signals. As the third layer noise reduction method, the wavelet threshold eliminates the modal aliasing phenomenon generated by EMD. Result: The experimental analysis shows that the proposed method has a good noise reduction effect on the simulation signal and the actual microseismic signal with different signal-to-noise ratios. Conclusion: It overcomes the disadvantage of the poor noise reduction effect of a single noise reduction method, can greatly improve the signal-to-noise ratio of the signal, and has a high similarity with the original signal, which provides a new idea for microseismic signal noise reduction. [ABSTRACT FROM AUTHOR]

Speckle noise in ultrasound images (UIs) significantly reduces the accuracy of disease diagnosis. The aim of this study was to quantitatively evaluate its feasibility in salivary gland ultrasound imaging by modeling the adaptive non-local means (NLM) algorithm. UIs were obtained using an open-source device provided by SonoSkills and FUJIFILM Healthcare Europe. The adaptive NLM algorithm automates optimization by modeling the isotropic search window, eliminating the need for manual configuration in conventional NLM methods. The coefficient of variation (COV), contrast-to-noise ratio (CNR), and edge rise distance (ERD) were used as quantitative evaluation parameters. UIs of the salivary glands revealed evident visualization of the internal echo shape of the malignant tumor and calcification line using the adaptive NLM algorithm. Improved COV and CNR results (approximately 4.62 and 2.15 times, respectively) compared with noisy images were achieved. Additionally, when the adaptive NLM algorithm was applied to the UIs of patients with salivary gland sialolithiasis, the noisy images and ERD values were calculated almost similarly. In conclusion, this study demonstrated the applicability of the adaptive NLM algorithm in optimizing search window parameters for salivary gland UIs.

The approach to reduce the amplitude noise of a vertical channel of the sampling oscilloscope is presented in this paper. In general, the vertical channel of the sampling oscilloscope consists of a high-frequency sampling circuit and a relatively low-frequency sample transmission path along with a high bit resolution analogto-digital converter. The paper presents a method to improve the sensitivity of the vertical channel of a stroboscopic oscilloscope by extending the conventional channel architecture. The main vertical channel unit of the oscilloscope is a sampling device (sampler), which made of discrete elements and usually implemented using high frequency diodes. The sampler performs a transformation of the sample of the high-frequency signal under test into a low-frequency equivalent signal (otherwise called a balance impulse). In a conventional sampling device, this pulse is quantized once the amplitude is at its highest, thus achieving the best signal-to-noise ratio. The paper analyzes the operating parameters of the sampling device circuit and their influence on the output signal of the sampler. In this approach uses the fastest (15 MHz) high-resolution (18-bit) analog-to-digital converters currently on the market to reduce the amplitude noise of vertical channel based on conventional architecture. Our research has shown that it is possible to obtain an increase in the signal-tonoise ratio of almost 1.3 times. Article in Lithuanian. Stroboskopinio osciloskopo vertikaliojo kanalo triukšmo mažinimo metodas Santrauka Straipsnyje yra pateikiamas metodas, leidžiantis pagerinti stroboskopinio osciloskopo vertikaliojo kanalo jautrumą praplečiant įprastą kanalo architektūrą. Pagrindinis osciloskopo vertikaliojo kanalo mazgas yra strobavimo įtaisas (signalų maišytuvas), kuris sistemose, gaminamose iš diskrečiųjų elementų, dažniausiai įgyvendinamas taikant aukštadažnius diodus. Strobavimo įtaise vykdoma tiriamojo aušktadažnio signalo imties transformacija į žemadažnį ekvivalentinį signalą (kitaip vadinamas balanso impulsu). Įprastos architektūros strobavimo įtaise šis impulsas yra kvantuojamas vieną kartą, kai amplitudė esti didžiausia, taip pasiekiamas geriausias signalo ir triukšmo santykis. Straipsnyje yra analizuojami strobavimo įtaiso grandinės darbo parametrai ir jų įtaka įtaiso išėjimo signalui, taikant šiuo metu rinkoje sparčiausius (15 MHz) didelio skiltiškumo (18-bit) analoginius skaitmeninius keitiklius siekiama sumažinti įprastos architektūros vertikaliojo kanalo amplitudės triukšmą. Reikšminiai žodžiai: stroboskopinis osciloskopas, strobavimo įtaisas, vertikalusis kanalas, analoginis skaitmeninis keitiklis, ekvivalentinio laiko strobavimo metodas.

