Local Filter Research Articles

Distributed Multiple Model Filtering for Markov Jump Systems With Measurement Outliers

In this paper, a distributed filtering problem is studied for a Markov jump system over sensor networks, where measurements are partially disturbed by outliers. A local multiple model filter is designed based on variational Bayesian approaches and interacting multiple model methods (IMM), the designed filter is able to identify and exclude outliers automatically, so as to mitigate the impact of outliers. A distributed filter is proposed by combining the designed local filter with consensus on information methods. Furthermore, a sufficient condition is given to guarantee the stability of the designed distributed filter, in which the estimation errors of each sensor are bounded in the mean square sense. Finally, both simulations and experiments of target tracking systems are done to show the effectiveness of the designed distributed filter.

Tilt angle filter effect on noise cancelation and structural edges detection in hydrocarbon sources in a gravitational potential field

Noise cancelation is the process done to remove out-of-range anomalies and make better edge boundaries interpretation. One of the most challenging issues in describing gravitational maps is separating the anomalies related to shallow sources from the deep ones. Furthermore, Existing noise can make it arduous to separate shallow blurred boundaries. In this study in SE Iran, gravitational surveying was carried out in shallow areas from the west of Qeshm to the Hormuz islands in a regular network with a distance of one kilometer. The range of gravitational Bouguer was -297 to -330 mGal. Modeling and determining geometrical parameters revealed five negative anomalies from salt penetration. The residual gravity was computed by deducting gravitational effects related to deep sources from measured gravitational data. Correspondingly, estimating the boundary and edge of the subsurface masses will be better than local filters, and anomalies will be highlighted with more intensity. Furthermore, three major fault systems in the Zagros basin were determined as the primary origin of activity and expansion of Hormuz salt. Sensitivity analysis was employed utilizing analytical signals and maps of tilt angle filtering, which both revealed the same satisfying results of -297 to -330 mGal. In this article, the effect of the tilt angle local phase filter on a synthetic model was accomplished through numerical coding. As a result, Total Horizontal Derivative (THDR) provides location of salt intrusion in Qeshm area; whereas the best image of salt intrusion, in terms of feature edge illumination, presented by Analytical signal of residual gravity map.

Localized adaptive waveform inversion: regularizations for Gabor deconvolution and 3-D field data application

SUMMARYRecently, we have developed a localized adaptive waveform inversion (LAWI) method to tackle the cycle-skipping issue in velocity reconstruction through seismic waveform inversion. The LAWI method employs a local matching filter, computed using Gabor deconvolution, to measure the instantaneous time-shift between observed and calculated data. Unlike the adaptive waveform inversion (AWI) approach, the LAWI method can take the non-stationarity between observed and calculated data into account. In this work, we investigate two types of regularization based on prior information about the expected filter, which could be a minimum-norm filter or a delta-shape filter, with regard to their effects on the robustness and resolution of inversion. We demonstrate on synthetic data the advantages and disadvantages of these two types of prior information, where the delta-type LAWI may handle multiple observed phases not initially predicted by the starting velocity model. Therefore, we apply the delta-type LAWI to a high-quality 3-D field data set in the North Sea, eliminating the need for data-windowing tuning, which can be tedious and time-consuming for 3-D data. Under different workflows with varying reliable initial models and frequency bands of the pressure data considered, we show that the LAWI approach is robust, effective and efficient for reconstructing the P-wave velocity, while other approaches such as AWI and graph-space optimal-transport method may require meticulous data-tuning strategies to converge to the correct model. Well logs and data fits, primarily from early arrivals, give us confidence that this LAWI approach could be applied to various acquisitions and subsurface targets, thanks to its phase-driven principle.

Distributed recursive filtering for multi-rate uniform sampling systems with packet losses in sensor networks

The distributed filtering problem is studied for a multi-rate uniform sampling linear discrete system over sensor networks, where packet losses may occur during data exchange between nodes. The multi-rate system is transformed into a single-rate system by using a virtual measurement method. At each sensor node, the estimates received from neighbour nodes are fused by matrix or scalar weighting according to their different estimation accuracy. A distributed recursive filter with the Kalman consensus filter structure is devised based on the fused estimate, where the filtering gain and consensus gain are solved by locally minimising an upper bound of the filtering error covariance. The obtained gains and the upper bound of the covariance depend on a group of free positive scalar parameters. The scalar parameters are optimised by minimising the upper bound of the covariance. It is proved that the upper bound of the covariance of the filter with optimal gains and parameters is smaller than the covariance of the local filter based on sensor measurements. It avoids calculating cross-covariance matrices between nodes and has small computational burden. The exponential boundedness in the mean square of the filtering error is analysed. A target tracking system verifies the effectiveness of the algorithm.

Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter

Super dust storms re-occurred over East Asia in 2021 spring and casted great health damages and property losses. It is essential to achieve an accurate dust forecast to reduce the damage for early warning. The forecasting system fundamentally relies on a numerical model which can forecast the full evolution of dust storms. However, large uncertainties exist in model forecasts. Meanwhile, various near-real-time observations are available that contain valuable dust information. A dust storm forecasting system is here developed through coupling a chemical transport model, LOTOS-EUROS, and Localized EnKF (LEnKF) assimilation approach. The assimilations are carried out via an interface of our self-designed assimilation toolbox, PyFilter v1.0. Ground-based PM10 measurements from air quality monitoring network are assimilated. Sequential assimilation tests are carried out over the 2021 spring super dust storms. The results show that the assimilation-based forecasting system produces a promising dust forecast than model-only forecast, and the improvements is also validated through comparing to the independent MODIS aerosol optical depth (AOD). Superior performance is obtained when LEnKF is implemented, as the localization helps EnKF in resolving the PM10 measurements that have a large spatial variability with limited ensemble members. In addition, sensitivity experiments are conducted to exploit the distance-dependent localization for the LEnKF. Considering both cases, the optimal choice of the distance is tested to be around 500 km: the larger distance is less effective in removing the spurious correction, while the smaller one easily falls into the local optimum and the model would become divergent rapidly.

Demonstrating the Potential Impacts of Assimilating FY-4A Visible Radiances on Forecasts of Cloud and Precipitation with a Localized Particle Filter

Abstract The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun-4A (FY-4A) satellite provides visible radiances that contain critical information on clouds and precipitation. In this study, the impact of assimilating FY-4A/AGRI all-sky visible radiances on the simulation of a convective system was evaluated with an observing system simulation experiment (OSSE) using a localized particle filter (PF). The localized PF was implemented into the Data Assimilation Research Testbed (DART) coupled with the Weather Research and Forecasting (WRF) Model. The results of a 2-day data assimilation (DA) experiment generated encouraging outcome at a synoptic scale. Assimilating FY-4A/AGRI visible radiances with the localized PF significantly improved the analysis and forecast of cloud water path (CWP), cloud coverage, rain rate, and rainfall areas. In addition, some positive impacts were produced on the temperature and water vapor mixing ratio in the vicinity of cloudy regions. Sensitivity studies indicated that the best results were achieved by the localized PF configured with a localization distance that is equivalent to the model grid spacing (20 km) and with an adequately short cycling interval (30 min). However, the localized PF could not improve cloud vertical structures and cloud phases due to a lack of related information in the visible radiances. Moreover, the localized PF was compared with the ensemble adjustment Kalman filter (EAKF) and it was indicated that the localized PF outperformed EAKF even when the number of ensemble members was doubled for the latter, indicating a great potential of the localized PF in assimilating visible radiances.

Adaptive Decentralized Cooperative Localization for Firefighters Based on UWB and Autonomous Navigation

Cooperative localization (CL) is a popular research topic in the area of localization. Research is becoming more focused on Unmanned Aerial Vehicles (UAVs) and robots and less on pedestrians. This is because UAVs and robots can work in formation, but pedestrians cannot. In this study, we develop an adaptive decentralized cooperative localization (DCL) algorithm for a group of firefighters. Every member maintains a local filter and estimates the position and the relative measurement noise covariance is estimated rather than a fixed value. We derived the explicit expressions for the inter-member collaboration instead of using approximations. This method reduces the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) ranging on the CL, eliminating the need for fixed UWB anchors. The proposed algorithm was validated by two experiments designed in the building and forest environments. The experimental results demonstrate that the proposed algorithm improved the accuracy of localization, and the proposed algorithm suppressed the localization errors by 14.23% and 47.01% compared to the decentralized cooperative localization extended Kalman filter (DCLEKF) algorithm, respectively.

Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism

This paper investigates the distributed resilient fusion filtering (DRFF) issue under inverse covariance intersection (ICI) fusion criterion and dynamic event-triggered mechanisms (DETMs), where the physical plant is described by stochastic nonlinear multi-sensor networked systems (MSNSs) with time-varying system parameters and multiple missing measurements (MMMs). The measurements from various sensor nodes to the fusion center may undergo the missing data, where this phenomenon is depicted by means of random variables governed by certain statistical principles. In addition, the DETM is adopted to regulate the communication process from each sensor node to fusion center, which can alleviate the network transmission situations with communication overload and energy consumption limitation. The purpose of the addressed issue is to construct a set of local resilient filters (LRFs) for stochastic nonlinear MSNSs with MMMs via the DETM, which can guarantee that the minimized upper bounds are derived and the desirable filter gain with easy-to-implementation form is given. Subsequently, via the obtained LRFs, a unified framework of the DRFF approach is formulated through using the ICI fusion criterion. In addition, the monotonicity analysis of the obtained upper bound in regard to the triggered parameter is examined by providing rigorous theoretical proof. Finally, the simulations with comparison experiment are provided to illustrate the validity of presented DRFF technique.

Distributed fusion filtering for multi-sensor systems under time-correlated fading channels and energy harvesters

In this paper, the distributed fusion filtering issue is investigated for multi-sensor systems with the constraints from both time-correlated fading channels and energy harvesters. A specific scenario is considered where the sensors can harvest energy from the natural environment and may consume a certain amount of energy when transmitting measurements to the filters. In order to properly deal with the energy supply relationship between a battery and multiple sensors, a dynamic energy-allocated rule is proposed in this paper, i.e., the storage battery provides energy to sensors in order of different sensors’ priorities. Additionally, the channel fading phenomenon is also taken into consideration and the fading coefficient is described by a dynamic process. In this paper, we are committed to designing a local filter such that, under the effects of the time-correlated fading channels and energy harvesters, an upper bound on the local filtering error covariance is firstly derived by using the mathematical induction and then the upper bound is minimized by designing the local filter gain. Next, the covariance intersection approach is employed to obtain the fusion estimates. Finally, a simulation is provided to verify that the presented filtering strategy is feasible and effective.

Distributed Estimation With Iterative Inverse Covariance Intersection

For distributed estimation in sensor network systems, intractable correlation among local estimates has a great impact on the accuracy and consistency of the fused estimate. An inconsistent or over conservative estimate would lead to degradation of estimation performance or even estimation divergence. In this brief, a distributed iterative diffusion estimation algorithm based on inverse covariance intersection (ICI) technique is proposed to address the unknown correlation and yield an estimate with uncertainty close to the true mean square error. The proposed algorithm is referred to as iterative ICI (It-ICI), which leverages the iteration of fusions that is only executed locally. Every sensor node only combines estimates from its neighbors. By iterations, the local information can be propagated to the entire sensor network and the accuracy can be incrementally improved. The fused estimate is proved to be unbiased with bounded covariance provided that the local filters’ estimates are unbiased and bounded. The performance of the proposed distributed estimation algorithm is illustrated by a collaborative target tracking problem in terms of enhanced tracking accuracy and consistency.

The effectiveness of using local exhaust ventilation, HEPA filter, and dental aerosol suction on indoor air quality

