M. H. M. Krishna Prasad

Quality Assessment of Pixel-Level ImageFusion Using Fuzzy Logic

Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported methods, wavelet transform based image fusion and weighted average discrete wavelet transform based image fusion using genetic algorithm.

Enhanced Cluster Based Routing Protocol for MANETS

Mobile ad-hoc networks (MANETs) are a set of self organized wireless mobile nodes that works without any predefined infrastructure. For routing data in MANETs, the routing protocols relay on mobile wireless nodes. In general, any routing protocol performance suffers i) with resource constraints and ii) due to the mobility of the nodes. Due to existing routing challenges in MANETs clustering based protocols suffers frequently with cluster head failure problem, which degrades the cluster stability. This paper proposes, Enhanced CBRP, a schema to improve the cluster stability and in-turn improves the performance of traditional cluster based routing protocol (CBRP), by electing better cluster head using weighted clustering algorithm and considering some crucial routing challenges. Moreover, proposed protocol suggests a secondary cluster head for each cluster, to increase the stability of the cluster and implicitly the network infrastructure in case of sudden failure of cluster head.

Quality evaluation measures of pixel - level image fusion using fuzzy logic

Image fusion is a technique to combine the registered images to increase the spatial resolution of acquired low detail multi-sensor images and preserving their spectral information. In fusing panchromatic and multispectral images, the objective is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information. Different fusion methods provide different results for different applications, medical imaging, automatic target guidance system, remote sensing, machine vision, automatic change detection, and biometrics. In this paper, we utilize a fuzzy logic approach to fuse images from different sensors, in order to enhance visualization. The work here further explores the comparison between image fusion using wavelet transform and fuzzy logic approach along with performance/quality evaluation measures like image quality index, entropy, mutual information measure, root mean square error, peak signal to noise ratio, fusion factor, fusion symmetry and fusion index. Experimental results prove that the use of the proposed method can efficiently preserve the spectral information while improving the spatial resolution of the remote sensing images.

Efficient DNA-Based Cryptographic Mechanism to Defend and Detect Blackhole Attack in MANETs

This paper addresses a novel method to detect and defend against the blackhole attack and cooperative blackhole attack using hybrid DNA-based cryptography (HDC) mechanism. Moreover, the proposed method upsurge the security issue with the underlying AODV routing protocol. Eventually, this HDC is one of the high potential candidates for advanced wireless ad hoc networks, which require less communication bandwidth and memory in comparison with other cryptographic systems. The simulation results of this proposed method provide better security and network performances as compared to existing schemes.

Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique

Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software reuse is a process in which the existing software is used to build new software. A metric is a quantitative indicator of an attribute of an item/thing. Reusability is the likelihood for a segment of source code that can be used again to add new functionalities with slight or no modification. A lot of research has been projected using reusability in reducing code, domain, requirements, design etc., but very little work is reported using software reuse in medical domain. An attempt is made to bridge the gap in this direction, using the concepts of clustering and classifying the data based on the distance measures. In this paper cardiologic database is considered for study. The developed model will be useful for Doctors or Para-medics to find out the patient’s level in the cardiologic disease, deduce the medicines required in seconds and propose them to the patient. In order to measure the reusability K-means clustering algorithm is used.

Light-Weighted DNA-Based Cryptographic Mechanism Against Chosen Cipher Text Attacks

DNA cryptography is a new cryptographic paradigm from hastily growing biomolecular computation, as its computational power will determine next generation computing. As technology is growing much faster, data protection is getting more important and it is necessary to design the unbreakable encryption technology to protect the information. In this paper, we proposed a biotic DNA-based secret key cryptographic mechanism, seeing as DNA computing had made great strides in ultracompact information storage, vast parallelism, and exceptional energy efficiency. This Biotic Pseudo DNA cryptography method is based upon the genetic information on biological systems. This method makes use of splicing system to improve security and random multiple key sequence to increase the degree of diffusion and confusion, which makes resulting cipher texts difficult to decipher and makes to realize a perfect secrecy system. Moreover, we also modeled the DNA-assembled public key cryptography for effective storage of public key as well as double binded encryption scheme for a given message. The formal and experimental analysis not only shows that this method is powerful against brute force attack and chosen cipher text attacks, but also it is very efficient in storage, computation as well as transmission.

