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.

Particle swarm optimization based nonlinear active noise control under saturation nonlinearity

Abstract: Saturation nonlinearity effect on the ANC performance is addressed.PSO based nonlinear structure ANC algorithm is proposed.Filter bank based FLANN structure is tuned with PSO.The proposed ANC algorithm does not require secondary path estimate.Computer simulations are carried out for performance evaluation.Sensitivity analysis is done for PSO parameter tuning. In this paper, a method is proposed to overcome the saturation non-linearity linked to the microphones and loudspeakers of active noise control (ANC) system. The reference microphone gets saturated when the acoustic noise at the source increases beyond the dynamic limits of the microphone. When the controller tries to drive the loudspeaker system beyond its dynamic limits, the saturation nonlinearity is also introduced into the system. The secondary path which is generally estimated with a low level auxiliary noise by a linear transfer function does not model such saturation nonlinearity. Therefore, the filtered-x least mean square (FXLMS) algorithm fails to perform when the noise level is increased. For alleviating the saturation nonlinearity effect a nonlinear functional expansion based ANC algorithm is proposed where the particle swarm optimization (PSO) algorithm is suitably applied to tune the parameters of a filter bank based functional link artificial neural network (FLANN) structure, named as PSO based nonlinear structure (PSO-NLS) algorithm. The proposed algorithm does not require any computation of secondary path estimate filtering unlike other conventional gradient based algorithms and hence has got computational advantage. The computer simulation experiments show its superior performance compared to the FXLMS, filtered-s LMS and genetic algorithms under saturation present at both at secondary and reference paths. The paper also includes a sensitivity analysis to study the effect of different parameters on ANC performance.

Seasonal variations of black carbon observed at the remote mountain site Happo in Japan

Abstract: [1] The emission of black carbon (BC) from East Asia and its long-range transport strongly influence the mass concentration of BC (MBC) over the western Pacific. However, reliable and long-term BC data are still limited in this region, especially at elevated altitudes. In this study, we present accurate measurements of MBC using a continuous soot monitoring system at Happo, a remote mountain site at an altitude of about 1.8 km in Japan, from August 2007 to August 2009. The annual average MBC at Happo was about 0.26 ± 0.18 (1σ) µg m−3. The monthly average MBC values exhibited similar seasonal variations during both years, with minimum values in winter. Around 40% of the air sampled at the site was of free tropospheric (FT) origin, with about 10% originating in North China (NC) origin, respectively. The MBC values for FT (0.24 µg m−3) and NC (0.23 µg m−3) air were representative of the MBC values (0.26 µg m−3) at 1.8 km height in the western Pacific, which are strongly influenced by BC emissions in North China. The MBC values calculated using a regional-scale model reproduced well the MBC observed at Happo. The model predicted that BC transported from northern China alone contributed ~53% to the measured MBC, consistent with trajectory analysis. The comparison of model-calculated and observed MBC values indicates that the minimum values of MBC in winter were caused by the suppressed upward transport of BC over the Asian continent. Biomass burning in Siberia substantially increased MBC in the spring of 2008.

Evaluation of Field Level Adaptation Measures Under the Climate Change Scenarios in Rice Based Cropping System in India

Abstract: With the rise in population, under future scenarios, global food demand is expected to increase. Biotic and abiotic factors such as climate change are threat to food security owing to its variability, occurrence, duration and magnitude of extreme events. Since, the climate change is unequivocal, the adaptations and mitigations for sustainable agricultural production can minimize its negative impact on agriculture and improve food grain productivity. The grain yield of rice and peanut was simulated for the base period (1969–1999) and future periods 2020 (2011–2040), 2050 (2041–2070) and 2080 (2071–2100) under A2 and B2 scenarios of HadCM3 Global Climate Model. Under the A2 and B2 scenarios, the yield of both rice and peanut declined from base period to the future periods 2011–2040, 2041–2070 and 2071–2100. The cultivar ‘IR 36’ was found to be more sensitive to rising temperature under future climate scenarios as compared to ‘Swarna.’ The yield decline for future climate scenarios increased with locations from higher (Kharagpur and Cuttack) to lower latitude (East Godavari and Vizianagaram). Earlier transplanting time of rice compared to normal (15 July) and sowing time of peanut from normal (9 February) increased the simulated yields of rice-peanut system at higher latitude and reduced the yield loss at lower latitude in future climate scenarios. Delayed planting caused maximum grain yield reduction of the cropping system in future climate scenarios at all the locations.

Spectra of Graphs Resulting from Various Graph Operations and Products: a Survey

Abstract: Abstract Let G be a graph on n vertices and A(G), L(G), and |L|(G) be the adjacency matrix, Laplacian matrix and signless Laplacian matrix of G, respectively. The paper is essentially a survey of known results about the spectra of the adjacency, Laplacian and signless Laplacian matrix of graphs resulting from various graph operations with special emphasis on corona and graph products. In most cases, we have described the eigenvalues of the resulting graphs along with an explicit description of the structure of the corresponding eigenvectors.