Noise Smoothing Research Articles

GNSS-corrected InSAR displacement time-series spanning the 2019 Ridgecrest, CA earthquakes

SUMMARYInSAR displacement time-series are emerging as a valuable product to study a number of Earth processes. One challenge to current time-series processing methods, however, is that when large earthquakes occur, they can leave sharp coseismic steps in the time-series. These discontinuities can cause current atmospheric correction and noise smoothing algorithms to break down, as these algorithms commonly assume that deformation is steady through time. Here, we aim to remedy this by exploring two methods for correcting earthquake offsets in InSAR time-series: a simple difference offset estimate (SDOE) process and a multiparameter offset estimate (MPOE) parametric time-series inversion technique. We apply these methods to a 2-yr time-series of Sentinel-1 interferograms spanning the 2019 Ridgecrest, CA earthquake sequence. Descending track results indicate that the SDOE method precisely corrects for only 20 per cent of the coseismic offsets at 62 study locations included in our scene and only partially corrects or sometimes overcorrects for the rest of our study sites. On the other hand, the MPOE estimate method successfully corrects the coseismic offset for the majority of sites in our analysis. This MPOE method allows us to produce InSAR time-series and data-derived estimates of deformation during each phase of the earthquake cycle. In order to better isolate and estimate the signal of post-seismic lithospheric deformation in the InSAR time-series, we apply a GNSS-based correction to our interferograms. This correction ties the interferograms to median-filtered weekly GNSS displacements and removes additional atmospheric artefacts. We present InSAR-based estimates of post-seismic deformation for the area around the Ridgecrest rupture, as well as a 2-yr coseismic-corrected, GNSS-corrected InSAR time-series data set. This GNSS-corrected InSAR time-series will enable future modelling of post-seismic processes such as afterslip in the near field of the rupture, poroelastic deformation at intermediate distances and viscoelastic deformation at longer timescales in the far field.

Diagnostic Imaging Analysis and Care of Patients with Endomyocardial Fibrosis Based on Wireless Network Smart Medical Application.

The heart is one of the most important organs of the human body, but in recent years heart disease has become one of the human health killers and this paper explores endomyocardial fibrosis, which is a common cardiomyopathy, commonly seen in infants and children, and refers to a diffuse elastic fibrous disease of the endocardium. The purpose of this paper is to explore the diagnostic imaging analysis and care of patients with endocardial heart machine fibrosis using wireless network intelligent medical technology, aiming to provide a new power basis for the treatment of the disease in related patients. This paper proposes a new endocardial segmentation algorithm that aims to process image information using image features, intervene in image noise reduction and smoothing, etc., and use image grayscale values to confirm cardiac cavity grayscale values as a basis for physicians to make certain judgments for the diagnosis of patients with endocardial machine fibrosis. The experimental results show that the atrial fibrillation group is distinctly higher compared to the sinus rhythm group, with values remaining between 25 and 39, which is a significant advantage compared to other methods.

Multi-objective neural network model selection with a graph-based large margin approach

This work presents a new decision-making strategy for multi-objective learning problem of artificial neural networks (ANN). The proposed decision-maker searches for the solution that minimizes a margin-based validation error amongst Pareto set solutions. The proposal is based on a geometric approximation to find the large margin (distance) of separation among the classes. Several benchmarks commonly available in the literature were used for testing. The obtained results showed that the proposal is more efficient in controlling the generalization capacity of neural models than other learning machines. It yields smooth (noise robustness) and well-fitted models straightforwardly, i.e., without the necessity of parameter set definition in advance or validation data use, as often required by learning machines.

