Modelling and Forecasting the Indian Re/US Dollar Exchange Rate

This paper develops vector autoregressive and Bayesian vector autoregressive models to forecast the Indian Re/US dollar exchange rate which is governed by a managed floating exchange rate regime. It considers extensions of the monetary model that include the forward premium, capital inflows, volatility of capital flows, order flows and central bank intervention. The study finds that the monetary model generally outperforms the naïve model. It also finds that forecast accuracy can be improved by extending the monetary model to include forward premium, volatility of capital inflows and order flow. Information on intervention by the central bank also helps to improve forecasts at the longer end. The study also reports that the Bayesian vector autoregressive models generally outperform their corresponding VAR variants.exchange rate; monetary model; VAR and Bayesian VAR models

A Case for Cooperative and Incentive-Based Coupling of Distributed Clusters

Research interest in Grid computing has grown significantly over the past five years. Management of distributed resources is one of the key issues in Grid computing. Central to management of resources is the effectiveness of resource allocation as it determines the overall utility of the system. The current approaches to superscheduling in a grid environment are non-coordinated since application level schedulers or brokers make scheduling decisions independently of the others in the system. Clearly, this can exacerbate the load sharing and utilization problems of distributed resources due to suboptimal schedules that are likely to occur. To overcome these limitations, we propose a mechanism for coordinated sharing of distributed clusters based on computational economy. The resulting environment, called \emph{Grid-Federation}, allows the transparent use of resources from the federation when local resources are insufficient to meet its users' requirements. The use of computational economy methodology in coordinating resource allocation not only facilitates the QoS based scheduling, but also enhances utility delivered by resources.Comment: 22 pages, extended version of the conference paper published at IEEE Cluster'05, Boston, M

E2XLRADR (Energy Efficient Cross Layer Routing Algorithm with Dynamic Retransmission for Wireless Sensor Networks)

The main focus of this article is to achieve prolonged network lifetime with overall energy efficiency in wireless sensor networks through controlled utilization of limited energy. Major percentage of energy in wireless sensor network is consumed during routing from source to destination, retransmission of data on packet loss. For improvement, cross layered algorithm is proposed for routing and retransmission scheme. Simulation and results shows that this approach can save the overall energy consumptio

End-to-End Privacy for Open Big Data Markets

The idea of an open data market envisions the creation of a data trading model to facilitate exchange of data between different parties in the Internet of Things (IoT) domain. The data collected by IoT products and solutions are expected to be traded in these markets. Data owners will collect data using IoT products and solutions. Data consumers who are interested will negotiate with the data owners to get access to such data. Data captured by IoT products will allow data consumers to further understand the preferences and behaviours of data owners and to generate additional business value using different techniques ranging from waste reduction to personalized service offerings. In open data markets, data consumers will be able to give back part of the additional value generated to the data owners. However, privacy becomes a significant issue when data that can be used to derive extremely personal information is being traded. This paper discusses why privacy matters in the IoT domain in general and especially in open data markets and surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end to end privacy for open data markets. We also highlight some of the major research challenges that need to be address in order to make the vision of open data markets a reality through ensuring the privacy of stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special Issue Cloud Computing and the La

Microstructural characterization of ferromagnetic materials using magnetic NDE techniques

Magnetic NDE techniques, namely, the acoustic Barkhausen noise, the magnetic Barkhausen noise and the magnetic hysteresis curves, were simultaneously used for microstructural characteriza- tion of nickel and steels. Results showed that, in nickel, the non-180(DEGREES) domain walls interact more strongly with dislocations than the 180(DEGREES) domain walls. A study of the grain size effect on the magnetic and acoustic Barkhausen noises showed a great potential as a NDE grain size measurement tool. Moreover, the Barkhausen signals indicate that the 180(DEGREES) domain walls in nickel seem to have a stronger inter- action with grain boundaries than the non-180(DEGREES) domain walls, as indicated by the acoustic Barkhausen signal. Based on the experi- mental observations, a theoretical model is being proposed to explain the grain size effect on the Barkhausen signals. The model takes into account the density of magnetic domain walls and their initial velocity, with both quantities being strongly dependent on microstructure. The acoustic and magnetic Barkhausen noises were also found to be very sensitive to the change in carbide morphology. It is proposed that the magnetic Barkhausen peak signal is caused by;mainly domain nucleation and the acoustic Barkhausen peak signal due to domain growth; (\u271)DOE Report IS-T-1131. This work was performed under contract No. W-7405-Eng-82 with the U.S. Department of Energy