A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal

Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.

Interpolation Parameters in Inverse Distance-Weighted Interpolation Algorithm on DEM Interpolation Error

Although DEM occupies an important basic position in spatial analysis, so far, the quality of DEM modeling has still not reached a satisfactory accuracy. This research mainly discusses the influence of interpolation parameters in the inverse distance-weighted interpolation algorithm on the DEM interpolation error. The interpolation parameters to be studied in this paper are the number of search points, the search direction, and the smoothness factor. In order to study the optimization of IDW parameters, the parameters that have uncertain effects on DEM interpolation are found through analysis, such as the number of search points and smoothing factor. This paper designs an experiment for the optimization of the interpolation parameters of the polyhedral function and finds the optimal interpolation parameters through experimental analysis. Of course, the “optimum” here is not the only one, but refers to different terrain areas, which makes the interpolation results relatively good. The selection of search points will be one of the research focuses of this article. After determining the interpolation algorithm, the kernel function is also one of the important factors that affect the accuracy of DEM. The value of the smoothing factor in the kernel function has always been the focus of DEM interpolation research. Different terrains, different interpolations, and functions will have different optimal smoothing factors. The search direction is to ensure that the sampling points are distributed in all directions when the sampling points are sparse and to improve the contribution rate of the sampling points to the interpolation points. The selection of search shape is to improve computing efficiency and has no effect on DEM accuracy; the search radius is mainly controlled by the number of search points, and there are two methods: adaptive search radius and variable length search radius. When the weight coefficient k = 1 , 2 , 3 , 4 , the number of sampling points involved in the interpolation calculation is different, and the error in the residual varies greatly, and both increase with the increase of the number of sampling points in the parameter interpolation calculation. This research will help improve the quality evaluation of DEM.

Assessment of Seawater Intrusion in Coastal Aquifers Using Multivariate Statistical Analyses and Hydrochemical Facies Evolution-Based Model

Groundwater (GW) studies have been conducted worldwide with regard to several pressures, including climate change, seawater intrusion, and water overexploitation. GW quality is a very important sector for several countries in the world, in particular for Tunisia. The shallow coastal aquifer of Sfax (located in Tunisia) was found to be under the combined conditions of continuous drop in GW and further deterioration of the groundwater quality (GWQ). This study was conducted to identify the processes that control GWQ mainly in relation to mineralization sources in the shallow Sfax coastal aquifer. To perform this task, 37 wells are considered. Data include 10 physico-chemical properties of groundwater analyzed in water samples: pH, EC, calcium (Ca), sodium (Na), magnesium (Mg), potassium (K), chloride (Cl), sulfate (SO4), bicarbonate (HCO3), and nitrate (NO3), i.e., investigation was based on a database of 370 observations. Principal component analysis (PCA) and hydrochemical facies evolution (HFE) were conducted to extract the main factors affecting GW chemistry. The results obtained using the PCA model show that GWQ is mainly controlled by either natural factors (rock–water interactions) or anthropogenic ones (agricultural and domestic activities). Indeed, the GW overexploitation generated not only the GWQ degradation but also the SWI. The inverse distance weighted (IDW) method, integrated in a geographic information system (GIS), is employed to achieve spatial mapping of seawater intrusion locations. Hydrochemical facies evolution (HFE) results corroborate the seawater intrusion and its spatial distribution. Furthermore, the mixing ratio showed that Jebeniana and Chaffar–Mahares localities are characterized by high SWI hazard. This research should be done to better manage GW resources and help to develop a suitable plan for the exploitation and protection of water resources.

Mapping and distribution of noise using IDW interpolation algorithm in Thuan An city, Binh Duong province

The study is conducted to assess the level of noise pollution from traffic activities in urban areas (in the case of Thuan An city, Binh Duong province) with the specific goal of applying geographic information systems. (GIS) in building noise pollution distribution maps in the study area. The research team collected noise data at peak hours and normal hours on weekdays and weekends using noise meters at 61 survey points. Noise measurement data was then interpolated using IDW (Inverse Distance Weighting) method to assess the spatial distribution of noise in Thuan An city. In addition, the study digitizes traffic routes and special areas (hospitals, schools, churches - pagodas) in the study area to identify areas affected by sound noisy. The results show that the areas near the main road are most affected by noise during rush hour, while areas in the small lane are noise level within the permitted range. In addition, the results also show the difference in noise level and noise time between weekdays and weekends.