Morphological Edge Detection on CT Images

In this paper, a novel mathematical morphology edge detection algorithm is proposed to detect lungs CT medical image edge. It is a better method for edge information detecting and noise filtering than differential operation, which is sensitive to noise. And it is a better compromise method between noise smoothing and edge orientation, but the computation is more complex than general morphological edge detection algorithms

Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information

This paper proposes a single-stage adaptive multi-scale noise filtering algorithm for point clouds, based on feature information, which aims to mitigate the fact that the current laser point cloud noise filtering algorithm has difficulty quickly completing the single-stage adaptive filtering of multi-scale noise. The feature information from each point of the point cloud is obtained based on the efficient k-dimensional (k-d) tree data structure and amended normal vector estimation methods, and the adaptive threshold is used to divide the point cloud into large-scale noise, a feature-rich region, and a flat region to reduce the computational time. The large-scale noise is removed directly, the feature-rich and flat regions are filtered via improved bilateral filtering algorithm and weighted average filtering algorithm based on grey relational analysis, respectively. Simulation results show that the proposed algorithm performs better than the state-of-art comparison algorithms. It was, thus, verified that the algorithm proposed in this paper can quickly and adaptively (i) filter out large-scale noise, (ii) smooth small-scale noise, and (iii) effectively maintain the geometric features of the point cloud. The developed algorithm provides research thought for filtering pre-processing methods applicable in 3D measurements, remote sensing, and target recognition based on point clouds.

Fall Detection using Deep Learning Algorithms and Analysis of Wearable Sensor Data by Presenting a New Sampling Method

Fall is one of the most critical health challenges in the community, which can cause severe injuries and even death. The primary purpose of this study is to develop a deep neural network using wearable sensor data to detect falls. Most datasets in this field suffer from the problem of data imbalance so that the instances belonging to the Fall classes are significantly less than the data of the normal class. This study offers a dynamic sampling technique for increasing the balance rate between the samples belonging to fall and normal classes to improve the accuracy of the learning algorithms. The Sisfall dataset was used in which human activity is divided into three categories: normal activity (BKG), moments before the fall (Alert), and role on the ground (Fall). Three deep learning models, CNN, LSTM, and a hybrid model called Conv-LSTM, were implemented on this dataset, and their performance was evaluated. Accordingly, the Conv-LSTM hybrid model presents 96.23%, 98.59%, and 99.38% in the Sensitivity parameter for the BKG, Alert, and Fall classes, respectively. For the accuracy parameter, we have managed to reach 97.12%. In addition, by using noise smoothing and removal techniques, we can hit a 97.83% accuracy rate. The results indicate the proposed model's superiority compared to other similar studies.

Adaptive Robust Least-Squares Smoothing Algorithm

The Kalman smoothing algorithm is widely use d in offline data processing in GNSS to improve filter calcu lation accuracy. When there are measurement outliers or b iased random models, the adaptive Kalman smoothing algo rithm can reduce their impact on the smoothing results to a certain extent. Nevertheless, the adaptive Kalman smoothi ng algorithm usually has difficulty obtaining optimal estim ation in a complex observation environment due to the inhe rent defects of the algorithm itself. Therefore, we propose a n adaptive robust least-squares smoothing algorithm. The row and column transformation of the least-square smooth noise covariance matrix is realized by constructing an orth ogonal space transformation matrix, and the noise covaria nce estimation problem of the Kalman filter is transformed into the problem of solving the variance covariance compo nent elements under a specific matrix structure. In additio n, by selecting the observation value with the largest stand ardized residual to determine whether it is gross error, we avoid the gross error misjudgment problem caused by the t wo-factor robust estimation method under least-square sm oothing, and further improve the smoothing accuracy. Fina lly, two experiments are studied to compare the performan ce of the new algorithm and the state-of-the-art algorithms. The proposed algorithm has demonstrated better perform ance in terms of robustness and estimation accuracy.

