• Rural Digital Europe
• 2014-2023close
• European University of Technology close

The purpose of this paper is to address current issues in industrial robotics applications and to address also the current trends occurring in the industry regarding smaller mobile units in factory and external manufacturing processes relating to the battery powering of robotics devices. Specifically, we shall concentrate the efforts herein on “Mobile Industrial Robotics”. The paper touches briefly on problems associated with both definitions of industrial robots and problems associated with power supply sources of mobile industrial robotics and provides an outline of future work with appropriate solution for smaller mobile industrial mobile units operating in the variable input range of 24V to 48v and outputs in the range of 3.3V to 12V @ 20A in three stages. The project required a fast, turn-key solution which did not allow for a “design from scratch” solution and so detailed herein is suitable design produced in other sectors and relating the details, design process, modelling and examination of a successful and efficient high current DC to DC switch mode power supply. Parameters have been adjusted to our own preferences in the design considerations regarding “Industrial Service Hybrids” [1] (ISH)1. Other design issues are under assessment and shall be included as the project moves forward.

Remote sensing, as the tool for spatially continuous measurements has become a trend for estimating Crop Yield since economically efficient agricultural management is highly dependent on detailed temporal and spatial knowledge of the processes affecting physiological crop development. This paper aims at examining the use of field spectroscopy along with Landsat's satellite imagery in order to test the accuracy of raw satellite data and the impact of atmospheric effects on determining crop yield derived from models using remotely sensed data. The spectroradiometric retrieved Vegetation Indices(VI) of Durum wheat, is directly compared to the corresponding VI of Landsat 7 ETM+ and 8 OLI, sourcing from both atmospherically corrected and not corrected satellite images in order to test the effects of atmosphere upon them. Vegetation Indices are vital in the procedure for estimating Crop Yield since they are used in stochastic or empirical models for describing or predicting crop yield. Leaf Area Index, which is also inferred using VI, is also compared to the real values of LAI that are measured using the SunScan instrument, during the satellite's overpass. Crop Yield is finally determined using the Cyprus Agricultural Research Institute's Crop Yield model for Durum wheat, adapted to satellite data, and is used to examine the impact of atmospheric effects. The results have prevailed that if crop yield models using remote sensing imagery, do not apply atmospheric effects algorithms, then there is statistically significant difference in the prediction from the real yield and hence a significant error regarding the model. The study's goal is to illustrate the need of atmospheric effects removal on remotely sensed data especially for models using satellite images.

Existen numerosas aplicaciones de IoT donde los gateways recopilan los datos en bruto de sensores y los envían a una nube para su procesamiento. Una alternativa consiste en hacer gran parte del procesamiento en el propio gateway y minimizar el envío de datos y la carga computacional en la nube; esta estrategia se denomina edge-computing y puede lograr un mayor ahorro de energía y reducción del ancho de banda empleado en las comunicaciones. El objetivo del proyecto es diseñar e implementar un gateway edge-computing basado en Raspberry Pi usando Python y tecnologías estándar; el gateway edgecomputing recopila datos de numerosos sensores IoT y hace un preprocesamiento y tratamiento de dichos datos minimizando el envío de datos a una nube. Se propone un caso de uso basado en sensores IoT de temperatura y humedad usando comunicación Bluetooth Low Energy. Escuela Técnica Superior de Ingeniería de Telecomunicación Universidad Politécnica de Cartagena

Unmanned aerial vehicles (UAVs) for use in remote sensing offer simple and affordable observation from the air. Due to the decreasing size of the sensors, receivers and antennas, it is now possible to create integrated low-altitude airborne systems. Remote sensing technologies on a UAV platform are an efficient, non-invasive and low cost resource for the detection, documentation and monitoring of various environmental (both natural and cultural) features, in high resolution and quality. Different remote sensing techniques can be used alongside to the UAVs, such as field spectroscopy, multispectral cameras, infrared cameras and thermal cameras. This integration in terms of techniques and technologies, contributes further to more solid outcomes depending on the case study. The possibility of adding a variety of sensors to the basic UAV unit amplifies both the application cases and the results. Among the types of camera sensors that can be used with the UAVs are the visible spectrum cameras, the multi-spectral cameras and the hyperspectral cameras. These cameras can extract more detailed information since an entire spectrum is acquired from each pixel of the image. In this paper, various UAVs used for environmental and archaeological applications in different areas of Cyprus are presented.

Abstract Concerns about drinking water (DW) quality contamination during water distribution raise a need for real-time monitoring and rapid contamination detection. Early warning systems (EWS) are a potential solution. The EWS consist of multiple conventional sensors that provide the real-time measurements and algorithms that allow the recognizing of contamination events from normal operating conditions. In most cases, these algorithms have been established with artificial data, while data from real and biological contamination events are limited. The goal of the study was the event detection performance of the Mahalanobis distance method in combination with on-line DW quality monitoring sensors and manual measurements of grab samples for potential DW biological contamination scenarios. In this study three contamination scenarios were simulated in a pilot-scale DW distribution system: untreated river water, groundwater and wastewater intrusion, which represent realistic contamination scenarios and imply biological contamination. Temperature, electrical conductivity (EC), total organic carbon (TOC), chlorine ion (Cl-), oxidation–reduction potential (ORP), pH sensors and turbidity measurements were used as on-line sensors and for manual measurements. Novel adenosine-triphosphate and flow cytometric measurements were used for biological water quality evaluation. The results showed contamination detection probability from 56% to 89%, where the best performance was obtained with manual measurements. The probability of false alarm was 5–6% both for on-line and manual measurements. The Mahalanobis distance method with DW quality sensors has a good potential to be applied in EWS. However, the sustainability of the on-line measurement system and/or the detection algorithm should be improved.

