A computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computervision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the SupportVectorMachines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the SupportVectorMachines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies

Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data

The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divided into multiple parties or when the dataset is too large to be handled by the data owner. In the latter case, usually some servers are hired to perform the task of clustering. The dataset is divided by the data owner among the servers who together perform the k-means and return the cluster labels to the owner. The major challenge in this method is to prevent the servers from gaining substantial information about the actual data of the owner. Several algorithms have been designed in the past that provide cryptographic solutions to perform privacy preserving k-means. We provide a new method to perform k-means over a large set using multiple servers. Our technique avoids heavy cryptographic computations and instead we use a simple randomization technique to preserve the privacy of the data. The k-means computed has exactly the same efficiency and accuracy as the k-means computed over the original dataset without any randomization. We argue that our algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems Security. Springer, Cham, 201

New data on the distribution and natural history of the lesser grison (Galictis cuja), hog-nosed skunk (Conepatus chinga) and culpeo (Pseudalopex culpaeus) in northwestern Argentina

We present a total of 190 new distribution records of three little-known mammalian carnivores (Conepatus chinga; Galictis cuja,and Lycalopex culpaeus) obtained using camera trap techniques and direct observation in the highlands of Jujuy province, northwestern Argentina. These new records extend the present known distributions of these three species to the west of the province and to higher altitudes, and provide additional information on habitat association and activity patterns.Fil: Tellaeche, Cintia Gisele. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Cátedra de Fisiología Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Reppucci, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Cátedra de Fisiología Animal; ArgentinaFil: Luengos Vidal, Estela Maris. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Cátedra de Fisiología Animal; ArgentinaFil: Lucherini, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Cátedra de Fisiología Animal; Argentin

Walk-through programming for robotic manipulators based on admittance control

The present paper addresses the issues that should be covered in order to develop walk-through programming techniques (i.e. a manual guidance of the robot) in an industrial scenario. First, an exact formulation of the dynamics of the tool the human should feel when interacting with the robot is presented. Then, the paper discusses a way to implement such dynamics on an industrial robot equipped with an open robot control system and a wrist force/torque sensor, as well as the safety issues related to the walk-through programming. In particular, two strategies that make use of admittance control to constrain the robot motion are presented. One slows down the robot when the velocity of the tool centre point exceeds a specified safety limit, the other one limits the robot workspace by way of virtual safety surfaces. Experimental results on a COMAU Smart Six robot are presented, showing the performance of the walk-through programming system endowed with the two proposed safety strategies

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper

Accuracy Evaluation of Dense Matching Techniques for Casting Part Dimensional Verification

Product optimization for casting and post-casting manufacturing processes is becoming compulsory to compete in the current global manufacturing scenario. Casting design, simulation and verification tools are becoming crucial for eliminating oversized dimensions without affecting the casting component functionality. Thus, material and production costs decrease to maintain the foundry process profitable on the large-scale component supplier market. New measurement methods, such as dense matching techniques, rely on surface texture of casting parts to enable the 3D dense reconstruction of surface points without the need of an active light source as usually applied with 3D scanning optical sensors. This paper presents the accuracy evaluation of dense matching based approaches for casting part verification. It compares the accuracy obtained by dense matching technique with already certified and validated optical measuring methods. This uncertainty evaluation exercise considers both artificial targets and key natural points to quantify the possibilities and scope of each approximation. Obtained results, for both lab and workshop conditions, show that this image data processing procedure is fit for purpose to fulfill the required measurement tolerances for casting part manufacturing processes.This research was partially funded by ESTRATEUS project (Reference IE14-396). given are accurate and use the standard spelling of funding agency names at https://search.crossref.org/funding, any errors may affect your future funding

Automatic segmentation of relevant textures in agricultural images

One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevantimage processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processin