Development of a drone‐borne electromagnetic survey system for searching for buried vehicles and soil resistivity mapping

ABSTRACTWe developed a drone‐borne electromagnetic survey system using a commercial multi‐frequency electromagnetic sensor equipped with a GPS receiver, a WiFi serial transceiver, and an ultrasonic distance sensor to measure the height of the electromagnetic sensor above the ground surface. The electromagnetic sensor was suspended from a drone with ropes. The distance between the drone and the electromagnetic sensor was adjusted to minimize the influence of electromagnetic noise generated by the drone, and to stabilize the electromagnetic sensor during flight. The system was tested at two experimental sites. The first site consisted of two buried vehicles to simulate a landslide. We assumed a scenario in which the search for the buried vehicles was urgent and accessibility to the area was limited. The second site consisted of wet and dry agricultural fields to test resistivity mapping. In the first test, we used the in‐phase component of the measured data to locate the vehicles. The shallower vehicle was identified clearly, while the deeper vehicle was located successfully, albeit less easily. In the second test, the quadrature component was used for one‐dimensional inversion after data processing, which included data smoothing, resampling and bias noise correction. The bias noise was measured while hovering the drone at a high altitude to negate the influence of ground conductivity. The results showed that the resistivity distributions could be mapped at some depths by using a five‐frequency‐processed quadrature component, and clearly showed the difference between the wet and dry fields. The crucial parameter in the evaluations of these targets was the height of the electromagnetic sensor above the ground surface, which was measured continuously during flight. The results demonstrated the potential of the survey system to search for buried metal objects and for shallow subsurface resistivity mapping over relatively large areas.

Application of Gaussian Radial Basis Functions for Fast Spatial Imaging of Ground Penetration Radar Data Obtained on an Irregular Grid

Spatial imaging of ground penetrating radar (GPR) measurement data is a difficult computational problem that is time consuming and requires substantial memory resources. The complexity of the problem increases when the measurements are performed on an irregular grid. Such grid irregularities are typical for handheld or flying GPR systems. In this paper, a fast and efficient method of GPR data imaging based on radial basis functions is described. A compactly supported modified Gaussian radial basis function (RBF) and a hierarchical approximation method were used for computation. The approximation was performed in multiple layers with decreasing approximation radius, where in successive layers, increasingly finer details of the imaging were exposed. The proposed method provides high flexibility and accuracy of approximation with a computational cost of N·log (N) for model building and N·M for function evaluation, where N is the number of measurement points and M is the number of approximation centres. The method also allows for the control smoothing of measurement noise. The computation of one high-quality imaging using 5000 measurement points utilises about 5 s on an Intel Core i5-7200U CPU 2.5 GHz, 8 GB RAM computer. Such short time enables real-time image processing during field measurements.

Comparison of point cloud filtering methods with data acquired by photogrammetric method and RGB-D sensors

Point clouds (PCs) are inevitable sources to generate digital solid model-based applications such as reverse engineering, differential 3D modelling, 3D sensing and modelling of environments, scene reconstruction, augmented reality. Photogrammetric methods, Terrestrial Laser Scanners and RGB-D sensors are relatively common among the technologies used to capture PCs. Because of their structural characteristics, measuring systems produce large amounts of noise that cannot be precisely predicted in type and amplitude. Due to the noisy measurements, the spatial orientations of the differential surface particles and the spatial locations of the corner points have a certain degree of deformation. In order to increase visual, spatial and physical quality of the solid model, which is frequently used in reverse engineering, PCs must be filtered to discard noise and outlier. In this paper PC produced from different methods was filtering with Shepard Inverse Distance Weighting method, Gaussian Filtering method, Single Value Decomposition Based Plane Fitting method and Optimization Based Plane Fitting method. Backtracking Search Optimization Algorithm (BSA) was used to fitting plane. Experimental results were compared visually and statistical according to the number of neighborhoods. The results showed that Backtracking Search Optimization based filtering supplied better noise smoothing results than its competitors.

Doppler Ultrasound Imaging Combined with Fetal Heart Detection in Predicting Fetal Distress in Pregnancy-Induced Hypertension under the Guidance of Artificial Intelligence Algorithm.