Abstract Agriculture is the second most significant source of greenhouse gas (GHG) emissions, with approximately 24 % of Latvia’s total GHG emissions in 2014. Emissions from agricultural soils contributed major share of the total emissions – 59.6 %, enteric fermentation emissions was second largest source – 32 %. The share of manure management emissions was evaluated as 7.5 % of total emissions in the sector, remaining 0.9 % of emissions refer to liming and urea application. GHG emissions increased in 2014 by 3.3 % comparing to 2013 due to increase of cattle, sheep and fur animal numbers. Statistics also showed increase of synthetic N fertilizer consumption approximately by 4.6 %, sown area by 0.3 % and lime application to soils 42.9 % [1]. Bearing in mind significant share of agricultural emissions in total GHG emissions in Latvia and the growing emission trend the additional attention is necessary for evaluation of impacts of agriculture on the environment. The purpose of this study is to explore indicators for assessing eco-efficiency in the Latvian agriculture sector. First the paper describes methods which can be used for measuring eco-efficiency, second availability of activity data and third, presents calculations of selected indicators for eco-efficiencies.

In this article we will present a transdisciplinary approach to carbon sequestration in agricultural soils. The software provides a method proposed to measure the amount of carbon that can be captured from different soil types and different crop. The application has integrated an intuitive interface, is portable and calculate the number of green certificates as a reward for farmers financial support for environmental protection. We plan to initiate a scientific approach to environmental protection through financial incentives for agriculture fits in EU rules by taxing big polluters and rewarding those who maintain a suitable environment for the development of ecological and competitive agriculture.

Proceedings of SPIE - The International Society for Optical Engineering, Volume 9229, 2014, Article number 922912 Accurate assessment of water use is an important issue in a globally changing climate and environment, where water is becoming a scarce but essential resource. The concept "?Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. This indicator provides valuable information for a global assessment of how water resources are used. Remote sensing (RS) provides physically-based, worldwide, and consistent spatial information over space and time, and has been used in hydrological applications in order to estimate relevant variables at different temporal and spatial scales. The paper focuses on exploring and exploiting the potential of using RS techniques and data for WF assessment in agriculture. Based on recent papers initiated in this research topic the investigation focuses on how variables needed in the calculation of water footprint are obtained (based on non RS and on RS approaches), on identifying the inputs required for estimating the WF of crops and whether it is feasible to integrate various RS approaches. The results of this study demonstrate the usefulness of satellite data for water footprint assessment, which were obtained by the Remote Sensing Working Group in the framework of the ESSEM COST Action ES1106, "Assessment of EUROpean AGRIculture WATer use and trade under climate change"? (EURO-AGRIWAT). © 2014 SPIE.

There are currently many classifications of IoT systems, many of which come from the industry and reflect the functional and structural characteristics of the solutions. One of the most popular practical classifications of loT systems is the Postscapes classification, which divides the whole set of solutions into seven different categories (“Smart Home”, “Smart Transport”, “Smart Healthcare”, “Smart Sales”, “Smart Transport”). “Smart Home” and “Smart Production” and “Smart City”, as well as “Smart Energy”.

This study aims at reviewing existing remote sensing approaches to assess drought impact on desertification in the East Mediterranean region. Drought and desertification are interconnected phenomena. The World Meteorological Organization (WMO) defines that an area is affected by drought when the annual precipitation is lower than 60 % of the normal values, at least during 2 consecutive years in more than 50 % of its area. Drought is a phenomenon that may trigger or exacerbate desertification. Desertification is usually reported as the process during which land becomes more arid and loses its vegetation, water bodies (lakes, streams), and wildlife. Being one of the major causes of desertification, drought is a complex phenomenon and its monitoring is crucial for early warning and risk management of desertification. A number of approaches are possible for assessing drought. This paper reviews remotely-sensed drought indices that are particularly relevant to the East Mediterranean Region, discussing their strengths and weaknesses, as well as their present challenges. As the East Mediterranean Region is dominated by semi-arid to arid climates, focus is here given to methods applied to assess drought in semi-arid or arid regions. The present paper analyses the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Normalized Difference Drought Index (NDDI), the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Vegetation Health Index (VHI) and the Composite Drought Index (CDI). For their validation, the indices need to be compared to ancillary data, recorded at meteorological stations or acquired from in-situ measurements. Thus, the paper suggests Goodness-of-fit criteria, which correlate the derived data spatially and temporally. Examples of such criteria are the Pearson product-moment correlation coefficient r, the coefficient of determination R2, the Root Mean Squared Error (RMSE) and the Nash Sutcliffe Efficiency (NSE).