Remote Sensing is extensively used for crop mapping and management in current era. High resolution multispectral data of every part of earth is available at relatively low cost. Selection of appropriate decision rule and appropriate spectral bands is critical for obtaining accurate classification results. Need to find an accurate decision rule which is less time consuming and needs less resources leads to the performance analysis of different classification algorithms on the basis of their classification accuracy, time consumption, computational requirements, reliability etc. A SPOT 5 image acquired on 2009-07-03 from Barcelona city of Spain located at 41.3833° N, 2.1833°, and the classification algorithms i.e. Maximum Likelihood, Parallelepiped, and Mahalanobis Distance classifiers were used for the classification of the SPOT image. A spatial subset of the original imagery was created with resolution half of the original image. In this research, imagery was first atmospherically corrected using QUAC (Quick Atmospheric Correction). The spectral signature of different land classes were extracted using the spectral profile of each individual land class. The whole imagery was then classified using three Classifiers/Decision Rules i.e. Maximum Likelihood, Mahalanobis Distance and Parallelepiped Classifier. The post classification procedures i.e. clump and sieve were applied to the classified imagery to improve classification results. Maximum Likelihood Classifier outperforms other classifiers i.e. Mahalanobis Distance and Parallelepiped Classifier with Overall Accuracy (OAA) of 99.17 per cent. However these classifiers show good accuracy for classification of some classes of interest for instance the Mahalanobis Classifier outperforms the Maximum Likelihood Classifier in classifying water bodies. Results also show that the band selection is also critical in accurate classification of the imagery. The spectrally subsetted images (NIR band removed) of the same place showed very less classification accuracy than that of the original image

The heat extraction from and cooling of computer microprocessors are challenging tasks in the modern era. Previously, the microprocessors were usually cooled by air, but now industry is shifting towards using nanofluids, as their properties are more thermo-physically stable. The experimental and numerical studies have revealed that the rate of heat transfer depends both on the thermal characteristics of the coolant and the geometry of the heat sink. For optimized results, it is recommended to analyze the combined effect of nanofluids and the geometry of the heat sink. Mini-channel heat sinks in combination with a nanofluid offered an excellent rate of heat transfer. However, passing nanofluids continuously through the system causes various problems over time; for example, the thermal stresses on the components are increased, which may lead to wear and tear of the system. In this study, a numerical investigation of mini-channel heat sinks was conducted through thermal-FSI. A numerical model was established with airfoil and Savonius pin-fin mini channel heat sinks, and they were analyzed at different flow rates from 0.25 LPM to 0.75 LPM with an increment of 0.25 LPM with different fluids, i.e., water, Al2O3–H2O, and Fe2O3–H2O nanofluids, varying their volumetric concentration. The minimum stresses were obtained while increasing the temperature drop and decreasing the pressure drop. The thermal stresses were calculated using the thermal-FSI technique and were found to be in the threshold range, and hence the material was within the yield limit at 0.75 LPM when using the Fe2O3-H2O Nanofluid at a 0° angle using the Savonius heat sink.