This study was to improve the feasibility and economic benefits of intelligent medical system Doppler ultrasound (DUS) imaging technology combined with fetal heart detection to predict the fetal distress in pregnancy-induced hypertension (PIH), so as to reduce the risk of deterioration of the patient's condition. The characteristics of DUS images were analyzed, and a diffusion filter reducing the specificity was adopted to improve the smooth speckle noise of DUS images. 120 pregnant women in hospital were the subjects of the study, all of whom received ultrasound cord blood flow testing and fetal heart monitoring. 88 PIH patients with fetal distress were diagnosed and included in the observation group, and 32 healthy pregnant women tested during the same period were identified as the control group. Clinical data were reviewed and analyzed. The diagnostic rates of fetal distress by simple fetal heart monitoring and DUS detection combined with fetal heart monitoring were compared. The results showed that 26.7% of fetal distress were diagnosed by fetal heart monitoring alone, and 73.3% of fetal distress were diagnosed by combined testing, so the diagnostic accuracy of the combined detection method was greatly higher than the single fetal heart detection (P < 0.05). The Pulsatility index (PI), resistance index (RI), and S/D values detected by the umbilical artery in the observation group were 1.48, 0.85, and 4.31, respectively. The PI, RI, and S/D values detected by the umbilical artery in the control group were 0.96, 0.64, and 3.59, respectively. The results of arterial detection were significantly higher than those of the control group, and the difference was of significant scientific significance (P < 0.05). In summary, the PI and RI values of the middle cerebral artery (MCA) detected by DUS diagnosis can effectively reflect the current status of the fetus in the uterus and reduce the mortality of the fetus. The images guided by DUS imaging technology can clearly show the current status of the fetus in the uterus, effectively improve the medical diagnostic efficiency, and have important reference value for the development of intelligent medical equipment.

Reduction of Multiplicative Noise in Radar Images

Introduction. A radar image is an image obtained by remote sensing the earth's surface with a radar device. Radar images are characterized by background graininess caused by speckle noise, which should be filtered to improve the quality of radar images. The structure of speckle noise reduction filters often comprise one or more parameters to control the level of noise smoothing. The values of these parameters have to be selected experimentally. In works devoted to speckle noise filtering, the methods used for selecting filter paraments are rarely clarified.Aim. To present a methodology for selecting the parameters of multiplicative speckle noise filters on a radar image that are optimal in terms of the quality of the resulting image.Materials and methods. The article presents a method for determining the optimal parameters of speckle noise reduction filters. This method was applied to the most conventionally used filters. The search for optimal parameters and testing of the filters were carried out using a specially designed image, which contained the objects most frequently found on radar images. The structural similarity index (SSIM) metric was chosen as a metric that assesses the quality of filtration.Results. After determining the optimal (in terms of SSIM) parameters of speckle noise reduction filters, the filters were compared to select the best filters in terms of the quality of radar image processing. In addition, the operation of the filters under study was tested on images containing various types of objects, namely: large objects, small objects and sharp borders. Knowing which filter copes best with smoothing speckle noise in a particular area and what values of the variable parameters this requires, an optimal quality of radar images can be achieved. Filtering not only improves human perception of radar images, but also reduces the influence of speckle noise during their further processing (object detection, segmentation of areas, etc.).Conclusion. The proposed algorithm allowed optimal parameters for several speckle noise filters to be determined. The quality of filtration was assessed using an expert method (visually) by comparing images before and after filtration, differential images and one-dimensional image slices. The Frost filter and the anisotropic diffusion filter with optimal parameters showed the best processing quality according to the SSIM metric.

Large Time Behavior of Deterministic and Stochastic 3D Convective Brinkman-Forchheimer Equations in Periodic Domains

