Real-Time Reconstruction of Remote Sensing Imagery: Aggregation of Robust Regularization with Neural Computing

The robustified numerical technique for real-time sensor array reconstructive image processing is developed as required for remote sensing imaging with large scale array/synthesized array radars. The addressed technique is designed via performing the regularized robustification of the fused Bayesian-regularization imaging method aggregated with the efficient real-time numerical implementation scheme that employs the neural network computing.CINVESTA

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Computational enhancement of large scale environmental imagery: aggregation of robust numerical regularization, neural computing and digital dynamic filtering

We address a new efficient robust optimisation approach to large-scale environmental image reconstruction/enhancement as required for remote sensing imaging with multi-spectral array sensors/SAR. First, the problem-oriented robustification of the previously proposed Fused Bayesian-Regularization (FBR) enhanced imaging method is performed to alleviate its ill-poseness due to system-level and model-model uncertainties. Second, the modification of the Hopfield-type Maximum Entropy Neural Network (MENN) is proposed that enables such MENN to perform numerically the robustified FBR technique via computationally efficient iterative scheme. The efficiency of the aggregated robust regularised MENN technique is verified through simulation studies of enhancement of the real-world environmental images.CINVESTA

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Ground-based radar is known as one of the most important systems for precipitation measurement at high spatial and temporal resolutions. Radar data are recorded in digital manner and readily ingested to any statistical analyses. These measurements are subjected to specific calibration to eliminate systematic errors as well as minimizing the random errors, respectively. Since statistical methods are based on mathematics, they offer more precise results and easy interpretation with lower data detail. Although they have challenge to interpret due to their mathematical structure, but the accuracy of the conclusions and the interpretation of the output are appropriate. This article reviews the advanced methods in using the calibration of ground-based radar for forecasting meteorological events include two aspects: statistical techniques and data mining. Statistical techniques refer to empirical analyses such as regression, while data mining includes the Artificial Neural Network (ANN), data Kriging, Nearest Neighbour (NN), Decision Tree (DT) and fuzzy logic. The results show that Kriging is more applicable for interpolation. Regression methods are simple to use and data mining based on Artificial Intelligence is very precise. Thus, this review explores the characteristics of the statistical parameters in the field of radar applications and shows which parameters give the best results for undefined cases. DOI: 10.17762/ijritcc2321-8169.15012

Localization Techniques for Water Pipeline Leakages: A Review

Pipeline leakages in water distribution network (WDN) is one of the prominent issues that has gain an interest among researchers in the past few years. Time and accuracy play an important role in leak localization as it has huge impact to the human population and economic point of view. The complexity of WDN has prompt numerous techniques and methods been introduced focusing on the accuracy and efficacy. In general, localization techniques can be divided into two broad categories; external and internal systems. This paper reviews some of the techniques that has been explored and proposed including the limitations of each techniques. Â

Spatio-temporal risk assessment models for Lobesia botrana in uncolonized winegrowing areas

The objective of this work was to generate a series of equations to describe the voltinism of Lobesia botrana in the quarantine area of the main winemaking area of Argentina, Mendoza. To do this we considered an average climate scenario and extrapolatedthese equations to other winegrowing areas at risk of being invaded. A grid of 4 km2was used to generate statistics on L. botrana captures and the mean temperature accumulation for the pixel. Four sets of logistic regression were constructed using the percentage of accumulated trap catches/grid/week and the degree-day accumulation above7°C, from 1st July. By means of a habitat model, an extrapolation of the phenologicalmodel generated to other Argentine winemaking areas was evaluated. According to ourresults, it can be expected that 50% of male adult emergence for the first flight occurs at248.79 ± 4 degree-days (DD), in the second flight at 860.18 ± 4.1 DD, while in the thirdand the fourth flights, 1671.34 ± 5.8 DD and 2335.64 ± 4.3 DD, respectively. Subsequentclimatic comparison determined that climatic conditions of uncolonized areas of Cuyo Region have a similar suitability index to the quarantine area used to adjust the phenologicalmodel. The upper valley of Río Negro and Neuquén are environmentally similar. Valleys ofthe northwestern region of Argentina showed lower average suitability index and greatervariability among SI estimated by the algorithm considered. The combination of two models for the estimation of adult emergence time and potential distribution, can provide greater certainties in decision-making and risk assessment of invasive species.Fil: Heit, Guillermo Eugenio. Ministerio de Agricultura, Ganadería, Pesca y Alimento. Servicio Nacional de Sanidad y Calidad Agroalimentaria; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; ArgentinaFil: Aceñolaza, Pablo Gilberto. Universidad Nacional de Entre Ríos; Argentina. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentin