High Error Values Research Articles

A Fast Converging Channel Estimation Algorithm for Wireless Sensor Networks

A set-membership affine projection algorithm is proposed that can estimate a complex-valued channel matrix using a set of complex-valued pilots in the presence of additive white Gaussian noise. It is shown that the algorithm converges faster than the well-known set-membership normalized least mean square algorithm (SM-NLMS) while it resolves the high steady-state error value and the complexity issues in the regular affine projection algorithm. The fast convergence of the proposed algorithm means that a shorter training sequence inside each data block is required, which in turn improves the effective bit rate. This fast convergence is more pronounced when the pilot vectors are highly correlated. We incorporated the set-membership filtering framework into our study to reduce the computational complexity of the algorithm and preserve energy in the WSNs. Other studies have shown the superiority of the adaptive filtering algorithms, and in particular the NLMS algorithm, over other alternatives in various signal processing areas in WSNs, therefore, our proposed algorithm is a powerful substitute for a variety of algorithms. In other words, the implementation of various signal processing algorithms for different purposes can be replaced with the implementation of the proposed multipurpose algorithm. In this paper, we combine the results of our previous studies and prove the convergence of the algorithm. Furthermore, the steady-state analysis in the output mean square error is presented for two cases of pilot signals, and in the conducted simulations, the MSE performance of the algorithm is compared with the regular affine projection algorithm and the SM-NLMS algorithm.

Estimation of circular-circular probability distribution

This paper aims to introduce an estimation algorithm for the joint densityof a circular-circular random variable, which is expressible in the form asdiscussed by Fernandez-Duran (2007). The performance of the algorithm hasbeen checked with the help of a simulation study and it is found to performeciently even for small sample sizes. Furthermore, the performance of theproposed algorithm is compared with that of an existing method of densityestimation and is found to perform better than the existing one, which isindicated by the higher mean square error values for the estimates obtainedby the latter method. Finally, the application of the algorithm is displayedby estimating the joint density of a circular-circular random variable arisingin a real-life data set.

A COMPARATIVE ANALYSIS OF ALGORITHMS FOR COUNTING OBJECTS IN IMAGES

The report presents a comparative analysis of algorithms for counting objects in images. They are used in counting eggs. From the three algorithms compared Threshold, Circular Hough and Wateshed, with high performance and small error values ​​is the algorithm Circular Hough. When recognizing the eggs, it is necessary to make a selection of the color model, by which to separate the eggs from the background according to their color and the breed of birds. More research is needed on the impact of the image capturing conditions on the accuracy of algorithms.

Estimated net radiation in an Amazon–Cerrado transition forest by Landsat 5 TM

The Amazon–Cerrado transition forest is an extensive region with unique characteristics of radiation exchanges. The measurements of the net radiation (Rn) in this ecosystem are limited to the local scale, and their spatial distribution can be carried out by remote sensing techniques, of which accuracy needs to be evaluated. Thus, the objective of this study was to analyze the accuracy of the model of surface Rn derived from measured solar radiation and estimates of normalized difference vegetation index (NDVI), surface albedo (α), and land surface temperature (LST) estimated by images of Landsat 5 TM in an Amazon–Cerrado transition forest. The Rn, NDVI, α, and LST were estimated by Landsat 5 TM images and related to micrometeorological measurements in a tower of the study area. There was seasonality of micrometeorological variables with higher values of incident solar radiation, air temperature, and vapor pressure deficit during the dry season. However, there was no seasonality of Rn. NDVI decreased and α increased during the dry season, while LST was nearly constant. The Rn had negative correlation with α and positive with NDVI. Both instantaneous and daily Rn estimated with Landsat 5 TM images showed high correlation and low error values when compared with Rn measured in the study area.