The large time behavior of deterministic and stochastic three dimensional convective Brinkman-Forchheimer (CBF) equations $$\begin{aligned} \partial _t{\varvec{u}}-\mu \Delta {\varvec{u}}+({\varvec{u}}\cdot \nabla ){\varvec{u}}+\alpha {\varvec{u}}+\beta |{\varvec{u}}|^{r-1}{\varvec{u}}+\nabla p={\varvec{f}},\ \nabla \cdot {\varvec{u}}=0, \end{aligned}$$ ∂ t u - μ Δ u + ( u · ∇ ) u + α u + β | u | r - 1 u + ∇ p = f , ∇ · u = 0 , for $$r\ge 3$$ r ≥ 3 ( $$\mu ,\beta >0$$ μ , β > 0 for $$r>3$$ r > 3 and $$2\beta \mu \ge 1$$ 2 β μ ≥ 1 for $$r=3$$ r = 3 ), in periodic domains is carried out in this work. Our first goal is to prove the existence of global attractors for the 3D deterministic CBF equations. Then, we show the existence of random attractors for the 3D stochastic CBF equations perturbed by small additive smooth noise. Furthermore, we establish the upper semicontinuity of random attractors for the 3D stochastic CBF equations (stability of attractors), when the coefficient of random perturbation approaches to zero. Finally, we address the existence and uniqueness of invariant measures of 3D stochastic CBF equations.

Research on Cell Counting Method Based on Flood Fill Algorithm

Cell counting has a wide range of applications in cytology research and clinical practice. Cell counts are widely used in the early diagnosis and treatment of serious diseases and the screening of effective drugs in the later stage. The method of studying cell counts is of great significance. This paper proposes a method for cell counting based on the highlight area of the cell center as a live cell marker. This method uses algorithms bilateral filtering to smooth noise in the original image; after adaptive threshold segmentation, an algorithm based on flood filling is used. Accurate segmentation of the highlighted area in the center of the cell; finally, counting is achieved through the connected domain labeling algorithm. The experimental results show that the accuracy of the cell counting method proposed in this paper is above 98%, which verifies the feasibility and practicability of the method proposed in this paper.

Incompressible viscous fluids in R2 and SPDEs on graphs, in presence of fast advection and non smooth noise

Le comportement asymptotique d’une classe d’equations stochastiques de reaction-diffusion-advection dans le plan est etudie. Nous montrons qu’a mesure que le terme d’advection sans divergence devient de plus en plus grand, les solutions de telles equations convergent vers la solution d’une EDP stochastique appropriee definie sur le graphe associe a l’Hamiltonien. Tout d’abord, nous traitons le cas ou la perturbation stochastique est donnee par un processus de Wiener spatialement homogene singulier prenant des valeurs dans l’espace des distributions de Schwartz. Comme dans les travaux precedents, nous supposons ici que la derivee de la periode du mouvement sur les level sets de l’Hamiltonien ne s’annule pas. Puis, dans la seconde partie, sans supposer cette condition sur la derivee de la periode, nous etudions un type de convergence plus faible pour les solutions d’une classe appropriee de EDPS lineaires.

Wheelchair Movement Based on Convolution Neural Network

This paper intends to develop a methodology for helping amputees and crippled people old, by ongoing voice direction and association between patient and personal computer (PC) where these blends offer a promising response for helping the debilitated people. The major objective of this work is accurately detected audio orders via a microphone of an English language (go, stop, right and left) in a noisy environment by the proposed system. Thus, a patient that utilizes the proposed system can be controlling a wheelchair movement. The venture depends on preparing an off-line dataset of audio files are included 10000 orders and background noise. The proposed system has two important steps of preprocessing to get accurate of specific audio orders, accordingly, the accurate direction of wheelchair movement. Firstly, a dataset was preprocessed to reduce ambient noise by using Butterworth (cutoff 500-5000 Hz) and Wiener filter. Secondly, in the input (a microphone) of the proposed discriminative model put a procedure of infinite impulse response filter (Butterworth), passband filter for cutoff input microphone from 150-7000 Hz for back-off the loud and environment noise and local polynomial approximation (Savitzky-Golay) smoothing filter that plays out a polynomial regression on the signal values. Thus, a better for filtering from ambient noise and keeping on a waveform from distortion that makes the discriminative model accurate when voice orders were recognized. The proposed system can work with various situations and speeds for steering; forward, stop, left and right. All datasets are trained by using deep learning with specific parameters of...

