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Remote sensing techniques are capable of mapping soil nutrient concentrations and preparing soil maps for long-term agricultural productivity and food security. Recently, hyperspectral imaging techniques have been widely used to quantify and map nitrogen levels in the soil in large areas. In this study, we employed a partial least square regression (PLSR) technique over PRISMA hyperspectral data on part of the Radhapuram area, Tirunelveli District, India to improve the accuracy of estimating soil nutrient levels. The results of the study show that the PLSR prediction accuracy rates using field observations provided the most accurate estimates of soil containing organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK). Soil nutrient predictions were carried out using bands in visible near-infrared and near-infrared regions. Analysis of 150 bands using random analyses provided an R2 value of 0.970 and the PLSR technique performed best while using the combined bands in the VNIR+NIR regions. Based on the analyses, PRISMA hyperspectral images using spectral angle mapper (SAM) image classification provided a better map of the soil consisting of organic carbon. The research findings are important references for the prediction of soil nutrients with high accuracy.

Abstract India has its unique and rich cultural heritage. One such uniqueness in India is ancient paintings. Especially in South India, Tanjore paintings are very popular. These paintings are made during 1010 AD with vibrant colours, gold, silver and precious stones. These paintings are the memorabilia of the great Chola kingdom. These paintings can be seen in great Brahadeeshwara Temple walls till now. Damages to these paintings happen due to varying environmental conditions and rituals followed throughout the year. Hence, preserving these heritages could be an additional source in National Cultural Museum and cultural libraries. This paper focuses towards the restoration of such ancient painting images that can be digitized and archived for the future use of aesthete. The painting images are preprocessed using Weiner filter for removing the background noises since its PSNR value is higher than Gaussian and Median filters. The preprocessed image is then applied to restoration algorithm. Two types of restoration algorithm is attempted, image segmentation and in-painting algorithm. The degraded image was restored efficiently with in-painting algorithm than segmentation algorithm. Further research can be focused towards automatic adaptive selection of patch based on the nature of images. From the results it is observed that with in-painting algorithm the image restoration is better than the segmentation algorithm for degraded painting images.

Entrepreneurship is of utmost importance in a country's economic development. Starting one's own business is not easy and it is even more difficult to sustain in this highly competitive environment. This research helps understand the relative importance of challenging factors that hinders the growth of an enterprise resulting in an entrepreneur deciding whether to continue the business or exit it in the early phase itself. The analytic hierarchy process (AHP) is the technique used here to determine the relative position of these factors in persuading an entrepreneur's decision to continue or exit the business. A thorough literature review identified the key challenging factors in early-stage entrepreneurship related to, Finance, Administration, Personal, Product & Market, Environment, and Human Resources. Pairwise comparison is done by collecting the opinions of eight specialists from the industry and from academia, for formulating the reciprocal matrix and analysis using AHP. The finding of this study helps entrepreneurs identify the major hurdles and their impact on the successful launch or growth of their enterprise. On the other side, this research can also be useful for the Government and other support agencies in framing policies, to wipe out hurdles that pave way for a conducive entrepreneurial environment. [ABSTRACT FROM AUTHOR]

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

When morphological capabilities are used for the class of high decision hyperspectral photographs from metropolitan areas, one must not forget two crucial problems. Among which the primary one is that traditional morphological openings and closings degrade the object obstacles and distorts the items shape. Morphological profiles (MP) opening and closing via reconstruction can keep us away from this problem, however this system ends in a few unwanted consequences. In this paper, first check out morphological summaries with subjective restoration and steering MPs for the classification of excessive decision hyperspectral snap shots from city areas. Secondly, broaden a supervised face extraction to lessen the dimensionality of the engendered morphological profiles for the prediction.