Volume 11, Number 3

Research in agriculture is increasing quality and quantity, but pest reduces it. To prevent the effect of these pests, insecticides are used. But excessive use of pesticides is very harmful to production and environment. So initially pest detection is necessary. We work on nocturnal pests because that can be easily attracting using night trapping tools. The purpose of this review article is to analyse the popular techniques and find the right technique for the initial diagnosis and early detection of major nocturnal flying pests like Pink Bollworm, White Grub, Helicoverpa and Spodoptera. The importance of early detection can be in identifying and classifying the pests in a digital view. We have concluded our results with the various methods and the prospects of future research.

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Fifty years ago instrumentation and control (I&C) systems at nuclear power plants (NPP) were analog and relied on a mixture of mechanical, pneumatic and electric components. Today analog technology has been replaced with digital technology. Digital I&C has over the years experienced difficulties in the licensing process, which has delayed and escalated costs of both NPP and I&C projects. In the paper it is argued that some of the difficulties are connected to misunderstandings regarding differences between analog and digital I&C. These misunderstandings have led to unrealistic expectations regarding proofs that selected I&C systems can be considered acceptable. To ensure a successful licensing process it would be necessary to agree on evidence for safety that can be considered sufficient. Such evidence should be collected both from the I&C design process and from testing intermediate and final I&C solutions. By a combination of evidence from different sources it should be possible to build a safety case that can be agreed to give sufficient proofs for acceptability. The first component in building the safety case is to make use of safety principles to provide structural evidence that certain classes of design errors have been avoided. The second component is to use simulators and targeted testing to demonstrate functionality of the I&C in different plant situations.

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Breast cancer affecting the women is known to cause high mortality unless detected in right time. Detection requires Mammography followed by biopsy of the tumour or lesions present in the breast tissue. Contemporary Mammographic hardware has incorporated digitization of output imagesfor increasing the scope for implementation of computational methods towards Computer Aided Diagnostics (CAD).CAD systems require Medical Image Processing, a multi-disciplinary science that involves development of computational algorithms on medical images. Histopathological slides are examined for determination of malignancy after biopsy is performed. Digital Images are required to be registered and enhanced prior to application of any deterministic algorithm. This paper provides both effective and efficient improvements over existing algorithms and introduces some innovative ideas based on image segmentation process to develop computer aided diagnosis tools that can help the radiologists in making accurate interpretation of the digital mammograms.

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Segmentation algorithms play an important role in image processing and analysis. The identification of objects and process monitoring strongly depends on the accuracy of the segmentation algorithms. Waste water treatment plants are used to treat wastewater from municipal and industrial plants. Activated sludge process is used in wastewater treatment plants to biodegrade the organic constituents present in waste water. This biodegradation is done with the help of microorganisms and bacteria. There are two important types of microscopic organisms present in the activated sludge plants, named as flocs as filaments, which are visible under microscope. In this paper we study the microscopic images of wastewater using phase contrast microscopy. The images are acquired from wastewater sample using a microscope. The samples of wastewater are collected from domestic wastewater treatment plant aeration tank. Our main aim is to segment threadlike organisms knows as filaments. Several segmentation algorithms (such as edge based algorithm, k-means algorithm, texture based algorithm, and watershed algorithm) will be explored and their performance will be compared using gold approximations of the images. The performance of the algorithms are evaluated using different performance metrics, such as Rand Index, specificity, variation of information, and accuracy. We have found that edge based segmentation works well for phase contrast microscopic images of activated sludge wastewater.

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