Triclosan (TCS) is a multi-purpose antimicrobial agent used as a common ingredient in everyday household personal care and consumer products. The expanded use of TCS provides a number of pathways for the compound to enter the environment and it has been detected in sewage treatment plant effluents; surface; ground and drinking water. The physico-chemical properties indicate the bioaccumulation and persistence potential of TCS in the environment. Hence, there is an increasing concern about the presence of TCS in the environment and its potential negative effects on human and animal health. Nevertheless, scarce monitoring data could be one reason for not prioritizing TCS as emerging contaminant. Conventional water and wastewater treatment processes are unable to completely remove the TCS and even form toxic intermediates. Considering the worldwide application of personal care products containing TCS and inefficient removal and its toxic effects on aquatic organisms, the compound should be considered on the priority list of emerging contaminants and its utilization in all products should be regulated.

Proteases are ubiquitous enzymes that occur in various biological systems ranging from microorganisms to higher organisms. Microbial proteases are largely utilized in various established industrial processes. Despite their numerous industrial applications, they are not efficient in hydrolysis of recalcitrant, protein-rich keratinous wastes which result in environmental pollution and health hazards. This paved the way for the search of keratinolytic microorganisms having the ability to hydrolyze "hard to degrade" keratinous wastes. This new class of proteases is known as "keratinases". Due to their specificity, keratinases have an advantage over normal proteases and have replaced them in many industrial applications, such as nematicidal agents, nitrogenous fertilizer production from keratinous waste, animal feed and biofuel production. Keratinases have also replaced the normal proteases in the leather industry and detergent additive application due to their better performance. They have also been proved efficient in prion protein degradation. Above all, one of the major hurdles of enzyme industrial applications (cost effective production) can be achieved by using keratinous waste biomass, such as chicken feathers and hairs as fermentation substrate. Use of these low cost waste materials serves dual purposes: to reduce the fermentation cost for enzyme production as well as reducing the environmental waste load. The advent of keratinases has given new direction for waste management with industrial applications giving rise to green technology for sustainable development.

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Automatic Number Plate Recognition (ANPR) is an imageprocessing technology that identifies vehicles by their number plates without direct human intervention. It is an application of computer vision and important area of research due to its many applications. The main process of ANPR is divided into four stages. This paper presents a simple and efficient method for the extraction of number plate from the vehicle image based on morphological operations, thresholding and sobel edge detection, and the connected component analysis.

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Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases:-Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.

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