Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Two new Schmitt trigger circuits based on current sink and current source inverters

Abstract: This paper presents two new Schmitt trigger circuits with eight enhancement-type MOS transistors are introduced in this paper. These two Schmitt trigger circuits are implemented based on current sink and current source inverters. The hysteresis curves of the proposed Schmitt triggers are presented, hysteresis width depends on the supply voltage and transistor geometry. These circuits are preferred for high speed applications and also useful in low power applications. The performances of proposed circuits are examined using Cadence and model parameters of 180 nm CMOS technology with supply rail voltage of +3V. The simulation results and layouts are presented with optimized sizing and spacing in compliance to the design rules of gpdk 180 nm CMOS process.

Threshold based soft partial parallel interference cancellation for MIMO-OFDM system

Abstract: The multiple input multiple output (MIMO) technology combined with orthogonal frequency division multiplexing (OFDM) is a dominant air interface technology for 4G and 5G wireless communication technologies. In this paper we proposed a new threshold based soft partial parallel interference canceller for MIMO-OFDM system. The MIMO detector based on the hybrid use of soft minimum mean square error (MMSE) equalization and proposed threshold based partial parallel interference cancellation (PPIC) is applied on each OFDM subcarrier. From simulation results, it is shown that proposed interference cancellation technique outperforms the conventional interference cancellation techniques such as parallel interference (PIC) and ordered successive interference cancellation (OSIC).

A hybrid rule extraction method for One-Class Support Vector Machines

Abstract: One-Class Support Vector Machines (OCSVM) is widely applied for those classification problems where one of the classes of data is completely not present or not properly sampled However, the knowledge presented by OCSVM is not interpretable by the human analyst To ameliorate this problem there is a need for providing explanation aids to OCSVM classification decisions Recently, several rule extraction methods were evolved to provide explanation ability to Support Vector Machines (SVM) and Artificial Neural Networks (ANN) Motivated from these methods this paper proposes a new hybrid rule extraction method for OCSVM, which is not widely studied yet Proposed hybrid is composed with Support Vector Data Description (SVDD) and RIPPER rule learning The viability of the proposed hybrid is tested over three benchmark datasets Obtained results have shown that SVDD+RIPPER hybrid outperformed SVDD and RIPPER alone in terms of classification performance and explanation ability