Development of Free Space Method for Material Characterisation and GPR Study & A Case Study on Biological Samples

The thesis mainly focuses on material characterization in different environments: freely available samples taken in planar fonn, biological samples available in small quantities and buried objects.Free space method, finds many applications in the fields of industry, medicine and communication. As it is a non-contact method, it can be employed for monitoring the electrical properties of materials moving through a conveyor belt in real time. Also, measurement on such systems at high temperature is possible. NID theory can be applied to the characterization of thin films. Dielectric properties of thin films deposited on any dielectric substrate can be determined. ln chemical industry, the stages of a chemical reaction can be monitored online. Online monitoring will be more efficient as it saves time and avoids risk of sample collection.Dielectric contrast is one of the main factors, which decides the detectability of a system. lt could be noted that the two dielectric objects of same dielectric constant 3.2 (s, of plastic mine) placed in a medium of dielectric constant 2.56 (er of sand) could even be detected employing the time domain analysis of the reflected signal. This type of detection finds strategic importance as it provides solution to the problem of clearance of non-metallic mines. The demining of these mines using the conventional techniques had been proved futile. The studies on the detection of voids and leakage in pipes find many applications.The determined electrical properties of tissues can be used for numerical modeling of cells, microwave imaging, SAR test etc. All these techniques need the accurate determination of dielectric constant. ln the modem world, the use of cellular and other wireless communication systems is booming up. At the same time people are concemed about the hazardous effects of microwaves on living cells. The effect is usually studied on human phantom models. The construction of the models requires the knowledge of the dielectric parameters of the various body tissues. lt is in this context that the present study gains significance. The case study on biological samples shows that the properties of normal and infected body tissues are different. Even though the change in the dielectric properties of infected samples from that of normal one may not be a clear evidence of an ailment, it is an indication of some disorder.ln medical field, the free space method may be adapted for imaging the biological samples. This method can also be used in wireless technology. Evaluation of electrical properties and attenuation of obstacles in the path of RF waves can be done using free waves. An intelligent system for controlling the power output or frequency depending on the feed back values of the attenuation may be developed.The simulation employed in GPR can be extended for the exploration of the effects due to the factors such as the different proportion of water content in the soil, the level and roughness of the soil etc on the reflected signal. This may find applications in geological explorations. ln the detection of mines, a state-of-the art technique for scanning and imaging an active mine field can be developed using GPR. The probing antenna can be attached to a robotic arm capable of three degrees of rotation and the whole detecting system can be housed in a military vehicle. In industry, a system based on the GPR principle can be developed for monitoring liquid or gas through a pipe, as pipe with and without the sample gives different reflection responses. lt may also be implemented for the online monitoring of different stages of extraction and purification of crude petroleum in a plant.Since biological samples show fluctuation in the dielectric nature with time and other physiological conditions, more investigation in this direction should be done. The infected cells at various stages of advancement and the normal cells should be analysed. The results from these comparative studies can be utilized for the detection of the onset of such diseases. Studying the properties of infected tissues at different stages, the threshold of detectability of infected cells can be determined.

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.

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems