Sai Vidya Institute of Technology

Bangalore, India

Sai Vidya Institute of Technology

Bangalore, India
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Pavan K.E.,Sai Vidya Institute of Technology | Rajesh G.N.,Sai Vidya Institute of Technology
2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings | Year: 2016

In current brain computer interfacing (BCI) system is used for many applications. This technology also used for capturing brain signals in the form of EEG. This technique have intensive applications in non-invasive technique of signal capturing, Due to its temporal resolution, low set-up cost, easy portability compared to all other techniques. In the EEG technique, as we are dealing with very low frequency signals, these signals have plagued with various noise and interferences, so there is an urge for designing and implementing an accurate and stabilized system for acquiring these low frequency signals. This paper is aiming for VLSI design and testing of EEG acquisition system to acquire brain signals. Initially the low power and high gain generalized operational amplifier is designed. Later a suitable filter is designed to eliminate the noise signals produced in low frequency operations, in order to capture EEG signals. The generalized amplifier design of this system consists of differential amplifier followed with voltage follower circuit. The above circuit is designed using the Cadence tool which facilitates the capturing of transient response and generation of virtual layouts. Each and every blocks of the system is designed and tested individually, for each blocks virtual layouts are generated. And the transient response of the system which contains EEG signals with different voltage levels and frequencies are captured. © 2016 IEEE.

Manjunath T.G.,Sai Vidya Institute of Technology | Kusagur A.,UBDT
2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques, ICEECCOT 2016 | Year: 2017

This paper is an attempt to develop a new technology, which is an advancement of the previously published paper [1] for fault diagnosis of multilevel inverter adopting the machine learning and optimization techniques. The advanced machine-learning algorithm called the Optimized Radial Basis Neural Network (ORBNN) method is developed in which the Neural Network uses Radial Basis function as the activation function. So in this paper the faulty condition of switch is identified using the neural network. Matlab based implementation is carried out using the neural network and means square error is minimized by using radial basis function neural network, trained with parameter optimization techniques gives better results. The fault diagnosis is carried out on a 7-level cascaded H-bridge inverter using neural network trained with Back-Propagation (BP), and optimized using Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA). The parameter chosen for the optimization is Mean Square Error(MSE), which has been minimized for the neural network and the radial basis neural network using different optimization algorithm as mentioned above. Matlab based simulation inferred that radial basis neural network performed better than the ordinary neural network and the CSA optimized radial basis neural network (ORBNN) delivered the lowest MSE concluding itself as the best method among the methods taken for analysis. © 2016 IEEE.

Manjunath T.G.,Sai Vidya Institute of Technology
International Journal of Electrical and Computer Engineering | Year: 2016

Multilevel Inverters (MLI) gains importance in Distribution systems, Electrical Drive systems, HVDC systems and many more applications. As Multilevel Inverters comprises of number of power switches the fault diagnosis of MLI becomes tedious. This paper is an attempt to develop and analyze the fault diagnosis method that utilizes Artificial Neural Network to get it trained with the fault situations. A performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA), which optimizes the Artificial Neural Network (ANN) that trains itself on the fault detection, and reconfiguration of the Cascaded Multilevel Inverters (CMLI) is attempted. The Total Harmonic Distortion (THD) occurring due to switch failures or driver failures occurring in the CMLI is considered for this comparative analysis. Elapsed time of recovery, Mean Square Error (MSE) and the computational budgets of ANN are the performance parameters considered in this comparative analysis. Optimization is involved in the process of updating the weight and the bias values in the ANN network. Matlab based simulation is carried out and the results are obtained and tabulated for the performance evaluation. It was observed that Modified Genetic Algorithm performed better than the Genetic Algorithm while optimizing the ANN training. Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved.

Shwetha M.,Sai Vidya Institute of Technology | Narayan K.,Sai Vidya Institute of Technology
AIP Conference Proceedings | Year: 2016

In this work, separation of platelets from red blood cells using Mach-Zehnder interferometer is shown using Dielectrophoretics (DEP). The proposed model demonstrates continuous separation of platelets from red blood cells. Mach-Zehnder Interferometer (MZI) has two arms, in which sensing arm will sense according to the applied voltage and separate the platelets from mixed blood cells. The platelets and the red blood cells will flow in two outlets of MZI. Microfluidic device is used to separate the RBC's and the platelets from the mixed blood cells. © 2016 AIP Publishing LLC.

Abhijith H.V.,Sai Vidya Institute of Technology
Souvenir of the 2015 IEEE International Advance Computing Conference, IACC 2015 | Year: 2015

Wireless sensor network (WSN) is considered as a most trusted technology because of its wide range of applications in various fields like healthcare, industry, military, agriculture etc. WSN consists of seveal sensor nodes with each having the capacity to sense the data, process the sensed data and communicate the processed data. Usually sensor nodes are densely, randomly deployed at the region of interest. This kind of deployment leads to the generation of enormous ammount of redundant sensor data. Routing of such redundant data consumes more energy and saturates the network resources. Hence Data fussion technique is used to reduce the redundant transmissions in the network by fussing the redundant data packets so that network lifetime is enhanced. There are different data fussion techniques which perform data fussion in a single level or in two levels. In this paper we are proposing a multilevel hierarchical data aggregation technique which handles the redundant transmissions in an efficient manner. © 2015 IEEE.

