Syam Sundar De

Bio: Syam Sundar De is an academic researcher from University of Calcutta. The author has contributed to research in topics: Ionosphere & Radio atmospheric. The author has an hindex of 7, co-authored 48 publications receiving 126 citations.

Thermal Anomalies Around the Time of Nepal Earthquakes M 7.8 April 25, 2015 And M 7.3 May 12, 2015

Abstract: Two consecutive large earthquakes having M values 7.8 and 7.3 occurred on April 25 and May 12, 2015, respectively at Nepal. During their occurrences, abrupt increase in greenhouse gases (like CO2, CH4, H2 etc.) and enhancement of radon emanations are found. These attain high momentum that introduce anomaly in the fluid expulsion from seismically active faults which produce air ionization before these large earthquakes. The process may be very much related to the latent heat release due to condensation of ionized aerosols, produced by energetic alpha particles from radon just before the earthquake. This probably introduces changes in the observed meteorological parameters in the region. Such variations may be due to siesmo tectonically induced radon anomaly before the earthquake.

Identification of the best architecture of a multilayer perceptron in modeling daily total ozone concentration over Kolkata, India

Abstract: Autoregressive neural network (AR-NN) models of various orders have been generated in this work for the daily total ozone (TO) time series over Kolkata (22.56°N, 88.5°E). Artificial neural network in the form of multilayer perceptron (MLP) is implemented in order to generate the AR-NN models of orders varying from 1 to 13. An extensive variable selection method through multiple linear regression (MLR) is implemented while developing the AR-NNs. The MLPs are characterized by sigmoid non-linearity. The optimum size of the hidden layer is identified in each model and prediction are produced by validating it over the test cases using the coefficient of determination (R 2) and Willmott’s index (WI). It is observed that AR-NN model of order 7 having 6 nodes in the hidden layer has maximum prediction capacity. It is further observed that any increase in the orders of AR-NN leads to less accurate prediction.

Detection of 2009 Leonid, Perseid and Geminid Meteor Showers through its effects on Transmitted VLF Signals

Abstract: The detection of 2009 Leonid, Perseid and Geminid meteor showers over Agartala, Tripura, India (Lat: 23.0° N, Long: 91.4° E) will be reported here by using two VLF receivers tuned to subionospheric transmitted VLF signals at the frequency 16.4 kHz from Aldra Island, Norway (Lat: 66.42° N, Long: 13.13° E) and the other at 18.2 kHz from Vijayananarayanam, India (Lat: 8.4° N; Long: 77.7° E). The received signals exhibited their peak values on November 17, 2009 when ZHR (Zenithal Hourly Rate) was highest. Some typical variations which are observed in the records of amplitude during the 2009 Leonid, Perseid and Geminid meteor showers will be presented in this paper.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Statistical model for standard seismicity and detection of anomalies by residual analysis

Abstract: A statistical point-process model is derived to describe the standard activity of earthquake occurrences by assuming that general seismicity is given by the superposition of aftershock sequences. The parameters are estimated ty the maximum likelihood method. Using the estimated model, the “residual point process” of the data is defined and used to find the anomalies which are included in the data set but not captured in the considered model for the standard seismicity. For instance, seismic quiescences can be measured quantitatively by using the residual process. Some examples are provided to illustrate such analyses. Furthermore, a time series of the magnitudes on the residual point process is considered, to investigate its dependence either on the time or on the history of the seismicity. By assuming the exponential distribution at each time and modelling of the b- value , we can examine such dependences and estimate them. Two practical examples are shown.

ELF Electromagnetic Waves from Lightning: The Schumann Resonances

Abstract: Lightning produces electromagnetic fields and waves in all frequency ranges. In the extremely low frequency (ELF) range below 100 Hz, the global Schumann Resonances (SR) are excited at frequencies of 8 Hz, 14 Hz, 20 Hz, etc. This review is aimed at the reader generally unfamiliar with the Schumann Resonances. First some historical context to SR research is given, followed by some theoretical background and examples of the extensive use of Schumann resonances in a variety of lightning-related studies in recent years, ranging from estimates of the spatial and temporal variations in global lighting activity, connections to global climate change, transient luminous events and extraterrestrial lightning. Both theoretical and experimental results of the global resonance phenomenon are presented. It is our hope that this review will increase the interest in SR among researchers previously unfamiliar with this phenomenon.