Magnetic field variations of the SI in the magnetosphere and correlated effects in interplanetary space

Explorer 26 magnetic field data in the magnetospheric region of L=3?6 and LT 1100–1500 hr with geomagnetic latitude range ?6° to 27° have been analyzed for studying nineteen SI and SC events. Most of the SI events observed in the magnetosphere at less than 15° geomagnetic latitude are compressional with magnetic perturbations along the ambient field. Elliptic polarizations with magnetic field variations in all three components have been observed between 10° and 27° geomagnetic latitude. Polarization directions have been shown to have similar patterns to those observed in the surface magnetic field data. Afternoon LT zone data in the magnetosphere indicate polarization patterns in general agreement with the results of Wilson and Sugiura (1961) obtained earlier from surface observations. The SI/SC perturbations are also qualitatively shown to be related to changes in the interplanetary magnetic field observed beyond 1 a.u.     

Boundary-layer development on circular cylinders

Mean-velocity measurements were made in the boundary layer on a smooth circular cylinder fitted with trip wires to model supercritical flow. The data are compared with the predictions of a boundary-layer calculation method to study the relative influence of surface curvature and low-Reynolds-number effects, and to assess the accuracy with which the location of separation can be determined. It is shown that the effects of surface curvature are most important close to separation while those of low Reynolds numbers are significant everywhere except near separation.     

Novel Algorithm for RS Image Classification

In the present era of modern technology, the efficacy and accuracy of output is demanding. Based on rigorous survey, Bharatkar and Patel (International Journal of Advanced Research in Computer Science 3(7):218–223, 2012) concluded that the incorporation of Block Truncation Coding (BTC) approach in the existing image classification algorithm can be used to improve the classification accuracy. Therefore, in the present study, an effort has been made to explore the Content Based Remote Sensing Image (CBRSI) classification algorithm to enhance classification accuracy with BTC approach. It is revealed from the study that BTC based maximum likelihood classifier gives better overall accuracy and kappa statistics.     

Mapping a Specific Crop—A Temporal Approach for Sugarcane Ratoon

Mapping a specific crop using single date multi-spectral imagery remains a challenging task because vegetation spectral responses are considerably similar. The use of multi-temporal images helps to discriminate specific crops as the classifier can make use of the uniqueness in the temporal evolution of the spectral responses of the different vegetated classes. However, one major concern in multi-temporal studies is the selection of optimum dates for the discrimination of crops as the use of all available temporal dates can be counterproductive. In this study this concern was addressed by selecting the best 2, 3, 4… combinations dates. This was done by conducting a separability analysis between the spectral response of the class of interest (here, sugarcane-ratoon) and non-interest classes. For this analysis, we used time series LISS-III and AWiFS sensors data that were classified using Possibilistic c-Means (PCM). This fuzzy classifier can extract single class sub-pixel information. The end result of this study was the detection of best (optimum) temporal dates for discriminating a specific crop, sugarcane-ratoon. An accuracy of 92.8 % was achieved for extracting ratoon crop using AWiFS data whereas the optimum temporal LISS-III data provided a least entropy of 0.437. Such information can be used by agricultural department in selecting an optimum number of strategically placed temporal images in the crop growing season for discriminating the specific crop accurately.     

Ichnology of the Goradongar Formation,Goradongar hill range,Patcham island,Kachchh, Western India

The middle Jurassic Goradongar Formation exposed in Goradongar hill represents a mixed siliciclasticcarbonate succession with shales and limestones. They contain a large number of well preserved trace fossils. Total 44 ichnospecies of 31 ichnogenera; representing diverse ethology, were grouped in five ichnoassemblages (Planolites, Palaeophycus, Gyrochorte, Rhizocorallium and Arenicolites assemblage). These recurring ichnoassemblages represent the Cruziana ichnofacies and occasionally a mixed Skolithos-Cruziana ichnofacies. Patterns of diversity and density of the trace fossils reveal changes in bathymetry, oxygen level, trophic level and the sub-strate conditions at the time of deposition. These paleoenvironment and palaeo-oceanography changes are co-relatable to world-wide Bathonian-Callovian (middle Jurassic) deposits.     

Desertification in western Rajasthan (India): an assessment using remote sensing derived rain-use efficiency and residual trend methods

Natural Hazards - Owing to its impact on global ecosystem, climate change and related effects is being perceived as a serious issue worldwide especially in the arid and semi-arid regions. Climatic...     

Post-emplacement kinematics and exhumation history of the Almora klippe of the Kumaun–Garhwal Himalaya,NW India: revealed by fission track thermochronology

International Journal of Earth Sciences - Tectonically transported crystalline thrust sheet over the Lesser Himalayan meta-sedimentary zone along the Main Central Thrust (MCT) is represented by...     

Hook-shaped type-3 superposed fold in granulite,Badarama complex,Rengali province,India

International Journal of Earth Sciences -     

Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naïve Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods.