Energy and Nutrient Intake Monitoring

A passive system to determine the in-flight intake of nutrients is developed. Nonabsorbed markers placed in all foods in proportion to the nutrients selected for study are analyzed by neutron activation analysis. Fecal analysis for each market indicates how much of the nutrients were eaten and apparent digestibility. Results of feasibility tests in rats, mice, and monkeys indicate the diurnal variation of several markers, the transit time for markers in the alimentary tract, the recovery of several markers, and satisfactory use of selected markers to provide indirect measurement of apparent digestibility. Recommendations are provided for human feasibility studies

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

Interrelationship between cloud cover and sensible heat flux over land during MONTBLEX-1990

Micro-meteorological tower observations of MONTBLEX (Monsoon Trough Boundary Layer Experiment)- 1990, combined with routine surface observations at Jodhpur in the dry convective sector of Indian summer monsoon trough are used to examine the interrelationship between total cloud cover (TCC) and surface sensible heat flux (SHF) during the summer monsoon of 1990. A significant inverse relationship between TCC and SHF is found during various Intensive Observation Periods of the experiment. This relationship holds for the various methods of estimation of SHF. © Printed in India

Two-Hop Routing with Traffic-Differentiation for QoS Guarantee in Wireless Sensor Networks

This paper proposes a Traffic-Differentiated Two-Hop Routing protocol for Quality of Service (QoS) in Wireless Sensor Networks (WSNs). It targets WSN applications having different types of data traffic with several priorities. The protocol achieves to increase Packet Reception Ratio (PRR) and reduce end-to-end delay while considering multi-queue priority policy, two-hop neighborhood information, link reliability and power efficiency. The protocol is modular and utilizes effective methods for estimating the link metrics. Numerical results show that the proposed protocol is a feasible solution to addresses QoS service differenti- ation for traffic with different priorities.Comment: 13 page

DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams

Similarity matching and join of time series data streams has gained a lot of relevance in today's world that has large streaming data. This process finds wide scale application in the areas of location tracking, sensor networks, object positioning and monitoring to name a few. However, as the size of the data stream increases, the cost involved to retain all the data in order to aid the process of similarity matching also increases. We develop a novel framework to addresses the following objectives. Firstly, Dimension reduction is performed in the preprocessing stage, where large stream data is segmented and reduced into a compact representation such that it retains all the crucial information by a technique called Multi-level Segment Means (MSM). This reduces the space complexity associated with the storage of large time-series data streams. Secondly, it incorporates effective Similarity Matching technique to analyze if the new data objects are symmetric to the existing data stream. And finally, the Pruning Technique that filters out the pseudo data object pairs and join only the relevant pairs. The computational cost for MSM is O(l*ni) and the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction Factor. We have performed exhaustive experimental trials to show that the proposed framework is both efficient and competent in comparison with earlier works.Comment: 20 pages,8 figures, 6 Table

Object Detection using Deep Learning with Hierarchical Multi Swarm Optimization

Till now there is a huge research had in the field of visual information retrieval, but with the growth of data and with less processing speed we are not meeting the needs of current problem. The main focus of this paper is to identify the objects with salient features and object highlighting. Till now object identification is done with the pixel based or with the region based. Different methodologies are compared in this work and they will be processed with the learning work. Multi scale contrast is one of the pixel based technology where object borders are identified but not the object. This can be done with the histogram contrast. Still it is not covering all the features of the object and it is not clear in identifying the objects at high contrast regions. To solve this issue region based contrasting method is used which is the better solution for all this object identification. After extracting the features and identifying the object, now auto classification or identification of the object should be done. The other part of the work mainly concentrates on the learning system which uses most popular neural network algorithms. Identifying the drawbacks of neural network algorithms and proposing the new methodology identify the objects is done in this paper

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