Screening of the alterations in qualitative characteristics of grape under the impacts of storage and harvest times using artificial neural network

The tested model showed that high reliability based on the obtained inputs for achieved data for RMSE and correlation coefficient between the obtained experimental and predicted values. An enhancement in the storage time, reduced the pH value and flavor index (fruit maturation), but boosted the acidity value of the fruits. On the other hand retardation in the harvest time led to an increase in pH value, total soluble solids and dextrose contents as well as flavor index of the samples. Artificial neural network design has been applied to predict the process of alterations during storage time. Back propagation feed forward neural network with the arrangement of 5:8:2 with a high correlation coefficient value (˃ 0.989) and low root mean square error value (< 0.0019) as well as sigmoid hyperbolic tangent activation function with Levenberg–Marquardt learning and learning cycle of 1000 were detected as the most reliable and appropriate neural network model in storage process.

An materials informatics approach to Ni-based single crystal superalloys lattice misfit prediction

The lattice misfit between γ and γ′ phase in Ni-based single crystal superalloys plays a critical role for microstructural stability and high-temperature creep and fatigue resistance. Making predictions of the lattice misfit rapidly and accurately is therefore of much practical importance, especially for costly and time-consuming material design by trial and error. In this study, we provide a machine learning approach to predict misfit using relevant material descriptors including the chemical composition, dendrite information and measurement temperature and so on. We perform support vector regression, sequential minimal optimization regression and multilayer perceptron algorithms with linear and poly kernels on experimental dataset for appropriate model selecting, and multilayer perceptron model works well for its distinguished prediction performance with high correlation coefficient and low error values. The approach is validated by comparing the predicted lattice misfit with a widely used empirical formula and experimental observation with respect to prediction accuracy.

Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties

The development of rapid, accurate, and cost-effective methods to determine forest soil properties such as visible and near infrared (Vis-NIR) spectroscopy is important for sustainable land management. The main objective of this study was to assess the suitability of Vis-NIR spectroscopy coupled with partial least squares regression (PLSR) to determine some forest soil properties such as organic carbon (SOC), total nitrogen (TN), pH, and soil texture (sand, silt, and clay) for a representative forest area in southern Italy. Soil samples (0–20 cm depth) were collected at 267 locations, oven-dried and passed through a 2-mm sieve, and analyzed for some chemical and physical soil properties using conventional laboratory methods. Vis-NIR reflectance of each soil sample was measured in laboratory under artificial light using an ASD FieldSpec IV 350–2500 nm spectroradiometer. Partial least squares regression (PLSR) was used to develop a calibration model for SOC, TN, pH, sand, silt, and clay. Samples were split into a calibration set (187 samples) to develop the models and a validation set (80 samples) to assess the prediction accuracy of the calibration models. Results showed a good agreement between measured and predicted values with high R 2 and low root mean square error (RMSE) values. Model validation using independent data was satisfactory for all the studied soil properties. Finally, findings confirmed that laboratory Vis–NIR spectroscopy has the potential to be a non-destructive and cost-effective tool for rapid determination of many soil properties. The spectral data collected in this study could contribute to build a regional soil spectral library to be used advantageously in support to soil survey in other areas of the Calabria region (southern Italy).

Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves

Vegetation biochemical and biophysical variables are important for many ecological, agronomic, and meteorological applications. Among the main variables, water and chlorophyll are essentials due to directly affect the plant photosynthetic capacity and crop productivity. The objective of this study was develop and validate models capable of estimating water and chlorophyll status in sunflower leaves under progressive water stress, based on the visible/near-infrared region (Vis/NIR) spectral reflectance and chemometric technique. The water and chlorophyll models were adjusted considering the spectral reflectance from the 500–1039 nm wavelengths by using partial least squares regressions (PLSR). In the external validation, high determination coefficient (0.8386 and 0.8097) and low mean bias error (−0.40 dry basis and 0.09 mg g−1) values for water and chlorophyll, respectively, indicating that their predictive capabilities and accuracies of the models were satisfactory. Results showed that spectrometry has potential to be applied as an alternative method in quantifying water and chlorophyll status in sunflower leaves in a non-destructive, quick, and consistent way.