A Comparative Study of Fractal Dimension Based Age Group Classification of Facial Images with Different Testing Strategies

The demand of estimation of age from facial images has tremendous applications in real world scenario like law enforcement, security control, and human computer interaction etc. However despite advances in automatic age estimation, the computer based age classification has become prevalent. The present paper evaluates the method of age group classification based on the Correlation Fractal Dimension (FD) of facial image using different validation techniques. To reduce variability, multiple rounds of cross validation are performed using different partitions to the data. The expected level of fit of the model classifying facial images into four categories based on FD value of a facial edge is estimated using multiple cross-validation techniques. The simulation is carried out and results are analyzed on different images from FG-NET database, Google database and from the scanned photographs as these are random in nature and help to indicate the efficiency and reliability of the proposed method. It is also a successful demonstration that Correlation Fractal Dimension of a facial edge is sufficient for a classification task with high percentage of classification accuracy.

Software Reuse in Medical Database for Cardiac Patients using Pearson Family Equations

Software reuse is a subfield of software engineering that is used to adopt the existing software for similar purposes. Reuse Metrics determine the extent to which an existing software component is reused in new software with an objective to minimize the errors and cost of the new project. In this paper, medical database related to cardiology is considered. The Pearson Type-I Distribution is used to calculate the probability density function (pdf) and thereby utilizing it for clustering the data. Further, coupling methodology is used to bring out the similarity of the new patient data by comparing it with the existing data. By this, the concerned treatment to be followed for the new patient is deduced by comparing with that of the previous patients’ case history. The metrics proposed by Chidamber and Kemerer are utilized for this purpose. This model will be useful for the medical field through software, particularly in remote areas.

An Empirical Study on Community Detection Algorithms

Social networks are simply networks of social interactions and personal relationships. They have several properties, and community is one among them. These communities can be arranged by individuals in such a way that within the group they can connect more frequently compared to the outside of the group. Community detection can discover groups within a network where individuals’ group memberships are not explicitly given. These networks are represented in the form of graph. When graph size is increased then the number of communities will also be increased. Because of this complexity and dynamic nature of the graph, community detection in social network becomes a challenging task. Hence, more research is going on community detection, resulting in plenty of algorithms that come into picture to find effective way of detecting communities in a graph. In this paper, authors have presented different community detection algorithms and also discussed their pros and cons. Finally, authors stated some of the research challenges in this area.

Access the number of speakers through visual access tendency for effective speech clustering

Speech clustering group the unlabeled speech utterances according to their similarity features and it requires prior information about number of speakers before assigning every speech utterance into its respective speaker cluster. Determine the number of speakers of speech dataset is a primary problem of speech clustering. Most methods follow the post clustering ideas for evaluation of number of speakers. After the recent study of cluster (or speakers) detection methods, it is found that visual access tendency (VAT) is most suitable approach for assessing the number of speakers information. However, it needs speaker model parameters for finding an accurate speakers information. By this motivation, the VAT is extended with Gaussian mixture model (GMM) for deriving of speakers information with model parameters. In the proposed work, speech data (i.e. speaker utterances or segment) is modeled by GMM, which derives GMM mean supervectors. Dissimilarity features are derived for a set of GMM mean supervectors in VAT for effective speech clustering. The GMM mean supervectors are high-dimensional and this dimensionality problem is addressed by generating intermediate vectors (i-vectors). Efficiency of proposed methods is demonstrated in the experimental study by real time datasets.Speech clustering group the unlabeled speech utterances according to their similarity features and it requires prior information about number of speakers before assigning every speech utterance into its respective speaker cluster. Determine the number of speakers of speech dataset is a primary problem of speech clustering. Most methods follow the post clustering ideas for evaluation of number of speakers. After the recent study of cluster (or speakers) detection methods, it is found that visual access tendency (VAT) is most suitable approach for assessing the number of speakers information. However, it needs speaker model parameters for finding an accurate speakers information. By this motivation, the VAT is extended with Gaussian mixture model (GMM) for deriving of speakers information with model parameters. In the proposed work, speech data (i.e. speaker utterances or segment) is modeled by GMM, which derives GMM mean supervectors. Dissimilarity features are derived for a set of GMM mean supervectors in VAT for effective speech clustering. The GMM mean supervectors are high-dimensional and this dimensionality problem is addressed by generating intermediate vectors (i-vectors). Efficiency of proposed methods is demonstrated in the experimental study by real time datasets.