Growth and Characterization of ZnO based Heterojunction diodes and ZnO Nanostructuresby Pulsed Laser Ablation

Transparent conducting oxides (TCO’s) have been known and used for technologically important applications for more than 50 years. The oxide materials such as In2O3, SnO2 and impurity doped SnO2: Sb, SnO2: F and In2O3: Sn (indium tin oxide) were primarily used as TCO’s. Indium based oxides had been widely used as TCO’s for the past few decades. But the current increase in the cost of indium and scarcity of this material created the difficulty in obtaining low cost TCO’s. Hence the search for alternative TCO material has been a topic of active research for the last few decades. This resulted in the development of various binary and ternary compounds. But the advantages of using binary oxides are the easiness to control the composition and deposition parameters. ZnO has been identified as the one of the promising candidate for transparent electronic applications owing to its exciting optoelectronic properties. Some optoelectronics applications of ZnO overlap with that of GaN, another wide band gap semiconductor which is widely used for the production of green, blue-violet and white light emitting devices. However ZnO has some advantages over GaN among which are the availability of fairly high quality ZnO bulk single crystals and large excitonic binding energy. ZnO also has much simpler crystal-growth technology, resulting in a potentially lower cost for ZnO based devices. Most of the TCO’s are n-type semiconductors and are utilized as transparent electrodes in variety of commercial applications such as photovoltaics, electrochromic windows, flat panel displays. TCO’s provide a great potential for realizing diverse range of active functions, novel functions can be integrated into the materials according to the requirement. However the application of TCO’s has been restricted to transparent electrodes, ii notwithstanding the fact that TCO’s are n-type semiconductors. The basic reason is the lack of p-type TCO, many of the active functions in semiconductor originate from the nature of pn-junction. In 1997, H. Kawazoe et al reported the CuAlO2 as the first p-type TCO along with the chemical design concept for the exploration of other p-type TCO’s. This has led to the fabrication of all transparent diode and transistors. Fabrication of nanostructures of TCO has been a focus of an ever-increasing number of researchers world wide, mainly due to their unique optical and electronic properties which makes them ideal for a wide spectrum of applications ranging from flexible displays, quantum well lasers to in vivo biological imaging and therapeutic agents. ZnO is a highly multifunctional material system with highly promising application potential for UV light emitting diodes, diode lasers, sensors, etc. ZnO nanocrystals and nanorods doped with transition metal impurities have also attracted great interest, recently, for their spin-electronic applications This thesis summarizes the results on the growth and characterization of ZnO based diodes and nanostructures by pulsed laser ablation. Various ZnO based heterojunction diodes have been fabricated using pulsed laser deposition (PLD) and their electrical characteristics were interpreted using existing models. Pulsed laser ablation has been employed to fabricate ZnO quantum dots, ZnO nanorods and ZnMgO/ZnO multiple quantum well structures with the aim of studying the luminescent properties.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

‘Reliability Concepts in Quantile-based Analysis of Lifetime Data

Reliability analysis is a well established branch of statistics that deals with the statistical study of different aspects of lifetimes of a system of components. As we pointed out earlier that major part of the theory and applications in connection with reliability analysis were discussed based on the measures in terms of distribution function. In the beginning chapters of the thesis, we have described some attractive features of quantile functions and the relevance of its use in reliability analysis. Motivated by the works of Parzen (1979), Freimer et al. (1988) and Gilchrist (2000), who indicated the scope of quantile functions in reliability analysis and as a follow up of the systematic study in this connection by Nair and Sankaran (2009), in the present work we tried to extend their ideas to develop necessary theoretical framework for lifetime data analysis. In Chapter 1, we have given the relevance and scope of the study and a brief outline of the work we have carried out. Chapter 2 of this thesis is devoted to the presentation of various concepts and their brief reviews, which were useful for the discussions in the subsequent chapters .In the introduction of Chapter 4, we have pointed out the role of ageing concepts in reliability analysis and in identifying life distributions .In Chapter 6, we have studied the first two L-moments of residual life and their relevance in various applications of reliability analysis. We have shown that the first L-moment of residual function is equivalent to the vitality function, which have been widely discussed in the literature .In Chapter 7, we have defined percentile residual life in reversed time (RPRL) and derived its relationship with reversed hazard rate (RHR). We have discussed the characterization problem of RPRL and demonstrated with an example that the RPRL for given does not determine the distribution uniquely

Personnel Management Practices in the Kerala based Scheduled commercial Banks

The thesis entitled Personnel Management Practices in the Kerala-Based Scheduled Commercial Banks. Personnel management function is of cardinal importance, requiring a sophisticated and scientific approach. In a labour-intensive, service industry like banking. Productivity and ultimate profitability of the entire organization depend considerably on the effectiveness with which personnel management function is executed; and the prudence with which personnel problems are handle. The main objectives of the study are to understand the current status of personnel management functions in the banks and to evaluate the practices in the light of the principles and theories of personnel management so as to identify the strengths and weaknesses. The universe of this study is the eight Scheduled Commercial Banks based in Kerala. The major limitation of the study is that as State Bank of Travancore, the lone public sector bank based in Kerala did not grant permission for collection of data, this study had to be confined to private sector banks only. Almost the entire data used for this study are primary and were collected from the files and other records or the concerned banks. This report has chapters dealing with the functional areas of personnel management such as determination of human resource requirements, recruitment and selection, training and development, performance appraisal, promotions and compensation. Findings reveal that the practice of personnel management in the Kerala-based private sector scheduled commercial banks has not gained a degree of sophistication compatible with its role in modern business management.

Quantile based entropy function

Quantile functions are efficient and equivalent alternatives to distribution functions in modeling and analysis of statistical data (see Gilchrist, 2000; Nair and Sankaran, 2009). Motivated by this, in the present paper, we introduce a quantile based Shannon entropy function. We also introduce residual entropy function in the quantile setup and study its properties. Unlike the residual entropy function due to Ebrahimi (1996), the residual quantile entropy function determines the quantile density function uniquely through a simple relationship. The measure is used to define two nonparametric classes of distributions

Quantile based reliability aspects of partial moments

Partial moments are extensively used in literature for modeling and analysis of lifetime data. In this paper, we study properties of partial moments using quantile functions. The quantile based measure determines the underlying distribution uniquely. We then characterize certain lifetime quantile function models. The proposed measure provides alternate definitions for ageing criteria. Finally, we explore the utility of the measure to compare the characteristics of two lifetime distributions

Image Denoising Using Sure-Based Adaptive Thresholding In Directionlet Domain

The standard separable two dimensional wavelet transform has achieved a great success in image denoising applications due to its sparse representation of images. However it fails to capture efficiently the anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi directional and anisotropic wavelet transform called directionlet is presented. The image denoising in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. The image is first segmented and the dominant direction of each segment is identified to make a directional map. Then according to the directional map, the directionlet transform is taken along the dominant direction of the selected segment. The decomposed images with directional energy are used for scale dependent subband adaptive optimal threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and visual quality