Drag and inertia coefficients for horizontally submerged rectangular cylinders in waves and currents

The results of an experimental investigation carried out to measure combined wave and current loads on horizontally submerged square and rectangular cylinders are reported in this paper. The wave and current induced forces on a section of the cylinders with breadth-depth (aspect) ratios equal to 1, 0.5, and 0.75 are measured in a wave tank. The maximum value of Keulegan-Carpenter (KC) number obtained in waves alone is about 5 and Reynolds (Re) number ranged from 6.3976103 to 1.186105. The drag (CD) and inertia (CM) coefficients for each cylinder are evaluated using measured sectional wave forces and particle kinematics calculated from linear wave theory. The values of CD and CM obtained for waves alone have already been reported (Venugopal, V., Varyani, K. S., and Barltrop, N. D. P. Wave force coefficients for horizontally submerged rectangular cylinders. Ocean Engineering, 2006, 33, 11-12, 1669-1704) and the coefficients derived in combined waves and currents are presented here. The results indicate that both drag and inertia coefficients are strongly affected by the presenceof the current and show different trends for different cylinders. The values of the vertical component inertia coefficients (CMY) in waves and currents are generally smaller than the inertia coefficients obtained in waves alone, irrespective of the current's magnitude and direction. The results also illustrate the effect of a cylinder's aspect ratio on force coefficients. This study will be useful in the design of offshore structures whose columns and caissons are rectangular sections

Targeted shark fishery in Kerala

The report deals with gear used for shark fishing, landings and catch compostion in Cochin Fisheries Harbour during the last quarter of 2002

Bimodal Biometric Verification Mechanism using fingerprint and face images(BBVMFF)

An increased demand of biometric authentication coupled with automation of systems is observed in the recent times. Generally biometric recognition systems currently used consider only a single biometric characteristic for verification or authentication. Researchers have proved the inefficiencies in unimodal biometric systems and propagated the adoption of multimodal biometric systems for verification. This paper introduces Bi-modal Biometric Verification Mechanism using Fingerprint and Face (BBVMFF). The BBVMFF considers the frontal face and fingerprint biometric characteristics of users for verification. The BBVMFF Considers both the Gabor phase and magnitude features as biometric trait definitions and simple lightweight feature level fusion algorithm. The fusion algorithm proposed enables the applicability of the proposed BBVMFF in unimodal and Bi-modal modes proved by the experimental results presented

An efficient cloud based architecture for integrating content management systems

The use of digital content is increasing day after day and now it is an essential element of our day today life. The amount of stored information is so huge that it is highly difficult to manage the content especially in a distributed cloud environment. There are many open source software solutions available in cloud to handle huge amount of digital data. However none of these solutions addresses all the requirements needed to manage the content spread out in multiple systems effectively. The user has to relay on multiple content management systems to do the work. This turns into ever more unwieldy, time consuming and leads to loss of data. Using robust and integrated content management systems, these issues could be solved effectively. In this paper we have identified various challenges of using the content management system in the cloud after surveying many Content Management System related article and proposed an integrated solution named Cloud based Architecture integrating Content Management System which is capable of interfacing with various unique features available at different content management system installations in the cloud. This maximizes the functionality and performance of any Content management systems. The Representational State Transfer (REST) protocol is used to integrate the best features of various open source content management systems. REST provides higher level of security compared to existing systems as it does not store the user sessions. The users can interact with the system with the help of an interface which abstracts the complexities of multiple content management systems running in the cloud. © 2017 IEEE

Molecular cloning of growth hormone encoding cDNA of Indian major carps by a modified rapid amplification of cDNA ends strategy

A modified rapid amplification of cDNA ends (RACE) strategy has been developed for cloning highly conserved cDNA sequences. Using this modified method, the growth hormone (GH) encoding cDNA sequences of Labeo rohita, Cirrhina mrigala and Catla catla have been cloned, characterized and overexpressed in Escherichia coli. These sequences show 96-98% homology to each other and are about 85% homologous to that of common carp. Besides, an attempt has been made for the first time to describe a 3-D model of the fish GH protein

Trust model genetic node recovery based on cloud theory for underwater acoustic sensor network

Underwater Acoustic Sensor Networks [UASNs] are becoming a very growing research topic in the field of WSNs. UASNs are harmful by many attacks such as Jamming attacks at the physical layer, Collision attacks at the data link layer and Dos attacks at the network layer. UASNs has a unique characteristic such as unreliable communication, mobility, and computation of underwater sensor network. Because of this the traditional security mechanism, e.g. cryptographic, encryption, authorization and authentications are not suitable for UASNs. Many trust mechanisms of TWSNs [Terrestrial Wireless Sensor Networks] had proposed to UASNs and failed to provide security for UASNs environment, due to dynamic network structure and weak link connection between sensors. In this paper, a novel Trust Model Genetic Algorithm based on Cloud Theory [TMC] for UASNs has been proposed. The TMC-GA suggested a genetic node recovery algorithm to improve the TMC network in terms of better network lifetime, residual energy and total energy consumption. Also ensures that sensor nodes are participating in the rerouting in the routing discovery and performs well in terms of successful packet delivery. Simulation result provides that the proposed TMC-Genetic node recovery algorithm outperforms compared to other related works in terms of the number of hops, end-to-end delay, total energy consumption, residual energy, routing overhead, throughput and network lifetime

EDSC: Efficient document subspace clustering technique for high-dimensional data

With the advancement in the pervasive technology, there is a spontaneous rise in the size of the data. Such data are generated from various forms of resources right from individual to organization level. Due to the characteristics of unstructured or semi-structuredness in data representation, the existing data analytics approaches are not directly applicable which leads to curse of dimensionality problem. Hence, this paper presents an Efficient Document Subspace Clustering (EDSC) technique for high-dimensional data that contributes to the existing system with respect to identification by eliminating the redundant data. The discrete segmentation of data points are used to explicitly expose the dimensionality of hidden subspaces in the clusters. The outcome of the proposed system was compared with existing system to find the effective document clustering process for high-dimensional data. The processing time of EDSC for subspace clustering is reduced by 50% as compared to the existing system

RMSC: Robust Modeling of Subspace Clustering for high dimensional data

Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel technique called as Robust Modeling of Subspace Clustering (RMSC) presented to solve the above problem. An analytical research methodology is used to formulate two algorithms for computing outliers and for extracting elite subspace from the highdimensional data inflicted by different forms of noise. RMSC was found to offer higher accuracy and lower error rate both in presence of noise and absence of noise over high-dimensional data. © 2017 IEEE