Informatics Publishing Limited

K. Duraisamy ¹, U. Ragavendran ²

Affiliations
1 K.S. Rangasamy College of Technology, Tiruchengode – 630 215, IN
2 Anna University of Technology, Coimbatore - 641 047, IN

Abstract

A novel 4-quadrant analog multiplier using floating gate mos (FGMOS) transistors operating in saturation region are implemented. Floating gate mosfets are being utilized in a number of new and existing analog applications. These devices are not only useful for designing memory elements but also we can implement circuit elements. The main advantage in FGMOS is that the drain current is proportional to square of the weighted sum of input signals. By using conventional transistors we obtain only few hundred mill volts range of the supply voltage and when we go for square law devices we obtain up to 50%. So in order to get 100% range of the supply voltage we go for FGMOS. This can be obtained by the control voltage applied at the gate of the FGMOS. This simulation is done with the SPICE tools.

Keywords

VLSI, MOSFET Circuits, MOS Logic Circuits, MOS Integrated Circuits.

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K. Duraisamy ¹, K. Haridass ²

Affiliations
1 Department of Computer Science, NGM College, Pollachi, IN
2 Department of Computer Application, NGM College, Pollachi, IN

Abstract

A cluster is a group of objects which are similar to each other within a cluster and are dissimilar to the objects of other clusters. The similarity is typically calculated on the basis of distance between two objects or clusters. Two or more objects present inside a cluster and only if those objects are close to each other based on the distance between them.The major objective of clustering is to discover collection of comparable objects based on similarity metric. Fuzzy Possibilistic C-Means (FPCM) is the effective clustering algorithm available to cluster unlabeled data that produces both membership and typicality values during clustering process. In this approach, the efficiency of the Fuzzy Possibilistic C-means clustering approach is enhanced by using the penalized and compensated constraints based FPCM (PCFPCM). The proposed PCFPCM approach differ from the conventional clustering techniques by imposing the possibilistic reasoning strategy on fuzzy clustering with penalized and compensated constraints for updating the grades of membership and typicality. The performance of the proposed approaches is evaluated on the University of California, Irvine (UCI) machine repository datasets such as Iris, Wine, Lung Cancer and Lymphograma. The parameters used for the evaluation is Clustering accuracy, Mean Squared Error (MSE), Execution Time and Convergence behavior.

Keywords

Unsupervised Learning, Fuzzy C-Mean, Fuzzy Possibilistic C-Means, Penalized and Compensated Constraints Based FPCM.

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