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Deployment and topology of a wireless sensor network for precision viticulture
Deployment and topology of a wireless sensor network for precision viticulture
Precision viticulture is a specialization of precision agriculture techniques applied to viticulture. Precision agriculture is the use of information system technologies applied to agricultural production. Some of the applicable technologies are; Wireless Sensor Networks (WSN), Global Positioning Systems (GPS), spectroscopy analysis of Near-Infrared (NIR), Geographic Information Systems (GIS). These systems provide means of observation, evaluation and control of agricultural activities. The farmers demand assistant systems to perform actions for saving time and avoiding risks. There are studies of maps crops and mesh-sampling techniques to predict the harvest volume in a vineyard with a certain varieties of grapes. The prediction is based on a previous study of crops over a period of three to four years. Along these three or four years a large volume of samples is taken to study several parameters. In this application area is where the wireless sensor networks technologies would have high incidence. In this context we intend to analyse, at first place, the specific characteristics of the operational environment of a vineyard. Second, we will analyse the most appropriate architecture for a sensor network in this environment. Application of wireless sensor networks technology can take many forms depending of environment, and implementation objectives. In this paper we discuss about the best procedure for deployment and the optimal topology of a wireless sensor network for viticulture.
Gregorio Corral
IWO_RUG_BA_IK
IWO_RUG_BA_IK
Template for the the final project of the course.
T. Caselli
Measurement of the dynamic viscosity of Canola Oil using a ball drop
Measurement of the dynamic viscosity of Canola Oil using a ball drop
The viscosity of a particular fluid is an interesting parameter that plays an important role in fluid dynamics of that fluid. We chose the common household cooking item canola oil. Using a ball drop, we set out to measure viscosity at various temperatures and create a model for the viscosity of canola oil as a function of temperature, as well as an accurate measurement for viscosity at room temperature. It was found that the viscosity between 0 and 40 degrees Celsius can be approximated using an exponential function and that an estimation for viscosity at room temperature was not very difficult to obtain. The precision of this measurement was limited by uncertainty in lab equipment used to measure various quantities as well as the image analysis software we used and the limited frame-rate of our camera.
Jamie Clark
The Parallelization and Optimization of the N-Body Problem using OpenMP and Cuda
The Parallelization and Optimization of the N-Body Problem using OpenMP and Cuda
This research paper aims at exploiting efficient ways of implementing the N-Body problem. The N-Body problem, in the field of physics, predicts the movements and planets and their gravitational interactions. In this paper, the efficient execution of heavy computational work through usage of different cores in CPU and GPU is looked into; achieved by integrating the OpenMP parallelization API and the Nvidia CUDA into the code. The paper also aims at performance analysis of various algorithms used to solve the same problem. This research not only aids as an alternative to complex simulations but also for bigger data that requires work distribution and computationally expensive procedures.
Tushaar Gangarapu
Survey on Bi-LSTM CNNs CRF for Italian Sequence Labeling and Multi-Task Learning
Survey on Bi-LSTM CNNs CRF for Italian Sequence Labeling and Multi-Task Learning
In the last few years the resolution of NLP tasks with architectures composed of neural models has taken vogue. There are many advantages to using these approaches especially because there is no need to do features engineering. In this paper, we make a survey of a Deep Learning architecture that propose a resolutive approach to some classical tasks of the NLP. The Deep Learning architecture is based on a cutting-edge model that exploits both word-level and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture has provided cutting-edge performance in several sequential labeling activities for the English language. The architecture that will be treated uses the same approach for the Italian language. The same guideline is extended to perform a multi-task learning involving PoS labeling and sentiment analysis. The results show that the system performs well and achieves good results in all activities. In some cases it exceeds the best systems previously developed for Italian.
leo.ranaldi
L'Amor al món medieval
L'Amor al món medieval
Breu resum sobre l'amor al món medieval. Basat en apunts de l'assignatura de la UB.
Muntsa Moral
Estudio de los factores de disipación de la mica-epoxi en pruebas de calentamiento para placas de circuitos eléctricos
Estudio de los factores de disipación de la mica-epoxi en pruebas de calentamiento para placas de circuitos eléctricos
Comprensión de un estudio realizado en la mica-epoxi para placas de circuitos. El estudio consistió en pruebas de resistencia para medir el desgaste en el tiempo del material y así determinar su tiempo de vida aproximado.
Luis Alberto González José
Estudio del proceso termodinámico en una máquina de expreso
Estudio del proceso termodinámico en una máquina de expreso
Estudio de los procesos termodinámicos que conllevan una máquina de expreso. Se utiliza solo agua, sin el filtro de café para utilizar solo la densidad del agua. Se encuentra que es similar a un proceso de Carnot, pero como un ciclo abierto.
Luis Alberto González José

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