Modelling and Mapping Total and Bioaccessible Arsenic and Lead in Stoke-on-Trent and Their Relationships with Industry

This study was based on a geochemical soil survey of Stoke-on-Trent in the UK of 747 surface soil samples analysed for 53 elements. A subset of 50 of these soil samples were analysed for their bioaccessible As and Pb content using the Unified Barge Method. Random Forest modelling, using the total element data as predictor variables, was used to predict bioaccessible As and Pb for all 747 samples. Random Forest modelling, using inverse distance weighed predictors and bedrock and superficial geology, was also used to map both total and bioaccessible As and Pb on a 400 × 400 spatial prediction grid with a 50 m resolution. The predicted bioaccessible As ranged from ca. 1 to 8 mg/kg and the total As ca. 8 to 45 mg/kg. The bioaccessible Pb and the total Pb both covered the range ca. 16–1200 mg/kg, with the highest values for both forms of Pb showing similar spatial distributions. Predictor variable importance and information on past industry suggest that the source of both of these elements is driven by anthropogenic causes.

Filling and validating rainfall data based on statistical techniques and artificial intelligence

The study of the hydric regime of rainfall helps in management analysis and decision-making in hydrographic basins, but a fundamental condition is the need for continuous time series of data. Therefore, this study compared gap filling methods in precipitation data and validated them using robust statistical techniques. Precipitation data from the municipality of Itirapina, which has four monitoring stations, were used. Four gap filling techniques were used, namely: normal ratio method, inverse distance weighting, multiple regression and artificial neural networks, in the period from 1979 to 1989. For validation and performance evaluation, the coefficient of determination (R²), mean absolute error (MAE), mean squared error (RMSE), Nash-Sutcliffe coefficient (Nash), agreement index (D), confidence index were used (C) and through non-parametric techniques with Mann-Witney and Kruskal-Wallis test. Excellent performances of real data were verified in comparison with estimated data, with values above 0.8 of the coefficient of determination (R²) and of Nash. Kruskal-Wallis and Mann-Whitney tests were not significant in Stations C1 and C2, demonstrating that there is a difference between real and estimated data and between the proposed methods. It was concluded that the multiple regression and neural network methods showed the best performance. From this study, efficient tools were found to fill the gap, thus promoting better management and operation of water resources. Keywords: artificial neural networks, inverse distance weighting, multiple regression, normal ratio method.

Spatial Behavior of Soil Erodibility in the La Villa River Basin, Panama

Introduction: Soil erodibility is an important factor in understanding the erosion that takes place in a territory. This is a parameter that can behave erratically in small spaces, but that describes a trend in larger spaces. Aim: Determine the K factor of soil erodibility in the La Villa-Panama river basin. Place and Duration of Study: La Villa River Basin-Azuero Peninsula, Panama. 2010-2012. Methodology: 98 points of the La Villa river watershed were sampled. Factor K was calculated using the adaptation to the sol-erodibility nomogram. The percentage of organic matter, structure class (in the field), permeability (combination permeameter) and the percentages of sand, silt and very fine sand (Bouyoucos method) were determined. To obtain the most complete information possible on the distribution of erodibility, a superficial interpolation of the point values ​​corresponding to the soil samples taken was carried out. The software used was Arcview 3.3 and the Spatial Analyst extension. The interpolation method was IDW (Inverse Distance Weight). The erodibility values ​​were categorized into seven intervals in such a way that it was possible to observe the differences on the map. Results: The erodibility values ​​were influenced by the content of organic matter and coarse particles (percentage of sand and silt + very fine sand) of the soil. In the province of Herrera, 86% of the land surface and 76% in the province of Los Santos presents susceptibility to erosion in the ranges of 0.032 to 0.043 Ton ha h ha-1 Mj-1 mm-1. Conclusion: The results indicate that 80% of the soils of the La Villa river basin present a moderately high erodibility factor, with the highest values ​​being registered in the upper middle zone.