Local Filter Research Articles

Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.

Backstepping-based distributed abnormality localization for linear parabolic distributed parameter systems

Abnormality localization for industrial processes described by distributed parameter systems (DPSs) is vital but seldom studied to the best of our knowledge. In this paper, a systematic framework is proposed to address this problem for a class of linear parabolic DPSs using a limited number of in-domain measurements plus one boundary measurement rather than the full-state measurement. The proposed methodology consists of an abnormality detection filter (ADF) and an abnormality localization filter (ALF) design based on the backstepping techniques and eigenspectrum. For the detection purpose, the residual is evaluated in a lumped manner; For the localization purpose, a distributed residual is first constructed and then evaluated in a distributed manner. Numerical simulations on a heat transfer rod are conducted to demonstrate the effectiveness of the proposed method.

The Elusive Cochlear Filter: Wave Origin of Cochlear Cross-Frequency Masking.

The mammalian cochlea achieves its remarkable sensitivity, frequency selectivity, and dynamic range by spatially segregating the different frequency components of sound via nonlinear processes that remain only partially understood. As a consequence of the wave-based nature of cochlear processing, the different frequency components of complex sounds interact spatially and nonlinearly, mutually suppressing one another as they propagate. Because understanding nonlinear wave interactions and their effects on hearing appears to require mathematically complex or computationally intensive models, theories of hearing that do not deal specifically with cochlear mechanics have often neglected the spatial nature of suppression phenomena. Here we describe a simple framework consisting of a nonlinear traveling-wave model whose spatial response properties can be estimated from basilar-membrane (BM) transfer functions. Without invoking jazzy details of organ-of-Corti mechanics, the model accounts well for the peculiar frequency-dependence of suppression found in two-tone suppression experiments. In particular, our analysis shows that near the peak of the traveling wave, the amplitude of the BM response depends primarily on the nonlinear properties of the traveling wave in more basal (high-frequency) regions. The proposed framework provides perhaps the simplest representation of cochlear signal processing that accounts for the spatially distributed effects of nonlinear wave propagation. Shifting the perspective from local filters to non-local, spatially distributed processes not only elucidates the character of cochlear signal processing, but also has important consequences for interpreting psychophysical experiments.

Distributed Kalman filter for UWB/INS integrated pedestrian localization under colored measurement noise

Colored Measurement Noise (CMN) has a great impact on the accuracy of human localization in indoor environments with Inertial Navigation System (INS) integrated with Ultra Wide Band (UWB). To mitigate its influence, a distributed Kalman Filter (dKF) is developed for Gauss–Markov CMN with switching Colouredness Factor Matrix (CFM). In the proposed scheme, a data fusion filter employs the difference between the INS- and UWB-based distance measurements. The main filter produces a final optimal estimate of the human position by fusing the estimates from local filters. The effect of CMN is overcome by using measurement differencing of noisy observations. The tests show that the proposed dKF developed for CMN with CFM can reduce the localization error compared to the original dKF, and thus effectively improve the localization accuracy.

Metagenomic analysis of Raphidiopsisraciborskii microbiome: beyond the individual.

Raphidiopsisraciborskii is a toxic, invasive bacteria with a defined biogeographic pattern attributed to the generation of ecotypes subjected to local environmental filters and to phenotypic plasticity. The interactions taking place between the cyanobacterium and the other bacteria inhabiting the external polysaccharide-rich matrix surrounding the cells, or phycosphere, may be ecotype-specific and would have different influence on the carbon and nutrient cycling in the ecosystem. Here, we describe the bacterial community or microbiome (assessed by 16S rRNA metagenomics) associated to two R.raciborskii strains that have been described as different ecotypes: the saxitoxin-producer MVCC19 and the non-toxic LB2897. Our results showed that both ecotypes share 50% of their microbiomes and differ in their dominant taxa. The taxon having the highest abundance in the microbiome of MVCC19 was Neorhizobium (22.5% relative abundance), while the dominant taxon in LB2897 was the Planctomycetes SM1A02 (26.2% relative abundance). These groups exhibit different metabolic capabilities regarding nitrogen acquisition (symbiotic nitrogen-fixing in Neorhizobium vs. anammox in SM1A02), suggesting the existence of ecotype-specific microbiomes that play a relevant role in cyanobacterial niche-adaptation. In addition, as saxitoxin and analogues are nitrogen-rich (7 atoms per molecule), we hypothesise that saxitoxin-producing R.raciborskii benefits from external sources of nitrogen provided by the microbiome bacteria. Based on these findings, we propose that the mechanisms involved in the assembly of the cyanobacterial microbiome community are ecotype-dependent.

