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.

GIS AND STATISTICAL EVALUATION OF SOIL QUALITY OF SIDI YAHYA, GHARBPLAIN (MOROCCO)

The study of soil quality in irrigated areas is necessary to evaluate the sustainability of the agricultural production system. Indeed, the assessment of this quality is based on the physicochemical and biological characterization of soil parameters, as well as the knowledge of their spatial distribution and their evolution over time. This work aims to make a diagnosis of the current situation of soil quality of SidiYahya in the Gharb plain, Morocco. For this, sampling was carried out from 33 sites distributed over the studied plain during 2019. In this study, different soil properties including specifically texture, pH, electrical conductivity (EC), organic matter (OM), phosphorus (P2O5), and potassium (K2O) were measured while exchangeable sodium percentage (ESP) was calculated using the standard formula. Based on the observed soil properties a map was prepared by using a geographic information system (GIS), which was based specifically on the inverse distance weighted (IDW) spatial interpolation method. Data were processed using different statistical tools like descriptive statistics, correlation, and principal component analysis (PCA). Results of the study revealed that 70% of the soils have a heavy clayey texture with a predominance of vertisols (55%). Further, the study area soil is mainly alkaline (70%), poor in organic matter (61%) and phosphorus (52%), while very rich in potassium (70%), and non-saline (88%) contents. Soil pH was reported to be the least variable whereas sand, phosphorus, and salinity were the highest variable. IDW allowed mapping the soil properties by moving from punctual information to whole extent information. Furthermore, correlations were found between various soil properties by using PCA, 3 principal components (PCs) were able to extract 76% of the information from the 9 initial soil properties. Collected soil samples were grouped into 3 groups, based on their scores on the 3 PCs. Based on these two kinds of information, delineation of management zones can be established for a site-specific supply of agricultural inputs leading to better management of soil and water resources for securing their sustainable use.

Detection and Delineation of Potential Areas for Tourism Activities in Coastal Zone of Ratnagiri District, Maharashtra (India)

Tourism is a device of economic development in developed, developing, and even in underdeveloped countries. The AHP based MCDA techniques used for detection and delineation of the tourism potential zones in the coastal area of Ratnagiri district in Maharashtra (India). The conventional and satellite data viz. ASTER data has been used for spatial analysis in GIS software. Seacoast, elevation, slope, river, LULC, available amenities, accessibility and connectivity, cultural and historical places, and density of settlements used as the criterions for detection of potential zones for tourism activities. The experts’ opinions, literature survey and fieldwork used as the source of information for the selection of criterions and determination of ranks. The weighted overlay technique used to find the comparative levels of tourism potentials: very high, high, moderate and less potentials for tourism. About 13% of TGA shows comparatively very high tourism potential, 25.80% shows high, 54.62 % shows moderate, and 6.57 % shows less tourism potential. The overall accuracy of the categorized map estimated about 89.60%. The thematic maps viz. amenities and settlement were processed using inverse distance weighted (IWD) interpolation technique. Therefore, some areas from the category, ‘high potential’ merged in the category, ‘moderate potential’ and reduced the producer’s accuracy of the classified map. The sandy beaches, lowland area with a gentle slope, river landscape features, road network, cultural and historical places, ideal size of settlements and number of amenities are attractions of tourists in the region. The technique used in this study can be an effective apparatus for precise analysis of site suitability for tourism activities.

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.

UAV-Assisted Three-Dimensional Spectrum Mapping Driven by Spectrum Data and Channel Model

As the number of civil aerial vehicles increase explosively, spectrum scarcity and security become an increasingly challenge in both the airspace and terrestrial space. To address this difficulty, this paper presents an unmanned aerial vehicle-assisted (UAV-assisted) spectrum mapping system and a spectrum data reconstruction algorithm driven by spectrum data and channel model are proposed. The reconstruction algorithm, which includes a model-driven spectrum data inference method and a spectrum data completion method with uniformity decision mechanism, can reconstruct limited and incomplete spectrum data to a three-dimensional (3D) spectrum map. As a result, spectrum scarcity and security can be achieved. Spectrum mapping is a symmetry-based digital twin technology. By employing an uniformity decision mechanism, the proposed completion method can effectively interpolate spatial data even when the collected data are unevenly distributed. The effectiveness of the proposed mapping scheme is evaluated by comparing its results with the ray-tracing simulated data of the campus scenario. Simulation results show that the proposed reconstruction algorithm outperforms the classical inverse distance weighted (IDW) interpolation method and the tensor completion method by about 12.5% and 92.3%, respectively, in terms of reconstruction accuracy when the collected spectrum data are regularly missing, unevenly distributed and limited.

The Comparison of Spatial Models in Peak Ground Acceleration (PGA) Study

This study was conducted to compare the performance of three different spatial analysis models: Inverse Distance Weighted (IDW), Ordinary Kriging,­­ and Regularized Spline interpolation technique to determine the best fit model representing Peak Ground Acceleration (PGA) in West Java Province, Indonesia. The three models are commonly used in spatial visualization, but have different calculation methods. The calculations were performed using available formulas while the spatial modeling was conducted using the algorithms in GIS software. Meanwhile, the accuracy of the spatial model and factual calculation was determined through the Root Mean Square Error (RMSE). The results showed differences for both spatial distribution and maximum and minimum values for each model. However, IDW was observed to be the model which approaches the factual value of the PGA calculation as indicated by its RMSE value of 0.772352 in comparison with the 7.169879 (Ordinary Kriging) and 1.140802 (Regularized Spline).

Comparison of spatial interpolation methods for estimation of weekly rainfall in West Bengal, India

Rainfall is one of the most eloquently researched contemporary meteorological phenomena affecting the agricultural practices dramatically, particularly along the humid, sub-tropics, where agriculture is predominantly rainfed. It is a key parameter of agricultural production in West Bengal due to lack irrigation facilities in most of the areas. Thus, it is very important to have detailed information of rainfall distribution pattern of West Bengal. In practice rainfall data is collected only at few discrete stations scattered all over the whole state. However, rainfall is a spatially continuous phenomenon rather than discrete. Thus it becomes essential to apply a robust spatial interpolation technique to transform the discrete values into a continuous spatial pattern. In the present study, three spatial interpolation techniques namely Kriging, Inverse Distance Weighted (IDW) and SPLINE, are used for a comparative analysis to identify the most efficient interpolation technique. Weekly average rainfall data available between 1901 and 1985 for 19 standard meteorological weeks (SMW), Week 22 to Week 40 are used for the analysis. The errors of the three interpolation techniques are analyzed and the best method is chosen based on the minimum mean absolute deviation (MAD) and the minimum mean squared deviation (MSD) criteria. The IDW method is found to be the best spatial interpolation technique.