Paper Type	::	Review Paper
Title	::	An Energy and Spectral Efficient In Underwater Communication Using Magneto Inductive Channel
Country	::	India
Authors	::	Kalpana.K,
Page No.	::	01-07
	::	10.9790/3021-03340107
		0.4/3021-03340107
		3021-0303-0407

The analysis of Magneto Inductive communication technique due to underwater communication having multipath fading, dynamic channel and high propagation delay . The performance studies of 3D network covering 100 of meter of sea depth and few km square area show fully connected multi coil network with communication bandwidth extending from few to tens of kHz. The low speed of sound in water, different attenuation characteristics and time varying multi-path fading make use of regular communication methods impractical. To get large area coverage and more bandwidth from sea area . In this work, highly power efficient and fully connected fuzzy scheduling is presented. It allows underwater channel more fair and efficient communication over larger distances.

 

Keywords: Underwater magnetic wireless communications, Magnetic- induction, induction coil, multiple access techniques, 3D grid network

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Paper Type	::	Research Paper
Title	::	Performance of Ann in Pattern Recognition For Process Improvement Using Levenberg- Marquardt And Quasi-Newton Algorithms
Country	::	India
Authors	::	A.Saravanan || Dr.P.Nagarajan
Page No.	::	08-13
	::	10.9790/3021-03340813
		0.4/3021-03340813
		3021-0303-0413

In Industrial manufacturing, Quality has become one of the most important consumer decision factors in the selection among competing products and services. Product inspection is an important step in the production process. Since product reliability is most important in mass production facilities. Neural networks are used to model complex relationships between inputs and outputs or to find patterns in data. Neural networks are being successfully applied across a wide range of application domains in business, medicine, geology and physics to solve problems of prediction, classification and control. In this paper, we investigate the use of different percentages of dataset allocation into training, validation and testing on the performance of ANN in pattern recognition for process improvement using two selected training algorithms (Levenberg- Marquardt and Quasi-Newton Algorithm). The result of this paper clearly indicates that L-M algorithm has fastest network convergence rate than Q-N algorithm in production process

 

Keywords: Back Propagation Neural Network (BPNN), Levenberg- Marquardt, Quasi-Newton Algorithm

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