Mamíferos puneños y altoandinos

Los mamíferos de la Puna argentina se encuentran representados por 54 especies, 33 géneros per tenecientes a 15 familias y 6 órdenes. Rodentia y Carnivora son los órdenes más ricos en especies comprendiendo el 69 y 15%, respectivamente, seguidos de Chiroptera con el 9%. En orden de importancia sigue Artiodactyla con una sola familia y dos especies. Cingulata y Marsupialia se ubican al final con una familia y una especie cada una. Considerando la totalidad de las especies de mamíferos de la Puna y las cinco áreas protegidas nacionales presentes en esta ecorregión se calculó una representatividad general del 65%, es decir 35 de las 54 especies de mamíferos se encuentran registradas en las áreas protegidas nacionales de Argentina. Chiroptera y Rodentia fueron los órdenes presentes en la Puna que tuvieron especies no registradas dentro del sistema nacional de áreas protegidas, alcanzando representatividades del 80 y 51%, respectivamente. Las principales amenazas para los mamíferos en estos ambientes se relacionan con actividades humanas, e incluyen: la cacería, la contaminación y desecación de las fuentes de agua, la introducción de especies exóticas, la degradación del hábitat, la contaminación causada por el turismo y/o las competencias deportivas y la disminución de la cobertura vegetal. Son pocos y muy específicos los esfuerzos científicos y gubernamentales por generar conocimiento sobre los mamíferos puneños, siendo sumamente difícil implementar políticas de uso comercial, manejo y/o conservación. En general, se sabe muy poco sobre los roedores y quirópteros, siendo este desconocimiento una de las amenazas más críticas en algunas circunstancias. Esto ha hecho sumamente difícil interpretar los impactos que una actividad determinada puede causar sobre sus poblaciones.“Puna and High-Andes Mammals”. Mammals at the Puna of Argentine are represented by 54 species, 33 genera belonging to 15 families and 6 orders. Rodentia and Carnivora are the richest orders (69 and 15%, respectively) followed by Chiroptera (9%). Artiodactyla includes one family and two species; Cingulata and Marsupialia are last with one species each. Considering all mammalian species of the Puna and High Andes and the four protected areas of national jurisdiction, a general representation of 61% is calculated: 33 of the 54 mammal species of these ecoregions are recorded in national protected areas of Argentina. Chiroptera and Rodentia orders have species not recorded in the national system of protected areas, reaching a representativeness of 80 and 46% respectively. The main threats to mammals in these environments due to interaction with human activities include: pollution and loss of water sources, hunting and introduction of exotic species, habitat degradation, pollution caused by tourism and/or sporting activities and decreased vegetation cover. Scientific and government efforts are scarce and very specific to generate knowledge about Puna mammals, which results in extremely difficult implementation of policies for commercial use, management and/or conservation. Very little is known about rodents and bats, and this ignorance is one of the most critical threats in some circumstances, since it is not possible to interpret the impacts of any activity to their populations.Fil: Perovic, Pablo Gastón. Administración Nacional de Parques Nacionales; ArgentinaFil: Trucco Aleman, Carlos Eduardo. Universidad Nacional de Salta; ArgentinaFil: Tellaeche, Cintia Gisele. Universidad Nacional de Jujuy; ArgentinaFil: Bracamonte, Julio Cesar. Universidad Nacional de Jujuy; ArgentinaFil: Cuello, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; ArgentinaFil: Novillo, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; ArgentinaFil: Lizárraga, Leónidas. Administración Nacional de Parques Nacionales; Argentin

La comprensión de los estudiantes de los diferentes tipos de texto: estrategias para identificar y mejorar la comprensión conceptual de los estudiantes

This article evaluates students’ conceptual understanding using the think-aloud method. The participants, four students who were studying the last year of the Spanish ab initio course in an international school in Prague, volunteered to take part in the think-aloud protocols. The results show that the think-aloud method helps to get a better sense of students’ understanding of different text types.Este artículo evalúa la comprensión conceptual de los estudiantes mediante el método de think-aloud. Los participantes, cuatro estudiantes del curso de Español ab initio de una escuela internacional en Praga, se ofrecieron como voluntarios para participar en los protocolos de think-aloud. Los resultados muestran que este método ayuda a los estudiantes a tener una mejor comprensión de los diferentes tipos de texto.

Estimation of the aggregate import demand function for Mexico: a cointegration analysis

Purpose: This study estimated total import demand elasticities concerning income, import prices and domestic prices. A high propensity to import constitutes a significant obstacle to economic growth in Mexico since the benefits of increased exports or any other aggregate demand expansion leak to the rest of the world. Design/methodology/approach: This paper estimated a Vector Error Correction Model of the total import demand elasticities concerning income, import prices and domestic prices. Total imports are a dependent variable, while Gross Domestic Product (GDP) and import and domestic prices are the independent variables. Findings: The principal finding is that an increase of 1 peso in the Mexican GDP leads to a rise of 0.50 pesos in Mexican imports; the elasticity of import demand for prices is low. Still, the elasticity of import demand for domestic prices is 2.14 times greater than that for import prices. These results have significant economic policy implications, such as promoting the expansion of the domestic market and the national content of exports. Research limitations/implications: It is tempting to estimate the import demand function for the entire 1993–2019 period since such data is available. But by doing so, the authors would overestimate the propensity to import, given that from 1993 to 2019, the proportion of imports as a percentage of GDP went from 11.37 in 1993 to 29.66 in 2019. Therefore, it makes more sense to estimate the import demand function from 2000 to 2019, a period with a stable proportion of imports to GDP. Originality/value: A high level of imports in developing countries means that much of their aggregate demand is filtered abroad. Therefore, the low impact of its exports on GDP is related to the Mexican economy’s high imports. The authors calculate this relationship with new data and methods