ABSTRACTIntroduction: The emergence of COVID-19 pandemic that occurred at the end of December 2019 in Wuhan, China, was a health crisis that greatly affected the world. Therefore, dentists and dental nurses are professionals who have a very high risk of being exposed to the droplets and aerosol particles generated during dental procedures. Furthermore, they also have a high risk of cross-infection from exposure to microorganisms in blood, saliva, droplets, and instruments contaminated with blood, saliva, and tissue debris. This study aims to analyze the effectiveness of using exhaust fan wall/Local Exhaust Ventilation (LEV), HEPA filter, and dental aerosol suction in the dental clinic of Academic Hospital Gadjah Mada University. Methods: The type of the research is analytical observational study. This study measured temperature, humidity, ACH and the microbial load in the 3 dental clinic rooms of Sadewa 1 RSA UGM in September 2020, using an analytical observational method with ANOVA to determine the effect of using a LEV, HEPA filter and dental aerosol suction in the dental clinic. Results: Dental clinic equipped with a LEV, HEPA filter, and dental aerosol suction, was negatively associated with room humidity levels, with a Pearson correlation of (-0.777), air humidity correlated with microbial load (0.242), but the correlation was not large. From the ANOVA results, the use of LEV, HEPA filter and dental aerosol suction against humidity has a significant effect on reducing the humidity in the dental clinic with p value 0.05. Conclusion: The use of an oral LEV, HEPA filter, and dental aerosol suction has an effect on reducing air humidity and decreasing the microbial load, affects the number of particles or droplets in dental clinics, and affects ACH.Keywords: local exhaust ventilation, HEPA filter and dental aerosol suction, microbial load, humidity

Visual Geometry Group based on U-Shaped Model for Liver/Liver Tumor Segmentation

Liver cancer is the primary reason of death around the globe. Manually detecting the infected tissues is a challenging and time-consuming task. The computerized methods help make accurate decisions and therapy processes. The segmentation accuracy might be increased to reduce the loss rate. Semantic segmentation performs a vital role in infected liver region segmentation. This article proposes a method that consists of two major steps; first, the local Laplacian filter is applied to improve the image quality. The second is the proposed semantic segmentation model in which features are extracted to the pre-trained VGG16 model and passed to the U-shaped network. This model consists of 51 layers: input, 23 convolutional, 4 max pooling, 4 transpose convolutional, 4 concatenated, 8 activation, and 7 batch-normalization. The proposed segmentation framework is trained on the selected hyperparameters that reduce the loss rate and increase the segmentation accuracy. The proposed approach more precisely segments the infected liver region. The proposed approach performance is accessed on two datasets such as 3DIRCADB and LiTS17. The proposed framework provides an average dice score of 0.98, which is far better compared to the existing works.

A Distributed Implementation of Steady-State Kalman Filter

This paper studies the distributed state estimation in sensor network, where <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$m$</tex></formula> sensors are deployed to infer the <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$n$</tex></formula> -dimensional state of a Linear Time-Invariant (LTI) Gaussian system. By a lossless decomposition of the optimal steady-state Kalman filter, we show that the problem of distributed estimation can be reformulated as that of the synchronization of homogeneous linear systems. Based on such decomposition, a distributed estimator is proposed, where each sensor node runs a local filter using only its own measurement, alongside with a consensus algorithm to fuse the local estimate of every node. We prove that the average of estimates from all sensors coincides with the optimal Kalman estimate, and under certain condition on the graph Laplacian matrix and the system matrix, the consensus error is bounded and the asymptotic error covariance is derived. As a result, the distributed estimator is stable for each single node. We further show that the proposed algorithm has a low message complexity of min( <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$m$</tex></formula> , <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$n$</tex></formula> n). Numerical examples are provided in the end to illustrate the efficiency of the proposed algorithm.

$\mathcal {H}_{2}$ and $\mathcal {H}_\infty$ Suboptimal Distributed Filters Design for Linear Systems

This paper investigates the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{2}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_\infty$</tex-math></inline-formula> suboptimal distributed filtering problems for continuous time linear systems. We consider a linear system monitored by a number of filters, where each of the filters receives only part of the measured output of the system. Each filter can communicate with the other filters according to an a priori given strongly connected weighted directed graph. The aim is to design filter gains that guarantee the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{2}$</tex-math></inline-formula> or <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_\infty$</tex-math></inline-formula> norm of the transfer matrix from the disturbance input to the output estimation error to be smaller than an a priori given upper bound, while all local filters reconstruct the full system state asymptotically. We provide a centralized design method for obtaining such <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{2}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_\infty$</tex-math></inline-formula> suboptimal distributed filters. The proposed design method is illustrated by a simulation example.

An Approach for the Reduction of Unwanted Edges in Contour Detection Based on Local Filtering

In this paper, an approach for the reduction of unwanted edges in contour detection based on local filtering is presented. Our approach can be used as a preprocessing step before contour detection. Also our approach is useful for object recognition based on feature extraction tasks, because many contour detection methods can’t delete all unwanted edges carefully. Our method consists of a computational algorithm that has 7 steps. Including smoothing, edge detection, smoothing, decreasing of pixels, thresholding, local filtering, and mask creation respectively. We use smoothing for adhering neighbor edge pixels and weakening alone edge pixels. So we can amplify the correct edge pixels and attenuate unwanted edge pixels by smoothing the edge image. In local filtering, we use a proposed casual template that determines noisy regions and correct regions and therefore can create a mask matrix that its elements related to mentioned regions. Finally we can use the "mask matrix" for improving contours by using a “And” operator and we ensure final contour that has a few context effect.

Distributed decentralized EKF for very large-scale networks with application to satellite mega-constellations navigation

The design of a decentralized and distributed filtering solution for large-scale networks of interconnected systems is addressed considering (i) generic nonlinear dynamics and (ii) generic coupled nonlinear outputs in a generic, possibly time-varying, topology. The local filters, which follow the structure of the extended Kalman filter, are implemented in each system, which estimates its own state exclusively. To be suitable for the heavily restricted implementation to very large-scale systems, a novel algorithm is proposed, which: (i) does not rely on instantaneous data transmission; (ii) allows local communication exclusively; and (iii) requires computational, memory, and data transmission resources for each system that do not scale with the dimension of the network. The scalability of the proposed algorithm allows for its application to the cooperative localization problem of very large-scale systems. In particular, it is applied herein to the on-board position estimation problem of LEO mega-constellations using GNSS featuring numerical simulations for the Starlink constellation.

Self-training algorithm based on density peaks combining globally adaptive multi-local noise filter

Self-training algorithm highlights the speed of training a supervised classifier through small labeled samples and large unlabeled samples. Despite its long considerable success, self-training algorithm has suffered from mislabeled samples. Local noise filters are designed to detect mislabeled samples. However, two major problem with this kind of application are: (a) Current local noise filters have not treated the spatial distribution of the nearest neighbors in different classes in much detail. (b) They are being disadvantaged when mislabeled samples are located in overlapping areas of different classes. Here, we develop an integrated architecture – self-training algorithm based on density peaks combining globally adaptive multi-local noise filter (STDP-GAMLNF), to improve detecting efficiency. Firstly, the spatial structure of the data set is revealed by density peak clustering, and it is used for empowering self-training to label unlabeled samples. In the meantime, after each epoch of labeling, GAMLNF can comprehensively judge whether a sample is a mislabeled sample from multiple classes or not, and it will reduce the influence of edge samples effectively. The corresponding experimental results conducted on eighteen UCI data sets demonstrate that GAMLNF is not sensitive to the value of the neighbor parameter k, and it is capable of adaptively finding the appropriate number of neighbors of each class.

A deep graph convolutional neural network architecture for graph classification.

Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to extract high-level features of nodes. There are two main reasons for this: 1) Overlaying too many graph convolution layers will lead to the problem of over-smoothing. 2) Graph convolution is a kind of localized filter, which is easily affected by local properties. To solve the above problems, we first propose a novel general framework for graph neural networks called Non-local Message Passing (NLMP). Under this framework, very deep graph convolutional networks can be flexibly designed, and the over-smoothing phenomenon can be suppressed very effectively. Second, we propose a new spatial graph convolution layer to extract node multiscale high-level node features. Finally, we design an end-to-end Deep Graph Convolutional Neural Network II (DGCNNII) model for graph classification task, which is up to 32 layers deep. And the effectiveness of our proposed method is demonstrated by quantifying the graph smoothness of each layer and ablation studies. Experiments on benchmark graph classification datasets show that DGCNNII outperforms a large number of shallow graph neural network baseline methods.

DecodeSTORM: A user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

Information accessibility and export quality: Evidence from China

AbstractExploiting Google's 2010 withdrawal from mainland China, we examine the causal impact of information accessibility on export quality, and find that export quality decreases after Google's exit. The effect is more pronounced for firms and products facing greater information frictions, for firms in regions with local web filters, for firms trading with countries with more online information, and for firms facing fiercer product market competition. The more intensively affected firms also become less productive and their capital intensity declines after Google's exit. Overall, these results suggest that limited information accessibility may impair a country's international competitiveness through deteriorating export quality.