The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model

Data assimilation has been widely applied in atmospheric and oceanic forecasting systems and particle filters (PFs) have unique advantages in dealing with nonlinear data assimilation. They have been applied to many scientific fields, but their application in geoscientific systems is limited because of their inefficiency in standard settings systems. To address these issues, this paper further refines the statistical observation and localization scheme which used in the classic localized equivalent-weights particle filter with statistical observation (LEWPF-Sobs). The improved method retains the advantages of equivalent-weights particle filter (EWPF) and the localized particle filter (LPF), while further refinements incorporate the effect of time series on the reanalyzed data into the statistical observation calculations, in addition to incorporating the statistical observation proposal density into the localization scheme to further improve the assimilation accuracy under sparse observation conditions. In order to better simulate the geoscientific system, we choose an intermediate atmosphere-ocean-land coupled model (COAL-IC) as the experimental model and divide the experiment into two parts: standard observation and sparse observation, which are analyzed by the spatial distribution results and root mean square error (RMSE) histogram. In order to better analyze the characteristics of the improved method, this method was chosen to be analyzed in comparison with the localized weighted ensemble Kalman filter (LWEnKF), the LPF and classical LEWPF-Sobs. From the experimental results, it can be seen that the improved method is better than the LWEnKF and LPF methods for various observation conditions. The improved method reduces the RMSE by about 7% under standard observation conditions compared to the traditional method, while the advantage of the improved method is even more obvious under sparse observation conditions, where the RMSE is reduced by about 85% compared to the traditional method. In particular, this improved filter not only combine the advantage of the two algorithms, but also overcome the computing resources.

Rapid Earthquake Source Description Using Variometric-Derived GPS Displacements toward Application to the 2019 Mw 7.1 Ridgecrest Earthquake

AbstractUsing near-field high-rate Global Positioning System (GPS) displacements to invert for earthquake fault slips in real time has the potential to improve the accuracy of earthquake early warning or tsunami early warning. For such applications, real-time retrieval of high-accuracy GPS displacements is essential. Here, we report on rapid modeling of the 2019 Mw 7.1 Ridgecrest earthquake with real-time GPS displacements derived from a variometric approach with readily available broadcast ephemeris. This method calculates station variations in real time by differencing continuous phase observations and does not rely on precise orbit and clock information. The phase ambiguity is also removed, and thus the method does not suffer from a relatively long convergence time. To improve the accuracy of variometric displacements, we use a local spatial filter to decrease the influence of residual errors that cannot be removed completely by the time difference. We invert for the centroid moment tensor, static fault slips, and fault rupture process from the derived displacements. Our results show that all inverted models are available within about 65 s after the origin time of the earthquake and are comparable with models inverted by real-time precise point positioning displacements. This study highlights the great value of variometric displacements for the rapid earthquake source description with only broadcast ephemeris.

Signaling between time steps does not allow for nonlocality beyond hidden nonlocality

Hidden nonlocality is the phenomenon that entangled states can be local in the standard Bell scenario but display nonlocality after local filtering. However, there exist entangled states for which all measurement statistics can be described via a local hidden variable model even after local filtering. In this work we consider the scenario that measurement outcomes and settings of Alice can influence measurements of Bob in subsequent time steps (and vice versa), however, there is no signaling among them for measurements at the same time step. We show that in this scenario states that only display local statistics after local filtering remain local even when considering the complete statistics of arbitrary sequences and therefore no advantage can be gained by performing longer sequences in this scenario. We first determine the extreme points of the polytope defined by the no-signaling conditions within the same time step and the arrow-of-time constraints. Based on these results we introduce a notion of locality and provide a complete representation of the corresponding local polytope in terms of inequalities in the simplest scenario. These results imply that in the scenario considered here there is no nonlocality beyond hidden nonlocality. We further propose a device-dependent Schmidt number witness and we compare our finding to known local models in the sequential scenario.

Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors

This paper is concerned with the recursive fusion estimation-based mobile robot localization (RL) problem by employing multiple energy harvesting sensors (EHSs). In the addressed RL problem, multiple sensors with energy harvesting capacity are deployed to produce measurements used for RL. When the sensors own sufficient energy, the sensors can output measurements and then send them to the corresponding local filter. Otherwise, the sensor energy-induced missing measurement phenomenon will occur. In order to obtain the missing measurement rate, at each time instant, the relationship between the totality of the sensor energy and its probability distribution is derived recursively. This paper aims at seeking out a practicable solution to the addressed mobile RL problem. First, in the presence of the sensor energy-induced measurement missing phenomenon, an upper bound (UB) of the local localization error covariance is recursively acquired. Then, such a derived UB is minimized by suitably devising the desired local filter parameter. Subsequently, the covariance intersection fusion method is adopted to achieve the addressed RL problem. In the end, a simulation is conducted to verify the practicability of the developed RL scheme.

