Modular sensor nodes for environmental data monitoring

A framework for modular wireless sensor networks (WSN) designed to capture and monitor micro-climates in a crop field. WSN is rapidly improving in automotive industry, agricultural, industrial and environmental monitoring and many other areas. Moulder architecture minimises the software upgrade down time and enables hardware reusability. Recent developments and advances in wireless technology as well as affordability give rise to this emerging field in the realm of Precision Agriculture (PA). Vineyard monitoring is an emerging application field in PA

Bioinformatics challenges and potentialities in studying extreme environments

Cold environments are populated by organisms able to contravene deleterious effects of low temperature by diverse adaptive strategies, including the production of ice binding proteins (IBPs) that inhibit the growth of ice crystals inside and outside cells. We describe the properties of such a protein (EfcIBP) identified in the metagenome of an Antarctic biological consortium composed of the ciliate Euplotes focardii and psychrophilic non-cultured bacteria. Recombinant EfcIBP can resist freezing without any conformational damage and is moderately heat stable, with a midpoint temperature of 66.4 degrees C. Tested for its effects on ice, EfcIBP shows an unusual combination of properties not reported in other bacterial IBPs. First, it is one of the best-performing IBPs described to date in the inhibition of ice recrystallization, with effective concentrations in the nanomolar range. Moreover, EfcIBP has thermal hysteresis activity (0.53 degrees C at 50 mu M) and it can stop a crystal from growing when held at a constant temperature within the thermal hysteresis gap. EfcIBP protects purified proteins and bacterial cells from freezing damage when exposed to challenging temperatures. EfcIBP also possesses a potential N-terminal signal sequence for protein transport and a DUF3494 domain that is common to secreted IBPs. These features lead us to hypothesize that the protein is either anchored at the outer cell surface or concentrated around cells to provide survival advantage to the whole cell consortium

Evaluation of Spatial Interpolation Techniques for Mapping Soil pH. Paper presented at the International Congress on Modelling and Simulation

Abstract: Soil pH has a major effect on plant nutrient availability by controlling the chemical structure of the nutrient. Adjusting soil acidity or alkalinity improves soil nutrition without adding extra fertilizers. Soil nutrients needed by plants in the largest amount are referred to as macronutrients. In addition to macronutrients, plants also need trace nutrients and both macro and trace nutrient availability is controlled by soil pH. Understanding of spatial variability of soil properties is important in site-specific management. Analysis of spatial variation of soil properties is fundamental to sustainable agricultural and rural development. The special variability of soil property is often measured using various interpolation methods resulting in map generation. Selecting a proper spatial interpolation method is crucial in surface analysis, since different methods of interpolation can lead to different surface results. Among statistical methods, geo-statistical kriging-based techniques have been frequently used for spatial analysis and surface mapping. [email protected] In this work, three common interpolation methods are used to study the spatial distributions of soil pH in a vineyard. Interpolation techniques were used to estimate the pH measurement in unsampled points and create a continuous dataset that could be represented over a map of the entire study area. The method investigated includes; Inverse Distance Weighting (IDW), Radial base Function (RBF) and Ordinary Kriging (OK). The performance of conventional statistics showed that soil pH had a law variation in this study. Experimental anisotropic semivariograms were fitted with the Spherical, Exponential, Gaussian and Exponential models and the Exponential model was found as the best fitted model using the crossvalidation method. The performances of interpolation methods were evaluated and compared using the cross-validation. The results showed that RBF method performed better than IDW and OK for prediction of the spatial distribution of topsoil p

An adaptive model of person identification combining speech and image information

The paper introduces a combination of adaptive neural network systems and statistical method for integrating speech and face image information for person identification. The method allows for the development of models of persons and their on-going adjustment based on new speech and face images. The method is illustrated with a modeling and classification of different persons, when speech and face images are presented in an incremental way. In this model, there are two sub - networks, one for face image and one for speaker recognition. A higher-level layer is applied to make a final decision. In the speaker recognition subnetwork, a text-dependant model is built using Evolving Connectionist Systems (ECOS) [1]. In the face image recognition sub-network, composite profile technique is applied for face image feature extraction and Zero Instruction Set Computing (ZISC) [2] technology is used to build the neural network. In the higher-level conceptual subsystem, final recognition decision is made using statistical method. The experiments show that ECOS and ZISC are appropriate techniques for the creation of evolving models for the task of speaker and face recognition individually. It is also shown that the integration of the speech and image information using statistical method improves the person identification rate. © 2004 IEEE

Spatial variability on soil pH gradient: a case study in vineyards

Soil pH gradient is an indicator of microorganisms and bacteria population in root zones with effects on the growth of plants, such as grapevine (Fernandez-Calviño, 2010). The pH gradient analysis can be used to determine management strategies – root development and soil quality - to reach the most suitable balance for a specific vineyard. Custom built management strategies lead to high vineyard productivity and avoid undesirable environmental impacts caused by surplus-nutrient runoff into streams, ground water reservoirs and the micro-fauna population. However, the pH gradient in agricultural soil varies in a spatialtemporal way, making its studies difficult and timeconsuming. Given this scenario, the interpolation techniques have been used to build spatial maps of soil attributes by sampled locations values. Such spatial maps give the soil condition of whole agricultural field, allowing the estimation of non-sampled locations values. A common methodology of spatial interpolation is the kriging, a popular statistical method that is grounded within the geostatistics field. Exemplifying, the Figure 1 shows the 2D maps of soil macronutrients spatial variability analyzed by kriging method (Cruvinel, 1999). The objective of this study is to evaluate the spatial variability of pH gradient in soil based on the use of geostatistical mapping obtained by pH measurements and semi-variogram models. For the analysis of the pH gradient, the soil samples were collected in 58 points considering three different horizons:~5-15 cm, ~15- 25 cm, and ~25-35 cm depths. To minimize error the soil pH gradient analyses were duplicated in the vineyard soils and at laboratory. The preliminary results have shown the existence of a significant pH gradient with values in the top layers of soil lower than the bottom layers. Therefore the pH level from top layers was more acid than the bottom ones. This study is part of “Enometrica Project”, which is an ongoing research project on micro-climate monitoring and modelling in vineyards and orchards to positively influence crop management and resulting quality production