Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I-V and P-V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported.

Real-time sensing of chemical warfare agents (CWAs) is, today, a crucial topic to prevent lethal effects of a chemical terroristic attack. For this reason, the development of efficient, selective, sensitive, and reversible sensoristic devices, which are able to detect by optical response the ppm levels of these compounds, both in water and in air, is strongly required. Here, we report the design and synthesis of a fluorescent nanosensor, based on carbon nanoparticles covalently functionalized with ethanolamine arms, which exploits the multitopic supramolecular interaction with nerve agents, ensuring highly efficient (log K 6.46) and selective molecular recognition. Moreover, given the aqueous dispersibility of carbon nanoparticles, these nanosensors ensure even higher sensitivity, detecting sub-ppt concentration of nerve agents in water, and subppm level in air by using a common digital camera or a smartphone. Our results pave the way to an innovative class of low-cost reusable CWA sensors, prompting, for the first time, the simultaneous detection of nerve agents through gaseous and aqueous media, thus extending the protection range to public water supplies.

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Internal organs, like lungs, are very often examined by the use of screening methods. For this purpose we present an evaluation model based on a composition of fuzzy system combined with a neural network. The input image is evaluated by means of custom rules which use type-1 fuzzy membership functions. The results are forwarded to a neural network for final evaluation. Our model was validated by using X ray images with lung nodules. The results shows the high performances of our approach with sensitivity and specificity reaching almost 95% and 90% respectively, with an accuracy of 92.56%. The new methodology lower considerably the computational demands and increases detection performances.

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Abstract-Nowadays the effective and fast detection of fruit defects is one of the main concerns for fruit selling companies. This paper presents a new approach that classifies fruit surface defects in color and texture using Radial Basis Probabilistic Neural Networks (RBPNN). The texture and gray features of defect area are extracted by computing a gray level co-occurrence matrix and then defect areas are classified by the applied RBPNN solution.

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