Reduction of Spatially Correlated Speckle in Textured SAR Images

Synthetic aperture radars (SARs) provide a lot of images that can be used for numerous applications. A problem with acquired images is that they are corrupted by speckle which is a noise-like phenomenon with multiplicative nature. In addition, speckle is non-Gaussian and it is often spatially correlated. A typical task in SAR image processing is despeckling and many methods have been already proposed. However, most of them do not take noise spatial correlation into account during denoising. In this paper, we show how this can be done in despeckling based on discrete cosine transform. The use of frequency-dependent thresholds leads to sufficient improvement of denoising efficiency in terms of visual quality metrics. Moreover, we consider quite complex structure texture images for which noise removal is usually problematic and can lead to information loss. Comparison to the well-known local statistic Lee and Frost filters, extended DCT-based filter is carried out for different remote sensing systems including Sentinel-1 and Sentinel-2.

Interacting elevational and latitudinal gradients determine bat diversity and distribution across the Neotropics.

New World bats are heavily affected by the biophysical setting shaped by elevation and latitude. This study seeks to understand the patterns of bat species diversity across elevational, latitudinal and vegetation height gradients throughout the Neotropics. Systematically gathered putative and empirical data on bat species distribution across the entire Neotropics were examined using descriptive statistics, spatial interpolation of bat taxonomic, functional and phylogenetic diversity, generalized linear models, generalized linear mixed models and phylogenetic generalized least squares. We uncoupled the effects of elevation, latitude and vegetation height to predict Neotropical bat diversity, showing that dietary level, home range and habitat breadth were the most important ecological traits determining coarse-scale bat distributions. Latitude was largely responsible for sorting the regional species pool, whereas elevation appears to apply an additional local filter to this regional pool wherever tropical mountains are present, thereby shaping the structure of montane assemblages. Bats provide multiple ecosystem services and our results can help pinpoint priority areas for bat research and conservation across all Neotropics, elucidate the thresholds of species distributions, and highlight bat diversity hotspots at multiple scales.

Pemanfaatan Media Filter Kearifan Lokal dalam Meningkatkan Kualitas Air dengan Proses Filtrasi

The need for clean water continues to increase with changing times and the passage of time, however, clean water that is suitable for consumption is not easily available in some areas, considering that the physical conditions of regional geomorphology and hydrology have different forms. Water that is suitable for drinking must be clean and minimal from pollutant loads and substances that can interfere with the health of the body, this is different from the water obtained by people in Rantau Karya Village, Geragai District, Tanjung Jabung Timur Regency, because the hydrological conditions of the area are dominated by peatlands so that the water consumed is included in peat water, where the majority of the people use dug well water, therefore a simple technology is needed in dug well water treatment by utilizing local wisdom filter media. The results showed the efficiency of removal of organic substances (KMnO4) where the initial parameter was 22.5 mg / l to be 11.218 mg / l. The efficiency of turbidity reduction is 56%, where the initial result of the turbidity parameter is 31 NTU and the final result is 15 NTU, and the final pH of well water is 6.26, where the initial test shows the number 5.6. Each thickness of the filter media to get optimum results with a thickness of 15 cm.

Ultrasonic tomographic reconstruction of local fiber orientation in multi-layer composites using Gabor filter-based information diagram method

Fiber waviness and ply stacking errors compromise the mechanical performance of multi-layer composites. Measuring the local in-plane fiber orientation layer-by-layer is therefore important to assure the structural integrity of a composite. The Radon transform and the Gabor filter methods have been reported to be appropriate for extracting the fiber orientation from an ultrasonic dataset, where the Radon transform method was found to be more accurate and stable. This paper proposes a tomographic reconstruction method on the basis of classical ultrasound pulse-echo testing, in view of reconstructing the local in-plane fiber orientation in multi-layer composites. A 3D ultrasonic dataset is first sliced successively through depth, and each 2D slice is locally analyzed by a Gabor filter method coupled to the concept of an Information Diagram (GF-ID). The GF-ID improves the Gabor filter method by constructing local Gabor filters with optimal orientation and wavelength from which the local in-plane fiber orientations can be obtained in an automated way. The performance of the developed GF-ID method is investigated on both synthetic texture images and an experimental ultrasonic dataset obtained from a 24-layer [45/0/-45/90]3S carbon fiber reinforced composite. It is found that the GF-ID method yields a high-quality tomographic reconstruction of the local orientations at different scales, and shows high noise resistance. Comparison with the classical Radon transform approach reveals the higher performance of the GF-ID method for ultrasonic reconstruction of the local in-plane fiber orientation in multi-layer composites.

Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches

Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.

