Furthermore, Figure 6 illustrates the predictive property of the optimized network, the ANN response and measurement values are compared, thus a good agreement between measurement and ANN model is founded. It��s noted that the full scale error (%FS) is expressed as a percentage of the ratio between the absolute error and the maximum output (wavelength) variation range.Figure 6.ANN model validation.Table 1.ANN optimized parameters.4.?Implementation and Simulation ResultsThe resulting currents of one BTJ cannot be directly expl
The rapid development of sensor and wireless communication technologies has increased the use of automatic (wireless) sensors in environmental monitoring and agriculture [1].

The availability of smarter, smaller and inexpensive sensors measuring a wider range of environmental parameters has enabled continuous-timed monitoring of environment and real-time applications [2,3]. This was not possible earlier, when monitoring was based on water sample collection and laboratory analyses or on automatic sensors wired to field loggers requiring manual data downloading. During the previous decades, environmental monitoring has developed from off-line sensors to real-time, operational sensor networks [4] and to open Sensor Webs. These are based on open, standard protocols, interfaces and web services [5�C7].Varying terminology, such as wireless sensor networks, environmental sensors networks [4] and geo-sensor networks [8], are used interchangeably to describe more or less the same basic concept of collecting, storing and sharing sensor data, but employing different technologies or having different functional focus [2].

All of these terms refer to a system comprised of a set of sensor nodes and a communication system that allows automatic data collection and sharing through internet based databases and services [2,4]. The sensor webs are also seen as an advanced part of sensor networks by some authors [4,8], while others differentiate between sensor networks and sensor webs. They emphasize that the latter are based on open Sensor Web enablement (SWE) standards and web services, and that sensor nodes are able to communicate with each other. This makes sensor webs interoperable and intelligent systems that can react to changing environmental conditions [2,5,6].

Along with developments in sensor and communication technology, complex environmental problems such as eutrophication and climate change have rapidly increased the need for temporally and spatially accurate data [4]. Adaptation to more variable weather and environmental conditions increases the importance of (near) real-time information Entinostat that is valuable in better timing and control of agricultural management practices such as irrigation and pesticide spraying, monitoring algae bloom, and developing flood and frost warning systems [1,3].