Complexity quantification of dense array EEG using sample entropy analysis

n this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.

Propagation of Microwaves in the Troposphere with Potential Application to GPS based Navigation and Meteorology with emphasis on Indian region

Global Positioning System (GPS), with its high integrity, continuous availability and reliability, revolutionized the navigation system based on radio ranging. With four or more GPS satellites in view, a GPS receiver can find its location anywhere over the globe with accuracy of few meters. High accuracy - within centimeters, or even millimeters is achievable by correcting the GPS signal with external augmentation system. The use of satellite for critical application like navigation has become a reality through the development of these augmentation systems (like W AAS, SDCM, and EGNOS, etc.) with a primary objective of providing essential integrity information needed for navigation service in their respective regions. Apart from these, many countries have initiated developing space-based regional augmentation systems like GAGAN and IRNSS of India, MSAS and QZSS of Japan, COMPASS of China, etc. In future, these regional systems will operate simultaneously and emerge as a Global Navigation Satellite System or GNSS to support a broad range of activities in the global navigation sector.Among different types of error sources in the GPS precise positioning, the propagation delay due to the atmospheric refraction is a limiting factor on the achievable accuracy using this system. The WADGPS, aimed for accurate positioning over a large area though broadcasts different errors involved in GPS ranging including ionosphere and troposphere errors, due to the large temporal and spatial variations in different atmospheric parameters especially in lower atmosphere (troposphere), the use of these broadcasted tropospheric corrections are not sufficiently accurate. This necessitated the estimation of tropospheric error based on realistic values of tropospheric refractivity. Presently available methodologies for the estimation of tropospheric delay are mostly based on the atmospheric data and GPS measurements from the mid-latitude regions, where the atmospheric conditions are significantly different from that over the tropics. No such attempts were made over the tropics. In a practical approach when the measured atmospheric parameters are not available analytical models evolved using data from mid-latitudes for this purpose alone can be used. The major drawback of these existing models is that it neglects the seasonal variation of the atmospheric parameters at stations near the equator. At tropics the model underestimates the delay in quite a few occasions. In this context, the present study is afirst and major step towards the development of models for tropospheric delay over the Indian region which is a prime requisite for future space based navigation program (GAGAN and IRNSS). Apart from the models based on the measured surface parameters, a region specific model which does not require any measured atmospheric parameter as input, but depends on latitude and day of the year was developed for the tropical region with emphasis on Indian sector.Large variability of atmospheric water vapor content in short spatial and/or temporal scales makes its measurement rather involved and expensive. A local network of GPS receivers is an effective tool for water vapor remote sensing over the land. This recently developed technique proves to be an effective tool for measuring PW. The potential of using GPS to estimate water vapor in the atmosphere at all-weather condition and with high temporal resolution is attempted. This will be useful for retrieving columnar water vapor from ground based GPS data. A good network of GPS could be a major source of water vapor information for Numerical Weather Prediction models and could act as surrogate to the data gap in microwave remote sensing for water vapor over land.

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.