Nondestructive Testing of Lettuce Nitrogen Stress Based on Multidimensional Image

Visible light near infrared (VS-NIR) hyperspectral combined with three-dimensional laser scanning was applied to extract the VS-NIR features of lettuce nitrogen between 400-1700 nm and 3D morphological features of the plants. Such combination realizes the rapid quantitative detection of lettuce nitrogen. This study is based on the hyperspectral image data cube achieved from lettuce leaves with different nitrogen levels. Stepwise regression sensitive area was used and adaptive band selection method was combined to extract the characteristic spectrum and feature image of lettuce nitrogen and characterize the average image intensity. Also; the error caused by moisture variation content in lettuce nitrogen image features was compensated. Then a model of lettuce nitrogen hyperspectral image diagnosis was built. The reverse engineering software Geomagic Qualify was used to repair and smooth interference noise and discontinuous range which are based on the 3D laser scanning data of lettuce. Accordingly, the stem diameter, plant height, leaf area, and biomass features of different nitrogen levels of lettuce are obtained and the model of nitrogen detection about lettuce growth features was built based on reverse engineering and integral method. Multi-scale fusion lettuce nitrogen detection model is built by using the acquired hyperspectral images with growing features of lettuce nitrogen and adopting genetic algorithm combined with partial least squares regression. Results show the correlation coefficient R of the built model is 0.95; the model precision is much better than single feature of hyperspectral images and 3D laser scanning model. The feature extraction algorithm and the eigenvectors provide the reference for development of facilities for online monitoring system of crop growth information.

3D Reconstruction using convolution smooth method

3D imagery is an image with depth data. The use of depth information in 3D images still has many drawbacks, especially in the image results. Raw data on the 3D camera even does not look smooth, and there is too much noise. Noise in the 3D image is in the form of imprecise data, which results in a rough image. This research will use the convolution smooth methods to improve the 3D image. Will smooth noise in the 3D image, so the resulting image will be better. This smoothing system is called the blurring effect. This research has been tested on flat objects and objects with a circle contour. The test results on the flat surface obtained a distance of 1.3177, the test in the object with a flat surface obtained a distance of 0.4937, and the test in circle contour obtained a distance of 0.3986. This research found that the 3D image will be better after applying the convolution smooth method.

Countermeasures Against Adversarial Examples in Radio Signal Classification

Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vulnerable to carefully crafted attacks called adversarial examples. Hence, the reliance of wireless networks on deep learning algorithms poses a serious threat to the security and operation of wireless networks. In this letter, we propose for the first time a countermeasure against adversarial examples in modulation classification. Our countermeasure is based on a neural rejection technique, augmented by label smoothing and Gaussian noise injection, that allows to detect and reject adversarial examples with high accuracy. Our results demonstrate that the proposed countermeasure can protect deep-learning based modulation classification systems against adversarial examples.

Sample size estimation in locomotion kinematics and electromyography for statistical parametric mapping

In biomechanics, kinematic and electromyographic data can be represented as one-dimensional (1D) waveforms and compared by using 1D hypothesis tests. These statistical techniques are increasingly applied in the study of locomotion. However, although widely agreed as a key step to obtain reliable and replicable findings, no a priori sample size estimation is usually conducted. This can also be done in 1D tests by calculating the statistical power – i.e., the probability of rejecting the null hypothesis when it is false – by using statistical parametric mapping. With the present study we characterised the parameters needed to estimate sample size in locomotion, and how they impact on statistical power in 1D tests. First, noise and signal in kinematics and electromyography were defined using experimental data on locomotion in physiological and pathological participants. Then, 1D power analysis was performed in representative conditions, and a dataset of tabulated sample sizes was generated. Kinematic and electromyographic data showed a smooth Gaussian noise, with amplitude and full-width-at-half-maximum depending on the physiological or pathological condition, and the considered joint or muscle. Given a certain noise, statistical power increased i) with greater signal amplitude and signal full-width-at-half-maximum, ii) when setting a region of interest and iii) when using a paired (vs. unpaired) study design. The present work provides initial benchmarks for appropriate sampling in 1D hypothesis testing, meant to evaluate statistical power in 1D tests and assists sample size estimation in studies on locomotion.