Earth Science research in India evolved over the years in tune with the trends in the global scenario from studying the geologic attributes and assessing the natural resources to addressing issues related to fundamental processes involving, for example, crustal evolution, geodynamics and other aspects of contemporary Earth Science problems. Apart from various Earth Science institutions the Department of Science and Technology (DST), Govt. of India, through the SERC (now SERB) has been sponsoring Earth Science research projects in the country, and thus playing a catalytic role by providing extra-mural funding. Although the DST started funding Earth Science projects since 1985, the extent of such support increased in the 1990s and thereafter with sponsoring of a number of specialized thematic Earth Science programmes. As a result of these initiatives a prodigious amount of data has been generated that are contained in the Project Completion Reports (PCR). An analysis of the database prepared from 440 PCRs for the period 1993-2015 indicated that the projects dealing mainly with 17 themes increased almost steadily in number from 1993 to 2012 with a slight decreasing trend till 2015. A similar trend is exhibited by the yearly total project funding and the average cost per project. These aspects and the relation between the number of projects under different themes, papers published, PhDs produced and scientific and technical personnel trained are discussed here.
Eye detection is a pre-requisite stage for many applications such as human-computer interfaces, iris recognition, driver drowsiness detection, security, and biology systems. In this paper, template based eye detection is described. The template is correlated with different regions of the face image. The region of face which gives maximum correlation with template refers to eye region. The method is simple and easy to implement. The effectiveness of the method is demonstrated in both the cases like open eye as well as closed eye through various simulation results.
Signal enhancement is useful in many areas like social, medicine and engineering. It can be utilized in data mining approach for social and security aspects. Signal decomposition method is an alternative choice due to the elimination of noise and signal enhancement. In this paper, two different algorithms such as Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are used. The bands are updated concurrently and adaptively in each mode. That performs better than the traditional methods for non-recursive signals. Further it has been investigated that VMD outperforms EMD due to its self-optimization methods as well as adaptively using Wiener filter. It is shown in the result section. Different noise levels as 0dB, 5dB, 10dB and 15dB are considered for input signal.
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