Assessment of the possibility of using data mining methods to predict sorption isotherms of selected organic compounds on activated carbon

The paper analyses the use of four data mining methods (Support Vector Machines. Cascade Neural Networks. Random Forests and Boosted Trees) to predict sorption on activated carbons. The input data for statistical models included the activated carbon parameters, organic substances and equilibrium concentrations in the solution. The assessment of the predictive abilities of the developed models was made with the use of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The computations proved that methods of data mining considered in the study can be applied to predict sorption of selected organic compounds 011 activated carbon. The lowest values of sorption prediction errors were obtained with the Cascade Neural Networks method (MAE = 1.23 g/g; MAPE = 7.90% and RMSE = 1.81 g/g), while the highest error values were produced by the Boosted Trees method (MAE=14.31 g/g; MAPE = 39.43% and RMSE = 27.76 g/g).

Purwarupa Sistem Prediksi Luas dan Hasil Panen Padi suatu Wilayah menggunakan Pengolahan Citra Digital dengan Metode Sobel dan Otsu

Area and paddy crop yield prediction system of an area using image processing by Sobel Otsu’s method is one of system that utilize aerial photo data for measuring area and prediction of its crop yield. Otsu’s method is used to thresholding process and Sobel’s method is used to detect paddy field’s edges that will calculate its area. Then filtering process so that the scanning process white pixels are counted only exist in the desired region. After the amount of white pixel(s) is obtained, their amount is multiplied with the scale that obtained from calibration process and crop yield prediction (kg/m2). Detection of yellow paddy color that ready-to-harvest is successfully performed by processing the HSV color, which is then detected by thresholding HSV. At the time of testing with variety of paddy color, the detected paddy color is the paddy color ready-to-harvest, which is brownish yellow that represented by white pixels, and will be used then to predict its area and crop yield. Thereafter, accuracy calculation test resulting in different error levels in different paddy fields. Error in testing of this system are 3,1 %, 8,7%, 4,9% dan 248%. The highest error value is caused by excessive exposure of light, with the result that the green color on paddy is detected by the system as yellow and some areas are covered by trees that, thereby reducing paddy fields area calculation.

An Evaluation of Inter-Fractional Set-Up Errors in Patients Treated with Distinct Immobilization Equipment for Varying Anatomical Regions

This study aims to evaluate inter-fractional set-up errors in patients treated with distinct immobilization equipment (thermoplastic mask, knee-fix and feet-fix, wing board and vac-lok) for four anatomical regions including brain, head and neck (HN), thorax and pelvis. Data of randomly selected 140 patients who were treated for four anatomical regions were obtained using Hi-Art Helical Tomotherapy (HT) system. Pre-treatment planning was based on automatic registration readings of computed tomography (CT) and mega-voltage computed tomography (MVCT) on a daily basis. Distinct immobilization equipment was used for varying anatomical regions. Individual mean set-up error (M), systematic error (Σ), and random error (σ) values were calculated through daily translational and rotational deviation values. The size of translational, systematic and random error was 1.31 - 4.93 mm for brain, 2.28 - 4.88 mm for HN, 4.04 - 9.90 mm for thorax, and 6.34 - 14.68 mm for pelvis. Rotational values were as follows: 0.06˚ - 0.73˚ for brain, 0.42˚ - 0.6˚ for HN, 0.48˚ - 1.14˚ for thorax and 0.65˚ - 1.05˚ for pelvis. The highest translational, systematic and random error value was obtained from the pelvic regional. The highest standard and random error value in pitch and roll was produced in the rotational direction of the pelvis (0.05˚ and 0.71˚), while the highest error value in yaw was (1.14˚) produced from thorax. Inter-fractional set-up errors were most commonly produced in the pelvis, followed by thorax. Our study results suggest that the highest systematic and random errors are found for thorax and pelvis. Distinct immobilization equipment was important in these results. Safety margins around the clinical target volume (CTV) are changeable for different anatomical regions. A future work could be developed to new equipment for immobilization because of the reduced margins CTV.