The approach to reduce the amplitude noise of a vertical channel of the sampling oscilloscope is presented in this paper. In general, the vertical channel of the sampling oscilloscope consists of a high-frequency sampling circuit and a relatively low-frequency sample transmission path along with a high bit resolution analogto- digital converter. The paper presents a method to improve the sensitivity of the vertical channel of a stroboscopic oscilloscope by extending the conventional channel architecture. The main vertical channel unit of the oscilloscope is a sampling device (sampler), which made of discrete elements and usually implemented using high frequency diodes. The sampler performs a transformation of the sample of the high-frequency signal under test into a low-frequency equivalent signal (otherwise called a balance impulse). In a conventional sampling device, this pulse is quantized once the amplitude is at its highest, thus achieving the best signal-to-noise ratio. The paper analyzes the operating parameters of the sampling device circuit and their influence on the output signal of the sampler. In this approach uses the fastest (15 MHz) high-resolution (18-bit) analog-to-digital converters currently on the market to reduce the amplitude noise of vertical channel based on conventional architecture. Our research has shown that it is possible to obtain an increase in the signal-tonoise ratio of almost 1.3 times. [ABSTRACT FROM AUTHOR]

Predicting the spatial distribution of Soil Organic Matter (SOM) through remote sensing images is an important part of precision agriculture and land resource management research. The emergence of Gaofen5 (GF-5) has facilitated quantitative soil monitoring based on hyperspectral remote sensing. As the first full spectrum hyperspectral satellite for comprehensive observations of the atmosphere and land, GF-5 has a high spatial resolution of 30 m, including a total of 330 bands, and the coverage is consistent with the range obtained by spectro-radiometerFieldSpec3 (Analytical Spectral Devices, Longmont, USA). However, there is no clear and appropriate noise reduction method in the relevant soil research. Therefore, this study was to explore the best noise reduction method for GF-5 application in soil organic matter inversion. The experimental area was Mingshui County (124°18°-125°21°E，46°44°-47°29°N), a typical black soil region of Heilongjiang Province. A total of 38 sampling points were selected at the depth of 0-20 cm in the study area and the spatial coordinates of sampling points were recorded by GPS. The non-cloud GF-5 hyperspectral remote sensing image was obtained from the website of China Resources Satellite Application Center. According to our prior knowledge, radiometric calibration and atmospheric correction pre-processing of hyperspectral remote sensing images in ENVI5.1 were performed. Singular Value Decomposition (SVD), Discrete Wavelet Transformation (DWT) and Median Filtering (MF) noise reduction method were used. The two-dimensional spectral indexes were calculated under different noise reduction methods. The Principal Component Analysis (PCA) method was used to decrease the dimensionality of the inputs, and the Random Forest (RF) method was used to predict the SOM content. A total of 26 data were used for model establishment and the rest 12 data were used for model validation. The SOM content of these samples were 3.45%-5.53% with mean of 4.44%, standard deviation of 4.28% and coefficient of variance of 9.6. The results showed that the best decomposition layer of DWT was 1 since the correlation coefficient between reflectance and SOM was the highest and the wavelength locations with high correlation were consistent with those in the original reflectance curves. Among different noise reduction methods, the correlation between the spectral curve and the SOM content was the highest for DWT, followed by SVD and MF. The small "burr" in the soil reflection spectrum curve was removed by using different noise reduction methods and the shapes of the spectral curves under different SOM contents were consistent. The principal component analysis results showed that the cumulative contribution rates for data experiencing noise reduction by SVD, DWT and MF methods were all about 95%. The DWT method combined with RF prediction model achieved the highest accuracy of SOM estimation for validation dataset (R²: 0.69, RMSE: 2.26%, RPD: 1.80) and the noise reduction effect was the most significant. The SOM spatial distribution under the DWT noise reduction model was mapped and the result was consistent with the real condition. The SOM content was relatively high and its distribution was uniform in the central areas of Mingshui County. However, it was low in the southeast region because of severe soil erosion. The research results can provide an effective way of the realization of remote sensing prediction of SOM at a large scale by using GF-5 hyperspectral satellite data, expanding the application scope of satellite images. [ABSTRACT FROM AUTHOR]