Engineering the beat phenomenon of quasi-Rayleigh waves for regions with minimal Surface Acoustic Wave (SAW) amplitude

Surface Acoustic Waves (SAW) are prevalent in a variety of scientific fields. Typically, the ability to manipulate these surface waves has a significant impact on the efficacy of any SAW-based device. In this paper, we present a simple method for designing localized regions that have minimal SAW amplitude. These localized regions have valley-like drops in SAW amplitude and are achieved by using the spatial beat phenomenon of quasi-Rayleigh waves. Controlling the beat phenomenon allows us to manipulate where and how these localized regions appear in the waveguide. In particular, the localized regions can be designed to have variable widths and can be positioned consecutively due to minimal wave scattering. The design methodology and physics are described in detail, and several supplementary simulations are provided to demonstrate the efficacy of the proposed method. Lastly, we briefly discuss how the proposed approach could potentially be exploited to implement a localized high-pass filter and enables SAW wave propagation that avoids surface obstacles.

Virtual Target-Based Longitudinal Motion Planning of Autonomous Vehicles at Urban Intersections: Determining Control Inputs of Acceleration With Human Driving Characteristic-Based Constraints

This article describes the development and implementation of virtual target-based longitudinal motion planning of autonomous vehicles at urban intersections ensuring safety and ride comfort. In this study, virtual targets are designed to cope with oncoming vehicles in the blind zone at the intersection for safety. The true field of view (FOV) of cognitive sensors and the virtual target states are constructed based on the sensor specification and intersection road information from a high-definition (HD) map. The future states and intention of sensor-detected targets are inferred and predicted with an interacting multiple model (IMM) filter. The local IMM filters are employed with an intelligent driver model (IDM). Based on predicted target states, two driving modes-"cross" and "stop"-under three different intersection stages-"approach," "intersection in," and "intersection out"-are determined. The model predictive control (MPC) is formulated to determine the control inputs of acceleration with human driving characteristics-based constraints. The proposed algorithm is evaluated through simulation to indicate the effectiveness of the virtual target. The suggested motion planning has been implemented on an autonomous driving vehicle and tested on urban roads.

A Novel Oversampling Method Based on SMOTE and Local Sets for Imbalanced Classification

Learning a classifier from imbalanced data continues to be a challenging issue. The oversampling methods can improve the imbalanced classification from the perspective of data preprocessing. Different oversampling methods have been proposed. Nevertheless, most tend to generate unnecessary noise, create redundant synthetic samples in the class center and heavily rely on the parameter k. To solve the above issues, this work presents an oversampling method based on local sets and SMOTE (LS-SMOTE). First, the local sets are searched to describe the local characteristic of imbalanced data. Second, a local sets-based noise filter is designed to remove noise and smooth the class boundary. Finally, on each local set, the interpolation of SMOTE between a base sample and a selected sample closest to the majority class is employed to create the synthetic samples. Experimental results with 12 real data sets have proved that LS-SMOTE outperforms representative oversampling methods in training k nearest neighbor classifier.

Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.

Event-based fusion estimation for multi-rate systems subject to sensor degradations

This paper is concerned with the event-based fusion estimation problem for a class of multi-rate systems (MRSs) subject to sensor degradations. The MRSs under consideration include several sensor nodes with different sampling rates. To facilitate the filter design, the MRSs are transformed into a single-rate system (SRS) by using an augmentation approach. A set of random variables obeying known probability distributions are used to characterize the phenomenon of the sensor degradations. For the purpose of saving the limited communication resources, the event-triggering mechanism (ETM) is adopted to regulate the transmission frequency of the measurements. For the addressed MRSs, we aim to design a set of event-based local filters for each sensor node such that the upper bound of each local filtering error covariance (FEC) is guaranteed and minimized by designing the filter parameter appropriately. Subsequently, the local estimates are fused with the aid of covariance intersection (CI) fusion approach. Finally, a numerical experiment is exploited to demonstrate the usefulness of the developed fusion estimation algorithm.

Variational Bayesian‐based adaptive distributed fusion target tracking with unknown sensor measurement losses

A new variational Bayesian-based adaptive distributed fusion unscented Kalman filter (ADFUKF-VB) algorithm is proposed for the problem of state estimation in a distributed fusion target tracking system with unknown sensor measurement losses. First, the Bernoulli random variables are introduced as the judgement factors of the measurement losses in each local unscented Kalman filter (UKF) of the distributed fusion target tracking system, and the probability density function (PDF) of the measurement loss probability is modelled as a Beta distribution. Then, the variational Bayesian (VB) approach is used to infer the estimated variables in each local UKF. The measurement losses are judged according to the expectations of the Bernoulli random variables, and three ways are adopted to estimate the state vector based on the obtained judgement results. Finally, the covariance intersection (CI) fusion strategy is used to achieve the fusion of all local state estimates. The simulation results show that the proposed ADFUKF-VB algorithm can accurately judge whether the measurement is lost and estimate the unknown measurement loss probability, moreover, effectively improve the accuracy of state estimation compared with the existing filtering algorithms with unknown measurement losses.