Adsorption of hazardous heavy metal copper(II) from aqueous effluents onto waste material fish (Dicentrarchus labrax) scales: optimization, equilibrium, kinetics, thermodynamic, and characterization studies

This study presents the adsorption results of copper(II) ions onto waste material fish (Dicentrarchus labrax) scales (FS). For this purpose; the optimization, equilibrium, kinetics, and adsorbent characterization studies were carried out. Optimum initial pH, initial Cu(II) concentration, temperature, and adsorbent concentration were determined to be 3.0, 200 mg L−1, 45°C, and 1.0g L−1, respectively. The monolayer coverage capacity of FS calculated from Langmuir isotherm model was found as 127.16 mg g−1 with high regression coefficient (R2 = 0.9932) and low error value (ARE = 4.9569) at optimum temperature. The studied adsorption kinetic was in the best agreement with the pseudo-second-order kinetic model; also, both intraparticle and film diffusion were effective in the removal. Furthermore, the adsorbent was characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), energy-dispersive X-ray (EDX) analyses for pre- and post-adsorption.

Discussion of “New Method for High-Speed Railway Bridge Dynamic Deflection Measurement” by Xianlong He, Xueshan Yang, and Lizhen Zhao

Discussion of “New Method for High-Speed Railway Bridge Dynamic Deflection Measurement” by Xianlong He, Xueshan Yang, and Lizhen Zhao

Electrical Performance and Effect of Frequency Electromagnetic Waves on Subterranean Termites &lt;i&gt;Coptotermes curvignathus&lt;/i&gt; Holmgren

The research aimed to propose a non-destructive technology to control subterranean termites Coptotermes curvignathus Holmgren infestation based on electromagnetic waves. A portable apparatus for this technology has been built and its experiment is presented in this paper. Some electrical parameters were measured and analyzed along with their effects to the termites. The experiment using frequency range between 30 Hz - 600 kHz has been done. The average error of the apparatus by comparing the result with the direct measurement using oscilloscope was also measured. The highest error value appeared at 600 kHz with frequency error 6.05 kHz. The highest error of voltage (i.e. 0.186 Volt) appeared at 100 kHz. For safetiness, the highest magnetic field at 300 kHz was 0.1815 μT and at 500 kHz was 0.00725 μT which were safe for human. The average value of termites mortality was higher on irradiation time 120 minutes than 60 minutes respectively in all test frequency: 300 kHz, 400 kHz, 500 kHz and 600 kHz. This paper presents an important information of the electromagnatic-based technology for environmental friendly termites control in spite of using the insecticides.

Automatic diagnosis of malaria based on complete circle–ellipse fitting search algorithm

Diagnosis of malaria parasitemia from blood smears is a subjective and time-consuming task for pathologists. The automatic diagnostic process will reduce the diagnostic time. Also, it can be worked as a second opinion for pathologists and may be useful in malaria screening. This study presents an automatic method for malaria diagnosis from thin blood smears. According to this fact that malaria life cycle is started by forming a ring around the parasite nucleus, the proposed approach is mainly based on curve fitting to detect parasite ring in the blood smear. The method is composed of six main phases: stain object extraction step, which extracts candidate objects that may be infected by malaria parasites. This phase includes stained pixel extraction step based on intensity and colour, and stained object segmentation by defining stained circle matching. Second step is preprocessing phase which makes use of nonlinear diffusion filtering. The process continues with detection of parasite nucleus from resulted image of previous step according to image intensity. Fourth step introduces a complete search process in which the circle search step identifies the direction and initial points for direct least-square ellipse fitting algorithm. Furthermore in the ellipse searching process, although parasite shape is completed undesired regions with high error value are removed and ellipse parameters are modified. Features are extracted from the parasite candidate region instead of whole candidate object in the fifth step. By employing this special feature extraction way, which is provided by special searching process, the necessity of employing clump splitting methods is removed. Also, defining stained circle matching process in the first step speeds up the whole procedure. Finally, a series of decision rules are applied on the extracted features to decide on the positivity or negativity of malaria parasite presence. The algorithm is applied on 26 digital images which are provided from thin blood smear films. The images are contained 1274 objects which may be infected by parasite or healthy. Applying the automatic identification of malaria on provided database showed a sensitivity of 82.28% and specificity of 98.02%.

