A Short Survey on Perceptual Hash Function

The authentication of digital image has become more important as these images can be easily manipulated by using image processing tools leading to various problems such as copyright infringement and hostile tampering to the image contents. It is almost impossible to distinguish subjectively which images are original and which have been manipulated. There are several cryptographic hash functions that map the input data to short binary strings but these traditional cryptographic hash functions is not suitable for image authentication as they are very sensitive to every single bit of input data. When using a cryptographic hash function, the change of even one bit of the original data results in a radically different value. A modified image should be detected as authentic by the hash function and at the same time must be robust against incidental and legitimate modifications on multimedia data. The main aim of this paper is to present a survey of perceptual hash functions for image authentication.Keywords: Hash function, image authentication*Cite as: Arambam Neelima, Kh. Manglem Singh, “A Short Survey on Perceptual Hash Function†ADBU-J.Engg Tech, 1(2014) 0011405(8pp

Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach

Every energy system which we consider is an entity by itself, defined by parameters which are interrelated according to some physical laws. In recent year tremendous importance is given in research on site selection in an imprecise environment. In this context, decision making for the suitable location of power plant installation site is an issue of relevance. Environmental impact assessment is often used as a legislative requirement in site selection for decades. The purpose of this current work is to develop a model for decision makers to rank or classify various power plant projects according to multiple criteria attributes such as air quality, water quality, cost of energy delivery, ecological impact, natural hazard, and project duration. The case study in the paper relates to the application of multilayer perceptron trained by genetic algorithm for ranking various power plant locations in India

A survey of Multi-Criteria Decision Making Technique used in Renewable Energy Planning

Fossil based oil, gas and coal reserves will exhaust in few decades and the accelerated demand for conventional energy have forced planners and policy makers to look for alternate sources of Energy. Renewable energies option serves as a solutions for a sustainable, environmentally friendly and long-term cost effective sources of energies to meet our ever increasing needs of energy.  Renewable energy sites selection can be viewed as a Multiple Criteria Decision Making (MCDM) problem. MCDM is a complex Decision Making (DM) tools as it involves both quantitative and qualitative criteria. In recent years, several MCDM techniques and approaches have been suggested to solve energy planning problems. The main objective of this paper is to systematically review MCDM techniques and approaches in sustainable and renewable energy planning problems. A review of more than 100 published papers based on MCDM analysis is studied and presented in this paper. Findings of this review paper confirm that MCDM techniques can assist stakeholders and decision makers in unravelling some of the uncertainties inherent in renewable energy decision making. Classification of methodology used, criteria selection and application area are summarized and presented

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter

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Not AvailableCounting the total number of pigs manually on a large-scale pig farm is a crucial and inefficient task. As this process is time-consuming and includes many critical points that can lead to miscalculation. Some of the challenging issues in pig counting include overlapping, partial occlusion, different viewpoint that limits the usage of traditional object detection methods. Image segmentation is used for object detection, which separate foreground and background pixels of the images. In this paper, we used Marker-Controlled Watershed segmentation method for counting pig in an image. Here, different image thresholding techniques such as Otsu threshold, Adaptive threshold and manual threshold is considered. The structural similarity of these thresholding techniques is determined using jaccards coefficient index. Otsu threshold gives the best similarity scores. The average processing time of these thresholding techniques is also determined. Further, the images obtained from Otsu threshold is checked for overlapping objects. In case of image with overlapping objects, the segmentation is done using marker-controlled watershed segmentation algorithm to segregate the overlapping objects and label the objects individually. In case of non overlapping, objects present in the images obtained from Otsu threshold are label directly to count the number of pigs present in the image. Hence, this segmentation process provides an efficient way for counting pigs in an image.Not Availabl