RMAC-M: Extending the R-MAC Protocol for an Energy Efficient, Delay Tolerant Underwater Acoustic Sensor Network application with a mobile data mule node

One of the major applications of underwater acoustic sensor networks (UWASN) is ocean environment monitoring. Employing data mules is an energy efficient way of data collection from the underwater sensor nodes in such a network. A data mule node such as an autonomous underwater vehicle (AUV) periodically visits the stationary nodes to download data. By conserving the power required for data transmission over long distances to a remote data sink, this approach extends the network life time. In this paper we propose a new MAC protocol to support a single mobile data mule node to collect the data sensed by the sensor nodes in periodic runs through the network. In this approach, the nodes need to perform only short distance, single hop transmission to the data mule. The protocol design discussed in this paper is motivated to support such an application. The proposed protocol is a hybrid protocol, which employs a combination of schedule based access among the stationary nodes along with handshake based access to support mobile data mules. The new protocol, RMAC-M is developed as an extension to the energy efficient MAC protocol R-MAC by extending the slot time of R-MAC to include a contention part for a hand shake based data transfer. The mobile node makes use of a beacon to signal its presence to all the nearby nodes, which can then hand-shake with the mobile node for data transfer. Simulation results show that the new protocol provides efficient support for a mobile data mule node while preserving the advantages of R-MAC such as energy efficiency and fairness.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

Optically transparent magnetic nanocomposites based on encapsulated Fe3O4 nanoparticles in a sol–gel silica network

Composite Fe3O4–SiO2 materials were prepared by the sol–gel method with tetraethoxysilane and aqueous-based Fe3O4 ferrofluids as precursors. The monoliths obtained were crack free and showed both optical and magnetic properties. The structural properties were determined by infrared spectroscopy, x-ray diffractometry and transmission electron microscopy. Fe3O4 particles of 20 nm size lie within the pores of the matrix without any strong Si–O–Fe bonding. The well established silica network provides effective confinement to these nanoparticles. The composites were transparent in the 600–800 nm regime and the field dependent magnetization curves suggest that the composite exhibits superparamagnetic characteristics

Re-Engineering Towed Arrays for Quality Enhancement - Network Based Towed Array

The Towed Array electronics is a multi-channel simultaneous real time high speed data acquisition system. Since its assembly is highly manpower intensive, the costs of arrays are prohibitive and therefore any attempt to reduce the manufacturing, assembly, testing and maintenance costs is a welcome proposition. The Network Based Towed Array is an innovative concept and its implementation has remarkably simplified the fabrication, assembly and testing and revolutionised the Towed Array scenario. The focus of this paper is to give a good insight into the Reliability aspects of Network Based Towed Array. A case study of the comparison between the conventional array and the network based towed array is also dealt with

Implementation of a Neural Network Classifier for Noise Sources in the Ocean

The paper investigates the feasibility of implementing an intelligent classifier for noise sources in the ocean, with the help of artificial neural networks, using higher order spectral features. Non-linear interactions between the component frequencies of the noise data can give rise to certain phase relations called Quadratic Phase Coupling (QPC), which cannot be characterized by power spectral analysis. However, bispectral analysis, which is a higher order estimation technique, can reveal the presence of such phase couplings and provide a measure to quantify such couplings. A feed forward neural network has been trained and validated with higher order spectral features