Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Higgs boson. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Search for CP Violation in D0 → π0 π0 Decays

Abstract: We search for CP violation in neutral charm meson decays using a data sample with an integrated luminosity of 966 fb(-1) collected with the Belle detector at the KEKB e(+)e(-) asymmetric-energy collider. The asymmetry obtained in the rate of D-0 and (D) over bar (0) decays to the pi(0)pi(0) final state, [-0.03 +/- 0.64(stat) +/- 0.10(syst)]%, is consistent with no CP violation. This constitutes an order of magnitude improvement over the existing result. We also present an updated measurement of the CP asymmetry in the D-0 -> K-S(0)pi(0) decay: A(CP)(D-0 -> K-S(0)pi(0)) = [-0.21 +/- 0.16(stat) +/- 0.07(syst)]%.

Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

Abstract: The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain .

Performance of Smart Grid Dynamic HAN With RQAM and GMSK Modulation

Abstract: In smart grid communications, home area network (HAN) plays an important role in managing data transmission among communicating devices through a smart meter. Performance of the network depends on the efficiency of modulation schemes used in the communicating devices such as rectangular quadrature amplitude modulation (RQAM) and Gaussian minimum shift keying (GMSK). Saleh-Valenzuela (S-V) and Weibull fading channels are appropriate models for indoor communication channels. Average symbol error rate (ASER) and average channel capacity (ACC) performance of indoor dynamic HANs with communicating devices using RQAM and GMSK are analyzed for these channel models. Numerical results are presented to demonstrate the effect of traffic intensity, number of active devices in HAN, and the modulation types with parameters of practical interest.

Cyclostationary Features Based Modulation Classification in Presence of Non Gaussian Noise Using Sparse Signal Decomposition

Abstract: Automatic modulation classification (AMC) is a salient component in the area of cognitive radio, signal detection, interference identification, electronic warfare, spectrum management and surveillance. The majority of the existing signals detection and classification methods presume that the received signal is corrupted by additive white Gaussian noise. The performance of the modulation classification algorithms degrades severely under the non-Gaussian impulsive noise. Hence, in this paper, we introduce a robust algorithm to identify the modulation type of digital signal contaminated with non-Gaussian impulse noise and additive white Gaussian noise (AWGN) using a sparse signal decomposition on hybrid dictionary. The algorithm first detects and removes the impulse noise using sparse signal decomposition thereafter it classifies the modulation schemes using cyclostationary feature extraction algorithm. Simulation results demonstrate the superiority of the proposed method under different non-Gaussian impulse noise and AWGN conditions. The performance of the proposed classifier is evaluated using well known classifiers available in the literature.

Design of ZVS based high gain DC-DC converter for PV applications

Abstract: In-dependency from fossil fuel to reduce pollution level has become a zenith issue in the field of power generation. Renewable energy sources have become a potential replacement of traditional generation. However the output voltage from the renewable energy sources like photovoltaic cells is low and is not sufficient for the integration to the utility grid. Many high gain DC-DC topologies have been proposed to achieve high DC voltage from renewable sources. In search of high gain, switching losses involved in the circuit are given minimal insight. In this work, a high gain DC-DC converter along with Zero Voltage Switching (ZVS) to minimize switching losses is proposed. In the proposed model, high gain is achieved by implementing multi level boost converter (MLBC). Various modes of operation to achieve ZVS in MLBC and the corresponding operating conditions are discussed in detail in this paper. The proposed ZVS in MLBC topology is simulated in MATLAB/SIMULINK. Simulation studies are carried out to verify the validity of the analytical design.