Arvind D.,BMS College of Engineering | Arvind D.,Sai Vidya Institute of Technology | Hegde G.,BMS College of Engineering
RSC Advances | Year: 2015

Supercapacitors are perfect energy storage devices; they can be charged almost instantly and release energy over a long time. They can be charged multiple times with minimal degradation in performance. Supercapacitor performance is determined by the composition of the electrode and advanced configurations. In this review, we compare the performance of different electrode materials which are obtained from biowaste based precursors. Our main interest in this review is to study the supercapacitor properties using carbon based spherical natured particles well known as carbon nanospheres. Carbon based electrodes, particularly bio-waste activated carbon nanospheres, have gained interest due to their excellent energy storage ability. In this paper, Activated Carbon Nanospheres derived from several bio-waste materials are reviewed on the basis of their cyclic voltammograms, specific capacitances, surface areas, electrolytes used and fabrication process. © The Royal Society of Chemistry.

Prasantha P.A.,Sai Vidya Institute of Technology | Sandhya N.C.,University of Mysore | Kempegowda B.K.,Maharanis Government Science College | Bhadregowda D.G.,University of Mysore | And 4 more authors.
Journal of Molecular Catalysis A: Chemical | Year: 2012

The kinetics of oxidation of α-amino acids (AAs) by chloramine-T (CAT) using β-cyclodextrin (BCD) as catalyst was studied in aqueous sodium hydroxide medium at 313 K. The kinetics of reactions was fractional-order with respect to [amino acids] and [β-cyclodextrin]. First-order with respect to [chloramine-T] and inverse fractional-order with respect to [OH -] have been found. Effect of ionic strength, added salt and reaction product (PTS) had no effect on reaction rate. The dependence of the reaction rate on temperature was studied and activation parameters were computed from Arrhenius-Eyring plots. The reaction mechanism and the derived rate law are consistent with the observed experimental results. © 2011 Elsevier B.V. All rights reserved.

Kiran N.C.,Sai Vidya Institute of Technology | Kumar G.N.,University Visvesvaraya College of Engineering
2013 4th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2013 | Year: 2013

The paper introduces a novel offline payment system in mobile commerce using the case study of micro-payments. The present paper is an extension version of our prior study addressing on implication of secure micropayment system deploying process oriented structural design in mobile network. The previous system has broad utilization of SPKI and hash chaining to furnish reliable and secure offline transaction in mobile commerce. However, the current work has attempted to provide much more light weight secure offline payment system in micro-payments by designing a new schema termed as Offline Secure Payment in Mobile Commerce (OSPM). The empirical operation are carried out on three types of transaction process considering maximum scenario of real time offline cases. Therefore, the current idea introduces two new parameters i.e. mobile agent and mobile token that can ensure better security and comparatively less network overhead. © 2013 IEEE.

Manjunath T.G.,Sai Vidya Institute of Technology
2015 International Conference on Condition Assessment Techniques in Electrical Systems, CATCON 2015 - Proceedings | Year: 2015

Fault diagnosis on Multilevel Inverter (MLI) has been a subject of research for about a decade. This paper is an attempt to deliver a performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA) working to optimize Artificial Neural Network (ANN) that trains itself on the fault detection and reconfiguration of the Cascaded Multilevel Inverters (CMLI). The open circuit (OC) faults occurring in the CMLI is considered for this comparative analysis of the performance. The parameters that are taken for the performance evaluation are elapsed time of recovery, Mean Square Error (MSE) and the computational budgets of ANN. Matlab/Simulink is used to develop the CMLI and M-files are used to develop the ANN and optimization algorithms like GA and MGA. The results are obtained and tabulated and performance evaluation carried out. © 2015 IEEE.

Nanjundappa C.E.,National Institute of Technology Jalandhar | Shivakumara I.S.,Bangalore University | Arunkumar R.,Sai Vidya Institute of Technology
Microgravity Science and Technology | Year: 2013

The effect of temperature dependent viscosity on the onset of Marangoni-Bénard ferroconvection under microgravity conditions in a horizontal ferrofluid layer in the presence of a uniform vertical magnetic field has been studied. The viscosity is considered to be varying exponentially with temperature. The lower rigid and the upper horizontal free boundaries are considered to be perfectly insulated to temperature perturbations. The resulting eigenvalue problem is solved numerically using the Galerkin technique as well as analytically by regular perturbation technique with wave number a as a perturbation parameter. It is observed that the analytical results agree well with those obtained numerically. The characteristics of stability of the system are strongly dependent on the viscosity parameter B. It is found that increase in the viscosity parameter B has a stabilizing effect on the onset of Marangoni-Bénard ferroconvection. Moreover, the nonlinearity of fluid magnetization M 3 is observed to have no consequence on the onset of convection in the case of fixed heat flux boundary conditions. © 2012 Springer Science+Business Media Dordrecht.

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