The bearing is the core part of the electric spindle, and the bearing failure directly affects the normal operation of the electric spindle. When the electric spindle breaks down, the mechanical system can not operate normally, which leads to great losses. In order to detect bearing faults, many traditional intelligent fault detection methods are proposed, and the fault category can not be accurately determined due to the existence of noise components in the signal. In this paper, a new noise reduction algorithm- Sparse Denoising Auto-Encode (SDAE) is proposed. The Denoising Auto-Encoder can learn the noise factors and extract the succinct expressions from raw data automatically, and sparsity is integrated on the basis of Denoising Auto-Encoder to improve the generalization of feature expression. More effective feature expressions are extracted to train Convolutional Neural Network (CNN), and the Adam optimization algorithm is used to fine-tune CNN to improve the accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

Super‐resolution (SR) applications aim to use information within one or more low‐resolution (LR) image(s) to obtain high‐resolution (HR) image(s). LR images might well be the consecutive frames of a video sequence. When multiple LR images are used as input, motion estimation (ME) between portions of images is an important step of the solution for this ill‐posed problem. In this study, the authors employed translational optical flow for ME, followed by common vector approach (CVA) for HR image reconstruction from multiple sources. CVA provides a way to reduce outliers caused by noise, occlusion, shadows and incorrect ME. HR image blocks are obtained by combining common and difference vectors of the blocks' class that are handled separately. Noise in difference vectors is reduced by a known noise reduction method before combining. Separate handling of common and difference parts guarantees better results and greatly reduce artefacts. Compared to the state‐of‐the‐art, experimental results confirm the authors' achievement by visual, peak signal‐to‐noise ratio and structural similarity index measures criteria. [ABSTRACT FROM AUTHOR]

Separating signal from unwanted noise is a major problem when analysing biomedical data, such as electrocardiography. Electrocardiogram (ECG) data are typically a mixture of real signal and various sources of noise, including baseline wander, power line interference, and electromagnetic interference. Since ECG signals are non‐stationary physiological signals, the wavelet transform has been proposed to be an effective tool for eliminating unwanted noise from the ECG data. Here, the authors proposed a new noise reduction method for ECG data based on the discrete wavelet transform and hidden Markov model. They performed Monte Carlo simulations to compare the performance of this new method with seven other well‐known denoising techniques. [ABSTRACT FROM AUTHOR]

In this article, we consider a test of the sphericity for high-dimensional covariance matrices. We produce a test statistic by using the extended cross-data-matrix (ECDM) methodology. We show that the ECDM test statistic is based on an unbiased estimator of a sphericity measure. In addition, the ECDM test statistic enjoys consistency properties and the asymptotic normality in high-dimensional settings. We propose a new test procedure based on the ECDM test statistic and evaluate its asymptotic size and power theoretically and numerically. We give a two-stage sampling scheme so that the test procedure can ensure a prespecified level both for the size and power. We apply the test procedure to detect divergently spiked noise in high-dimensional statistical analysis. We analyze gene expression data by the proposed test procedure. [ABSTRACT FROM AUTHOR]

Landing gear is one of the most important aeroacoustic noise sources during modern large aircrafts takeoff and landing. This paper some research achievements and latest developments made through wind tunnel experiments, flyover experiments and numerical simulations in the aeroacoustic investigation field of landing gear in the last decades. This paper a brief overview of four aspects including the noise generation mechanisms, the noise reduction techniques, the similarity rule of wind tunnel aeroacoustic experiments and the prediction methods for landing gears. Landing gear broadband noise can be generally categorized into two parts is the flow separation induced noise and the other is the interaction noise between upstream and downstream components. Moreover, the resonant phenomenon appeared in some cavity configurations can also generate tonal noise. Some active and passive noise reduction methods such as fairing and plasma actuators which can suppress the flow separation and interaction phenomena are used to reduce landing gear noise. Finally, prediction of further research on landing gear noise is presented. [ABSTRACT FROM AUTHOR]

Abstract: We describe a signal-processing program for a data acquisition system for cryogenic particle detectors. The program is based on an optimal-filtering method for high-resolution measurement of calorimetric signals with a significant amount of noise of unknown origin and non-stationary behavior. The program was applied to improve the energy resolution of the alpha particle spectrum of an 241Am source. [Copyright &y& Elsevier]

Proposed is a noise reduction method for magnetic resonance (MR) images. This method can be considered a new adaptive non‐local means filtering technique since different weights based on the edgeness of an image are applied. Unlike conventional noise reduction methods, which typically fail in preserving detailed information, the proposed method preserves fine structures while significantly reducing noise in MR images. For comparing the proposed method with other noise reduction methods, both a simulated ground truth data set and real MR images were used. The experiment shows that the proposed method outperforms conventional methods in terms of both restoration accuracy and quality. [ABSTRACT FROM AUTHOR]