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This paper uses the data from seven wind farms at Muppandal, Tamil Nadu, India, collected for three years from April 2002 to March 2005 for the estimation of energy yield from wind farms. The model is developed with the help of neural network methodology, and it involves three input variables-wind speed, relative humidity, and generation hours-and one output variable, which give the energy output from wind farms. The modeling is done using MATLAB software. The most appropriate neural network configuration after trial and error is found to be 3-5-1 (3 input layer neurons, 5 hidden layer neurons, 1 output layer neuron). The mean square error for the estimated values with respect to the measured data is 7.6 × 10-3. The results demonstrate that this work is an efficient energy yield estimation tool for wind farms. [ABSTRACT FROM AUTHOR]

Tea is an extensively consumed beverage worldwide with an expanding market. The major quality attributes of tea are flavor, aroma, color, and strength. Out of these, flavor and aroma are the most important attributes. Human experts called "tea tasters" conventionally evaluate tea quality, and they usually assign scores to samples of tea that are under evaluation on a scale of 1 to 10, depending on the flavor, the aroma, and the taste of the sample. This paper presents a study where, first, the selection of appropriate sensors was carried out based on sensitivity with the major aroma-producing chemicals of black tea. Then, this sensor array was exposed to black tea samples that were collected from the tea gardens in India, and the computational model has been developed based on artificial neural network methods to correlate the measurements with the tea taster's scores. With unknown tea samples, encouraging results have been obtained with a more than 90% classification rate. [ABSTRACT FROM AUTHOR]

By studying the spectral reflectance features of different land cover types and leveraging information of primarily “BLUE” band along with “RED” and “NIR” bands, this article seeks to introduce a new built-up index such as powered B1 built-up index (PB1BI). The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas from Landsat7 satellite images efficiently. For Landsat7 satellite imagery, classification performances of the proposed index along with support vector machine (SVM), artificial neural network (ANN), and three existing built-up indices have been examined for three study sites of 1° Latitude $\times 1^\circ $ Longitude ($\approx 12\,100~{\mathrm {sq}}\cdot {\mathrm {km}}$) area from three diverse geographical regions in India. The computed value of the M-Statistics for PB1BI is consistently greater than 1.80, indicating a better spectral separability between built-up and nonbuilt-up classes by the index. In order to improve the performance of the built-up indices, this article has suggested a bootstrapping method for threshold estimation in addition to the existing Otsu’s method for the same. It has been found that using bootstrapping method instead of Otsu’s method for threshold estimation has helped to improve the classification performance of built-up indices up to 17.75% and 40.49% in terms of overall accuracy and kappa ($\kappa $) coefficient, respectively. It has been observed that for the validation set, average overall accuracy (97.45%) and kappa ($\kappa $) coefficient (0.907) of PB1BI for considered study sites are not only significantly higher than existing indices but also comparable with the same of SVM (99.10% and 0.942) and ANN (87.24% and 0.450). This article has also shown that the proposed index provides a stable performance for multitemporal analysis of the study sites and is able to capture growth in built-up region in time horizon. The classification performance of PB1BI has also been verified for Landsat8 imagery across 11 study sites from different continents around the globe, and the results show overall accuracy and $\kappa $ to be consistently more than 90% and 0.75, respectively. For considered study sites, the reported values of average accuracy and $\kappa $ of PB1BI for built-up classification using Landsat8 satellite data are 95.7151% and 0.8843, respectively. [ABSTRACT FROM AUTHOR]