Evaluation of the estimated continuous cardiac output monitoring system in adults and children undergoing kidney transplant surgery: a pilot study

Evaluation of the estimated continuous cardiac output (esCCO) allows non-invasive and continuous assessment of cardiac output. However, the applicability of this approach in children has not been assessed thus far. We compared the correlation coefficient, bias, standard deviation (SD), and the lower and upper 95 % limits of agreement for esCCO and dye densitography-cardiac output (DDG-CO) measurements by pulse dye densitometry (PDD) in adults and children. On the basis of these assessments, we aimed to examine whether esCCO can be used in pediatric patients. DDG-CO was measured by pulse dye densitometry (PDD) using indocyanine green. Modified-pulse wave transit time, obtained using pulse oximetry and electrocardiography, was used to measure esCCO. Correlations between DDG-CO and esCCO in adults and children were analyzed using regression analysis with the least squares method. Differences between the two correlation coefficients were statistically analyzed using a correlation coefficient test. Bland-Altman plots were used to evaluate bias and SD for DDG-CO and esCCO in both adults and children, and 95 % limits of agreement (bias ± 1.96 SD) and percentage error (1.96 SD/mean DDG-CO) were calculated and compared. The average age of the adult patients (n = 10) was 39.3 ± 12.1 years, while the average age of the pediatric patients (n = 7) was 9.4 ± 3.1 years (p < 0.001). For adults, the correlation coefficient was 0.756; bias, -0.258 L/min; SD, 1.583 L/min; lower and upper 95 % limits of agreement for DDG-CO and esCCO, -3.360 and 2.844 L/min, respectively; and percentage error, 42.7 %. For children, the corresponding values were 0.904; -0.270; 0.908; -2.051 and 1.510 L/min, respectively; and 35.7 %. Due to the high percentage error values, we could not establish a correlation between esCCO and DDG-CO. However, the 95 % limits of agreement and percentage error were better in children than in adults. Due to the high percentage error, we could not confirm a correlation between esCCO and DDG-CO. However, the agreement between esCCO and DDG-CO seems to be higher in children than in adults. These results suggest that esCCO can also be used in children. Future studies with bigger study populations will be required to further investigate these conclusions.

Terrestrial Earth couple climate–carbon spatial variability and uncertainty

Underlying patterns of varied and rapidly changing weather phenomena characterized the atmospheric environment of Earth regions and Earth as a whole. Simplified models for the Thornthwaite moisture index estimation based on precipitation and evapotranspiration were developed. Geostatistics was used to characterize spatial patterns of the terrestrial Earth climates using the structure of covariance between the moisture index and net primary production (NPP) or gross primary production (GPP) derived from the MODerate Resolution Imaging Spectroradiometer sensor (MODIS) data products. Global terrestrial snow cover information was used to differentiate climates of the Earth Northern and Southern hemispheres. Two moisture index models were implemented globally using Thornthwaite evapotranspiration derived from high resolution temperature and the evapotranspiration derived from the MODIS data products. The inclusion of NPP or GPP in the climate models, improved the prediction of the moisture index in the terrestrial Earth, with better application of the models with NPP. Regions with higher error values in the Earth indicated random climate conditions and major difficult for climate spatial prediction. The moisture index model using Thornthwaite evapotranspiration determined less dry climates when compared to the moisture index model using Penman–Monteith evapotranspiration.

Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region

This study evaluates the potential of C- and L-band polarimetric SAR data for the discrimination of iron-mineralized laterites in the Brazilian Amazon region. The study area is the N1 plateau located on the northern border of the Carajás Mineral Province, the most important Brazilian mineral province which has numerous mineral deposits, particularly the world’s largest iron deposits. The plateau is covered by low-density savanna-type vegetation (campus rupestres) which contrasts visibly with the dense equatorial forest. The laterites are subdivided into three units: chemical crust, iron-ore duricrust, and hematite, of which only the latter two are of economic interest. Full polarimetric data from the airborne R99B sensor of the SIVAM/CENSIPAM (L-band) system and the RADARSAT-2 satellite (C-band) were evaluated. The study focused on an assessment of distinct schemes for digital classification based on decomposition theory and hybrid approach, which incorporates statistical analysis as input data derived from the target decomposition modeling. The results indicated that the polarimetric classifications presented a poor performance, with global Kappa values below 0.20. The accuracy for the identification of units of economic interest varied from 55% to 89%, albeit with high commission error values. In addition, the results using L-band were considered superior compared to C-band, which suggest that the roughness scale for laterite discrimination in the area is nearer to L than to C-band.

Reservoir lithofacies analysis using 3D seismic data in dissimilarity space

Seismic data interpretation is one of the most important steps in exploration seismology. Seismic facies analysis (SFA) with emphasis on lithofacies can be used to extract more information about structures and geology, which results in seismic interpretation enhancement. Facies analysis is based on unsupervised and supervised classification using seismic attributes. In this paper, supervised classification by a support vector machine using well logs and seismic attributes is applied. Dissimilarity as a new measuring space is employed, after which classification is carried out. Often, SFA is carried out in a feature space in which each dimension stands as a seismic attribute. Different facies show lots of class overlap in the feature space; hence, high classification error values are reported. Therefore, decreasing class overlap before classification is a necessary step to be targeted. To achieve this goal, a dissimilarity space is initially created. As a result of the definition of the new space, the class overlap between objects (seismic samples) is reduced and hence the classification can be done reliably. This strategy causes an increase in the accuracy of classification, and a more trustworthy lithofacies analysis is attained. For applying this method, 3D seismic data from an oil field in Iran were selected and the results obtained by a support vector classifier (SVC) in dissimilarity space are presented, discussed and compared with the SVC applied in conventional feature space.

Development and verification of a non-linear disaggregation method (NL-DisTrad) to downscale MODIS land surface temperature to the spatial scale of Landsat thermal data to estimate evapotranspiration

A nonlinear method (NL-DisTrad) was developed and tested to disaggregate satellite-derived estimates of land surface temperature of MODIS (Moderate Resolution Imaging Spectrometer) with a resolution of 960m to the scale of Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) at 60m. This method uses the relationship that is captured at the hot edge pixels in the feature space between the Normalised Difference Vegetation Index (NDVI) and the land surface temperature (LST) at a coarse resolution to disaggregate the LST to a finer resolution. The residuals that are generated at the coarse resolution are modelled using an Artificial Neural Network model (ANN), and the resulting residuals are added to the disaggregated LST at a fine resolution. The ANN model was built using the NDVI from the neighbourhood pixels. The hypothesis is that the LST of a pixel will not only be affected by the vegetation within the pixel but also by the vegetation of surrounding pixels. The performance of this hybrid model NL-DisTrad (Hot edge model+ANN model) is assessed by comparing the results to the existing disaggregation method, TsHARP, and the observed Landsat LST. The NL-DisTrad disaggregation results were comparable to the observed Landsat LST even for pixels with non-uniform vegetation. The statistical analysis indicated that the proposed model disaggregates the LST better than TsHARP, based on the high Nash Sutcliffe Efficiency (NSE>0.80) and low root mean square error value (RMSE<0.96K). Furthermore, using SEBAL (Surface Energy Balance Algorithm for Land), it was found that the estimates of daily evapotranspiration (ET) from the LST that were disaggregated using NL-DisTrad were comparable to the ET estimates from the observed Landsat LST data. As the disaggregation method NL-DisTrad only needs the LST–NDVI relationship at the coarse resolution, the model could be used to disaggregate the coarse resolution MODIS temperature data to the fine resolution of satellites such as IRS-P6 or SPOT-5 that do not carry any thermal sensors.