Accelerator based activities in India

In India, major accelerator related programs are being pursued at BARC/TIFR-Mumbai, VECC-Calcutta, NSCDelhi and CAT-Indore. At BARC/TIFR, research programs are being pursued with the 14MV Pelletron accelerator and at NSC-Delhi the 15MVpelletron accelerator is being used to carry out a variety of accelerator based research studies. At VECC-Calcutta, the existing cyclotron is being upgraded to also deliver heavy ion beams through injection by a ECR ion source. Work towards installing a K=500 superconducting cyclotron is also in progress at VECC, Calcutta. At CAT-Indore, a synchrotron radiation facility based on 450 MeV electron storage ring is being commissioned and work is also in progress to build a synchrotron source based on a 2.0 GeV storage ring in the near future. We have also the international collaboration programs, particularly involving high energy accelerator facilities abroad. This report gives an overview of the different accelerator based programs in India

Accelerator-driven sub-critical reactor system (ADS) for nuclear energy generation

In this talk we present an overview of accelerator-driven sub-critical reactor systems (ADS), and bring out their attractive features for the elimination of troublesome long-lived components of the spent fuel, as well as for nuclear energy generation utilizing thorium as fuel. In India, there is an interest in the programmes of development of high-energy and high-current accelerators due to the potential of ADS in utilizing the vast resources of thorium in the country for nuclear power generation. The accelerator related activities planned in this direction will be outlined

Dynamic maintenance of 2-d convex hulls and order decomposable problems

In this paper, we consider dynamic data structures for order decomposable problems. This class of problems include the convex hull problem, the Voronoi diagram problem, the maxima problem, and intersection of halfspaces. This paper first describes a scheme for maintaining convex hulls in the plane dynamically in $O(\log n)$ amortized time for insertions and $O(\log^2 n)$ time for deletions. $O(n)$ space is used. The scheme improves on the time complexity of the general scheme by Overmars and Van Leeuwen. We then consider the general class of Order Decomposable Problems. We show improved behavior for insertions in the presence of deletions, under some assumptions. The main assumption we make is that the problems are required to be {\em change sensitive}, i.e., updates to the solution of the problem at an insertion can be obtained in time proportional to the changes

Learning and Reasoning Strategies for User Association in Ultra-dense Small Cell Vehicular Networks

Recent vehicular ad hoc networks research has been focusing on providing intelligent transportation services by employing information and communication technologies on road transport. It has been understood that advanced demands such as reliable connectivity, high user throughput, and ultra-low latency required by these services cannot be met using traditional communication technologies. Consequently, this thesis reports on the application of artificial intelligence to user association as a technology enabler in ultra-dense small cell vehicular networks. In particular, the work focuses on mitigating mobility-related concerns and networking issues at different mobility levels by employing diverse heuristic as well as reinforcement learning (RL) methods. Firstly, driven by rapid fluctuations in the network topology and the radio environment, a conventional, three-step sequence user association policy is designed to highlight and explore the impact of vehicle speed and different performance indicators on network quality of service (QoS) and user experience. Secondly, inspired by control-theoretic models and dynamic programming, a real-time controlled feedback user association approach is proposed. The algorithm adapts to the changing vehicular environment by employing derived network performance information as a heuristic, resulting in improved network performance. Thirdly, a sequence of novel RL based user association algorithms are developed that employ variable learning rate, variable rewards function and adaptation of the control feedback framework to improve the initial and steady-state learning performance. Furthermore, to accelerate the learning process and enhance the adaptability and robustness of the developed RL algorithms, heuristically accelerated RL and case-based transfer learning methods are employed. A comprehensive, two-tier, event-based, system level simulator which is an integration of a dynamic vehicular network, a highway, and an ultra-dense small cell network is developed. The model has enabled the analysis of user mobility effects on the network performance across different mobility levels as well as served as a firm foundation for the evaluation of the empirical properties of the investigated approaches