Heavy metals occur in immobilized form in sediments and as ores in nature. However due to various human activities like ore mining and industrial processes the natural biogeochemical cycles are disrupted causing increased deposition of heavy metals in terrestrial and aquatic environment. Release of these pollutants without proper treatment poses a significant threat to both environment and public health, as they are non biodegradable and persistent. Through a process of biomagnification, they further accumulate in food chains. Thus their treatment becomes inevitable and in this endeavor, biosorption seems to be a promising alternative for treating metal contaminated waters. This technology employs various types of biomass as source to trap heavy metals in contaminated waters. The biosorbent is prepared by subjecting biomass to various processes like pretreatment, granulation and immobilization, finally resulting in metal entrapped in bead like structures. These beads are stripped of metal ions by desorption which can be recycled and reused for subsequent cycles. This technology out-performs its predecessors not only due to its cost effectiveness but also in being ecofriendly i.e., where other alternatives fail.

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Image Retrieval system is an effective and efficient tool for managing large image databases. A content based image retrieval system allows the user to present a query image in order to retrieve images stored in the database according to their similarity to the query image. In this paper content based image retrieval method is used on digital image data set. The main objective of this paper is to evaluate the retrieval system based on Hybrid features. The texture features are extracted by using pyramidal wavelet transform and the shape features are extracted by using Fourier descriptor. And the hybrid technique is the combination of both texture and shape. The major advantage of such an approach is that little human intervention is required. It is ascertained that the performance is superior when the image retrieval based on the Hybrid features, and better results than primitive set.

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