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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
Household daily-peak electricity load forecasting with statistical models
Household daily-peak electricity load forecasting with statistical models
This article proposes to obtain a statistical model of the daily peak electricity load of a household located in Austin-TX,USA. The Box-Jenkins methodology was followed to obtain the best fit for the time-series. Four models provided a good fit: ARIMA(0,1,2), ARIMA(1,1,2), SARIMA(0,1,2)(0,1,1) and SARIMA(1,1,2)(0,1,1). The model with the highest Akaike Information Criteria was the ARIMA(1,2,2). However, the model with the highest forecast accuracy was the SARIMA(1,1,2)(0,1,1), which obtained an RMSE of 0.296 and a MAPE Of 15.00.
Luciano Viola
QM_hw
QM_hw
homework on quantum mechanics course
Stas Kelvich
Run2A Plan, progress and performance.
Run2A Plan, progress and performance.
Run2A Plan, progress and performance (preliminary).
Chanpreet Amole
Improving the performance of medical CT image reconstruction on multicore processor
Improving the performance of medical CT image reconstruction on multicore processor
Image reconstruction is observed to be one of the most common problem because of it's large data movement and non-trivial data dependencies. In the past, these problems were tackled by many high performance hardware such as FPGA's and GPGPU's. This also reflects the investemts to be made in these supercomputers for real time reconstruction of clinical computed tomography (CT) applications. Medical imaging systems are employing high performance computing (HPC) technology to meet their time constraints. This paper presents different optimizations to the volume reconstruction and implement it on a commodity hardware such as x86 based multicore system. This paper chooses to perform its implementaion on Intel Xeon X5365 multicore processor. We perform different levels of parallelization and analyse each of them and report their results with respect to serial implementation. The objective of this paper is to understand the constraints of volume reconstruction in multicore architecture and optimize them while preserving the quality of the reconstructed image.
shiv
Introducción a MATLAB
Introducción a MATLAB
En el presente post, haremos una breve introducción de la herramienta MATLAB. Presentaremos funciones, operaciones y gráficos con más de dos datos, para asi luego ordenar y hacer cálculos con matrices.
Benjamin Pastene Rebolledo
Programacion WEB CGI vs JSP
Programacion WEB CGI vs JSP
CGI vs JSP
Juan Carlos Valdivia Berrios
Cifras Significativas
Cifras Significativas
Suma, resta, multiplicación y división.
Juan Diego
Lab 2 Fisica
Lab 2 Fisica
Laboratorio de Fisica.
Maritza